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2023 International Conference on Applied Physics and Computing, ICAPC 2023

作者： Yu, Jianxiang Maalla, Maher Maalla, Allam School of Engineering Guangzhou College of Technology and Business Guangdong Guangzhou510850 China School of Computer Technologies and Control Itmo University Saint Petersburg197101 Russia

ISBN: (纸本)9798350359244

The escalating challenges faced by the agricultural industry, characterized by labor shortages, and rising costs, underscore the imperative for innovative solutions. This study explores the transformative potential of Intelligent Harvesting Systems in revolutionizing fruit harvesting, with a focus on assessing feasibility and understanding implications within the fruit industry. The integration of cutting-edge technologies, specifically 5G communication and artificial intelligence (AI), plays a pivotal role in the design and evaluation of a control module for an agricultural picking robot. The feasibility assessment encompasses an in-depth exploration of the technological, economic, operational, and environmental. Experimental evaluations conducted in controlled environments and real-world settings showcase the system's high picking speed, accuracy, and adaptability to diverse produce sizes. The outcomes of this research not only align with the initial objectives of enhancing harvesting efficiency and reducing labor costs but also hold promise for a sustainable and resource-efficient agricultural future. The positive synergy between 5G communication and AI technology emerges as a driving force behind the system's exceptional performance. This study not only contributes to the immediate advancements in intelligent harvesting but also lays a robust foundation for continued innovation, setting the stage for further exploration and refinement in this dynamic field. © 2023 IEEE.

关键词： 5G mobile communication systems

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Fast Finite-Time Adaptive Neural control for Pure-Feedback Stochastic Nonlinear Systems

Fast Finite-Time Adaptive Neural Control for Pure-Feedback S...

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2023 IEEE International Conference on Memristive Computing and Applications, ICMCA 2023

作者： Zhao, Ximei Zhang, Hanyu Shao, Lijun Huang, Lili Li, Jiao School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Jinan China Qilu Institute of Technology Jinan China College of Computer and Communication Engineering Qilu Institute of Technology Jinan China

ISBN: (纸本)9798350316230

In this article, we solve the fast finite-time stabilization as well as adaptive neural control design issues for a class uncertain stochastic nonlinear systems. By employing the mean value theorem, the pure-feedback form can be transformed into strict-feedback case. Firstly, a new fast finite-time control scheme is proposed in virtue of generalizing the fast finite-time adaptive control strategy for deterministic systems to the stochastic case. Next, a novel adaptive neural control strategy is developed to simultaneously deal with the stochastic nonlinear systems with completely unknown nonlinearities as well as the disturbances term. Then, stability analysis have been given based on a Jensen's inequality. © 2023 IEEE.

关键词： Feedback

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Insulator Defect Detection Based on YOLOv4-tiny with Improved Feature Fusion 12

Insulator Defect Detection Based on YOLOv4-tiny with Improve...

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12th International Conference on Information Technology in Medicine and Education, ITME 2022

作者： Zhong, Yuning Hu, Rong Li, Zuoyong Cai, Yuanzheng Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Fuzhou350121 China

ISBN: (纸本)9798350310153

Insulator exposed to a wild environment for long-term running usually suffers from the damage of natural calamities that makes them become one of the most fragile components in the whole power system. In this case, even a slight fault will seriously affect the power supply safety of the power grid. Therefore, it is critical to find and identify the faulty insulator efficiently and accurately. However, an insulator is quite a small component in comparison with a huge power system which makes it hard to visible with naked eyes. This characteristic leads to poor recognition accuracy for existing detection methods. In order to solve the above problems, we propose an insulator defect detection algorithm based on an improved YOLOv4-tiny model. It differs from existing insulator detection models, our algorithm applies the effective channel attention network to the feature extraction network of YOLOv4-tiny, which significantly enhances the feature extraction ability of the backbone network. These two-way features of the pyramid can be fully fused in the two-way feature fusion stages seamlessly. Experimental results show that compared with, our proposed algorithm outperforms Faster RCNN and YOLOv3 in average classification accuracy, missed detection, and false detection. © 2022 IEEE.

关键词： Feature extraction

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Joint Power Allocation and Task Scheduling for Data Offloading in Non-Geostationary Orbit Satellite Networks

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2882-2896页

作者： He, Lijun Jia, Ziye Wang, Juncheng Lansard, Erick Han, Zhu Yuen, Chau China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Xidian University State Key Laboratory of Integrated Services Networks Shaanxi Xi'an710071 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China Southeast University National Mobile Communications Research Laboratory Nanjing211111 China Hong Kong Baptist University Department of Computer Science China Nanyang Technological University School of Electrical and Electronics Engineering 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

In Non-Geostationary Orbit Satellite Networks (NGOSNs) with a large number of battery-carrying satellites, proper power allocation and task scheduling are crucial to improving data offloading efficiency. In this work, we jointly optimize power allocation and task scheduling to achieve energy-efficient data offloading in NGOSNs. Our goal is to properly balance the minimization of the total energy consumption and the maximization of the sum weights of tasks. Due to the tight coupling between power allocation and task scheduling, we first derive the optimal power allocation solution to the joint optimization problem with any given task scheduling policy. We then leverage the conflict graph model to transform the joint optimization problem into an Integer Linear Programming (ILP) problem with any given power allocation strategy. We explore the unique structure of the ILP problem to derive an efficient semidefinite relaxation-based solution. Finally, we utilize the genetic framework to combine the above special solutions as a two-layer solution for the original joint optimization problem. Simulation results demonstrate that our proposed solution can properly balance the reduction of total energy consumption and the improvement of the sum weights of tasks, thus achieving superior system performance over the current literature. © 2004-2012 IEEE.

关键词： Geostationary satellites

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Image Recognition In Complex Environments Based On Improved FCM Algorithm 43

Image Recognition In Complex Environments Based On Improved ...

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43rd Chinese control Conference, CCC 2024

作者： Pengxin, Zhao Qing, Lv Qiang, Li Jin, Wang Hebei Normal University College of Computer and Cyberspace Security Shijiazhuang050010 China College of Engineering of Hebei Normal University Hebei Normal University Shijiazhuang050010 China Hebei Provincial Key Laboratory of Information Fusion and Intelligent Control China

ISBN: (纸本)9789887581581

In recent years, image recognition has been commonly utilized in the field of computer vision, while the challenge of image recognition in complex environments has become a topical study. The fuzzy C-mean (FCM) is a clustering algorithm based on fuzzy set theory and achieves clustering by assigning affiliation to each data point. However, in complex environments, the FCM algorithm may fall into local optimal solutions and lead to a decrease in recognition accuracy. To solve this problem, this paper proposes an improved approach based on a genetic algorithm and simulated annealing algorithm applied in the FCM algorithm. The genetic algorithm searches and optimizes the clustering centers by simulating the biological evolution process to find more optimal parameters for the experiment. The simulated annealing algorithm performs global optimization. It generates new solutions by adding a small perturbation to avoid the clustering centers from falling into local optima. Finally, a series of image recognition experiments are conducted in complex environments. The experiment results show that the proposed approach has higher recognition accuracy and robustness in complex environments compared with the traditional FCM algorithm. The algorithm can improve the accuracy of image recognition and has good adaptability in complex environments. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Genetic algorithms

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An Entropy-Integrated Adaptive Coding and Scheduling Framework for Optimized Data Transmission in Fog-Cloud IoT Architectures

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IEEE Internet of Things Journal 2025年

作者： Lu, Yang Zang, Yuting Bian, Ziyi Zhao, Shuai Zheng, Yan Northeast Forestry University College of Computer and Control Engineering Harbin China Harbin Normal University College of Computer and Information Engineering Harbin China La Trobe University School of Computing Engineering and Mathematical Sciences MelbourneVIC Australia

Entropy-driven network coding has been used as a basis for optimizing data transmission and enhancing resource utilization in Fog-Cloud IoT architectures, including applications in smart cities, industrial automation, environmental monitoring, and healthcare. In Fog-Cloud IoT architectures, conventional data transmission protocols are inefficient because they cannot adapt to dynamic entropy levels, resulting in underutilized bandwidth, increased latency, and higher energy consumption. In this paper, we propose an Enhanced Entropy-Driven Network Coding (E-EDNC) framework to address these problems. Our framework integrates real-time entropy estimation with adaptive coding strategies and employs a hybrid evolutionary-reinforcement learning (HE-RL) algorithm to dynamically optimize coding parameters and scheduling decisions. Experimental results demonstrate that E-EDNC improves bandwidth utilization by 25%, reduces latency by 25%, and decreases energy consumption by 12%, thereby enhancing overall data transmission efficiency and reliability in Fog-Cloud IoT environments. © 2014 IEEE.

关键词： Network coding

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Data Poisoning: Issues, Challenges, and Needs 7

Data Poisoning: Issues, Challenges, and Needs

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7th IET Smart Cities Symposium, SCS 2023

作者： Aljanabi, Mohammad Omran, Alaa Hamza Mijwil, Maad M. Abotaleb, Mostafa El-Kenawy, El-Sayed M. Mohammed, Sahar Yousif Ibrahim, Abdelhameed Department of computer College of Education Aliarqai university Baghdad Iraq University of information technology and communications Baghdad Iraq Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt Computer Science Department Computer Science &Information Technology College Anbar University Anbar Iraq Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9781839539831

Data poisoning attacks, where adversaries manipulate training data to degrade model performance, are an emerging threat as machine learning becomes widely deployed in sensitive applications. This paper provides a comprehensive overview of data poisoning including attack techniques, adversary incentives, impacts on security and reliability, detection methods, defenses, and key research gaps. We examine label flipping, instance injection, backdoors, and other attack categories that enable malicious outcomes ranging from IP theft to accidents in autonomous systems. Promising detection approaches include statistical tests, robust learning, and forensics. However, significant challenges remain in translating academic defenses like adversarial training and sanitization into practical tools ready for operational use. With safety and trustworthiness at stake, more research on benchmarking evaluations, adaptive attacks, fundamental tradeoffs, and real-world deployment of defenses is urgently needed. Understanding vulnerabilities and developing resilient machine learning pipelines will only grow in importance as data integrity is fundamental to developing safe artificial intelligence. © The Institution of engineering & Technology 2023.

关键词： Machine learning

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Empirical Analysis of Vulnerabilities Life Cycle in Golang Ecosystem 24

Empirical Analysis of Vulnerabilities Life Cycle in Golang E...

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44th ACM/IEEE International Conference on Software engineering, ICSE 2024

作者： Hu, Jinchang Zhang, Lyuye Liu, Chengwei Yang, Sen Huang, Song Liu, Yang College of Command and Control Engineering Army Engineering University of Pla Nanjing China Continental-NTU Corporate Lab Nanyang Technological University Singapore Singapore School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Academy of Military Science Nanjing China

ISBN: (纸本)9798400702174

Open-source software (OSS) greatly facilitates program development for developers. However, the high number of vulnerabilities in open-source software is a major concern, including in Golang, a relatively new programming language. In contrast to other commonly used OSS package managers, Golang presents a distinctive feature whereby commits are prevalently used as dependency versions prior to their integration into official releases. This attribute can prove advantageous to users, as patch commits can be implemented in a timely manner before the releases. However, Golang employs a decentralized mechanism for managing dependencies, whereby dependencies are upheld and distributed in separate repositories. This approach can result in delays in the dissemination of patches and unresolved vulnerabilities. To tackle the aforementioned concern, a comprehensive investi-gation was undertaken to examine the life cycle of vulnerability in Golang, commencing from its introduction and culminating with its rectification. To this end, a framework was established by gathering data from diverse sources and systematically amalgamating them with an algorithm to compute the lags in vulnerability patching. It turned out that 66.10% of modules in the Golang ecosystem were affected by vulnerabilities. Within the vulnerability life cycle, we found two kinds of lag impeding the propagation of vulnerability fixing. By analyzing reasons behind non-lagged and lagged vul-nerabilities, timely releasing and indexing patch versions could significantly enhance ecosystem security. © 2024 ACM.

关键词： Open source software

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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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Enhancing 6G-Satellite Network Integration with Artificial Intelligence: A Future Communication Paradigm 22

Enhancing 6G-Satellite Network Integration with Artificial I...

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22nd International Learning and Technology Conference, L and T 2025

作者： Salah, Ibrahim Hussein, Aziza I. Wageh Lotfy, Mostafa Mabrook, Mohamed Mourad Almaaqal University College of Engineering Control and Computer Department Basrah61001 Iraq Effat University College of Engineering Energy and Technology Research Center Saudi Arabia Faculty of Technology and Education Beni-Suef University Dept. of Process Control Technology Beni-Suef Egypt Beni-Suef University Faculty of Navigation Science & Space Technology Space Communication Dept. Beni-Suef Egypt

ISBN: (纸本)9798331532215

The integration of 6G networks and satellite communications is set to revolutionize global connectivity, offering seamless coverage across terrestrial and non-terrestrial environments. Artificial Intelligence (AI) is essential for improving this integration, addressing challenges such as dynamic resource management, latency reduction, and network optimization. AI techniques like machine learning, deep learning, and reinforcement learning offer innovative solutions to handle the complexities of 6G-satellite networks. These advancements promise to significantly improve network efficiency, enhance data transmission reliability, and ensure seamless connectivity across different areas. Potential use cases include smart cities, autonomous vehicles, and Internet of Things (IoT) applications, where AI-driven 6G-satellite integration will be crucial. The proposed AI-enhanced 6G-satellite framework not only addresses current challenges but also lays the groundwork for a resilient, scalable, and globally interconnected communication infrastructure, offering a promising future. © 2025 IEEE.

关键词： Satellite communication systems

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