**1. Introduction**

In the last two decades, research on automatic collision avoidance and optimal path planning for surface vessels has intensified, driven by the ever-growing density of maritime traffic in narrow waterways, such as gulfs, ports, and canals [1]. Motivated by the design of autonomous surface vehicles (ASV) controllers, but also aiming for the decision support of officers on watch in conventional vessels [2], control and optimization tools that ensure the safety and the cost effectiveness of navigational actions are being intensively developed. These tools are perceptive of the surrounding environment through arrays of sensors, radars, and other positioning and communication aids. In this context, the automatic identification system (AIS) encompasses most of the aforementioned technologies in order to gather positioning and other vessel data. The already vast AIS comprises an everexpanding worldwide maritime trajectory dataset, which is made available by vessels, port authorities, and other platforms in charge of efficient and safe maritime path planning. Given the fact that the majority of vessel accidents are related to erroneous handling rather than equipment failure or environmental conditions [3], these tools aim to phase out the human officers on watch as vessel controllers, or at least augment their navigational decision-making using optimization- and prediction-based methods.

The formulation of the trajectory optimization problem used in collision avoidance controllers must take multiple aspects of vessel navigation into account, while being perceptive of their surrounding environment in real time. The generation of control actions that will result in a trajectory remaining sufficiently clear from any stationary or moving objects is not the sole objective; an efficient controller should also ensure the

**Citation:** Papadimitrakis, M.; Stogiannos, M.; Sarimveis, H.; Alexandridis, A. Multi-Ship Control and Collision Avoidance Using MPC and RBF-Based Trajectory Predictions. *Sensors* **2021**, *21*, 6959. https:// doi.org/10.3390/s21216959

Academic Editors: Baochang Zhang, Reza Ghabcheloo and Antonio M. Pascoal

Received: 3 August 2021 Accepted: 16 October 2021 Published: 20 October 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

economy of the control actions, as well as the adherence to the collision avoidance rules, commonly known as the COLREGs [4]. Multiple collision avoidance controllers have been proposed that fulfil the aforementioned specifications; in [5], a hierarchical multiobjective optimization problem is formulated, which generates an intermediate waypoint for the controlled vessel while accounting for the good seamanship rules. In [6], a fuzzy-Bayesian collision avoidance controller is formulated capable of addressing multiple obstacle vessels at once. In [7], optimal trajectories for the collision avoidance problem are generated using a B-Spline-based search algorithm. Lastly, in [8], a collision avoidance controller utilizes a probabilistic method in order to infer the one-step-ahead position of obstacle vessels, while also accounting for non-COLREG-compliant obstacle vessels.

In general, it has been observed that controllers that are not model-based can have trouble incorporating crucial aspects of the trajectory optimization problem, thus compromising practicality. Without a working model of the controlled vessel, its maneuvering capabilities cannot be easily included in the formulation, and neither can the effect of environmental conditions be quantified [9,10]. For these reasons, model predictive control (MPC) emerges as an effective control method for the problem at hand because it utilizes a model of the plant in order to compute an optimal control trajectory based on the predicted trajectory of other ships in the vicinity. As a framework, MPC can account for the uncertainties of both the utilized model of the plant and the trajectory prediction models of other ships, while also incorporating all possible control objectives (such as navigational risk, course smoothness, or deviation from the original path) in a single cost function. Some collision avoidance controllers based on MPC have been proposed in the literature; a robust MPC controller utilizing straight-line obstacle vessel trajectory predictions is proposed in [9], capable of COLREG compliance and handling of multiple obstacles. In [11], motion planning for an autonomous vessel using a sampling-based MPC method takes place. In [12], an MPC controller for the collision avoidance task is built by approximating the behavior of an LQR controller, thus ensuring asymptotic stability of the system. In [13], a neural network used to approximate the MPC response for the generation of COLREG compliant trajectories for multi ship encounters is presented. In addition, MPC has been integrated in distributed control frameworks of multi-ship schemes; for example, a distributed MPC scheme has been employed for a multi-vessel formation controller with collision avoidance capabilities [14], or for the robust distributed control of multiple vessels operating for the inter-terminal transport of containers [15].

It becomes apparent that for the scope of the collision avoidance task, information about the future trajectories of other ships plays a central role. Prevalent in non-data driven methodologies already used for the vessel trajectory prediction (VTP) problem is the first principles-based modeling technique [16], carrying a number of significant shortcomings, such as their inherent complexity, which has a greater negative impact due to the fact that the model is usually employed multiple times within the duration of each MPC sample. In order to simplify the solution of the employed kinematic differential equations and facilitate the real-time prediction of future states, these types of models are usually created using several assumptions that try to approximate real-world conditions, but also make the final model far less accurate. Therefore, one should employ a more sophisticated, data-driven approach for the creation of effective trajectory prediction models that are included in MPC controllers. Machine learning has answered the call of producing highly accurate models, which may be easily integrated in predictive frameworks through the use of black-box modeling, and more specifically, artificial neural network (NN) approaches [17]. NNs employ different architectures in order to remap the original non-linear problem to a higher-dimensional input space and approximate its dynamics utilizing standard functions. In this context, various NN techniques have been successfully utilized in control frameworks solving the vessel trajectory prediction problem.

Feedforward NN architectures, most commonly represented by the multilayer perceptrons (MLPs), have been employed to solve the vessel trajectory prediction problem as in [18,19], where MLP NNs are trained using the well-established backpropagation algorithm outperforming rival methodologies, i.e., linear models and Kalman filters. In [19], a real AIS dataset gathered from the confined space of a river waterway is used to approximate the vessel dynamics in such environments. Backpropagation has been the baseline of more efficient training methods as in [20], where different computational intelligence approaches like differential evolution, genetic algorithms, and swarm-based techniques are used to modify the original backpropagation algorithm in order to create more accurate feedforward NN models. Other NN architectures, like support vector machines (SVMs), have been employed in conjunction with computational intelligence optimization techniques, i.e., the particle swarm optimization (PSO) algorithm, on AIS datasets to solve the vessel trajectory prediction problem [21]. In most cases, the inherent abilities of NN architectures that can meet the standard of high accuracy are limited to a one-step ahead prediction horizon, in the sense that multi-step ahead predictions would require an approximation of unknown future states to be made and present an error enlarged through propagation to the end of the prediction horizon. Such an error would become critically high after a small number of steps, rendering the control framework useless.

To overcome this problem, long-term trajectory prediction approaches have been devised with the inclusion of memory features, such as the recurrent neural networks (RNNs), with their most notable representative, i.e., the long short-term memory (LSTM) NNs already used in the context of the vessel trajectory prediction problem [22–25]. Besides trajectory modeling and prediction in open waters, advances have also been made in crowded port waters as in [26], where another modification of the RNNs, namely the bidirectional gated recurrent unit, is used to address the vessel trajectory prediction problem, outperforming standard NN methods in such scenarios. Gated recurrent units are promising candidates for predicting the collective behavior of vessel fleets [27].

Radial basis function (RBF) networks form a unique NN architecture belonging to the general feedforward NN category. RBFs differ from other NN architectures, having simpler structures, employing faster training algorithms, and usually producing more accurate models than MLPs. Within the context of vessel trajectory prediction, RBFs have been integrated in control frameworks by approximating unknown vessel parameters [28–30]. Recently, RBFs have been applied on real AIS data in order to produce highly accurate models for one-step and multi-step ahead predictions [31], showing their potential in being integrated to receding horizon control methodologies.

Remarkably, in the collision avoidance research literature, there are no instances where the multi-step-ahead trajectory prediction of moving obstacles is addressed in such a systematic manner; usually these trajectories are either known a priori, or there are no obstacle ships present whatsoever. An exception occurs in [9], where straight line trajectory predictions are employed, based on estimates of current course and speed for the moving obstacle. In this work, a multi-ship MPC controller utilizing RBF obstacle trajectory prediction models trained using real AIS data is presented for the collision avoidance task. The main contributions of this work are as follows: first, we introduce a novel MPC scheme for collision avoidance control, where nonlinear data-driven models are used to predict the trajectories of obstacle ships; to the authors' best knowledge, this is the first such instance in the literature. The previous state-of-the-art approach of using straight-line obstacle trajectory predictions may have yielded satisfactory approximation results in open sea case studies, where ships are expected to travel in a straight line, but is of limited practical use for the cases of narrow gulfs, ports, or canals, where ships are expected to maneuver while navigating through, thus resulting to nonlinear trajectories. Secondly, the aforementioned nonlinear models are trained using an AIS data-driven methodology, which, once again, constitutes an original approach in the context of vessel collision avoidance. Using real trajectories as training data increases the practicality of the proposed scheme, since the resulting trajectory prediction models capture the dynamics of real vessels. The proposed method is tested in a collision avoidance case study at the Miami port area, and its performance is illustrated by the comparison with an MPC controller

employing straight-line obstacle prediction models, which corresponds to the current state-of-the-art approach [9].

The paper is structured as follows. In Section 2, the AIS-data-driven methodology for the creation of the RBF trajectory prediction models is presented. In Section 3, some preliminaries on maritime collision avoidance and optimal trajectory generation are described, and later, the proposed method is presented. In Section 4, the case study based on the port of Miami is outlined, and the simulation results are discussed in depth. Lastly, in Section 5, concluding remarks are made.

#### **2. AIS-Data-Based Trajectory Prediction Models**

#### *2.1. Radial Basis Function Neural Networks*

RBF NNs have been successfully employed to approximate nonlinear system dynamics in order to predict future system states in numerous diverse applications [32,33]. Their success can be mainly attributed to their structure, which is simpler when compared to other NN architectures, as they comprise a single hidden layer, attached linearly to the network output. This property not only allows for using very fast training algorithms, but also makes RBF NNs suitable for integration in MPC schemes, as (a) it facilitates the controller design [34], and (b) helps to solve the MPC online optimization problem in shorter computational times [35], thus rendering such schemes applicable to systems with fast dynamics [36]. Another property that makes RBF NNs a popular modeling method in predictive control schemes is related to their increased approximation capabilities [37,38]. Indeed, notwithstanding their simple structure, RBF NNs have proven to be more accurate in modeling various nonlinear systems when compared to other machine learning methods, including MLPs, support vector machines (SVMs), random forests, etc. [39]. Especially within the context of the vessel modeling and control problems, RBFs have already shown great potential in modeling unknown vessel parameters [28,29], in order to create models capable to be integrated in trajectory tracking control algorithms. Furthermore, in a recent publication [31], it has been shown that RBF NNs trained with the fuzzy means (FM) algorithm outperform other data-driven techniques such as MLPs when modeling vessel trajectories based on AIS data.

Training an RBF NN is a process consisting mainly of two phases. The first phase is performed by applying a clustering technique on the training dataset in order to identify the centerpoint and optimized parameters of a number of radially symmetric basis functions called nodes. The incorporation of radial basis functions (e.g., Gaussian, quadratic, etc.) is the first main difference between RBFs and other feedforward NNs. The linear combination of these nodes produces the output prediction of the network. Finding the node weights is the goal of the second phase, a problem trivially solved by least squares.

A typical RBF network can be seen in Figure 1. The structure comprises three distinct layers, the first of which is the input layer and has the sole purpose of distributing the *N* inputs to the *L* nodes of the hidden layer. The second point differentiating RBFs from other architectures is the existence of only one hidden layer of *N*-dimensional nodes. In order to produce a prediction ˆ *y*(*k*) given an input datapoint **x**(*k*), at first the Euclidean norm is used to calculate the activity *μl*(**x**(*k*)) for every node *l* by using the difference between the *k*-th input vector **x**(*k*) and the *l*-th node center **^ u***l*, such that

$$\mu\_l(\mathbf{x}(k)) = \|\mathbf{x}(k) - \hat{\mathbf{u}}\_l\| = \sqrt{\sum\_{i=1}^{N} \left(\mathbf{x}\_i(k) - \hat{\mathbf{u}}\_{i,l}\right)^2}, k = 1, \dots, K \tag{1}$$

The activity acts as input to the free parameters of each node according to the chosen RBF. The hidden node response vector **z**(*k*) for the *k*-th datapoint is given by

$$\mathbf{z}(k) = \left| \lg(\mu\_1(\mathbf{x}(k))), \lg(\mu\_2(\mathbf{x}(k))), \dots, \lg(\mu\_L(\mathbf{x}(k))) \right| \tag{2}$$

where *g* corresponds to the chosen activation function. Note that in this work, a thin plate spline function is employed

$$\log(\mu\_l(\mathbf{x}(k))) = \mu\_l^2(\mathbf{x}(k)) \cdot \log \mu\_l(\mathbf{x}(k)) \tag{3}$$

due to the fact that it is an established choice as an RBF kernel producing models of high accuracy [40], but also because there are no tunable parameters other than the actual input to the function. Such parameters would require optimization techniques to be included in the training process, e.g., employment of the Gaussian function would need kernel width optimization, which is usually performed by cpu-intensive iterative algorithms or suboptimal trial-and-error techniques.

Finally, the network's prediction is calculated by linearly combining the hidden note responses such that

$$
\hat{y}(k) = \mathbf{z}(k) \cdot \mathbf{w} \tag{4}
$$

where **w** is a vector containing the node weights.

**Figure 1.** A typical radial basis function network structure using Gaussian RBFs.

#### *2.2. The Fuzzy Means (Fm) Training Algorithm*

For a given training dataset where **Y** denotes the real outputs, and after the hidden node responses **Z** are formulated, the weight vector **w** can be trivially calculated by least squares in matrix form

$$\mathbf{w}^T = \mathbf{Y}^T \cdot \mathbf{Z} \cdot \left(\mathbf{Z}^T \cdot \mathbf{Z}\right)^{-1} \tag{5}$$

thus completing the second training phase in one easy step.

The first phase of training requires a clustering algorithm to be applied to the training dataset, in which case the fuzzy means (FM) algorithm is a great candidate for this task [39]. Following the notation of the previous example, let us suppose a system with *N* normalized input variables *xi*. At first, each input variable domain must be partitioned into *s* 1-D fuzzy sets (FS). Each fuzzy subspace **A***l* , where *l* = 1, 2, ... ,*sN*, is formed by the selected *sN* fuzzy sets according to the respective input variable. This process creates a *N*-dimensional grid, where all intersection points, also called nodes, are candidates to become RBF centerpoints. The FM algorithm undertakes the task of selecting the appropriate candidate nodes that will be assigned as RBF centers. To perform the selection procedure, a membership function

$$\mu\_{\mathbf{A}^l}(\mathbf{x}(k)) = \begin{cases} 1 - d\_r^l(\mathbf{x}(k)), & \text{if } d\_r^l(\mathbf{x}(k)) \le 1 \\ 0, & \text{if } other\text{wise} \end{cases} \tag{6}$$

determines whether an input vector lies within the domain of an RBF centered around a candidate node. In the simple case where all input variable spaces are equally partitioned, the following distance metric can be used to assign *N*-dimensional spherical domains to each candidate node

$$d\_r^l(\mathbf{x}(k)) = \sqrt{\sum\_{i=1}^N \left(a\_{i, \overline{i}i}^l - \mathbf{x}\_i(k)\right)^2} / \sqrt{N} \delta a \tag{7}$$

where **x**(*k*) is the *k*-th input vector, *al i*,*ji* is the centerpoint of fuzzy subspace **A***l* , and *δa* is the sphere radius, which is the same for each input. The FM algorithm uses a fast non-iterative procedure to find a subset of the subspaces, so that the final RBF NN's hidden layer comprises only the fuzzy sets, which sufficiently cover all training datapoints, in the sense that each datapoint is included in at least one fuzzy set. For an in-depth description of the FM algorithm, the reader may refer to [39].

#### *2.3. Data Preprocessing*

Best modeling practices mandate that a training dataset should be error- and noisefree, a case that is far from truth when using data from AIS transceivers [41–45]. AIS data are irregularly sampled and contain heavy noise, missing data, and erroneous values. Thus, before employing any modeling technique, rigorous preprocessing is in order.

The Marine Cadastre service (www.marinecadastre.gov, accessed on 25 July 2021) has been the source of all data used in this work. MarineCadastre.gov is a is a service that gathers and publicly provides AIS data to marine planning initiatives. In this work, data from all days between 1 July 2019 and 30 June 2020 have been included and filtered to keep vessels sailing an area around the port of Miami covered by the geolocation rectangle defined by the latitudes of 25.720◦ through 25.840◦ and the longitudes of −80.145◦ through −80.042◦. To conform to the initial assumption of similar size and similar dynamics, we allowed only cargo ships sailing on engine power into the dataset, further filtering the dataset to yield a total of 180 vessels.

To address the problems of sample irregularity, noise, and erroneous values, the dataset was resampled to 120 s, which was deemed enough to capture the high inertia dynamics of large cargo ships. The interpolation technique applied on the data to perform the resampling was the Akima piecewise cubic interpolation [46], which is quite effective on geolocation data, performing a mild denoising as well. A heuristic that rejects very far-off outlier values due to GPS errors was also applied. The trajectories were split in data samples, with each one containing ten consecutive vessel positions. Note that each trajectory's starting point should be the last point of the previous one resulting in an overlap of one point, but this final position will be used as the model's output, so no actual overlapping exists within the input data. The resampling and splitting process yielded a total of about 14 k samples from 3.1 k resampled trajectories of the initial 180 vessels. Algorithm 1 depicts the step-by-step procedure of preprocessing.

**Algorithm 1** Preprocessing Stage

1: Process each entry in the common dataset so that it contains only the following: Vessel ID, timestamp, latitude, and longitude. Reject all other information.

### *2.4. Modeling Procedures*

AIS transceiver equipped vessels are able to record and exchange timestamped information, including geolocation, speed, direction, vessel identification, and specifications

2: Sort dataset by vessel ID and sort each vessel data by date.

3: Apply resampling and outlier filtering on the data of each vessel to achieve a resampling of 120 s.

4: Split vessel data into trajectories containing ten consecutive vessel positions each.

5: Create final preprocessed dataset, which should contain the vessel ID and final

10-position trajectories.

data. Vessel trajectory prediction algorithms integrated with collision avoidance techniques can incorporate trajectory prediction models in order to identify imminent threats and navigate safely and efficiently within heavily crowded port areas or open seas.

Let us suppose an available AIS dataset, comprising an arbitrary number of *Tv* trajectories for a total of *V* vessels, where *v* = 1, 2, ... , *V*. Let us also suppose that the included trajectories contain an arbitrary number of *Kv*,*t* AIS messages *AISmv*,*t k* (timestamped geolocation and other data). In this work, for simplicity reasons, we employ the following format in AIS messages

$$ASm\_k^{v,t} = \left\{ \begin{array}{cc} Ts\_k^{v,t} & y\_k^{v,t} & x\_k^{v,t} \end{array} \right\} \tag{8}$$

where *k* = 1, 2, ... , *Kv*,*t* , and *Tsv*,*t k* denotes the message timestamp, while *yv*,*t k* and *xv*,*t k* are the respective latitude and longitude contained in the *k*-th AIS message for the *t*-th trajectory of the *v*-th vessel. The fact that there are unknown parameters, e.g., the state and controls of the vessels, prohibits the use of kinematics in calculating future vessel states. Nevertheless, the vessel dynamics exist in the information hidden within the dataset and can be extracted and, in most cases, approximated by using a black-box modeling technique such as RBF NNs. We can assume that a common underlying pattern exists in the dynamics of samesize vessels executing similar maneuvers, for example, when approaching or leaving a port, when berthing, when crossing waterway paths, etc. Thus, if a suitable dataset of sufficient size is made available, an RBF NN can be trained to perform one-step-ahead predictions about a vessel's future geolocation by using past AIS messages as seen in the following equations

$$\left\{ \begin{array}{c} \Delta y\_{k+1}^{\upsilon, t} \\ \stackrel{\scriptstyle \upsilon, t}{\end{array}} \right\} = RBF \text{ NN} \left( \begin{array}{c} AISm\_{k}^{\upsilon, t} \\ \end{array} \dots \quad AISm\_{k-N}^{\upsilon, t} \right) \tag{9}$$

$$\begin{cases} \overset{\circ}{y}\_{k+1}^{v,t} = y\_k^{v,t} + \Delta \overset{\circ}{y}\_{k+1}^{v,t} \\ \mathbf{x}\_{k+1} = \mathbf{x}\_k^{v,t} + \Delta \mathbf{x}\_{k+1} \end{cases} \tag{10}$$

where *N* is the number of past AIS messages given as inputs to the RBF NN.

The accuracy and simple structures of FM-trained RBF NNs make them ideal to be integrated in multi-step-ahead predictive control formulations. Receding horizon techniques require models of very high accuracy due to the inevitable error enlargement through propagation. This effect appears when the incorporated model is expected to recurrently make future predictions based on its own previous output throughout the prediction horizon. The problem is further worsened with the increase of the prediction horizon and can ultimately drive the control algorithm to failure. Another important point to be noted is that the linear combination used to produce an RBF NN's prediction is a simple and very fast calculation, a fact that benefits model predictive control (MPC) frameworks [36], which require an optimization problem to be solved iteratively and expect a significant number of model predictions to be made in order to converge to the optimal solution at each timestep.

Delta values of the last position of each sample were used as the model's output, while the first nine positions were the model's input

$$\left\{ \begin{array}{cccc} \text{\reflectbox{ $\upsilon$ }}\_{\text{v},t} \\ \text{\reflectbox{ $\upsilon$ }}\_{\text{x},t} \\ \text{\reflectbox{ $\upsilon$ }}\_{k+1} \end{array} \right\} = RBF \text{ NN} \left( \begin{array}{cccc} \text{\reflectbox{ $\upsilon$ }}\_{k} & \text{\reflectbox{ $\upsilon$ }}\_{k} & \text{\reflectbox{ $\upsilon$ }}\_{k} & \dots & \text{\reflectbox{ $\upsilon$ }}\_{k-8} & \text{\reflectbox{ $\upsilon$ }}\_{k-8} \end{array} \right) \tag{11}$$

The Δˆ *y v*,*t k*+1 and Δˆ *x v*,*t k*+1 values may be added to the last input position to calculate the final predicted vessel position. Based on the above procedure, the results of the modeling process produced an RBF model of very high accuracy [31]. The step-by-step procedure of the modeling stage can be seen in Algorithm 2.

Note that the number of past inputs was determined after a trial-and-error procedure, where several RBF models were trained using a different number of inputs. After testing inputs in the range of 3 to 15 past vessel positions, data obtained on model performance showed that using less than nine inputs produced models with reduced prediction accuracy, while using more than nine inputs increased the model's complexity without any significant accuracy gain compared to the model using nine inputs.

**Algorithm 2** Modeling Stage

1: Load final preprocessed dataset.

2 : Replace the final value of all included 10 −

position trajectories with the respective delta value according to - Δ*yv*,*t* 10 Δ*xv*,*t* 10 . = *yv*,*t* 10 − *yv*,*t* 9 *xv*,*t* 10 − *xv*,*t* 9 .

,

so that each trajectory sample is in the form /

*yv*,*t* 1 *xv*,*t* 1 *yv*,*t* 2 *xv*,*t* 2 ... *yv*,*t* 9 *xv*,*t* 9 Δ*yv*,*t* Δ*xv*,*t*

3: Randomly permute the trajectory samples of each vessel.

4: Split the trajectory samples of each vessel into training, validation, and testing subsets (in this work a 50%–5%–25% percentage split is used). Do this so that all vessels contribute to all three subsets according to the chosen splitting.

0 .

5: Merge all subset samples, e.g., all training samples of all vessels together in one single dataset that will be used for training. Do the same for the validation and testing subsets.

6: Normalize the inputs and outputs of the training subset. Apply the normalization coefficients to the validation and testing subsets.

7 : Apply the fuzzy means algorithm on the training and validation dataset using the nine first sets of *yv*,*t*

− *xv*,*t* values as inputs and the last set of Δ*yv*,*t* − Δ*xv*,*t* values as output.

8: Final model is in the form of Equation (11).

Moreover, a series of tests has been performed by the recurrent application of this model based on a horizon of 5 timesteps for all trajectories of the testing subset, where, at each successive timestep, the model had to use an increasing number of its own previous predictions. As the model uses more of its past predictions, accuracy decreases due to the enlargement of the propagated prediction error. Such a test can provide intuition on the models' ability to be incorporated in receding horizon predictive frameworks. The quality metrics used for these tests were the root mean squared error (RMSE) and the root mean squared haversine formula distance (RMSHFD). The haversine formula is commonly used to measure great circle distances on spherical surfaces. Table 1 presents the performance metrics obtained after the recurrent application of the chosen model in order to make predictions for the full length of the trajectories included in the testing subset of the training procedure. Mean RMSE values for the two outputs of the model, namely the latitude and longitude, are provided in degrees, wherein can be seen that the error lies in the order of 1.5 thousandth of a degree. The mean RMSHFD metric shows the respective error margin in meters when combining the two model outputs to get the actual predicted future vessel position for all tested trajectories. More details on the modeling procedure for the one-step ahead models, including detailed results and comparison with other machine learning approaches, can be found in [31].

**Table 1.** Performance metrics of the produced RBF NN model.

#### **3. MPC for Multi-Ship Collision Avoidance**

#### *3.1. Preliminaries on Maritime Collision Avoidance and Trajectory Generation*

The objective of maritime collision avoidance is the generation of a risk-free trajectory that the controlled vessel should follow. A well-defined and effective method of assessing collision risk in the near future is the closest point of approach (CPA). Stemming from the concept of the CPA, two metrics are defined: time to CPA (TCPA) and distance to CPA (DCPA) (see Figure 2). A discussion regarding the quick calculation of TCPA and DCPA using the line-of-sight (LOS) distance between the controlled vessel and the obstacle ship is presented in [5]. These metrics depict the urgency of the collision danger of vessel *i* with another vessel *j* as well as its magnitude, and by specifying lowest acceptable thresholds *dmin* and *tmin* concerning the minimum DCPA and minimum TCPA, respectively, one can construct a risk cost function, as presented in [5].

**Figure 2.** Illustration of the CPA metrics, as well as the LOS angle concept.

Here, *a*0 is a scaling parameter, and *Ti* denotes the trajectory matrix containing the x-y position of the *i* vessel for every timestep

$$T\_i = \left[ \begin{array}{cc} x\_1 & y\_1 \\ \vdots & \vdots \\ x\_n & y\_n \end{array} \right]. \tag{13}$$

By combining TCPA and DCPA, the spatial-temporal nature of a maritime collision risk with vessel *i* is successfully reflected. The physical interpretation of Equation (12) is that a candidate trajectory with larger minimum distance from an obstacle ship occurring at an earlier time will always be safer than a path with a smaller minimum distance and/or earlier time of occurrence. Common values for *tmin* and *dmin* are 10 min and 0.6 nm; because the present paper is concerned with collision avoidance in busy waterways such as ports, a lower *dmin* value of 0.4 nm is used. In any case, Equation (12) can be readily incorporated in the cost function of an MPC optimization problem formulation.

A second item in the domain of trajectory generation is efficiency. Vessels should strive to not deviate too much from their original course when addressing a collision risk with another vessel. The efficiency of the generated trajectory *Ti* for vessel *i* can be reflected by calculating the sum of absolute deviations from the original trajectory *TOG*,*i*

$$f\_{d,i} = \left\|{T\_{OG,i} - T\_i}\right\|.\tag{14}$$

Next, an important requirement to be fulfilled when addressing the problem of collision avoidance are the COLREGs [4]. The implementation of the COLREGs restricts the domain of possible candidate paths according to the type of encounter, for example 'head-on,' 'crossing,' and 'overtaking.' Head-on vessels should pass each other on the port side, while a vessel crossing from the starboard side should be given the right of way. A visual depiction of the encounter rules takes place in Figure 3. Multiple approaches for

the modeling of the COLREG rules have been made in the literature [2,5,8], although these are usually concerned with a one-step-ahead calculation. However, for the case of an MPC controller, in order to ensure COLREG compliance for a candidate trajectory, all of its waypoints must be taken into account. By assuming that the LOS angle is increasing in the anti-clockwise direction, one needs to evaluate whether the LOS angles of each sequential trajectory timestep position are increasing monotonically, in order to confirm the compliance of the trajectory for the 'head-on' and 'give-way' situations.

**Figure 3.** (**a**) A head-on situation between two ships; (**b**) a crossing situation between two ships (give-way); the orange ship must give way to the crossing ship on its starboard side.

The idea is depicted in Figure 4, where a head-on encounter between vessels *i* and *j* occurs; here, the LOS angles for trajectory *Ti* monotonically increase, and therefore, it is deemed as compliant. In contrast, the monotonically decreasing LOS angles of the *T i* trajectory confirm its non-compliance as per the COLREGs intentions. A penalty for noncompliance of a vessel *i* encountering a vessel *j* in a 'head-on' or 'give-way' situation can therefore be formulated,

$$P\_{ij} = \begin{cases} \quad 1, & \text{if } \mathfrak{a}\_{LOS\_{ij}} \searrow \\ \quad 0, & \text{if } \mathfrak{otherwise} \end{cases} \tag{15}$$

where *aLOSij* is the LOS angle vector, calculated for each trajectory point of encountering vessel *i* and the current position of encountered vessel *j*.

**Figure 4.** Head-on situation between vessels *i* and *j*; the LOS angle can be used to assess the COLREG compliance of a candidate trajectory.

Next, the generated vessel trajectory, apart from being safe and COLREG compliant, should also take into account the maneuvering capabilities of the controlled vessel, i.e., it should be guaranteed that the trajectory is technically possible to be tracked by the vessel. The feasible search domain of the trajectory optimization problem can be constructed by a purely geometric approach in the case of a one-step-ahead calculation, such as in [5], where the design variables are the vessel's next position and course. However, the extension of this geometric approach to multiple-steps-ahead requires the application of nonlinear

constraints that would bound every sequential vessel position with its previous one, in order to enforce technical feasibility. For this reason, a model-based approach is preferred. The Nomoto models constitute a class of vessel course models that are tailored for this task, and have been widely adopted, not only for the design of collision avoidance schemes [47], but also for path tracking controllers [48]. The 1st order linear Nomoto model is shown as follows:

$$T\_s \frac{d\omega}{dt} + \omega = K\_s a. \tag{16}$$

Here, *ω* is the angular velocity of the vessel, while *a* is the control input to the vessel's rudder. The maneuvering capabilities of the vessel are reflected by the *Ts* and *Ks* constants, called time constants and rudder gain constants, respectively, while typical values are in the [0.5, 2] range for both. Solving the differential Equation (16) by assuming constant rudder angle input for a *t* time interval, the 1st order linear Nomoto model can be discretized as follows [8]:

$$
\Delta\theta(t) = K\_s \left( t - T\_s + T\_s \exp\left(\frac{t}{T\_s}\right) \right) \tag{17}
$$

Here, Δ*θ* is the course change that would occur if a control input of *a* was applied and held for a time period of *t*. By setting this time period *t* as the discretization interval Δ*t*, a course model can be used to create a discrete vessel position model as follows

$$\begin{aligned} \theta\_{k+1} &= \theta\_{k-1} + \Delta\theta\_k(a\_k) \\ x\_{k+1} &= x\_k + \cos(\theta\_{k+1}) V\_k \,\Delta t \\ y\_{k+1} &= y\_k + \sin(\theta\_{k+1}) V\_k \,\Delta t \end{aligned} \tag{18}$$

Here, *θk*, *xk*, *yk* is the current course, horizontal displacement, and vertical displacement according to a global reference frame, respectively, while *Vk* is the vessel velocity. The discretization interval Δ*t* can be set according to the simulation resolution required. Equation (18) constitute a discrete position model *Li* for the *i*-th vessel,

$$\mathbf{x}\_{i}(k+1) = L\_{i}(\mathbf{u}\_{i}, \mathbf{x}\_{i}(k)),\tag{19}$$

with input vector *ui* = *a V* and state vector *xi* = *θ x y* . By evaluating the discrete vessel position model *Li* for {1, 2, . . . , *n*} consecutive timesteps, where *n* the total timesteps, a trajectory *Ti* can be created for the i-th vessel, as shown in Equation (13).

#### *3.2. Collision Avoidance with Mpc and Obstacle Trajectory Prediction Models*

The MPC framework has demonstrated its aptitude in handling the uncertainties and nonlinearities of the collision avoidance problem multiple times in the literature [9,49]; however, no other works have incorporated a nonlinear data-driven obstacle trajectory prediction model in their formulation. In MPC, the optimal moves of the controlled vessels are calculated for multiple steps ahead by solving a constrained optimization problem, with constraints in real time, for each controller sample time *tcst*. The cost function of the optimization problem is constituted by two horizons, namely the prediction horizon *hp* and the control horizon *hc*; the first accounts for the total discrete timesteps ahead that the model can be evaluated, while the second for the number of timesteps that the control variables can be modified. Given a set of controlled vessels *Vc* = {1, 2, . . . , *Nc*} and a set of non-controlled or obstacle vessels *Vo* = {1, 2, . . . , *No*} where *Nc* and *No* are the total number of controlled and non-controlled vessels, respectively, the optimization problem's cost function can be formulated as the summation of all the cost functions of the respective controlled vessels for the *k*th timestep:

$$\begin{array}{lcl}\min\_{\mathbf{U}(k)}\sum\_{i\in V} \mathbf{C}\_{i}(\mathbf{X}\_{\varepsilon}(k), \mathbf{X}\_{o}(k))\\ \text{s.t. } \mathbf{U}\_{\mathsf{u}} \le \mathbf{U}(k) \le \mathbf{U}\_{l} \\\ \mathbf{N}(\mathbf{X}\_{\varepsilon}(k), \mathbf{X}\_{o}(k)) \ge \mathbf{d}\_{\varepsilon} \\\ \mathbf{P}(\mathbf{X}\_{\varepsilon}(k), \mathbf{X}\_{o}(k)) = 0 \end{array} \tag{20}$$

Here, *U*(*k*) is the input matrix and is created by the horizontal concatenation of the input vectors of all controlled vessels *Vc*, up until the control horizon *hc*:

$$\mathbf{U}(k) = \begin{bmatrix} \mathfrak{u}\_1(k) & \mathfrak{u}\_1(k+1) & \cdots & \mathfrak{u}\_1(k+h\_c-1) \\ \vdots & \ddots & \vdots \\ \mathfrak{u}\_{N\_c}(k) & \mathfrak{u}\_{N\_c}(k+1) & \cdots & \mathfrak{u}\_{N\_c}(k+h\_c-1) \end{bmatrix}. \tag{21}$$

Next *Xc*(*k*) and *Xo*(*k*) are the controlled and non-controlled vessel state matrices, respectively, and are created by the horizontal concatenation of the state vectors of all controlled and non-controlled vessels *Vc* and *Vo*, respectively, up to the prediction horizon *hp*.

$$\begin{aligned} \mathbf{X}\_{\varepsilon}(k) &= \begin{bmatrix} \mathbf{x}\_{\varepsilon,1}(k) & \mathbf{x}\_{\varepsilon,1}(k+1) & \cdots & \mathbf{x}\_{\varepsilon,1}\left(k+h\_{p}-1\right) \\ \vdots & \ddots & \vdots \\ \mathbf{x}\_{\varepsilon,N\_{\varepsilon}}(k) & \mathbf{x}\_{\varepsilon,N\_{\varepsilon}}(k+1) & \cdots & \mathbf{x}\_{\varepsilon,N\_{\varepsilon}}\left(k+h\_{p}-1\right) \\ \mathbf{x}\_{o,1}(k) & \mathbf{x}\_{o,1}(k+1) & \cdots & \mathbf{x}\_{o,1}\left(k+h\_{p}-1\right) \\ \vdots & \ddots & \vdots \\ \mathbf{x}\_{o,N\_{\varepsilon}}(k) & \mathbf{x}\_{o,N\_{o}}(k+1) & \cdots & \mathbf{x}\_{o,N\_{\varepsilon}}\left(k+h\_{p}-1\right) \end{bmatrix} \end{aligned} \tag{22}$$

For simplicity, because consecutive states *xi*(*k*) up to *xi k* + *hp* − 1 constitute a single trajectory *Ti*(*k*), one can write *Xc*(*k*) and *Xo*(*k*) as the concatenation of the trajectories of the respective vessel sets *Vc*, *Vo* as per Equation (13):

$$\mathbf{X}\_{\mathcal{E}}(k) = \begin{bmatrix} \ & T\_{c,1}(k) \\ \vdots \\ \ & T\_{c,N\_{\mathcal{E}}}(k) \end{bmatrix} \quad \mathbf{X}\_{\mathcal{o}}(k) = \begin{bmatrix} \ & T\_{o,1}(k) \\ \vdots \\ \ & T\_{o,N\_{\mathcal{B}}}(k) \end{bmatrix}. \tag{23}$$

Next, *Ci*(*k*) is the cost function of the *i*-th controlled vessel, formulated as follows:

$$\mathbf{C}\_{i}(k) = F\_{i}(\mathbf{X}(k)) + a\_{G}\mathbf{G}^{2}(\mathbf{U}\_{i}(k)) \tag{24}$$

Here, *X*(*k*) is the vertical concatenation of the two state matrices *Xc*(*k*), *Xo*(*k*), containing the trajectories of all vessels *V* = *Vc* 1 *Vo*

$$X(k) = \begin{bmatrix} \ T\_{c,1}(k) & \dots & \ T\_{c,N\_c}(k) & T\_{o,1}(k) & \dots & T\_{o,N\_0}(k) & \end{bmatrix} \tag{25}$$

The cost function is comprised by two terms *Fi* and *G*, each concerned with the prediction and control horizon, respectively. The presence of *G* term, weighted by the *aG* parameter, encourages the smoothness of the control actions and, consequently, the generated trajectories of the controlled vessels

$$G(\mathcal{U}\_i(k)) = \sum\_{j=1}^{h\_\epsilon - 1} \left\| \mathcal{U}\_{i, j+1}(k) - \mathcal{U}\_{i, j}(k) \right\|. \tag{26}$$

Term *Fi* consolidates the collision avoidance and course keeping objectives, and is specific to the *i*-th vessel

$$F\_{\bar{i}}(\mathbf{X}(k)) = a\_r \sum\_{j \in V \backslash i} \left( f\_{r, \bar{i}j} ^2(\mathbf{X}\_i(k), \mathbf{X}\_j(k)) \right) \frac{1}{|V \backslash i|} + a\_d \, f\_{d, \bar{i}} ^2(\mathbf{X}\_i(k)). \tag{27}$$

In Equation (26), *fr*,*ij* is the collision risk between the *i*-th and the *j*-th vessel, as calculated using their respective trajectories *Xi*(*k*), *Xj*(*k*) by applying Equation (12), and *fd*,*i* is the deviation from the original trajectory *TOG*, *i*, as expressed in Equation (14). Both terms are weighted by the *ar* and *ad* weighting parameters, respectively. Since we are concerned with the safety of the generated trajectory throughout the whole prediction horizon, the mean

collision risk from all vessels in *V*\*i* is evaluated, in contrast to other approaches [5], where only the maximum collision risk at time *k* is minimized. This way, all possible collision risks are addressed and reduced simultaneously, thus avoiding the adverse possibility of evading one collision risk and increasing another. Moreover, the reason that risk avoidance is used as a control objective in Equation (27) and not as a hard optimization constraint is to ensure that the MPC optimization problem of Equation (20) will not fail in the case of the existence of an inescapable collision risk; as shown in Equation (12), risk is a function of distance to CPA, meaning that the controller will continue to attempt to maximize that distance, thus fulfilling the control intention in such an encounter. However, in order to guarantee that collisions will be avoided, one more constraint to the MPC optimization problem is added by setting an emergency distance d*e* (where d*e* < *dmin*); to be more specific, the vector *N* contains the DCPAs of all controlled vessels *Vc*, which are required to be above the emergency distance.

At this point, it must be noted that since the state matrix *X*(*k*) consolidates all vessel trajectories, controlled and non-controlled alike, a degree of cooperation is induced between the respective controlled vessels *Vc*. Lastly, returning to the optimization problem denoted in Equation (20), the *U*(*k*) input matrix is bounded by the upper and lower matrices *Uu***,** *Ul*, respectively. The vector *P* contains the COLREG non-compliance penalties for the controlled vessels *Vc* as calculated in Equation (15), which are required to be zero via an equality constraint.

The next item to be addressed regarding the MPC formulation is the used model that maps the input variables *U* to the state variables of the controlled vessels *Xc*. Here, the 1st order linear Nomoto model is used, as described in Equation (18), with the addition of input noise that accounts for modeling error *e* and environmental parameters:

$$\begin{aligned} \mathbf{x}\_{\varepsilon,i}(k+1) &= \hat{L}\_i(\mathbf{u}\_{i\prime}, \mathbf{x}\_{\varepsilon,i}(k)), \text{ where } i \in V\_{\varepsilon} \\ \hat{L}\_i(\mathbf{u}\_{i\prime}, \mathbf{x}\_{\varepsilon,i}(k)) &= L\_i(\mathbf{u}\_i + \varepsilon(\mathbf{u}\_i), \mathbf{x}\_{\varepsilon,i}(k)), \text{where } \varepsilon(\mathbf{u}\_i) = \mathbf{u}\_i \odot (0, \sigma^2). \end{aligned} \tag{28}$$

Here, *G* is a random variable sampled from a Gaussian distribution with a standard deviation of *σ*. Finally, the state matrix of the non-controlled vessels *Xo*(*k*) is assumed to be unknown for the scope of this research, and thus, an estimation is required, based on past positions. For this task, the RBF prediction model presented in Section 2.3 is employed for each non-controlled vessel *j*, and its trajectory for the *k*-th timestep is estimated using its past nine positions:

$$\hat{T}\_{o,j}(k) = RBF\{\mathbf{x}\_{o,j}(k), \mathbf{x}\_{o,j}(k-1), \dots, \mathbf{x}\_{o,j}(k-9)\}, \text{ where } j \in V\_o. \tag{29}$$

Next, in order to alleviate a possible computational burden for the MPC optimization problem, an important assumption should be made. The formulation of the control scheme as-presented would give rise to a high-dimensional search space for the MPC optimization problem, thus greatly hindering its effective solution. It is assumed then that all vessels retain their initial speed, with the only controllable variable being the vessel's rudder angle; this way, the total number of control variables is reduced by half. This approach to the collision avoidance problem has occurred in the literature [5], and is not simplistic for two reasons: first, good seafaring practice dictates that course change maneuvers are preferred over speed ones, not only because they conserve energy, but also because they better emphasize the intentions of the vessel to outside observers, such as other vessels in the vicinity. Second, since large container ships will be examined in the scope this case study, their large longitudinal inertia [48] confirms the assumption that the speed remains almost constant during the timeframe of a typical collision avoidance maneuvering scenario. Therefore, for the scope of this paper, the input matrix *U* at timestep *k* is formulated as follows:

$$\mathbf{U}(k) = \begin{bmatrix} a\_1(k) & a\_1(k+1) & \cdots & a\_1(k+h\_c-1) \\ \vdots & \ddots & \vdots \\ a\_{N\_c}(k) & a\_{N\_c}(k+1) & \cdots & a\_{N\_c}(k+h\_c-1) \end{bmatrix}' \tag{30}$$

where *ai*(*k*) is the rudder angle of vessel *i* at timestep *k.*

Having defined all aspects of the MPC optimization problem, a reiteration of the challenges of the collision avoidance control problem and how they are addressed by the controller is in order: firstly, the goal of the control design is to generate trajectories for the controlled vessels that are risk-free (Equation (12)), smooth (Equation (26)), COLREGcompliant (Equation (15)), and do not deviate from the original course (Equation (14)). Possible collision risks are assessed by utilizing trajectory predictions for non-controlled (obstacle) vessels in the vicinity. The controllable variables are the rudder angles of the vessels (vessel speed is considered constant), while a discrete 1st order Nomoto model (Equation (28)) is used for the modeling of the vessel dynamics, which was also infused with a noise signal for the purpose of accounting for uncertainties and environmental factors. The aforementioned vessel dynamics model has been compared to its higher-order nonlinear counterparts in [50], and it was shown that vessel course inaccuracies occur only for high yaw rates. Given the fact that the proposed collision avoidance method is concerned with large vessels with slow dynamics, the used vessel dynamics model is deemed adequate for the case. In addition, MPC has shown to be robust against model uncertainties or input noise [36]. Finally, the constraints that must be adhered to when searching for the optimal solution (Equation (20)) are the technical bounds on the controlled variables (i.e., maximum and minimum rudder angles) and the COLREG compliance of the result trajectory.

#### *3.3. Control Framework*

Having presented the proposed MPC controller, this section describes its integration within a general control framework. As shown in Figure 5, the framework is comprised by an offline and an online process. The offline process corresponds to the RBF trajectory prediction model training, using data from a specific area of interest (for example, a port) naturally then, it could be undertaken by the port authority. The online process corresponds to the real-time control of autonomous vessels in the presence of obstacle vessels in the area of interest. The MPC collision avoidance controller, as described in Section 3.2, is integrated here and is supplied with real time trajectory predictions of all obstacle vessels in order to calculate the optimal control actions for the controlled vessels. Since the RBF trajectory prediction model has been trained offline in the port authority premises, it is sensible to place the MPC controller there too, and to communicate the computed control actions per control timestep via a communications link with the controlled vessels. Figure 6 demonstrates this concept.

The MPC optimization problem described in Equation (20) is solved using the sequential quadratic programming (SQP) algorithm, which involves iterative calls to the objective function [35]. As shown in Figure 5, the integration of the MPC controller in the control framework requires the calculation of the obstacle vessel trajectory predictions for every controller timestep. Therefore, two main sources of computational complexity arise: the first is the evaluation of the RBF trajectory prediction model, which is shown to be in the order of magnitude of milliseconds [31], meaning that multiple obstacle vessels can be accounted for by the control scheme. The second is the solution of the optimization problem (Equation (20)) by the SQP algorithm, which is known to converge quickly and with few objective function calls [51]. It is concluded that a typical controller timestep duration, comprised by the two aforementioned sources, will not exceed the order of magnitude of seconds, which is considered reasonable given the slow dynamics of large vessels.

**Figure 5.** The proposed control framework.

**Figure 6.** Communications within the proposed control framework.

#### **4. Case Study**

In this section, the performance of the proposed multi-ship MPC controller is assessed using real-life obstacle ship trajectories, which were sourced and preprocessed as described in Section 2.4. In order to underline the importance of using sophisticated trajectory prediction models in the context of collision avoidance controller design, the proposed method is compared to an MPC controller that uses straight-line predictions for the trajectories of obstacle ships based on their current course and speed [9]. To this end, two crossing scenarios are examined, while performance indicators of the generated trajectories are extracted and discussed in detail. The simulations were coded and executed on Matlab 2020b, on a computer with an Intel i7 processor and 16 GB RAM. The simulation sample time is 30". Lastly, the tuning and parameters of the methods are shown in Table 2, while the vessel parameters are shown in Table 3.

**Table 2.** MPC tuning parameters.

**Table 3.** Vessel parameters.


*Edited by Bernardo Llamas Moya, M. Dolores Storch de Gracia and Luis F. Mazadiego*

In a global world, where the acceleration of technological changes is happening in all industrial sectors, a special focus is forced on innovation and creativity. The book has gathered a small number of sectors where innovation is being the main vector to achieve the competitiveness that companies are craving. The motivation to choose these sectors has been preceded by a careful selection in which we wanted to pick up those in which innovation is a key today. Different aspects push to create and innovate: the environment in general and in particular climate change is forcing to rethink sectors such as energy, infrastructure, water, biotechnology, materials, defense, education, or health. Dear reader, in your hand is a work that reflects the same spirit of the human being: curiosity and eagerness to overcome have allowed humanity to have evolved and still continue today.

ISBN 978-953-51-3447-3

Case Study of Innovative Projects - Successful Real Cases

Photo by your\_photo / iStock


*Article* **An Agent-Based Decision Support Platform for Additive Manufacturing Applications**

#### **Nikolaos Papakostas \*, Anthony Newell and Abraham George**

Laboratory for Advanced Manufacturing Simulation and Robotics, School of Mechanical and Materials Engineering, University College Dublin, D04 V1W8 Dublin 4, Ireland<EMAIL_ADDRESS>(A.N<EMAIL_ADDRESS>(A.G.)

**\*** Correspondence<EMAIL_ADDRESS>Tel.: +353-1-716-1741

Received: 23 June 2020; Accepted: 14 July 2020; Published: 18 July 2020

**Abstract:** The effective estimation and consideration of process cost, time, and quality for additive manufacturing operations, when a series of suitable technologies and resources are available, is very important for making informed product design and development decisions. The main objective of this paper is to propose the design, deployment, and use of an agent-based decision support platform, which is capable of proposing alternative additive manufacturing resources and process configurations to design engineers while reducing the number of communication steps among engineering teams and organizations. Different computer-aided systems are utilised and interfaced for automating the information exchange as well as for accelerating the overall product development process.

**Keywords:** process planning; scheduling; design for additive manufacturing; multiple criteria


**1.1.1 Block matching algorithm**

Block Matching Algorithm (BMA) (IEG Richardson 2003) is the most popular motion estimation algorithm. BMA calculates motion vector for an entire block of pixels instead of individual pixels. The same motion vector is applicable to all the pixels in the block. This reduces computational requirement and also results in a more accurate motion vector since the objects are typically a cluster of pixels. BMA algorithm is illustrated in figure 1.

#### Fig. 1. Block Matching Algorithm

The current frame is divided into pixel blocks and motion estimation is performed independently for each pixel block. Motion estimation is done by identifying a pixel block from the reference frame that best matches the current block, whose motion is being estimated. The reference pixel block is generated by displacement from the current block's location in the reference frame. The displacement is provided by the Motion Vector (MV). MV consists of is a pair (x, y) of horizontal and vertical displacement values. There are various criteria available for calculating block matching.

The reference pixel blocks are generated only from a region known as the search area. Search region defines the boundary for the motion vectors and limits the number of blocks to evaluate. The height and width of the search region is dependent on the motion in video sequence. The available computing power also determines the search range. Bigger search region requires more computation due to increase in number of evaluated candidates. Typically the search region is kept wider (i.e. width is more than height) since many video sequences often exhibit panning motion. The search region can also be changed adaptively depending upon the detected motion. The horizontal and vertical search range, Sx & Sy, define the search area (+/-Sx and +/- Sy) as illustrated in figure 1.

#### **1.1.2 Full search block matching**

58 Video Compression

there is also the phase plane correlation technique, which generates motion vectors via correlation between current frame and reference frame. However, the most popular technique is Block Matching methodology which is the prime topic of discussion here.

Block Matching Algorithm (BMA) (IEG Richardson 2003) is the most popular motion estimation algorithm. BMA calculates motion vector for an entire block of pixels instead of individual pixels. The same motion vector is applicable to all the pixels in the block. This reduces computational requirement and also results in a more accurate motion vector since

The current frame is divided into pixel blocks and motion estimation is performed independently for each pixel block. Motion estimation is done by identifying a pixel block from the reference frame that best matches the current block, whose motion is being estimated. The reference pixel block is generated by displacement from the current block's location in the reference frame. The displacement is provided by the Motion Vector (MV). MV consists of is a pair (x, y) of horizontal and vertical displacement values. There are

The reference pixel blocks are generated only from a region known as the search area. Search region defines the boundary for the motion vectors and limits the number of blocks to evaluate. The height and width of the search region is dependent on the motion in video sequence. The available computing power also determines the search range. Bigger search region requires more computation due to increase in number of evaluated candidates. Typically the search region is kept wider (i.e. width is more than height) since many video sequences often exhibit panning motion. The search region can also be changed adaptively depending upon the detected motion. The horizontal and vertical search range, Sx & Sy,

the objects are typically a cluster of pixels. BMA algorithm is illustrated in figure 1.

**1.1.1 Block matching algorithm**

Fig. 1. Block Matching Algorithm

various criteria available for calculating block matching.

define the search area (+/-Sx and +/- Sy) as illustrated in figure 1.

Full search block matching algorithm (Alois, 2009) evaluates every possible pixel block in the search region. Hence, it can generate the best block matching motion vector. This type of BMA can give least possible residue for video compression. But, the required computations are prohibitively high due to the large amount of candidates to evaluate in a defined search region. The number of candidates to evaluate are ((2\*Sx) +1)\*((2\*Sy) +1) which is predominantly high compared to any of the search algorithms. There are several other fast block-matching algorithms, which reduce the number of evaluated candidates yet try to keep good block matching (Yu-Wen, 2006) accuracy. Note that since these algorithms test only limited candidates, they might result in selecting a candidate corresponding to local minima, unlike full search, which always results in global minima. Some of the algorithms are listed below.

#### **1.1.3 Fast search algorithms**

There are many other block matching algorithms (Nuno, 2002) and their variants available, but differs in the manner how they select the candidate for comparison and what is the motion vector resolution. Although, the full search algorithm is the best one in terms of the quality of the predicted image and its resolution of the motion vector it is very computationally intensive. With the realization that motion estimation is the most computationally intensive operation in the coding and transmitting of video streams, people started looking for more efficient algorithms. However, there is a trade-off between the efficiency of the algorithm and the quality of the prediction image. Keeping this trade-off in mind a lot of algorithms have been developed. These algorithms are called Sub-Optimal (Alois, 2009) because although they are computationally more efficient than the Full search, they do not give as good a quality as in the full search.

#### **1.1.4 Three step search**

In a three-step search (TSS) algorithm (Alan Bovik, 2009), the first iteration evaluates nine candidates as shown in figure 2. The candidates are centered on the current block's position. The step size for the first iteration is typically set to half the search range. These algorithms operate by calculating the energy measure (e.g. SAD) at a subset of locations within the search window as illustrated (TSS, sometimes described as N-Step Search) in Figure.2. SAD is calculated at position (0, 0) (the centre of the Figure) and at eight locations ±2*N*−1 (for a search window of ± (2*N* −1) samples). The first nine search locations are numbered '1'. The search location that gives the smallest SAE is chosen as the new search centre and a further eight locations are searched, this time at half the previous distance from the search centre (numbered '2' in the figure). Once again, the 'best' location is chosen as the new search origin and the algorithm is repeated until the search distance cannot be subdivided further. This is the last iteration of the three-step search algorithm. The best matching candidate from this iteration is selected as the final candidate. The motion vector corresponding to this candidate is selected for the current block. The number of candidates evaluated during three-step search is very less compared to the full search algorithm. The TSS is considerably simpler than Full Search (8*N* + 1 search compared with (2*N*+1 −1)2 searches for Full Search) but the TSS (and other fast search algorithms) do not usually perform as well as Full Search.

H.264 Motion Estimation and Applications 61

During each ite ration, a set of three neighboring candidates along the x-axis are tested in Fig.2. The three-candidate set is shifted towards the best matching candidate, with the best matching candidate forming the centre of the set for the next iteration. The process stops if the best matching candidate happens to be the centre of the candidate set. The location of this candidate on the x-axis is used as the x-component of the motion vector. The search now continues parallel to the y-axis. A procedure similar to x-axis search is followed to estimate y-component of the motion vector. One-step at a time search on average tests less number of

Integer pixel motion estimation (also called as full search method) is carried out in the process of motion estimation that is mainly used to reduce the duplication (redundant data) among adjacent frames. But in practice, the distance of real motion is not always made by multiplier (which is constant) at the sampling interval.The actual motion in the video sequence can be much finer. Hence, the resulting object might not lie on the integer pixel (Iain Richardson, 2010) grid. To get a better match, the motion estimation needs to be performed on a sub-pixel grid. The sub-pixel grid can be either at half pixel resolution or

Therefore it is advantageous to use the subpixel motion estimation technique to ensure high compression with high PSNR ratio of reconstructed image. The motion vector can be calculated at 1/2, 1/4, 1/8 subpixel (Young et.al 2010) positions. The motion vector is to be calculated at 1/4 pixel gives more detailed information than at 1/2 pixel position. Since, the image has been enlarged, interpolation must be implemented to compensate for the pixel

candidates. However, the motion vector accuracy is poor.

quarter pixel resolution.

value in case of enlargement.

Fig. 3. 6-tap Directional interpolation filter for luma

**1.1.7 Sub-pixel motion estimation (Fractional Pel Motion Estimation)**

Fig. 2. Fast Search Algorithms


**Abstract**

Aneurysmal subarachnoid hemorrhage is an important group of intracranial hemorrhage with a high risk of disability and mortality. The initial amount of bleeding, rebleeding, and delayed cerebral ischemia are considered as the most important factors in determining the prognosis of aneurysm-induced bleeding. In arteriovenous malformations, its location and deep venous drainage play a role in the prognosis. Cardiac complications, neurogenic pulmonary edema, hypertension, hyperglycemia, infections, and prolonged mechanical ventilation in aneurysmal subarachnoid hemorrhage lead to morbidity and mortality. Aneurysm bleeding control, appropriate fluid replacement to ensure euvolemia, when necessary external ventricular drainage and/or decompressive craniectomy, mannitol or hypertonic saline application, and infection control are the main principles of treatment.

**Keywords:** aneurysmal subarachnoid hemorrhage,

cerebral arteriovenous malformation, cerebral vasospasm, delayed cerebral ischemia, intracranial pressure


**3. Results**

*Orchis anthropophora* (Figure 1a) returned the results reported in Table 1 and Figure 2. The main volatile organic compounds detected were caryophyllene (11.32%), tetradecanal (57.17%) and hexadecanal (12.10%). Caryophyllene has a scent described as sweet, woody and terpenic, while the aroma of tetradecanal is described as fatty, waxy, amber, incense, citrus peel and musk.

The SPME analysis of *Orchis purpurea* (Figure 1b) showed that the main components of the aroma were aromatic compounds such as 3,5-dimethoxytoluene (35.29%) and elemicin (4.76%) (Table 1). The scent of elemicin is described as spicy and floral. When *Orchis italica* (Figure 1c) was examined, eucalyptol (3.93%), caryophyllene (47.29%) and 4-(3-hydroxy-2-methoxyphenyl)butan-2-one (4.96%) were the most abundant compounds found in the scent (Table 1). The scent of eucalyptol is recognized as eucalyptus, herbal, camphoreous and medicinal. *Orchis pauciflora* (Figure 1d) showed the presence of linalool (26.12%), 1,4-dimethoxybenzene (15.01%), germacrene D (9.34%) and 6,10,14-trimethyl-4,8,12-tetradecadrienal (3.60%). The scent of linalool is described as citrus, floral, sweet and woody, while that of 1,4-dimethoxybenzene is sweet, green and hay.

The SPME analysis of the scent of *Orchis mascula* (Figure 3a) showed the presence of eucalyptol (7.80%), linalool (21.28%), tetradecane (3.70%), pentadecane (4.41%) and 6,10,14-trimethyl-2-pentadecanone (Table 1 and Figure 4). *Orchis quadripunctata* (Figure 3b) had a scent composition where only hydrocarbons were found. Thus, tridecane (9.70%), tetradecane (9.38%), pentadecane (26.85%) and heptadecane were the main components of the scent (Table 1). In the case of *Orchis provincialis* (Figure 3c), the volatile organic compounds found in the SPME analysis were β-farnesene (44.16%), 3,7,11-trimethyl2,6,10-dodecatrienal (29.25%) and 6,10,14-trimethyl-2-pentadecanone (6.32%) (Table 1). β-Farnesene's scent is described as woody, citrus, herbal and sweet. Finally, the aroma components of *Orchis pallens* (Figure 3d) were α-zingiberene (14.67%), di-*epi*-α-cedrene (10.64%), β-curcumene (33.29%) and diethyltoluamide (13.95%) (Table 1). α-Zingiberene has a scent described as spicy, fresh and sharp, while that of di-*epi-*α-cedrene is described as woody, cedar, sweet and fresh.

**Figure 3.** (**a**) Orchis mascula; (**b**) Orchis quadripunctata; (**c**) Orchis provincialis; (**d**) Orchis pallens. Photos of V. A. Romano.

**Figure 4.** Chromatogram of volatile organic compounds from *Orchis mascula*; (a) β-terpinene; (b) 2,2,4,6,6-pentamethyl-3-heptene; (c) limonene; (d) eucalyptol; (e) linalool; (f) dodecane; (g) tridecane; (h) tetradecane; (k) germacrene D; (i) βsesquiphellandrene; (j) hexadecane; (l) heptadecane; (m) pristane; (n) octadecane; (o) 6,10,14-trimethyl-2-pentadecanone.

**Table 1.** SPME-GC-MS analysis of *Orchis* species.

## **4. Discussion**

It is interesting to note the large differences between our reported results and those reported in the Introduction section. For *O. anthropophora*, two different analyses are available [16,20]. While in the work of Cozzolino [16], only hydrocarbons with an extremely high molecular weight were found, the other article [20] reported that β-caryophyllene was the main component. In our study, β-caryophyllene was present, but the main component was tetradecanal. In the case of *O. purpurea*, no other results on the composition of the scent are available. For *O. italica*, only an article published by Cozzolino is available [16], and, also in this case, only high-molecular weight hydrocarbons were found. In our experiment, on the contrary, β-caryophyllene was the main component of the scent.

The scent of *O. pauciflora* has been determined through headspace analysis, showing the presence of 2-methyl-6-methylene-3,7-octadiene-2-ol as the main component [15]. However, in our analysis, linalool and 1,4-dimethoxybenzene were found as the main components. *O. mascula* was the object of an intense study where several different analytical methods were used. This way, headspace analysis returned *E*-ocimene as the main component of the scent [13,14]. This result was confirmed by SPME analysis [15,17]. In our analysis, as it is evident considering Figure 4, linalool was the main component of the aroma. For *O. quadripunctata*, the work of Schiestl and Cozzolino found only hydrocarbons [16]. Only hydrocarbons were found in this work, but with a significant difference in the molecular weight of the detected compounds. In the case of *O. provincialis*, the work of Schiestl and Cozzolino determined only the presence of hydrocarbons [17], while the presence of relevant amounts of β-farnesene was determined in this study. Finally, while an SPME analysis of the scent of *O. pallens* found phenethyl alcohol, farnesene and farnesol [21], our analysis of the same species found β-curcumene as the main component.

## **5. Conclusions**

This work shows the analysis of *Orchis* samples from Basilicata. The analyses were performed by using the same procedure and the same fiber in SPME-GC-MS, which allowed achieving a homogenous dataset. The analyses showed different scent compositions from those determined on samples deriving from different sites. These observed differences, when SPME of other headspace techniques is used, can depend both on the different absorption rates of the analytes on the fiber and on the variation in the scent due to natural adaptation of the plant to different environmental conditions, due, for example, to different pollination insects. A completely different consideration can be found in the work of Schiestl and Cozzolino, where a completely different analytical method was used (hexane extraction of labellum, and GC-MS analysis of the extracts). In their case, only hydrocarbons were determined. Probably, their analytical procedure was not the correct method for the determination of the orchid scent.

**Author Contributions:** Conceptualization, M.D. and V.A.R.; methodology, R.R.; investigation, R.R., L.V. and M.M.; data curation, R.L.; writing—original draft preparation, M.D. and V.A.R.; writing— review and editing, M.D., V.A.R. and R.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.


**5.3 Improvement of regional carrying capacity under sustainable development goals**

Use comparative advantage, guide population and industry to move to the area with higher carrying capacity. Northeast China is rich in resources and has the resource base to develop the primary industry and the secondary industry. However, after the service industry of the tertiary industry has become the main engine of economic development, most areas in northeast China are affected by natural and climatic conditions, which makes it difficult for them to gather and attract more population and industries, and resource-based cities that used to absorb a large number of jobs are gradually facing the pressure of population outflow. In the future, it needs to guide the migration of population and industries to the big cities in northeast China, improve the core carrying capacity of big cities, and develop the industries of higher education, scientific research, military industry and heavy industry with northeast characteristics in big cities. In other large areas, at least in the short term, resource-based cities in northeast China, as an important energy supply base, still need to continue to assume the responsibility and pressure of supplying resources and energy in order to ensure the rigid demand for resources and energy and national strategic security.

In the long run, we should strengthen the endogenous driving force of economic growth in resource-exhausted cities by cultivating diversified industrial systems. Utilize local advantages to develop diversified industries. Take Fuxin, a coal city in Liaoning province, as an example, it has vast land and great agricultural development potential, and the agricultural products processing industry is booming. Fuxin is rich in fluorite resources, and the fluorine chemical industry based on this raw material has begun to develop. Taking coal gangue produced in coal mining process as raw material, a number of building materials enterprises are emerging gradually. In view of the increasingly depleted resources, we should plan ahead to promote the development of emerging industries. For example, Volvo's plant in Daqing, is a model for the introduction of new industries. Daqing, which has no automobile industry base at all, has initially set up a high-end automobile factory, injecting new vitality into this increasingly exhausted resource-based city. In 2017, Daqing exported more than 25,000 vehicles, with an export volume of \$897 million, accounting for 74% of the city's total exports. Therefore, it is feasible to construct capital intensive industries such as automobiles in remote, cold and remote areas due to their low labor cost, high degree of automation and low transportation cost.


**1. Introduction**

The electric power sector contributes to about a quarter of the total CO2 emissions worldwide. Therefore, in most mitigation scenarios for climate change the share of low-carbon electricity supply (comprising renewable energy, nuclear and carbon capture and storage) increases from the current share of approximately 30% to more than 80% by 2050 [1].

A power plant based on integrated gasification combined cycle (IGCC) is a very suitable addition to any future power system, because it offers the possibility to capture CO2 in a very efficient pre-combustion process. Furthermore, in a poly-generation configuration, this technology is able to accommodate the intermittent renewable power generation from wind and solar and operate the gasification island at full load by producing synthetic chemical products like hydrogen, SNG, methanol, and Fischer–Tropsch fuels. For high-ash and low-rank coals, fluidized bed gasifiers are especially suitable [2].

The rate of char gasification is the limiting step in gasifiers and most relevant for determining residence times of the particles and size of the reactors. Therefore, an understanding of the mechanics of char gasification for the chosen fuel is essential for the design of gasifiers. Considerable work has been done already in the field of char gasification processes. Irfan et al. [3] did a comprehensive review on CO2 gasification of coal regarding different factors of influence like coal rank, pressure, gas composition, temperature, and mineral matter. In this study, it was concluded that CO2 gasification characteristics are hard to conclude with full authenticity and the researchers observed those differently for a variety of coals. Generally, the same is true for steam gasification [4]. Ye et al [5] investigated the kinetics and reactivity of two South Australian low-rank coals and quantified the reaction rate for steam and CO2 gasification as well as the influence of mineral content and particle size. Nevertheless, the carbon conversion in the presented data never exceeded 70% and the inhibiting effect of the products has not been included in the model. Another study by Huang et al. [6] focused on the influence of H2 and CO at different temperatures, but only worked with the reaction rate at 50% carbon conversion and omitted a comparison of different conversion models. Fermoso et al. [7] used non-isothermal experiments to determine a suitable conversion model and made statements on the errors of the models but again omitted any inhibiting influence of product gases. Everson et al. [8] assessed the gasification kinetics with steam and CO2 including the influence of the product gases of an inertinite-rich coal with isothermal measurements in a temperature range of 150 K. They used data for almost the complete carbon conversion and validated their assumed kinetic and conversion model with the measurement results but neglected to test other possible models.

It can be stated that most existing work focuses on analysis of single aspects of the gasification, and is not suitable to describe the conversion process in a gasifier completely with its changing gas compositions, temperatures, and particles of varying carbon conversion. For correct prediction of the gasifier behavior with Computational Fluid Dynamic (CFD), a sound modelling of the reaction properties of the fuel is imperative. Therefore, this work aims at demonstrating a methodology to find a complete model for the char conversion during gasification of one specific lignite char that takes into account all relevant temperatures and gas compositions for the full range of char conversion.


7. Occupational health

The OGP will evaluate the status of working environment factors that are relevant for life time extension, prior to the commencement of implementing ALE. Factors that should be include considerations for chemical/radiation exposure, lightning and ventilation, ergonomics, noise/vibration pollution, material handling and storage, outdoor operations and accommodation facilities.

The main objective of the evaluation is to provide a status of the working environment according to both technical and operational requirements. The assessment/evaluation are appropriately based upon existing conditions at the facility, and if necessary, follow-up with new evaluations and assessments as required. The operational risks of each from each finding and the future risks shall be evaluated before deciding on the implementation of measures for improving working environment.

#### 8. Engineering design

All assets are required to have design documentation available and accessible, which supports effective design at all stages of the asset life cycle and in relation to the management of aging life extension. All engineering activity to be undertaken throughout the anticipated service life of an asset should properly address life extension considerations.

### 9. Asset life extension for fixed offshore structures

Zettlemoyer [8] provides a template for the asset life of fixed offshore structures. In general, the main source of interest or ALE involves the jacket substructure which are essentially made up of tubular steel sections welded together to form a truss system. For fixed offshore structures, the jacket template or truss system is

Maintenance Management of Aging Oil and Gas Facilities DOI: http://dx.doi.org/10.5772/intechopen.82841

structure, competency or training requirements, and succession planning. Human factors analysis shall be performed where changes are made or where extended life challenges the established human, technology and organizational context. Organizational system is also a factor to be considered, which aspects include engineering design, contract and procurement management. Engineering design and related procurement activities require a thorough and careful consideration of asset aging and life extension factor. The risk from each finding and the overall potential (future) risks shall be evaluated before deciding on the implementation of

The OGP is to ensure that experience on lifetime extension from other installations and operating areas is applied to the analyses and evaluations carried out for the application. Any specific relevant information shall be included in the application document. OGPs are to ensure that the analyses and evaluation work has been carried out in accordance with the regulations, the relevant company standards and

The OGP will evaluate the status of working environment factors that are relevant for life time extension, prior to the commencement of implementing ALE. Factors that should be include considerations for chemical/radiation exposure, lightning and ventilation, ergonomics, noise/vibration pollution, material handling

The main objective of the evaluation is to provide a status of the working environment according to both technical and operational requirements. The assessment/evaluation are appropriately based upon existing conditions at the facility, and if necessary, follow-up with new evaluations and assessments as required. The operational risks of each from each finding and the future risks shall be evaluated before deciding on the implementation of measures for improving working

All assets are required to have design documentation available and accessible, which supports effective design at all stages of the asset life cycle and in relation to the management of aging life extension. All engineering activity to be undertaken throughout the anticipated service life of an asset should properly address life

Zettlemoyer [8] provides a template for the asset life of fixed offshore structures. In general, the main source of interest or ALE involves the jacket substructure which are essentially made up of tubular steel sections welded together to form a truss system. For fixed offshore structures, the jacket template or truss system is

have been verified by the appropriate technical discipline authority.

and storage, outdoor operations and accommodation facilities.

9. Asset life extension for fixed offshore structures

measures.

Maintenance Management

6. Assurance and verification

7. Occupational health

environment.

70

8. Engineering design

extension considerations.

considered as a structural safety critical element (SCE), so the integrity must always intact (Figure 9).

The jacket template substructure is to be assessed for ALE in terms of ultimate strength of the structure and fatigue life assessments. The values for the ultimate strength results are represented in terms of an RSR (Reserve Strength Ratio). The RSR is the ratio of the base shear at collapse/base shear at the 100 Yr environmental loading (i.e. the design condition). Ultimate strength analysis is also called a pushover analysis or collapse analysis and involves non-linear analytical methods (Figure 10).

For new structures a RSR value is generally over 2.0. As platforms operate for some time, degradation due to corrosion, damage due to accidental damage (vessel collision, dropped objects) is possible. Offshore structures are inspected and anomaly management is performed to detect and repair damage based on severity levels over its operations. It is expected that the RSR may be compromised and reduced if damage is unmitigated.

For every operating region the acceptance criteria need to be determined as it varies from region to region for varying levels of environmental loading. In the Gulf of Mexico (GoM) minimum acceptance criteria based on API RP2A are provided in Table 1.

The operations for fixed offshore structures can be extended if the platform in principle has greater than its minimum RSR values. For asset life extension, it is expected that all severe damage to structures has either been repaired or reduced to a manageable condition, prior to the migration to ALE. In essence the topsides of the fixed offshore needs to be appropriately assessed. This is generally done by using a risk based maintenance (RBM) program where anomalies are rectified due to severity levels. These topside RBM should be aligned to other topsides programs including piping, equipment skids and vessels to ensure that the maximum use can be made of allocated resources.

Figure 9. Typical jacket substructure for a fixed offshore structure [8].

10. Case study

support.

10.2 Situation

HSE exposure (Figure 11).

10.3 Problem analysis

Figure 11.

73

10.1 Aging electrical component

Maintenance Management of Aging Oil and Gas Facilities

DOI: http://dx.doi.org/10.5772/intechopen.82841

Facilities are designed typically with a life span of 20–25 years. However, it is becoming common for facilities, both on-shore and off-shore, to be operated beyond its life span. While assets are designed for 20–25 years, equipment age differently and suffer from different age-related failure mechanisms. Other than aging, electrical, control and instrumentation equipment suffer from obsolescence. This is primarily due to unavailability of components and end of hardware/software

A downstream refinery experienced control issues with its two reactor and regenerator slide valves on its Residue Fluid Catalytic Cracker (RFCC) unit. The symptom, initially, manifested as valve hunting. This progressively worsen to the point where the valves had to be put on manual hand wheel control. While this action temporarily stopped the valve hunting, it made control of the reactor and regenerator catalyst level very difficult as operators had to be on-site to adjust the valve opening manually. Left unresolved, the likely consequence of this situation was a process upset and RFCC unit trip. This would also cause a cascade effect, resulting in the shutdown of other units, incurring significant production losses and

The problem was initially thought to be due to a failure of the HPU control module. However, the problem was traced, eventually, to a failing DC power supply unit (PSU) which powers the control module. Once the problem was identified, replacement of the power supply unit resolved the issue. What should be noted from this seemingly straightforward problem is that both reactor and regenerator HPU units had suffered from the same problem. On closer inspection, both power supply units were of the same make and had been first installed (as part of the HPU units) at around the same time. At the time of the incident, the power supply units

A typical FCCU slide valve actuator (left) and HPU & control module (right) [12].

Figure 10. Typical schematic for load displacement curve—ultimate strength [8].

#### Table 1.

Minimum RSR values for the Gulf of Mexico (API RP 2A, 2014) [9].

The other key performance indicator for tubular joints is fatigue life prediction. In recent years through the use of proper joint configuration in design, use of joint flexibility approaches in analysis, the issue of fatigue life estimation has been argued away. In principle the acceptance criteria for ALE for a fixed offshore structure is the exceedance of the minimum RSR values. In recent years, integrity management codes of practice including API RP2SIM (2014) [10], ISO 19902:2007 [11] provide guidance on the ALE for fixed operating steel structures.


*Article* **Comparative Study on Seismic Response of Pile Group Foundation in Coral Sand and Fujian Sand**

#### **Qi Wu 1 , Xuanming Ding 1,2,\*, Yanling Zhang 1 and Zhixiong Chen 1,2**

Received: 12 January 2020; Accepted: 9 March 2020; Published: 11 March 2020

**Abstract:** The physical and mechanical properties of coral sand are quite different from those of common terrestrial sands due to the special marine biogenesis. Shaking table tests of three-story structures with nine-pile foundation in coral sand and Fujian sand were carried out in order to study the dynamic response characteristics of pile-soil-structure system in coral sand under earthquake. The influence of shaking intensity on the dynamic response of the system was taken into consideration. The results indicated that the peak value of the excess pore pressure ratio of coral sand was smaller than that of Fujian sand under two kinds of shaking intensities; moreover, the development speed of excess pore pressure ratio of coral sand was smaller than that of Fujian sand. The liquefaction of coral sand was more difficult than Fujian sand under the same relative density and similar grain-size distribution. The horizontal displacement, settlement, column bending moment, and pile bending moment of coral sand were smaller than those of Fujian sand, respectively. The magnification effect of column bending moment of buildings in coral sand was less than that in Fujian sand with increasing shaking intensity. This study can provide some supports for the seismic design of coral reef projects.

**Keywords:** coral sand; Fujian sand; shaking table test; dynamic response; pile group


**2. Conclusion(s)**

Many reports have suggested that human health may be affected by exposure to even low levels of BPA, especially during the gestation period. However, the detailed mechanisms of BPA's effects remain unknown. To further elucidate the mechanism governing the detrimental effects of EDCs on target organs, it is essential to clarify both the metabolism and elimination pathways of such chemicals in the body. However, BPA is highly glucuronidated in the intestine and liver, and the resultant formation of BPA-GA prevents a complete understanding of metabolism and disposition by facilitating deconjugation during enterohepatic circulation and systematic circulation in the body. Given that exposure to BPA could adversely affect the fetus in pregnant animals, it is critical that further work be done to determine the fate of venous GA compounds in the complete BPA pathway before excretion.

In modern society, we are continually exposed to many chemical substances. We have to deal with all of these chemicals to ensure good health. Many studies of the effects of chemical substances have focused only on terminal mechanisms. We originally developed the prominent drug metabolism systems to eliminate various chemicals in the process of evolution. The various mechanisms that determine the effects of EDCs can only be productively discussed after a more complete understanding of their metabolism systems is achieved. At that point, new precautions to avoid the risks of adverse effects could be developed.


**2. Identification of factors influencing traditional cheese productions**

CoRFiLaC is a dairy research centre that carries out research actions taking into account all the aspects of the dairy production chain, from the animal nutrition, management and animal welfare and quality of milk and dairy products to finally their consumers' acceptance and product valorization. For these purposes, it has developed several projects, together with other national and international partners, with the aim of creating a network between the research and public institutions involved in the agricultural sector, the control bodies that certify quality products and cheese producers. To strengthen this network, a common shared strategy to support the actors of the dairy chain is necessary in order to improve the quality of the productions, to reduce the geographical isolation limits and to raise consumers' awareness.

The real challenge is to demonstrate scientifically that traditional dairy products are healthy, high-quality and pleasant for consumers and carriers of cultural elements that provide an identity to a specific area of production.

Several and specific factors are responsible for the bio-organoleptic diversity of the traditional dairy products.

Every traditional cheese originates from complex production systems characterized by several "biodiversity factors", such as the environment, the macro- and microclimate, the natural pasture, the breed (often autochthonous) of the animals, the use of raw milk and its natural microflora, the use of natural coagulants, the use of natural ingredients, the use of traditional equipment and the ageing conditions [1]. The characterization of such production systems is basically

important both from a scientific point of view and for the valorization of the deriving productions.


**Acknowledgements**

*21st Century Surface Science - a Handbook*

TPU decorated with modified nanosilicas

Teflon AF (sheath)/PCL

(core)

PVC-ZnO PS-ZnO

PS-TiO2 PVC-TiO2

**Electrospun fiber coating Solvent Research field Ref.** TTSBI + DFBP Tetrachloroethane (TCE) Adsorption applications [41]

EPS and EPS/zeolite Tetrahydrofuran (THF) Water-oil separation [45]

Water-oil separation [42]

Water-oil separation [43]

Water-oil separation [44]

Water-oil separation [47]

Self-cleaning [50]

Water-oil separation [54]

Corrosion protection [57]

Corrosion protection [58]

Corrosion protection [59]

Corrosion protection [60]

Corrosion protection [62]

Corrosion protection [63]

antibacterial performance, and deodorization

Energy systems (DSSC

devices)

[49]

[52]

[55]

[56]

[61]

[65]

Desalination by membrane

distillation

skin regeneration

Water-saline solution

Desalination by membrane

separation

distillation

2,2,2-trifluoroethanol (TFE) Microfluidics [51]

N,N-dimethylformamide (DMF) and tetrahydrofuran

(DMF) and acetone

(DMAc) and acetone

(DMAc) and acetone

(DMAc) and tetrahydrofuran

PLA:PVP/PLA:PEG Dichloromethane Burn wound healing and

(DMF) and chloroform

(DMAc) and acetone

(DMF) and acetone

(DMAc) and toluene

(DMF) and tetrahydrofuran

N,N-dimethylformamide (DMF) and tetrahydrofuran

N,N-dimethylformamide (DMF) and N,N-

dimethylacetamide (DMAc)

(DMAc) and tetrahydrofuran

PLA/TiO2 Chloroform and acetone Water immobilization,

PLLA Toluene and chloroform Biomedical applications [64]

*Summary of the different sensitive electrospun coatings, the metallic substrates used, and the resultant corrosion tests for the development of new and innovative protective coatings with good anticorrosion properties.*

PCL Chloroform and methanol Fabrics [53]

(THF)

(DMF)

(THF)

PVDF N,N-dimethylformamide

PMMA N,N-dimethylacetamide

PS/TEOS/camphene N,N-dimethylformamide

PSF N,N-dimethylacetamide

PAA N,N-dimethylacetamide

PCL N,N-dimethylformamide

Cellulose acetate N,N-dimethylacetamide

PVDF-HFP N,N-dimethylformamide

PVDF/SA N,N-dimethylformamide

PVDF-ZnO N,N-dimethylacetamide

PVC-ZnO N,N-dimethylformamide

PS-TiO2 N,N-dimethylformamide

PS-Al2O3 N,N-dimethylacetamide

(DMF)

(THF)

(THF)

(DMF)

(THF)

**200**

**Table 1.**

This work was supported by the Ministerio de Ciencia, Innovación y Universidades-Retos (Project RTI2018-096262-B-C41-MAITAI) and by the Public University of Navarre (Project PJUPNA1929).