UNRAVELING FALSE POSITIVES IN UNSUPERVISED DEFECT DETECTION MODELS: A STUDY ON ANOMALY-FREE TRAINING DATASETS

Unraveling False Positives in Unsupervised Defect Detection Models: A Study on Anomaly-Free Training Datasets

Unsupervised defect detection methods have garnered substantial attention in industrial defect detection owing to their capacity to circumvent complex fault sample collection.However, these models grapple with establishing a robust boundary between normal and abnormal conditions in intricate scenarios, leading to a heightened frequency of false-pos

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Kinesthetic imagery of musical performance

Musicians use different kinds of imagery.This review focuses on kinesthetic imagery, which has been shown to be an effective complement to actively playing an instrument.However, experience in actual movement performance seems to be a requirement for a recruitment of those brain areas representing movement ideation during imagery.An internal model

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An overview of emulgels for topical application

In recent years, gels have been preferentially used for cosmetics and topical pharmaceutical preparations due to their favorable characteristics, such as being greaseless, readily spreadable and easily removable.However, one obstacle that faced it was the inability to Baking enclose hydrophobic compounds.Therefore, a novel approach was developed to

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Leaf Area Index Estimation Algorithm for GF-5 Hyperspectral Data Based on Different Feature Selection and Machine Learning Methods

Leaf area index (LAI) is an essential vegetation parameter that represents the light energy utilization and vegetation canopy structure.As the only in-operation hyperspectral satellite launched by China, GF-5 is potentially useful for accurate LAI estimation.However, there is no research focus on evaluating GF-5 data for LAI estimation.Hyperspectra

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