In this paper, a digital image processing algorithm i s designed based on digital image processing technology to achieve accurate acquisition of rock porosity data, and the parametric relationship between concrete por...
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Survival prediction in PD-1 inhibitor patients has received extensive attention in recent years. Existing diffusion models generally focus blurring on key lesion regions, and the masks are weakly matched to CT images ...
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This research integrates Sensor Pattern Noise (SPN) with Error Level Analysis (ELA) using multi-modal deep learning techniques to enhance image forgery detection. ELA is effective at detecting compression artifacts bu...
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Bearing is the key part of mechanical equipment, and its state of health directly affects the property and safety of equipment traditional bearing health monitoring methods are usually based on vibration analysis and ...
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The smart healthcare system plays a more critical role than ever before. It is designed to share electronic patient records (EPR) to improve medical care and facilitate research. However, protecting the security of EP...
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This paper examines the automatic classification of skin photos using the state-of-the-art deep learning methods, with a focus on the categorization of skin lesions. One of the main goals is to improve the accuracy an...
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In recent years, the COVID-19 has made it difficult for people to interact with each other face-to-face, but various kinds of social interactions are still needed. Therefore, we have developed an online interactive sy...
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Most anomaly detection models are often sensitive to unavoidable disturbance or non-defective "visual defects", and such near abnormal samples are easily identified as anomalies, resulting in a high false de...
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ISBN:
(纸本)9781728198354
Most anomaly detection models are often sensitive to unavoidable disturbance or non-defective "visual defects", and such near abnormal samples are easily identified as anomalies, resulting in a high false detection rate. To this end, we propose a novel multi-scale Cross-image Knowledge Transfer anomaly detection model, namely CKT. Different from most existing intra-image distillation methods, our model transfers both the intra-image knowledge of the normal image and the inter-image knowledge of the normal image and the near-anomaly prototype, to assist the model to learn more robust normal patterns. Furthermore, we develop a cross-image attention module for explicitly enhancing the near-abnormal pattern learning during the distillation procedure, to alleviate the problem of high false detection rate induced by near-abnormal instances. Extensive experiments on texture datasets, such as KSDD2, MT, AITEX, and the textural subset of Mvtec-AD, show that the proposed CKT model can outperform most of the current unsupervised anomaly detection methods. Compared with the existing distillation based anomaly detection frameworks, our work can get significant gains with a margin of 2%.
Time Division Multiple Access (TDMA) is a practical multiple access technology in modern communication systems. In TDMA communication system, terminals share wireless channel resources in the form of frame-time slots....
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Digital Twin is a digital replication of physical things. This technology has been in works for over a decade, and has been implemented in various regions. This study shows the integration of internet of Things(IoT) w...
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