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International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM)

作者： Qi Feng Zhenhua Shao College of Electrical and Electronic Engineering Wenzhou University Wenzhou China College of Computer and Control Engineering Minjiang University Fuzhou China

ISBN: (纸本)9781665417334

Partial discharge (PD) is the initial manifestation of insulation deterioration in power equipment. When PD accumulates to a certain extent, it will lead to equipment damage. PD detection in advance is an effective method to prevent insulation deterioration. In this paper, AE method, TEV method and UHF method are used to study the internal discharge, suspension discharge and surface discharge respectively, pointing out the limitations of a single detection method, and using clustering method to extract the discharge characteristic parameters of different defect types to classify. The results of K-means clustering show that the accuracy of internal discharge is 83%, surface discharge is 65%, and suspension discharge is 66%. FCM clustering results show that the accuracy of internal discharge is 63%, surface discharge is 60%, and suspension discharge is 50%. The results of hierarchical clustering show that the accuracy of internal discharge is 73%, surface discharge is 70%, and suspension discharge is 67%. From the perspective of sensitivity, TEV method has the highest sensitivity, AE method and UHF method have their advantages in the sensitivity of different discharge defects. In order to overcome the limitation of a single detection method, combined with the experimental results and the actual noise interference and local location requirements, the application of multi-means combined detection is analyzed to provide some reference for the selection of joint detection method. UHF/AE method can avoid electromagnetic interference. UHF/TEV method can avoid acoustic interference; TEV/AE method can improve the localization accuracy.

关键词： Partial discharges Location awareness Insulation Surface discharges Sensitivity analysis Clustering methods Electromagnetic interference

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SIAVC: Semi-Supervised Framework for Industrial Accident Video Classification

arXiv

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arXiv 2024年

作者： Li, Zuoyong Lin, Qinghua Fan, Haoyi Zhao, Tiesong Zhang, David The Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China The School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China The School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou45000 China The Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou350108 China Shenzhen518172 China

Semi-supervised learning suffers from the imbalance of labeled and unlabeled training data in the video surveillance scenario. In this paper, we propose a new semi-supervised learning method called SIAVC for industrial accident video classification. Specifically, we design a video augmentation module called the Super Augmentation Block (SAB). SAB adds Gaussian noise and randomly masks video frames according to historical loss on the unlabeled data for model optimization. Then, we propose a Video Cross-set Augmentation Module (VCAM) to generate diverse pseudo-label samples from the high-confidence unlabeled samples, which alleviates the mismatch of sampling experience and provides high-quality training data. Additionally, we construct a new industrial accident surveillance video dataset with frame-level annotation, namely ECA9, to evaluate our proposed method. Compared with the state-of-the-art semi-supervised learning based methods, SIAVC demonstrates outstanding video classification performance, achieving 88.76% and 89.13% accuracy on ECA9 and Fire Detection datasets, respectively. The source code and the constructed dataset ECA9 will be released in https://***/AlchemyEmperor/SIAVC. Copyright © 2024, The Authors. All rights reserved.

关键词： Classification (of information)

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An Empathetic AI Coach for Self-Attachment Therapy

arXiv

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arXiv 2022年

作者： Alazraki, Lisa Ghachem, Ali Polydorou, Neophytos Khosmood, Foaad Edalat, Abbas Department of Computing Imperial College London United Kingdom Computer Science and Software Engineering California Polytechnic State University San Luis Obispo United States

In this work, we present a new dataset and a computational strategy for a digital coach that aims to guide users in practicing the protocols of self-attachment therapy. Our framework augments a rule-based conversational agent with a deep-learning classifier for identifying the underlying emotion in a user’s text response, as well as a deep-learning assisted retrieval method for producing novel, fluent and empathetic utterances. We also craft a set of human-like personas that users can choose to interact with. Our goal is to achieve a high level of engagement during virtual therapy sessions. We evaluate the effectiveness of our framework in a non-clinical trial with N=16 participants, all of whom have had at least four interactions with the agent over the course of five days. We find that our platform is consistently rated higher for empathy, user engagement and usefulness than the simple rule-based framework. Finally, we provide guidelines to further improve the design and performance of the application, in accordance with the feedback received. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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A Fast Blockchain-based Federated Learning Framework with Compressed Communications

arXiv

引用

arXiv 2022年

作者： Cui, Laizhong Su, Xiaoxin Zhou, Yipeng The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The School of Computing FSE Macquarie University Macquarie ParkNSW2113 Australia

Recently, blockchain-based federated learning (BFL) has attracted intensive research attention due to that the training process is auditable and the architecture is serverless avoiding the single point failure of the parameter server in vanilla federated learning (VFL). Nevertheless, BFL tremendously escalates the communication traffic volume because all local model updates (i.e., changes of model parameters) obtained by BFL clients will be transmitted to all miners for verification and to all clients for aggregation. In contrast, the parameter server and clients in VFL only retain aggregated model updates. Consequently, the huge communication traffic in BFL will inevitably impair the training efficiency and hinder the deployment of BFL in reality. To improve the practicality of BFL, we are among the first to propose a fast blockchain-based communication-efficient federated learning framework by compressing communications in BFL, called BCFL. Meanwhile, we derive the convergence rate of BCFL with non-convex loss. To maximize the final model accuracy, we further formulate the problem to minimize the training loss of the convergence rate subject to a limited training time with respect to the compression rate and the block generation rate, which is a bi-convex optimization problem and can be efficiently solved. To the end, to demonstrate the efficiency of BCFL, we carry out extensive experiments with standard CIFAR-10 and FEMNIST datasets. Our experimental results not only verify the correctness of our analysis, but also manifest that BCFL can remarkably reduce the communication traffic by 95-98% or shorten the training time by 90-95% compared with BFL. Copyright © 2022, The Authors. All rights reserved.

关键词： Blockchain

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Towards An Efficient Searching Approach of ROS Message by Knowledge Graph

Towards An Efficient Searching Approach of ROS Message by Kn...

引用

IEEE Annual International computer software and Applications Conference (COMPSAC)

作者： Sun Bo Xinjun Mao Shuo Yang Long Chen College of Computer National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

The Robot Operating System (ROS) has become the most popular robot development framework in the last few years, which has loosely coupled structure and provides remote communications between different component nodes. The ROS messages are critical to bridge the communication channels and clearly define the data structures. The developers can use the standardized or user-customized ROS message types to construct a communication channel between two component nodes uniquely. However, it becomes increasingly difficult for developers to find the required ROS message type from thousands of diverse ROS message types in ROS-based robotic software development. Finding the proper ROS message type is a non-trivial task because developers may hardly know the exact names of required ROS messages but only has a rough knowledge of the task domain features. To tackle this challenge, we construct a novel ROS Message Knowledge Graph (RMKG) with 4543 entities and 14320 relationships, including all ROS message types and message packages. We take the shortest path algorithm to search ROS message in RMKG by searching with ROS message feature or ROS message package and visualize the subgraph structure of the search results. Moreover, we develop a ROS message package library that supports fuzzy queries to find the required message package. A comprehensive evaluation of RMKG shows the high accuracy of our knowledge construction approach. A user study indicates that RMKG is promising in helping developers find suitable ROS message types for robotics software development tasks. An effect evaluation of message package fuzzy query shows the good effects of our fuzzy query method under different situations.

关键词： Bridges Operating systems Conferences Communication channels Data structures software Libraries

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TAD-Bench: A Comprehensive Benchmark for Embedding-Based Text Anomaly Detection

arXiv

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arXiv 2025年

作者： Cao, Yang Yang, Sikun Li, Chen Xiang, Haolong Qi, Lianyong Liu, Bo Li, Rongsheng Liu, Ming School of Computing and Information Technology Great Bay University China Great Bay Institute for Advanced Study Great Bay University China Graduate School of Informatics Nagoya University Japan School of Software Nanjing University of Information Science and Technology China College of Computer Science and Technology China University of Petroleum [East China China College of Cyberspace Security Zhengzhou University China School of Computer Harbin Engineering University China School of IT Deakin University Australia

Text anomaly detection is crucial for identifying spam, misinformation, and offensive language in natural language processing tasks. Despite the growing adoption of embedding-based methods, their effectiveness and generalizability across diverse application scenarios remain under-explored. To address this, we present TAD-Bench, a comprehensive benchmark designed to systematically evaluate embedding-based approaches for text anomaly detection. TAD-Bench integrates multiple datasets spanning different domains, combining state-of-the-art embeddings from large language models with a variety of anomaly detection algorithms. Through extensive experiments, we analyze the interplay between embeddings and detection methods, uncovering their strengths, weaknesses, and applicability to different tasks. These findings offer new perspectives on building more robust, efficient, and generalizable anomaly detection systems for real-world applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Embeddings

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Smart Health Monitoring System with Wireless Networks to Detect Kidney Diseases

引用

Computational Intelligence and Neuroscience 2022年 2022卷 3564482页

作者： Dhanke, Jyoti Rathee, Naveen Vinmathi, M.S. Janu Priya, S. Abidin, Shafiqul Tesfamariam, Mikiale Bharati Vidyapeeth's College of Engineering Lavale Maharashtra Pune412115 India Department of Electronics and Communication Engineering IIMT College of Engineering Uttar Pradesh Greater Noida201310 India Department of CSE Panimalar Engineering College Bangalore Trunk Road Nazarethpet Poonamallee Tamilnadu Chennai600123 India Department of Electronics and Communication Engineering K. Ramakrishnan College of Engineering Samayapuram Tamilnadu Tiruchirappalli621112 India Department of Computer Science Aligarh Muslim University Uttar Pradesh Aligarh202002 India Department of Software Engineering College of Computing and Informatics Haramaya University Dire Dawa Ethiopia

It is essential to change health services from a hospital to a patient-centric platform since medical costs are steadily growing and new illnesses are emerging on a worldwide scale. This study provides an optimal decision support system based on the cloud and Internet of Things (IoT) for identifying Chronic Kidney Disease (CKD) to provide patients with efficient remote healthcare services. To identify the presence of medical data for CKD, the proposed technique uses an algorithm named Improved Simulated Annealing-Root Mean Square -Logistic Regression (ISA-RMS-LR). The four subprocesses that make up the proposed model are a collection of data, preprocessing, feature selection, and classification. The incorporation of Simulated Annealing (SA) during Feature Selection (FS) enhances the ISA-RMS-LR model's classifier outputs. Using the CKD benchmark dataset, the ISA-RMS-LR model's efficacy has been verified. According to the experimental findings, the proposed ISA-RMS-LR model effectively classifies patients with CKD, with high sensitivity at 99.46%, accuracy at 99.26%, Specificity at 98%, F-score at 99.63%, and kappa value at 98.29%. The proposed system has many benefits including the fast transmission of medical data to the medical personnel, real-time tracking, and registration condition of the patient through a medical record. Potential enhancement of the performance measures the provider system's hospital capacity and monitoring of a significant number of patients with a concentrated average delay. © 2022 Jyoti Dhanke et al.

关键词： Simulated annealing

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Multi-Objective Evolving Long-Short Term Memory Networks with Attention for Network Intrusion Detection

SSRN

引用

SSRN 2022年

作者： Wei, Wenhong Chen, Yi Lin, Qiuzhen Ji, Junkai Wong, Ka-Chun Li, Jianqiang School of Computer Science and Technology Dongguan University of Technology Dongguan China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong

Cyber security has received more and more attention, as people use more Internet applications in their life and are concerned about the security of their personal data. Intrusion Detection System (IDS) is an important security tool that can detect abnormal Internet network activities. Deep Learning (DL) techniques have been widely applied in the field of IDS and achieved promising performance in terms of detection accuracy. However, the design of DL architectures usually requires professional knowledge and dramatically impacts the performance of the DL model. Moreover, due to the existence of a small ratio of abnormal traffic in a large number of network traffic, this serious imbalance data problem leads to poor performance of the DL model in detecting minority attack classes. To alleviate these problems, this paper proposes a hybrid evolutionary DL model (called EvoBMF) to detect intrusion behaviors in the network, which uses bidirectional Long-short Term Memory (BiLSTM) for preliminary feature extraction, Multi-Head Attention (MHA) for further capturing features and global information of the network traffic, and full-connected layer module to do final classification. To deal with the problem of parameter tuning manually in the DL model when tackling different tasks, the EvoBMF model is first encoded as the chromosome of Multi-objective Evolutionary Algorithm (MOEA) and then its two conflicting objectives (the complexity and classification ability of the model) will be optimized. Later, a state-of-the-art MOEA (MOEA/D-DRA) is modified to optimize the above two objectives, which can obtain the optimal architecture for EvoBMF. The proposed EvoBMF model can be easily deployed in Cloud computing scenarios to resist network intrusion. Moreover, in order to alleviate the severe imbalance in routine network traffic, the synthetic minority over-sampling technique is introduced to generate representative samples of minority classes in order to improve the overall performanc

关键词： Brain

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A Comparative Study of Agile Methods, Testing Challenges, Solutions Tool Support 14

A Comparative Study of Agile Methods, Testing Challenges, So...

引用

14th International Conference on Open Source Systems and Technologies, ICOSST 2020

作者： Rehman, Attique Ur Nawaz, Ali Ali, Mohammad Tahir Abbas, Muhammad College of Electrical and Mechanical Engineering Nust Department of Computer and Software Engineering Rawalpindi Pakistan College of Art and Science Gulf University of Science and Technology Computer Science Department Mubarak Al-Abdullah Kuwait

ISBN: (纸本)9781728190501

Agile development is conventional these days and with the passage of time software developers are rapidly moving from Waterfall to Agile development. Agile methods focus on delivering executable code quickly by increasing the responsiveness of software companies while decreasing development overhead and consider people as the strongest pillar of software development. As agile development overshadows Waterfall methodologies for software development, it comes up with some distinct challenges related to testing of such software. Our study is going to discuss the challenges this approach has stirred up. Some of the challenges are discussed in this paper with possible solutions and approaches used for resolving these challenges. Also, the tools in practice are mentioned to improve the efficiency of the process. (Abstract) © 2020 IEEE.

关键词： software testing

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Significant enhancement in the electrical conductance properties of ethylene propylene diene monomer using the nano‐SiO_(2) particles

引用

High Voltage 2021年第3期6卷 487-494页

作者： Chunyu Xu Ruixue Zhao Chuanyang Li Yuanyuan Li Guodong Zhang Tohid Shahsavarian Mengqiang Yuan Zhipeng Lei Shanxi Key Laboratory of Mining Electrical Equipment and Intelligent Control College of Electrical and Power EngineeringTaiyuan University of TechnologyTaiyuanChina Electrical and Computer Engineering University of ConnecticutStorrsConnecticutUSA State Grid Jincheng Power Supply Company JinchengChina Shanxi Jincheng Anthracite Mining Group Co. LtdJinchengChina School of Electrical Engineering Shandong UniversityJinanChina

To study the effect of nano‐silica particles on the conductivity characteristics of ethylene propylene diene monomer(EPDM)insulation,EPDM nanocomposites with different mass fractions of nano silica were prepared via the melt‐blending *** electron microscopy was used to analyze the dispersion of nanoparticles in *** on the Fourier‐transform infrared spectrum,the bonding properties between the nanoparticles and EPDM were *** steady‐state current at 30-100°C and 1-20 kV/mm was ***,the conductivity characteristic was *** experimental results show that as the content of nano‐silica increases,the electrical conductivity of EPDM nanocomposite as well as the threshold field strength gradually *** with a small number of nanoparticles will increase the activation energy of the carrier conductivity,and the interface region effect introduced by nanoparticles will reduce the carrier mobility and concentration,resulting in a lower *** the calculated value of the dielectric constant with the measured value can conclude that the high‐field conductance process is a combination of the electrode effect and the body effect,which in this case,Schottky effect dominates.

关键词： propylene conductivity conductance

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