**1. Introduction**

Essential oils (EOs) have been widely used in folk medicine throughout the history of humankind. The application of EOs covers a wide range from therapeutic, hygienic, and spiritual to ritualistic purposes. EOs are aromatic, volatile, lipophilic liquids extracted from different parts of plant materials such as barks, buds, flowers, fruits, seeds, and roots [1]. EOs are mixtures of complex compounds with variable individual chemical composition and concentrations that includes primarily terpenoids, like monoterpenes (C10), sesquiterpenes (C15), diterpenes (C20), acids, alcohols, aldehydes, aliphatic hydrocarbons, acyclic esters or lactones, rare nitrogen- and sulfur-containing compounds, coumarin, and homologues of phenylpropanoids [1,2]. The biological effects of EOs cover a wide range of effects, including antioxidant, antimicrobial, antitumor, anti-inflammatory, and antiviral activity [3].

The increase in demand for the use of aromatherapy as complementary and alternative medicine has led people to believe in the myth that EOs are harmless because they are natural and have been used for a long time [4]. However, there might be several side effects of EOs even if topical administration is applied and, among these, allergic reactions are the most frequent (many EOs can cause, e.g., rashes on the skin). Some of them can be poisonous if absorbed through the skin, breathed, or swallowed. Previous studies also report the interaction of EOs with other drugs [5]. The continuous production of new aroma chemicals and their widespread and uncontrolled usage as alternative therapies together with many carrier diluents have brought serious problems, especially among children. In this regard, it is of utmost importance to study the mode of action of essential oils and to find a proper, unharmful formulation. Another serious problem is the highly lipophilic nature of EOs, which makes it impossible to measure their biological effects in an aqueous environment [1,6,7].

One major characteristic and application of EOs are their strong antimicrobial activity, including antibacterial and antifungal effects without the development of microbial resistance. Numerous studies are found in the literature describing the antimicrobial activities of a large variety of EOs [8–13]. Most of these assays include conventional broth dilution method, disk diffusion method, and bioautography assay to measure the antimicrobial activity of EOs. Efforts have been made to overcome the lipophilic nature of the oils usually by application of EOs diluted in seemingly suitable solvents/detergents. In the case of natural lipophilic volatile compounds like EOs, solvents of varying polarity, e.g., DMSO, ethanol, and methanol, are most commonly used. However, previous studies have reported the antimicrobial effects of the solvents themselves (DMSO, ethanol, and other solvents in various microbial assays) or their influence on the true antimicrobial effects of EOs [14]. The usage of solubilizing agents limits the precise determination of the antimicrobial activities of EOs. Also, a major problem might arise in the classical assays due to the evaporation of EOs during the assay or the inability of the test microbes to reach the lipophilic range of the tested EOs (in bioautography, as an example) [15,16].

Therefore, new formulations have been determined to increase the solubility or to emulsify the EOs in an aqueous environment. These efforts help to stabilize the oils, to produce an even release of the active components into the required environment, and to maintain their antioxidant and antimicrobial activities [6,11,17,18]. Detergents and organic solvents are not welcome in this regard. Attempts have been made to entrap EOs by modified cyclodextrins for the exact determination of their antimicrobial characteristics [19,20].

The application of Pickering nanoemulsion is a quite novel approach to stabilize oil-in-water (O/W) and water-in-oil (W/O) emulsions by solid particles instead of surfactants. The mechanism involves the adsorption of solid particles on the oil–water interface, causing a significant decrease in the interfacial surface tension that results in high emulsion stability [18]. Previous studies have reported decreased evaporation of EOs from O/W emulsion of nanoparticle-stabilized formulations versus EO–surfactant systems to be a beneficial factor [21,22].

Despite the numerous existing studies on EO–Pickering emulsion, we could not find any literature data on chamomile volatile oil–nanoparticle formulation [7,23]. The main aim of the present work is to use Pickering emulsion of chamomile EO stabilized with modified Stöber silica nanoparticles and characterize its antimicrobial effect using Gram-positive and Gram-negative bacteria as well as *Candida* fungal species. We could demonstrate the strong antimicrobial effects of the chamomile EO–Pickering emulsion and suggest a plausible mode of action of this formulation. Experimental efforts were made to support the suggested mode of action.

#### **2. Results**

#### *2.1. Characteristics of Stöber Silica Nanoparticles*

The mean diameter, PDI value (polydispersity index), and zeta potential of modified Stöber silica nanoparticles (SNPs) were determined by dynamic light scattering (DLS), and these values were 20 nm, 0.01, and −21.3 mV, respectively. The size and morphology of SNPs were examined by TEM (see Figure 1). The size distribution obtained by DLS was confirmed by TEM, which showed that the mean diameter of silica samples was 20 nm; they are highly monodisperse and have a spherical shape.

**Figure 1.** TEM images of silica nanoparticles (SNPs) with different resolutions: 100,000× magnification (**A**) and 500,000× magnification (**B**), accelerating voltage: 80 kV; dTEM = 20 nm. PDI = 0.015.


*Article* **An Experimental Study on the Performance of Corrugated Cardboard as a Sustainable Sound-Absorbing and Insulating Material**

**Chun-Won Kang 1, Mina K. Kim 2 and Eun-Suk Jang 1,3,\***

**Abstract:** The continuing development of industrialization and increasing population density has led to the emergence of noise as an increasingly common problem, requiring various types of sound absorption and insulation methods to address it. Meanwhile, the recycling of resources to ensure a sustainable future for the planet and mankind is also required. Therefore, this study investigates the potential of corrugated cardboard as a resource for noise reduction. The sound absorption and insulation performance of non-perforated corrugated cardboard (NPCC) were measured, and modified corrugated boards were fabricated by drilling holes either through the surface of the corrugated board alone or through the corrugated board in its entirety. The sound-absorption/insulation performance both of perforated corrugated cardboard (PCC) and perforated corrugated cardboard with multi-frequency resonators (PCCM) were measured using the transfer function method and the transmission matrix method. To determine the effectiveness of NPCC, PCC, and PCCM in noise reduction, the sound pressure level was analyzed by applying it to a home blender. The results showed PCCM's sound absorption and insulation performance to be excellent. On the basis of these findings, we propose the use of PMMC as an eco-friendly noise-reduction material.

**Keywords:** eco-friendly sound-absorbing material; corrugated cardboard; perforated corrugated cardboard; sound-absorption coefficient; sound transmission loss; transfer function method; transfer matrix method; multi-frequency resonator

#### **1. Introduction**

Because Republic of Korea is a country with small land mass and high population density, apartment housing is common [1]. Noise complaints in these apartment complexes are an unavoidable reality [2]. According to the National Noise Information System (NOISEINFO), a government agency that monitors noise problems in the country, the number of reported noise-related problems has increased exponentially, from 1829 cases in 2012 to 10,142 cases in 2019 [3].

Overall noise level reduction is necessary for maintaining an agreeable sound environment [4]. Blocking or reducing noise through sound absorption and sound insulation is a mainstream method [5]. Accordingly, a significant amount of research has been conducted in order to identify materials with excellent sound absorption and sound insulation properties [6,7]. Sound-absorbing materials typically include one or more components from porous materials [8], plate or membrane vibration materials [9], or resonators [10,11].

Traditionally, the most important metric by which a potential sound-absorbing material is assessed is its absorption ability. However, while the materials typically relied on for indoor acoustic control are typically derived from petroleum [12], recent years have

**Citation:** Kang, C.-W.; Kim, M.K.; Jang, E.-S. An Experimental Study on the Performance of Corrugated Cardboard as a Sustainable Sound-Absorbing and Insulating Material. *Sustainability* **2021**, *13*, 5546. https://doi.org/10.3390/su13105546

Academic Editor: Shervin Hashemi

Received: 8 April 2021 Accepted: 14 May 2021 Published: 16 May 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**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/).

seen a new focus on the utilization of sustainable green materials, including agricultural by-products, to fulfill this function [13–15]. In this spirit, a wide range of studies have been conducted of eco-friendly sound-absorption materials such as rice straw [16], rice husks [17], palm fibers [18], giant reeds [19], egg cartons [20], and wood paper [21].

Corrugated cardboard is a bio-degradable, eco-friendly paper material that is inexpensive and robust in relation to its weight [22]. Its thickness and empty middle space make it a useful sound insulating material [23]. It can be used as a resonance sound-absorbing material either by perforating only the surface of the corrugated board or by penetrating the entire thickness of the corrugated board. Its sound-absorption properties for specific frequency bands can be enhanced by modifying hole size and depth [24]. Corrugated cardboard can also be used as an interior building material and, when discarded, can be reused for pulp or paper [25]. Corrugated cardboard is known for its utility as a building material [25,26], and some studies have suggested the potential of hydrophobic treatment to avoid moisture absorption and further expand its versatility [27,28]. Moreover, it may act as a flame retardant, improving building safety [29].

With an eye toward these benefits, we set out to determine whether corrugated cardboard could be utilized as a sustainable noise-reducing building material.

Berardi and Iannace [30] measured cardboard's sound-absorption coefficient by inserting its veins in a direction parallel to the impedance tube. This resulted in excellent sound-absorption performance at medium and high frequencies but poor performance at low frequencies below 400 Hz. In short, while the material performed well in the veins of the cardboard direction, as a practical matter, it is not easy to use the material in this way.

Kang and Seo [31] investigated changes to the resonant frequency of the cardboard as a function of changes to the aperture ratio. They found no significant changes but reported that sound absorption at a specific frequency was significantly increased by perforations of a certain depth and size. Kang et al. [32] reported that applying porous polyurethane foam attached to a corrugated cardboard to a household blend reduced the sound-absorption coefficient and sound transmission loss. Polyurethane foam, however, is not an eco-friendly sound-absorbing material, and sound-absorbing performance may be improved with additional research into the corrugated board itself.

This study developed a natural sound absorber using triple-layer-corrugated cardboard whose inner surface layers were pierced with holes to enhance its resonance soundabsorbing properties. Three types of corrugated cardboard were prepared: non-perforated corrugated cardboard (NPCC), perforated corrugated cardboard (PCC), and perforated corrugated cardboard with multi-frequency resonator (PCCM). The sound absorption and insulation properties of each of these types were then measured using the transfer function method and the transmission matrix method.

To test these corrugated sound-absorbing materials, they were applied to the use case of home blenders. A home blender was selected as a noise generator since it is one of the most common noise-producing household appliances [33].

The noise level of the blender's rotor was first analyzed. After this, a Helmholtz resonator actively formed in the thickness direction corresponding to the frequency characteristics of the noise source was created on the surface of the corrugated cardboard.

An additional cavity layer was installed between the single-resonator corrugated cardboard and the NPCC, while part of the surface perforation was connected to the rear layer in order to create a multi-resonator with multiple frequencies. The noise reduction effect on the blender was evaluated by measuring and comparing the sound-absorption rate, acoustic transmission loss, and noise level of the fabricated single-resonator and multi-resonator-perforations in relation to hole diameter and perforation ratio.

#### **2. Materials and Methods**

*2.1. Sample Preparation*

As shown in Figure 1, triple-walled, seven-layer corrugated cardboard with 1800 g/m2 base weight and 15.0 mm thickness was sourced from a Korean market (Daeyoung Packaging Co, Ltd., Ansan-si, Korea).

**Figure 1.** Triple-wall corrugated cardboard structure.

In this study, three types of corrugated cardboard were used as sound absorbers. Figure 2 shows the respective structures of the three corrugated cardboard types, while Figure 3 shows application of the cardboard to the blender. NPCC denotes non-perforated corrugated cardboard (Figure 3a). PCC denotes single-resonance sound-absorbing corrugated cardboard whose surface liner paper was pierced with 2.3 mm diameter holes at 14 mm intervals (Figure 3b).

**Figure 2.** Three corrugated cardboard type structures. (**a**): NPCC, (**b**): PCC and, (**c**): PCCM.

PCCM denotes PCC with additional 3.0 mm diameter holes (1/4 of the number of 2.3 mm diameter holes). The 3.0 mm diameter holes pierced all 7 layers of the corrugated cardboard. There was a 4 cm air cavity at the back where we added NPCC (Figure 3c). Resonance occurs at different frequencies depending on perforated hole diameter, surface liner paper thickness, perforated hole area, and distance from the inner liner paper.

The resonance sound-absorption frequency was calculated as follows:

$$f\_0 = \text{c} / 2\pi (\text{G} / V)^{0.5} \tag{1}$$

$$G = \text{s} / \text{le} \tag{2}$$

$$le = l + \delta \tag{3}$$

where *c*: sound velocity; *G*: neck conductivity; *V*: cavity volume; *s*: neck surface area; *le*: effective neck length; *l*: neck length; and *δ*: end correction (- 0.8d) Therefore,

$$f\_0 = \text{c/} 2\pi (\text{s/}V(1+\delta))^{0.5} \tag{4}$$

**Figure 3.** Three types of corrugated cardboard types for sound-absorbing materials applied to the blender. (**a**): NPCC, (**b**): PCC, and (**c**): PCCM.

#### *2.2. Measurement of Sound-Absorption Coefficient Using Transfer Function Method*

The sound-absorption coefficient (SAC) of the NPCC, PCC, and PCCM was measured using a B&K type 4206 impedance tube according to ISO 10534-2 [34] (Figure 4). We additionally calculated the noise-reduction coefficient (NRC) as the average of the soundabsorption rates of 200, 500, 1000, and 2000 Hz.

**Figure 4.** Schematic for B&K type 4206 impedance tube used to measure SAC.

Specimens were cut into 29 mm diameter pieces and inserted into an impedance tube. We added silicon O-rings to prevent experimental errors due to gaps between the sample and the wall of the impedance tube. SAC was measured in the 100–6400 Hz frequency range. Temperature, relative humidity, and air pressure were 25.8 ◦C, 53%, and 1012.00 hPa, respectively. Sound velocity, air density, and acoustic impedance were 346.62 m/s, 1.177 kg/m3, and 408.0 Pa/(m/s), respectively.

#### *2.3. STL Measurements Using Transmission Matrix Method*

Sound transmission loss (STL), the ratio of the difference between incident sound energy to the material and transmitted sound energy to the material, represents the material's sound insulation performance. In this study, STL was measured in the 100–6400 Hz frequency band by the transmission matrix method according to ASTM E-2611 [35], using a B&K type 4206-T impedance tube to measure acoustic transmission loss (Figure 5). Temperature, relative humidity, and air pressure during measurement were 26.3 ◦C, 50%, and 1010.0 hPa, respectively.

**Figure 5.** Schematic for B&K 4206-T impedance used to measure STL.

#### *2.4. Sound Pressure Level Analysis*

To verify the actual noise reduction effect, we fabricated a cover using NPCC, PCC, and PCCM and applied this to the blender. First, we performed a sound pressure level analysis. Sound pressure level was measured using a B&K type 2250 handheld sound analyzer in the 63–16,000 Hz frequency range with a 1/3 octave analyzer. Sound pressure level can be measured from 63 Hz. However, there is an experimental error due to noise generation below 50 Hz; therefore, it was measured at frequencies above 100 Hz [33,34]. Sound pressure was measured approximately 1 m from the top of the blender, and the sound pressure level is given as the average of the measured values over 20 s. At the blender's maximum power, the maximum noise peak was in the 1–2 kHz frequency band. Using this information, the resonance cover was optimized for the 1–2 kHz frequency band. The blender was enclosed with the NPCC, PCC, and PCCM covers, and noise levels measured. Temperature, relative humidity, and air pressure during measurements were 28.7 ◦C, 54%, and 1017.2 hPa, respectively.

#### **3. Results and Discussion**

*3.1. SAC Results from Transfer Function Method*

Figure 6 shows the SAC for NPCC, PCC, and PCCM in the 100–6400 Hz frequency range measured by impedance tube. The average SAC and NRC of NPCC were 0.062 (SD 0.010) and 0.055 (SD 0.012), respectively. This indicates almost no sound absorption. The average SAC and NRC of PCC were 0.331 (SD 0.009) and 0.346 (SD 0.007), respectively. The average SAC and NRC of PCCM were 0.362 (SD 0.017) and 0.423 (0.009), respectively.

**Figure 6.** SAC results for NPCC, PCC, and PCCM.

As shown in Figure 2b, the PCC's void volume between the perforated surface liner paper and the inner liner paper becomes a single resonator. This is why it can resonate at specific frequencies.

The theoretical resonance frequency of PCC calculated from Equation 4 was 1102 Hz. As shown in Figure 6, the resonance frequency of the PCC obtained experimentally was 936 Hz. As Figure 2c shows, the PCCM's void volume is equal to that of the PCC, plus additional void volume between the corrugated cardboard and the rear space. This means that resonance occurs in two places, at 380 and 1050 Hz. The theoretically calculated resonance frequency and experimentally measured resonance frequency were little bit different. The cause of such an error is that the corrugated cardboard does not only absorb sound by resonance, but an effect due to weak plate vibration may be added. In addition, in this study, since the hole was drilled by the experimenter, not by a machine such as CNC, the hole size may not be constant; therefore, the frequency mismatch can be regarded as an experimental error.

As a result, the average SAC of the PCC increased approximately 5 times more than that of the NPCC, while the NRC of the PCC increased approximately 6 times more than that of the NPCC. In addition, the PCC had peak values of SAC, which were 0.754 at 936 Hz and 0.457 at 4264 Hz. The SAC of the PCC was significantly increased at a specific frequency.

The average SAC of the PCCM was similar to that of the PCC, but the NRC of the PCCM increased approximately 1.2 times more than that of the PCC. The SAC peak values for the PCCM were 0.680 at 1232 Hz, 0.628 at 2704 Hz, and 0.469 at 240 Hz. The PCCM is a multi-resonator and showed peak SAC values at various frequencies.

Compared with other NRC natural fiber composite board (Bagasse: 0.32, Bamboo: 0.35, Banana 0.40, Coir of 0.29, Corn: 0.36) [36], the PCCM demonstrated higher soundabsorption capabilities.

We also compared the sound-absorption performance against Wooden MPP (microperforated panels), which are an eco-friendly sound-absorbing material in wide use. We extracted raw data for the sound-absorbing graph result of wooden MPP with holes of 2 mm in diameter and 10 mm intervals in a 5 mm wooden board and the rear air cavity set to 50 mm from the previous study [37] using Engauge Digitzer software [38], and compared the performance of this material with our cardboard. In addition, we added to compare with parallel direction of NPCC from Beradi et al. [27].

As shown Figure 7, PCC's SAC absorbed sound better over 680 Hz than Wooden MPP, and PCCM generally performed better at sound absorption than Wooden MPP, with the exception of the 400–600 Hz range. The wooden MPP had a rear space of 50 mm, while that of the PMCC was 40 mm. Were the space behind the PCC and PCCM to be further increased, the SAC at low frequencies might increase even more.

**Figure 7.** Compared SAC results for Wooden MPP from Song et al. [37], NPCC (Parallel direction) from Berardi et al. [27], NPCC, PCC, and PCCM from this work.

PMCC generally performed better at sound absorption than parallel direction of NPCC from Beradi et al. [27], with the exception of the 320–940 Hz range.

In sum, PCC and PMCC did not perform worse than Wooden MPP and parallel direction of NPCC. Corrugated cardboard is cheaper than wooden boards, lighter, easier to install, and easier to recycle. In addition, parallel direction of NPCC is not easy to use practically as sound-absorption material. Therefore, PCC's and PMCC's many advantages make them ideal environmentally friendly sound-absorbing materials.

In sum, PCC and PCCM did not perform worse than Wooden MPP. Corrugated cardboard is cheaper than wooden boards, lighter, easier to install, and easier to recycle; PCC's and PMCC's many advantages make them ideal environmentally friendly soundabsorbing materials.

#### *3.2. STL Results from Transmission Matrix Method*

Figure 8 shows the SLT of NPCC, PCC, and PCCM in the 100–6400 Hz frequency range.

**Figure 8.** STL for CC, PCC, and PCCM.

The average STL of NPCC, PCC, and PCCM were 48.246 (SD 4.683) dB, 25.590 (SD 1.839) dB, and 65.011 (0.878) dB, respectively.

NPCC is a good sound insulation material in itself. The PCC's STL was significantly lower than that of the NPCC due to the surface liner paper perforations. However, the PCCM also showed good sound-insulation performance because the multi-perforated corrugated cardboard on the front side and the NPCC on the rear-side block the sound energy.

Corrugated cardboard has a low specific gravity and is thick; therefore, transmission loss is generally high. As frequency increases, transmission loss decreases. Below 1000 Hz, corrugated cardboard is a good sound-insulation material.

#### *3.3. Sound Pressure Level Analysis*

The average sound pressure level of the blender without a cover was 86.267 (SD 1.840) dB, while the levels using NPCC, PCC, and PCCM were 75.500 (SD 0.432) dB, 72.133 (SD 1.096) dB, and 64.367 (SD 0.573) dB, respectively (Figure 9).

NPCC application already reduced the blender's sound level pressure by 11 dB solely on account of its sound insulation effect. PCC application lowered the noise reduction rate by 13 dB, and the PCCM reduced the noise by 22 dB. There was no difference in the average SAC between the PCC and the PCCM, but the PCCM's blender noise reduction effect was greater than that of the PCC. This is because the PCCM's sound-absorption peak frequency range was similar to the blender's noise frequency range.

#### **4. Conclusions**

The possibility of using corrugated cardboard as an eco-friendly sound-absorbing and insulating material was investigated. The material was applied to a blender to evaluate its noise reduction effect. The results of the study are as follows:

On the basis of the findings in this study, it will be possible to develop a variety of sound-absorbing and sound-insulating materials using the method of corrugated cardboard perforation. In the low-carbon era, in which the recycling of resources has become a necessity for the sustainable future of the earth and humanity, corrugated cardboard is likely to become an increasingly valuable resource as an eco-friendly sound-absorbing material.

**Author Contributions:** C.-W.K.: conceptualization, investigation, methodology, writing—original draft, writing—review and editing. M.K.K.: conceptualization, methodology, supervision, writing review and editing. E.-S.J.: experiment, data analysis, supervision, writing—original draft, writing review and editing. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2019R1I1A3A02059471). It was supported under the international cooperation program framework managed by the NRF of Korea (NRF-2020K2A9A2A08000181).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

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

#### **References**


**ALTERNATIVE FUELS, TECHNICAL AND ENVIRONMENTAL CONDITIONS**

Edited by **Krzysztof Biernat**

### **Alternative Fuels, Technical and Environmental Conditions**

http://dx.doi.org/10.5772/60628 Edited by Krzysztof Biernat

### **Contributors**

Maciej Mikulski, Sławomir Wierzbicki, Marta Ambrosewicz-Walacik, Kamil Duda, Andrzej Piętak, Maciej Paczuski, Andrzej Pankowski, Ryszard Puławski, Kamil Kurpiel, Marcin Przedlacki, Marcin Marchwiany, Ph.D. Anna Matuszewska, Miloslaw Kozak, Jerzy Merkisz, Pawel Fuc, Piotr Lijewski, Michal Dobrzynski

### **© The Editor(s) and the Author(s) 2016**

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**Deceleration Planning Algorithm Based on Classified Multi-Layer Perceptron Models for Smart Regenerative Braking of EV in Diverse Deceleration Conditions**

#### **Gyubin Sim 1, Kyunghan Min 2, Seongju Ahn 2, Myoungho Sunwoo 2 and Kichun Jo 3,\***

Received: 12 August 2019; Accepted: 16 September 2019; Published: 18 September 2019

**Abstract:** The smart regenerative braking system (SRS) is an autonomous version of one-pedal driving in electric vehicles. To implement SRS, a deceleration planning algorithm is necessary to generate the deceleration used in automatic regenerative control. To reduce the discomfort from the automatic regeneration, the deceleration should be similar to human driving. In this paper, a deceleration planning algorithm based on multi-layer perceptron (MLP) is proposed. The MLP models can mimic the human driving behavior by learning the driving data. In addition, the proposed deceleration planning algorithm has a classified structure to improve the planning performance in each deceleration condition. Therefore, the individual MLP models were designed according to three different deceleration conditions: car-following, speed bump, and intersection. The proposed algorithm was validated through driving simulations. Then, time to collision and similarity to human driving were analyzed. The results show that the minimum time to collision was 1.443 s and the velocity root-mean-square error (RMSE) with human driving was 0.302 m/s. Through the driving simulation, it was validated that the vehicle moves safely with desirable velocity when SRS is in operation, based on the proposed algorithm. Furthermore, the classified structure has more advantages than the integrated structure in terms of planning performance.

**Keywords:** deceleration planning; multi-layer perceptron; smart regenerative braking; driving behavior; electric vehicles

#### **1. Introduction**

"One-pedal driving" is one of the many remarkable changes as the paradigm of vehicle platforms switches from an internal combustion engine to electric vehicles (EVs) [1–3]. It makes driving with only the accelerator pedal possible by generating regenerative braking torque when the accelerator pedal is released. This can increase driver convenience as there are fewer pedals to shift.

The smart regenerative braking system (SRS) is one of the "one-pedal driving" technologies, which can take advantage of both the improvement of driver convenience and energy efficiency [4]. It is a type of advanced driver assistance system (ADAS) in that it supports driving using a radar sensor. Unlike general one-pedal driving, it does not always generate regenerative torque when the accelerator pedal is released. However, it does generate adequate regenerative torque according to car-following situations. The amount of regenerative torque is appropriately determined by relative distance, relative velocity, and speed of the ego vehicle.

SRS is an intelligent braking system which controls the friction braking or regenerative braking to meet certain goals such as safety, energy efficiency, and braking performance. There were various studies regarding the intelligent braking system. In Reference [5], an ADAS system regarding brake assistance based on the driver behavior and situation was proposed. Lin et al. suggested active control of regenerative braking to improve the braking performance of an electric vehicle [6]. In Reference [7], a brake-by-wire actuator was designed to shorten the braking distance and time. Similar to Reference [5], SRS improves the safety of the vehicle by intelligent braking. In addition, the system can enhance driver convenience.

Since SRS requires the deceleration to be used as a reference value in the automatic regenerative torque control, a deceleration planning algorithm is necessary. There are some points to consider when designing a deceleration planning algorithm for SRS. The first essential point is the harmony between the acceleration by the human driver and the deceleration by the SRS. It can be achieved by generating a deceleration profile that is similar to human driving [8,9]. A second crucial point is the applicability of SRS to diverse deceleration conditions to maximize the advantages of SRS, such as driver convenience and energy efficiency. A third important point is the usability in vehicles online.

In order to mimic human driving, a machine learning technique is an appropriate method because it can learn the characteristics of human driving. Various researches were conducted to predict acceleration and velocity using an artificial neural network (ANN) [10–16]. In particular, Reference [17] proposed an ANN model to predict acceleration with four inputs: relative distance, relative velocity, velocity, and desired speed. In Reference [18], a fuzzy rule-based neural network which used relative distance, relative speed, vehicle speed, and actions of the previous time step was designed to capture the vehicle motions. Khodayari et al. proposed a neural network model focusing on human driving behavior in Reference [16]. Unlike other researches, it used estimated instantaneous reaction delay to capture the realistic driver behavior of moving the foot between the accelerator and the brake pedal. These researches showed an acceptable performance of acceleration prediction. However, they lacked harmonization with the acceleration by human driver when applied to SRS because they could not accurately represent the driver behavior, especially in deceleration scenarios. In addition, these algorithms could only be applied in car-following conditions.

To maximize the advantages of SRS, the deceleration planning algorithm should generate deceleration in not only car-following condition, but other deceleration conditions as well. Intelligent transportation system (ITS) information is actively used in ADAS and energy management systems in various driving situations [19,20]. The deceleration planning algorithm can be applied in diverse deceleration situations using ITS information.

A third important point is the usability in vehicles. The deceleration planning algorithm should operate in vehicles online. Accordingly, only the information which is acquired in vehicles on-board can be used as an input of the algorithm.

Some researches related to deceleration planning and speed prediction were conducted. Yeon et al. developed a recurrent neural network (RNN) model to predict vehicle speed with a 10-s prediction horizon [21]. The proposed algorithm showed better prediction performance than other algorithms that were suggested. However, it had a limitation in that it could only be applied in the specific route used to train the RNN model because the algorithm used the position in the route as an input of the model. Min et al. proposed an RNN model to generate a deceleration profile at braking conditions. The accuracy of the model was improved by using a physical constraint to stop at the specific location. However, the model could only be used in braking conditions at a traffic light.

To overcome these limitations of previous research, this paper proposes a deceleration planning algorithm using classified multi-layer perceptron (MLP) models. The noticeable feature of the proposed algorithm is the classified structure. To improve the planning performance of the model, the deceleration models were developed individually in three different deceleration conditions: car-following, speed bump, and intersection. Each model was trained with the driving data acquired through vehicle experiments. Unlike the previous studies on driver models, the suggested MLP model considers the human reaction delay in deceleration, which results in the generated deceleration mimicking the coasting behavior. In particular, learning of the coasting behavior was adequately performed by appropriately processing the target data. In addition, the reference value of acceleration was used as input. As a result, the vehicle reached the required velocity successfully with the suggested algorithm in the three deceleration conditions. Moreover, the model was applicable in more diverse situations by using ITS information.

Because there are three models which are specialized in the three deceleration conditions, the planning algorithm should select the MLP model to be used. For this, a state recognition algorithm was designed. Using data such as accelerator pedal position, velocity, relative distance, and distance to speed bump, it recognizes the necessity of deceleration and the cause of it. Using the cause of deceleration, the adequate model which has specialized inputs in each deceleration condition is selected and used in planning. This results in acceptable performance in using the suggested algorithm in the SRS.

The proposed algorithm was validated through driving simulations using driving data. Then, the safety of the proposed algorithm was evaluated. In addition, the similarity to human driving was analyzed. Moreover, the planning results of the proposed algorithm were compared to the results with integrated structure.

The rest of the paper is organized as follows: Section 2 illustrates an overview of the entire algorithm. Section 3 describes the state recognition algorithm which determines the driving state and the cause of deceleration, called the "deceleration condition". The set of input and hidden layers of the MLP model, and the hyper-parameter optimization methods are described in Section 4. In Section 5, the training of the MLP models and the data used in the training are described. In Section 6, the simulation results of the suggested algorithm are shown and compared to the algorithm of integrated structure. Section 7 discusses the results and concludes the paper.

#### **2. System Overview**

#### *2.1. Description of Deceleration Conditions*

To minimize the number of braking instances by a driver, the deceleration planning algorithm should be able to generate deceleration in diverse situations. In this research, three deceleration situations were selected: car-following, speed bump, and intersection. Car-following was chosen because car-following is most common in both urban and highway driving. Speed bump and intersection were selected because they are major deceleration causes in urban driving. With these three deceleration conditions, the algorithm can be applied in most deceleration situations. In urban driving, traffic lights represent a major deceleration situation. However, its signal cannot be acquired in the target vehicle, which was used in the vehicle experiments on-board; thus, the condition is excluded in this research because the proposed algorithm was designed considering its usability in vehicles online. Details of each deceleration condition are given below.

The car-following condition is a driving situation where the leading car gets close to the ego vehicle. In this situation, the relative velocity is negative and the relative distance decreases for a few seconds.

The second situation is a speed bump, where the human pushes the brake pedal to pass through it smoothly. The location of the speed bump and ego vehicle in the route are acquired from the navigation device in the vehicle used for the experiment in this research. Distance to speed bump was calculated using the locations and used to check the reason for deceleration.

The third condition is an intersection. At an intersection, there are three options of driving according to the path: right turn, left turn, and straight. In right-driving countries, the movement of the vehicle is decided by the traffic signal for a left turn and going straight. To design the deceleration model in the two situations, the signal of the traffic light is required. However, it cannot be acquired from the current navigation device in the vehicle. Therefore, only the right turn was included in the intersection condition.

#### *2.2. Algorithm Overview*

As mentioned in Section 1, the suggested algorithm has a classified structure. Compared to the integrated structure, the number of inputs in each MLP model is reduced, and the size of the model decreases. In addition, better planning performance can be anticipated, because each model is trained with the deceleration profile which is matched to each deceleration situations.

Because of the classified structure, the model used in the deceleration planning should be selected. In addition, the vehicle motion should be monitored because automatic regeneration can operate only when the driver does not push the pedals. To monitor the vehicle motion and determine the current cause of deceleration, a state recognition algorithm was designed.

Figure 1 shows the overall structure of the entire algorithm. Firstly, the state recognition algorithm determines the driving state which refers to the vehicle motion based on the driver's behavior of pushing the pedal. Then, it determines the cause of deceleration, called the "deceleration condition". After the deceleration condition is selected, the deceleration model generate the deceleration of the next time step using the trained MLP model suitable for the deceleration condition.

**Figure 1.** Overall structure of the deceleration planning algorithm.

#### **3. State Recognition Algorithm**

#### *3.1. Driving State Recognition Algorithm*

The SRS operates only in situations where deceleration is needed. Specifically, the SRS should not work when the driver pushes the accelerator pedal. To determine whether SRS can operate or not, the "driving state" has to be defined. It refers to the state of vehicle movement based on the behavior of drivers pushing the accelerator or brake pedal. There are four driving states categorized by the driving state recognition algorithm: driving, coasting, deceleration, and stopping. The meaning of each driving state is as follows:

The driving state recognition algorithm was designed as a simple state flow chart, as shown in Figure 2. State transition occurs from one state to another using on-board sensor data. Data such as accelerator pedal position (AP), brake pedal position (BP), and velocity are used as conditions for state transitions. In Figure 2, "AP on" and "BP on" refer to pushing each pedal. Likewise, "AP off" and "BP off" refer to releasing each pedal.

**Figure 2.** State flow chart of driving state recognition algorithm.

#### *3.2. Deceleration Condition Recognition Algorithm*

"Deceleration condition" means the cause of deceleration. There are three deceleration factors as described in Section 2.1: car-following, speed bump, and intersection. The deceleration condition recognition algorithm compares the effect of each deceleration factor and chooses one of the three factors as the deceleration condition.

When there are more than two deceleration factors at the same time, the algorithm should determine which deceleration factor has a greater impact on the necessity of deceleration. A constant acceleration (CA) model is used to evaluate the significance of each deceleration factor. It is a parametric equation which calculates the acceleration to reach the required velocity at a specific location. It consists of current vehicle speed, required velocity, and the distance to a specific location, as shown in Equation (1) and Figure 3.

$$a\_{CA} = \frac{v\_2^2 - v\_1^2}{2d}.\tag{1}$$

**Figure 3.** Driving situation for constant acceleration model.

Three parameters in the CA model (current vehicle speed (*v*1), required velocity (*v*2), and the distance (*d*) to the object) should be determined to calculate the acceleration. In the three parameters, the required velocity and the distance to the object depend on the deceleration factors. Their categorization according to deceleration factors is shown in Table 1. The values of minimum velocity for speed bump and intersection were confirmed as fixed values in each deceleration condition based on experimental data.

After the values of acceleration were calculated by the CA model in the three deceleration factors, they were compared to each other to determine the most influential deceleration factor. A large absolute value of acceleration in a deceleration factor means that the deceleration factor is dominant. However, the method of selecting the biggest value can cause a problem of frequent switching when two deceleration factor values are the same level. To prevent this type of problem, hysteresis was

applied in determining the deceleration condition. Thus, the deceleration condition changes from one deceleration factor to another when the difference in absolute value calculated by the CA model is bigger than 0.2 m/s2.

#### **4. Deceleration Model Based on Multi-Layer Perceptron**

The suggested algorithm uses an MLP model to generate the deceleration used in automatic regeneration. MLP is one of the simplest types of ANN. Although it has a simpler structure compared to other types of ANN such as RNN or convolutional neural network (CNN), it can capture the nonlinear characteristics between multiple inputs and outputs, which is adequate when considering the human driving behavior. In addition, it is more suitable for application in vehicles because it usually has a smaller size than other ANN structures.

In this section, the input layer of the MLP model was designed based on the analysis of the human driving behavior. Then, the grid search algorithm was used to optimize the structure of the hidden layer. In the grid search, the data acquired from the vehicle experiments were used to train the model.

#### *4.1. Design of the Input Layer*

#### 4.1.1. Driver Behavior during Deceleration

Prior to designing the deceleration model, deceleration profiles in each deceleration situation were analyzed to select the proper input set of the MLP model. In the data acquired from the vehicle experiments, the deceleration part in the three deceleration conditions were sliced and analyzed.

The deceleration profiles in each deceleration situation are shown in Figure 4. They are sliced deceleration profiles from when the accelerator pedal was released to when the brake pedal was released. The deceleration when both the accelerator and brake pedal were released was processed as zero. Additionally, the deceleration profiles of a human driver were compared to the deceleration profiles calculated by the CA model, which was modified from the original version.

**Figure 4.** Deceleration profiles in (**a**) car-following; (**b**) speed hump; and (**c**) intersection.

The deceleration profiles had two common features regardless of the deceleration conditions. Firstly, drivers constantly released both the accelerator and the brake pedal. This driving behavior is usually called "coasting". Although the duration of coasting behavior is different depending on the driving situation, and although there is uncertainty in that behavior, human drivers always display the coasting behavior.

Secondly, the deceleration gradually finished as the acceleration by a CA model operated in accordance with the deceleration condition. Therefore, the shape of the deceleration profiles in each deceleration condition were highly similar to the acceleration profiles generated from the modified constant acceleration model. The modification depending on the deceleration condition is described in the next few paragraphs.

In a car-following condition, drivers usually control the speed of the vehicle to follow the speed of the preceding vehicle. However, the following vehicle's velocity can often range between 0.5 m/s slower and 0.5 m/s faster than the preceding vehicle. Therefore, Equation (1) was modified to Equation (2) to consider the driver intention in each sliced deceleration profile, where *v*1 and *v*2 in the equation are the speed of the ego and leading vehicle, *v*1, *final* and *v*2, *final* are the specific parameters of the ego and leading vehicle's speed at the end of the each sliced deceleration profile, and *d* is relative distance.

$$a\_{\triangle A} = \frac{\left(\upsilon\_2 + \left(\upsilon\_{1,final} - \upsilon\_{2,final}\right)\right)^2 - \upsilon\_1^2}{2d}.\tag{2}$$

In speed bump and intersection conditions, the minimum velocity is considered in calculating constant acceleration instead of the velocity of the leading vehicle. However, the minimum velocity is different in each deceleration profile because the deceleration aim of the driver differs. Likewise, the minimum velocity depends on the driver's intention at intersections. Therefore, the original constant acceleration model was modified to Equation (3), to consider the driver's intention in the sliced deceleration profiles.

$$a\_{\rm CA} = \frac{v\_{1,final}^2 - v\_1^2}{2d}.\tag{3}$$

#### 4.1.2. Selection of the Input Set

The selection of the input set for the ANN model is important because the model performance highly depends on the appropriate set of inputs [22]. There were various researches to represent the microscopic motion of vehicles in car-following conditions. Regardless of the type of methods used in car-following models, there are three common elements in their input sets: ego vehicle speed, relative distance, and relative velocity, which shows that they are highly correlated with the movement of the vehicle in car-following conditions.

The three inputs mentioned in the previous paragraph were also used in this research. However, there were no relative distance and relative velocity data in the speed bump and intersection conditions. Therefore, these two inputs were replaced with other information that can be acquired by the navigation device. The relative distance was replaced with distance to speed bump and intersection. The relative velocity was replaced with difference between the speed of ego vehicle and minimum velocity. The minimum velocity changes depending on each deceleration profile and deceleration condition. Therefore, it is defined as the velocity at the end of each deceleration profile.

The deceleration models use two more inputs in addition to these three inputs. One is "coasting time" to simulate the coasting behavior of human driving and the other one is "reference acceleration" to deal with the end condition in each deceleration condition. One of the important points in designing the deceleration model for the SRS is the harmony between the acceleration of the human driver and the deceleration by the SRS. The stability is increased by mimicking the coasting behavior because drivers usually feel discomfort when the regenerative torque is generated right after the accelerator pedal is released. In addition, to guarantee safety and reach adequate velocity, the performance of the model should be improved. The reference acceleration helps the training of the MLP models and improves the model performance.

Costing time means the time taken after the accelerator pedal is released. The learning of coasting behavior with only the velocity, relative distance, and relative velocity is possible to some extent, but it cannot take the delay from the human reaction into account. When the coasting time is added to the input set of models, the algorithm can consider the nonlinear characteristic of coasting behavior based on the reaction delay and driving situations. The coasting time is calculated by counting the tick every 100 ms after the value of the accelerator pedal position sensor (APS) becomes zero.

Reference acceleration refers to the acceleration calculated by the modified CA model shown in Equations (2) and (3). When the modified constant acceleration model is applied, the deceleration profile follows the profile generated from the modified CA model in the final part of the profile in each deceleration condition as shown in Figure 4. Therefore, the acceleration calculated from the modified CA model is used as an input for the MLP models to help training by providing the standard value in the final part of the deceleration.

#### 4.1.3. Normalization of Inputs

In addition to selecting an adequate set of inputs, normalization of inputs is crucial in designing the input layer. The selected inputs have different ranges of values depending on their type. The different scales of values can slow down the training process, and produce a poor performance result. Therefore, normalization of each input was conducted by min–max normalization method as shown in Equation (4), which makes the value of each parameter between 0 and 1. The overall structure of the MLP model based on the selected input set and normalization is shown in Figure 5.

$$\chi\_{\text{ITOM}} = \frac{\mathbf{x}\_{\text{in}} - \min(\mathbf{x}\_{\text{in}})}{\max(\mathbf{x}\_{\text{in}}) - \min(\mathbf{x}\_{\text{in}})}. \tag{4}$$

**Figure 5.** Overall structure of the multi-layer perceptron (MLP) model.

#### *4.2. Design of the Hidden Layer*

To design the hidden layer of the MLP model, various hyper-parameters should be selected such as the number of hidden layers and hidden nodes, the activation function, the optimizer, and the training iteration. Before optimizing the hyper-parameter set, hyper-parameters in an adequate range were found by hand-tuning as shown in Table 2. The number of hidden layers was fixed as two considering the small number of inputs. The model with only two hidden layers showed acceptable performance. As candidates of optimizers, four types of optimizer were used in optimization: Stochastic gradient descent (SGD), Adaptive subgradient (ADAGRAD), nesterov-accelerated ADAM (NADAM), and'RMSprop which was proposed by Geoff Hinton in his lecture.

Because the size of the model with determined range of the hyper-parameter set was small, the set was optimized based on a grid-search algorithm. The grid-search algorithm is the optimization method which tries all candidates and selects the best set. Due to the small model size, training time of the model was short, which made it possible to use the grid-search algorithm.

#### **5. Experiments**

In this section, the experimental environments in each deceleration condition are described. The experimental data were divided into three different parts to prevent overfitting. Then, the MLP models were trained with the training dataset, and the optimum set of hyper-parameters was selected by the grid-search algorithm.

#### *5.1. Experiment Environments*

#### 5.1.1. Test Vehicle Configuration

Vehicle experiments were conducted to acquire the data used to train the MLP models. The vehicle used in the experiment and the data acquisition system are shown in Figure 6. The specifications of the radar sensor are in Table 3. We obtained information related to the leading car such as the relative distance velocity through the radar sensor. Moreover, the ITS information, such as location of speed bumps and intersections, was collected using the navigation device in the test vehicle.

**Figure 6.** Test vehicle and logging environment.

#### **Table 3.** Specifications of the radar sensor.

#### 5.1.2. Test Route

The vehicle experiment was conducted in Incheon, Korea in the routes shown in Figures 7–9. A test driver drove the vehicle; thus, the different driving characteristics depending on the driver were excluded. In addition, in each experiment, the cause of the deceleration was excluded. For example, there was no preceding vehicle in the experiment for the speed bump situations.

Experiments were conducted in the route shown in Figure 7. The ego vehicle decelerated multiple times depending on the velocity change of the preceding vehicle. To maintain a safe distance, the ego vehicle was decelerated by the test driver. The deceleration started from 10 to 20 m/s and finished at various speeds, including zero.

The experiments for speed bumps and intersections were conducted in the routes shown in Figures 8 and 9, and the locations of speed bumps and intersections are represented in each figure. To exclude the effect of the preceding vehicle, the experiment was conducted in situations without a leading car.

**Figure 8.** Test route for speed bump situations.

**Figure 9.** Test route for intersection situations.

#### *5.2. Training*

#### 5.2.1. Input and Target Data

From the experimental data acquired through the vehicle experiments illustrated in Section 5.1, the input and target datasets were generated. The five types of inputs in each deceleration condition were extracted, and they were normalized as described at Section 4.1.3. The target data were the measured acceleration of the next time step because the model should generate the deceleration of the next time step. Therefore, there was a gap in the time step between the input and target data. The target data were processed as zero when both the accelerator and brake pedals were not pushed to assist the model learning the coasting behavior. The time step of the input and target data was set as 100 ms.

#### 5.2.2. Dataset Splitting

If the hyper-parameters are not suitable to estimate the target value, the model's ability to estimate with new data is aggravated, which is called overfitting. Cross-validation is used to check overfitting. For cross-validation, there are three types of dataset: training, validation, and test datasets. The training dataset is a group of data used in training the model, which usually takes 70% of the entire data. The validation dataset is unseen data in training to check the overfitting. After the model performance is measured using the validation dataset, hyper-parameters are tuned to improve the prediction performance. Although the validation dataset is not used in the training directly, it is used to improve the model performance because hyper-parameters are selected by validation results. Therefore, a new dataset is needed to check the performance of the model, which is called the test dataset.

The dataset in this research was split in the way mentioned in the previous paragraph. The proportions of training, validation, and test datasets were 70%, 20%, and 10%, respectively. As for the evaluation index of validation, root-mean-square error (RMSE) was used, which is a representative method to compare two types of values, as shown in Equation (5), where *n* is the number of data, *ameas*,*i* is the *i*-th measured acceleration from human driving, and *apred*,*i* is the *i*-th predicted acceleration by MLP models.

$$RMSE = \sqrt{\frac{1}{n} \sum\_{i=1}^{n} \left(a\_{m\text{ars},i} - a\_{pred,i}\right)^2}. \tag{5}$$

#### 5.2.3. Training and Hyper-Parameter Optimization

Each model which has one of the combinations of hyper-parameters described in Table 2 was trained with training dataset. Then, the best model which had the smallest RMSE in the validation dataset was selected through the grid-search algorithm. To train the models, Keras, one of the representative libraries for deep learning, was used based on Anaconda, which is the software platform for data science. The optimized sets of hyper-parameters depending on each deceleration condition are shown in Table 4. The number of hidden nodes in each hidden layer was different according to deceleration conditions, but they commonly used Relu as the activation function.

**Table 4.** Optimized hyper-parameter set in each deceleration condition.

The results of training, validation, and testing with the MLP models with optimized hyper-parameters are shown in Figures 10–12. They compare the target value, which is the measured acceleration from human driving, and the predicted value by trained MLP models in each deceleration condition. They are expressed as the absolute value for visibility. In addition, the results of RMSE and the number of points in training, validation, and test are also provided.

**Figure 10.** Results of (**a**) training; (**b**) validation; (**c**) test; (**d**) and total using the best model in car-following conditions.

**Figure 11.** Results of (**a**) training; (**b**) validation; (**c**) test; (**d**) and total using the best model in speed hump conditions.

**Figure 12.** Results of (**a**) training; (**b**) validation; (**c**) test; (**d**) and total using the best model in intersection conditions.

In the comparison graphs shown in Figures 10–12, there are some points in the *x*-axis and *y*-axis generated by inaccurate prediction of deceleration start timing. In human driving, there is a high level of uncertainty in the coasting behavior, which makes the prediction of coasting duration difficult. The total numbers of data points were 5693, 2153, and 1458 in each deceleration condition, reflecting 569.3, 215.3, and 145.8 s of data. The three datasets were from 90, 44, and 15 deceleration scenarios in the three deceleration conditions.

#### **6. Validation through Driving Simulation**

Since the proposed deceleration planning algorithm is for use in the SRS of EVs, the motion of the vehicle when the algorithm is applied should be validated. Thus, a driving simulation was conducted for algorithm validation using driving data. The driving data were acquired in the vehicle tests including car-following, speed bump, and intersection situations. The motion of the vehicle was simulated using the generated deceleration by the algorithm with the assumption that the vehicle moves in accordance to the generated value. The results of driving simulation were compared to the human driving data, and they were analyzed in terms of satisfaction of required velocity and safety. In addition, the proposed algorithm using the classified MLP models was compared to a deceleration planning algorithm with an integrated structure in terms of model performance.

#### *6.1. Driving Simulation Process*

In the driving simulation, the deceleration planning algorithm shown in Figure 1 operated every 100 ms, which was the sampling time. However, there was one more step not represented in Figure 1 called "vehicle motion simulation". In the driving simulation, the vehicle moves as per the generated deceleration by the algorithm. Therefore, the states such as velocity, relative distance, and distance to speed bump and intersection are calculated by the generated deceleration. Then, the planning algorithm generates the deceleration again with the calculated states during the vehicle motion simulation.

Reference acceleration was used as an input of the MLP models, which refers to the acceleration calculated from the modified constant acceleration model mentioned in Section 4.1.2. However, unlike when the models were trained, the values of *v*1, *final* − *v*2, *final* in car-following conditions and *v*1, *final* in speed bump and intersection conditions were not defined from the given driving data when the driving simulation was in progress. Therefore, these values were predetermined as constant values. The value of *v*1, *final* − *v*2, *final* in car-following conditions refers to the speed difference at the end of deceleration. It was fixed at −0.5, which means that the model had the intention to decelerate to achieve a speed 0.5 m/s slower than the leading vehicle, considering the safety of the vehicle. The value of *v*1, *final* refers to the minimum velocity in speed bump and intersection conditions. These values were determined as 30 and 15 km/h in speed bump and intersection conditions, which were average values calculated from the experimental data.

#### *6.2. Data for Driving Simulation*

New data for the driving simulation were acquired from vehicle experiments to validate the model performance in the three deceleration conditions. The vehicle test was conducted in the routes shown in Figure 13 with the data acquisition system shown in Figure 7. In Figure 13, the locations of speed bumps and intersections are expressed with different marks. Although the route for the driving simulation was the same as the route for the intersection test shown in Figure 10, a leading vehicle drove in front of the ego vehicle throughout the experiment, which is different from the previous experiment described in Section 5.1. Therefore, the newly acquired data included car-following, speed bump, and intersection situations.

**Figure 13.** Driving route for driving simulation data acquisition.

The data acquired from the experiment are shown in Figure 14. It shows various information related to the vehicle states, the preceding vehicle, and the ITS. Some descriptions of the data are given below.

**Figure 14.** Data of (**a**) acceleration; (**b**) velocity; (**c**) relative distance; (**d**) relative velocity; (**e**) pedal position; (**f**) distance to speed hump; (**g**) and distance to intersection used in driving simulation.

#### *6.3. Results of the Driving Simulation*

#### 6.3.1. Overall Description of the Driving Simulation Results

The driving simulation was conducted using the data shown in Figure 14. The entire simulation results are shown in Figure 15. It shows various data such as the results of the state recognition algorithm, the vehicle states, and the ITS information.

The driving conditions and deceleration conditions are shown in Figure 15e,f. As mentioned in Section 3, the SRS does not operate when the driving state is "acceleration" or "stopping". When the driving state is "coasting" or "deceleration", the deceleration is generated depending on the recognized deceleration condition. When the deceleration condition is "none", the generated value of deceleration is 0. When the deceleration condition is one of the other values, the deceleration is generated using the deceleration model adequate for the deceleration condition.

In Figure 15a–c,g,h, the red lines signify the simulated results and the yellow lines denote the measured data. The red line only appears when the SRS is in operation. When the driving state changes from acceleration to coasting, the simulation of the SRS starts. At that time, the values of simulation are initialized as the same value of the measured value. Then, the simulation is conducted while the driving state is kept as coasting or deceleration. The details of simulation results are analyzed for each deceleration condition in the next section.

**Figure 15.** Result of (**a**) acceleration; (**b**) velocity; (**c**) relative distance; (**d**) pedal position; (**e**) driving state; (**f**) deceleration condition; (**g**) distance to speed hump; (**h**) and distance to intersection in driving simulation.

#### 6.3.2. Results in Car-Following Condition

The sliced results in the car-following condition are shown in Figure 16. The driver released the accelerator pedal at about 57 s, and the driving state transited from driving to coasting at the same time. Then, the driving simulation started. The deceleration generated by the car-following model is shown in Figure 16a. Although the duration of coasting was not exactly the same as with the human driver because of the uncertainty of driver behavior, the result of deceleration planning initially shows the coasting behavior. Therefore, the deceleration profile generated by the model has a similar shape to the measured data from human driving.

In addition to checking the coasting behavior and similarity to human driving of the simulated results, the time to collision (TTC) was measured to check the safety in car-following conditions. TTC is a safety indicator to guarantee safety in car-following conditions that was widely used in various researches, calculated by dividing the relative distance by the velocity. The distribution of TTC from the planning results is shown in Figure 17. Furthermore, the minimum value of TTC was 1.443 s, which means that it would take 1.443 s to collide with the leading vehicle if the velocity was maintained at the same level.

**Figure 16.** Sliced results of (**a**) acceleration; (**b**) velocity; (**c**) relative distance; (**d**) pedal position; (**e**) driving state; (**f**) deceleration condition; (**g**) distance to speed hump; (**h**) and distance to intersection in car-following condition.

**Figure 17.** Distribution of time to collision (TTC) in car-following condition.

#### 6.3.3. Results from the Speed Bump Condition

The sliced results where the vehicle passes through the speed bump are shown in Figure 18. Because the deceleration was generated by the speed bump model, the vehicle speed at the end of the profile should meet the criterion of the minimum velocity. As mentioned in Section 6.1, the minimum velocity was set to 30 km/h in the speed bump situation. This requirement seems to be satisfied because the velocity at the end was 8.24 m/s, which is almost the same as 30 km/h.

**Figure 18.** Sliced results of (**a**) acceleration; (**b**) velocity; (**c**) relative distance; (**d**) pedal position; (**e**) driving state; (**f**) deceleration condition; (**g**) distance to speed hump; (**h**) and distance to intersection in speed bump condition.

#### 6.3.4. Results in Intersection Condition

The sliced results from where the vehicle turns right at the intersection are shown in Figure 19. Unlike the previous two sliced results shown in Figures 16 and 18, there were two deceleration factors in Figure 19: car-following and intersection. Therefore, the deceleration condition was determined by comparing the acceleration from the CA model as shown in Figure 19h. Therefore, the deceleration condition was the intersection initially, before changing to car-following in the middle, and transiting to intersection again toward the end of the deceleration.

Because the deceleration condition was intersection toward the end of the deceleration, the vehicle speed at the end of the profile should meet the minimum velocity. As mentioned in Section 6.1, the minimum velocity was set to 15 km/h in the intersection situation. This requirement seems to be satisfied because the velocity at the end was 4.16 m/s, which is almost the same as 15 km/h.

Because the objective of this research was to design a deceleration planning algorithm, and not to predict the acceleration, the motion of the vehicle when the algorithm was applied was compared to human driving using the values of velocity. Table 5 shows the RMSE of planning results and driving data in each sliced deceleration profile. An RMSE of 0.088 m/s was found in the car-following condition, while it was 0.015 m/s in the speed bump condition and 0.27 m/s in the intersection condition.

**Figure 19.** Sliced results of (**a**) acceleration; (**b**) velocity; (**c**) relative distance; (**d**) pedal position; (**e**) driving state; (**f**) deceleration condition; (**g**) distance to intersection; (**h**) and acceleration by CA model in intersection condition.

**Table 5.** Root-mean-square error (RMSE) of velocity in each sliced result.

#### *6.4. Comparison with the Integrated ANN Model*

As mentioned above, a noticeable feature of the proposed algorithm is the classified MLP model, where it is expected that the performance of the model is improved. Therefore, the proposed algorithm was compared with the integrated MLP model-based deceleration planning algorithm in terms of the similarity to human driving.

The integrated MLP model has more inputs to generate deceleration in the three deceleration conditions. In addition, it does not require a deceleration condition recognition algorithm because there is no need to select which MLP model to use. The structure of the integrated MLP model is shown in Figure 20. Hyper-parameters are also optimized in the integrated model. The number of hidden layers is two, and the number of hidden nodes in each hidden layer is 40 in the integrated model. In addition, it uses Relu as an activation function.

**Figure 20.** Model structure of integrated MLP model.

The comparison results of velocity RMSE are shown in Table 6. The velocity RMSE was calculated using the entire simulation results shown in Figure 15. The proposed algorithm showed a velocity RMSE of 0.312 m/s, and the planning algorithm with integrated structure showed a velocity RMSE of 0.890 m/s, which indicates that the classified structure improved the planning performance of MLP models.

**Table 6.** Comparison of classified and integrated structures.

According to the comparison results of the two different structures, the classified structure showed better performance in planning the deceleration for automatic regeneration. The classified structure has three different MLP models which have specialized inputs in each deceleration condition. The models have a reference acceleration as the common input, which is described in Section 4.1.2. This value provides a standard to reach the desirable velocity and keep a safe distance in each deceleration condition. Although the integrated structure uses three types of reference acceleration, as shown in Figure 20, it does not have the structure shown in Figure 1, which has a state recognition algorithm. As a result, the model seems to have difficulty in determining the most influential element without recognizing the deceleration condition. This results in weaker performance of the integrated structure compared to the classified structure.

#### **7. Conclusions**

This study suggested a deceleration planning algorithm which consisted of classified MLP models. The MLP models were trained with the human driving data acquired from vehicle experiments. In addition, diverse hyper-parameters were selected by hand-tuning and a grid-search algorithm. The validation results in each deceleration condition are summarized as follows:

The driving simulation was conducted using trained models with the best hyper-parameters. Through the driving simulation, it was validated that the vehicle motion with SRS based on the

proposed algorithm is safe in car-following conditions and satisfies the minimum velocity at speed bumps and intersections. In addition, the deceleration planning results showed the coasting behavior in the initial part of the deceleration by using the "coasting time" and the "reference acceleration" as inputs for the MLP models. This can reduce the driver discomfort in using the SRS and improve the acceptability of the SRS. The proposed algorithm was compared with other deceleration planning algorithms with an integrated MLP model. The results showed that the planning algorithm with a classified structure has more similarity with human driving than the integrated structure.

In the future, this research will be applied to the SRS in EVs via integration with a regenerative torque controller. Furthermore, the deceleration conditions will be extended by using more types of ITS information such as curvature, traffic lights, and speed limits.

**Author Contributions:** Conceptualization, G.S. and K.M.; investigation, G.S.; methodology, G.S.; validation, G.S. and S.A.; visualization, K.M. and S.A.; writing—review and editing, M.S. and K.J.

**Funding:** This work was financially supported by the BK21 plus program (22A20130000045) under the Ministry of Education, Republic of Korea, the Industrial Strategy Technology Development Program (No. 10039673, 10060068, 10079961), the International Collaborative Research and Development Program (N0001992) under the Ministry of Trade, Industry and Energy (MOTIE Korea), and National Research Foundation of Korea (NRF) grant funded by the Korean government (MEST) (No. 2011-0017495).

**Acknowledgments:** The experiment vehicle was supported by the Hyundai motor company.

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

#### **References**

© 2019 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 (http://creativecommons.org/licenses/by/4.0/).

*Article*


**1. Introduction**

406 Advances in Hematopoietic Stem Cell Research

Zubair AC, Kao G, Daley H, Schott D, Freedman A, Ritz J. (2006). CD34(+) CD38(-) and

autologous transplantation. Cytotherapy. 8:399-407.

CD34(+) HLA-DR(-) cells in BM stem cell grafts correlate with short-term engraftment but have no influence on long-term hematopoietic reconstitution after

> Cytomegalovirus (CMV) end-organ disease is a serious complication after stem cell transplantation (SCT) (Boeckh M, 2003). Within the first one hundred days after SCT, 50% of recipients develop CMV infection determined by positive antigenemia and 65 to 86.5% when viral replication is determined by real-time PCR (RT-PCR) (Ljungman et al., 2006; Solano et al., 2001). Described risk factors for CMV infection concern donor type, graft source, positive CMV serostatus of donor and recipient, CD34+ graft selection, preconditioning regimen, GvHD prophylaxis regimen, incidence of acute and chronic GvHD and prophylaxis and treatment for GvHD (Ljungman et al., 2002; Ozdemir et al., 2007). Pre-emptive therapy is currently based on viral replication determined by either antigenemia or RT-PCR (Drew, 2007). Although antigenemia has been extensively used (Drew, 2007), RT-PCR has been shown to be more sensitive (Hakki et al., 2003; Solano et al., 2001).

> The use of techniques based on nucleic acid amplification for the detection of CMV in clinical samples are in expansion and in many hospitals have replaced the use of other assays such as viral cultures or pp65 antigenemia. Several studies have assessed the performance between the different CMV viral load assays available. However, no many studies have compared the differences between the DNA extraction methods used (Fahle & Fischer, 2000; Caliendo et al., 2007; Kalpoe et al., 2004; Leruez-Ville et al., 2003; Avetisyan et al., 2006; Boeckh et al., 2009; Gerna et al., 2008; Gimeno et al., 2008). Although during the DNA extraction the majority of the methods use internal controls as a measurement of the DNA loss during the extraction procedure, in the downstream amplification not many of the assays use DNA standards that will facilitate the comparison among the different kits and standardization of the results between hospitals. In fact the availability of an optimal and efficient DNA extraction procedure that can use a broad type of samples with minimum modifications in the procedure may be practical and affordable for use in the clinical practice (Fahle & Fischer, 2000).

> One of the differences between results using the methods available is type of clinical specimen used to perform the CMV DNA extraction. Samples collected vary from plasma,

Detection of CMV Infection in Allogeneic SCT Recipients: The Multiple Assays 409

et al., 2004; Mengelle et al., 2011), and several studies performed on different herpesviruses have shown increased sensibility when automated extraction was performed compared to manual extraction kits (Nicholson et al.; 1997; Griffiths et al.; 1984), our laboratory recently demonstrated that the DNA extraction method from Affigene was more efficient than the automated system from Abbott providing a more accurate estimation of CMV DNA load (Gracia-Ahufinger et al., 2010). Our data proving that the manual DNA extraction method from Affigene resulted in a more efficient DNA extraction in comparison with that of an automated procedure from Abbott were somewhat surprising and are in contrast to previously published studies showing just the opposite (Kalpoe et al., 2004; Limaye et al., 2001). In this context, our data underscore the fact that the DNA extraction efficiency of distinct automated systems may not be comparable and should be thoroughly evaluated. This finding translates into critical therapeutic consequences, as patients would be treated depending on a threshold viral load, which will be different depending on the method used. In this context, these data reinforce the idea that local guidelines for the initiation of pre-emptive therapy based on commercial assays must be established as long as universally accepted standards for

CMV viral load determination is used to diagnose active CMV infection, to adopt treatment strategies to prevent CMV infection after transplantation and to monitor CMV after therapy. For this reason it is necessary to establish robust and reproducible assays to make possible to detect CMV levels within a wide range from low to very high number of copies (Abbate

The pp65 antigenemia developed in the late 1980s was the first non-cell culture based quantitative assay used in clinic to detect CMV infection (Atkinson & Emery), making obsolete the previous techniques such as shell vial assays (Gleaves et al., 1984; Nicholson et al., 1997), or the detection of early antigen fluorescent foci (DEAFF) test (Griffiths et al., 1984). The pp65 antigenemia assay is based on the detection of the pp65 phosphoprotein of CMV in peripheral blood leukocytes (Van der Bij et al., 1988), and it has been widely used for years to quantify and guiding the administration of therapy and monitoring active CMV infection of STC recipients (Bonon et al., 2005; Tormo et al., 2010). However, the antigenemia assay has many disadvantages such as, it requires quite a lot blood volume as well as intensive labour and need to process samples within 6h from the time of collection to achieve optimal sensitivity (Kim et al., 2007; Mhiri et al., 2007), it restricts the numbers of samples that can be analyzed simultaneously and it requires a high number of leukocytes (at least more than 200 leukocytes) for acceptable performance of the assay (Preiser et al., 2001), being unfeasible during periods of severe neutropenia. In addition, due to the fact that antigenemia results can be elevated after following ganciclovir treatment despite of a decrease of DNAemia levels, results using antigenemia for monitoring efficacy of the pre-emptive therapy of CMV infection in SCT recipients may be mislead (Sia et al., 2000). Other molecular techniques have reduced the

quantitative analysis of CMV DNAemia are not available.

**3. Detection of CMV infection**

**3.1 CMV detection using antigemia assays**

turnaround time for monitoring CMV infection.

et al., 2008).

whole blood or leukocytes, with the optimal sample for monitoring CMV viral being controversial. Since CMV infect cells, the viral load results obtained from leukocytes isolated from peripheral blood or whole blood samples tend to be higher than the results obtained from plasma. However, it has been reported a high correlation between CMV viral load results from plasma samples and whole blood samples (Caliendo et al., 2007; Kalpoe et al., 2004; Leruez-Ville et al., 2003). Moreover, some authors believe that the presence of CMV particles in plasma is related with the level of viral replication (Kalpoe et al., 2004), representing the infectious viral particles able to spread to other host cells. Thus whole blood and plasma samples are equally suitable for testing CMV infection in SCT recipients.

Molecular techniques for CMV quantification such as RT-PCR have been shown to be useful for the rapid diagnosis of CMV infection and for monitoring clinical responses to antiviral therapy. This technique offers some advantages over others PCR methods, including increased precision, accuracy, reproducibility and a shorter turnaround time. To date, the clinical utility of using the RT-PCR test to guide preemptive therapy in transplant recipients has been mainly studied in SCT recipients (Avetisyan et al., 2006; Boeckh M, 2009; Gerna et al., 2008; Gimeno et al., 2008; Harrington et al., 2007; Kalpoe et al., 2004; Lilleri et al., 2004; Limaye et al., 2001; Machida et al., 2000; Ruell et al., 2007; Verkruyse et al., 2006). However, it has not been established a cutoff threshold for initiating antiviral therapy against CMV probably due to the significant differences between the different techniques used to determine the CMV viral load. In the absence of standardization the current clinical guidelines recommend to each individual laboratory to establish their own viral thresholds for CMV management (Kotton et al., 2010; Razonable & Emery, 2004).


**6. Conclusions**

Biosurfactants offer an attractive proposition for biotechnological application across various sectors and are considered superior to synthetic surfactants. Diverse MACA produce biosurfactants with interesting properties that have been explored in the context of biomedicine and environmental remediation. However, many MACA have not yet been investigated for biosurfactant production and various potential applications are yet to receive significant research. Rapid, reliable methods for high throughput screening for biosurfactant production are essential as are robust standard methods for biosurfactant purification and characterisation. Efforts to evaluate and expand the knowledge of structural characteristics and gene regulation of biosurfactants are warranted to improve their effectiveness and productivity. Commercial-scale production will need to employ various existing and new strategies to become economic and sustainable. Cutting-edge technologies such high-throughput omics-based tools should accelerate the development of commercial production of biosurfactants. Furthering our understanding of biosurfactants produced by MACA will facilitate their commercial exploitation thereby contributing to a sustainable bio-based economy.


*Edited by Zhengchao Wang*

This book includes two sections: Clinical Features, and Basic Research of Polycystic Ovary Syndrome (PCOS). This book provides a comprehensive overview of latest PCOS research to benefit the population of women with PCOS. We sincerely thank Dr Alsadi Bassim, Prof. Orbetzova Maria, Prof. Abduljabbar Hassan, and Dr Shaobing Wang for their contributions to the section of PCOS clinical features and thank Dr Zhenghong Zhang, Dr Zhengchao Wang, Dr Fan Wang, Prof. Dolžan Vita, and Dr Mohamed-Hussein Zeti-Azura for their contributions to the section of PCOS basic research.At last, we hope that this book is meaningful to the clinicians who care for women with PCOS and to the researchers who investigate the complexities of this disorder.

ISBN 978-1-83880-289-9

Polycystic Ovarian Syndrome

Published in London, UK © 2020 IntechOpen © Razvan25 / iStock


**1. Introduction**

Over the years, engineers have worked on developing solutions to meet the needs of society, including housing, basic services and communications. In the area of transport infrastructure, there is an identifiable process of development, which began with Roman roads and continually evolved to produce the improved paved roads that allow for the driving of motorized vehicles. These roads were later transformed into the high-capacity roads (the roads that we know today) that have recently begun to incorporate more sustainable and smarter materials and practices of building and design.

Therefore, roads are beginning to be regarded as more than simply a means of transporting goods and services between cities; they are required to provide extra value for the investments made to build them and the spaces that they use. In particular, new trends have emerged in the design and production of advanced materials with the capacity to overcome the challenges associated with recent technological advances, as well as the effects of extreme weather resulting from climate change. One example is the implementation of methodologies for evaluating the environmental impact of traditional processes of production and construction, and for the definition of strategies to make these processes cleaner and more sustainable (e.g., low temperature manufacturing technologies, the use of waste as a substitute for natural resources and LCA studies). A further example is the design of new materials capable of dealing with extreme temperature changes and the associated problems, including permanent deformations produced at high temperatures, aging generated by high exposure to UV rays and chemical pollutants, fatigue cracking produced by freeze-thaw cycles and thermal cracking caused by the retraction of the material at low temperatures.

In general terms, this new generation of roads could be built using resilient and smart materials that have the capacity to extend the life of the pavement, improve sustainability, reduce maintenance costs and improve road safety, in addition to avoiding the traffic disruptions that lead to significant economic losses and, therefore, the slower development of countries. Additionally, these roads could contribute towards meeting the requirements established by recent technological advances in the autonomous vehicle industry.

Therefore, some of the main characteristics of the materials developed to be included in this new generation of roads include the capacity to self-heal the cracks developed as a result of traffic-induced fatigue, to show more visible colors at night in order to enhance road safety, to register the level of pressure and strain experienced by the effect of vehicle loads, to capture energy from the environment (wind, solar, hydraulic) in order to feed other components of the traffic system, to communicate with autonomous vehicles to assist in their guidance, as well as to send traffic signals to generate an integral system of communications.

Included among this new generation of materials are mechanomutable asphalt materials (MAMs), which are composed of a bituminous matrix with magnetically susceptible materials activated by the effect of external magnetic fields, if needed. MAMs can be used in three main potential applications [1,2]: (1) the mechanical control of the modulus of the bituminous matrix, to improve the performance of specific areas of the road; (2) the generation of thermal changes in order to propose alternative solutions for road maintenance and (3) the construction of encoded roads to assist self-guided vehicle systems.

The present paper presents a general description of MAMs, the mechanisms associated with their functioning and use, as well as the specific areas in which they could be most conveniently placed, in order to consider their use as a potential solution for the construction of smart pavements.

## **2. Mechanomutable Asphalt Materials**

Mechanomutable asphalt materials can be defined as a bituminous matrix modified with ferromagnetic materials, which can modify mechanical performance using the effect of magnetic fields produced in permanent magnets [3–7]. As shown in Figure 1, this concept can be extended to thermomutable materials due to the thermal changes produced by the effect of magnetic fields generated in induction coils, which can also allow for the construction of encoded roads for smart cities (communication with self-driving vehicles, cars, scooters, bikes people with limited mobility, traffic signals and for collecting traffic count data, among other uses) [2].

**Figure 1.** Mechanomutable asphalt materials.

Table 1 displays the materials with ferromagnetic properties that have been used until now in the construction of asphalt materials. These can be summarized as: steel slag, steel grit, ferrite fillers, fibers (carbon, steel, steel wood and carbon nanofibers), magnetite (tailings and powered), carbonyl iron powder, graphene, graphite, carbon nanotubes and carbon black. Depending on the type of material, these can serve as total or partial substitutes for the fine/coarse aggregate. The main parameters evaluated in previous studies are associated with the typical distresses developed in the material during practice, but without the application of magnetic fields (Table 1). These are: cracking (dynamic and quasi-static tests to represent fatigue and fracture), permanent deformation (Marshall stability and rutting) and moisture damage (stiffness loss).

More recent studies (Table 1) have also evaluated the changes produced by magnetic fields in terms of dynamic modulus values, phase angle and the healing capacity of these materials (understood as the mechanical recovery of the material following the application of heat and resting periods), as well as susceptibility to detection by magnetic field sensors, a characteristic that makes these materials a viable option for the encoding of the roads that is needed for the autonomous vehicle industry.

**Table 1.** Ferromagnetic materials used in the construction of asphalt materials.