Video interpretation systems are widely used for assisting people with visual impairments. The main goal of a video interpreter system is to help people with visual impairments. By leveraging technologies such as Text...
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Human cognition naturally excels at identifying irregular patterns in images, enabling the distinction between expected variations and anomalies. Industrial defect classification presents unique challenges, encompassi...
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ISBN:
(纸本)9798350327533
Human cognition naturally excels at identifying irregular patterns in images, enabling the distinction between expected variations and anomalies. Industrial defect classification presents unique challenges, encompassing a wide range of potential errors, from minor nuances to critical structural issues. This research work introduces PaDiM (Patch Distribution Modeling Framework) model, a novel approach for cold-start anomaly detection in industrial imagery. Prior research predominantly focuses on acquiring a model representing the standard distribution, often achieved through techniques like auto encoding, GANs, or other unsupervised adaptation methods. Even without explicit adaptation, these models exhibit robust anomaly detection capabilities, effectively localizing defects within the spatial context. PaDiM excels in industrial anomaly localization, positioning itself as a state-of-the-art solution. It maintains efficiency with swift inference times, obviating the need for specific dataset training. This characteristic renders PaDiM highly attractive for practical applications in industrial anomaly detection. In this research work, PaDiM model is implemented for real-world industry manufactured products such as Bottle, Cable, Capsule, Hazelnut and Screw using computer Vision for anomaly detection on MVTec AD dataset. And obtained an accuracy of 83%. Additional experiments highlight PaDiM high sample efficiency, matching the performance of existing anomaly detection methods while utilizing only a fraction of the nominal training data. Furthermore, PaDiMs versatility extends to its adaptability in various industrial settings. Its ability to effectively identify and classify a wide range of defects, from subtle imperfections to major structural discrepancies, showcases its potential for widespread applicability across diverse manufacturing processes. This adapta bility is a testament to Patch Core's robustness and underscores its significance as a powerful tool in mode
Analyzing remote network packet data can help improve network performance, enhance security, and ensure compliance with regulatory requirements. It's very important to monitor the network, especially in critical s...
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Knowledge graph (KG) completion is a critical task in Artificial Intelligence (AI) focused on deducing absent connections between entities. The diverse nature of data, encompassing text, images, and numerical informat...
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An irregular heart rhythm that can result from various heart-related conditions are arrhythmia. Globally an estimate of 3% to 5% of population is affected by arrhythmia. If it is not diagnosed and treated quickly, it ...
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The project labelled 'DOH Integrator tool' aims to improve internet privacy and security by implementing the DNS over HTTPS (DoH) protocol. Traditional DNS queries are sent in cleartext, which makes them subje...
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This research paper explores the design and implementation of an event-driven monitoring system for Wi-Fi6 Access Points (APs) using Java-based microservices architecture. WiFi 6's primary advantage over other sta...
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Cloud storage auditing research is dedicated to solving the data integrity problem of outsourced storage on the cloud. In recent years, researchers have proposed various cloud storage auditing schemes using different ...
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Cloud storage auditing research is dedicated to solving the data integrity problem of outsourced storage on the cloud. In recent years, researchers have proposed various cloud storage auditing schemes using different techniques. While these studies are elegant in theory, they assume an ideal cloud storage model;that is, they assume that the cloud provides the storage and compute interfaces as required by the proposed schemes. However, this does not hold for mainstream cloud storage systems because these systems only provide read and write interfaces but not the compute interface. To bridge this gap, this work proposes a serverless computing-based cloud storage auditing system for existing mainstream cloud object storage. The proposed system leverages existing cloud storage auditing schemes as a basic building block and makes two adaptations. One is that we use the read interface of cloud object storage to support block data requests in a traditional cloud storage auditing scheme. Another is that we employ the serverless computing paradigm to support block data computation as traditionally required. Leveraging the characteristics of serverless computing, the proposed system realizes economical, pay-as-you-go cloud storage auditing. The proposed system also supports mainstream cloud storage upper layer applications(e.g., file preview) by not modifying the data formats when embedding authentication tags for later auditing. We prototyped and open-sourced the proposed system to a mainstream cloud service, i.e., Tencent Cloud. Experimental results show that the proposed system is efficient and promising for practical use. For 40 GB of data, auditing takes approximately 98 s using serverless computation. The economic cost is 120.48 CNY per year, of which serverless computing only accounts for 46%. In contrast, no existing studies reported cloud storage auditing results for real-world cloud services.
This paper explores the integration of machine learning (ML) techniques with magnesium-based biomedical applications, focusing on predictive modeling and personalized treatment strategies. Magnesium's biocompatibi...
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This paper explores advanced techniques in developing a Friends Recommendation System on social media platforms, specifically Facebook. By leveraging user behavior data such as the number of followers, followings, and...
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