Many applications of principal component analysis (PCA) can be found in dimensionality reduction. But linear PCA method is not well suitable for nonlinear chemical processes. A new PCA method based on im-proved input ...Many applications of principal component analysis (PCA) can be found in dimensionality reduction. But linear PCA method is not well suitable for nonlinear chemical processes. A new PCA method based on im-proved input training neural network (IT-NN) is proposed for the nonlinear system modelling in this paper. Mo-mentum factor and adaptive learning rate are introduced into learning algorithm to improve the training speed of IT-NN. Contrasting to the auto-associative neural network (ANN), IT-NN has less hidden layers and higher training speed. The effectiveness is illustrated through a comparison of IT-NN with linear PCA and ANN with experiments. Moreover, the IT-NN is combined with RBF neural network (RBF-NN) to model the yields of ethylene and propyl-ene in the naphtha pyrolysis system. From the illustrative example and practical application, IT-NN combined with RBF-NN is an effective method of nonlinear chemical process modelling.展开更多
The existing computer and network technology makes the enterprise training transform from the traditional mode into new mode. The paper studies how to combine enterprise knowledge management and network training to ma...The existing computer and network technology makes the enterprise training transform from the traditional mode into new mode. The paper studies how to combine enterprise knowledge management and network training to make the enterprise training meet the demands of knowledge management and improve the competitiveness of enterprises. And the paper puts forwards the new opinion combining enterprise knowledge management and network training system. The purpose of applying knowledge map and knowledge push to training system is to integrate knowledge management into training system to make the enterprises face the challenge of knowledge economy.展开更多
Training neural network to recognize targets needs a lot of samples.People usually get these samples in a non-systematic way,which can miss or overemphasize some target information.To improve this situation,a new meth...Training neural network to recognize targets needs a lot of samples.People usually get these samples in a non-systematic way,which can miss or overemphasize some target information.To improve this situation,a new method based on virtual model and invariant moments was proposed to generate training samples.The method was composed of the following steps：use computer and simulation software to build target object＇s virtual model and then simulate the environment,light condition,camera parameter,etc.;rotate the model by spin and nutation of inclination to get the image sequence by virtual camera;preprocess each image and transfer them into binary image;calculate the invariant moments for each image and get a vectors＇ sequence.The vectors＇ sequence which was proved to be complete became the training samples together with the target outputs.The simulated results showed that the proposed method could be used to recognize the real targets and improve the accuracy of target recognition effectively when the sampling interval was short enough and the circumstance simulation was close enough.展开更多
Although train modeling research is vast, most available simulation tools are confined to city-or trip-scale analysis, primarily offering micro-level simulations of network segments. This paper addresses this void by ...Although train modeling research is vast, most available simulation tools are confined to city-or trip-scale analysis, primarily offering micro-level simulations of network segments. This paper addresses this void by developing the Ne Train Sim simulator for heavy long-haul freight trains on a network of multiple intersecting tracks. The main objective of this simulator is to enable a comprehensive analysis of energy consumption and the associated carbon footprint for the entire train system. Four case studies were conducted to demonstrate the simulator's performance. The first case study validates the model by comparing Ne Train Sim output to empirical trajectory data. The results demonstrate that the simulated trajectory is precise enough to estimate the train energy consumption and carbon dioxide emissions. The second application demonstrates the train-following model considering six trains following each other. The results showcase the model ability to maintain safefollowing distances between successive trains. The next study highlights the simulator's ability to resolve train conflicts for different scenarios. Finally, the suitability of the Ne Train Sim for modeling realistic railroad networks is verified through the modeling of the entire US network and comparing alternative powertrains on the fleet energy consumption.展开更多
Based on the"Understandable Output Hypothesis"a practical study on the construction of college oral English network training camp is set up,through speech learning and imitation,building language input in na...Based on the"Understandable Output Hypothesis"a practical study on the construction of college oral English network training camp is set up,through speech learning and imitation,building language input in natural environment,exploring effective output mode based on information technology platform,providing foreign language learners with opportunities to express language and get feedback.Students use relevant resources on the Internet to complete the oral activities of"thematic activities"together,so as to cultivate students'cooperative learning,communication skills,team spirit and language communication ability.展开更多
Artificial intelligence(AI),encompassing machine learning and deep learning,is being extensively used in medical sciences.It is slated to positively impact the diagnosis and prognostication of various diseases.Deep le...Artificial intelligence(AI),encompassing machine learning and deep learning,is being extensively used in medical sciences.It is slated to positively impact the diagnosis and prognostication of various diseases.Deep learning,a subset of AI,has been instrumental in diagnosing diabetic retinopathy(DR),diabetic macular edema,glaucoma,age-related macular degeneration,and numerous other ocular diseases.AI performs equally well in the early prediction of glaucoma and agerelated macular degeneration.Integrating AI with telemedicine promises to improve healthcare delivery,although challenges persist in implementing AI algorithms,especially in deve-loping countries.This review provides a compre hensive summary of AI,its applications in ophthalmology,particularly DR,the diverse algorithms utilized for different ocular conditions,and prospects for the future integration of AI in eye care.展开更多
Whether using a shallow neural network with one hidden layer,or a deep network with many hidden layers,the training data must represent subgroups of the deposit type being explored to be useful.Published examples of n...Whether using a shallow neural network with one hidden layer,or a deep network with many hidden layers,the training data must represent subgroups of the deposit type being explored to be useful.Published examples of neural networks have mostly been limited to one individual mineral deposit for training.Variation of geologic features among deposits within a type are so large that a single deposit cannot provide proper information to train a neural net to generalize and guide exploration for other deposits.Models trained with only one deposit tend to be academic successes but are not of practical value in exploration for other deposits.This is why it takes much experience examining many deposits to properly train an economic geologist—a neural network is not any different.Two examples of shallow neural networks are used to demonstrate the power of neural networks to possibly locate undiscovered deposits and to provide some suggestions of how to deal with missing data.The training data needs to include information spatially related to known deposits and hopefully information from many different deposits of the type.Lessons learned from these and other examples point to a proposed sampling plan for data that could lead to a generalized neural network for exploration.In this plan,10 or more well-explored gold-rich porphyry copper deposits from around the world with 100 or more sample sites near and some distance from each deposit would probably capture important variability among such deposits and provide proper data to train and test a shallow neural network to predict locations of undiscovered deposits.展开更多
To handle the handover challenge in Express Train Access Networks(ETAN).mobility fading effects in high speed railway environments should be addressed first.Based on the investigation of fading effects in this paper,w...To handle the handover challenge in Express Train Access Networks(ETAN).mobility fading effects in high speed railway environments should be addressed first.Based on the investigation of fading effects in this paper,we obtain two theoretical bounds:HOTiming upper bound and HO-Margin lower bound,which are helpful guidelines to study the handover challenge today and in the future.Then,we apply them to analyze performance of conventional handover technologies and our proposal in ETAN.This follow-up theory analyses and simulation experiment results demonstrate that the proposed handover solution can minimize handover time up to 4ms(which is the fastest one so far),and reduce HO-Margin to 0.16 dB at a train speed of 350km/h.展开更多
This research work investigates the use of Artificial Neural Network (ANN) based on models for solving first and second order linear constant coefficient ordinary differential equations with initial conditions. In par...This research work investigates the use of Artificial Neural Network (ANN) based on models for solving first and second order linear constant coefficient ordinary differential equations with initial conditions. In particular, we employ a feed-forward Multilayer Perceptron Neural Network (MLPNN), but bypass the standard back-propagation algorithm for updating the intrinsic weights. A trial solution of the differential equation is written as a sum of two parts. The first part satisfies the initial or boundary conditions and contains no adjustable parameters. The second part involves a feed-forward neural network to be trained to satisfy the differential equation. Numerous works have appeared in recent times regarding the solution of differential equations using ANN, however majority of these employed a single hidden layer perceptron model, incorporating a back-propagation algorithm for weight updation. For the homogeneous case, we assume a solution in exponential form and compute a polynomial approximation using statistical regression. From here we pick the unknown coefficients as the weights from input layer to hidden layer of the associated neural network trial solution. To get the weights from hidden layer to the output layer, we form algebraic equations incorporating the default sign of the differential equations. We then apply the Gaussian Radial Basis function (GRBF) approximation model to achieve our objective. The weights obtained in this manner need not be adjusted. We proceed to develop a Neural Network algorithm using MathCAD software, which enables us to slightly adjust the intrinsic biases. We compare the convergence and the accuracy of our results with analytic solutions, as well as well-known numerical methods and obtain satisfactory results for our example ODE problems.展开更多
In this study we investigate neural network solutions to nonlinear differential equations of Ricatti-type. We employ a feed-forward Multilayer Perceptron Neural Network (MLPNN), but avoid the standard back-propagation...In this study we investigate neural network solutions to nonlinear differential equations of Ricatti-type. We employ a feed-forward Multilayer Perceptron Neural Network (MLPNN), but avoid the standard back-propagation algorithm for updating the intrinsic weights. Our objective is to minimize an error, which is a function of the network parameters i.e., the weights and biases. Once the weights of the neural network are obtained by our systematic procedure, we need not adjust all the parameters in the network, as postulated by many researchers before us, in order to achieve convergence. We only need to fine-tune our biases which are fixed to lie in a certain given range, and convergence to a solution with an acceptable minimum error is achieved. This greatly reduces the computational complexity of the given problem. We provide two important ODE examples, the first is a Ricatti type differential equation to which the procedure is applied, and this gave us perfect agreement with the exact solution. The second example however provided us with only an acceptable approximation to the exact solution. Our novel artificial neural networks procedure has demonstrated quite clearly the function approximation capabilities of ANN in the solution of nonlinear differential equations of Ricatti type.展开更多
Train network system is a distributed bus system based on MVB bus, and its core component is vehicle control unit (VCU). The software architecture of VCU is developed in a flat and distributed way based on function or...Train network system is a distributed bus system based on MVB bus, and its core component is vehicle control unit (VCU). The software architecture of VCU is developed in a flat and distributed way based on function orientation, which is convenient for expansion, upgrading and testing.展开更多
In High-Speed Railways(HSRs),the Train Control and Management System(TCMS)plays a crucial role.However,as the demand for train networks grows,the limitations of traditional wired connections have become apparent.This ...In High-Speed Railways(HSRs),the Train Control and Management System(TCMS)plays a crucial role.However,as the demand for train networks grows,the limitations of traditional wired connections have become apparent.This paper designs and implements a Wireless Train Communication Network(WTCN)to enhance the existing train network infrastructure.To address the challenges that wireless communication technology faces in the unique environment of high-speed rail,this study first analyzes various onboard environments and simulates several typical scenarios in the laboratory.Integrating the specific application scenarios and service characteristics of the high-speed train control network,we conduct measurements and validations of WiFi performance,exploring the specific impacts of different factors on throughput and delay.展开更多
With the increasing data volume of train on-board system,real-time performance has become the most critical factor to ensure the safety of train operation.Considering that standard Ethernet cannot meet the real-time r...With the increasing data volume of train on-board system,real-time performance has become the most critical factor to ensure the safety of train operation.Considering that standard Ethernet cannot meet the real-time requirement of existing train communication network(TCN),the time-sensitive network(TSN)technology for TCN is introduced.To solve the time-delay problem,an adaptive switch queue selection mechanism for traffic scheduling is proposed.Firstly,the topology model of TCN based on TSN and the traffic model are described.Then,the K shortest path routing algorithm based on load balancing provides the optimal routing for the scheduling process.Finally,the adaptive switch queue selection mechanism is introduced to solve the aggregation flow conflict problem effectively,queue resources are properly allocated,and the gate control list(GCL)of each frame in the queue is obtained.Experimental results show that compared with the traditional constraint model,the schedulability of the model with an adaptive switch queue selection mechanism increases by 33.0%,and the maximum end-to-end delay and network jitter decrease by 19.1%and 18.6%on average respectively.It can provide theoretical support and application reference for the real-time performance optimization of TCN based on TSN.展开更多
Ethernet technology is widely applied in train communication networks(TCNs),serving as a crucial foundation for the enhancement of train intelligence.However,with its extensive deployment,some reliability issues have ...Ethernet technology is widely applied in train communication networks(TCNs),serving as a crucial foundation for the enhancement of train intelligence.However,with its extensive deployment,some reliability issues have been exposed,particularly those at the physical layer.Certain faults have significantly impacted the daily operations and services of trains.Focusing on the diagnosis of the health status of the Ethernet physical layer,this paper proposes a window-voting Support Vector Machine(SVM)classification method based on multi-feature fusion.It aims to identify various fault conditions in TCNs and to detect potential communication issues in advance.Initially,the specific problems in TCNs are analysed,examining data waveform characteristics under four health statuses:normal,interference,aging and fault.Subsequently,the weights of the waveform features are calculated using the Fuzzy Analytic Hierarchy Process and the Grey Relational Analysis method,and a window-voting SVM classifier is then constructed to categorize the data waveforms.Finally,a test system is set up in the laboratory to simulate different health statuses of the Ethernet physical layer,and to acquire experimental data for validating the effectiveness of the proposed method.The results show that the accuracy of recognizing the health status of the Ethernet physical layer exceeds 95%.展开更多
Quantum optimal control(QOC)relies on accurately modeling system dynamics and is often challenged by unknown or inaccessible interactions in real systems.Taking an unknown collective spin system as an example,this wor...Quantum optimal control(QOC)relies on accurately modeling system dynamics and is often challenged by unknown or inaccessible interactions in real systems.Taking an unknown collective spin system as an example,this work introduces a machine-learning-based,data-driven scheme to overcome the challenges encountered,with a trained neural network(NN)assuming the role of a surrogate model that captures the system’s dynamics and subsequently enables QOC to be performed on the NN instead of on the real system.The trained NN surrogate proves effective for practical QOC tasks and is further demonstrated to be adaptable to different experimental conditions,remaining robust across varying system sizes and pulse durations.展开更多
The Multilayer Perceptron(MLP)is a fundamental neural network model widely applied in various domains,particularly for lightweight image classification,speech recognition,and natural language processing tasks.Despite ...The Multilayer Perceptron(MLP)is a fundamental neural network model widely applied in various domains,particularly for lightweight image classification,speech recognition,and natural language processing tasks.Despite its widespread success,training MLPs often encounter significant challenges,including susceptibility to local optima,slow convergence rates,and high sensitivity to initial weight configurations.To address these issues,this paper proposes a Latin Hypercube Opposition-based Elite Variation Artificial Protozoa Optimizer(LOEV-APO),which enhances both global exploration and local exploitation simultaneously.LOEV-APO introduces a hybrid initialization strategy that combines Latin Hypercube Sampling(LHS)with Opposition-Based Learning(OBL),thus improving the diversity and coverage of the initial population.Moreover,an Elite Protozoa Variation Strategy(EPVS)is incorporated,which applies differential mutation operations to elite candidates,accelerating convergence and strengthening local search capabilities around high-quality solutions.Extensive experiments are conducted on six classification tasks and four function approximation tasks,covering a wide range of problem complexities and demonstrating superior generalization performance.The results demonstrate that LOEV-APO consistently outperforms nine state-of-the-art metaheuristic algorithms and two gradient-based methods in terms of convergence speed,solution accuracy,and robustness.These findings suggest that LOEV-APO serves as a promising optimization tool for MLP training and provides a viable alternative to traditional gradient-based methods.展开更多
We propose an optical tensor core(OTC) architecture for neural network training. The key computational components of the OTC are the arrayed optical dot-product units(DPUs). The homodyne-detection-based DPUs can condu...We propose an optical tensor core(OTC) architecture for neural network training. The key computational components of the OTC are the arrayed optical dot-product units(DPUs). The homodyne-detection-based DPUs can conduct the essential computational work of neural network training, i.e., matrix-matrix multiplication. Dual-layer waveguide topology is adopted to feed data into these DPUs with ultra-low insertion loss and cross talk. Therefore, the OTC architecture allows a large-scale dot-product array and can be integrated into a photonic chip. The feasibility of the OTC and its effectiveness on neural network training are verified with numerical simulations.展开更多
This paper introduces the high-speed electrical multiple unit （EMO） life cycle, including the design, manufacturing, testing, and maintenance stages. It also presents the train control and monitoring system （TCMS）...This paper introduces the high-speed electrical multiple unit （EMO） life cycle, including the design, manufacturing, testing, and maintenance stages. It also presents the train control and monitoring system （TCMS） software development platform, the TCMS testing and verification bench, the EMU driving simulation platform, and the EMU remote data transmittal and maintenance platform. All these platforms and benches combined together make up the EMU life cycle cost （LCC） system. Each platform facilitates EMU LCC management and is an important part of the system.展开更多
The present study aims to conduct 2 types of statistical analysis to reveal the impact of the spread of COVID-19 on train delays by comparing the potential contributing factors before, during and after the outbreak of...The present study aims to conduct 2 types of statistical analysis to reveal the impact of the spread of COVID-19 on train delays by comparing the potential contributing factors before, during and after the outbreak of the virus in the metropolitan train lines in Japan. First of all, the result of the present study clearly revealed the changes in contributing factors for train delays caused by the spread of COVID-19. Specifically, the contributing factors for train delays changed due to the decrease of passengers by the effect of the outbreak of the virus. Additionally, though large terminal stations were considered to be a major contributing factor in causing and increasing train delays in the past, this was not the case after the spread of COVID-19. Therefore, under such conditions, it is more effective to make improvements in small to medium stations and tracks rather than terminal stations. Furthermore, as the decrease in passengers also decreased train delays in commuter lines going to the suburbs due to the spread of COVID-19, the contributing factor for such lines is the excessive number of passengers. Therefore, as for countermeasures for train delays after the effects of COVID-19, it is necessary to disperse passengers in order to avoid passengers concentrating in the same time zones and train lines.展开更多
The development of automatic methods to recognize cracks in surfaces of concrete has been under focus in recent years,firstly through computer vision methods and more recently focusing on convolutional neural networks...The development of automatic methods to recognize cracks in surfaces of concrete has been under focus in recent years,firstly through computer vision methods and more recently focusing on convolutional neural networks that are delivering promising results.Challenges are still persisting in crack recognition,namely due to the confusion added by the myriad of elements commonly found on concrete surfaces.The robustness of these methods would deal with these elements if access to correspondingly heterogeneous datasets was possible.Even so,this would be a cumbersome methodology,since training would be needed for each particular case and models would be case dependent.Thus,efforts from the scientific community are focusing on generalizing neural network models to achieve high per-formance in images from different domains,slightly different from those in which they were effectively trained.The generalization of networks can be achieved by domain adaptation techniques at the training stage.Domain adapta-tion enables finding a feature space in which features from both domains are invariant,and thus,classes become separable.The work presented here proposes the DA-Crack method,which is a domain adversarial training method,to generalize a neural network for recognizing cracks in images of concrete surfaces.The domain adversarial method uses a convolutional extractor followed by a classifier and a discriminator,and relies on two datasets:a source labeled dataset and a target unlabeled small dataset.The classifier is responsible for the classification of images randomly chosen,while the discriminator is dedicated to uncovering to which dataset each image belongs.Backpropagation from the discriminator reverses the gradient used to update the extractor.This enables fighting the convergence promoted by the updating backpropagated from the classifier,and thus generalizing the extractor enabling it for crack recognition of images from both source and target datasets.Results show that the DA-Crack training method improved accuracy in crack classification of images from the target dataset in 54 percentage points,while accuracy on the source dataset remains unaffected.展开更多
基金Supported by Beijing Municipal Education Commission (No.xk100100435) and the Key Research Project of Science andTechnology from Sinopec (No.E03007).
文摘Many applications of principal component analysis (PCA) can be found in dimensionality reduction. But linear PCA method is not well suitable for nonlinear chemical processes. A new PCA method based on im-proved input training neural network (IT-NN) is proposed for the nonlinear system modelling in this paper. Mo-mentum factor and adaptive learning rate are introduced into learning algorithm to improve the training speed of IT-NN. Contrasting to the auto-associative neural network (ANN), IT-NN has less hidden layers and higher training speed. The effectiveness is illustrated through a comparison of IT-NN with linear PCA and ANN with experiments. Moreover, the IT-NN is combined with RBF neural network (RBF-NN) to model the yields of ethylene and propyl-ene in the naphtha pyrolysis system. From the illustrative example and practical application, IT-NN combined with RBF-NN is an effective method of nonlinear chemical process modelling.
文摘The existing computer and network technology makes the enterprise training transform from the traditional mode into new mode. The paper studies how to combine enterprise knowledge management and network training to make the enterprise training meet the demands of knowledge management and improve the competitiveness of enterprises. And the paper puts forwards the new opinion combining enterprise knowledge management and network training system. The purpose of applying knowledge map and knowledge push to training system is to integrate knowledge management into training system to make the enterprises face the challenge of knowledge economy.
基金Supported by the Ministerial Level Research Foundation(404040401)
文摘Training neural network to recognize targets needs a lot of samples.People usually get these samples in a non-systematic way,which can miss or overemphasize some target information.To improve this situation,a new method based on virtual model and invariant moments was proposed to generate training samples.The method was composed of the following steps：use computer and simulation software to build target object＇s virtual model and then simulate the environment,light condition,camera parameter,etc.;rotate the model by spin and nutation of inclination to get the image sequence by virtual camera;preprocess each image and transfer them into binary image;calculate the invariant moments for each image and get a vectors＇ sequence.The vectors＇ sequence which was proved to be complete became the training samples together with the target outputs.The simulated results showed that the proposed method could be used to recognize the real targets and improve the accuracy of target recognition effectively when the sampling interval was short enough and the circumstance simulation was close enough.
基金funded in part by the Advanced Research Projects AgencyEnergy (ARPA-E), U.S. Department of Energy, under award number DE-AR0001471。
文摘Although train modeling research is vast, most available simulation tools are confined to city-or trip-scale analysis, primarily offering micro-level simulations of network segments. This paper addresses this void by developing the Ne Train Sim simulator for heavy long-haul freight trains on a network of multiple intersecting tracks. The main objective of this simulator is to enable a comprehensive analysis of energy consumption and the associated carbon footprint for the entire train system. Four case studies were conducted to demonstrate the simulator's performance. The first case study validates the model by comparing Ne Train Sim output to empirical trajectory data. The results demonstrate that the simulated trajectory is precise enough to estimate the train energy consumption and carbon dioxide emissions. The second application demonstrates the train-following model considering six trains following each other. The results showcase the model ability to maintain safefollowing distances between successive trains. The next study highlights the simulator's ability to resolve train conflicts for different scenarios. Finally, the suitability of the Ne Train Sim for modeling realistic railroad networks is verified through the modeling of the entire US network and comparing alternative powertrains on the fleet energy consumption.
文摘Based on the"Understandable Output Hypothesis"a practical study on the construction of college oral English network training camp is set up,through speech learning and imitation,building language input in natural environment,exploring effective output mode based on information technology platform,providing foreign language learners with opportunities to express language and get feedback.Students use relevant resources on the Internet to complete the oral activities of"thematic activities"together,so as to cultivate students'cooperative learning,communication skills,team spirit and language communication ability.
文摘Artificial intelligence(AI),encompassing machine learning and deep learning,is being extensively used in medical sciences.It is slated to positively impact the diagnosis and prognostication of various diseases.Deep learning,a subset of AI,has been instrumental in diagnosing diabetic retinopathy(DR),diabetic macular edema,glaucoma,age-related macular degeneration,and numerous other ocular diseases.AI performs equally well in the early prediction of glaucoma and agerelated macular degeneration.Integrating AI with telemedicine promises to improve healthcare delivery,although challenges persist in implementing AI algorithms,especially in deve-loping countries.This review provides a compre hensive summary of AI,its applications in ophthalmology,particularly DR,the diverse algorithms utilized for different ocular conditions,and prospects for the future integration of AI in eye care.
文摘Whether using a shallow neural network with one hidden layer,or a deep network with many hidden layers,the training data must represent subgroups of the deposit type being explored to be useful.Published examples of neural networks have mostly been limited to one individual mineral deposit for training.Variation of geologic features among deposits within a type are so large that a single deposit cannot provide proper information to train a neural net to generalize and guide exploration for other deposits.Models trained with only one deposit tend to be academic successes but are not of practical value in exploration for other deposits.This is why it takes much experience examining many deposits to properly train an economic geologist—a neural network is not any different.Two examples of shallow neural networks are used to demonstrate the power of neural networks to possibly locate undiscovered deposits and to provide some suggestions of how to deal with missing data.The training data needs to include information spatially related to known deposits and hopefully information from many different deposits of the type.Lessons learned from these and other examples point to a proposed sampling plan for data that could lead to a generalized neural network for exploration.In this plan,10 or more well-explored gold-rich porphyry copper deposits from around the world with 100 or more sample sites near and some distance from each deposit would probably capture important variability among such deposits and provide proper data to train and test a shallow neural network to predict locations of undiscovered deposits.
基金supported by the National Basic Research Program of China (973 Program)(No.2012CB315606 and 2010CB328201)
文摘To handle the handover challenge in Express Train Access Networks(ETAN).mobility fading effects in high speed railway environments should be addressed first.Based on the investigation of fading effects in this paper,we obtain two theoretical bounds:HOTiming upper bound and HO-Margin lower bound,which are helpful guidelines to study the handover challenge today and in the future.Then,we apply them to analyze performance of conventional handover technologies and our proposal in ETAN.This follow-up theory analyses and simulation experiment results demonstrate that the proposed handover solution can minimize handover time up to 4ms(which is the fastest one so far),and reduce HO-Margin to 0.16 dB at a train speed of 350km/h.
文摘This research work investigates the use of Artificial Neural Network (ANN) based on models for solving first and second order linear constant coefficient ordinary differential equations with initial conditions. In particular, we employ a feed-forward Multilayer Perceptron Neural Network (MLPNN), but bypass the standard back-propagation algorithm for updating the intrinsic weights. A trial solution of the differential equation is written as a sum of two parts. The first part satisfies the initial or boundary conditions and contains no adjustable parameters. The second part involves a feed-forward neural network to be trained to satisfy the differential equation. Numerous works have appeared in recent times regarding the solution of differential equations using ANN, however majority of these employed a single hidden layer perceptron model, incorporating a back-propagation algorithm for weight updation. For the homogeneous case, we assume a solution in exponential form and compute a polynomial approximation using statistical regression. From here we pick the unknown coefficients as the weights from input layer to hidden layer of the associated neural network trial solution. To get the weights from hidden layer to the output layer, we form algebraic equations incorporating the default sign of the differential equations. We then apply the Gaussian Radial Basis function (GRBF) approximation model to achieve our objective. The weights obtained in this manner need not be adjusted. We proceed to develop a Neural Network algorithm using MathCAD software, which enables us to slightly adjust the intrinsic biases. We compare the convergence and the accuracy of our results with analytic solutions, as well as well-known numerical methods and obtain satisfactory results for our example ODE problems.
文摘In this study we investigate neural network solutions to nonlinear differential equations of Ricatti-type. We employ a feed-forward Multilayer Perceptron Neural Network (MLPNN), but avoid the standard back-propagation algorithm for updating the intrinsic weights. Our objective is to minimize an error, which is a function of the network parameters i.e., the weights and biases. Once the weights of the neural network are obtained by our systematic procedure, we need not adjust all the parameters in the network, as postulated by many researchers before us, in order to achieve convergence. We only need to fine-tune our biases which are fixed to lie in a certain given range, and convergence to a solution with an acceptable minimum error is achieved. This greatly reduces the computational complexity of the given problem. We provide two important ODE examples, the first is a Ricatti type differential equation to which the procedure is applied, and this gave us perfect agreement with the exact solution. The second example however provided us with only an acceptable approximation to the exact solution. Our novel artificial neural networks procedure has demonstrated quite clearly the function approximation capabilities of ANN in the solution of nonlinear differential equations of Ricatti type.
文摘Train network system is a distributed bus system based on MVB bus, and its core component is vehicle control unit (VCU). The software architecture of VCU is developed in a flat and distributed way based on function orientation, which is convenient for expansion, upgrading and testing.
基金support from the Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications(BHRC-2024-1)Beijing Jiaotong University,the National Natural Science Foundation of China(U21A20445).
文摘In High-Speed Railways(HSRs),the Train Control and Management System(TCMS)plays a crucial role.However,as the demand for train networks grows,the limitations of traditional wired connections have become apparent.This paper designs and implements a Wireless Train Communication Network(WTCN)to enhance the existing train network infrastructure.To address the challenges that wireless communication technology faces in the unique environment of high-speed rail,this study first analyzes various onboard environments and simulates several typical scenarios in the laboratory.Integrating the specific application scenarios and service characteristics of the high-speed train control network,we conduct measurements and validations of WiFi performance,exploring the specific impacts of different factors on throughput and delay.
基金supported by the National Natural Science Foundation of China(52072081)Major Project of Science and Technology of Guangxi Province of China(Guike AB23075209)+2 种基金Guangxi Manufacturing Systems and Advanced Manufacturing Technology Key Laboratory Director Fund(24050-44-S015)Innovation Project of Guangxi Graduate Education(YCSW2024135)Major Talent Project in Guangxi Zhuang Autonomous Region。
文摘With the increasing data volume of train on-board system,real-time performance has become the most critical factor to ensure the safety of train operation.Considering that standard Ethernet cannot meet the real-time requirement of existing train communication network(TCN),the time-sensitive network(TSN)technology for TCN is introduced.To solve the time-delay problem,an adaptive switch queue selection mechanism for traffic scheduling is proposed.Firstly,the topology model of TCN based on TSN and the traffic model are described.Then,the K shortest path routing algorithm based on load balancing provides the optimal routing for the scheduling process.Finally,the adaptive switch queue selection mechanism is introduced to solve the aggregation flow conflict problem effectively,queue resources are properly allocated,and the gate control list(GCL)of each frame in the queue is obtained.Experimental results show that compared with the traditional constraint model,the schedulability of the model with an adaptive switch queue selection mechanism increases by 33.0%,and the maximum end-to-end delay and network jitter decrease by 19.1%and 18.6%on average respectively.It can provide theoretical support and application reference for the real-time performance optimization of TCN based on TSN.
基金supported in part by funds from the National Key Research and Development Program(Grant No.2022YFB4301202)the Regional Joint Fund Project of Natural Science Foundation of Hunan Province(Grant No.2025JJ70016).
文摘Ethernet technology is widely applied in train communication networks(TCNs),serving as a crucial foundation for the enhancement of train intelligence.However,with its extensive deployment,some reliability issues have been exposed,particularly those at the physical layer.Certain faults have significantly impacted the daily operations and services of trains.Focusing on the diagnosis of the health status of the Ethernet physical layer,this paper proposes a window-voting Support Vector Machine(SVM)classification method based on multi-feature fusion.It aims to identify various fault conditions in TCNs and to detect potential communication issues in advance.Initially,the specific problems in TCNs are analysed,examining data waveform characteristics under four health statuses:normal,interference,aging and fault.Subsequently,the weights of the waveform features are calculated using the Fuzzy Analytic Hierarchy Process and the Grey Relational Analysis method,and a window-voting SVM classifier is then constructed to categorize the data waveforms.Finally,a test system is set up in the laboratory to simulate different health statuses of the Ethernet physical layer,and to acquire experimental data for validating the effectiveness of the proposed method.The results show that the accuracy of recognizing the health status of the Ethernet physical layer exceeds 95%.
基金supported by the Innovation Program for Quantum Science and Technology(Grant No.2021ZD0302100)the National Natural Science Foundation of China(Grant Nos.12361131576,92265205,and 92476205).
文摘Quantum optimal control(QOC)relies on accurately modeling system dynamics and is often challenged by unknown or inaccessible interactions in real systems.Taking an unknown collective spin system as an example,this work introduces a machine-learning-based,data-driven scheme to overcome the challenges encountered,with a trained neural network(NN)assuming the role of a surrogate model that captures the system’s dynamics and subsequently enables QOC to be performed on the NN instead of on the real system.The trained NN surrogate proves effective for practical QOC tasks and is further demonstrated to be adaptable to different experimental conditions,remaining robust across varying system sizes and pulse durations.
基金supported by the National Natural Science Foundation of China(Grant Nos.62376089,62302153,62302154)the Key Research and Development Program of Hubei Province,China(Grant No.2023BEB024)+1 种基金the Young and Middle-Aged Scientific and Technological Innovation Team Plan in Higher Education Institutions in Hubei Province,China(Grant No.T2023007)the National Natural Science Foundation of China(Grant No.U23A20318).
文摘The Multilayer Perceptron(MLP)is a fundamental neural network model widely applied in various domains,particularly for lightweight image classification,speech recognition,and natural language processing tasks.Despite its widespread success,training MLPs often encounter significant challenges,including susceptibility to local optima,slow convergence rates,and high sensitivity to initial weight configurations.To address these issues,this paper proposes a Latin Hypercube Opposition-based Elite Variation Artificial Protozoa Optimizer(LOEV-APO),which enhances both global exploration and local exploitation simultaneously.LOEV-APO introduces a hybrid initialization strategy that combines Latin Hypercube Sampling(LHS)with Opposition-Based Learning(OBL),thus improving the diversity and coverage of the initial population.Moreover,an Elite Protozoa Variation Strategy(EPVS)is incorporated,which applies differential mutation operations to elite candidates,accelerating convergence and strengthening local search capabilities around high-quality solutions.Extensive experiments are conducted on six classification tasks and four function approximation tasks,covering a wide range of problem complexities and demonstrating superior generalization performance.The results demonstrate that LOEV-APO consistently outperforms nine state-of-the-art metaheuristic algorithms and two gradient-based methods in terms of convergence speed,solution accuracy,and robustness.These findings suggest that LOEV-APO serves as a promising optimization tool for MLP training and provides a viable alternative to traditional gradient-based methods.
基金supported by the National Key R&D Program of China (No.2019YFB2203700)the National Natural Science Foundation of China (No.61822508)。
文摘We propose an optical tensor core(OTC) architecture for neural network training. The key computational components of the OTC are the arrayed optical dot-product units(DPUs). The homodyne-detection-based DPUs can conduct the essential computational work of neural network training, i.e., matrix-matrix multiplication. Dual-layer waveguide topology is adopted to feed data into these DPUs with ultra-low insertion loss and cross talk. Therefore, the OTC architecture allows a large-scale dot-product array and can be integrated into a photonic chip. The feasibility of the OTC and its effectiveness on neural network training are verified with numerical simulations.
文摘This paper introduces the high-speed electrical multiple unit （EMO） life cycle, including the design, manufacturing, testing, and maintenance stages. It also presents the train control and monitoring system （TCMS） software development platform, the TCMS testing and verification bench, the EMU driving simulation platform, and the EMU remote data transmittal and maintenance platform. All these platforms and benches combined together make up the EMU life cycle cost （LCC） system. Each platform facilitates EMU LCC management and is an important part of the system.
文摘The present study aims to conduct 2 types of statistical analysis to reveal the impact of the spread of COVID-19 on train delays by comparing the potential contributing factors before, during and after the outbreak of the virus in the metropolitan train lines in Japan. First of all, the result of the present study clearly revealed the changes in contributing factors for train delays caused by the spread of COVID-19. Specifically, the contributing factors for train delays changed due to the decrease of passengers by the effect of the outbreak of the virus. Additionally, though large terminal stations were considered to be a major contributing factor in causing and increasing train delays in the past, this was not the case after the spread of COVID-19. Therefore, under such conditions, it is more effective to make improvements in small to medium stations and tracks rather than terminal stations. Furthermore, as the decrease in passengers also decreased train delays in commuter lines going to the suburbs due to the spread of COVID-19, the contributing factor for such lines is the excessive number of passengers. Therefore, as for countermeasures for train delays after the effects of COVID-19, it is necessary to disperse passengers in order to avoid passengers concentrating in the same time zones and train lines.
基金support from the Fundação para a Ciência e Tecnologia through the Ph.D.grant SFRHBD/144924/2019the individual project CEECIND/04463/2017.
文摘The development of automatic methods to recognize cracks in surfaces of concrete has been under focus in recent years,firstly through computer vision methods and more recently focusing on convolutional neural networks that are delivering promising results.Challenges are still persisting in crack recognition,namely due to the confusion added by the myriad of elements commonly found on concrete surfaces.The robustness of these methods would deal with these elements if access to correspondingly heterogeneous datasets was possible.Even so,this would be a cumbersome methodology,since training would be needed for each particular case and models would be case dependent.Thus,efforts from the scientific community are focusing on generalizing neural network models to achieve high per-formance in images from different domains,slightly different from those in which they were effectively trained.The generalization of networks can be achieved by domain adaptation techniques at the training stage.Domain adapta-tion enables finding a feature space in which features from both domains are invariant,and thus,classes become separable.The work presented here proposes the DA-Crack method,which is a domain adversarial training method,to generalize a neural network for recognizing cracks in images of concrete surfaces.The domain adversarial method uses a convolutional extractor followed by a classifier and a discriminator,and relies on two datasets:a source labeled dataset and a target unlabeled small dataset.The classifier is responsible for the classification of images randomly chosen,while the discriminator is dedicated to uncovering to which dataset each image belongs.Backpropagation from the discriminator reverses the gradient used to update the extractor.This enables fighting the convergence promoted by the updating backpropagated from the classifier,and thus generalizing the extractor enabling it for crack recognition of images from both source and target datasets.Results show that the DA-Crack training method improved accuracy in crack classification of images from the target dataset in 54 percentage points,while accuracy on the source dataset remains unaffected.
