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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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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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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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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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Joint Optimization of Trajectory and Task Offloading for Cellular-Connected Multi-UAV Mobile Edge Computing

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Chinese Journal of Electronics 2024年第3期33卷 823-832页

作者： Jingming XIA Yufeng LIU Ling TAN School of artificial intelligence Nanjing University of Information Science and Technology Engineering research center of Digital Forensics Ministry of education Nanjing University of Information Science and Technology

Since the computing capacity and battery energy of unmanned aerial vehicle(UAV) are constrained,UAV as aerial user is hard to handle the high computational complexity and time-sensitive *** paper investigates a cellular-connected multi-UAV network supported by mobile edge *** UAVs carrying tasks fly from a given initial position to a termination position within a specified *** handle the large number of tasks carried by UAVs,we propose a energy cost of all UAVs based problem to determine how many tasks should be offloaded to high-altitude balloons(HABs) for computing,where UAV-HAB association,the trajectory of UAV,and calculation task splitting are jointly ***,the formulated problem has nonconvex ***,an efficient iterative algorithm by applying successive convex approximation and the block coordinate descent methods is put ***,in each iteration,the UAV-HAB association,calculation task splitting,and UAV trajectory are alternately ***,for the nonconvex UAV trajectory optimization problem,an approximate convex optimization problem is *** numerical results indicate that the scheme of this paper proposed is guaranteed to converge and also significantly reduces the entire power consumption of all UAVs compared to the benchmark schemes.

关键词： Energy consumption Multi-access edge computing Costs Power demand Autonomous aerial vehicles Iterative algorithms Convex functions

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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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Identity-Preserving Adversarial Training for Robust Network Embedding

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Journal of Computer Science & Technology 2024年第1期39卷 177-191页

作者：岑科廷沈华伟曹婍徐冰冰程学旗 data intelligence System research center Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 101480China Beijing Academy of artificial intelligence Beijing 100000China Chinese Academy of Sciences Key Laboratory of Network data Science and Technology Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Network embedding,as an approach to learning low-dimensional representations of nodes,has been proved extremely useful in many applications,e.g.,node classification and link ***,existing network embed-ding models are vulnerable to random or adversarial perturbations,which may degrade the performance of network em-bedding when being applied to downstream *** achieve robust network embedding,researchers introduce adversari-al training to regularize the embedding learning process by training on a mixture of adversarial examples and original ***,existing methods generate adversarial examples heuristically,failing to guarantee the imperceptibility of generated adversarial examples,and thus limit the power of adversarial *** this paper,we propose a novel method Identity-Preserving Adversarial Training(IPAT)for network embedding,which generates imperceptible adversarial exam-ples with explicit identity-preserving *** formalize such identity-preserving regularization as a multi-class classification problem where each node represents a class,and we encourage each adversarial example to be discriminated as the class of its original *** experimental results on real-world datasets demonstrate that our proposed IPAT method significantly improves the robustness of network embedding models and the generalization of the learned node representations on various downstream tasks.

关键词： network embedding identity-preserving adversarial training adversarial the example

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Part-Whole Relational Few-Shot 3D Point Cloud Semantic Segmentation

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Computers, Materials & Continua 2024年第3期78卷 3021-3039页

作者： Shoukun Xu Lujun Zhang Guangqi Jiang Yining Hua Yi Liu School of Computer Science and artificial intelligence Aliyun School of Big Data and School of SoftwareChangzhou UniversityChangzhou213164China Department of Computer Science University of AberdeenKing’s CollegeAberdeenAB243FXUK

This paper focuses on the task of few-shot 3D point cloud semantic *** some progress,this task still encounters many issues due to the insufficient samples given,e.g.,incomplete object segmentation and inaccurate semantic *** tackle these issues,we first leverage part-whole relationships into the task of 3D point cloud semantic segmentation to capture semantic integrity,which is empowered by the dynamic capsule routing with the module of 3D Capsule Networks(CapsNets)in the embedding ***,the dynamic routing amalgamates geometric information of the 3D point cloud data to construct higher-level feature representations,which capture the relationships between object parts and their ***,we designed a multi-prototype enhancement module to enhance the prototype ***,the single-prototype enhancement mechanism is expanded to the multi-prototype enhancement version for capturing rich ***,the shot-correlation within the category is calculated via the interaction of different samples to enhance the intra-category *** studies prove that the involved part-whole relations and proposed multi-prototype enhancement module help to achieve complete object segmentation and improve semantic ***,under the integration of these two modules,quantitative and qualitative experiments on two public benchmarks,including S3DIS and ScanNet,indicate the superior performance of the proposed framework on the task of 3D point cloud semantic segmentation,compared to some state-of-the-art methods.

关键词： Few-shot point cloud semantic segmentation CapsNets

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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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Ensemble successor representations for task generalization in offline-to-online reinforcement learning

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Science China(Information Sciences) 2024年第7期67卷 240-255页

作者： Changhong WANG Xudong YU Chenjia BAI Qiaosheng ZHANG Zhen WANG Space Control and Inertial Technology research center Harbin Institute of Technology Shanghai artificial intelligence Laboratory Shenzhen research Institute of Northwestern Polytechnical University School of Cybersecurity Northwestern Polytechnical University

In reinforcement learning(RL), training a policy from scratch with online experiences can be inefficient because of the difficulties in exploration. Recently, offline RL provides a promising solution by giving an initialized offline policy, which can be refined through online interactions. However, existing approaches primarily perform offline and online learning in the same task, without considering the task generalization problem in offline-to-online adaptation. In real-world applications, it is common that we only have an offline dataset from a specific task while aiming for fast online-adaptation for several tasks. To address this problem, our work builds upon the investigation of successor representations for task generalization in online RL and extends the framework to incorporate offline-to-online learning. We demonstrate that the conventional paradigm using successor features cannot effectively utilize offline data and improve the performance for the new task by online fine-tuning. To mitigate this, we introduce a novel methodology that leverages offline data to acquire an ensemble of successor representations and subsequently constructs ensemble Q functions. This approach enables robust representation learning from datasets with different coverage and facilitates fast adaption of Q functions towards new tasks during the online fine-tuning *** empirical evaluations provide compelling evidence showcasing the superior performance of our method in generalizing to diverse or even unseen tasks.

关键词： offline reinforcement learning online fine-tuning task generalization successor representations ensembles

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