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Enhancing Diagnostic Accuracy in Plasma applications: Overcoming Data Imbalance and Uncertainty with Advanced Machine Learning Techniques

Enhancing Diagnostic Accuracy in Plasma Applications: Overco...

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IEEE International conference on Plasma Science (ICOPS)

作者： N.S. Ali H.Z. Huang B. Ali M.U. Bashir S.A. Shah Center for System Reliability and Safety University of Electronic Science and Technology of China Chengdu China School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Mechanical Engineering MUET SZAB campus Khairpur Mir’s Sindh Pakistan Khairpur Mir’s Pakistan

ISBN: (数字)9798350360912

ISBN: (纸本)9798350360929

In plasma applications critical to industries like aerospace, automotive, material processing, advance manufacturing, and nuclear fusion, temperature sensor accuracy and reliability are essential for ensuring operational safety, efficiency, and equipment longevity. These sensors are vital in managing processes at extreme temperatures but face challenges due to data imbalance and uncertainty. Typically, extreme temperature events, anomalies are infrequent, leading to machine learning models that are undertrained for critical conditions, potentially missing failure signs. Additionally, sensor data uncertainty, from degradation, harsh environments, and response variability, can compromise data integrity for anomaly detection through diagnostic. This situation underscores the need for advanced methods to address challenges. Traditional diagnostic techniques fall short in complex plasma environments, often achieving suboptimal diagnostic accuracy. The initiation of Generative Adversarial Networks (GAN) for plasma temperature sensor signals with combination of Artificial Neural Networks (ANN) offers a breakthrough, with these models learning from complex data patterns and generating synthetic data to represent rare events better. This approach has significantly improved diagnostic accuracy in plasma applications, reaching up to 96.12%, a marked improvement over traditional methods. The integration of proposed diagnostic frameworks sets new standards for safety, efficiency, and reliability in plasma application engineering. It highlights the importance of developing machine learning algorithms that can manage data imbalance and incorporate uncertainty measures, ensuring models are accurate, reliable, and capable of identifying critical events. This advancement not only enhances plasma application reliability but also contributes to high-temperature operational management and sensor technology development, paving the way for more resilient and intelligent sensing systems in

关键词： Temperature sensors Plasma applications Accuracy Uncertainty Machine learning Artificial neural networks Reliability engineering

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4th International conference on Futuristic Trends in Networks and Computing Technologies, FTNCT 2021

4th International Conference on Futuristic Trends in Network...

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4th International conference on Futuristic Trends in Networks and Computing Technologies, FTNCT 2021

ISBN: (纸本)9789811950360

The proceedings contain 80 papers. The special focus in this conference is on Futuristic Trends in Networks and Computing Technologies. The topics include: Design of Mobile Application for Farmers;secured Fog-Based System for Smart Healthcare Application;Meta-Heuristic Optimal Path Planning in Blockchain-Aided UAV Swarm Network;internet of Behavior in Cybersecurity: Opportunities and Challenges;fusion of Federated Learning and 6G in Internet-of-Medical-Things: Architecture, Case Study and Emerging Directions;rail Track Monitoring System Using Quantum Key Distribution in IoT Scenario;Evaluating Tip Selection algorithms for IOTA Blockchain;LEAPS: Load and Emission Performance Characteristics for sensor-Driven Green Transport Systems;a Smart Home Automation System Based on Internet of Things (IoT) Using Arduino;generative Adversarial Network for Colorization of Mammograms;brain Depletion Recognition Through Iot sensors Empowered with Computational Intelligence;auction-Based Deadline and Priority-Enabled Resource Allocation in Fog–IoT Architecture;research of Basic Characteristics of Wireless sensor Networks for Energy Monitoring System;An Energy Efficient Routing for Emergency Rescue in IoT-Based WSN;an Energy-Efficient Data Sensing Technique Using Compressive Sensing for IoT-Based Systems;dual-Hop Direct Diffusion Routing Protocol for Energy-Efficient Wireless sensor Network;design and Development of Smart Waste Bin for Effective Waste Collection and Management;randomly Hiding Secret Data Using I-Blocks and E-Blocks for Image Steganography;on the Efficacy of Deep Image Denoising for Computer Vision applications;diabetic Retinopathy Classification Using Pixel-Level Lesion Segmentation;Coverage, Capacity and Cost Analysis of 4G-LTE and 5G Networks: A Case Study of Ahmedabad and Gandhinagar;password-Protected Lossless Visible Watermarking Technique for Digital Image;survey: Emotion Recognition from Text Using Different Approaches;cyber-Awareness for Dummies.

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Time Synchronization Accuracy Verification for Multi-sensor System

Time Synchronization Accuracy Verification for Multi-Sensor ...

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2021 International conference on Artificial Intelligence, Big Data and algorithms, CAIBDA 2021

作者： Yuan, Songyu Li, Yuehua Chen, Yong Wang, Chenghao Intelligent Robotics Research Center Zhejiang Lab Hangzhou China

ISBN: (纸本)9781665424905

High-precision time synchronization is essential for applications such as perception and localization in autonomous mobile robots. For multi-sensor systems, high-precision time synchronization can reduce the ghosting in perception, improve the association accuracy between prediction and measurement in localization, reduce the number of corner cases caused by data asynchronous in fusion and improve the robustness of autonomous intelligent system. However, the present researches are mainly focused on the design of time synchronization system or the calibration of time shift between different sensors. Few methods are focused on verifying the accuracy of time synchronization multi-sensor system. In this paper, a novel verification method is proposed to verify the accuracy of time synchronization in multi-sensor system. The design idea of the method is to convert and amplify the small time deviation of the synchronized multi-sensor system into other physical quantities, which can be easily measured for calculation. Two different time synchronized multi-sensor systems are evaluated by our methods in the experiment and the time synchronization accuracy levels of the two systems are verified, which also prove the feasibility of our methods. Furthermore, our verification methods are suitable for various multi-sensor application systems containing camera, LiDAR or IMU, including Visual-Inertial SLAM, LiDAR-Inertial SLAM and Multi-Mode mapping. © 2021 IEEE.

关键词： Synchronization

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Distributed Detection of Critical Nodes in Wireless sensor Networks Using Maximum Independent Set

Distributed Detection of Critical Nodes in Wireless Sensor N...

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IEEE conference on Wireless Communications and Networking

作者： Jieyu Gao Jie Li Qihui Wu Youbiao Wu Haikuo Xu Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Identifying key nodes in a network is crucial for practical applications, especially when it comes to accurately detecting cut vertices, which play a vital role in maintaining network stability. As network complexity increases, relying on a single method to identify key nodes often fails to provide a comprehensive assessment of node importance in real-world scenarios. To address this, this paper proposes a novel framework for cut vertex identification that evaluates node importance from three perspectives: weighted fusion centrality metrics, the spanning tree algorithm, and topology graph properties. Additionally, existing methods based on centrality metrics often struggle with low accuracy in identifying cut vertices. To overcome this limitation, we have devised a method, termed the MIS-CV algorithm, that enables the accurate identification of network cut vertices by solely evaluating the intersection of node neighborhoods within the maximum independent set. We validated our proposed algorithm through simulation analysis on the Barabasi-Albert scale-free network. The results demonstrate that the MIS-CV key nodes identification algorithm surpasses traditional search tree algorithms in terms of both accuracy and operational efficiency. This proves that the MIS-CV algorithm has higher recognition accuracy without increasing computational complexity.

关键词： Measurement Wireless sensor networks Technological innovation Accuracy Network topology Wireless networks Simulation Stability criteria Topology Indexes

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An Ensemble Learning Approach for Malicious Traffic Detection in Wireless sensor Networks

An Ensemble Learning Approach for Malicious Traffic Detectio...

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IEEE International conference on Cloud Computing in Emerging Markets (CCEM)

作者： Rakshith B H Santhosh Anand Kannan M Department of Computer Science School of Computing Amrita Vishwa Vidyapeetham (Mysuru Campus) Mysuru Karnataka

ISBN: (数字)9798350360059

ISBN: (纸本)9798350360066

Wireless sensor Networks (WSNs) are essential for many applications, but they are also vulnerable to security risks since they are open to malicious assaults. The Support Vector Machine (SVM) and Random Forest machine learning methods are used in this research to offer an enhanced algorithm for identifying malicious traffic in WSNs. The combination approach, also known as the integrated model, makes use of the advantages of both Random Forest and SVM to increase accuracy and resistance to different assault scenarios. Through its collective intelligence, the integrated algorithm achieves high detection rates and minimizes false positives. The system architecture incorporates this combined algorithm into the current WSN framework, guaranteeing seamless integration and potent detection capabilities. The combined technique achieves an excellent accuracy of 96.33%, which is superior to the separate SVM and Random Forest models' 94.83% and 91.00%, accuracy values respectively. This illustrates how well the combined algorithm can predict hazardous traffic. The suggested technique improves WSN security by offering a dependable way to identify and stop hazardous activity early on. Subsequent studies will evaluate the algorithm's performance in large-scale WSN deployments and look into other combination fusion techniques.

关键词： Support vector machines Wireless sensor networks Accuracy Machine learning algorithms Intrusion detection Prediction algorithms Feature extraction Classification algorithms Security Random forests

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Multi sensor Signal-based Multi-level Association strategy for Real-time Track Association and fusion

Multi Sensor Signal-based Multi-level Association strategy f...

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International conference on Big Data and Information Analytics (BigDIA)

作者： Jiaxin Guo Zhi Fang Zengwen Yu Hang Zhang Yifan Zheng Hong Zhang Beijing Institute of Computer Technology and Application Beijing China Laboratory for Big Data and Decision National University of Defense Technology Changsha China

The real-time track association and fusion task(the AF task) aims to integrate the tracks of maritime targets collected by sensors into a coherent track. In real-world scenarios, track data collected by a single sensor often lacks the required stability, falling short of meeting the demands of various applications. While adopting multi-sensor-based AF task methods is prevalent, existing approaches have overlooked critical aspects, including temporal consistency. This paper tackles crucial challenges in the multi-sensor AF tasks, focusing on problems such as asynchronous radar scanning and real-time matching across multiple sensors. Utilizing multi-level association and integrated learning algorithms, we enhance matching accuracy and efficiency while ensuring track continuity. Leveraging the dataset provided by the 2023 National Big Data and Computational Intelligence Challenge 1 , our approach significantly outperforms traditional methods in three key aspects: the AF task of radar point trace , the AF task of ESM bearing line, and the AF task of rader and ESM. In comparison with conventional methods, three values are reduced by 1.1325, 0.0870, and 0.2637 on the difference metrics between the generated tracks and the actual tracks, respectively. These results further validate the significant improvements of our method in enhancing the effectiveness of the AF task.

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Robust sensor fusion algorithms Against Voice Command Attacks in Autonomous Vehicles 20

Robust Sensor Fusion Algorithms Against Voice Command Attack...

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20th IEEE International conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom)

作者： Guan, Jiwei Zheng, Xi Wang, Chen Zhou, Yipeng Jolfaei, Alireza Macquarie Univ Dept Comp Sydney NSW Australia CSIRO Data61 Sydney NSW Australia

ISBN: (纸本)9781665416580

With recent advances in autonomous driving, voice control systems have become increasingly adopted as human-vehicle interaction methods. This technology enables drivers to use voice commands to control the vehicle and will be soon available in Advanced Driver Assistance Systems (ADAS). Prior work has shown that Siri, Alexa and Cortana, are highly vulnerable to inaudible command attacks. This could be extended to ADAS in real-world applications and such an inaudible command threat is difficult to detect due to microphone nonlinearities. In this paper, we aim to develop a more practical solution by using camera views to defend against inaudible command attacks where ADAS are capable of detecting their environment via multi-sensors. To this end, we propose a novel multimodal deep learning classification system to defend against inaudible command attacks. Our experimental results confirm the feasibility of the proposed defense methods and the best classification accuracy reaches 89.2%. Code is available at https://***/ITSEG-MQ/sensor-fusion-Against-VoiceCommand-Attacks.

关键词： Advanced driver assistance systems

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A Novel Localization Method for Indoor Mobile Robot Based on Adaptive Weighted fusion

A Novel Localization Method for Indoor Mobile Robot Based on...

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第35届中国控制与决策会议

作者： Jincheng Sun Yang Luo Yunxiang Jiang Dan Ye College of Information Science and Engineering Northeastern University State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences

Localization is a critical issue in autonomous robot navigation and path *** this paper,a novel localization method based on adaptive weighted fusion(AWF)is *** method incorporates visual recognition of artificial landmarks and employs different adaptive fusion algorithms based on the pose and position of the mobile ***,the method consists of three ***,a discrete motion model of the mobile robot is ***,the NDT-based laser rangefinder localization method is employed and visual odometry is replaced with artificial landmark recognition,with an analysis of the error model of the IMU and ***,Adaptive Weighted fusion(AWF) algorithm is utilized to fuse the sensor information mentioned above and correct the position and direction of the mobile *** results demonstrate that the proposed AWF localization method can significantly improve the localization accuracy of the mobile *** research direction is promising and is expected to have broader applications in practical settings.

关键词： Mobile robot adaptive weighted fusion multisensors fusion localization

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Foundations of Smart Healthcare: Emerging IoT Technologies in Wireless sensor Networks

Foundations of Smart Healthcare: Emerging IoT Technologies i...

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2025 International conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Nisha, B. Mohamed, Hassan Devadasu, G. Punitha, M. Poomani Alias Sandeep, Kandula Karthikeyan, T. Saveetha Medical College and Hospital Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Cmr College of Engineering & Technology Department of Eee Telangana Hydrabad India Sethu Institute of Technology Department of Information Technology Pulloor Tamil Nadu Kariapatti626115 India Gokaraju Rangaraju Institute of Engineering and Technology Department of Information Technology Telangana Hyderabad India Sona College of Technology Biomedical Engineering Tamilnadu Salem India

ISBN: (纸本)9798331527495

The study examines the relationship between new technologies and healthcare, with a particular emphasis on how WSNs, or wireless sensor networks, and the World of Things (IoT) work together to create smart healthcare systems. The introduction emphasises the need for creative healthcare solutions by highlighting the growth in life expectancy throughout the world and the resulting issues created by an ageing society. The literature study explores WSN-based communication technologies for healthcare, including standards and technology for both Low Power Wide-Area Networks (LP-WAN) and Low Rate Wireless Small Area Networks. Security and privacy issues in smart healthcare are also covered. The section on the suggested methodology describes the latest advancements and applications in smart healthcare, with a focus on application domains, system architectures, and the function of crowd and crowdsensing in these applications. There is discussion of the technical obstacles in intelligent health care design as well as the issues in crowdsourcing and crowdsensing. The discussion part outlines the essential elements of a successful smart healthcare system, such as energy-efficient equipment, secure communication channels, bandwidth, and Quality of Services (QoS) support. The conclusion emphasises how crucial it is to embrace technological innovations in order to reorganise healthcare systems while also recognising the need of resolving current issues, especially with regard to communication standards that deal with integrity, servicing, dependability, silence, and security. In order to improve early sickness identification and individualised treatment, the study promotes a preventative-focused healthcare approach that makes use of IoT technology, complex algorithms, and artificial intelligence © 2025 IEEE.

关键词： Patient treatment

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Weighted fusion of Multiple Classifiers for Human Activity Recognition

Weighted Fusion of Multiple Classifiers for Human Activity R...

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International conference on Information fusion

作者： Kezhu Zuo Xinde Li Jean Dezert Yilin Dong School of Cyber Science and Engineering Southeast University Nanjing China Key Laboratory of Measurement and Control of CSE School of Automation Southeast University Nanjing China The French Aerospace Lab ONERA DTIS/MIDL Palaiseau France College of Computer Science and Engineering Shanghai Maritime University Shanghai China

Human Activity Recognition (HAR) based on wear-able device has become a hot topic of research due to its wide range of applications in health-care, fitness and smart homes. However, the classification of some activities with similar sensor readings, such as standing and sitting, is usually more challenging for the design of efficient activity recognition algorithms. Considering the inconsistent performance of different classifiers, which can provide information complementary for individual classifier, we propose a novel multi-classifier fusion method based on belief functions (BFs) theory for HAR. Specifically, at first, four classifiers are trained using time-domain and frequency-domain features to obtain basic belief assignments (BBA) of activity, respectively. Then, three assessment criteria are utilized to evaluate the reliability of the classifiers and a scoring matrix is constructed. Next, the algorithm of Belief Function based the Technique for Order Preference by Similarity to Ideal Solution (BF-TOPSIS) is employed to calculate the weighting coefficients for each classifier. Finally, the discounting and Dempster’s rules are adopted to combine the multiple classifiers and further decision making. Several experiments were conducted to illustrate the performance of the proposed method using the UCI smartphone dataset, and the results show that the proposed method is more accurate than the state-of-art methods.

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