Although seemingly disparate,high-energy nuclear physics(HENP)and machine learning(ML)have begun to merge in the last few years,yielding interesting results.It is worthy to raise the profile of utilizing this novel mi...Although seemingly disparate,high-energy nuclear physics(HENP)and machine learning(ML)have begun to merge in the last few years,yielding interesting results.It is worthy to raise the profile of utilizing this novel mindset from ML in HENP,to help interested readers see the breadth of activities around this intersection.The aim of this mini-review is to inform the community of the current status and present an overview of the application of ML to HENP.From different aspects and using examples,we examine how scientific questions involving HENP can be answered using ML.展开更多
State of health(SOH)estimation of e-mobilities operated in real and dynamic conditions is essential and challenging.Most of existing estimations are based on a fixed constant current charging and discharging aging pro...State of health(SOH)estimation of e-mobilities operated in real and dynamic conditions is essential and challenging.Most of existing estimations are based on a fixed constant current charging and discharging aging profiles,which overlooked the fact that the charging and discharging profiles are random and not complete in real application.This work investigates the influence of feature engineering on the accuracy of different machine learning(ML)-based SOH estimations acting on different recharging sub-profiles where a realistic battery mission profile is considered.Fifteen features were extracted from the battery partial recharging profiles,considering different factors such as starting voltage values,charge amount,and charging sliding windows.Then,features were selected based on a feature selection pipeline consisting of filtering and supervised ML-based subset selection.Multiple linear regression(MLR),Gaussian process regression(GPR),and support vector regression(SVR)were applied to estimate SOH,and root mean square error(RMSE)was used to evaluate and compare the estimation performance.The results showed that the feature selection pipeline can improve SOH estimation accuracy by 55.05%,2.57%,and 2.82%for MLR,GPR and SVR respectively.It was demonstrated that the estimation based on partial charging profiles with lower starting voltage,large charge,and large sliding window size is more likely to achieve higher accuracy.This work hopes to give some insights into the supervised ML-based feature engineering acting on random partial recharges on SOH estimation performance and tries to fill the gap of effective SOH estimation between theoretical study and real dynamic application.展开更多
The reliability of real-time embedded software directly determines the reliability of the whole real-time embedded sys- tem, and the effective software testing is an important way to ensure software quality and reliab...The reliability of real-time embedded software directly determines the reliability of the whole real-time embedded sys- tem, and the effective software testing is an important way to ensure software quality and reliability. Based on the analysis of the characteristics of real-time embedded software, the formal method is introduced into the real-time embedded software testing field and the real-time extended finite state machine (RT-EFSM) model is studied firstly. Then, the time zone division method of real-time embedded system is presented and the definition and description methods of time-constrained transition equivalence class (timeCTEC) are presented. Furthermore, the approaches of the testing sequence and test case generation are put forward. Finally, the proposed method is applied to a typical avionics real- time embedded software testing practice and the examples of the timeCTEC, testing sequences and test cases are given. With the analysis of the testing result, the application verification shows that the proposed method can effectively describe the real-time embedded software state transition characteristics and real-time requirements and play the advantages of the formal methods in accuracy, effectiveness and the automation supporting. Combined with the testing platform, the real-time, closed loop and automated simulation testing for real-time embedded software can be realized effectively.展开更多
BACKGROUND Non-alcoholic fatty liver disease(NAFLD)is the most common chronic liver disease,affecting over 30% of the United States population.Early patient identification using a simple method is highly desirable.AIM...BACKGROUND Non-alcoholic fatty liver disease(NAFLD)is the most common chronic liver disease,affecting over 30% of the United States population.Early patient identification using a simple method is highly desirable.AIM To create machine learning models for predicting NAFLD in the general United States population.METHODS Using the NHANES 1988-1994.Thirty NAFLD-related factors were included.The dataset was divided into the training(70%)and testing(30%)datasets.Twentyfour machine learning algorithms were applied to the training dataset.The bestperforming models and another interpretable model(i.e.,coarse trees)were tested using the testing dataset.RESULTS There were 3235 participants(n=3235)that met the inclusion criteria.In the training phase,the ensemble of random undersampling(RUS)boosted trees had the highest F1(0.53).In the testing phase,we compared selective machine learning models and NAFLD indices.Based on F1,the ensemble of RUS boosted trees remained the top performer(accuracy 71.1%and F10.56)followed by the fatty liver index(accuracy 68.8% and F10.52).A simple model(coarse trees)had an accuracy of 74.9% and an F1 of 0.33.CONCLUSION Not every machine learning model is complex.Using a simpler model such as coarse trees,we can create an interpretable model for predicting NAFLD with only two predictors:fasting C-peptide and waist circumference.Although the simpler model does not have the best performance,its simplicity is useful in clinical practice.展开更多
Internet communication protocols define the behavior rules of network components when they communicate with each other.With the continuous development of network technologies,many private or unknown network protocols ...Internet communication protocols define the behavior rules of network components when they communicate with each other.With the continuous development of network technologies,many private or unknown network protocols are emerging in endlessly various network environments.Herein,relevant protocol specifications become difficult or unavailable to translate in many situations such as network security management and intrusion detection.Although protocol reverse engineering is being investigated in recent years to perform reverse analysis on the specifications of unknown protocols,most existing methods have proven to be time-consuming with limited efficiency,especially when applied on unknown protocol state machines.This paper proposes a state merging algorithm based on EDSM(Evidence-Driven State Merging)to infer the transition rules of unknown protocols in form of state machines with high efficiency.Compared with another classical state machine inferring method based on Exbar algorithm,the experiment results demonstrate that our proposed method could run faster,especially when dealing with massive training data sets.In addition,this method can also make the state machines have higher similarities with the reference state machines constructed from public specifications.展开更多
We show that the secret key generation rate can be balanced with the maximum secure distance of four-state continuous-variable quantum key distribution(CV-QKD) by using the linear optics cloning machine(LOCM). Ben...We show that the secret key generation rate can be balanced with the maximum secure distance of four-state continuous-variable quantum key distribution(CV-QKD) by using the linear optics cloning machine(LOCM). Benefiting from the LOCM operation, the LOCM-tuned noise can be employed by the reference partner of reconciliation to achieve higher secret key generation rates over a long distance. Simulation results show that the LOCM operation can flexibly regulate the secret key generation rate and the maximum secure distance and improve the performance of four-state CV-QKD protocol by dynamically tuning parameters in an appropriate range.展开更多
采用UML分析与设计的业务信息系统,业务流程经过层层的抽象迭代,缺乏一种透明的业务流程实现。WF提供了可视化的业务过程编程模型,便于实现业务流程自动化,在对比分析WF State Machine和UML状态图的基础上,研究从UML状态图到WF State Ma...采用UML分析与设计的业务信息系统,业务流程经过层层的抽象迭代,缺乏一种透明的业务流程实现。WF提供了可视化的业务过程编程模型,便于实现业务流程自动化,在对比分析WF State Machine和UML状态图的基础上,研究从UML状态图到WF State Machine业务流程映射关系,选取UML中典型状态图,依据一定的命名转换规则,实现了从UML状态图分析设计到WF状态机业务过程可视化的构建,完成了动态测试。展开更多
A state machine can make program designing quicker,simpler and more efficient. This paper describes in detail the model for a state machine and the idea for its designing and gives the design process of the state mach...A state machine can make program designing quicker,simpler and more efficient. This paper describes in detail the model for a state machine and the idea for its designing and gives the design process of the state machine through an example of audio signal generator system based on Labview. The result shows that the introduction of the state machine can make complex design processes more clear and the revision of programs easier.展开更多
With the popularization of vessel satellite AIS(automatic identification system)equipment and the continuous improve-ment of the AIS data’s coverage,continuity and effectiveness,AIS has become an important data sourc...With the popularization of vessel satellite AIS(automatic identification system)equipment and the continuous improve-ment of the AIS data’s coverage,continuity and effectiveness,AIS has become an important data source to study the navigation char-acteristics of vessel groups.This study established an identification model to extract the fishing state and intensity information of fishing vessels,based on the AIS data of purse seine fishing vessels,combined with the variables of vessel position,speed and course.Expert experience,spatial statistics and data mining analysis methods were applied to establish the model,and the Western and Cen-tral Pacific Ocean areas were studied.The results showed that the overall accuracy of identification of the fishing state using Support Vector Machine method is higher,and the method has a good modeling effect.The spatial distribution characteristics of the vessels’fishing intensity based on AIS data showed a significant cluster distribution pattern.The obtained high-intensity fishing area can be used as a prediction of purse seine fishing grounds in the Western and Central Pacific areas.Through the processing and research of AIS data,this study provided important scientific support for the identification of fishing state of purse seine fishing vessels.The spatial fishing intensity of fishing vessels based on AIS data can also be used for the analysis of fishery resources and fishing grounds,and further serve the sustainable development of marine fisheries.展开更多
Fishing logbook records the fishing behaviors and other information of fishing vessels.However,the accuracy of the recorded information is often difficult to guarantee due to the misreport and concealment.The fishing ...Fishing logbook records the fishing behaviors and other information of fishing vessels.However,the accuracy of the recorded information is often difficult to guarantee due to the misreport and concealment.The fishing vessel monitoring system(VMS)can monitor and record the navigation information of fishing vessels in real time,and it may be used to improve the accuracy of identifying the state of fishing vessels.If the VMS data and fishing logbook are combined to establish their relationships,then the navigation characteristics and fishing behavior of fishing vessels can be more accurately identified.Therefore,first,a method for determining the state of VMS data points using fishing log data was proposed.Secondly,the relationship between VMS data and the different states of fishing vessels was further explored.Thirdly,the state of the fishing vessel was predicted using VMS data by building machine learning models.The speed,heading,longitude,latitude,and time as features from the VMS data were extracted by matching the VMS and logbook data of three single otter trawl vessels from September 2012 to January 2013,and four machine learning models were established,i.e.,Random Forest(RF),Adaptive Boosting(AdaBoost),K-Nearest Neighbor(KNN),and Gradient Boosting Decision Tree(GBDT)to predict the behavior of fishing vessels.The prediction performances of the models were evaluated by using normalized confusion matrix and receiver operator characteristic curve.Results show that the importance rankings of spatial(longitude and latitude)and time features were higher than those of speed and heading.The prediction performances of the RF and AdaBoost models were higher than those of the KNN and GBDT models.RF model showed the highest prediction performance for fishing state.Meanwhile,AdaBoost model exhibited the highest prediction performance for non-fishing state.This study offered a technical basis for judging the navigation characteristics of fishing vessels,which improved the algorithm for judging the behavior of fishing vessels based on VMS data,enhanced the prediction accuracy,and upgraded the fishery management being more scientific and efficient.展开更多
An effective power quality prediction for regional power grid can provide valuable references and contribute to the discovering and solving of power quality problems. So a predicting model for power quality steady sta...An effective power quality prediction for regional power grid can provide valuable references and contribute to the discovering and solving of power quality problems. So a predicting model for power quality steady state index based on chaotic theory and least squares support vector machine (LSSVM) is proposed in this paper. At first, the phase space reconstruction of original power quality data is performed to form a new data space containing the attractor. The new data space is used as training samples for the LSSVM. Then in order to predict power quality steady state index accurately, the particle swarm algorithm is adopted to optimize parameters of the LSSVM model. According to the simulation results based on power quality data measured in a certain distribution network, the model applies to several indexes with higher forecasting accuracy and strong practicability.展开更多
Railway transportation system is a critical sector where design methods and techniques are defined by international standards in order to reduce possible risks to an acceptable minimum level. CENELEC 50128 strongly re...Railway transportation system is a critical sector where design methods and techniques are defined by international standards in order to reduce possible risks to an acceptable minimum level. CENELEC 50128 strongly recommends the utilization of finite state machines during system modelling stage and formal proof methods during the verifi- cation and testing stages of control algorithms. Due to the high importance of interlocking table at the design state of a sig- nalization system, the modelling and verification of inter- locking tables are examined in this work. For this purpose, abstract state machines are used as a modelling tool. The developed models have been performed in a generalized structure such that the model control can be done automatically for the interlocking systems. In this study, NuSMV is used at the verification state. Also, the consistency of the developed models has been supervised through fault injection. The developed models and software components are applied on a real railway station operated by Metro Istanbul Co.展开更多
With the widespread use of lithium ion batteries in portable electronics and electric vehicles,further improvements in the performance of lithium ion battery materials and accurate prediction of battery state are of i...With the widespread use of lithium ion batteries in portable electronics and electric vehicles,further improvements in the performance of lithium ion battery materials and accurate prediction of battery state are of increasing interest to battery researchers.Machine learning,one of the core technologies of artificial intelligence,is rapidly changing many fields with its ability to learn from historical data and solve complex tasks,and it has emerged as a new technique for solving current research problems in the field of lithium ion batteries.This review begins with the introduction of the conceptual framework of machine learning and the general process of its application,then reviews some of the progress made by machine learning in both improving battery materials design and accurate prediction of battery state,and finally points out the current application problems of machine learning and future research directions.It is believed that the use of machine learning will further promote the large-scale application and improvement of lithium-ion batteries.展开更多
For thin-walled parts,uniform allowance to each machining surface is allocated by the traditional machining method.Considering the sequence of the adjacent machining features,it may cause poor stiffness for some side ...For thin-walled parts,uniform allowance to each machining surface is allocated by the traditional machining method.Considering the sequence of the adjacent machining features,it may cause poor stiffness for some side walls due to a minor wall thickness,which may cause the deformation of the final formed parts to be large,or deduce machining efficiency for some machining features due to too thick remains.In order to address this issue,a non-uniform allowance allocation method based on interim state stiffness of machining features for the finishing of thin-walled structural parts is proposed in this paper.In this method,the interim state model of machining features is constructed according to the machining sequence of the parts,and the stiffness of the side wall is taken as the evaluation index to allocate reasonable allowance value to the corresponding machining surface to ensure the stiffness requirement of the parts in the machining process.According to the finite element simulation results,the non-uniform allowance allocation method proposed in this paper can effectively improve the stiffness of the parts and reduce the deformation of the parts,when compared with the traditional uniform allowance machining method.展开更多
According to the basic emotional theory, the artificial emotional model based on the finite state machine(FSM) was presented. In finite state machine model of emotion, the emotional space included the basic emotional ...According to the basic emotional theory, the artificial emotional model based on the finite state machine(FSM) was presented. In finite state machine model of emotion, the emotional space included the basic emotional space and the multiple emotional spaces. The emotion-switching diagram was defined and transition function was developed using Markov chain and linear interpolation algorithm. The simulation model was built using Stateflow toolbox and Simulink toolbox based on the Matlab platform. And the model included three subsystems: the input one, the emotion one and the behavior one. In the emotional subsystem, the responses of different personalities to the external stimuli were described by defining personal space. This model takes states from an emotional space and updates its state depending on its current state and a state of its input (also a state-emotion). The simulation model realizes the process of switching the emotion from the neutral state to other basic emotions. The simulation result is proved to correspond to emotion-switching law of human beings.展开更多
As the intersection of disciplines deepens,the field of battery modeling is increasingly employing various artificial intelligence(AI)approaches to improve the efficiency of battery management and enhance the stabilit...As the intersection of disciplines deepens,the field of battery modeling is increasingly employing various artificial intelligence(AI)approaches to improve the efficiency of battery management and enhance the stability and reliability of battery operation.This paper reviews the value of AI methods in lithium-ion battery health management and in particular analyses the application of machine learning(ML),one of the many branches of AI,to lithium-ion battery state of health(SOH),focusing on the advantages and strengths of neural network(NN)methods in ML for lithium-ion battery SOH simulation and prediction.NN is one of the important branches of ML,in which the application of NNs such as backpropagation NN,convolutional NN,and long short-term memory NN in SOH estimation of lithium-ion batteries has received wide attention.Reports so far have shown that the utilization of NN to model the SOH of lithium-ion batteries has the advantages of high efficiency,low energy consumption,high robustness,and scalable models.In the future,NN can make a greater contribution to lithium-ion battery management by,first,utilizing more field data to play a more practical role in health feature screening and model building,and second,by enhancing the intelligent screening and combination of battery parameters to characterize the actual lithium-ion battery SOH to a greater extent.The in-depth application of NN in lithium-ion battery SOH will certainly further enhance the science,reliability,stability,and robustness of lithium-ion battery management.展开更多
Using state assignment to minimize power dissipation and area for finite state ma-chines is computationally hard. Most of published results show that the reduction of switchingactivity often trades with area penalty. ...Using state assignment to minimize power dissipation and area for finite state ma-chines is computationally hard. Most of published results show that the reduction of switchingactivity often trades with area penalty. In this paper, a new approach is proposed. Experimentalresults show a significant reduction of switching activity without area penalty compared withprevious publications.展开更多
基金supported in part by the National Natural Science Foundation of China under contract Nos.11890714,12147101(Ma),12075098(Pang),12247107,12075007(Song)the Germany BMBF under the ErUM-Data project(Zhou)the Guangdong Major Project of Basic and Applied Basic Research No.2020B0301030008(Ma).
文摘Although seemingly disparate,high-energy nuclear physics(HENP)and machine learning(ML)have begun to merge in the last few years,yielding interesting results.It is worthy to raise the profile of utilizing this novel mindset from ML in HENP,to help interested readers see the breadth of activities around this intersection.The aim of this mini-review is to inform the community of the current status and present an overview of the application of ML to HENP.From different aspects and using examples,we examine how scientific questions involving HENP can be answered using ML.
基金funded by China Scholarship Council.The fund number is 202108320111 and 202208320055。
文摘State of health(SOH)estimation of e-mobilities operated in real and dynamic conditions is essential and challenging.Most of existing estimations are based on a fixed constant current charging and discharging aging profiles,which overlooked the fact that the charging and discharging profiles are random and not complete in real application.This work investigates the influence of feature engineering on the accuracy of different machine learning(ML)-based SOH estimations acting on different recharging sub-profiles where a realistic battery mission profile is considered.Fifteen features were extracted from the battery partial recharging profiles,considering different factors such as starting voltage values,charge amount,and charging sliding windows.Then,features were selected based on a feature selection pipeline consisting of filtering and supervised ML-based subset selection.Multiple linear regression(MLR),Gaussian process regression(GPR),and support vector regression(SVR)were applied to estimate SOH,and root mean square error(RMSE)was used to evaluate and compare the estimation performance.The results showed that the feature selection pipeline can improve SOH estimation accuracy by 55.05%,2.57%,and 2.82%for MLR,GPR and SVR respectively.It was demonstrated that the estimation based on partial charging profiles with lower starting voltage,large charge,and large sliding window size is more likely to achieve higher accuracy.This work hopes to give some insights into the supervised ML-based feature engineering acting on random partial recharges on SOH estimation performance and tries to fill the gap of effective SOH estimation between theoretical study and real dynamic application.
基金supported by the Aviation Science Foundation of China
文摘The reliability of real-time embedded software directly determines the reliability of the whole real-time embedded sys- tem, and the effective software testing is an important way to ensure software quality and reliability. Based on the analysis of the characteristics of real-time embedded software, the formal method is introduced into the real-time embedded software testing field and the real-time extended finite state machine (RT-EFSM) model is studied firstly. Then, the time zone division method of real-time embedded system is presented and the definition and description methods of time-constrained transition equivalence class (timeCTEC) are presented. Furthermore, the approaches of the testing sequence and test case generation are put forward. Finally, the proposed method is applied to a typical avionics real- time embedded software testing practice and the examples of the timeCTEC, testing sequences and test cases are given. With the analysis of the testing result, the application verification shows that the proposed method can effectively describe the real-time embedded software state transition characteristics and real-time requirements and play the advantages of the formal methods in accuracy, effectiveness and the automation supporting. Combined with the testing platform, the real-time, closed loop and automated simulation testing for real-time embedded software can be realized effectively.
文摘BACKGROUND Non-alcoholic fatty liver disease(NAFLD)is the most common chronic liver disease,affecting over 30% of the United States population.Early patient identification using a simple method is highly desirable.AIM To create machine learning models for predicting NAFLD in the general United States population.METHODS Using the NHANES 1988-1994.Thirty NAFLD-related factors were included.The dataset was divided into the training(70%)and testing(30%)datasets.Twentyfour machine learning algorithms were applied to the training dataset.The bestperforming models and another interpretable model(i.e.,coarse trees)were tested using the testing dataset.RESULTS There were 3235 participants(n=3235)that met the inclusion criteria.In the training phase,the ensemble of random undersampling(RUS)boosted trees had the highest F1(0.53).In the testing phase,we compared selective machine learning models and NAFLD indices.Based on F1,the ensemble of RUS boosted trees remained the top performer(accuracy 71.1%and F10.56)followed by the fatty liver index(accuracy 68.8% and F10.52).A simple model(coarse trees)had an accuracy of 74.9% and an F1 of 0.33.CONCLUSION Not every machine learning model is complex.Using a simpler model such as coarse trees,we can create an interpretable model for predicting NAFLD with only two predictors:fasting C-peptide and waist circumference.Although the simpler model does not have the best performance,its simplicity is useful in clinical practice.
基金This work is supported by the National Natural Science Foundation of China(Grant Number:61471141,61361166006,61301099)Basic Research Project of Shenzhen,China(Grant Number:JCYJ20150513151706561)National Defense Basic Scientific Research Program of China(Grant Number:JCKY2018603B006).
文摘Internet communication protocols define the behavior rules of network components when they communicate with each other.With the continuous development of network technologies,many private or unknown network protocols are emerging in endlessly various network environments.Herein,relevant protocol specifications become difficult or unavailable to translate in many situations such as network security management and intrusion detection.Although protocol reverse engineering is being investigated in recent years to perform reverse analysis on the specifications of unknown protocols,most existing methods have proven to be time-consuming with limited efficiency,especially when applied on unknown protocol state machines.This paper proposes a state merging algorithm based on EDSM(Evidence-Driven State Merging)to infer the transition rules of unknown protocols in form of state machines with high efficiency.Compared with another classical state machine inferring method based on Exbar algorithm,the experiment results demonstrate that our proposed method could run faster,especially when dealing with massive training data sets.In addition,this method can also make the state machines have higher similarities with the reference state machines constructed from public specifications.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61379153 and 61572529)
文摘We show that the secret key generation rate can be balanced with the maximum secure distance of four-state continuous-variable quantum key distribution(CV-QKD) by using the linear optics cloning machine(LOCM). Benefiting from the LOCM operation, the LOCM-tuned noise can be employed by the reference partner of reconciliation to achieve higher secret key generation rates over a long distance. Simulation results show that the LOCM operation can flexibly regulate the secret key generation rate and the maximum secure distance and improve the performance of four-state CV-QKD protocol by dynamically tuning parameters in an appropriate range.
文摘采用UML分析与设计的业务信息系统,业务流程经过层层的抽象迭代,缺乏一种透明的业务流程实现。WF提供了可视化的业务过程编程模型,便于实现业务流程自动化,在对比分析WF State Machine和UML状态图的基础上,研究从UML状态图到WF State Machine业务流程映射关系,选取UML中典型状态图,依据一定的命名转换规则,实现了从UML状态图分析设计到WF状态机业务过程可视化的构建,完成了动态测试。
文摘A state machine can make program designing quicker,simpler and more efficient. This paper describes in detail the model for a state machine and the idea for its designing and gives the design process of the state machine through an example of audio signal generator system based on Labview. The result shows that the introduction of the state machine can make complex design processes more clear and the revision of programs easier.
基金supported by the Project of Developing of Tuna Fishing Grounds Forecasting(No.ZD 202101-06).
文摘With the popularization of vessel satellite AIS(automatic identification system)equipment and the continuous improve-ment of the AIS data’s coverage,continuity and effectiveness,AIS has become an important data source to study the navigation char-acteristics of vessel groups.This study established an identification model to extract the fishing state and intensity information of fishing vessels,based on the AIS data of purse seine fishing vessels,combined with the variables of vessel position,speed and course.Expert experience,spatial statistics and data mining analysis methods were applied to establish the model,and the Western and Cen-tral Pacific Ocean areas were studied.The results showed that the overall accuracy of identification of the fishing state using Support Vector Machine method is higher,and the method has a good modeling effect.The spatial distribution characteristics of the vessels’fishing intensity based on AIS data showed a significant cluster distribution pattern.The obtained high-intensity fishing area can be used as a prediction of purse seine fishing grounds in the Western and Central Pacific areas.Through the processing and research of AIS data,this study provided important scientific support for the identification of fishing state of purse seine fishing vessels.The spatial fishing intensity of fishing vessels based on AIS data can also be used for the analysis of fishery resources and fishing grounds,and further serve the sustainable development of marine fisheries.
基金Supported by the Public Welfare Technology Application Research Project of China(No.LGN21C190009)the Science and Technology Project of Zhoushan Municipality,Zhejiang Province(No.2022C41003)。
文摘Fishing logbook records the fishing behaviors and other information of fishing vessels.However,the accuracy of the recorded information is often difficult to guarantee due to the misreport and concealment.The fishing vessel monitoring system(VMS)can monitor and record the navigation information of fishing vessels in real time,and it may be used to improve the accuracy of identifying the state of fishing vessels.If the VMS data and fishing logbook are combined to establish their relationships,then the navigation characteristics and fishing behavior of fishing vessels can be more accurately identified.Therefore,first,a method for determining the state of VMS data points using fishing log data was proposed.Secondly,the relationship between VMS data and the different states of fishing vessels was further explored.Thirdly,the state of the fishing vessel was predicted using VMS data by building machine learning models.The speed,heading,longitude,latitude,and time as features from the VMS data were extracted by matching the VMS and logbook data of three single otter trawl vessels from September 2012 to January 2013,and four machine learning models were established,i.e.,Random Forest(RF),Adaptive Boosting(AdaBoost),K-Nearest Neighbor(KNN),and Gradient Boosting Decision Tree(GBDT)to predict the behavior of fishing vessels.The prediction performances of the models were evaluated by using normalized confusion matrix and receiver operator characteristic curve.Results show that the importance rankings of spatial(longitude and latitude)and time features were higher than those of speed and heading.The prediction performances of the RF and AdaBoost models were higher than those of the KNN and GBDT models.RF model showed the highest prediction performance for fishing state.Meanwhile,AdaBoost model exhibited the highest prediction performance for non-fishing state.This study offered a technical basis for judging the navigation characteristics of fishing vessels,which improved the algorithm for judging the behavior of fishing vessels based on VMS data,enhanced the prediction accuracy,and upgraded the fishery management being more scientific and efficient.
文摘An effective power quality prediction for regional power grid can provide valuable references and contribute to the discovering and solving of power quality problems. So a predicting model for power quality steady state index based on chaotic theory and least squares support vector machine (LSSVM) is proposed in this paper. At first, the phase space reconstruction of original power quality data is performed to form a new data space containing the attractor. The new data space is used as training samples for the LSSVM. Then in order to predict power quality steady state index accurately, the particle swarm algorithm is adopted to optimize parameters of the LSSVM model. According to the simulation results based on power quality data measured in a certain distribution network, the model applies to several indexes with higher forecasting accuracy and strong practicability.
文摘Railway transportation system is a critical sector where design methods and techniques are defined by international standards in order to reduce possible risks to an acceptable minimum level. CENELEC 50128 strongly recommends the utilization of finite state machines during system modelling stage and formal proof methods during the verifi- cation and testing stages of control algorithms. Due to the high importance of interlocking table at the design state of a sig- nalization system, the modelling and verification of inter- locking tables are examined in this work. For this purpose, abstract state machines are used as a modelling tool. The developed models have been performed in a generalized structure such that the model control can be done automatically for the interlocking systems. In this study, NuSMV is used at the verification state. Also, the consistency of the developed models has been supervised through fault injection. The developed models and software components are applied on a real railway station operated by Metro Istanbul Co.
基金financial supports from the National Key Research and Development Program of China(2018YFA0209600)the Natural Science Foundation of China(22022813,21878268,52075481)。
文摘With the widespread use of lithium ion batteries in portable electronics and electric vehicles,further improvements in the performance of lithium ion battery materials and accurate prediction of battery state are of increasing interest to battery researchers.Machine learning,one of the core technologies of artificial intelligence,is rapidly changing many fields with its ability to learn from historical data and solve complex tasks,and it has emerged as a new technique for solving current research problems in the field of lithium ion batteries.This review begins with the introduction of the conceptual framework of machine learning and the general process of its application,then reviews some of the progress made by machine learning in both improving battery materials design and accurate prediction of battery state,and finally points out the current application problems of machine learning and future research directions.It is believed that the use of machine learning will further promote the large-scale application and improvement of lithium-ion batteries.
基金supported by the National Science and Technology Major Project of the Ministry of Science and Technology of China(No.2015ZX04001002).
文摘For thin-walled parts,uniform allowance to each machining surface is allocated by the traditional machining method.Considering the sequence of the adjacent machining features,it may cause poor stiffness for some side walls due to a minor wall thickness,which may cause the deformation of the final formed parts to be large,or deduce machining efficiency for some machining features due to too thick remains.In order to address this issue,a non-uniform allowance allocation method based on interim state stiffness of machining features for the finishing of thin-walled structural parts is proposed in this paper.In this method,the interim state model of machining features is constructed according to the machining sequence of the parts,and the stiffness of the side wall is taken as the evaluation index to allocate reasonable allowance value to the corresponding machining surface to ensure the stiffness requirement of the parts in the machining process.According to the finite element simulation results,the non-uniform allowance allocation method proposed in this paper can effectively improve the stiffness of the parts and reduce the deformation of the parts,when compared with the traditional uniform allowance machining method.
基金Acknowledgements Project supported by the National Natural Science Foundation of China (Grant No.60932003), the National High Technology Development 863 Program of China (Grant No.2007AA01Z452, No. 2009AA01 Z118 ), Project supported by Shanghai Municipal Natural Science Foundation (Grant No.09ZRI414900), National Undergraduate Innovative Test Program (091024812).
基金Project(2006AA04Z201) supported by the National High-Tech Research and Development Program of China
文摘According to the basic emotional theory, the artificial emotional model based on the finite state machine(FSM) was presented. In finite state machine model of emotion, the emotional space included the basic emotional space and the multiple emotional spaces. The emotion-switching diagram was defined and transition function was developed using Markov chain and linear interpolation algorithm. The simulation model was built using Stateflow toolbox and Simulink toolbox based on the Matlab platform. And the model included three subsystems: the input one, the emotion one and the behavior one. In the emotional subsystem, the responses of different personalities to the external stimuli were described by defining personal space. This model takes states from an emotional space and updates its state depending on its current state and a state of its input (also a state-emotion). The simulation model realizes the process of switching the emotion from the neutral state to other basic emotions. The simulation result is proved to correspond to emotion-switching law of human beings.
基金supported by the National Key R&D Program of China(Grant No.2021YFB2401800)the Research Fund Program for Young Scholars(Chen Lai)of Beijing Institute of Technology,and the National Natural Science Foundation of China(Grant No.52074037).
文摘As the intersection of disciplines deepens,the field of battery modeling is increasingly employing various artificial intelligence(AI)approaches to improve the efficiency of battery management and enhance the stability and reliability of battery operation.This paper reviews the value of AI methods in lithium-ion battery health management and in particular analyses the application of machine learning(ML),one of the many branches of AI,to lithium-ion battery state of health(SOH),focusing on the advantages and strengths of neural network(NN)methods in ML for lithium-ion battery SOH simulation and prediction.NN is one of the important branches of ML,in which the application of NNs such as backpropagation NN,convolutional NN,and long short-term memory NN in SOH estimation of lithium-ion batteries has received wide attention.Reports so far have shown that the utilization of NN to model the SOH of lithium-ion batteries has the advantages of high efficiency,low energy consumption,high robustness,and scalable models.In the future,NN can make a greater contribution to lithium-ion battery management by,first,utilizing more field data to play a more practical role in health feature screening and model building,and second,by enhancing the intelligent screening and combination of battery parameters to characterize the actual lithium-ion battery SOH to a greater extent.The in-depth application of NN in lithium-ion battery SOH will certainly further enhance the science,reliability,stability,and robustness of lithium-ion battery management.
基金Supported by NNSF of China(Key International Cooperative Project No.60010121219)
文摘Using state assignment to minimize power dissipation and area for finite state ma-chines is computationally hard. Most of published results show that the reduction of switchingactivity often trades with area penalty. In this paper, a new approach is proposed. Experimentalresults show a significant reduction of switching activity without area penalty compared withprevious publications.