The event-triggered scheme from the plant to the fault detection filter is utilized to reduce the frequency of transmission . Our attention is focused on the design of a fault detection filter such that the residual system is stochastically stable and satisfies some expected performances . Sufficient conditions is established for the existed of the fault detection filter . The proposed time-frequency method integrates the advantages of ELMD and TKEO to detect localized defects in rolling-element bearings . First , a raw signal is decomposed into an ensemble of PFs and a residual component using ELMD . A novel sensitive parameter ( SP ) is introduced to select the sensitive PF that contains the most fault-related information . The focus lies on defining these processes using the conditional intensity function . Furthermore , likelihood inference , methods of simulation and residual analysis for temporal point processes specified by a conditional intensity function are considered . Much of the development in fault detection schemes have relied on the system being linear and the noise and disturbances being Gaussian . In such cases , optimal filtering ideas based on Kalman filtering is utilised in estimation followed by a residual analysis for which whiteness tests are typically carried out . Linearised approximations have been used in the nonlinear systems case . This paper describes xvc – a format for efficient compression of visual data – originally developed for compression of video sequences , but in the context of this paper applied to still images . The xvc codec is a block based lossy codec using a traditional approach for prediction , residual representation and entropy coding . There are no elements of Machine Learning or Artificial Neural Networks in the xvc encoder or decoder . This paper presents our recent work on the development of a 4.8/9.6 kbps baseband LPC coder , using vector quantization in both vocal tract modelling and residual representation . The vector quantizing approach has been proved highly effective for achieving good quality in voice communications applications at low and very-low data rates . By using improved simplex method and residuals method , a calculation based on the parameters of compartment model of pharmacokinetics is presented . It is shown from numerical results that the accuracy of the combinative method of the former two methods is much better , the convergence rate faster , and the stability higher . It demonstrates the efficiency of this approach for street level elevation images , derived from 3D point clouds acquired by terrestrial mobile mapping systems . An area-stability term is introduced in the residual definition , reducing the over-segmentation of the vegetation while preserving small significant regions . Such adaptivity is shown to reduce the number of undetected outliers , even when implemented in the afore validation schemes . In addition , the authors present an alternative residual definition considering vector magnitude and angle adopting a modified Gaussian-weighted distance-based averaging median . This procedure is able to adapt the degree of acceptable background fluctuations in velocity to the local displacement magnitude . The output of one hidden layer is modelled as the sum of a mixture and residual models . The mixture model forms an activation function contour while the residual one models fluctuations around the contour . The use of the mixture model gives two advantages : First , it introduces a novel regularisation on the DNN . The two generators form a W-shaped network ( W-net ) and they are trained jointly with a weight-sharing constraint . Additionally , we also propose two novel discriminators , the residual one focusing on attribute generation and the triplex one helping to generate realistic looking sketches . To validate our model , we have created a new large dataset with 8,804 images , named the Attribute Face Photo & Sketch ( AFPS ) dataset which is the first dataset containing attributes associated to face sketch images . Aiming at overcoming those defects , this paper proposes a novel hybrid model for short-term PV output power interval forecasting based on ensemble empirical mode decomposition ( EEMD ) as well as relevance vector machine ( RVM ) . Firstly , the EEMD is used to decompose the PV output power sequences into several intrinsic mode functions ( IMFs ) and residual ( RES ) components . After that , based on the decomposed components , the sample entropy ( SE ) algorithm is utilized to reconstruct those components where three new components with typical characteristics are obtained . The outcomes of the research are outlined as below . Firstly , the effects of these factors , defined as respectively variances of error of observed data , ones of residual of model and ones of error of input variables , are formulated in the case of multiregression model . It shows that Mahalanobis ' generalized distance between means of data and forecasts as well affects on the accuracy of forecasts . Models are then matched to the reference speakers using distance-based comparisons . The second technique is based on first determining the number of participants in the conversation using a speaker count method termed the " Residual Ratio Algorithm " ( RRA ) , and then indexing based on this count . The RRA involves an elimination process in which speech segments matching a chosen set of reference models are sequentially removed from the conversation and the relative amount of residual speech is observed to determine the count . In this paper , we address the degradation problem by introducing a deep residual learning framework . Instead of hoping each few stacked layers directly fit a desired underlying mapping , we explicitly let these layers fit a residual mapping . Formally , denoting the desired underlying mapping as H ( x ) , we let the stacked nonlinear layers fit another mapping of F ( x ) : = H ( x ) section : Related Work Residual Representations . In image recognition , VLAD [ reference ] is a representation that encodes by the residual vectors with respect to a dictionary , and Fisher Vector [ reference ] can be formulated as a probabilistic version [ reference ] of VLAD . For ResNet - 110 , we run it 5 times and show " best ( mean±std ) " as in [ reference ] . so our residual models have exactly the same depth , width , and number of parameters as the plain counterparts . We use a weight decay of 0.0001 and momentum of 0.9 , and adopt the weight initialization in [ reference ] and BN [ reference ] but with no dropout . Deep Residual Network ( ResNet ) is one of the first works that successfully adopt skip connections , where each mirco - block , a.k.a . residual function , is associated with a skip connection , called residual path . The residual path element - wisely adds the input features to the output of the same mirco - block , making it a residual unit . Depending on the inner structure design of the mirco - block , the residual network has developed into a family of various architectures , including WRN , Inception - resnet , and ResNeXt . In , the residual networks are bridged with recurrent neural networks ( RNNs ) , which helps people better understand the deep residual network from the perspective of RNNs . In , several different residual functions are unified , trying to provide a better understanding of designing a better mirco structure with higher learning capacity . But still , for the densely connected networks , in addition to several intuitive explanations on better feature reusage and efficient gradient flow introduced , there have been few works that are able to provide a really deeper understanding . This design , which is discussed in depth together with our new insights , has proven to be an effective means of improving generalization ability . Furthermore , we propose a novel residual unit capable of further improving the classification accuracy with our new network architecture . Experiments on benchmark CIFAR - 10 , CIFAR - 100 , and ImageNet datasets have shown that our network architecture has superior generalization ability compared to the original residual networks . The output of ℱ ( , ) is used for down - sampling and up - sampling layers in the convolutional encoding and decoding units of the RU - Net model respectively . In the case of R2U - Net , the final outputs of the RCNN unit are passed through the residual unit that is shown Fig . 4 ( d ) . Let 's consider that the output of the RRCNN - block is + 1 and can be calculated as follows : In this paper we propose an architecture for sequence to sequence modeling that is entirely convolutional . Our model is equipped with gated linear units and residual connections [ reference ] . We also use attention in every decoder layer and demonstrate that each attention layer only adds a negligible amount of overhead . paragraph : Network Design We designed a sequence - to - sequence network with LSTM units and residual connections on the decoder side to predict a temporally coherent sequence of 3D poses given a sequence of 2D joint locations . Figure [ reference ] shows the architecture of our network . Second , these networks exhibit a type of deep supervision lee2014deeply , as near - identity layers effectively reduce distance to the loss . speculate that the former , the residual formulation itself , is crucial . include / fig - overview Since our CoupleNet introduces a few more parameters compared with the single branch detectors , to further verify effectiveness of the coupling structure , here we increase the parameters of the prediction head for each single branch implementation to maintain the same amount of parameters with CoupleNet for comparison . In detail , we add a new residual variant block with three convolution layers , where the kernel size is 1x1x256 , 3x3x256 and 1x1x1024 respectively , to the prediction sub - network . We found that the standard R - FCN with one or two extra heads got a mAP of and respectively in VOC07 , which is slightly higher than our re - implemented version ( ) in as shown in Table [ reference ] . Therefore , to force a sufficiently early interaction between the two representations , we also considered a multiplicative function , where denotes the element - wise ( Hadamard ) product of two vectors , as well as a ‘ residual ’ function . Early experiments showed that a simple linear embedding of was insufficient to achieve good performance , and the residual version of did n’t show any marked difference to the multiplicative variant , so we do not include results for these here . For the other configurations , Figure [ reference ] shows pairwise comparisons between 1 ) a cosine basis function embedding and a completely learned MLP embedding , 2 ) an embedding size ( hidden layer size or number of cosine basis elements ) 32 and 64 , 3 ) ReLU and sigmoid nonlinearity following the embedding , and 4 ) concatenation and a multiplicative interaction between and . [ reference ] , we parameterize a 3D bounding box by its center ( , , ) , size ( , , ) and heading angle ( along up - axis ) . We take a “ residual ” approach for box center estimation . The center residual predicted by the box estimation network is combined with the previous center residual from the T - Net and the masked points ’ centroid to recover an absolute center ( Eq . In this study , analytical , numerical and experimental models were used in parallel to explore the pressure response of the human head as a result of low velocity impact . The aim of the study is to investigate whether it is possible to predict the response of the head for a particular impact scenario using image-based modeling techniques . In this paper , we present a image based tree modeling method , based on a number of techniques and algorithms in the field of image processing , image morphology , computer vision , as well as computer graphics . Due to the nature of image based modeling , the result model is quite realistic . Efforts were made in every step of the reconstruction process for the sake of acceleration and simplification , which include : 1.A modification of “ the Intelligent Scissors ” for branch segmentation . The Chinese painting modelling way is different to other pictures plants , both does not pursue absolutely practicalizes and did not advocate excessively exaggerates distorts , but is in the respect objective object foundation , carries on suitable processing to the image , causes to display the image even more to conform to painter 's subjective intention , vividly causes the image tohave the performance strength . " image modeling " is the basic modelling method which the Chinese painting uses , also is remarkable characteristic . ( 1 ) We exploit SfM information and enforce not only hard non - matching ( negative ) but also hard matching ( positive ) examples to be learned by the CNN . This is shown to enhance the derived image representation . ( 2 ) Finally , we describe how , after fine - tuning , we use the same training data to learn projections that appear to be an effective post - processing step . subsection : Image representation We adopt a compact representation that is derived from activations of convolutional layers and is shown to be effective for particular object retrieval . We refer deep features from each layer as convn , pooln , or fcn for the th layer of the CNN , which is the output of a convolutional , pooling , or fully connected layer respectively . We use fc6 to train R - CNN object and part detectors as well as image representation for classification . For , nearest neighbors are computed using pool5 and cosine distance metric . In this paper we present some reflections stemming from a case study aimed at building a socio-technical infrastructure for supporting participatory design and development . We summarize and abstract our experience into an hourglass approach defined by the intersection of two co-evolving dimensions of infrastructuring : the social and the technical ones . Different subsets of the community , characterized by the increasing involvement of volunteers , position themselves along the two axes and have different roles in the design and use of the generated artefact . Named Data Networking ( NDN ) is a proposed future Internet architecture that uses data names instead of host addresses for data delivery . The new architecture incorporates principles that have made the IP protocol suite widely adopted and globally scaled ( e.g. , the hourglass design and end-to-end principle ) , but changes the fundamental layer of the architecture to one better suited to modern networks and emerging communication patterns . In this dissertation , we study how to support mobile and distributed applications through Named Data Networking . We propose a novel hourglass-based system that delivers composite multimedia content to users in pervasive environments , which considers both users ' interest in the content and the presentation capabilities of various display devices . This hourglass-based system relies on our content and service composition framework that allows for customized production , distribution and presentation of multimedia content for pervasive environments . We present the hourglass design , possible solutions for some content composition scenarios and validate the solutions via selected experiments . A genealogical graph , such as family tree , can be taken as an example of such a case . Classical ancestor trees , descendant trees , Hourglass charts , and their visual variants such as node-link diagrams or fan charts are suitable for assessment of peoples relationships when one is focused on a particular person and his/her direct ancestors and descendants . Such tree-based representations miss a broader context of relationships and do not allow the quick assessment of several interlinked families together . The system architecture which is controlled by Micro-Controller Unit ( MCU ) includes main alarm clock module , lucky module and security module . In the main alarm clock module , we create a novel hourglass clock model and intelligent alarm clock features based on grey relational analysis . In the lucky module , we design a unique structure of live round according to the medical theory of psychology and hashing function for randomization . The hourglass concept has been undisputable ruler of networking visions on the last years . As network evolution is now a hot topic , this article aims to reflect on this concept , highlighting its current shortcomings , and suggesting its evolution . The aim is to propose an original and compact UWB bandpass filter ( BPF ) with a notched band to mitigate the WLAN interference namely , the 5.2 and 5.8 GHz frequencies . The electrical model of the BPF has been synthesized using Hourglass function . The notch was integrated by cascading the BPF with a rejection cell to cancel the undesired band ( 5 - 6 GHz ) . Currently the Hourglass mode and OGSA are the two main kinds of grid architecture . However they have some serious defects , such as low standardization , less dynamic , weak transparency and high complexity in application development . Our more practical hourglass schemes exploit common cloud infrastructure characteristics , such as limited file-system parallelism and the use of rotational hard drives for at-rest files . For files of modest size , we describe an hourglass scheme that exploits trapdoor one-way permutations to prove correct file encryption whatever the underlying storage medium . We also experimentally validate the practicality of our proposed schemes , the fastest of which incurs minimal overhead beyond the cost of encryption . The success of the IP protocol stack and also the success of the Internet as a technology lies in its simplicity . Based on the classical hourglass visualization , a wide variety of first and second layer technologies are all related to the IP and the intelligence is provided by the higher layers in most cases on the edge of the network . In theory , the details of the infrastructure are hidden from the applications and the application programmers . CornerNet - Squeeze speeds up inference by reducing the amount of processing per pixel . It incorporates ideas from SqueezeNet and MobileNets , and introduces a new , compact hourglass backbone that makes extensive use of 1 1 convolution , bottleneck layer , and depth - wise separable convolution . With the new hourglass backbone , CornerNet - Squeeze achieves an AP of 34.4 % on COCO at 30ms , simultaneously more accurate and faster than YOLOv3 ( 33.0 % at 39ms ) . CornerNet - Squeeze is the first to integrate SqueezeNet with the stacked hourglass architecture and to apply such a combination on object detection . Prior works that employ the hourglass architecture have excelled at achieving competitive accuracy , but it was unclear whether and how the hourglass architecture can be competitive in terms of efficiency . Our design and results show that this is possible for the first time , particularly in the context of object detection . The feature maps are obtained from the backbone network in CornerNet - Saccade , which is an hourglass network . Each hourglass module in the network applies several convolution and downsampling layers to downsize the input feature maps . It then upsamples the feature maps back to the original input resolution by multiple convolution and upsampling layers . subsection : Backbone Network We design a new hourglass backbone network that works better in CornerNet - Saccade . The new hourglass network consists of 3 hourglass modules and has a depth of 54 layers , while Hourglass - 104 in CornerNet consists of 2 hourglass modules and has a depth of 104 . A weight initialization scheme is then provided , which can be used for many network structures including inception models and ResNets . We observe that the problem of activation variance accumulation introduced by identity mapping may be harmful in some scenarios , , adding outputs of multiple residual units implemented by identity mapping together in the Hourglass structure . A simple yet effective solution is introduced for solving this issue . The building block of our network is the proposed Pyramid Residual Module ( PRM ) . We first briefly review the structure of hourglass network . Then a detailed discussion of our pyramid residual module is presented . Another novelty of ours is the corner pooling layers that help better localize the corners . We also significantly modify the hourglass architecture and add our novel variant of focal loss to help better train the network . section : CornerNet In addition , GPN formulates the inference procedure for joint configurations of human poses as a graph partition problem , and conducts local optimization for each person detection with reliable global affinity cues , leading to complexity reduction and performance improvement . GPN is implemented with the Hourglass architecture as the backbone network to simultaneously learn joint detector and dense regressor . Extensive experiments on benchmarks MPII Human Pose Multi - Person , extended PASCAL - Person - Part , and WAF , show the efficiency of GPN with new state - of - the - art performance . section : Network Architecture subsection : Hourglass Design The design of the hourglass is motivated by the need to capture information at every scale . We reintegrate intermediate predictions back into the feature space by mapping them to a larger number of channels with an additional 1x1 convolution . These are added back to the intermediate features from the hourglass along with the features output from the previous hourglass stage ( visualized in Figure [ reference ] ) . The resulting output serves directly as the input for the following hourglass module which generates another set of predictions . Refer to Sec 5.5 for experimental evidences . To pursue even better performance , we introduce the hourglass version in Fig 2 ( bottom ) , inspired by [ reference ] . This deep network conducts the encoding - decoding operations recurrently , thus has stronger representation power and can mix global and local information better .
The event-triggered scheme from the plant to the fault detection filter is utilized to reduce the frequency of transmission . Our attention is focused on the design of a fault detection filter such that the residual system is stochastically stable and satisfies some expected performances . Sufficient conditions is established for the existed of the fault detection filter .
The proposed time-frequency method integrates the advantages of ELMD and TKEO to detect localized defects in rolling-element bearings . First , a raw signal is decomposed into an ensemble of PFs and a residual component using ELMD . A novel sensitive parameter ( SP ) is introduced to select the sensitive PF that contains the most fault-related information .
The focus lies on defining these processes using the conditional intensity function . Furthermore , likelihood inference , methods of simulation and residual analysis for temporal point processes specified by a conditional intensity function are considered .
Much of the development in fault detection schemes have relied on the system being linear and the noise and disturbances being Gaussian . In such cases , optimal filtering ideas based on Kalman filtering is utilised in estimation followed by a residual analysis for which whiteness tests are typically carried out . Linearised approximations have been used in the nonlinear systems case .
This paper describes xvc – a format for efficient compression of visual data – originally developed for compression of video sequences , but in the context of this paper applied to still images . The xvc codec is a block based lossy codec using a traditional approach for prediction , residual representation and entropy coding . There are no elements of Machine Learning or Artificial Neural Networks in the xvc encoder or decoder .
This paper presents our recent work on the development of a 4.8/9.6 kbps baseband LPC coder , using vector quantization in both vocal tract modelling and residual representation . The vector quantizing approach has been proved highly effective for achieving good quality in voice communications applications at low and very-low data rates .
By using improved simplex method and residuals method , a calculation based on the parameters of compartment model of pharmacokinetics is presented . It is shown from numerical results that the accuracy of the combinative method of the former two methods is much better , the convergence rate faster , and the stability higher .
It demonstrates the efficiency of this approach for street level elevation images , derived from 3D point clouds acquired by terrestrial mobile mapping systems . An area-stability term is introduced in the residual definition , reducing the over-segmentation of the vegetation while preserving small significant regions .
Such adaptivity is shown to reduce the number of undetected outliers , even when implemented in the afore validation schemes . In addition , the authors present an alternative residual definition considering vector magnitude and angle adopting a modified Gaussian-weighted distance-based averaging median . This procedure is able to adapt the degree of acceptable background fluctuations in velocity to the local displacement magnitude .
The output of one hidden layer is modelled as the sum of a mixture and residual models . The mixture model forms an activation function contour while the residual one models fluctuations around the contour . The use of the mixture model gives two advantages : First , it introduces a novel regularisation on the DNN .
The two generators form a W-shaped network ( W-net ) and they are trained jointly with a weight-sharing constraint . Additionally , we also propose two novel discriminators , the residual one focusing on attribute generation and the triplex one helping to generate realistic looking sketches . To validate our model , we have created a new large dataset with 8,804 images , named the Attribute Face Photo & Sketch ( AFPS ) dataset which is the first dataset containing attributes associated to face sketch images .
Aiming at overcoming those defects , this paper proposes a novel hybrid model for short-term PV output power interval forecasting based on ensemble empirical mode decomposition ( EEMD ) as well as relevance vector machine ( RVM ) . Firstly , the EEMD is used to decompose the PV output power sequences into several intrinsic mode functions ( IMFs ) and residual ( RES ) components . After that , based on the decomposed components , the sample entropy ( SE ) algorithm is utilized to reconstruct those components where three new components with typical characteristics are obtained .
The outcomes of the research are outlined as below . Firstly , the effects of these factors , defined as respectively variances of error of observed data , ones of residual of model and ones of error of input variables , are formulated in the case of multiregression model . It shows that Mahalanobis ' generalized distance between means of data and forecasts as well affects on the accuracy of forecasts .
Models are then matched to the reference speakers using distance-based comparisons . The second technique is based on first determining the number of participants in the conversation using a speaker count method termed the " Residual Ratio Algorithm " ( RRA ) , and then indexing based on this count . The RRA involves an elimination process in which speech segments matching a chosen set of reference models are sequentially removed from the conversation and the relative amount of residual speech is observed to determine the count .
In this paper , we address the degradation problem by introducing a deep residual learning framework . Instead of hoping each few stacked layers directly fit a desired underlying mapping , we explicitly let these layers fit a residual mapping . Formally , denoting the desired underlying mapping as H ( x ) , we let the stacked nonlinear layers fit another mapping of F ( x ) : = H ( x )
section : Related Work Residual Representations . In image recognition , VLAD [ reference ] is a representation that encodes by the residual vectors with respect to a dictionary , and Fisher Vector [ reference ] can be formulated as a probabilistic version [ reference ] of VLAD .
For ResNet - 110 , we run it 5 times and show " best ( mean±std ) " as in [ reference ] . so our residual models have exactly the same depth , width , and number of parameters as the plain counterparts . We use a weight decay of 0.0001 and momentum of 0.9 , and adopt the weight initialization in [ reference ] and BN [ reference ] but with no dropout .
Deep Residual Network ( ResNet ) is one of the first works that successfully adopt skip connections , where each mirco - block , a.k.a . residual function , is associated with a skip connection , called residual path . The residual path element - wisely adds the input features to the output of the same mirco - block , making it a residual unit . Depending on the inner structure design of the mirco - block , the residual network has developed into a family of various architectures , including WRN , Inception - resnet , and ResNeXt .
In , the residual networks are bridged with recurrent neural networks ( RNNs ) , which helps people better understand the deep residual network from the perspective of RNNs . In , several different residual functions are unified , trying to provide a better understanding of designing a better mirco structure with higher learning capacity . But still , for the densely connected networks , in addition to several intuitive explanations on better feature reusage and efficient gradient flow introduced , there have been few works that are able to provide a really deeper understanding .
This design , which is discussed in depth together with our new insights , has proven to be an effective means of improving generalization ability . Furthermore , we propose a novel residual unit capable of further improving the classification accuracy with our new network architecture . Experiments on benchmark CIFAR - 10 , CIFAR - 100 , and ImageNet datasets have shown that our network architecture has superior generalization ability compared to the original residual networks .
The output of ℱ ( , ) is used for down - sampling and up - sampling layers in the convolutional encoding and decoding units of the RU - Net model respectively . In the case of R2U - Net , the final outputs of the RCNN unit are passed through the residual unit that is shown Fig . 4 ( d ) . Let 's consider that the output of the RRCNN - block is + 1 and can be calculated as follows :
In this paper we propose an architecture for sequence to sequence modeling that is entirely convolutional . Our model is equipped with gated linear units and residual connections [ reference ] . We also use attention in every decoder layer and demonstrate that each attention layer only adds a negligible amount of overhead .
paragraph : Network Design We designed a sequence - to - sequence network with LSTM units and residual connections on the decoder side to predict a temporally coherent sequence of 3D poses given a sequence of 2D joint locations . Figure [ reference ] shows the architecture of our network .
Second , these networks exhibit a type of deep supervision lee2014deeply , as near - identity layers effectively reduce distance to the loss . speculate that the former , the residual formulation itself , is crucial . include / fig - overview
Since our CoupleNet introduces a few more parameters compared with the single branch detectors , to further verify effectiveness of the coupling structure , here we increase the parameters of the prediction head for each single branch implementation to maintain the same amount of parameters with CoupleNet for comparison . In detail , we add a new residual variant block with three convolution layers , where the kernel size is 1x1x256 , 3x3x256 and 1x1x1024 respectively , to the prediction sub - network . We found that the standard R - FCN with one or two extra heads got a mAP of and respectively in VOC07 , which is slightly higher than our re - implemented version ( ) in as shown in Table [ reference ] .
Therefore , to force a sufficiently early interaction between the two representations , we also considered a multiplicative function , where denotes the element - wise ( Hadamard ) product of two vectors , as well as a ‘ residual ’ function . Early experiments showed that a simple linear embedding of was insufficient to achieve good performance , and the residual version of did n’t show any marked difference to the multiplicative variant , so we do not include results for these here . For the other configurations , Figure [ reference ] shows pairwise comparisons between 1 ) a cosine basis function embedding and a completely learned MLP embedding , 2 ) an embedding size ( hidden layer size or number of cosine basis elements ) 32 and 64 , 3 ) ReLU and sigmoid nonlinearity following the embedding , and 4 ) concatenation and a multiplicative interaction between and .
[ reference ] , we parameterize a 3D bounding box by its center ( , , ) , size ( , , ) and heading angle ( along up - axis ) . We take a “ residual ” approach for box center estimation . The center residual predicted by the box estimation network is combined with the previous center residual from the T - Net and the masked points ’ centroid to recover an absolute center ( Eq .
In this study , analytical , numerical and experimental models were used in parallel to explore the pressure response of the human head as a result of low velocity impact . The aim of the study is to investigate whether it is possible to predict the response of the head for a particular impact scenario using image-based modeling techniques .
In this paper , we present a image based tree modeling method , based on a number of techniques and algorithms in the field of image processing , image morphology , computer vision , as well as computer graphics . Due to the nature of image based modeling , the result model is quite realistic . Efforts were made in every step of the reconstruction process for the sake of acceleration and simplification , which include : 1.A modification of “ the Intelligent Scissors ” for branch segmentation .
The Chinese painting modelling way is different to other pictures plants , both does not pursue absolutely practicalizes and did not advocate excessively exaggerates distorts , but is in the respect objective object foundation , carries on suitable processing to the image , causes to display the image even more to conform to painter 's subjective intention , vividly causes the image tohave the performance strength . " image modeling " is the basic modelling method which the Chinese painting uses , also is remarkable characteristic .
( 1 ) We exploit SfM information and enforce not only hard non - matching ( negative ) but also hard matching ( positive ) examples to be learned by the CNN . This is shown to enhance the derived image representation . ( 2 )
Finally , we describe how , after fine - tuning , we use the same training data to learn projections that appear to be an effective post - processing step . subsection : Image representation We adopt a compact representation that is derived from activations of convolutional layers and is shown to be effective for particular object retrieval .
We refer deep features from each layer as convn , pooln , or fcn for the th layer of the CNN , which is the output of a convolutional , pooling , or fully connected layer respectively . We use fc6 to train R - CNN object and part detectors as well as image representation for classification . For , nearest neighbors are computed using pool5 and cosine distance metric .
In this paper we present some reflections stemming from a case study aimed at building a socio-technical infrastructure for supporting participatory design and development . We summarize and abstract our experience into an hourglass approach defined by the intersection of two co-evolving dimensions of infrastructuring : the social and the technical ones . Different subsets of the community , characterized by the increasing involvement of volunteers , position themselves along the two axes and have different roles in the design and use of the generated artefact .
Named Data Networking ( NDN ) is a proposed future Internet architecture that uses data names instead of host addresses for data delivery . The new architecture incorporates principles that have made the IP protocol suite widely adopted and globally scaled ( e.g. , the hourglass design and end-to-end principle ) , but changes the fundamental layer of the architecture to one better suited to modern networks and emerging communication patterns . In this dissertation , we study how to support mobile and distributed applications through Named Data Networking .
We propose a novel hourglass-based system that delivers composite multimedia content to users in pervasive environments , which considers both users ' interest in the content and the presentation capabilities of various display devices . This hourglass-based system relies on our content and service composition framework that allows for customized production , distribution and presentation of multimedia content for pervasive environments . We present the hourglass design , possible solutions for some content composition scenarios and validate the solutions via selected experiments .
A genealogical graph , such as family tree , can be taken as an example of such a case . Classical ancestor trees , descendant trees , Hourglass charts , and their visual variants such as node-link diagrams or fan charts are suitable for assessment of peoples relationships when one is focused on a particular person and his/her direct ancestors and descendants . Such tree-based representations miss a broader context of relationships and do not allow the quick assessment of several interlinked families together .
The system architecture which is controlled by Micro-Controller Unit ( MCU ) includes main alarm clock module , lucky module and security module . In the main alarm clock module , we create a novel hourglass clock model and intelligent alarm clock features based on grey relational analysis . In the lucky module , we design a unique structure of live round according to the medical theory of psychology and hashing function for randomization .
The hourglass concept has been undisputable ruler of networking visions on the last years . As network evolution is now a hot topic , this article aims to reflect on this concept , highlighting its current shortcomings , and suggesting its evolution .
The aim is to propose an original and compact UWB bandpass filter ( BPF ) with a notched band to mitigate the WLAN interference namely , the 5.2 and 5.8 GHz frequencies . The electrical model of the BPF has been synthesized using Hourglass function . The notch was integrated by cascading the BPF with a rejection cell to cancel the undesired band ( 5 - 6 GHz ) .
Currently the Hourglass mode and OGSA are the two main kinds of grid architecture . However they have some serious defects , such as low standardization , less dynamic , weak transparency and high complexity in application development .
Our more practical hourglass schemes exploit common cloud infrastructure characteristics , such as limited file-system parallelism and the use of rotational hard drives for at-rest files . For files of modest size , we describe an hourglass scheme that exploits trapdoor one-way permutations to prove correct file encryption whatever the underlying storage medium . We also experimentally validate the practicality of our proposed schemes , the fastest of which incurs minimal overhead beyond the cost of encryption .
The success of the IP protocol stack and also the success of the Internet as a technology lies in its simplicity . Based on the classical hourglass visualization , a wide variety of first and second layer technologies are all related to the IP and the intelligence is provided by the higher layers in most cases on the edge of the network . In theory , the details of the infrastructure are hidden from the applications and the application programmers .
CornerNet - Squeeze speeds up inference by reducing the amount of processing per pixel . It incorporates ideas from SqueezeNet and MobileNets , and introduces a new , compact hourglass backbone that makes extensive use of 1 1 convolution , bottleneck layer , and depth - wise separable convolution . With the new hourglass backbone , CornerNet - Squeeze achieves an AP of 34.4 % on COCO at 30ms , simultaneously more accurate and faster than YOLOv3 ( 33.0 % at 39ms ) .
CornerNet - Squeeze is the first to integrate SqueezeNet with the stacked hourglass architecture and to apply such a combination on object detection . Prior works that employ the hourglass architecture have excelled at achieving competitive accuracy , but it was unclear whether and how the hourglass architecture can be competitive in terms of efficiency . Our design and results show that this is possible for the first time , particularly in the context of object detection .
The feature maps are obtained from the backbone network in CornerNet - Saccade , which is an hourglass network . Each hourglass module in the network applies several convolution and downsampling layers to downsize the input feature maps . It then upsamples the feature maps back to the original input resolution by multiple convolution and upsampling layers .
subsection : Backbone Network We design a new hourglass backbone network that works better in CornerNet - Saccade . The new hourglass network consists of 3 hourglass modules and has a depth of 54 layers , while Hourglass - 104 in CornerNet consists of 2 hourglass modules and has a depth of 104 .
A weight initialization scheme is then provided , which can be used for many network structures including inception models and ResNets . We observe that the problem of activation variance accumulation introduced by identity mapping may be harmful in some scenarios , , adding outputs of multiple residual units implemented by identity mapping together in the Hourglass structure . A simple yet effective solution is introduced for solving this issue .
The building block of our network is the proposed Pyramid Residual Module ( PRM ) . We first briefly review the structure of hourglass network . Then a detailed discussion of our pyramid residual module is presented .
Another novelty of ours is the corner pooling layers that help better localize the corners . We also significantly modify the hourglass architecture and add our novel variant of focal loss to help better train the network . section : CornerNet
In addition , GPN formulates the inference procedure for joint configurations of human poses as a graph partition problem , and conducts local optimization for each person detection with reliable global affinity cues , leading to complexity reduction and performance improvement . GPN is implemented with the Hourglass architecture as the backbone network to simultaneously learn joint detector and dense regressor . Extensive experiments on benchmarks MPII Human Pose Multi - Person , extended PASCAL - Person - Part , and WAF , show the efficiency of GPN with new state - of - the - art performance .
section : Network Architecture subsection : Hourglass Design The design of the hourglass is motivated by the need to capture information at every scale .
We reintegrate intermediate predictions back into the feature space by mapping them to a larger number of channels with an additional 1x1 convolution . These are added back to the intermediate features from the hourglass along with the features output from the previous hourglass stage ( visualized in Figure [ reference ] ) . The resulting output serves directly as the input for the following hourglass module which generates another set of predictions .
Refer to Sec 5.5 for experimental evidences . To pursue even better performance , we introduce the hourglass version in Fig 2 ( bottom ) , inspired by [ reference ] . This deep network conducts the encoding - decoding operations recurrently , thus has stronger representation power and can mix global and local information better .
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