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Achieving data-driven actionability by combining learning and planning

计算机科学前沿 2018年第5期12卷 939-949页

作者： Qiang LV Yixin CHEN Zhaorong LI Zhicheng CUI Ling CHEN Xing ZHANG Haihua SHEN College of Information Engineering Yangzhou University Yangzhou 225127 China Department of Computer Science and Engineering Washington University in St.Louis St.Louis MO 63130 USA School of Management Fudan University Shanghai 200433 China School of Computer and Control Engineering University of Chinese Academy of Science Beijing 100049 China

A main focus of machine learning research has been improving the generalization accuracy and efficiency of prediction ***,what emerges as missing in many applications is actionability,i.e.,the ability to turn prediction results into *** effort in deriving such actionable knowledge is few and limited to simple action models while in many real applications those models are often more complex and harder to extract an optimal *** this paper,we propose a novel approach that achieves actionability by combining learning with planning,two core areas of *** particular,we propose a framework to extract actionable knowledge from random forest,one of the most widely used and best off-the-shelf *** formulate the actionability problem to a sub-optimal action planning (SOAP) problem,which is to find a plan to alter certain features of a given input so that the random forest would yield a desirable output,while minimizing the total costs of ***,the SOAP problem is formulated in the SAS+ planning formalism,and solved using a Max-SAT based *** experimental results demonstrate the effectiveness and efficiency of the proposed approach on a personal credit dataset and other *** work represents a new application of automated planning on an emerging and challenging machine learning paradigm.

关键词： actionable knowledge extraction machine learning planning random forest

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A two-stage HV-driven adaptive multi-objective evolutionary algorithm and its application in fixed polarity reed-muller circuits

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Expert Systems with Applications 2025年 288卷

作者： Lu Yang Shengsheng Wang Ruyi Dong Zihao Fu College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China College of Information and Control Engineering Jilin Institute of Chemical Technology Changchun 130012 China

To achieve a balance between convergence and diversity, we proposed a two-stage HV-driven adaptive multi-objective evolutionary algorithm (TSAMEA). TSAMEA employs a sinusoidal decreasing parameter adjustment method to enhance exploration pace in the first stage. An adaptive parameter control mechanism utilizes historical memory pools and an HV-driven degree adjustment strategy to achieve better exploitation in the second stage. Extensive experimental data demonstrate that TSAMEA outperforms nine other compared MOEAs. The component analysis illustrates the efficacy of each component of TSAMEA. In addition, area and power optimization are now the main limitations in chip design, TSAMEA is applied to area and power optimization for Fixed Polarity Reed-Muller (FPRM) logic circuits and perform well, which further verifies the ability of the TSAMEA to solve practical problems.

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Integrating V2X solutions in intelligent green cities: an AI-driven point exchange system approach

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Neural Computing and Applications 2025年

作者： Talaat, Fatma M. Shaban, Warda M. ZainEldin, Hanaa Badawy, Mahmoud Elhosseini, Mostafa Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science & Engineering New Mansoura University Gamasa35712 Egypt Department of Communication and Electronics Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence and informatics Horus University New Damietta Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Computer Science and Information Department Applied College Taibah University Madinah42353 Saudi Arabia College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia

With rapid urbanization, smart cities have become essential for enhancing urban management and sustainability by integrating technological, social, and institutional innovations. Among these innovations, vehicle-to-everything (V2X) communications and electric vehicles (EVs) play a critical role in reducing carbon emissions and optimizing urban mobility. To address the existing gaps in holistic V2X integration, this paper presents a novel eco-assistive fog-based traffic management system (EAFTMS) that leverages a four-tier architecture (IoT, fog, cloud, and application) for scalable, real-time traffic optimization. A key innovation of this system is the AI-driven point exchange system (PES), designed to incentivize sustainable behaviors such as reducing unnecessary vehicle usage and promoting green lifestyle choices. Unlike conventional models, the proposed framework incorporates real-time behavioral monitoring, rewards-based sustainability programs, and V2X-enabled dynamic traffic control. Empirical validation demonstrates that EAFTMS outperforms existing models, including support vector regression (SVR), achieving a 30% reduction in latency, a 40% improvement in response time, a 25% increase in traffic flow efficiency, and a 35% reduction in CO2 emissions. These results highlight the framework’s potential to set new standards in intelligent green cities by offering scalable, practical, and environmentally impactful solutions to urban transportation challenges. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Urban transportation

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Open-source dataset for control-oriented modelling in diesel engines

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science China(Information sciences) 2019年第7期62卷 223-224页

作者： Jinghua ZHAO Sitong ZHOU Yunfeng HU Mingjun JU Ruixue REN Hong CHEN State Key Laboratory of Automotive Simulation and Control Jilin University Department of Control Science and Engineering Jilin University Computer College Jilin Normal University Changchun FAW Sihuan Engine Manufacture Co. Ltd

In this study, an experimental dataset of a diesel engine under the European transient cycle (ETC)is demonstrated and opened for academic *** dataset with 7 dimensional features is collected from the advanced diesel engine manufactured by Changchun FAW Sihuan Engine Manufacture Co., *** experimental data of diesel engines is very important for system modeling and identification,

关键词： SOI Open-source dataset for control-oriented modelling in diesel engines

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Hybrid patterns recognition of control chart based on WA-PCA-PSO-SVM

International Journal of Control and Automation

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International Journal of control and Automation 2014年第10期7卷 91-98页

作者： Yan-Zhong, Liu Hong-Lie, Zhang Yan-Ju, Liu Jin-Gang, Jiang College of Computer and Control Engineering Qiqihar University Qiqihar China Computing Center Qiqihar University Qiqihar China College of Mechanical and Power Engineering Harbin University of Science and Technology Harbin China

Based on the analysis of the defect of traditional model, this paper proposes a new control chart pattern recognition model, which includes Wavelet Analysis (WA), Principal Component Analysis (PCA), Particle Swarm Optimization (PSO) and Support Vector Machine (SVM). WA is good to eliminate noise control chart anomaly pattern recognition of the adverse effect. PCA eliminates the redundant information of data between SVM and reduces the input dimension and computational complexity. PSO algorithm optimizes the parameters of SVM and the establishment of the optimal control chart anomaly pattern classifier can solve the problem optimal parameters of SVM. The simulation results show that the model is feasible, the results are reliable. This algorithm improves the control chart abnormal state average recognition accuracy and be used in the machining process real-time monitoring.

关键词： control charts

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Dynamic Scheduling of Mobile-Robotic Warehouse Logistics System 35

Dynamic Scheduling of Mobile-Robotic Warehouse Logistics Sys...

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第35届中国控制会议

作者： JIN Xiaochuang ZHONG Mengzhi QUAN Xiongwen LI Siyuan ZHANG Heng College of Computer and Control Engineering Nankai University School of Optical-Electrical and Computer Engineering University Of Shanghai For Science And Technology Library of Nankai University

ISBN: (纸本)9781509009107

For B2C enterprises, the efficiency and quality of the storage determines some key factors such as service level and customer satisfaction. In this paper, we present a solution to the multi-robot system with highly dynamic environment based on dispatching rules against the background of B2 C mobile-robotic warehouses logistics system. With multi-task for multi-station in one work circle outbound task mode, series dispatching rules which are suitable for dynamic warehousing environment are proposed, including shelf selection rules and task selection rules. Through some simulation experiments using Alphabet Soup software, we study the effects of different warehouse scale and different number of robots on the combined strategy. The numerical results show the validity of the combined strategy.

关键词： Warehouses Logistics System Dynamic Scheduling Dispatching Rules Multi-robots

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Evaluating and improving the interpretability of item embeddings using item-tag relevance information

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Frontiers of computer science 2020年第3期14卷 143-158页

作者： Tao LIAN Lin DU Mingfu ZHAO Chaoran CUI Zhumin CHEN Jun MA College of Data Science Taiyuan University of TechnologyJinzhong 030600China Software College Shandong UniversityJinan 250101China School of Computer and Control Engineering University of Chinese Academy of SciencesBeijing 101408China School of Computer Science and Technology Shandong University of Finance and EconomicsJinan 250014China School of Computer Science and Technology Shandong UniversityQingdao 266237China

Matrix factorization(MF)methods have superior recommendation performance and are flexible to incorporate other side information,but it is hard for humans to interpret the derived latent ***,the item-item cooccurrence information is exploited to learn item embeddings and enhance the recommendation ***,the item-item co-occurrence information,constructed from the sparse and long-tail distributed user-item interaction matrix,is over-estimated for rare items,which could lead to bias in learned item *** this paper,we seek to evaluate and improve the interpretability of item embeddings by leveraging a dense item-tag relevance ***,we design two metrics to quantitatively evaluate the interpretability of item embeddings from different viewpoints:interpretability of individual dimensions of item embeddings and semantic coherence of local neighborhoods in the latent *** also propose a tag-informed item embedding(TIE)model that jointly factorizes the user-item interaction matrix,the item-item co-occurrence matrix and the item-tag relevance matrix with shared item embeddings so that different forms of information can co-operate with each other to learn better item *** on the MovieLens20M dataset demonstrate that compared with other state-of-the-art MF methods,TIE achieves better top-N recommendations,and the relative improvement is larger when the user-item interaction matrix becomes *** leveraging the itemtag relevance information,individual dimensions of item embeddings are more interpretable and local neighborhoods in the latent space are more semantically coherent;the bias in learned item embeddings are also mitigated to some extent.

关键词： recommender system matrix factorization item embedding item-tag relevance interpretability

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Enhance Smart Grid Security With A Coordinated Cyber-Physical Defensive Mechanism 41

Enhance Smart Grid Security With A Coordinated Cyber-Physica...

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第41届中国控制会议

作者： Zhenyong Zhang Xin Che Xuguo Jiao Jingpei Wang State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou University College of Control Science and Engineering Zhejiang University School of Information and Control Engineering Qingdao University of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Smart grid,as one of the largest evolutionary critical infrastructures,witnesses the deep integration of cyber technologies and physical ***,recent events show that the vulnerabilities exposed in the cyber side and the physical side can be exploited to cause coupled and severe ***,the adversaries are able to mask the pernicious physical attack using the cyber attack by compromising the digitized data transmitted to the control *** cooperative cyber-physical attack(CCPA) is stealthy to the current defense *** this article,we propose a cyber-physical defensive mechanism(CPDM) to thwart the *** changing the physical value of the line admittance,the model parameters known by the adversary are not useful anymore,which is the basic idea to design the *** implementation of CPDM depends on the deployment of the distributed flexible AC transmission system(D-FACTS).To reduce the infrastructure cost for the CPDM,we investigate the minimum number of required D-FACTS devices to thwart the *** consider both weak constraints on the CCPA and CPDM and practical limitations for the CCPA and *** each case,we propose a method to compute the optimal deployment of D-FACTS devices,that is,where the DFACTS device should be deployed.

关键词： CPA Enhance Smart Grid Security With A Coordinated Cyber-Physical Defensive Mechanism Security

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A U-Net-like full convolutional pavement crack segmentation network based on multi-layer feature fusion

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International Journal of Pavement engineering 2025年第1期26卷

作者： Jin Wang Zhigao Zeng Fengxiang Huang R. Simon Sherratt Osama Alfarraj Amr Tolba Jianming Zhang a School of Computer and Communication Engineering Changsha University of Science and Technology Changsha People's Republic of China a School of Computer and Communication Engineering Changsha University of Science and Technology Changsha People's Republic of Chinad Key Laboratory of Safety Control of Bridge Engineering Ministry of Education (Changsha University of Science and Technology) Changsha People's Republic of China b Department of Biomedical Engineering The University of Reading Reading UK c Computer Science Department Community College King Saud University Riyadh Saudi Arabia

ABSTRACTCracks are an important indicator of pavement health, and it is difficult to achieve pixel-level segmentation of small and thin cracks. The existing network often experiences false segmentation and missed segmentation. Accordingly, a novel end-to-end U-Net-like full convolutional crack segmentation network is constructed. First, we propose a multi-layer feature fusion module to aggregate the texture and semantic features at each stage of encoder, so that the network can find smaller and thinner crack. Second, we design a novel residual structure with a pointwise convolution. Each stage of the encoder and decoder incorporates a residual structure to facilitate the fusion of feature maps with different spatial dimensions. It can also prevent the gradient vanish in the network training process. Finally, we utilise the maximum unpooling to restore spatial structure in up-sampling, which exploits the indices of maximum feature value in down-sampling. Therefore, high-frequency information is better preserved to help accurately restore the details of crack edges. To verify the proposed network performance, experiments are carried out on four open datasets, the proposed network can achieve better performance among five classical networks.

关键词： Crack segmentation deep learning semantic segmentation pavement damage detection

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A two-mode offspring generation selection mechanism with co-evolution for sparse large-scale multiobjective optimization

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Information sciences 2025年 718卷

作者： Xiaodong Huang Jian Wang Gaige Wang Yong Zhang Dunwei Gong Yaochu Jin Nikhil R. Pal College of Control Science and Engineering China University of Petroleum (East China) Qingdao 266580 China College of Science China University of Petroleum (East China) Qingdao 266580 China School of Computer Science and Technology Ocean University of China Qingdao Shandong 266580 China School of Information and Control Engineering China University of Mining and Technology Xuzhou Jiangsu 221000 China College of Automation and Electronic Engineering Qingdao University of Science and Technology Qingdao Shandong 266061 China College of Engineering Westlake University Hangzhou Zhejiang 310024 China Electronics and Communication Sciences Unit Indian Statistical Institute Kolkata India

Sparse large-scale multiobjective optimization problems (LSMOPs) have a wide range of practical applications. In recent years, numerous multiobjective evolutionary algorithms (MOEAs) have been developed to address the complexities of these problems. However, many existing MOEAs designed to solve sparse LSMOPs typically rely on fixed, experience-based vectors to guide offspring generation, which often makes it challenging to determine the optimal guiding vectors for different population states, leading to premature convergence and loss of population diversity. To some extent, this leads to a subjective selection of the vector used for offspring generation. To address this issue, we propose a two-mode offspring generation selection mechanism (TOGSM) that incorporates diversified sparse knowledge into the offspring generation process. The switching between these two modes is based on a designed offspring performance indicator. We also divide the population into two subpopulations by employing techniques of Pareto dominance relationship and fitness values. In each generation, the loser subpopulation generates offspring solutions during the reproduction process, under the guidance of the winner subpopulation. Experimental results confirm that TOGSM incorporating two-mode mechanism and co-evolution strategy can generate higher quality Pareto optimal solutions with faster convergence speed than the state-of-the-art (SOTA) comparative algorithms.

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