Multiple kernel clustering is an unsupervised data analysis method that has been used in various scenarios where data is easy to be collected but hard to be labeled.However,multiple kernel clustering for incomplete da...Multiple kernel clustering is an unsupervised data analysis method that has been used in various scenarios where data is easy to be collected but hard to be labeled.However,multiple kernel clustering for incomplete data is a critical yet challenging task.Although the existing absent multiple kernel clustering methods have achieved remarkable performance on this task,they may fail when data has a high value-missing rate,and they may easily fall into a local optimum.To address these problems,in this paper,we propose an absent multiple kernel clustering(AMKC)method on incomplete data.The AMKC method rst clusters the initialized incomplete data.Then,it constructs a new multiple-kernel-based data space,referred to as K-space,from multiple sources to learn kernel combination coefcients.Finally,it seamlessly integrates an incomplete-kernel-imputation objective,a multiple-kernel-learning objective,and a kernel-clustering objective in order to achieve absent multiple kernel clustering.The three stages in this process are carried out simultaneously until the convergence condition is met.Experiments on six datasets with various characteristics demonstrate that the kernel imputation and clustering performance of the proposed method is signicantly better than state-of-the-art competitors.Meanwhile,the proposed method gains fast convergence speed.展开更多
One problem with the existing dynamic exclusive bus lane strategies is that bus signal priority strategies with multi-phase priority request at intersections are not adequately considered.The principle of bus signal p...One problem with the existing dynamic exclusive bus lane strategies is that bus signal priority strategies with multi-phase priority request at intersections are not adequately considered.The principle of bus signal priority level was designed based on the isolated multi-phase structure principle consideration of the bus signal priority,and a new priority approach for the dynamic exclusive bus lane was proposed.Two types of priority strategies,green extension and red truncation,were proposed for current phase and next phase buses,respectively.The control parameters including minimum green time,green extension time,maximum green time and bus arrival time are calculated.The case studies for this paper were carried out using four consecutive intersections of Huaide Middle Road in Changzhou City.The signal control scheme was designed using the conventional,exclusive bus lane method,the dynamic exclusive bus lane without signal priority method,and the proposed approach,respectively.The authors used the VISSIM simulation platform to evaluate the efficiency of each approach.Results showed that the method of approach can significantly decrease delays caused by social and conventional buses and make up for the negative impact social buses have on the bus rapid transit(BRT)operation,which allows the method to complement the dynamic,exclusive bus lane design.展开更多
The purpose of the paper is to develop a solution for application of PV （photovoltaic） generators in MV （medium voltage） distribution system without unacceptable voltage changes due to drops of PV power output. Th...The purpose of the paper is to develop a solution for application of PV （photovoltaic） generators in MV （medium voltage） distribution system without unacceptable voltage changes due to drops of PV power output. The proposed solution includes operation of PV with predetermined leading power factor and addition of a capacitor bank in parallel to PV plant in order to compensate the reactive power absorbed by the PV inverters. The analytical expression of required power factor angle is derived. Adding a capacitor bank in parallel to PV power plant may pose a problem because of space limitations. The dimensions and cost of small MV capacitor banks depend significantly on the capacitor bank protection against internal faults. Application of the developed negative-sequence current difference method for the unbalance protection of the capacitor banks enables to achieve a compact and cost-reduced design of the banks connected in parallel to PV power plants. A real-world example of operation of the PV plant in parallel to the capacitor bank with the novel protection scheme is described.展开更多
To ensure the safety of vehicle driving and the accuracy of trajectory tracking,intelligent vehicles must make prompt and precise control decisions when performing trajectory tracking control in complex traffic enviro...To ensure the safety of vehicle driving and the accuracy of trajectory tracking,intelligent vehicles must make prompt and precise control decisions when performing trajectory tracking control in complex traffic environments.Nonlinear model predictive control demonstrates a clear advantage in terms of trajectory tracking accuracy.However,solving complex optimization problems leads to significant computational burdens,posing a challenge for improving the real-time performance of control systems.The nonlinear model predictive control,combining dynamic adjustment in the horizon with an adaptive event-triggering mechanism,is proposed.This algorithm employs an improved adaptive event-triggering mechanism to reduce the frequency of solving the optimization control problem,thus improving the control system's real-time performance.Additionally,it utilizes the particle swarm optimization algorithm to dynamically optimize the prediction horizon under varying vehicle velocities and road curvatures,establishing a predictive horizon dynamic adjustment strategy that enhances the trajectory tracking accuracy.The algorithm also dynamically adjusts the control time horizon based on the trajectory tracking accuracy,balancing real-time performance with tracking accuracy.Numerical simulation results demonstrate that the algorithm proposed in this paper significantly reduces the computational burden and effectively improves the real-time performance of the control system,while also enhancing the trajectory tracking accuracy of intelligent vehicles.展开更多
Class imbalance has long been a problem in traffic safety research,as it significantly reduces the prediction accuracy for minority class samples.In this study,a Control Method for Synthetic Samples(CMSS)is proposed t...Class imbalance has long been a problem in traffic safety research,as it significantly reduces the prediction accuracy for minority class samples.In this study,a Control Method for Synthetic Samples(CMSS)is proposed to control samples synthesized in a direction which is conducive to the improvement of prediction performance.This method first employs several oversampling methods to synthesize samples.Then,a regression model is established based on the cluster centres of synthetic samples and the corresponding value of performance improvement after adding the synthetic samples.The Shapley Additive Explanations(SHAP)technique is used to interpret it,and thus determine the features that have the most significant influence on the improvement.After that,random disturbance terms are added to several important features to make a directional adjustment.To achieve better prediction performance,the particle swarm optimization(PSO)algorithm is utilized to search for the number of features and the value of random perturbation terms.The highD dataset is used to validate the effectiveness of CMSS by predicting the lane-changing risk.In addition,the impact of factors such as imbalance rates on the effect of CMSS is discussed.Results show that CMSS can effectively improve the prediction performance of the non-integrated learning models under different class imbalance ratios.It is also revealed that the category of the regression model has a significant effect on the results of CMSS.As a novel method,CMSS can be combined with various kinds of oversampling methods to bring new perspectives for solving the class imbalance problem.展开更多
This paper presents the concept,a system-overview,and the evaluation of EnArgus,the central information system for energy research funding in Germany.Initiated by the German Federal Ministry for Economic Affairs and E...This paper presents the concept,a system-overview,and the evaluation of EnArgus,the central information system for energy research funding in Germany.Initiated by the German Federal Ministry for Economic Affairs and Energy(BMWi),EnArgus establishes a one-stop information system about all recent and ongoing energy research funding projects in Germany.Participants ranging from laypersons to experts were surveyed in three workshops to evaluate both the public and expert interfaces of the EnArgus system in comparison to peer systems.The results showed that the EnArgus system was predominantly evaluated positively by the various participants.It contributes to making the energy sector more transparent and offers clear advantages for professional use compared to similar systems.The system’s semantic processing enables more precise hits and better coverage by including semantically related terms in search results;its intelligence makes it fail-safe,rendering it suitable for areas where poor results can have dire consequences.Reporting on an actual real-world system,the paper also provides a roadmap-view of how electronic filing of administrative project data can be semantically enhanced and opened-up to provide the basis for new ways into the data that are key for future breakthrough AI interfaces.展开更多
基金Projects(52102405,71901223)supported by the National Natural Science Foundation of ChinaProjects(2021JJ40746,2021JJ40603)supported by the Natural Science Foundation of Hunan Province,China+2 种基金Project(kfj220701)supported by the Open Fund of Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems(Changsha University of Science and Technology),ChinaProject(21B0335)supported by the Scientific Research Program of the Education Department of Hunan Province,ChinaProject(2023M731962)supported by the China Postdoctoral Science Foundation。
基金funded by National Natural Science Foundation of China under Grant Nos.61972057 and U1836208Hunan Provincial Natural Science Foundation of China under Grant No.2019JJ50655+3 种基金Scientic Research Foundation of Hunan Provincial Education Department of China under Grant No.18B160Open Fund of Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle Infrastructure Systems(Changsha University of Science and Technology)under Grant No.kfj180402the“Double First-class”International Cooperation and Development Scientic Research Project of Changsha University of Science and Technology under Grant No.2018IC25the Researchers Supporting Project No.(RSP-2020/102)King Saud University,Riyadh,Saudi Arabia.
文摘Multiple kernel clustering is an unsupervised data analysis method that has been used in various scenarios where data is easy to be collected but hard to be labeled.However,multiple kernel clustering for incomplete data is a critical yet challenging task.Although the existing absent multiple kernel clustering methods have achieved remarkable performance on this task,they may fail when data has a high value-missing rate,and they may easily fall into a local optimum.To address these problems,in this paper,we propose an absent multiple kernel clustering(AMKC)method on incomplete data.The AMKC method rst clusters the initialized incomplete data.Then,it constructs a new multiple-kernel-based data space,referred to as K-space,from multiple sources to learn kernel combination coefcients.Finally,it seamlessly integrates an incomplete-kernel-imputation objective,a multiple-kernel-learning objective,and a kernel-clustering objective in order to achieve absent multiple kernel clustering.The three stages in this process are carried out simultaneously until the convergence condition is met.Experiments on six datasets with various characteristics demonstrate that the kernel imputation and clustering performance of the proposed method is signicantly better than state-of-the-art competitors.Meanwhile,the proposed method gains fast convergence speed.
基金This research was funded by National Natural Science Foundation of China(NSFC),grant number 51678076Hunan Provincial Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems,grant number 2017TP1016.
文摘One problem with the existing dynamic exclusive bus lane strategies is that bus signal priority strategies with multi-phase priority request at intersections are not adequately considered.The principle of bus signal priority level was designed based on the isolated multi-phase structure principle consideration of the bus signal priority,and a new priority approach for the dynamic exclusive bus lane was proposed.Two types of priority strategies,green extension and red truncation,were proposed for current phase and next phase buses,respectively.The control parameters including minimum green time,green extension time,maximum green time and bus arrival time are calculated.The case studies for this paper were carried out using four consecutive intersections of Huaide Middle Road in Changzhou City.The signal control scheme was designed using the conventional,exclusive bus lane method,the dynamic exclusive bus lane without signal priority method,and the proposed approach,respectively.The authors used the VISSIM simulation platform to evaluate the efficiency of each approach.Results showed that the method of approach can significantly decrease delays caused by social and conventional buses and make up for the negative impact social buses have on the bus rapid transit(BRT)operation,which allows the method to complement the dynamic,exclusive bus lane design.
文摘The purpose of the paper is to develop a solution for application of PV （photovoltaic） generators in MV （medium voltage） distribution system without unacceptable voltage changes due to drops of PV power output. The proposed solution includes operation of PV with predetermined leading power factor and addition of a capacitor bank in parallel to PV plant in order to compensate the reactive power absorbed by the PV inverters. The analytical expression of required power factor angle is derived. Adding a capacitor bank in parallel to PV power plant may pose a problem because of space limitations. The dimensions and cost of small MV capacitor banks depend significantly on the capacitor bank protection against internal faults. Application of the developed negative-sequence current difference method for the unbalance protection of the capacitor banks enables to achieve a compact and cost-reduced design of the banks connected in parallel to PV power plants. A real-world example of operation of the PV plant in parallel to the capacitor bank with the novel protection scheme is described.
基金supported by the National Natural Science Foundation of China under Grant(Grant No.52472399,52275086)the Hunan Provincial Natural Science Foundation of China under Grant(Grant No.2022JJ50020)the Scientific Research Fund of Hunan Provincial Education Department(Grant No.20A018).
文摘To ensure the safety of vehicle driving and the accuracy of trajectory tracking,intelligent vehicles must make prompt and precise control decisions when performing trajectory tracking control in complex traffic environments.Nonlinear model predictive control demonstrates a clear advantage in terms of trajectory tracking accuracy.However,solving complex optimization problems leads to significant computational burdens,posing a challenge for improving the real-time performance of control systems.The nonlinear model predictive control,combining dynamic adjustment in the horizon with an adaptive event-triggering mechanism,is proposed.This algorithm employs an improved adaptive event-triggering mechanism to reduce the frequency of solving the optimization control problem,thus improving the control system's real-time performance.Additionally,it utilizes the particle swarm optimization algorithm to dynamically optimize the prediction horizon under varying vehicle velocities and road curvatures,establishing a predictive horizon dynamic adjustment strategy that enhances the trajectory tracking accuracy.The algorithm also dynamically adjusts the control time horizon based on the trajectory tracking accuracy,balancing real-time performance with tracking accuracy.Numerical simulation results demonstrate that the algorithm proposed in this paper significantly reduces the computational burden and effectively improves the real-time performance of the control system,while also enhancing the trajectory tracking accuracy of intelligent vehicles.
基金sponsored by the Science and Technology Innovation Program of Hunan Province(Grant No.2023RC3143)the National Natural Science Foundation of China(Grant No.52102405)+1 种基金the China Postdoctoral Science Foundation(Grant No.2023M731962)the Fundamental Research Funds for the Central Universities,CHD(Grant No.300102343504).
文摘Class imbalance has long been a problem in traffic safety research,as it significantly reduces the prediction accuracy for minority class samples.In this study,a Control Method for Synthetic Samples(CMSS)is proposed to control samples synthesized in a direction which is conducive to the improvement of prediction performance.This method first employs several oversampling methods to synthesize samples.Then,a regression model is established based on the cluster centres of synthetic samples and the corresponding value of performance improvement after adding the synthetic samples.The Shapley Additive Explanations(SHAP)technique is used to interpret it,and thus determine the features that have the most significant influence on the improvement.After that,random disturbance terms are added to several important features to make a directional adjustment.To achieve better prediction performance,the particle swarm optimization(PSO)algorithm is utilized to search for the number of features and the value of random perturbation terms.The highD dataset is used to validate the effectiveness of CMSS by predicting the lane-changing risk.In addition,the impact of factors such as imbalance rates on the effect of CMSS is discussed.Results show that CMSS can effectively improve the prediction performance of the non-integrated learning models under different class imbalance ratios.It is also revealed that the category of the regression model has a significant effect on the results of CMSS.As a novel method,CMSS can be combined with various kinds of oversampling methods to bring new perspectives for solving the class imbalance problem.
基金The EnArgus®project was funded by the German Federal Ministry of Economic Affairs and Energy as part of the energy research programme.Consortium codes 01100245/1(EnArgus,July 2011-June 2013)and 01142005/1(EnArgus2.0,July 2013-June 2017).The responsibility of this publication lies with the authors.Please see EnArgus.de for more details about our funding.
文摘This paper presents the concept,a system-overview,and the evaluation of EnArgus,the central information system for energy research funding in Germany.Initiated by the German Federal Ministry for Economic Affairs and Energy(BMWi),EnArgus establishes a one-stop information system about all recent and ongoing energy research funding projects in Germany.Participants ranging from laypersons to experts were surveyed in three workshops to evaluate both the public and expert interfaces of the EnArgus system in comparison to peer systems.The results showed that the EnArgus system was predominantly evaluated positively by the various participants.It contributes to making the energy sector more transparent and offers clear advantages for professional use compared to similar systems.The system’s semantic processing enables more precise hits and better coverage by including semantically related terms in search results;its intelligence makes it fail-safe,rendering it suitable for areas where poor results can have dire consequences.Reporting on an actual real-world system,the paper also provides a roadmap-view of how electronic filing of administrative project data can be semantically enhanced and opened-up to provide the basis for new ways into the data that are key for future breakthrough AI interfaces.
