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Attribute Reduction of Hybrid Decision Information Systems Based on Fuzzy Conditional Information Entropy

Computers, Materials & Continua 2024年第5期79卷 2063-2083页

作者： Xiaoqin Ma Jun Wang Wenchang Yu Qinli Zhang School of big data and Artificial intelligence Chizhou UniversityChizhou247000China Anhui Education big data Intelligent Perception and Application engineering research center Chizhou247000China

The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable *** available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal *** order to address the aforementioned issues,this paper delves into the study of attribute reduction *** of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute *** newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute ***,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute ***,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy *** effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information ***,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is *** experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction *** comprehensive analysis o

关键词： Hybrid decision information systems fuzzy conditional information entropy attribute reduction fuzzy relationship rough set theory(RST)

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A Method of Emergency Volunteer Team Internal Participation in Rescue Decision-Making Considering Psychological Behavior

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Journal of Systems Science and Information 2024年第1期12卷 64-80页

作者： Jie BAI Shengqun CHEN College of Computer and Information Sciences Fujian Agriculture and Forestry UniversityFuzhou 350002China School of Information engineering Fujian Business UniversityFuzhou 350506China engineering research center of big data analytics for business intelligence Fujian Province UniversityFuzhou 350506China

A method for internal participation in rescue decision-making of emergency volunteer teams considering psychological behavior is proposed to address the time sequence of rescue ***,the problem of multi-tasking and multi-operation within the emergency volunteer team is ***,considering that task leaders are influenced by behavioral and psychological factors in the evaluation,the required time for the job is used as a reference point,and the expected time that volunteers can complete the job is used as an attribute *** task leader's prospect satisfaction value for each volunteer is calculated based on prospect theory,and the perceived utility values of disappointment theory and regret theory are calculated to measure the task leader's satisfaction with each ***,a multilayer coded genetic algorithm is used to construct an optimization model for emergency volunteer decision-making with the objective of maximizing the satisfaction ***,the feasibility and effectiveness of this method are illustrated by an example *** result shows that the efficiency of rescue tasks can be improved through decision optimization within the volunteer team.

关键词： emergency volunteers rescue task multi-layer coding genetic algorithm satisfaction value psychological behavior

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Multi-trial Vector-based Whale Optimization Algorithm

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Journal of Bionic engineering 2024年第3期21卷 1465-1495页

作者： Mohammad H.Nadimi-Shahraki Hajar Farhanginasab Shokooh Taghian Ali Safaa Sadiq Seyedali Mirjalili Faculty of Computer engineering Najafabad BranchIslamic Azad UniversityNajafabad***Iran big data research center Najafabad BranchIslamic Azad UniversityNajafabad***Iran Department of Computer Science Nottingham Trent UniversityNottinghamNG118NSUK Centre for Artificial intelligence research and Optimisation Torrens UniversityBrisbane4006Australia

The Whale Optimization Algorithm(WOA)is a swarm intelligence metaheuristic inspired by the bubble-net hunting tactic of humpback *** spite of its popularity due to simplicity,ease of implementation,and a limited number of parameters,WOA’s search strategy can adversely affect the convergence and equilibrium between exploration and exploitation in complex *** address this limitation,we propose a new algorithm called Multi-trial Vector-based Whale Optimization Algorithm(MTV-WOA)that incorporates a Balancing Strategy-based Trial-vector Producer(BS_TVP),a Local Strategy-based Trial-vector Producer(LS_TVP),and a Global Strategy-based Trial-vector Producer(GS_TVP)to address real-world optimization problems of varied degrees of ***-WOA has the potential to enhance exploitation and exploration,reduce the probability of being stranded in local optima,and preserve the equilibrium between exploration and *** the purpose of evaluating the proposed algorithm's performance,it is compared to eight metaheuristic algorithms utilizing CEC 2018 test ***,MTV-WOA is compared with well-stablished,recent,and WOA variant *** experimental results demonstrate that MTV-WOA surpasses comparative algorithms in terms of the accuracy of the solutions and convergence ***,we conducted the Friedman test to assess the gained results statistically and observed that MTV-WOA significantly outperforms comparative ***,we solved five engineering design problems to demonstrate the practicality of *** results indicate that the proposed MTV-WOA can efficiently address the complexities of engineering challenges and provide superior solutions that are superior to those of other algorithms.

关键词： Swarm intelligence algorithms Metaheuristic algorithms Optimization engineering design problems Whale optimization algorithm

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Combining permissioned blockchain and Bayesian best-worst method for transparent supplier selection in supply chain management

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Science China Technological Sciences 2024年

作者： LIU JiaJun ZHANG Jie LENG JieWu Shanghai engineering research center of Industrial big data and Intelligent System Institute of Artificial Intelligence Donghua University State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment Guangdong University of Technology

Supplier selection is an important business activity in order to realize the purchasing function in supply chain management. The supplier selection process includes four stages, i.e., bidding inviting, bidding, group decision-making, and results disclosure,involving the participation of manufacturing service demanders(MSDs), manufacturing service suppliers(MSSs), and decisionmakers. Nowadays, all the participants have raised concerns about the increased transparency in supplier selection. Therefore,this study proposes a transparent supplier selection method by considering the engagement of suppliers. In this method, the Bayesian best-worst method(Bayesian BWM) is used to aggregate decision-makers' preferences into the overall optimal weights of the alternative MSSs, and the MSS with the largest weight is considered the suitable MSS for MSDs. Furthermore,blockchain is introduced to record the decision-making process information about supplier selection through a customized smart contract, where MSSs act as supervisors to supervise the decision-making process through the distributed consensus mechanism rather than directly participate in the decision-making process. Finally, a case study of supplier selection in purchasing vibration acceleration sensors is presented. The result shows that the proposed method can support MSDs in selecting suitable MSS from alternative MSSs by aggregating decision-makers' preferences, and blockchain can provide credible information about the supplier selection process for MSSs, MSDs, and decision-makers. In this way, the transparency of supplier selection is enhanced.

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Deployment optimization for target perpetual coverage in energy harvesting wireless sensor network

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Digital Communications and Networks 2024年第2期10卷 498-508页

作者： Zhenkun Jin Yixuan Geng Chenlu Zhu Yunzhi Xia Xianjun Deng Lingzhi Yi Xianlan Wang Wuhan business University 430056China Wuhan research Institute of Post and Telecommunication Wuhan430074China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data SecuritySchool of Cyber Science and EngineeringHuazhong University of Science and TechnologyWuhan430074China Hubei Chutian Expressway Digital Technology Co.Ltd Wuhan430074China School of Information and Safety engineering Zhongnan University of Economics and LawWuhan430073China

Energy limitation of traditional Wireless Sensor Networks(WSNs)greatly confines the network lifetime due to generating and processing massive sensing data with a limited *** energy harvesting WSN is a novel network architecture to address the limitation of traditional ***,existing coverage and deployment schemes neglect the environmental correlation of sensor nodes and external energy with respect to physical *** considering the spatial correlation of the environment and the uneven distribution of energy in energy harvesting WSN,we investigate how to deploy a collection of sensor nodes to save the deployment cost while ensuring the target perpetual *** Confident Information Coverage(CIC)model is adopted to formulate the CIC Minimum Deployment Cost Target Perpetual Coverage(CICMTP)problem to minimize the deployed sensor *** the CICMTP is NP-hard,we devise two approximation algorithms named Local Greedy Threshold Algorithm based on CIC(LGTA-CIC)and Overall Greedy Search Algorithm based on CIC(OGSA-CIC).The LGTA-CIC has a low time complexity and the OGSA-CIC has a better approximation *** simulation results demonstrate that the OGSA-CIC is able to achieve lower deployment cost and the performance of the proposed algorithms outperforms GRNP,TPNP and EENP algorithms.

关键词： Energy harvesting WSN Deployment optimization Confident information coverage(CIC) Target perpetual coverage

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Deep learning-based semantic segmentation of human features in bath scrubbing robots

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Biomimetic intelligence & Robotics 2024年第1期4卷 70-79页

作者： Chao Zhuang Tianyi Ma Bokai Xuan Cheng Chang Baichuan An Minghuan Yin Hao Sun School of Artificial intelligence and data Science Hebei University of TechnologyTianjin 300401China engineering research center of Intelligent Rehabilitation Device and Detection Technology Ministry of EducationTianjin 300401China

With the rise in the aging population,an increase in the number of semidisabled elderly individuals has been noted,leading to notable challenges in medical and healthcare,exacerbated by a shortage of nursing *** study aims to enhance the human feature recognition capabilities of bath scrubbing robots operating in a water fog *** investigation focuses on semantic segmentation of human features using deep learning ***,3D point cloud data of human bodies with varying sizes are gathered through light detection and ranging to establish human ***,a hybrid filtering algorithm was employed to address the impact of the water fog environment on the modeling and extraction of human ***,the network is refined by integrating the spatial feature extraction module and the channel attention module based on *** results indicate that the algorithm adeptly identifies feature information for 3D human models of diverse body sizes,achieving an overall accuracy of 95.7%.This represents a 4.5%improvement compared with the PointNet network and a 2.5%enhancement over mean intersection over *** conclusion,this study substantially augments the human feature segmentation capabilities,facilitating effective collaboration with bath scrubbing robots for caregiving tasks,thereby possessing significant engineering application value.

关键词： 3D point cloud Human model LiDAR Semantic segmentation

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Mathematics of multi-agent learning systems at the interface of game theory and artificial intelligence

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Science China(Information Sciences) 2024年第6期67卷 445-447页

作者： Long WANG Feng FU Xingru CHEN center for Systems and Control College of EngineeringPeking University center for Multi-Agent research Institute for Artificial IntelligencePeking University Department of Mathematics Dartmouth College Department of Biomedical data Science Geisel School of Medicine at Dartmouth School of Science Beijing University of Posts and Telecommunications

Evolutionary game theory (EGT) and artificial intelligence(AI) are two fields that,at first glance,might seem distinct,but they have notable connections and *** former focuses on the evolution of behaviors (or strategies)in a population,where individuals interact with others and update their strategies based on imitation (or social learning).

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Variable projection algorithms with sparse constraint for separable nonlinear models

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Control Theory and Technology 2024年第1期22卷 135-146页

作者： Hui-Lang Xu Guang-Yong Chen Si-Qing Cheng Min Gan Jing Chen College of Computer and data Science Fuzhou UniversityFuzhou350116FujianChina Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhou350116FujianChina Key Laboratory of Intelligent Metro of Universities in Fujian Fuzhou UniversityFuzhou350116FujianChina engineering research center of big data intelligence Ministry of EducationFuzhou UniversityFuzhou350116FujianChina School of Science Jiangnan UniversityWuxi214122JiangsuChina

Separable nonlinear models are widely used in various fields such as time series analysis, system modeling, and machine learning, due to their flexible structures and ability to capture nonlinear behavior of data. However, identifying the parameters of these models is challenging, especially when sparse models with better interpretability are desired by practitioners. Previous theoretical and practical studies have shown that variable projection (VP) is an efficient method for identifying separable nonlinear models, but these are based on \(L_2\) penalty of model parameters, which cannot be directly extended to deal with sparse constraint. Based on the exploration of the structural characteristics of separable models, this paper proposes gradient-based and trust-region-based variable projection algorithms, which mainly solve two key problems: how to eliminate linear parameters under sparse constraint;and how to deal with the coupling relationship between linear and nonlinear parameters in the model. Finally, numerical experiments on synthetic data and real time series data are conducted to verify the effectiveness of the proposed algorithms.

关键词： Variable projection(VP) Non-smooth constraint Separable nonlinear models

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Integrating sequence and graph information for enhanced drug-target affinity prediction

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Science China(Information Sciences) 2024年第2期67卷 325-326页

作者： Haohuai HE Guanxing CHEN Calvin Yu-Chian CHEN Artificial intelligence Medical research center School of Intelligent Systems EngineeringShenzhen Campus of Sun Yat-sen University

Drug discovery is a pivotal discipline involving the identification and development of innovative pharmaceuticals designed to combat a wide array of illnesses. It plays a crucial role in advancing global health outcomes and improving the standard of living for individuals worldwide. However,drug discovery often relies on laborious in vitro experiments,

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Adaptive Sensor-Fault Tolerant Control of Unmanned Underwater Vehicles With Input Saturation

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 907-918页

作者： Xuerao Wang Qingling Wang Yanxu Su Yuncheng Ouyang Changyin Sun the School of Artificial intelligence the Engineering Research Center of Autonomous Unmanned System Technologyand the Anhui Provincial Engineering Research Center for Unmanned System and Intelligent TechnologyMinistry of EducationAnhui UniversityHefei 230601China the School of Automation Southeast UniversityNanjing 210096China

This paper investigates the tracking control problem for unmanned underwater vehicles(UUVs)systems with sensor faults,input saturation,and external disturbance caused by waves and ocean *** active sensor fault-tolerant control scheme is ***,the developed method only requires the inertia matrix of the UUV,without other dynamic information,and can handle both additive and multiplicative sensor ***,an adaptive fault-tolerant controller is designed to achieve asymptotic tracking control of the UUV by employing robust integral of the sign of error feedback *** is shown that the effect of sensor faults is online estimated and compensated by an adaptive *** the proposed controller,the tracking error and estimation error can asymptotically converge to ***,simulation results are performed to demonstrate the effectiveness of the proposed method.

关键词： Asymptotic stability fault-tolerant control input saturation robust integral of the sign of error unmanned underwater vehicle

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