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13th International Conference on Intelligent control and Information Processing, ICICIP 2025

作者： Chen, Shirui Gao, Huanli Hu, Shaolin Zhang, Zhijun The School of Automation Science and Engineering South China University of Technology Guangzhou510640 China The School of Automation Guangdong University of Petrochemical Technology Maoming Guangzhou525000 China The Guangzhou Key Laboratory of Brain-Computer Interaction and Applications Guangzhou China The Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering South China University of Technology Guangzhou510640 China Guangzhou510555 China The Jiangxi Thousand Talents Plan Nanchang University Nanchang330031 China The College of Computer Science and Engineering Jishou University Jishou416000 China Guangzhou510335 China The Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Shaanxi University of Technology Hanzhong723001 China The School of Information Science and Engineering Changsha Normal University Changsha410100 China The School of Automation Science and Engineering Maoming525000 China The Institute of Artificial Intelligence and Automation Guangdong University of Petrochemical Technology Maoming525000 China

ISBN: (纸本)9798331516147

To achieve low joint-angle drift and avoid mutual collision between dual redundant manipulators (DRMs) when they are doing collaboration works, a recurrent neural network based bicriteria repetitive motion collision avoidance (BRMCA) scheme is proposed for motion planning of DRMs. By designing a combined optimization objective encompassing both repetitive motion criterion and collision avoidance criterion, the BRMCA scheme has larger feasible region and less constraint contradiction issues compared with traditional inequality constraints based method. Furthermore, by incorporating velocity-level kinematic model and physical limits of DRMs into the scheme, a time varying quadratic programming (TVQP) problem is formulated. The linear variational inequality-based primal–dual neural network (LVI-PDNN) is used to solve the proposed scheme due to the high accuracy and low computation complexity. Finally, through comparison simulations of tracking Circle-shape and C-shape trajectories between BRMCA scheme and minimum velocity norm (MVN) scheme, it exhibits that the proposed BRMCA scheme can achieve low joint-angle drift, collision avoidance and high tracking accuracy when DRMs are doing cooperative tasks. © 2025 IEEE.

关键词： Recurrent neural networks

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Oxidation of emerging organic contaminants by in-situ H_(2)O_(2) fenton system

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Green Energy & Environment 2024年第3期9卷 417-434页

作者： Yuqin Ni Chuxiang Zhou Mingyang Xing Yi Zhou State Environmental Protection Key Lab of Environmental Risk Assessment and Control on Chemical Processes School of Resources&Environmental EngineeringEast China University of Science and TechnologyShanghai200237China Key Laboratory for Advanced Materials and Institute of Fine Chemicals National Engineering Laboratory for Industrial Wastewater TreatmentSchool of Chemistry and Molecular EngineeringEast China University of Science and Technology130 Meilong RoadShanghai200237China Department of Applied Natural Sciences Technische Hochschule LubeckMonkhofer Weg 23923562 LubeckGermany

The existence and risk of emerging organic contaminants(EOCs)have been under consideration and paid much effort to degrade these *** system is one of the most widely used technologies to solve this *** original Fenton system relies on the hydroxyl radicals produced by Fe(Ⅱ)/H_(2)O_(2) to oxidize the organic ***,the application of the Fenton system is limited by its low iron cycling efficiency and the high risks of hydrogen peroxide transportation and *** introduction of external energy(including light and electricity etc.)can effectively promote the Fe(Ⅲ)/Fe(Ⅱ)cycle and the reduction of oxygen to produce hydrogen peroxide in *** review introduces three in-situ Fenton systems,which are electro-Fenton,Photo-Fenton,and chemical *** mechanism,influencing factors,and catalysts of these three in-situ Fenton systems in degrading EOCs are discussed *** review strengthens the understanding of Fenton and in-situ Fenton systems in degradation,offering further insight into the real application of the in-situ Fenton system in the removal of EOCs.

关键词： In-situ H_(2)O_(2)production Fenton Emerging organic contaminants Photocatalysis Electrocatalysis

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Water quality prediction of copper-molybdenum miningbeneficiation wastewater based on the PSO-SVR model

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Frontiers of Environmental Science & engineering 2023年第8期17卷 81-94页

作者： Xiaohua Fu Qingxing Zheng Guomin Jiang Kallol Roy Lei Huang Chang Liu Kun Li Honglei Chen Xinyu Song Jianyu Chen Zhenxing Wang Ecological Environment Management and Assessment Center Central South University of Forestry and TechnologyChangsha 410004China State Environmental Protection Key Laboratory of Water Environmental Simulation and Pollution Control South China Institute of Environmental SciencesMinistry of Ecology and EnvironmentGuangzhou 510655China Chinese Non-Ferrous Industrial Engineering Center of Pollution Control Technology&Equipment Science Environment Protection Co.Ltd.Changsha 410036China Institute of Computer Science University of TartuTartu 51009Estonia School of Environmental Science and Engineering Guangzhou UniversityGuangzhou 510006China A.B Freeman School of Business Tulane UniversityNew OrleansLA 70118USA Guangzhou Huacai Environmental Protection Technology Co. Ltd.Guangzhou 511480China

The mining-beneficiation wastewater treatment is highly complex and *** factors like influent quality,flow rate,pH and chemical dose,tend to restrict the effluent effectiveness of miningbeneficiation wastewater *** oxygen demand(COD)is a crucial indicator to measure the quality of mining-beneficiation *** COD concentration accurately of miningbeneficiation wastewater after treatment is essential for achieving stable and compliant *** reduces environmental risk and significantly improves the discharge quality of *** paper presents a novel AI algorithm PSO-SVR,to predict water *** optimization of our proposed model PSO-SVR,uses particle swarm optimization to improve support vector regression for COD *** generalization capacity tested on out-of-distribution(OOD)data for our PSOSVR model is strong,with the following performance metrics of root means square error(RMSE)is 1.51,mean absolute error(MAE)is 1.26,and the coefficient of determination(R2)is *** compare the performance of PSO-SVR model with back propagation neural network(BPNN)and radial basis function neural network(RBFNN)and shows it edges over in terms of the performance metrics of RMSE,MAE and R2,and is the best model for COD prediction of mining-beneficiation *** is because of the less overfitting tendency of PSO-SVR compared with neural network *** proposed PSO-SVR model is optimum for the prediction of COD in copper-molybdenum mining-beneficiation wastewater *** addition,PSO-SVR can be used to predict COD on a wide variety of wastewater through the process of transfer learning.

关键词： Chemical oxygen demand Mining-beneficiation wastewater treatment Particle swarm optimization Support vector regression Artificial neural network

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Data-Driven Active Power Dispatch for Wind Farms

Data-Driven Active Power Dispatch for Wind Farms

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Data Driven control and Learning Systems (DDCLS)

作者： Xuguo Jiao Daoyuan Zhang Xin Wang Zhaoxing Ma Zhenyong Zhang Wenfeng Liu School of Information and Control Engineering Qingdao University of Technology Qingdao China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China School of Civil Engineering Qingdao University of Technology Qingdao China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Active power dispatch of wind farms plays an important role in power grid scheduling. In this paper, a data-driven active power dispatch strategy for wind farms is proposed, which uses the key point of minimizing the standard deviation of wind turbines' output power series to reduce their fluctuations. First, to build the optimization problem, the historical active power output data of each wind turbine is collected from the Supervisory control And Data Acquisition (SCADA) system. Then, the objective function is developed based on the idea of minimizing the standard deviation of the output power time series. Meanwhile, considering the constraints of wind turbines' output power and active power balance of the grid, the feasible domain of the cost function is constructed. Further, to solve the proposed nonlinear optimization problem with equality and inequality constraints, the trust region interior point method (TRIPM) is employed to explore optimal solution within the feasible domain. Finally, actual data collected from wind farms is used to test effectiveness of the designed approach and experimental results show that our method can achieve better output power smoothing performance than conventional Proportional Dispatch (PD) method.

关键词： Fluctuations Time series analysis SCADA systems Power smoothing Wind farms Linear programming Power grids

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Analysis of the Influence of Decision Module Performance on Transient Events in Steam Generator Level control 1

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6th International Symposium on Software Reliability, industrial Safety, Cyber Security and Physical Protection of Nuclear Power Plant, ISNPP 2021

作者： Liu, Dao-Guang Luan, Zhen-Hua Fang, Zi-Rui Yan, Xin-hong Liu, Shuang-Jin Jun, Liang State Key Lab of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China New Materials and New Energy College Shenzhen Technology University Lantian Road Pingshan District Shenzhen518124 China State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment Guangdong Shenzhen518172 China

ISBN: (数字)9789811911811

ISBN: (纸本)9789811911804

Decision module is widely used in nuclear power plant, participating in important control and protection system. Decision module performance directly affects the response of important control and protection, which is important for the security and stable operation of the nuclear power plant. This paper researches the impact of the decision module used in the steam generator level control system, which causes most of the unexpected reactor trip events. The transient response of two units is used to explain the process of decision module failure. The optimization scheme and configuration strategy of the application effect is provided. The scheme has been used in several units and is an important reference for the commissioning tests of nuclear power plant. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Transient analysis

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Semantic and Emotional Dual Channel for Emotion Recognition in Conversation

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Zhenyu Zhang, Zhibo Cheng, Yuhu Zhang, Tong Wang, Xuesong Jinan250353 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China China University of Mining and Technology Engineering Research Center of Intelligent Control for Underground Space Ministry of Education Xuzhou Key Laboratory of Artificial Intelligence and Big Data School of Information and Control Engineering Xuzhou221116 China South China University of Technology Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Emotion recognition in conversation (ERC) aims at accurately identifying emotional states expressed in conversational content. Existing ERC methods, although relying on semantic understanding, often encounter challenges when confronted with incomplete or misleading semantic information. In addition, when dealing with the interaction between emotional and semantic information, existing methods are often difficult to effectively distinguish the complex relationship between the two, which affects the accuracy of emotion recognition. To address the problems of semantic misdirection and emotional cross-talk encountered by traditional models when confronted with complex conversational data, we propose a semantic and emotional dual channel (SEDC) strategy for emotion recognition in conversations to process emotional and semantic information independently. Under this strategy, emotion information provides an auxiliary recognition function when the semantics are unclear or lacking, enhancing the accuracy of the model. Our model consists of two modules: the emotion processing module accurately captures the emotional features of each utterance through contrastive learning, and then constructs a dialogue emotion propagation map to simulate the emotional information conveyed in the dialogue;the semantic processing module combines an external knowledge base to enhance the semantic expression of the dialogue through knowledge enhancement strategies. This divide-and-conquer approach allows us to more deeply analyze the emotional and semantic dimensions of complex dialogues. Experimental results on the IEMOCAP, EmoryNLP, MELD, and DailyDialog datasets show that our approach significantly outperforms existing techniques and effectively improves the accuracy of dialogue emotion recognition. Our code is available at https://***/r/SEDC-FCF1. © 2010-2012 IEEE.

关键词： Contrastive Learning

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Gradient sparsification for efficient wireless federated learning with differential privacy

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Science China(Information Sciences) 2024年第4期67卷 272-288页

作者： Kang WEI Jun LI Chuan MA Ming DING Feng SHU Haitao ZHAO Wen CHEN Hongbo ZHU School of Electronic and Optical Engineering Nanjing University of Science and Technology Zhejiang Lab Key Laboratory of Computer Network and Information Integration(Southeast University) Ministry of Education Data61 Commonwealth Scientific and Industrial Research Organisation School of Information and Communication Engineering Hainan University School of Communications and Information Engineering Nanjing University of Posts and Telecommunications School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University

Federated learning(FL) enables distributed clients to collaboratively train a machine learning model without sharing raw data with each other. However, it suffers from the leakage of private information from uploading models. In addition, as the model size grows, the training latency increases due to the limited transmission bandwidth and model performance degradation while using differential privacy(DP)protection. In this paper, we propose a gradient sparsification empowered FL framework with DP over wireless channels, to improve training efficiency without sacrificing convergence performance. Specifically, we first design a random sparsification algorithm to retain a fraction of the gradient elements in each client's local model, thereby mitigating the performance degradation induced by DP and reducing the number of transmission parameters over wireless channels. Then, we analyze the convergence bound of the proposed algorithm, by modeling a non-convex FL problem. Next, we formulate a time-sequential stochastic optimization problem for minimizing the developed convergence bound, under the constraints of transmit power, the average transmitting delay, as well as the client's DP requirement. Utilizing the Lyapunov drift-plus-penalty framework, we develop an analytical solution to the optimization problem. Extensive experiments have been implemented on three real-life datasets to demonstrate the effectiveness of our proposed algorithm. We show that our proposed algorithms can fully exploit the interworking between communication and computation to outperform the baselines, i.e., random scheduling, round robin, and delay-minimization algorithms.

关键词： federated learning differential privacy gradient sparsification Lyapunov drift convergence analysis

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Security Issues and Defensive Approaches in Deep Learning Frameworks

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Tsinghua Science and Technology 2021年第6期26卷 894-905页

作者： Hongsong Chen Yongpeng Zhang Yongrui Cao Jing Xie Department of Computer Science University of Science and Technology Beijing(USTB)Beijing 100083China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing 100083China Department of Computer Science and Technology University of Science and Technology Beijing(USTB)Beijing 100083China Defense Electronics Institute China Industrial Control System Cyber Emergency Response TeamBeijing 100040China

Deep learning frameworks promote the development of artificial intelligence and demonstrate considerable potential in numerous ***,the security issues of deep learning frameworks are among the main risks preventing the wide application of *** on deep learning frameworks by malicious internal or external attackers would exert substantial effects on society and *** start with a description of the framework of deep learning algorithms and a detailed analysis of attacks and vulnerabilities in *** propose a highly comprehensive classification approach for security issues and defensive approaches in deep learning frameworks and connect different attacks to corresponding defensive ***,we analyze a case of the physical-world use of deep learning security *** addition,we discuss future directions and open issues in deep learning *** hope that our research will inspire future developments and draw attention from academic and industrial domains to the security of deep learning frameworks.

关键词： adversarial examples deep learning frameworks defensive approaches security issues

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Multi-component quantitative and feed-forward neural network for pattern classification of raw and wine-processed Corni Fructus

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Traditional Medicine Research 2023年第1期8卷 12-19页

作者： Yu Liu Ying-Fang Cui Dan-Dan Shi Shu-Li Man Xia Li Wen-Yuan Gao State Key Laboratory of Food Nutrition and Safety/Key Laboratory of Industrial Microbiology Ministry of Education/Tianjin Key Laboratory of Industry Microbiology/National and Local United Engineering Lab of Metabolic Control Fermentation Technology/China International Science and Technology Cooperation Base of Food NutritionSafety and Medicinal Chemistry/College of BiotechnologyTianjin University of Science&TechnologyTianjin 300457China Tianjin Key Laboratory for Modern Drug Delivery&High-Efficiency School of Pharmaceutical Science and TechnologyTianjin UniversityTianjin 300072China.

Background:To promote the quality evaluation,clarify the processing mechanism and distinguish origins of Corni Fructus(cornus)from different ***:This study developed a high performance liquid chromatography method for simultaneous determination of 5-hydroxymethylfurfural,2 phenolic acids and 4 iridoid glycosides and the reference fingerprint of cornus from different *** addition,the feedforward neural network model provided a pattern classification of sample ***:The content of morroniside and loganin were the highest in all raw cornus samples ranging from 9.45μg/mg to 16.3μg/mg and 6.64μg/mg to 13.7μg/mg,*** level of sweroside in raw cornus from Henan(0.83μg/mg^(-1).39μg/mg)and Zhejiang(0.64μg/mg^(-1).17μg/mg)were greater than other *** wine-processing,the glucose or fructose were dehydrated to increase the levels of *** C-4 position of-COOCH3 of hot-sensitive iridoid glycosides was hydrolyzed to generate-COOH as stable *** derivatives may be degraded to increase the content of phenolic ***,an excellent feedforward neural network model for identification of raw cornus and wine-prepared cornus was established which could distinguish the sample ***:This work provided a trustworthy method to evaluate the quality and distinguish the sources of ***,the clear processing mechanism provided a scientific foundation for controlling the cornus quality during wine-processing.

关键词： Cornus officinalis Sieb.et Zucc. quality evaluation fingerprints processing mechanism feedforward neural network

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Decentralized Game-based Auction Algorithm for Scheduling Robotic Chargers to Service EVs with Uncertain Demands

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-14页

作者： Fang, Qiuyang Zhang, Jianlei Wang, Chen Xie, Guangming Zhang, Chunyan Department of Automation College of Computer and Control Engineering Nankai University Tianjin China National Engineering Research Center of Software Engineering Peking University Beijing China State Key Laboratory for Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China

In this study, we introduce a novel auction-based algorithm modeled as a decentralized coalition formation game, designed for the complex requirements of large-scale multi-robot task allocation under uncertain demand. This context is particularly illustrative in scenarios where robots are tasked to charge electric vehicles. The algorithm begins by partitioning a composite task sequence into distinct subsets based on spatial similarity principles. Subsequently, we employ a coalition formation game paradigm to coordinate the assembly of robots into cooperative coalitions focused on these distinct subsets. To mitigate the impact of unpredictable task demands on allocations, our approach utilizes the conditional value-at-risk to assess the risk associated with task execution, along with computing the potential revenue of the coalition with an emphasis on risk-related outcomes. Additionally, integrating consensus auctions into the coalition formation framework allows our approach to accommodate assignments for individual robot-task pairings, thus preserving the stability of individual robotic decision autonomy within the coalition structure and assignment distribution. Simulative analyses on a prototypical parking facility layout confirm that our algorithm achieves Nash equilibrium within the coalition structure in polynomial time and demonstrates significant scalability. Compared to competing algorithms, our proposal exhibits superior performance in resilience, task execution efficiency, and reduced overall task completion times. The results demonstrate that our approach is an effective strategy for solving the scheduling challenges encountered by multi-robot systems operating in complex environments. IEEE

关键词： Polynomial approximation

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