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作者： Dai, Xuefeng Wang, Jiazhi Zhao, Jianqi Li, Dahui Yao, Zhifeng School of Computer and Control Engineering Qiqihar University Qiqihar China School of Mechatronics Engineering Qiqihar University Qiqihar China

To improve time efficiency for structured environment exploration of multi-robot systems, a novel coordinated strategy based on predicate logic reasoning is proposed in this paper. The strategy assigns the room with maximal utility to the robot with the optimal exploring property, and realizes cooperative exploration by considering the room capacity. A set of predicate logic reasoning formulas is established to implement the coordination. The coordination takes into account robot performance and environment property and is conducted in a distributed manner. In addition, to save computing and storage resources, a triple set named as task information was proposed instead of traditional unfinished job list for environment information sharing among robots. The results are validated by computer simulations for two simulated office like environments. © 2020 John Wiley & Sons, Ltd.

关键词： Multipurpose robots

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Novel Optimized Feature Selection Using Metaheuristics Applied to Physical Benchmark Datasets

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computers, Materials & Continua 2023年第2期74卷 4027-4041页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Fadwa Alrowais Sunil Kumar Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt School of Computer Science University of Petroleum and Energy StudiesDehradun248001India Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

In data mining and machine learning,feature selection is a critical part of the process of selecting the optimal subset of features based on the target *** are 2n potential feature subsets for every n features in a dataset,making it difficult to pick the best set of features using standard ***,in this research,a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm(ASSOA)has been *** using metaheuristics to pick features,it is common for the selection of features to vary across runs,which can lead to *** of this,we used the adaptive squirrel search to balance exploration and exploitation duties more evenly in the optimization *** the selection of the best subset of features,we recommend using the binary ASSOA search strategy we developed *** to the suggested approach,the number of features picked is reduced while maximizing classification accuracy.A ten-feature dataset from the University of California,Irvine(UCI)repository was used to test the proposed method’s performance *** other state-of-the-art approaches,including binary grey wolf optimization(bGWO),binary hybrid grey wolf and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hybrid GWO and genetic algorithm 4028 CMC,2023,vol.74,no.2(bGWO-GA),binary firefly algorithm(bFA),and *** results confirm the superiority and effectiveness of the proposed algorithm for solving the problem of feature selection.

关键词： Metaheuristics adaptive squirrel search algorithm optimization methods binary optimizer

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Incremental Learning Based on V-I Trajectory Feature for Non-Intrusive Load Monitoring 7

Incremental Learning Based on V-I Trajectory Feature for Non...

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7th IEEE Conference on Energy Internet and Energy System Integration, EI2 2023

作者： Li, Yuchun Li, Keke Wang, Sheng Cao, Zhiao School of Computer and Communication Engineering Northeastern University At Qinhuangdao Qinhuangdao China School of Electrical and Information Engineering Tianjin University Tianjin China School of Information Science and Engineering Northeastern University Shenyang China School of Control Engineering Northeastern University At Qinhuangdao Qinhuangdao China

ISBN: (纸本)9798350345094

Non-intrusive load monitoring (NILM) implements energy decomposition in a non-intrusive manner and provides a promising path to tap demand response potential for flexible load resources in residential and commercial buildings. Modern deep neural networks (DNNs) have succeeded in NILM fields, especially under offline settings where the task for a network is fixed and all training data are provided simultaneously. However, these algorithms face an inevitable challenge. When new or unknown appliances are added, the learning algorithms mentioned above exhibit a pronounced susceptibility to catas-trophic forgetting as they undertake the incremental acquisition of knowledge associated with these new appliance classes. To tackle this challenge, a novel incremental learning method is proposed in this paper. An effective linear model is introduced to correct the strong deviation when learning unknown appliance class. Numerical results on the PLAID public datasets illustrate the effectiveness of the presented method. © 2023 IEEE.

关键词： Taps

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Zero-phase CARIMA Filtering and Application in Wind-storage System Sizing and Power Dispatch Optimization

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CSEE Journal of Power and Energy Systems 2024年第6期10卷 2283-2295页

作者： Wei Wang Peng Chen Guorui Ren Jizhen Liu Fang Fang Zhe Chen State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China Department of Energy Technology Aalborg University9220 AalborgDenmark

Hybrid energy storage system(HESS)is an effective way to mitigate wind power fluctuations on multi-time scale,and can improve influence of large-scale grid-connected wind power on stability and reliability of power system.A novel methodology named zero-phase controlled auto-regressive integrated moving-average(CARIMA)filter is proposed to integrate HESS to smooth wind power ***,a design method for zero-phase CARIMA filter is provided,and then used to determine grid-connected power for a wind storage system and size *** reasons,direct current(DC)component caused by energy storage efficiency and grid-connected power delay caused by phase shift,for causing superfluous energy storage configuration are *** addition,a nonlinear programming scheduling strategy considering battery degradation is *** imbalance caused by efficiency difference during dynamic adjustment of energy storage output power is ***,thermostatically controlled loads(TCLs)are integrated in sizing and scheduling HESS to reduce energy storage demand and improve operating conditions of energy ***,effectiveness of the proposed strategy is verified by a case study.

关键词： CARIMA demand response energy storage optimization wind power zero-phase

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Unpredictable Switching for Cyber-Physical Security Against Worst-case Attackers 63

Unpredictable Switching for Cyber-Physical Security Against ...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Kanellopoulos, Aris Fotiadis, Filippos Vamvoudakis, Kyriakos G. Sandberg, Henrik Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm10044 Sweden Georgia Institute of Technology Daniel Guggenheim School of Aerospace Engineering AtlantaGA30332 United States

ISBN: (纸本)9798350316339

In this paper, we investigate the problem of securing a system against actuator attacks. Specifically, we employ an unpredictability-based defense algorithm according to the principles of Moving Target Defense, while explicitly considering the worst-case attack that can be launched into the system. Consequently, we allow the system to alternate between different actuating modes, but also between optimal and robust control schemes. Via game-theoretic methods, we derive mixed strategies dictating the probabilities of utilizing different controllers for a given mode of operation, whereas an entropy-augmented optimization problem gives the probabilities of using these different modes. Finally, the overall stability of the system is analyzed, and simulation results showcase the efficacy of our proposed method. © 2024 IEEE.

关键词： Cyber attacks

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Research of Improved TD3 Robotic Arm Path Planning using Evolutionary Algorithm 5

Research of Improved TD3 Robotic Arm Path Planning using Evo...

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5th World Robot Conference Symposium on Advanced Robotics and Automation, WRC SARA 2023

作者： Zhang, Feng Xiong, Jianqiang Yuan, Shuai Wen, Ke Shenyang Jianzhu University Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang Branch of National Special Computer Engineering Technology Research Center School of Electrical and Control Engineering Shenyang110168 China Shenyang Jianzhu University School of Electrical and Control Engineering Shenyang110168 China Shenyang Jianzhu University School of Computer Science and Engineering Shenyang110168 China

ISBN: (纸本)9798350307320

Nowadays, application of automated intelligent robot arm devices to improve industrial production efficiency has become a popular research field in the world. The previous off-line path planning method of robotic arm has the inadequacies of low efficiency and slow speed. Although the deep reinforcement learning has accomplished many achievements in the path planning of control manipulator, there are still some problems such as long training time and low planning accuracy. To solve the abovementioned issues, we propose an improved Twin Delayed Deep Deterministic policy gradient (TD3) algorithm (Improved Cross-Entropy Method-TD3: ICEM-TD3) for the path planning of the robotic arm. First, this paper combines evolutionary strategies with TD3 to generate action networks. Then the exploration of TD3 algorithm in the action space is replaced by the exploration in the parameter space. In addition, this paper designs a new reward function to weaken the redundancy of planning and accelerate the convergence speed of training. Finally, the Gazebo simulator is adopted to verify the proposed algorithm, and the results illustrate that the proposed algorithm can greatly improve the accuracy of the path planning of the manipulator using deep reinforcement learning. © 2023 IEEE.

关键词： Motion planning

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Mobile Crowdsourcing Task Assignment Algorithm Based on ConvNeXt and GRU 18th

Mobile Crowdsourcing Task Assignment Algorithm Based on Con...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Fan, Zequn Pan, Qingxian Gao, Zhaolong Luan, Peng Wei, Kai Li, Jinru School of Computer and Control Engineering Yantai University Yantai264005 China Yantai Institute of Technology Yantai264005 China Yantaishan Hospital Shandong Province Yantai264003 China

ISBN: (纸本)9783031714634

Task assignment is a crucial aspect of mobile crowdsourcing research. The focus is on allocating appropriate perceptual tasks based on task and worker characteristics to optimize perceptual quality. Tasks arrive dynamically in a crowdsourcing system, and the platform cannot assign all tasks at the beginning. This can lead to the task allocation process falling into a local optimal solution. To address the problem, this paper proposes a two-stage prediction algorithm that utilises historical spatio-temporal data of crowdsourcing services. Firstly, the temporal data is converted into image data through Markov Transition Field. Then, the task availability is transformed into a categorisation problem. In the first stage, a ConvNeXt-based network is used to predict the task availability. In the second stage, a GRU-based network is used to predict the task duration. This paper presents the results of several experiments that confirm the effectiveness of the proposed algorithm. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Spatio-temporal data

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Composing Like an Ancient Chinese Poet:Learn to Generate Rhythmic Chinese Poetry

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Journal of computer Science & Technology 2023年第6期38卷 1272-1287页

作者：何明陈艳赵洪科刘淇吴乐崔羽曾贵华刘贵全 Department of Electronic Engineering Shanghai Jiao Tong UniversityShanghai 200240China AI Laboratory Lenovo ResearchBeijing 100193China Baidu Incorporated Beijing 100193China College of Management and Economics Tianjin UniversityTianjin 300072China School of Computer Science and Technology University of Science and Technology of ChinaHefei 230027China School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China Risk Control Department OPayBeijing 100010China

Automatic generation of Chinese classical poetry is still a challenging problem in artificial ***-cently,Encoder-Decoder models have provided a few viable methods for poetry ***,by reviewing the pri-or methods,two major issues still need to be settled:1)most of them are one-stage generation methods without further polishing;2)they rarely take into consideration the restrictions of poetry,such as tone and ***,some an-cient Chinese poets tended first to write a coarse poem underlying aesthetics and then deliberated its semantics;while oth-ers first create a semantic poem and then refine its *** this basis,in order to better imitate the human creation procedure of poems,we propose a two-stage method(i.e.,restricted polishing generation method)of which each stage fo-cuses on the different aspects of poems(i.e.,semantics and aesthetics),which can produce a higher quality of generated *** this way,the two-stage method develops into two symmetrical generation methods,the aesthetics-to-semantics method and the semantics-to-aesthetics *** particular,we design a sampling method and a gate to formulate the tone and rhyme restrictions,which can further improve the rhythm of the generated *** results demon-strate the superiority of our proposed two-stage method in both automatic evaluation metrics and human evaluation met-rics compared with baselines,especially in yielding consistent improvements in tone and rhyme.

关键词： aesthetics poetry generation polishing semantics two-stage method

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Overview of Task Offloading of Wireless Sensor Network in Edge Computing Environment 6

Overview of Task Offloading of Wireless Sensor Network in Ed...

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6th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2023

作者： Hou, XiQiang Hu, YiFan Wang, Fuqiang School of Information and Control Engineering Qingdao University of Technology Qingdao China Shandong Computer Science Center Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350399059

This review article introduces the concepts, server architecture and application scenarios of Mobile Edge Computing (MEC) and Wireless Sensor Network (WSN). By differentiating between rechargeable and non-rechargeable systems, it presents the research status of task offloading in WSN in an edge computing environment. Various strategies are analyzed and compared in terms of their characteristics and application domains. The existing methods can reduce offloading latency and energy consumption, but still face some issues. Based on this, the research hotspots of edge computing task offloading for WSN are summarized, and its research and development trends are pointed out. © 2023 IEEE.

关键词： Energy harvesting

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SensoryT5: Infusing Sensorimotor Norms into T5 for Enhanced Fine-grained Emotion Classification 8

SensoryT5: Infusing Sensorimotor Norms into T5 for Enhanced ...

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8th Workshop on Cognitive Aspects of the Lexicon, CogALex 2024

作者： Xia, Yuhan Zhao, Qingqing Long, Yunfei Xu, Ge Wang, Jia School of Computer Science and Electronic Engineering University of Essex United Kingdom Institute of Linguistics Chinese Academy of Social Sciences China College of Computer and Control Engineering Minjiang University China Department of Intelligent Science Xi’an Jiaotong-Liverpool University China

ISBN: (纸本)9782493814456

In traditional research approaches, sensory perception and emotion classification have traditionally been considered separate domains. Yet, the significant influence of sensory experiences on emotional responses is undeniable. The natural language processing (NLP) community has often missed the opportunity to merge sensory knowledge with emotion classification. To address this gap, we propose SensoryT5, a neuro-cognitive approach that integrates sensory information into the T5 (Text-to-Text Transfer Transformer) model, designed specifically for fine-grained emotion classification. This methodology incorporates sensory cues into the T5’s attention mechanism, enabling a harmonious balance between contextual understanding and sensory awareness. The resulting model amplifies the richness of emotional representations. In rigorous tests across various detailed emotion classification datasets, SensoryT5 showcases improved performance, surpassing both the foundational T5 model and current state-of-the-art works. Notably, SensoryT5’s success signifies a pivotal change in the NLP domain, highlighting the potential influence of neuro-cognitive data in refining machine learning models’ emotional sensitivity. © 2024 ELRA Language Resource Association.

关键词： Classification (of information)

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