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A Parallel Slime Mould Algorithm with Boundary Rebound and Knowledge-Sharing Mechanism 15th

A Parallel Slime Mould Algorithm with Boundary Rebound and...

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15th international conference on Genetic and Evolutionary computing, ICGEC 2023

作者： Fan, HongYe Chu, Shu-Chuan Sui, Xiao Pan, Jeng-Shyang School of Computer Science Northeast Electric Power University Jilin Jilin City132012 China College of Computer Science and Engineering Shandong University of Science and Technology Shandong Qingdao266590 China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan

ISBN: (纸本)9789819700677

The conventional slime mould method has high global search ability but has certain drawbacks such as sluggish convergence speed, limited accuracy, and the tendency to slip into a locally optimum solution. Aiming at the algorithm’s shortcomings, we propose an improved stochastic optimization algorithm—a parallel Slime Mould Algorithm with Boundary rebound and Knowledge-sharing mechanism (PKB-SMA). The main improvement measures are as follows: (1) Introduce a parallel technique to balance the algorithm’s capacity for exploration and development. (2) Propose a knowledge-sharing system to improve individual quality. (3) Using boundary rebound method to improve individual exploration ability. (4) The population was initialized by Hammersley sampling method. Several test functions are utilized to validate the performance of the PKB-SMA method, and its experimental findings are compared to those of competing algorithms. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Optimization

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Revolutionizing Data Transmission - A Comprehensive Review of Deep Learning-Based Routing Mechanism for 5G Wireless Sensor Networks 10

Revolutionizing Data Transmission - A Comprehensive Review o...

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10th international conference on Communication and Signal Processing, ICCSP 2024

作者： Vijayakumar, K. Thirumaraiselvan, P. Sivakumar, B. Seenuvasan, P. Adhiparasakthi Engineering College Department of It Tamilnadu India Adhiparasakthi Engineering College Department of ECE Tamilnadu India Srm Institute of Science and Technology Department of Computing Technologies Tamilnadu India University College of Engineering Department of It Villupuram India

ISBN: (纸本)9798350353068

The convergence of 5G technology and Wireless Sensor Networks (WSNs) has lead in a transformative era in wireless communication, demanding intelligent and adaptive routing protocols. This review explores into the monarchy of deep learning-based routing protocols, dissecting their potential applications, challenges, and impact on the efficiency of 5G-enabled WSNs. Lack of understanding and variance, two crucial factors in the details of deep learning-based routing. The review scrutinizes the complexity of routing decisions and explores how deep learning algorithms introduce a layer of adaptability significant of human perception. As we examine the landscape of 5G WSNs, the benefits and challenges of incorporating deep learning into routing practices come to light. Adaptive decision-making, energy efficiency, resilience to dynamic environments, and enhanced data accuracy emerge as promising outcomes. However, the review also sheds light on the computational complexities and potential biases in training data that necessitate careful consideration. This review serves as a compass, navigating through the terrain of deep learning-based routing protocols for 5G WSNs. By sorting out the hints of incomprehension and variance within this context, we aim to provide a comprehensive understanding of the evolving landscape of intelligent routing. As 5G technology continues to redefine connectivity standards, this review contributes to the foundation of knowledge guiding the development and implementation of effective and adaptive routing protocols in the era of wireless sensor networks. © 2024 IEEE.

关键词： Wireless sensor networks

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FedLMT: Tackling System Heterogeneity of Federated Learning via Low-Rank Model Training with Theoretical Guarantees 41

FedLMT: Tackling System Heterogeneity of Federated Learning ...

引用

41st international conference on Machine Learning, ICML 2024

作者： Liu, Jiahao Zhou, Yipeng Wu, Di Hu, Miao Guizani, Mohsen Sheng, Quan Z. School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Computing Faculty of Science and Engineering Macquarie University Sydney Australia Abu Dhabi United Arab Emirates

Federated learning (FL) is an emerging machine learning paradigm for preserving data privacy. However, diverse client hardware often has varying computation resources. Such system heterogeneity limits the participation of resource-constrained clients in FL, and hence degrades the global model accuracy. To enable heterogeneous clients to participate in and contribute to FL training, previous works tackle this problem by assigning customized sub-models to individual clients with model pruning, distillation, or low-rank based techniques. Unfortunately, the global model trained by these methods still encounters performance degradation due to heterogeneous sub-model aggregation. Besides, most methods are heuristic-based and lack convergence analysis. In this work, we propose the FedLMT framework to bridge the performance gap, by assigning clients with a homogeneous pre-factorized low-rank model to substantially reduce resource consumption without conducting heterogeneous aggregation. We theoretically prove that the convergence of the low-rank model can guarantee the convergence of the original full model. To further meet clients' personalized resource needs, we extend FedLMT to pFedLMT, by separating model parameters into common and custom ones. Finally, extensive experiments are conducted to verify our theoretical analysis and show that FedLMT and pFedLMT outperform other baselines with much less communication and computation costs. Copyright 2024 by the author(s)

关键词： Federated learning

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Empowering Autonomous Decision-Making Through Quantum Reinforcement Learning and Cognitive Neuromorphic Frameworks 4

Empowering Autonomous Decision-Making Through Quantum Reinfo...

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4th IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Balasubramani, R. Biradar, Vidyadevi G. Nmam Institute of Technology Department of Information Science and Engineering Udupi India

ISBN: (纸本)9798350352931

The concept of Agentic AI, which focuses on empowering autonomous systems with human-like decision-making capabilities, has gained substantial attention in recent years. By leveraging advanced reinforcement learning (RL) techniques and cognitive frameworks, Agentic AI aims to enable machines to make complex decisions in dynamic environments with minimal human intervention. This paper introduces Agentic AI, a novel approach that combines Quantum Reinforcement Learning (QRL) and Cognitive Neuromorphic Frameworks to empower autonomous decision-making in complex environments. The proposed model leverages quantum computing principles to accelerate learning in reinforcement-based systems, while cognitive neuromorphic frameworks enhance adaptability and real-time decision-making by mimicking human brain functionality. The integration of QRL allows for faster convergence in learning algorithms, improving decision accuracy by 25% and reducing training time by 40%. Cognitive neuromorphic architectures offer enhanced scalability and resilience in dynamic environments, resulting in a 35% improvement in system robustness during testing. Simulations in autonomous navigation and robotic control show that Agentic AI achieves 20% more efficient task execution compared to traditional AI models. These values underscore the potential of the proposed system to revolutionize autonomous systems, providing a robust foundation for decision-making across a range of applications. © 2024 IEEE.

关键词： Quantum computers

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Application Research of Ant Colony Algorithm in Cross Basin Reservoir Earthquake and Dam Strong Earthquake

Application Research of Ant Colony Algorithm in Cross Basin ...

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2023 IEEE international conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Wang, Kouwu Chen, Zuwen Huang, Jie Luo, Xin Du, Weihua Aruna, T.M. Power China Guiyang Engineering Corporation Ltd. Guizhou Guiyang China Engineering Technology Research Center Guizhou Guiyang China GuiZhou Vocational Technology Institute Guizhou Guiyang China Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798350398465

Ant colony algorithm is a modern intelligent optimization algorithm, which is essentially a pseudo-random search and parallel algorithm. It makes some complex problems that are difficult to be solved by conventional optimization algorithms better, and improves people's ability to solve practical engineering optimization problems. for the problems of reservoir earthquake and dam strong earthquake, this paper analyzes the optimal solution under different parameter settings through the study of ant colony algorithm and process, combined with sample *** results show that the improved ant colony algorithm can speed up the convergence speed of the algorithm and improve the overall quality of the solution. © 2023 IEEE.

关键词： Multiobjective optimization

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Design of an Early Learning Car for Garbage Sorting Based on Object Detection 3

Design of an Early Learning Car for Garbage Sorting Based on...

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3rd international conference on Intelligent computing, Communication and convergence, ICI3C 2023

作者： Hu, Yuhao Dai, Zhihao Jiang, Zhenxiao Ji, Jiawei Yan, Yuxuan Wang, Junjie Li, Juanjuan School of Electronics and Information Technology Soochow University China

ISBN: (纸本)9798350343922

China has comprehensively launched the work of living rubbish classification in cities at the prefecture level and above across the country since 2019, and rubbish classification has begun to be integrated into the daily life of all people. However, due to the lack of awareness of rubbish classification for a long time, it has become a habit for adult groups not to classify rubbish, so the education of rubbish classification for young children is particularly important. In this thesis, an intelligent trolley control system is designed in the context of the implementation of the waste classification policy, aiming to improve the efficiency and accuracy of waste classification in the future by means of waste classification education for young children. The system is based on STM32, K210 and LD3320 control. Firstly, a lightened yolov2 target detection model is used to detect the gesture card teaching aids, and then Kalman filtering is applied to the detection results to achieve the tracking of the detected target on the image and to improve the stability of target detection. Then, PID control algorithm is used to achieve motion control of the trolley so that it can advance to the vicinity of the gesture sign as expected. Finally, the LD3320 recognises the voice of the toddler and opens the corresponding category of bins to achieve trash classification. The experimental results show that the intelligent cart system has good accuracy and stability in target detection and dynamic tracking, and high accuracy in speech recognition, which demonstrates good application prospects in young children's rubbish classification education, and promises to effectively improve the efficiency of rubbish classification and young children's participation, which is of positive significance for the promotion of the implementation of rubbish classification policy. © 2023 IEEE.

关键词： Three term control systems

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Unveiling the Synergy: IoT and Digital Twins in Transforming Industries 3

Unveiling the Synergy: IoT and Digital Twins in Transforming...

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3rd international conference on Smart Generation computing, Communication and Networking, SMART GENCON 2023

作者： Rajora, Ricky Rajora, Aarju Sharma, Bhanu Aggarwal, Priyanshi Thapliyal, Siddhant Chitkara University Institute of Engineering and Technology Chitkara University Punjab 140401 India Chandigarh Engineering College Department of Computer Science & Engineering Mohali Jhanjeri India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350319125

The convergence of the Internet of Things (IoT) and digital twins significantly transforms several sectors by establishing a seamless linkage between the physical and digital domains, enabling improved monitoring, analysis, and optimization capabilities. This research study explores the complex interplay among various technologies, providing a comprehensive analysis of the differentiation between digital models, shadows, and authentic digital twins to clarify prevalent misunderstandings. Digital twins have a wide range of practical applications that include several industries, including manufacturing, healthcare, smart cities, and agriculture. The methodology section delineates the methodical approach used in this work, with particular emphasis on the reciprocal connection between tangible elements and their virtual equivalents. The incorporation of insights derived from relevant literature enhances the development of a thorough comprehension of the intricate relationship between the IoT and digital twins. © 2023 IEEE.

关键词： Digital Twins IoT-enabled Twins Physical-Digital Integration Urban Digital Twins Virtual Replicas

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Consensus MPC-Based hierarchical control of Islanded AC Microgrid 3

Consensus MPC-Based hierarchical control of Islanded AC Micr...

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3rd international conference on Communication, computing and Digital Systems, C-CODE 2023

作者： Khan, Zawar Aamir, Muhammad Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology Electrical & Computer Engineering Department Haripur Pakistan

ISBN: (纸本)9798350332391

In this paper, the consensus model predictive control (MPC)-based hierarchical control scheme is presented for the primary and secondary layers of an islanded AC microgrid (MG). The secondary control is designed on the tracker consensus method, in this way a distributed controller in collaboration with the finite time observer will set the referenced values of voltage magnitude and frequency by significantly speeding up the method of synchronous convergence. Furthermore, the primary control has been implemented for each DG separately and comprises Finite Control Set-Model Predictive control (FCS-MPC), and droops control. FCS-MPC regulates PCC reference voltages for each DG unit. Droop Control regulates power exchange within and among DG units. In the proposed hierarchical control approach, information sharing is accomplished by a neighbouring node creating a sparse communication network while fully eliminating the requirement for a centralized controller. The controller effectiveness have been validated against unbalanced load, transients, and load plug-and-play capabilities. The simulation result shows that MG operates effectively using FCS-MPC for parallel inverters of DG units, and the consensus algorithm based distributed secondary controller in MG effectively delivers the anticipated power, voltage, and frequency. © 2023 IEEE.

关键词： Model predictive control

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Optimal Finite- Time Prescribed Performance of Servo Pneumatic Positioning with PID Control Tuning using an Evolutionary Mating Algorithm 13

Optimal Finite- Time Prescribed Performance of Servo Pneumat...

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13th IEEE international conference on Control System, computing and Engineering, ICCSCE 2023

作者： Irawan, Addie Sulaiman, Mohd Herwan Azahar, Mohd Iskandar Putra Universiti Malaysia Pahang Faculty of Electrical & Electronics Engineering Technology Pahang Pekan26600 Malaysia

ISBN: (纸本)9798350323184

This paper presents an optimum tuning on finite-time prescribed performance with PID (FT-PPC-PID) controller using the Evolutionary Mating Algorithm (EMA) approach for a pneumatic servo system's (PSS) rod-piston positioning. The design objective is to optimize the convergence rate and finite time of the prescribed performance function in error transformation in parallel with PID controller's gains. The multi-step input trajectory on the PPVDC model plant was used for simulations with specific load and random noise as disturbances. The results demonstrate that the controller optimized with EMA outperforms the same controller optimized with other methods in achieving dynamic multi-step positioning of the rod-piston. This highlights the significant enhancement in overall performance of PPVDC positioning, including the stability of its internal system, through the EMA-optimized finite-time prescribed performance controller with PID. © 2023 IEEE.

关键词： Pneumatic actuators

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Transforming Agriculture: Navigating the Challenges and Embracing the Opportunities of Artificial Intelligence of Things

Transforming Agriculture: Navigating the Challenges and Embr...

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2023 IEEE international conference on Agrosystem Engineering, technology and Applications, AGRETA 2023

作者： Leong, Ying Mei Lim, Ean Heng Subri, Nor Fatiha Binti Jalil, Norazira Binti A Quest International University Faculty of Computing and Engineering Ipoh Malaysia Universiti Tunku Abdul Rahman Faculty of Information and Communication Technology Malaysia

ISBN: (纸本)9798350347333

Agriculture faces significant challenges, including climate change, limited resources, and population growth, necessitating innovative solutions to ensure sustainable productivity and food security. Artificial Intelligence of Things (AIoT), a convergence of Al and IoT technologies, offers transformative potential for agriculture by optimizing resource utilization, improving production management, and reducing labor dependency. This paper presents an overview of AIoT applications in agriculture, highlighting its benefits, opportunities, and challenges. AIoT's benefits include increased efficiency and productivity through automation, reduced labor dependency with AI-powered robots, improved decisionmaking by analyzing real-time data, and enhanced sustainability by optimizing resource use. Opportunities for AIoT in agriculture encompass precision farming, predictive analytics, autonomous farming, resource optimization, supply chain efficiency, and decision support systems. However, implementing AIoT in agriculture faces challenges such as data quality, connectivity, cost, privacy, and user adoption. Addressing these challenges is essential to harness AIoT's potential fully. Future research directions include developing advanced Al algorithms, exploring edge computing, promoting interoperability and standards, and investigating AIoT's role in climate resilience and resource management. Research should also focus on AIoT adoption and usability, social and ethical implications, and AIoT-based supply chain integration. AIoT offers the opportunity to revolutionize agriculture, address challenges and unlock opportunities for sustainable productivity and food security. By adopting AIoT technologies and following relevant research directions, the agricultural sector can overcome challenges and achieve a resilient and food-secure future. © 2023 IEEE.

关键词： Artificial intelligence

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