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Defect Detection Model Using Time Series Data Augmentation and Transformation

Computers, Materials & Continua 2024年第2期78卷 1713-1730页

作者： Gyu-Il Kim Hyun Yoo Han-Jin Cho kyungyong chung Department of Computer Science Kyonggi UniversitySuwon-si16227Korea Contents Convergence Software Research Institue Kyonggi UniversitySuwon-si16227Korea Department of Energy IT Engineering Far East UniversityEumsung-gun27601Korea Division of AI Computer Science and Engineering Kyonggi UniversitySuwon-si16227Korea

Time-series data provide important information in many fields,and their processing and analysis have been the focus of much ***,detecting anomalies is very difficult due to data imbalance,temporal dependence,and ***,methodologies for data augmentation and conversion of time series data into images for analysis have been *** paper proposes a fault detection model that uses time series data augmentation and transformation to address the problems of data imbalance,temporal dependence,and robustness to *** method of data augmentation is set as the addition of *** involves adding Gaussian noise,with the noise level set to 0.002,to maximize the generalization performance of the *** addition,we use the Markov Transition Field(MTF)method to effectively visualize the dynamic transitions of the data while converting the time series data into *** enables the identification of patterns in time series data and assists in capturing the sequential dependencies of the *** anomaly detection,the PatchCore model is applied to show excellent performance,and the detected anomaly areas are represented as heat *** allows for the detection of anomalies,and by applying an anomaly map to the original image,it is possible to capture the areas where anomalies *** performance evaluation shows that both F1-score and Accuracy are high when time series data is converted to ***,when processed as images rather than as time series data,there was a significant reduction in both the size of the data and the training *** proposed method can provide an important springboard for research in the field of anomaly detection using time series ***,it helps solve problems such as analyzing complex patterns in data lightweight.

关键词： Defect detection time series deep learning data augmentation data transformation

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Explainable Anomaly Detection Using Vision Transformer Based SVDD

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Computers, Materials & Continua 2023年第3期74卷 6573-6586页

作者： Ji-Won Baek kyungyong chung Department of Computer Science Kyonggi UniversitySuwon-si 16227Korea Division of AI Computer Science and Engineering Kyonggi UniversitySuwon-si 16227Korea

Explainable AI extracts a variety of patterns of data in the learning process and draws hidden information through the discovery of semantic *** is possible to offer the explainable basis of decision-making for inference *** the causality of risk factors that have an ambiguous association in big medical data,it is possible to increase transparency and reliability of explainable decision-making that helps to diagnose disease *** addition,the technique makes it possible to accurately predict disease risk for anomaly *** transformer for anomaly detection from image data makes classification through ***,in MLP,a vector value depends on patch sequence information,and thus a weight *** should solve the problem that there is a difference in the result value according to the change in the *** addition,since the deep learning model is a black box model,there is a problem that it is difficult to interpret the results determined by the ***,there is a need for an explainablemethod for the part where the disease *** solve the problem,this study proposes explainable anomaly detection using vision transformerbasedDeep Support Vector Data Description(SVDD).The proposed method applies the SVDD to solve the problem of MLP in which a result value is different depending on a weight change that is influenced by patch sequence information used in the vision *** order to draw the explainability of model results,it visualizes normal parts through *** health data,both medical staff and patients are able to identify abnormal parts *** addition,it is possible to improve the reliability of models and medical *** performance evaluation normal/abnormal classification accuracy and f-measure are evaluated,according to whether to apply *** Results The results of classification by applying the proposed SVDD are evaluated ***,through the proposed meth

关键词： Explainable AI anomaly detection vision transformer SVDD health care deep learning classification

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Deep Learning-Based Action Classification Using One-Shot Object Detection

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Computers, Materials & Continua 2023年第8期76卷 1343-1359页

作者： Hyun Yoo Seo-El Lee kyungyong chung Contents Convergence Software Research Institute Kyonggi UniversitySuwon-si16227Korea Department of Public Safety Bigdata Kyonggi UniversitySuwon-si16227Korea Division of Computer Science and Engineering Kyonggi UniversitySuwon-si16227Korea

Deep learning-based action classification technology has been applied to various fields,such as social safety,medical services,and *** an action on a practical level requires tracking multiple human bodies in an image in real-time and simultaneously classifying their *** are various related studies on the real-time classification of actions in an ***,existing deep learning-based action classification models have prolonged response speeds,so there is a limit to real-time *** addition,it has low accuracy of action of each object ifmultiple objects appear in the ***,it needs to be improved since it has a memory overhead in processing image *** learning-based action classification using one-shot object detection is proposed to overcome the limitations of multiframe-based analysis *** proposed method uses a one-shot object detection model and a multi-object tracking algorithm to detect and track multiple objects in the ***,a deep learning-based pattern classification model is used to classify the body action of the object in the image by reducing the data for each object to an action *** to the existing studies,the constructed model shows higher accuracy of 74.95%,and in terms of speed,it offered better performance than the current studies at 0.234 s per *** proposed model makes it possible to classify some actions only through action vector learning without additional image learning because of the vector learning feature of the posterior neural ***,it is expected to contribute significantly to commercializing realistic streaming data analysis technologies,such as CCTV.

关键词： Human action classification artificial intelligence deep neural network pattern analysis video analysis

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Multiple-Object Tracking Using Histogram Stamp Extraction in CCTV Environments

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Computers, Materials & Continua 2023年第12期77卷 3619-3635页

作者： Ye-Yeon Kang Geon Park Hyun Yoo kyungyong chung Division of AI Computer Science and Engineering Kyonggi UniversitySuwon16227Korea Contents Convergence Software Research Institute Kyonggi UniversitySuwon16227Korea

Object tracking,an important technology in the field of image processing and computer vision,is used to continuously track a specific object or person in an *** technology may be effective in identifying the same person within one image,but it has limitations in handling multiple images owing to the difficulty in identifying whether the object appearing in other images is the *** tracking the same object using two or more images,there must be a way to determine that objects existing in different images are the same ***,this paper attempts to determine the same object present in different images using color information among the unique information of the ***,this study proposes a multiple-object-tracking method using histogram stamp extraction in closed-circuit television *** proposed method determines the presence or absence of a target object in an image by comparing the similarity between the image containing the target object and other *** this end,a unique color value of the target object is extracted based on its color distribution in the image using three methods:mean,mode,and interquartile *** Top-N accuracy method is used to analyze the accuracy of each method,and the results show that the mean method had an accuracy of 93.5%(Top-2).Furthermore,the positive prediction value experimental results show that the accuracy of the mean method was 65.7%.As a result of the analysis,it is possible to detect and track the same object present in different images using the unique color of the *** the results,it is possible to track the same object that can minimize manpower without using personal information when detecting objects in different *** the last response speed experiment,it was shown that when the mean was used,the color extraction of the object was possible in real time with 0.016954 *** this,it is possible to detect and track the same object in real time when using the proposed

关键词： Data mining deep learning object detection object tracking real-time object detection multiple object image processing

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Abnormal Behavior Detection Using Deep-Learning-Based Video Data Structuring

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Intelligent Automation & Soft Computing 2023年第8期37卷 2371-2386页

作者： Min-Jeong Kim Byeong-Uk Jeon Hyun Yoo kyungyong chung Department of Computer Science Kyonggi UniversitySuwon-si16227Gyeonggi-doKorea Contents Convergence Software Research Institute Kyonggi UniversitySuwon-si16227Gyeonggi-doKorea Division of AI Computer Science and Engineering Kyonggi UniversitySuwon-si16227Gyeonggi-doKorea

With the increasing number of digital devices generating a vast amount of video data,the recognition of abnormal image patterns has become more ***,it is necessary to develop a method that achieves this task using object and behavior information within video *** methods for detecting abnormal behaviors only focus on simple motions,therefore they cannot determine the overall behavior occurring throughout a *** this study,an abnormal behavior detection method that uses deep learning(DL)-based video-data structuring is *** and motions are first extracted from continuous images by combining existing DL-based image analysis *** weight of the continuous data pattern is then analyzed through data structuring to classify the overall *** performance of the proposed method was evaluated using varying parameter settings,such as the size of the action clip and interval between action *** model achieved an accuracy of 0.9817,indicating excellent ***,we conclude that the proposed data structuring method is useful in detecting and classifying abnormal behaviors.

关键词： Deep learning object detection abnormal behavior recognition classification data structuring

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Missing Value Imputation Model Based on Adversarial Autoencoder Using Spatiotemporal Feature Extraction

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Intelligent Automation & Soft Computing 2023年第8期37卷 1925-1940页

作者： Dong-Hoon Shin Seo-El Lee Byeong-Uk Jeon kyungyong chung Department of Computer Science Kyonggi UniversitySuwon-siGyeonggi-do16227Korea Department of Public Safety Bigdata Kyonggi UniversitySuwon-siGyeonggi-do16227Korea Division of AI Computer Science and Engineering Kyonggi UniversitySuwon-siGyeonggi-do16227Korea

Recently,the importance of data analysis has increased significantly due to the rapid data *** particular,vehicle communication data,considered a significant challenge in Intelligent Transportation Systems(ITS),has spatiotemporal characteristics and many missing *** missing values in data lead to the decreased predictive performance of *** missing value imputation models ignore the topology of transportation net-works due to the structural connection of road networks,although physical distances are close in spatiotemporal image ***,the learning process of missing value imputation models requires complete data,but there are limitations in securing complete vehicle communication *** study proposes a missing value imputation model based on adversarial autoencoder using spatiotemporal feature extraction to address these *** proposed method replaces missing values by reflecting spatiotemporal characteristics of transportation data using temporal convolution and spatial *** results show that the proposed model has the lowest error rate of 5.92%,demonstrating excellent predictive *** this,it is possible to solve the data sparsity problem and improve traffic safety by showing superior predictive performance.

关键词： Missing value adversarial autoencoder spatiotemporal feature extraction

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SlowFast Based Real-Time Human Motion Recognition with Action Localization

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Computer Systems Science & Engineering 2023年第11期47卷 2135-2152页

作者： Gyu-Il Kim Hyun Yoo kyungyong chung Department of Computer Science Kyonggi UniversitySuwon16227Korea Contents Convergence Software Research Institute Kyonggi UniversitySuwon16227Korea Division of AI Computer Science and Engineering Kyonggi UniversitySuwon16227Korea

Artificial intelligence is increasingly being applied in the field of video analysis,particularly in the area of public safety where video surveillance equipment such as closed-circuit television(CCTV)is used and automated analysis of video information is ***,various issues such as data size limitations and low processing speeds make real-time extraction of video data *** analysis technology applies object classification,detection,and relationship analysis to continuous 2D frame data,and the various meanings within the video are thus analyzed based on the extracted basic *** recognition is key in this *** recognition is a challenging field that analyzes human body movements,requiring the interpretation of complex movements of human joints and the relationships between various *** deep learning-based human skeleton detection algorithm is a representative motion recognition ***,motion analysis models such as the SlowFast network algorithm,have also been developed with excellent ***,these models do not operate properly in most wide-angle video environments outdoors,displaying low response speed,as expected from motion classification extraction in environments associated with high-resolution *** proposed method achieves high level of extraction and accuracy by improving SlowFast’s input data preprocessing and data structure *** input data are preprocessed through object tracking and background removal using YOLO and DeepSORT.A higher performance than that of a single model is achieved by improving the existing SlowFast’s data structure into a frame unit *** on the confusion matrix,accuracies of 70.16%and 70.74%were obtained for the existing SlowFast and proposed model,respectively,indicating a 0.58%increase in *** detection,based on behavioral classification,the existing SlowFast detected 2,341,164 cases,whereas the proposed model detected 3,11

关键词： Artificial intelligence convolutional neural network video analysis human action recognition skeleton extraction

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