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2009 ieee circuits and systems international conference on testing and diagnosis, ICTD'09

ISBN: (纸本)9781424425877

The proceedings contain 172 papers. The topics discussed include: study of the test flow optimization method in radar fault isolation;network cable fault location based on the wide pulse time domain reflection;research and application about general-purpose electronic system test platform;research and implementation of glitch capture technology;research on clock synchronization of wireless distributed measurement system;design and implementation of FC-AE-1553 point transmission hardware platform;a survey of neuromorphic engineering-biological nervous system realized on silicon;the research and application of the electric capacitor detecting sea ice thickness and its detecting system;a platform based on CORBA for open networked measurement system;and design for low power testing of computation modules with contiguous subspace in VLSI.

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2009 ieee circuits and systems international conference on testing and diagnosis, ICTD 09: Preface

2009 IEEE Circuits and Systems International Conference on T...

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2009 ieee circuits and systems international conference on testing and diagnosis, ICTD'09 2009年

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Chair's Introduction to 2009 ieee circuits and systems international conference on testing and diagnosis

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Journal of Electronic Science and Technology of China 2009年第4期7卷 289-289页

作者： Rueywen Liu General Chair of ICTD‘09 University of Notre Dame Notre Dame

Based on the recommendation of ICTD＇09 TPC members, this Special Issue of the Journal of Electronic Science ＆ Technology of China （JESTC） contained 22 high quality papers selected from the Proceedings of 2009 ieee circuits and systems international conference on testing and diagnosis （ICTD ＇09） which is fully sponsored by the ieee circuits and systems Society （CASS）, and is technically co-sponsored by the University of Electronic Science and Technology of China （UESTC）, the Chinese Institute of Electronics （CIE）, the China Instrument ＆ Control Society （CIS）, and organized by UESTC.

关键词： ieee this Chair’s Introduction to 2009 ieee circuits and systems international conference on testing and diagnosis

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Chairs Introduction to 2009 ieee circuits and systems international conference on testing and diagnosis

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Journal of Electronic Science and Technology 2009年第4期000卷

作者： Rueywen Liu University of Notre Dame Notre Dame Indiana 46556

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Comparative Study of Intelligent Algorithms in Fault diagnosis of Aviation Mechanical systems 1

Comparative Study of Intelligent Algorithms in Fault Diagnos...

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1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Shen, Jingjing Comac Shanghai Aircraft Design and Research Institute Shanghai China

ISBN: (纸本)9798331529246

The key components such as engines and turbines in aviation machinery systems operate under extreme conditions such as high temperature, high pressure, and strong vibration, which can easily lead to complex faults and pose significant challenges to accurate and timely fault diagnosis. In response to this issue, this article compares and studies the application of various intelligent algorithms in the fault diagnosis of aviation mechanical systems. Firstly, typical intelligent algorithms such as Convolutional Neural Networks (CNN) are selected to extract features and preprocess the collected fault data;Subsequently, corresponding fault diagnosis models are constructed, and each model is trained and parameter optimized;Finally, use the model for diagnostic testing. The CNN algorithm achieved a diagnostic accuracy of 0.935000 samples after 100 iterations, with a training time of 80 seconds and an inference time of 22 milliseconds. In the above data conclusions, the CNN algorithm has shown advantages in the fault diagnosis of aviation mechanical systems, providing important references for improving the efficiency and reliability of fault diagnosis. © 2025 ieee.

关键词： Convolutional neural networks

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Foundational Techniques and Principles for VLSI testing using Machine Learning 6

Foundational Techniques and Principles for VLSI Testing usin...

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6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Nalla, Sindhu Nagarajan, G. Dept of Electronic Engineering Satyabhama Institute of Science and Technology Chennai India Dept. of CSE Satyabhama institute of Science and Technology Chennai India

ISBN: (纸本)9798331522667

With the advent of Very-Large-Scale Integration (VLSI), testing has turned out to be much more troublesome as their size develops. Effective as these traditional VLSI testing methods are in simplification, implementing when it comes to modern circuits teeming strewn with millions of components is suitable and this is where they often falter. Machine learning (ML), as a recent sibling in the family of curtain technologies has opened doors to innovative solutions that may improve VLSI testing, ranging from faster detection and lower fake p/f ratio causing masking costs. A systematic survey of existing methods, techniques and frameworks that utilize ML for VLSI testing is presented in this paper. The survey has encompassed a wide set of ML paradigms, many under the umbrella of supervised/unsupervised/reinforcement learning to study their relevance in different VLSI testing phases (e.g., for fault diagnosis/test pattern generation/design-for-testability considerations). Lastly, the paper discusses how ML frameworks have to be integrated into established VLSI testing workflows and what possible opportunities/trade-offs with current incorporation practices. Performance Metrics: It also aims to evaluate key performance metrics, such as the accuracy/efficiency of a method or its scalability that may be thoroughly analyzed for an ML-based VLSI testing practice. The survey also points out vital gaps in the existing research and suggests possible ways to fill in these gaps by focusing on maybe developing more scalable, explainable & universally applicable ML models for VLSI testing. In addition, by organizing the vast body of work in this area and providing insights into how different approaches have been employed towards achieving specific testing objectives, it is envisaged that researchers and practitioners working on VLSI test research using machine learning will find a useful resource for advancing their contributions. © 2025 ieee.

关键词： Design-for-Testability Fault diagnosis Machine Learning Test Pattern Generation VLSI testing

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Advancing IC Fault Localization and diagnosis Through Machine Learning

Advancing IC Fault Localization and Diagnosis Through Machin...

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2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Rajasekar, P. Kumar, Alok Prema Latha, V. Jayapal, N. Santhoshi, Boddu Soundarya Karthik, K. Sivaramkumar, Mathiyalagan School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur603203 India Guru Gobind Singh Educational Society Department of Electrical and Electronics Engineering Technical Campus Jharkhand Bokaro827013 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522302 India Kongunadu College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Trichy621215 India S.R.K.R Engineering College Department of Mechanical Engineering Andhra Pradesh Bhimavaram534204 India Velammal College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Madurai625009 India Saveetha University Department of Biosciences Chennai602105 India

ISBN: (纸本)9798331509828

Fault localization and diagnosis of Integrated circuits (ICs) are essential for maintaining dependability in contemporary electronic systems. This research presents a sophisticated system utilizing Data Augmentation, Wrapper-based feature selection, and deep learning to improve the precision of defect identification and localization. Data Augmentation methods, such as synthetic fault data generation via SMOTE, rectify dataset imbalances, facilitating the model's generalization across various fault scenarios. Wrapper-based feature selection utilizes Recursive Feature Elimination (RFE) to discern the most significant characteristics, including timing delays, voltage levels, and signal patterns, so diminishing dimensionality while preserving essential information. The framework is centred around a deep learning model that integrates Convolutional Neural Networks (CNNs) for spatial fault detection and Long Short-Term Memory (LSTM) networks for temporal analysis. Experimental findings indicate a notable enhancement in fault localization precision, less false positives, and improved model interpretability. This method offers a scalable and resilient alternative for integrated circuit defect diagnosis, tackling the increasing complexity of contemporary electronic systems. © 2025 ieee.

关键词： Convolutional neural networks

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Optimizing Stroke diagnosis: Application of Machine Learning Algorithms for Early Detection

Optimizing Stroke Diagnosis: Application of Machine Learning...

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2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Sharma, Chetan Anooja, A. Kishore, Jaydeep Bhardwaj, Vivek UpGrad Education Private Limited Bangalore India School of Computer Application Jecrc University Rajasthan Jaipur India School of Computer Science and Engineering Manipal University Jaipur Jaipur India

ISBN: (纸本)9798331509828

Being a top cause of mortality and serious disability, stroke demands urgent diagnosis and impactful prevention. Current innovations in machine learning (ML) show a great capacity for predicting the likelihood of a stroke. This study reports on applying ML algorithms to improve the timely and early identification of stroke, leading to more effective interventions and lowering morbidity and mortality numbers. This work employs a complete dataset that integrates demographic information, medical history, data on lifestyle, and physical measurements. Engineering techniques of a high level were utilized in pre-processing the data to reduce the assumptions that lay underneath. This research applied five machine learning models for the prompt identification of stroke: Decision Tree, Support Vector Machine (SVM), Gradient Boosting, K-Nearest Neighbours (KNN), and XG Boost. The number of datasets used for training was 80%, and 20% went to testing. The accuracy of the Gradient Boosting model stood at 96%, outperforming XG Boost at 92% and Decision Tree at 91%. The model with Support Vector Machines (SVM) gave 84% accuracy, but all three models illustrated high precision and recall. These results indicate that machine learning models could significantly augment stroke prediction and enhance clinical decision processes. © 2025 ieee.

关键词： Support vector machines

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Medical Equipment Detection based on Resnet-50 Deep Learning Model 1

Medical Equipment Detection based on Resnet-50 Deep Learning...

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1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Yu, Hairong Hou, Tiedong Shen, Dong Liu, Yang Liu, Huanling Du, Qihang Shandong Institute of Metrology Institute of Medical Metrology Jinan China Shandong Institute of Metrology Energy Measurement Technology Center Jinan China

ISBN: (纸本)9798331529246

The increasing reliance on medical equipment for accurate diagnostics and treatment highlights the critical need for ensuring their operational reliability. Faulty medical devices can lead to severe consequences, including incorrect diagnoses and compromised patient safety, underscoring the need for effective fault detection. Despite enhancements, existing fault detection approaches face significant problems in accurately identifying defects due to limited feature extraction capabilities, often resulting in suboptimal classification efficiency. To address these challenges, this research proposes an efficient and accurate fault detection system for medical equipment using the ResNet-50 deep learning model. By leveraging residual learning, the proposed method effectively enhances feature extraction, enabling the model to capture intricate patterns within medical equipment images. The study utilizes a comprehensive collection of image samples from various medical devices, providing a diverse and reliable foundation for fault classification. The proposed model achieves a remarkable classification accuracy of 98.15%, demonstrating its effectiveness in distinguishing between functional and faulty devices. Key performance metrics, like Precision (97.3%), Recall (97.9%), Specificity (98.4%), and F1-Score (97.5%), were systematically analysed, validating the model's strength, consistency, and real-world applicability. Experimental findings showcase the model's consistent and outstanding efficiency, highlighting its superiority over current state-of-the-art techniques. The results emphasize the real-world feasibility of implementing such intelligent systems in healthcare settings, marking a notable progression in the automation of medical equipment testing. © 2025 ieee.

关键词： diagnosis

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A Fault diagnosis Strategy for Analog circuits Based on Composite Neural Networks

A Fault Diagnosis Strategy for Analog Circuits Based on Comp...

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Integrated circuits and Communication systems (ICICACS), ieee international conference on

作者： Tengyue Gui Haobin Xu Weimin Xu Lifeng Chen China Tobacco Zhejiang Industrial Co. Ltd Zhejiang Hangzhou China

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Analog circuit fault diagnosis is a technology that leads and has basic significance in electronic engineering and is vital in ensuring the maintainability and safety of electronic systems. This paper presents a fault diagnosis based on composite neural networks. The method used allows for the benefits of the feedforward neural network and the recurrent neural networks to be absorbed, while learning some static and dynamic characteristics of the behavior of the circuit. A precondition for success in this method is the introduction of an adjustable weighting schema used in weighting the importance of the components in the network. This enhances the accuracy and reliability of the diagnosis process. The experimental results show that the composite neural network diagnostic method has achieved remarkable performance for different types of faults, thus proving its superiority as a fault diagnosis method compared with the standard neural network model. In addition, this composite neural network possesses wide compatibility, which is well demonstrated in this paper, indicating that it can be applied in fault diagnosis for different circuit topologies and working environments.

关键词： Fault diagnosis Knowledge engineering Recurrent neural networks Neural networks Analog circuits Feedforward neural networks Safety Circuit faults Integrated circuit modeling Standards

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