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Research on fault diagnosis and analysis of AC relays based on CNN

Research on fault diagnosis and analysis of AC relays based ...

引用

Artificial Intelligence, Robotics, and Communication (ICAIRC), IEEE International conference on

作者： Jianchun Lin Yanlong Guo Jiazhi Li Yonghuan Piao School of Mechanical and Automotive Engineering Xiamen University of Technology Xiamen China Innovation Department Sucheon (Xiamen) Technologies Xiamen China

ISBN: (数字)9798331531225

ISBN: (纸本)9798331531232

Relay detection is an important step to ensure the reliable operation of relays in electrical systems. this paper proposes a fault diagnosis system for alternating current(AC) relays based on Convolutional Neural Networks(CNN). the system uses a vibration sensor to collect signals from the AC relay, and then transmits the vibration signals to the edge processing unit embedded with CNN. the edge processing unit extracts the time domain features of the AC relay vibration signal, and then classifies the features in the CNN model. Finally, the system outputs the results of abnormal vibration detection. the experimental results show that the system proposed in this paper can effectively identify the abnormal vibration of AC relays, reaching an accuracy rate of 98.3%, which is better than the traditional Support Vector Machines(SVM) or Random Forest(RF) methods. this paper provides a standardized, automated and intelligent solution for the field of abnormal vibration detection of AC relays, which helps to improve product quality and production efficiency.

关键词： Vibrations Fault diagnosis Accuracy image edge detection Production Feature extraction Robot sensing systems Convolutional neural networks Relays Time-domain analysis

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DLASE: A light-weight framework supporting Deep Learning for Edge Devices

DLASE: A light-weight framework supporting Deep Learning for...

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International conference on Recent Advances in signal processing, Telecommunications & Computing (SigTelCom)

作者： Khanh-Hoi Le Minh Kim-Hung Le Quan Le-Trung University of Information Technology Vietnam National University Ho Chi Minh City Ho Chi Minh Vietnam

ISBN: (数字)9781728168661

ISBN: (纸本)9781728168678

With the rapid growth of the Internet of things (IoT), there is a massive number of constrained devices connected to the internet, resulting in the generation of large data volume. the deep learning (DL) has become a promised solution to extract more valuable knowledge from collected data but struggle to execute DL algorithms due to limited computing resources of IoT devices. Leveraging the computation power of cloud computing, we could offload the data to cloud servers for processing and response results to the devices. But, this paradigm leads to a significant increase in latency and security risks. therefore, there have been many efforts to perform DL algorithms on edge devices, which are close to the data sources. Deploying various DL systems to such edge devices a complex process due to the diversity of DL models and running environment configuration. In this paper, we propose a light-weight framework supporting to deploy deep learning as a service running on edge devices, namely DLASE. there are two key advantages of our proposal. the first one is that we enable remote deployment of various deep learning models to edge devices. the other one is that these models are run as a service to serve requests from IoT devices. To demonstrate the light-weight and flexibility of DLASE, we practically evaluate the framework performances (consumed memory, CPU, latency) by running various combinations of DL models and edge devices.

关键词： Computational modeling Machine learning Cloud computing Servers Edge computing image edge detection Internet of things

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STTSBI: A Fast Inference Framework for Small Object Detection in Ultra-High-Resolution images

STTSBI: A Fast Inference Framework for Small Object Detectio...

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Intelligent Technology and Embedded Systems (ICITES), International conference on

作者： Zihan Chen Gang Chen School of Computer Science and Engineering Sun Yat-sen University Guangdong China

ISBN: (数字)9798331506292

ISBN: (纸本)9798331506308

Ultra-high-resolution images (UHR images), such as satellite images, aerial photographs, and panoramic images, provide high-quality detection information that enhances small and distant object detection performance. However, UHR images often contain hundreds of millions of pixels, leading to considerable inference time with existing frameworks. To address the aforementioned issue, we extend the YOLOv8 network and propose a novel tiling-based object detection framework called Selective Tiling and Two-Stage Batch Inference (STTSBI). In this framework, the input image is first divided into tiles. then the inserted pre-classification (PCLS) module filters out tiles without target objects and a two-stage batch inference process is employed to enhance GPU utilization efficiency by maintaining batch processing throughout the entire workflow, thereby significantly reducing inference time. According to our experimental setup, our proposed model can infer an image with over a hundred million pixels in no more than 0.4 seconds, achieving a 10× to 40× increase in inference speed compared to the existing methods. Furthermore, we apply binary quantization to the backbone module of YOLOv8 based on the PhoneBit framework, which further reduces the inference consumption. the binary quantization model achieves a 2× speedup compared to the fullprecision model.

关键词： Quantization (signal) Filters Embedded systems Inference mechanisms Computational modeling Graphics processing units Object detection Feature extraction Real-time systems Satellite images

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Saliency guided wavelet compression for low-bitrate image and Video coding

Saliency guided Wavelet compression for low-bitrate Image an...

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IEEE Global conference on signal and Information processing

作者： Souptik Barua Kaushik Mitra Ashok Veeraraghavan Rice University USA Indian Institute of Technology Madras India

ISBN: (纸本)9781479975921

We propose an improved saliency guided wavelet compression scheme for low-bitrate image/video coding applications. Important regions (faces in security camera feeds, vehicles in traffic surveillance) get degraded significantly at low bitrates by existing compression standards, such as JPEG/JPEG-2000/MPEG-4, since these do not explicitly utilize any knowledge of which regions are salient. We design a compression algorithm which, given an image/video and a saliency value for each pixel, computes a corresponding saliency value in the wavelet transform domain. Our algorithm ensures wavelet coefficients representing salient regions have a high saliency value. the coefficients are transmitted in decreasing order of their saliency. this allows important regions in the image/video to have high fidelity even at very low bitrates. Further, our compression scheme can handle several salient regions with different relative importance. We compare the performance of our method with the JPEG/JPEG-2000 image standards and the MPEG-4 video standard through two experiments: face detection and vehicle tracking. We show improved detection rates and quality of reconstructed images/videos using our Saliency Based Compression (SBC) algorithm.

关键词： Saliency wavelet transform image coding Video coding

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Research on Unmanned Aircraft Environment Perception Methods in Urban Low Altitude Complex Backgrounds 23

Research on Unmanned Aircraft Environment Perception Methods...

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Proceedings of the 4th International conference on Artificial Intelligence and Computer Engineering

作者： Jinyi Qu Wenping Gao Haoyu Wang Daquan Huang Tianjin Key Laboratory of Advanced Signal Processing Civil Aviation University of China China

ISBN: (纸本)9798400708831

With the continuous development of drone-related technologies, the application of UAV (Unmanned Aerial Vehicle) in urban low-altitude scenes is gradually increasing. Autonomous localization and perception of the surrounding environment are prerequisites for autonomous control. As a result, our team apply the VDO-SLAM algorithm to the scene of coexistence of dynamic and static obstacles in the complex background of urban low altitude. Effective segmentation of static background and moving objects is achieved by combining semantic segmentation and optical flow estimation algorithms. Since there is a lack of relevant public data for urban low-altitude scenarios, a binocular image dataset containing both static and dynamic objects is created by Airsim simulator, which is utilized for training a semantic segmentation model. In addition, a novel numbering maintenance algorithm is introduced for the semantic segmentation of continuous image sequences. the experimental results show that the algorithm can realize the autonomous perception of UAV posture and the motion state of dynamic objects in low altitude complex urban environment. this method provides the necessary self-pose information and reliable environmental data for autonomous obstacle avoidance of UAVs, and provides important support for the application of UAVs in urban low-altitude environment.

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A Proactive Context-Aware Recommender System for In-Vehicle Use 2020

A Proactive Context-Aware Recommender System for In-Vehicle ...

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Proceedings of the 2020 4th International conference on Vision, image and signal processing

作者： Maria Guinea Isabel Litton Rigel Smiroldo Irma Nitsche Eric Sax Mercedes-Benz Research and Development Germany Mercedes-Benz Research and Development North America Institute for Information Processing Technologies (ITIV) Karlsruhe Institute of Technology (KIT)

ISBN: (纸本)9781450389532

In the last years, the amount of in-vehicle functionalities has drastically increased. Consequently, users frequently have to navigate through a complex menu structure to control a desired functionality. this information overload is typically the cause of distracted driving, which can have unwelcome consequences. the main contribution of this paper is to provide a user behavioral model to represent the recurrent preferences of users for in-vehicle functionalities and to employ it to construct a proactive context-aware recommender system. this work is based on the assumption that most drivers tend to use a given feature within similar contextual environments (i.e. have routines). therefore, the proposed method aims to reduce the cognitive workload by automatically executing the regularly used features. We believe, this system will not only minimize the number of driver interactions, and hence, the distractions, but also let passengers have a more comfortable and personalized experience. the viability and efficacy of the proposed algorithm are justified by theory and demonstrated on a user study.

关键词： Context-Aware Behavior Modeling Bayesian Non-Parametric Methods Proactive Recommender System

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EEG-GNet: GCN-Based EEG signal Fatigue Detection Network for Edge Devices

EEG-GNet: GCN-Based EEG Signal Fatigue Detection Network for...

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IEEE International conference on Bioinformatics and Biomedicine (BIBM)

作者： Fuquan Zhang Yu Wu Xinxin Xu Chuansheng Wang Minjia Ma Antoni Grau Jiayan Huang School of Computer and Big Data Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China College of New Engineering Industry Putian University Putian China Department of Automatic Control Polytechnic University of Catalonia Barcelona Spain School of Robotics Xi’an Jiaotong-Liverpool University Suzhou China College of New Engineering Industry Putian University Fujian Ruituo Information Technology Limited Company Putian China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Fatigue driving is a common potential traffic safety hazard. To detect and analysis the driver Fatigue degree in real-time, more attention have been increasing paid to application research of EEG fatigue detection device. However, existing methods suffer from complex model structure or large-scale model parameters. In the other words, they requires high computational consumption and large memory footprint, which creates a contradiction between model and portable EEG machine with limited hardware resources. In this work, we take both time and frequency domains of EEG signal into account and design an lightweight and efficient driver fatigue detection network based on GCN (EEG-GNet), which is available edge-end device application. Experiments on 4th Raspberry Pi on a public baseline dataset show that our EEG-GNet outperforms the state-of-the-art methods and takes good tradeoff between detection accuracy and resource consumption. Our EEG-GNet provides a new potential opportunity for future applications in practical portable/wearable (P/W) devices.

关键词： Accuracy Computational modeling image edge detection Frequency-domain analysis Fatigue Brain modeling Feature extraction Electroencephalography Real-time systems Time-domain analysis

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the Development of a Gait Speed Detection System for Older Adults Using Video-based processing 19

The Development of a Gait Speed Detection System for Older A...

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Proceedings of the 2019 4th International conference on Biomedical Imaging, signal processing

作者： Teerawat Kamnardsiri Pattaraporn Khuwuthyakorn Sirinun Boripuntakul Department of Digital Game College of Arts Media and Technology Chiang Mai University Suthep Muang Chiang Mai Thailand Department of Modern Management and Information Technology College of Arts Media and Technology Chiang Mai University Suthep Muang Chiang Mai Thailand Department of Physical Therapy Chiang Mai University Sripoom Muang Chiang Mai Thailand

ISBN: (纸本)9781450372954

this study aimed to develop the gait speed detection system for measuring the instantaneous walking speed of older adults. the proposed system employed a standard camera 60 Hz and fixed on a tripod with 3-way head to collect the body motion. Besides, the proposed system was to assess the validity of instantaneous horizontal speed with the three-dimension motion analysis system. the cross-sectional study was used to design in this study. the proposed system consists of ten steps, which are: (1) Input video, (2) Extraction frames, (3) Calibration of a camera and the capture volume, (4) Colour detection and filling into the body, (5) the human body region detection, (6) Filtering of the foreground regions from image difference, (7) Centroid of the human body detection, (8) Identification of the human body position, (9) Feature tracking of the human speed and (10) Estimation of the human speed. the proposed system employed MATLAB (2015a) with the Computer Vision Toolbox and the image processing Toolbox for developing and testing. the fifteen older adults with mean age 67 (SD = 4.19) years performed three walking conditions that comprises: 1) walking at a slow speed, 2) walking at usual speed, and 3) walking at a fast speed. Besides, participants walked along a 10-metre walkway in the motion capture laboratory room. the results demonstrate that the proposed system measures have an excellent correlation with the motion analysis system measures, with correlation coefficients between 0.936 and 0.987. Hence, the proposed system is to be one of the useful tools for assessing instantaneous walking speed among older adults in both clinical and community settings.

关键词： older adults image processing Speed detection system gait speed computer vision motion capture

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Modelling Mobile signal Strength by Combining Geospatial Big Data and Artificial Intelligence 2020

Modelling Mobile Signal Strength by Combining Geospatial Big...

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Proceedings of the 2020 4th International conference on Vision, image and signal processing

作者： Paolo Fraccaro Matt Benatan Katharina Reusch Clyde Fare Blair Edwards Edward Pyzer-Knapp IBM Research Europe Hartree Centre Daresbury UK

ISBN: (纸本)9781450389532

Estimating mobile signal strength accurately is a crucial task for network providers and their customers. However, current methodologies to estimate mobile signal strength present limitations in their practical implementation (i.e. physical based models), portability (i.e. spatial interpolation methods), simplicity and accuracy (i.e. path loss models). In this paper we present a novel approach that takes advantage of geospatial Big Data and advanced Artificial Intelligence to predict mobile signal strength at scale. Particularly, we used open access geo-spatial information about weather, tree coverage, land use, imperviousness, altitude and network infrastructure (i.e. a total of 174 features) to train and test uncertainty-aware artificial neural networks to predict mobile signal strength on data from the NetBravo crowdsourcing platform across all the United Kingdom (UK). Our model scored a best performance of 7.9 (standard deviation of 0.2) dBm for Root Mean Squared Error and 5.7 (standard deviation of 0.4) dBm for the Mean Absolute Error. Feature importance analysis showed that mobile cell tower characteristics and geospatial features showing the distribution of imperviousness and tree cover density over the line of sight between the mobile cell tower and the receiver as well as relative humidity were among the top 20 most important features.

关键词： Mobile signal Strength Artificial Neural Networks Learning Big Data Geospatial

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the Research on Malicious Load Recognition Based on Principal Component Analysis

The Research on Malicious Load Recognition Based on Principa...

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Electronic Information Engineering and Computer Technology (EIECT), International conference on

作者： Zhangzhen Nie Yunhe Chen School of Computer and Information Engineering Tianjin Normal University Tianjin P.R.China

ISBN: (数字)9798331528850

ISBN: (纸本)9798331528867

As household electrical loads become increasingly diverse and electricity demand continues to rise, the identification of malicious loads has become a critical area of focus in smart grid research. Existing approaches primarily concentrate on current signal processing and the application of machine learning algorithms. Existing approaches, however, face limitations when dealing with complex nonlinear relationships and noise interference, particularly in diverse load environments. To address these challenges, this paper introduces a novel method for malicious load identification based on Principal Component Analysis (PCA) and waveform image generation. the primary innovation of this approach lies in the transformation of current data into waveform images, facilitating enhanced feature extraction and classification. First, the current data is converted into waveform images to visually represent malicious load characteristics. Due to the high dimensionality and redundancy, PCA is used for dimensionality reduction, preserving key features while removing noise and improving efficiency. Given the small sample size and complexity of malicious load data, the K-nearest neighbor (KNN) algorithm is applied for classification. KNN excels in small-sample environments by classifying based on similarity, making it ideal for handling the non-linear characteristics of malicious loads. the combination of PCA and KNN enables effective identification of malicious loads in high-dimensional, small-sample data. Experimental validation demonstrates that the proposed method delivers superior classification performance, achieving an accuracy of 0.9845, significantly surpassing the SVM classifier's 0.9617. these findings underscore the robustness and effectiveness of the waveform image-based approach for malicious load identification. the method offers promising new insights for smart grid monitoring and represents a significant advancement in the accurate and efficient identification of mali

关键词： Accuracy signal processing algorithms Nearest neighbor methods Feature extraction Robustness Real-time systems Classification algorithms Smart grids Object recognition Principal component analysis

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