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SSRN 2023年

作者： Wang, Shengdong Liu, Zhenbao Jia, Zhen Vong, Chi-Man Li, Zihao School of Civil Aviation Northwestern Polytechnical University Xi'An710072 China School of Mechanical and Electrical Engineering Xi'an University of Architecture and Technology Xi'An710055 China Dept. of Computer and Information Science University of Macau 999078 China

Intermittent faults of analog circuits are major causes of built-in test (BIT) false alarms. To enhance the reliability and maintainability of electronic system, an end-to-end approach based on enhanced one-dimensional Vision Transformer (1DViT) is constructed to realize intelligent diagnosis of intermittent faults. One multiscale convolution fusion module (MSC) is introduced into 1DViT to mine fault features on multiple time scales to enhance the interaction between global and local information. In addition, due to the complex operation and measurement conditions, it is difficult and time-consuming to obtain a large amount of data from practical test. To cope with this problem, transfer learning is introduced. The model will be first pre-trained with adequate simulation data which is readily accessible, and then fine-tuned with actual test data to match the practical distribution. Experiments on two typical circuits demonstrate that the proposed method could achieve excellent diagnostic result in practical test. © 2023, The Authors. All rights reserved.

关键词： Fault detection

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A Deep Neural Network for Detecting Broken and Scattered Power Lines in Complex Aerial Images

A Deep Neural Network for Detecting Broken and Scattered Pow...

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International Conference on Artificial Intelligence and computer Information technology (AICIT)

作者： Qiugen Pei Yuxuan Lai Nianxing Wei Shiting Wu Enterprise Architecture and Digitalization Department Guangdong Power Grid Co. Ltd. Guangzhou China Dept. School of Computer Science Guangdong University of Technology Guangzhou China Huizhou Boluo Power Supply Bureau Guangdong Power Grid Co. Ltd. Huizhou China

ISBN: (数字)9798350365443

ISBN: (纸本)9798350365450

Object detection algorithms constitute a pivotal task in the realm of computer vision, aiming to precisely identify and locate objects within images across diverse scenarios. In power systems, especially in aerial images used to detect wire breaks or scattered anomalies, object detection faces significant challenges. The distribution of wires is often uneven, appearing in different sizes in high-resolution images, and the diversity of fault shapes and sizes makes it difficult for traditional object detection models to recognize accurately. Moreover, the detection of small targets also limits the precision of traditional methods. To address the issue of diversity in fault shapes and sizes in high-resolution images, which makes it difficult for traditional object detection models to recognize accurately, first, we collect fault images of wire breaks and scatter under common scenarios, constructing the “EWF _detection” (Electric wire fault) dataset, and propose a Pyramid Multi-scale Object Detection Model, establishing the Hybrid Spatial Attention Pooling module, HSAP, which significantly enhances the multi-scale feature extraction capability. It not only improves the model's ability to focus on key features, but also suppresses unnecessary features, enhancing the expressiveness of the network, and effectively adapting to complex backgrounds, variable lighting conditions, and different line scales and directions for transmission line detection needs. This improvement makes our model perform exceptionally well in detecting rare faults in wires and accurately locate fault positions. These achievements provide reliable technical support for wire anomaly detection in the power industry and indicate a broad prospect of application in future power scenarios. Our method not only improves detection accuracy, but also maintains efficient detection speed, reducing reliance on a large number of fault line images. These advantages make our model an important technical tool for pow

关键词： Adaptation models Image recognition Shape Lighting Object detection Feature extraction Power industry Robustness Maintenance Wire

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A Machine Vision-Based Person Detection under Low-Illuminance Conditions Using High Dynamic Range Imagery for Visual Surveillance System 3

A Machine Vision-Based Person Detection under Low-Illuminanc...

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3rd International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2022

作者： Susa, Julie Ann B. Militante, Sammy V. Acoba, Aimee G. Evangelista, Ryan Soriente Lacatan, Luisito Lolong Lavina, Charlemagne G. Tanguilig, Bartolome T. Southern Luzon State University Dept. of Computer Engineering Quezon Lucban Philippines College of Engineering and Architecture University of Antique Antique Philippines Technological University of the Philippines Electronics Eng'g Technology Department Manila Philippines School of Graduate Studies Sulu State College Sulu Philippines College of Computing and Engineering Pamantasan Ng Cabuyao Laguna Philippines

ISBN: (纸本)9781665456647

The most difficult component of any computer vision application objects recognition and tracking. Video surveillance is a major study subject in computer vision in a dynamic environment, especially for security purposes. Video surveillance technology is critical in preventing crime, terrorism, and other threats. Several existing technologies can accurately monitor a person's mobility in interior environments. As well as regulated outdoor settings, object recognition and tracking at night, on the other hand, remain significant challenges for visual surveillance. The objects are usually farther away, inconspicuous, and have low brightness against the background. In this paper, detecting objects specifically people under low lighting conditions with the help of deep learning algorithms was used. For applications like in-car cameras and surveillance systems that operate in low light, these algorithms might improve picture identification performance. © 2022 IEEE.

关键词： Learning algorithms

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Dynamic Optimizations in GPU Using Roofline Model

Dynamic Optimizations in GPU Using Roofline Model

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IEEE International Symposium on Circuits and Systems

作者： Winnie Thomas Suryakant Toraskar Virendra Singh Computer Architecture and Dependable Systems Laboratory Dept. of Electrical Engineering Indian Institute of Technology Bombay India

ISBN: (纸本)9781728192017;9781728192024

Massively parallel processors such as graphics processing units (GPUs) often face the challenge of resource underutilization due to varying resource proclivity of workloads. Running multiple applications on a GPU has been an efficient and known alternative to mitigate underutilization. This paper proposes a multi-application oriented framework that carries out dynamic optimizations based on the operational intensities of various applications. Our framework analyzes applications based on operational intensities to identify their bottleneck resources using Roofline model. We demonstrate that the proposed optimizations improve the utilization and system-wide throughput of the GPU co-running applications with irregular resource demands. The dynamic optimizations improve the performance by 14.8% on average and up to 72.4% over a state-of-the-art spatial multitasking technique.

关键词： Heuristic algorithms Graphics processing units Throughput Multitasking Resource management Optimization Faces

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Machine Learning based Cybersecurity Technique for Detection of Upcoming Cyber Attacks

Machine Learning based Cybersecurity Technique for Detection...

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International Conference on Communication and Electronics Systems (ICCES)

作者： Kanchi Lohitha Lakshmi S.Shobana Bathula Prasanna Kumar K.B.Glory Bonda Kiran Kumar Sandya Rani Department of Computer Science and Engineering Vasireddy Venkatadri Institute Of Technology Guntur Department of IT R.M.K Engineering College Department of Computer Science and Engineering KKR & KSR Institute of Technology and Sciences Guntur AP Department of Engineering English Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh State India Department of Architecture KL School of Architecture KL Deemed to be University Vaddeswaram Dept. of IT St. Martin's Engineering College Secunderabad Telangana India

Cyberattacks are prevalent in the age of the Internet. Each year, both the quantity and severity of cybercrimes increase. Protection against cyber-attacks has become a primary responsibility, Significant in the internet society of today. However, providing cyber security is a highly difficult task that requires experience in the field of attacks and the ability to evaluate the possibility of threats. The continual evolution of cyberattacks is the biggest challenge in this industry. This article describes the significance of cyber security and enumerates the hazards that exist in the present digital environment. The statistics and evaluation of cyberattacks demonstrate the seriousness of these occurrences. Several sorts of cyber security threats are outlined, along with the machine learning approaches that may be used to detect these attacks.

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Diagnosis of Paratuberculosis in Histopathological Images Based on Explainable Artificial Intelligence and Deep Learning

arXiv

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arXiv 2022年

作者： Yiǧit, Tuncay Şengöz, Nilgün Özmen, Özlem Hemanth, Jude Işik, Ali Hakan Dept. of Computer Engineering Faculty of Engineering Süleyman Demirel University Isparta32260 Turkey Dept. of Pathology Faculty of Veterinary Medicine Burdur Mehmet Akif Ersoy University Burdur15030 Turkey Karunya Institute of Technology & Sciences Coimbatore641114 India Dept. of Computer Engineering Faculty of Engineering and Architecture Burdur Mehmet Akif Ersoy University Burdur15030 Turkey

Artificial intelligence holds great promise in medical imaging, especially histopathological imaging. However, artificial intelligence algorithms cannot fully explain the thought processes during decision-making. This situation has brought the problem of explainability, i.e., the black box problem, of artificial intelligence applications to the agenda: an algorithm simply responds without stating the reasons for the given images. To overcome the problem and improve the explainability, explainable artificial intelligence (XAI) has come to the fore, and piqued the interest of many researchers. Against this backdrop, this study examines a new and original dataset using the deep learning algorithm, and visualizes the output with gradient-weighted class activation mapping (Grad-CAM), one of the XAI applications. Afterwards, a detailed questionnaire survey was conducted with the pathologists on these images. Both the decision-making processes and the explanations were verified, and the accuracy of the output was tested. The research results greatly help pathologists in the diagnosis of *** Codes 68T07 © 2022, CC BY.

关键词： Medical imaging

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Lightweight Task-Oriented Semantic Communication Empowered by Large-Scale AI Models

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IEEE Transactions on Vehicular technology 2025年

作者： Liu, Chuanhong Guo, Caili Yang, Yang Chen, Mingzhe Quek, Tony Q. S. Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL United States Singapore University of Technology and Design Dept. of Information Systems Technology and Design 487372 Singapore

Recent studies have focused on leveraging large-scale artificial intelligence (LAI) models to improve semantic representation and compression capabilities. However, the substantial computational demands of LAI models pose significant challenges for real-time communication scenarios. To address this, this paper proposes utilizing knowledge distillation (KD) techniques to extract and condense knowledge from LAI models, effectively reducing model complexity and computation latency. Nevertheless, the inherent complexity of LAI models leads to prolonged inference times during distillation, while their lack of channel awareness compromises the distillation performance. These limitations make standard KD methods unsuitable for task-oriented semantic communication scenarios. To address these issues, we propose a fast distillation method featuring a pre-stored compression mechanism that eliminates the need for repetitive inference, significantly improving efficiency. Furthermore, a channel adaptive module is incorporated to dynamically adjust the transmitted semantic information based on varying channel conditions, enhancing communication reliability and adaptability. In addition, an information bottleneck-based loss function is derived to guide the fast distillation process. Simulation results verify that the proposed scheme outperform baselines in term of task accuracy, model size, computation latency, and training data requirements. © 1967-2012 IEEE.

关键词： NP-hard

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Emotion detection in infants using an ensemble classifier with a novel member selection technique

Emotion detection in infants using an ensemble classifier wi...

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2020 Summer computer Simulation Conference, SCSC 2020, Held at the 2020 Summer Simulation Multi-Conference, SummerSim 2020

作者： Fahmy, Hesham Ahmed Fahmy, Sherif Fadel Del Barrio García, Alberto A. Juan, Guillermo Botella Dept. of Electronics and Communications Dept. of Computer Engineering Arab Academy for Science Technology and Maritime Transport 2401 Smart Village Campus Giza12577 Egypt Dept. of Computer Architecture and Automation Complutense University of Madrid Ciudad Universitaria C/ Jose Santesmases 9 Madrid28040 Spain

ISBN: (纸本)9781713819462

In this paper, we develop a method for detecting infant emotion from images using machine learning techniques. It is important to develop automatic infant emotion detection algorithms as they can become part of an integrated system that monitors the well-being of infants at home and in institutional settings. In particular, we develop an ensemble classifier that combines the results from diverse individual classifiers to produce better classification results. We present a novel method for selecting members of the ensemble that is based on the Pearson correlation coefficient of the location of the errors in the classifiers. The results show that the ensemble classifier outperforms individual classifiers by at least 1.4 times, and outperforms a state-of-the-art open-source algorithm for adult emotion detection by a larger margin. The results also indicate that Pearson Correlation among classifiers at a certain number of classifier count is a good criterion for selecting ensemble members. ©2020 Society for Modeling & Simulation (SCS) International

关键词： Machine learning

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Predicting directional sound-localization of human listeners in both horizontal and vertical dimensions 148

Predicting directional sound-localization of human listeners...

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148th Audio Engineering Society International Convention 2020

作者： Barumerli, Roberto Majdak, Piotr Reijniers, Jonas Baumgartner, Robert Geronazzo, Michele Avanzini, Federico Dept. of Information Engineering University of Padova Italy Acoustics Research Institute Austrian Academy of Sciences Austria Dept. of Engineering Management University of Antwerp Belgium Dept. of Architecture Design and Media Technology Aalborg University Denmark Dept. of Computer Science University of Milano Italy

Measuring and understanding spatial hearing is a fundamental step to create effective virtual auditory displays (VADs). The evaluation of such auralization systems often requires psychoacoustic experiments. This process can be time consuming and error prone, resulting in a bottleneck for the evaluation complexity. In this work we evaluated a probabilistic auditory model for sound localization intended as a tool to assess VAD's abilities to provide static sound-localization cues to listeners. The outcome of the model, compared with actual results of psychoacoustic experiments, shows the advantages and limitations of this systematic evaluation. © 2020 148th Audio Engineering Society International Convention. All rights reserved.

关键词： Audition

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OCT based scanning system to enable 3D viewing, sharing and printing of artworks

OCT based scanning system to enable 3D viewing, sharing and ...

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3D Image Acquisition and Display: technology, Perception and Applications, 3D 2021 - Part of Imaging and Applied Optics Congress 2021

作者： Zhou, Xingyu In, Darlene Xiong, Xinchang Yang, Kunze Chen, Xing McCune Bruhn, Heather Liu, Xuan Yang, Yi Dept. of Electrical and Computer Engineering New Jersey Institute of Technology University Heights NewarkNJ07102 United States Division of Science and Engineering Penn State Abington AbingtonPA19001 United States The College of Arts and Architecture Penn State University ParkPA16802 United States

We acquired 3D volumetric data of artworks with an OCT-based hybrid scanning platform. The data is then exported into standard 3D data formats that can be 3D printed, shared, and viewed in VR and AR. © 2021 The A... 详细信息

ISBN: (纸本)9781557528209

关键词： Volumetric analysis

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