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The Application of intelligent computing and Machine Learning in OPGW Temperature Monitoring and Early Warning System

The Application of Intelligent Computing and Machine Learnin...

引用

2023 international conference on Integrated Intelligence and Communication systems, ICIICS 2023

作者： Zhang, Yongting Qiu, Guihong Feng, Yitong Jiang, Yuhuan Naval Aeronautical University Zhongshan Power Supply Bureau of Guangdong Provincial Power Grid Co. Ltd Guangdong Province Zhongshan City China

ISBN: (纸本)9798350315455

The construction scale of transmission lines continues to expand, and the safety, stability and reliability of their operation have become particularly important. This article proposes a temperature monitoring and early warning system based on Internet of Things technology to address the temperature problem in OPGW (Optical Power Grounded Wave) lines. This article uses intelligent computing and machine learning technology to monitor and provide early warning of OPGW temperature data, and applies Savitzky-Golay filtering, Bayesian and other algorithms to process and analyze the temperature data, thereby achieving temperature monitoring and early warning. In the experiment, different solutions were used to test the OPGW temperature data. Experimental data shows that as the temperature increases, its Brillouin frequency value changes in proportion to the difference between 0.01 and 0.02, respectively, and then increases. The system uses advanced temperature sensors, wireless communication technology and big data analysis methods to achieve real-time temperature monitoring and early warning of the OPGW line. © 2023 ieee.

关键词： early warning system intelligent computing machine learning temperature monitoring

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Humanoid Control Technology for Lower Limb Rehabilitation Robots Based on Human Gait data

Humanoid Control Technology for Lower Limb Rehabilitation Ro...

引用

2024 international conference on advanced Mechatronic systems, ICAMechS 2024

作者： Gao, Shengda Wang, Aihui Duan, Huichao Yue, Xuebing Li, Hengyi Dong, Jinkang Zhongyuan University of Technology School of Automation and Electrical Engineering Zhengzhou450007 China

ISBN: (纸本)9798350366204

The lower limb rehabilitation robot is a wearable exoskeleton bionic device that integrates robotic features with human walking characteristics. This paper explores the control strategies, gait data acquisition and processing methods, as well as the design and experimental validation of a humanoid control system for lower limb rehabilitation robots. By collecting gait data from healthy individuals using the NOKOV 3D infrared passive optical motion capture system, a gait dataset was established, and methods such as spline interpolation and Gaussian regression were employed to integrate the gait data. A humanoid control system based on real gait data was designed, utilizing a high-performance computer, R4SE controller, and joint motors to simulate gait patterns. Experimental results demonstrate that the system effectively follows target gait trajectories, achieving high trajectory tracking accuracy and smoothness, ensuring the safety and effectiveness of rehabilitation training. Future research will incorporate a more diverse group of subjects and intelligent control algorithms to enhance the system's adaptability and intelligence. © 2024 ieee.

关键词： Exoskeleton (Robotics)

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A Review on Automatic Energy Consumption Detection, Billing and Monitoring Device 9

A Review on Automatic Energy Consumption Detection, Billing ...

引用

9th international conference on advanced computing and Communication systems, ICACCS 2023

作者： Maule, Linda Cecilia Tracy Premkumar, R. Sai Balaji, Avvaru Nohit Venkata Seetha Thilak, K. Raj Devi, B. Gayathri Franklin, N. Sri Eshwar College of Engineering Department of Electrical and Electronics Engineering Coimbatore India Goldwin Industries Coimbatore India

ISBN: (纸本)9798350397376

In contemporary production and distribution systems, intelligent energy meters have become a dire necessity to make use of the available renewable energies with availability. This creates future scope in the sectors of developments and improvements for smart cities. A lot of new intelligent energy meters are being installed in homes of people for many reasons mainly that it can furnish numerous enlightening recordings of exhaustion alongside minute clarification of billing. Automated Readings, data Detersion, Theft Detection and early warnings to make efficient use of energy being consumed. To make full use of the benefits of the intelligence system, major technical issues need to be addressed. Numerous big data analytical machine engines are needed to address areas such as outage control, asset control and fault detection chiefly incase systems that do not identify gas consumption, consumer segmentations and so on. data Analytic point of view transform large volumes of data into information for customer's, utility, and various authorities. With the availability of numerous analytical algorithms, we will be able to access larger volumes of information but many systems are not able to complete this task sufficiently and efficiently. The advancements and of this intelligent energy meter are also discussed below. Researchers, utility authorities as well as stake holders may make use of the available information. © 2023 ieee.

关键词： intelligent systems

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Post-Quantum Cryptography Security with CSPM for Secure data Transmission in Cloud Environments 4

Post-Quantum Cryptography Security with CSPM for Secure Data...

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4th international conference on Ubiquitous computing and intelligent Information systems, ICUIS 2024

作者： Rajkumar, N. Kumar, K. Kishore Gokul, M. Durai, S. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Cse Department Chennai India

ISBN: (纸本)9798331529635

Quantum computing poses a significant threat to traditional cryptographic systems, necessitating the exploration of robust alternatives to secure data transmission in the cloud. This abstract delves into the integration of Post-Quantum Cryptography (PQC) and Cloud Security Posture Management (CSPM) as a comprehensive solution. The proposed framework advocates for the adoption of a variant of the McEliece cryptosystem within the cloud infrastructure, providing resistance against potential quantum attacks. Encryption, fortified by Post-Quantum Cryptography, emerges as the dominant method for ensuring the security of cloud-stored data. In parallel, Cloud Security Posture Management (CSPM) is introduced as a set of security protocols designed to identify and remediate cloud misconfigurations, enhancing the overall security posture. This research highlights the synergy between PQC and CSPM, showcasing their collective effectiveness in mitigating emerging threats in cloud computing. The integration of Quantum Key Distribution (QKD) further enhances data security, ensuring the confidentiality and integrity of transmitted information. The proposed model not only addresses current data breach challenges but also anticipates the evolving landscape of quantum computing, providing a forward-looking solution for secure data transmission in cloud environments. © 2024 ieee.

关键词： Cloud security

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PentraFormer: Learning Agents for Automated Penetration Testing via Sequence Modeling 17

PentraFormer: Learning Agents for Automated Penetration Test...

引用

ieee Congress on Cybermatics: 17th ieee international conference on Internet of Things, iThings 2024, 20th ieee international conference on Green computing and Communications, GreenCom 2024, 17th ieee international conference on Cyber, Physical and Social computing, CPSCom 2024, 10th ieee international conference on Smart data, Smartdata 2024

作者： Wang, Yunfei Liu, Shixuan Wang, Wenhao Zhu, Cheng Fan, Changjun Huang, Kuihua Chen, Chao National University of Defense Technology National Key Laboratory of Information Systems Engineering Changsha China National University of Defense Technology Laboratory for Big Data and Decision Changsha China National University of Defense Technology Changsha China

ISBN: (纸本)9798350351637

The exponential growth of computer networks has intensified the requirement for robust security measures, rendering penetration testing - an essential practice that assesses vulnerabilities by simulating attacks - critically important. Automated penetration testing (APT) has evolved from rule-based approaches to intelligent decision-making, such as reinforcement learning (RL), better emulating the adaptability and decision-making process of human penetration testers. However, using RL for APT is challenged by the high cost of real-network interactions, the scarcity of security datasets, and the unique complexities of APT scenarios, which include long decision sequences and delayed rewards, complicating RL efficacy and convergence. Inspired by the Decision Transformer's proficiency in predicting action sequences for offline RL, coupled with its effectiveness in dealing with data efficiency and adaptability, we introduce a novel sequence-based methodology for designing APT agents. This approach is tailored to effectively manage the intricate aspects of data-scarce and complex APT scenarios. Our proposed model, PentraFormer, adeptly addresses these challenges prevalent in APT scenarios. The robustness of our model is attested to through extensive empirical evaluations conducted on APT-dedicated tasks. © 2024 ieee.

关键词： Adversarial machine learning

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Energy Harvesting Design for Cooperative Reconfigurable intelligent Surface with Multi-Agent Deep Reinforcement Learning 10

Energy Harvesting Design for Cooperative Reconfigurable Inte...

引用

10TH international conference ON intelligent data AND SECURITY (IDS)

作者： Tao, Yihang Wu, Jun Pan, Qianqian Chen, Xiuzhen Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China Waseda Univ Grad Sch Informat Prod & Syst Fukuoka Japan Univ Tokyo Sch Engn Dept Syst Innovat Tokyo Japan

ISBN: (纸本)9798350389487;9798350389494

Cooperative reconfigurable intelligent surface (Cooperative-RIS)-assisted communication systems have been proven to provide more smart computing and communication ability than single-RIS or multi-distributed-RIS systems regarding beamforming gain and channel diversity. However, the increased number of deployed RISs brings heavier energy consumption, which makes the cooperative-RIS system less sustainable. The majority of previous works mainly study energy harvesting (EH) for single-RIS systems which cannot be directly applied to the cooperative-RIS systems. In this paper, we study the EH for cooperative-RIS-assisted communication systems. Specifically, we first model the cooperative beamforming and EH optimization as a non-convex problem. Besides, we propose a multi-agent deep deterministic policy gradient (MADDPG)-based EH efficiency maximization scheme for cooperative RISs while satisfying an acceptable sum rate. Finally, experimental results show the effectiveness of our proposed scheme.

关键词： smart communication reconfigurable intelligent surface energy harvesting cooperative beamforming multi-agent deep reinforcement learning

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Blockchain and DL-based Brain Tumour Prediction Scheme for Telesurgery systems in Healthcare 4.0 20

Blockchain and DL-based Brain Tumour Prediction Scheme for T...

引用

20th ieee international Wireless Communications and Mobile computing (ieee IWCMC) - Green and intelligent Communications

作者： Patel, Yogi Gupta, Rajesh Kakkar, Riya Tanwar, Sudeep Guizani, Mohsen Nirma Univ Inst Technol Dept Comp Sci & Engn Ahmadabad Gujarat India Mohamed Bin Zayed Univ Artificial Intelligence MB Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350361261;9798350361278

With the changing lifestyle of people, they are getting more prone to various diseases, some of which might prove to be dangerous and can cause serious health problems. The chances of getting brain tumors are increasing, with new alarming cases being found every day, and the detection and classification for further surgery are very crucial and should be done without due. Therefore, we propose here a Deep Learning (DL) CNN-based brain tumor classification framework that helps in analyzing the image and classifying it in its respective type. In addition, we integrate the blockchain technology to securely store the data, make it easily accessible at the time of need, and prevent it from malware practices. We have used the IPFS protocol in order to make our framework cost-efficient. The results of various DL models are compared and a CNN-based framework is proposed. The CNN-based model demonstrated an exceptional performance by achieving an accuracy of 97.91%.

关键词： Brain Tumour Deep Learning Telesurgery Blockchain IPFS CNN

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Study on Multi-Viral Infection on Lungs using data and Predictive Analysis Techniques 7

Study on Multi-Viral Infection on Lungs using Data and Predi...

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7th international conference on intelligent computing and Control systems, ICICCS 2023

作者： Varalakshmi, Sudha Vijayalakshmi, P. Rajendran, V. Vels Institute of Science Technology and Advanced Studies Department of Electronics and Communication Engineering Tamil Nadu Chennai600117 India

ISBN: (纸本)9798350397253

This research study has used deep learning techniques (DL) to classify spectrograms of acoustic signals. In addition, this study intends to differentiate spectrograms that contain a cough and those that do not contain a cough, and once it is known that a spectrogram contains a cough, it is possible to understand the underlying disease. The main goal is to obtain a system with better performance than those proposed so far in the literature in the field of cough detection and make a first approximation to the classification of diseases based on audio clips with cough. © 2023 ieee.

关键词： Convolutional neural networks

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Feature-based Map Merging with Mesh Networks in Swarm Robot systems

Feature-based Map Merging with Mesh Networks in Swarm Robot ...

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ieee international conference on Big data and Smart computing (BigComp)

作者： Jang, Insik Park, JuHyeon Lee, Heoncheol Kumoh Natl Inst Technol Gumi South Korea

ISBN: (纸本)9781665475785

In unknown environments, it is highly important to acquire efficiently the map of the unknown environments, which can be realized with swarm robot systems. However, swarm robots suffer from the lack of sensor data due to inevitably limited resources. Then, the individual maps of swarm robots degenerate as time goes, and thus the collective map of merging the individual maps may be inaccurate. This paper presents a feature-based map merging system with mesh networks to acquire individual maps efficiently and merge them accurately. The proposed system was tested with two small robots equipped with relatively low-cost sensors in unknown indoor environments. The experimental results showed that the proposed system acquired successfully individual maps and merged them accurately.

关键词： Small robot system SLAM Map merge Feature extraction Swarm robot Mesh network

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Pressure, Queue, and Average Speed - Based Multi-Agent DQN for Optimizing Traffic Signal Control

Pressure, Queue, and Average Speed - Based Multi-Agent DQN f...

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2024 international conference on Advances in data Engineering and intelligent computing systems, ADICS 2024

作者： Angela Jennifa Sujana, J. Namasivaayam, L. Mepco Schlenk Engineering College Department Of Artificial Intelligence And Data Science Tamil Nadu Sivakasi India

ISBN: (纸本)9798350364828

Traffic congestion poses a significant challenge in urban areas, and deep reinforcement learning (DRL) offers an encouraging method for traffic signal control. We evaluate the DQN algorithm, with PQAS (Pressure, Queue, Average Speed) using the SUMO traffic simulator. Our results demonstrate that DQN improves traffic flow and reduces congestion compared to traditional methods. Our findings highlight the potential of DRL in developing more effective traffic signal control systems, with DQN, combined with PQAS. PQAS represents three reward functions pressure-based, queue-based, and average speed-based reward functions. emerging as a promising candidate indicating that optimizing for average speed and minimizing queue lengths are key factors in achieving efficient traffic signal control. © 2024 ieee.

关键词： Reinforcement learning

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