"十三五"期间是农村饮水安全工程巩固提升的重要阶段,工程措施是重要基础,信息化等非工程措施是重要技术支撑手段。基于此,设计实现了一个服务于农村饮水安全巩固提升的信息化监管平台,主要汇聚辖区内各类分散、异构的农饮工..."十三五"期间是农村饮水安全工程巩固提升的重要阶段,工程措施是重要基础,信息化等非工程措施是重要技术支撑手段。基于此,设计实现了一个服务于农村饮水安全巩固提升的信息化监管平台,主要汇聚辖区内各类分散、异构的农饮工程SCADA(Supervisory Control and Data Acquisition)数据进行集中管理与应用。平台采用地址目录服务器注册及协商机制实现异构网络间的网路互通,采用通信服务器中间件对异构网络协议的报文进行解析和封装,解决了农饮工程分布式SCADA系统的网络级同步和系统接口级互联问题。该平台运行1 a多的实践表明,生产管理更及时、更全面,行业监管更到位、评价更客观,供水保证率和保障率均有明显提高。展开更多
Deep reinforcement learning(DRL) has demonstrated significant potential in industrial manufacturing domains such as workshop scheduling and energy system management.However, due to the model's inherent uncertainty...Deep reinforcement learning(DRL) has demonstrated significant potential in industrial manufacturing domains such as workshop scheduling and energy system management.However, due to the model's inherent uncertainty, rigorous validation is requisite for its application in real-world tasks. Specific tests may reveal inadequacies in the performance of pre-trained DRL models, while the “black-box” nature of DRL poses a challenge for testing model behavior. We propose a novel performance improvement framework based on probabilistic automata,which aims to proactively identify and correct critical vulnerabilities of DRL systems, so that the performance of DRL models in real tasks can be improved with minimal model modifications.First, a probabilistic automaton is constructed from the historical trajectory of the DRL system by abstracting the state to generate probabilistic decision-making units(PDMUs), and a reverse breadth-first search(BFS) method is used to identify the key PDMU-action pairs that have the greatest impact on adverse outcomes. This process relies only on the state-action sequence and final result of each trajectory. Then, under the key PDMU, we search for the new action that has the greatest impact on favorable results. Finally, the key PDMU, undesirable action and new action are encapsulated as monitors to guide the DRL system to obtain more favorable results through real-time monitoring and correction mechanisms. Evaluations in two standard reinforcement learning environments and three actual job scheduling scenarios confirmed the effectiveness of the method, providing certain guarantees for the deployment of DRL models in real-world applications.展开更多
Software defect feature selection has problems of feature space dimensionality reduction and large search space.This research proposes a defect prediction feature selection framework based on improved shuffled frog le...Software defect feature selection has problems of feature space dimensionality reduction and large search space.This research proposes a defect prediction feature selection framework based on improved shuffled frog leaping algorithm(ISFLA).Using the two-level structure of the framework and the improved hybrid leapfrog algorithm's own advantages,the feature values are sorted,and some features with high correlation are selected to avoid other heuristic algorithms in the defect prediction that are easy to produce local The case where the convergence rate of the optimal or parameter optimization process is relatively slow.The framework improves generalization of predictions of unknown data samples and enhances the ability to search for features related to learning tasks.At the same time,this framework further reduces the dimension of the feature space.After the contrast simulation experiment with other common defect prediction methods,we used the actual test data set to verify the framework for multiple iterations on Internet of Things(IoT)system platform.The experimental results show that the software defect prediction feature selection framework based on ISFLA is very effective in defect prediction of IoT communication software.This framework can save the testing time of IoT communication software,effectively improve the performance of software defect prediction,and ensure the software quality.展开更多
In this paper,the optimization problem subject to N nonidentical closed convex set constraints is studied.The aim is to design a corresponding distributed optimization algorithm over the fixed unbalanced graph to solv...In this paper,the optimization problem subject to N nonidentical closed convex set constraints is studied.The aim is to design a corresponding distributed optimization algorithm over the fixed unbalanced graph to solve the considered problem.To this end,with the push-sum framework improved,the distributed optimization algorithm is newly designed,and its strict convergence analysis is given under the assumption that the involved graph is strongly connected.Finally,simulation results support the good performance of the proposed algorithm.展开更多
文摘"十三五"期间是农村饮水安全工程巩固提升的重要阶段,工程措施是重要基础,信息化等非工程措施是重要技术支撑手段。基于此,设计实现了一个服务于农村饮水安全巩固提升的信息化监管平台,主要汇聚辖区内各类分散、异构的农饮工程SCADA(Supervisory Control and Data Acquisition)数据进行集中管理与应用。平台采用地址目录服务器注册及协商机制实现异构网络间的网路互通,采用通信服务器中间件对异构网络协议的报文进行解析和封装,解决了农饮工程分布式SCADA系统的网络级同步和系统接口级互联问题。该平台运行1 a多的实践表明,生产管理更及时、更全面,行业监管更到位、评价更客观,供水保证率和保障率均有明显提高。
基金supported by the Shanghai Science and Technology Committee (22511105500)the National Nature Science Foundation of China (62172299, 62032019)+2 种基金the Space Optoelectronic Measurement and Perception LaboratoryBeijing Institute of Control Engineering(LabSOMP-2023-03)the Central Universities of China (2023-4-YB-05)。
文摘Deep reinforcement learning(DRL) has demonstrated significant potential in industrial manufacturing domains such as workshop scheduling and energy system management.However, due to the model's inherent uncertainty, rigorous validation is requisite for its application in real-world tasks. Specific tests may reveal inadequacies in the performance of pre-trained DRL models, while the “black-box” nature of DRL poses a challenge for testing model behavior. We propose a novel performance improvement framework based on probabilistic automata,which aims to proactively identify and correct critical vulnerabilities of DRL systems, so that the performance of DRL models in real tasks can be improved with minimal model modifications.First, a probabilistic automaton is constructed from the historical trajectory of the DRL system by abstracting the state to generate probabilistic decision-making units(PDMUs), and a reverse breadth-first search(BFS) method is used to identify the key PDMU-action pairs that have the greatest impact on adverse outcomes. This process relies only on the state-action sequence and final result of each trajectory. Then, under the key PDMU, we search for the new action that has the greatest impact on favorable results. Finally, the key PDMU, undesirable action and new action are encapsulated as monitors to guide the DRL system to obtain more favorable results through real-time monitoring and correction mechanisms. Evaluations in two standard reinforcement learning environments and three actual job scheduling scenarios confirmed the effectiveness of the method, providing certain guarantees for the deployment of DRL models in real-world applications.
基金This work was supported by Liaoning Natural Fund Guidance Plan Project(No.20180550021)Dalian Science and Technology Star Project(No.2017RQ021)2019 Qingdao Binhai University-level Science and Technology Plan Research Project(No.2019KY09).
文摘Software defect feature selection has problems of feature space dimensionality reduction and large search space.This research proposes a defect prediction feature selection framework based on improved shuffled frog leaping algorithm(ISFLA).Using the two-level structure of the framework and the improved hybrid leapfrog algorithm's own advantages,the feature values are sorted,and some features with high correlation are selected to avoid other heuristic algorithms in the defect prediction that are easy to produce local The case where the convergence rate of the optimal or parameter optimization process is relatively slow.The framework improves generalization of predictions of unknown data samples and enhances the ability to search for features related to learning tasks.At the same time,this framework further reduces the dimension of the feature space.After the contrast simulation experiment with other common defect prediction methods,we used the actual test data set to verify the framework for multiple iterations on Internet of Things(IoT)system platform.The experimental results show that the software defect prediction feature selection framework based on ISFLA is very effective in defect prediction of IoT communication software.This framework can save the testing time of IoT communication software,effectively improve the performance of software defect prediction,and ensure the software quality.
基金Project supported by the Science and Technology Project from State Grid Zhejiang Electric Power Co.,Ltd.,China(No.5211JY20001Q)。
文摘In this paper,the optimization problem subject to N nonidentical closed convex set constraints is studied.The aim is to design a corresponding distributed optimization algorithm over the fixed unbalanced graph to solve the considered problem.To this end,with the push-sum framework improved,the distributed optimization algorithm is newly designed,and its strict convergence analysis is given under the assumption that the involved graph is strongly connected.Finally,simulation results support the good performance of the proposed algorithm.
