Abstract: A model adjustment method, comprises: by a processing device, performing: obtaining inferred data that is inferred using a model, performing a feedback mechanism on the inferred data to obtain a feedback command associated with correctness of the inferred data, adjusting the inferred data according to the feedback command to generate adjusted data, and using the adjusted data as one of a plurality of pieces of training data for retraining the model. The present disclosure further provides a model adjustment system and non-transitory computer readable medium.

Abstract: A defect-detecting device stores a plurality of audio image data and a target audio image data. The plurality of audio image data include image data of normal audio and image data of defective audio of a first audio device and image data of normal audio of a second audio device, and the target audio image data corresponds to the second audio device. The defect-detecting device generates a plurality of simulated audio image data according to the plurality of audio image data, and trains a defect detection model according to the simulated audio image data. The defect-detecting device also analyzes, through the defect detection model, the target audio image data, so as to determine whether the second audio device is defective.

Abstract: An apparatus and method for generating a motor diagnosis model. Each first dimension-reduced datum of a first motor corresponds to a normal state, an offline state, or an abnormal state, while each second dimension-reduced datum corresponds to the normal state or the offline state. The apparatus rotates the first dimension-reduced data or the second dimension-reduced data and integrates the rotated results with the dimension-reduced data that have not been rotated as a to-be-analyzed dataset. The apparatus trains a classification model for distinguishing data sources by using a subset of the to-be-analyzed dataset, derives an accuracy rate by testing the classification model for distinguishing data sources by using another subset of the to-be-analyzed dataset, and determines whether to use the to-be-analyzed dataset to generate the motor diagnosis model for the second motor.