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Anomaly Detection Control Through One-class Learning in Defect Inspection Systems

Journal of Institute of Control, Robotics and Systems 2025年第4期31卷 328-333页

作者： Jeon, Janggun Kim, Namgi Dept of AI Computer Engineering and Dept of computer science Kyonggi University Korea Republic of

This paper presents a novel image-level anomaly detection control method for defect inspection, which comprises featureextraction from high-dimensional data and feature-learning to analyze the observed patterns. we focus on the feature-learning step, especially in Deep SVDD. To determine anomalies from images of object suspected to be defective, Deep SVDD learns the decision boundaries for the normal feature distribution in the latent space. When a new image is received by the defect inspection system, Deep SVDD maps its features to the latent space and detects anomalies based on whether the observed features fall within the decision boundaries for normal features. The decision boundary of Deep SVDD is determined based on the center-point, which is the center position of the decision boundary. The center-point is randomly fixed at the beginning of training of Deep SVDD. However, this limits the possibility of finding the optimal decision boundary. Therefore, we propose a center-point-moving method to optimize the center-point location and improve the performance of Deep SVDD. To confirm the suitability of the proposed method, we conducted experiments on MVTec-AD, a defect inspection dataset for several products in the manufacturing industry. Based on the results, the proposed method improved the performance of Deep SVDD in 11 of the 15 class domains in the MVTec-AD dataset. © ICROS 2025.

关键词： Inspection equipment

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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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A survey on cross-user federated recommendation

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science China(Information sciences) 2025年第4期68卷 7-32页

作者： Enyue YANG Yudi XIONG Wei YUAN Weike PAN Qiang YANG Zhong MING College of Computer Science and Software Engineering Shenzhen University School of Electrical Engineering and Computer Science The University of Queensland WeBank AI Lab WeBank Department of Computer Science and Engineering Hong Kong University of Science and Technology College of Big Data and Internet Shenzhen Technology University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)

Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.

关键词： cross-user federated recommendation federated recommendation federated learning recommender systems user privacy

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Dynamic Framerate SlowFast Network for Improving Autonomous Driving Performance

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IEIE Transactions on Smart Processing and Computing 2023年第3期12卷 261-268页

作者： Jeon, Byeong-Uk Chung, Kyungyong Department of Computer Science Kyonggi University Suwon Korea Republic of Division of AI Computer Science and Engineering Kyonggi University Suwon Korea Republic of

computer vision technology is used for autonomous driving and road traffic safety. Accordingly, studies on deep learning models that detect and analyze objects through images or videos are ongoing. On the other hand, deep learning algorithms that detect even the action of things require high computing performance. In addition, the computing performance of autonomous driving vehicles for processing such tasks is inferior. These classification processes are not used for recognizing and determining autonomous driving vehicles because it is impossible to process the classification of the action of autonomous driving vehicles in an autonomous driving vehicle on a real-time basis. This paper proposes a Dynamic Framerate SlowFast network for improving autonomous driving performance. Unlike pre-existing studies, the proposed model includes a cropping process through the YOLO model. In addition, it measures the similarity between unit frames through the SSIM and skips the input when the similarity exceeds a certain level. This process made it possible to reduce the number of frames entered into the model. Compared to the existing SlowFast Network, the performance evaluation compared the time required to analyze one image and the AUC of classification results when the number of input frames was reduced through similarity analysis techniques. The similarity analysis technique achieved the highest AUC when the SSIM was applied. The Dynamic Framerate SlowFast network proposed in this study achieved an AUC of 0.7126 and took an FPS of 0.7912 to analyze the entire verification video data. Compared to the pre-existing SlowFast network, which took an FPS of 0.5285 to achieve an AUC of 0.7531, the Dynamic Framerate SlowFast network achieved faster and more accurate results. Therefore, using the proposed technique, it is possible to achieve faster detection results while maintaining the object action detection AUC of the SlowFast network. © 2023 Institute of Electronics and Informatio

关键词： Autonomous vehicles

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Question classification task based on deep learning models with self-attention mechanism

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Multimedia Tools and Applications 2025年第10期84卷 7777-7806页

作者： Mondal, Subhash Barman, Manas Nag, Amitava Computer Science and Engineering Central Institute of Technology Kokrajhar Assam Kokrajhar India Computer Science and Engineering [AI & ML Dayananda Sagar University Karnataka Bengaluru India

Question classification (QC) is a process that involves classifying questions based on their type to enable systems to provide accurate responses by matching the question type with relevant information. To understand and respond to natural language questions posed by humans, machine learning models or systems must comprehend the type of information requested, which can often be inferred from the structure and wording of the question. The high dimensionality and sparse nature of text data lead to challenges for text classification. These tasks can be improved using deep learning (DL) approaches to process complex patterns and features within input data. By training on large amounts of labeled data, deep learning algorithms can automatically extract relevant features and representations from text, resulting in more accurate and robust classification. This study utilizes a dataset comprising 5452 instances of questions and six output labels and uses two different word embedding techniques, like GloVe and Word2Vec, tested on the dataset using three deep learning models, LSTM, BiLSTM, and GRU, followed by a convolution layer. Additionally, a self-attention layer is included, which helps the model to focus on more relevant information when making predictions. Finally, an analytical discussion of the proposed models and their performance results provide insight into how GloVe and Word2Vec perform on the above-mentioned models. The GloVe embedding outperforms by achieving 97.68% accuracy and a moderate loss of 16.98 with the GRU model. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Embeddings

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Extended linear regression model for vessel trajectory prediction with a-priori aiS information

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地球空间信息科学学报（英文版） 2024年第1期27卷 202-220页

作者： Christiaan Neil Burger Waldo Kleynhans Trienko Lups Grobler Computer Science Division Stellenbosch UniversityStellenboschSouth Africa Electrical Electronic and Computer EngineeringUniversity of PretoriaPretoriaSouth Africa

As maritime activities increase globally,there is a greater dependency on technology in monitoring,control,and surveillance of vessel *** of the most prominent systems for monitoring vessel activity is the Automatic Identification System(aiS).An increase in both vessels fitted with aiS transponders and satellite and terrestrial aiS receivers has resulted in a significant increase in aiS messages received *** resultant rich spatial and temporal data source related to vessel activity provides analysts with the ability to perform enhanced vessel movement analytics,of which a pertinent example is the improvement of vessel location *** this paper,we propose a novel strategy for predicting future locations of vessels making use of historic aiS *** proposed method uses a Linear Regression Model(LRM)and utilizes historic aiS movement data in the form of a-priori generated spatial maps of the course over ground(LRMAC).The LRMAC is an accurate low complexity first-order method that is easy to implement operationally and shows promising results in areas where there is a consistency in the directionality of historic vessel *** areas where the historic directionality of vessel movement is diverse,such as areas close to harbors and ports,the LRMAC defaults to the *** proposed LRMAC method is compared to the Single-Point Neighbor Search(SPNS),which is also a first-order method and has a similar level of computational complexity,and for the use case of predicting tanker and cargo vessel trajectories up to 8 hours into the future,the LRMAC showed improved results both in terms of prediction accuracy and execution time.

关键词： Automatic Identification System(aiS)data Linear Regression Model(LRM) trajectory mining spatial map historic data trajectory prediction

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Power synchronization: taming massive diversified serverless functions under power constraints

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science China(Information sciences) 2025年第3期68卷 47-65页

作者： Du LIU Lu ZHANG Yechen XU Xinkai WANG Lingyu SUN Yifei PU Xiaofeng HOU Chao LI Minyi GUO Department of Computer Science and Engineering Shanghai Jiao Tong University AI Chip Center for Emerging Smart Systems The Hong Kong University of Science and Technology

Carbon neutrality has become an important design objective worldwide. However, the on-going shift to cloud-naive era does not necessarily mean energy efficiency. From the perspective of power management, co-hosted serverless functions are difficult to tame. They are lightweight, short-lived applications sensitive to power capping activities. In addition, they exhibit great individual and temporal variability, presenting idiosyncratic performance/power scaling goals that are often at odds with one another. To date, very few proposals exist in terms of tailored power management for serverless platforms. In this work, we introduce power synchronization, a novel yet generic mechanism for managing serverless functions in a power-efficient way. Our insight with power synchronization is that uniform application power behavior enables consistent and uncompromised function operation on shared host machines. We also propose PowerSync, a synchronization-based power management framework that ensures optimal efficiency based on a clear understanding of functions. Our evaluation shows that PowerSync can improve the energy efficiency of functions by up to 16% without performance loss compared to conventional power management strategies.

关键词： serverless computing variability energy efficiency power management power synchronization

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Comparative research on block design-based protocol and blowfish for secured key generation and data sharing on cloud

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Soft Computing 2024年第23期28卷 13701-13713页

作者： Hombal, Uma Rangapura Basavaraju, Dayananda Shinde, Ashwini S. Department of Computer Science and Engineering KSIT Bengaluru India Department of Computer Science and Engineering MSRIT Bengaluru India Department of CSE-AI Nutan College of Engineering and Research Pune India

Confidentiality of maintaining the Electronic Health Records of patients is a major concern to both the patient and Doctor. Sharing the data on cloud is one of the most efficient technology infrastructures with extensive potential application, but at the same time there is a threat of hacking the data. Here we implement the concept for sharing of electronic health record on cloud that permits multiple members of the group to easily share the group data. With the increase in size of healthcare data the time and space complexity increases. The main challenge here is data security. This research article emphasis on a secured method for sharing the data on cloud and generation of the key that is safe from unauthorized user. In this Research article, we implement the soft computing approach for key agreement protocol using Block based design to support several users that can adapt it-self to upsurge the number of users in the cloud which depends on the assembly of the block design. This protocol generates a common conference key for all members. On comparing our results with some prevailing techniques, we accomplish that the results of our research have a better computational complexity. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Personal computing

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Research on Memory-Efficient Approach to Sleep Quality Estimation for Edge Devices 15

Research on Memory-Efficient Approach to Sleep Quality Estim...

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Kang, Seungeun Kim, Jin Kim, Namgi Kyonggi University Division of Ai Computer Science and Engineering Suwon Korea Republic of

ISBN: (纸本)9798350364637

Lifelog data has become easier to collect due to the development of wearable technology and IoT devices, and its importance has increased with the growth of the healthcare industry. In particular, personalized services such as personalized healthcare and stress management are possible because of the personal characteristics of lifelog data. In this study, we build a sleep quality classification system using lifelog datasets. As a preprocessing of the time series data, we propose a method for using different time periods depending on the type of question to be considered, a method for synchronizing variable lengths, and a process for normalizing the data. In addition, this paper suggests Weighted Validation Loss Propagation and Set Lower Bound methods to enhance generalization performance in the process of building and training classification deep learning models with Convolution Neural Network (CNN) and Attention-based models. Then, we discuss model significance, applications, and limitations. We also propose future research to overcome the limitations. © 2024 IEEE.

关键词： Sleep research

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DeepGAN: Utilizing generative adversarial networks for improved deep learning

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International Journal of Knowledge-Based and Intelligent engineering Systems 2024年第4期28卷 732-748页

作者： V, Edward Naveen A, Jenefa T.M, Thiyagu A, Lincy Taurshia, Antony Department of Computer Science and Engineering Sri Shakthi Institute of Engineering and Technology India Department of Computer Science and Engineering Karunya Institute of Technology and Sciences India Division of Computer Science and Engineering Karunya Institute of Technology and Sciences India Department of Computer Science and Engineering National Engineering College India

In the realm of deep learning, Generative Adversarial Networks (GANs) have emerged as a topic of significant interest for their potential to enhance model performance and enable effective data augmentation. This paper addresses the existing challenges in synthesizing high-quality data and harnessing the capabilities of GANs for improved deep learning outcomes. Unlike traditional approaches that heavily rely on manually engineered data augmentation techniques, our work introduces a novel framework that leverages DeepGANs to autonomously generate diverse and high-fidelity data. Our experiments encompass a diverse spectrum of datasets, including images, text, and time series data. In the context of image classification tasks, we conduct experiments on the widely recognized CIFAR-10 dataset, which consists of 50,000 image samples. Our results demonstrate the remarkable efficacy of DeepGANs in enhancing model performance across various data domains. Notably, in image classification using the CIFAR-10 dataset, our innovative approach achieves an impressive accuracy of 97.2%. This represents a substantial advancement beyond conventional CNN models, underscoring the profound impact of DeepGANs in the realm of deep learning. In summary, this research sheds light on DeepGANs as a fundamental component in the pursuit of enhanced deep learning performance. Our framework not only overcomes existing limitations but also heralds a new era of data augmentation, with generative adversarial networks leading the way. The attainment of an accuracy rate of 97.2% on CIFAR-10 serves as a compelling testament to the transformative potential of DeepGANs, solidifying their pivotal role in the future of deep learning. This promises the development of more robust, adaptive, and accurate models across a myriad of applications, marking a significant contribution to the field. © 2024 – IOS Press. All rights reserved.

关键词： Generative adversarial networks

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