Abstract: An anomaly labeled-assistant detection system and a method thereof are provided. The anomaly labeled-assistant detection system includes a computing apparatus and a storage apparatus. The computing apparatus includes an anomaly labeled detection model, where the computing apparatus detects a plurality of pieces of labeled image data with a labeled category through the anomaly labeled detection model, the anomaly labeled detection model respectively generates an inference category corresponding to each piece of labeled image data, and the computing apparatus compares the labeled category and the inference category according to each piece of labeled image data, and automatically lists the labeled image data as anomaly labeled data when the labeled category of the labeled image data is different from the inference category. The storage apparatus is electrically connected to the computing apparatus, to store the labeled image data.

Abstract: A power supply health check system for checking a health state of an under-test power supply is provided. The under-test power supply supplies power to a main board which has a voltage signal during operation. The health check system includes a detecting module, a deep learning model, and a processing unit. The detecting module is electrically connected to the main board to detect the voltage signal and convert the voltage signal into a digital signal. The deep learning model is established by using frequency-domain voltage data of a plurality of healthy power. The processing unit is configured to: collect the digital signal and store the digital signal as under-test time-domain voltage data; convert the under-test time-domain voltage data into under-test frequency-domain voltage data; and calculate, based on the under-test frequency-domain voltage data and the deep learning model, a health indicator for determining the health state of the under-test power supply.

Abstract: Disclosed are an image detection auxiliary system and an image detection method. An image capture device is used to generate an original image. A computing device receives the original image and preprocesses the original image as an input image. The computing device uses a classification model to obtain a feature parameter corresponding to the input image, and inputs the feature parameter to a probability function to calculate an image focus score. Finally, the computing device performs formal detection or subsequent processing of generating a second warning signal according to the image focus score.

Abstract: The disclosure provides an artificial intelligence model control system, including a plurality of artificial intelligence model service modules, a plurality of model plugins, and a model controller. Each model plugin corresponds to one artificial intelligence model service module, so that the model plugin communicates with the corresponding artificial intelligence model service module. The model controller is connected to the model plugins and controls, through the model plugin, the corresponding artificial intelligence model service module to perform a task. Therefore, in the disclosure, a single model controller controls all artificial intelligence model service modules to perform tasks.

Abstract: Disclosed are an intelligent replenishment monitoring system and method. The intelligent replenishment monitoring system includes at least one image capture device and a computing device. The image capture device captures at least one shelf to generate a shelf image. The computing device is in signal connection to the image capture device to receive the shelf image. The computing device performs first-stage identification on the shelf image according to a commodity region positioning model, to identify a commodity region image from the shelf image. The computing device performs second-stage identification on the commodity region image according to the at least one commodity replenishment model, to obtain commodity information corresponding to the commodity region image, and generates replenishment information according to the commodity information, so that a staff member replenishes the commodities according to the replenishment information.

Abstract: A power supply health check system for checking a health state of an under-test power supply is provided. The under-test power supply supplies power to a main board which has a voltage signal during operation. The health check system includes a detecting module, a deep learning model, and a processing unit. The detecting module is electrically connected to the main board to detect the voltage signal and convert the voltage signal into a digital signal. The deep learning model is established by using frequency-domain voltage data of a plurality of healthy power. The processing unit is configured to: collect the digital signal and store the digital signal as under-test time-domain voltage data; convert the under-test time-domain voltage data into under-test frequency-domain voltage data; and calculate, based on the under-test frequency-domain voltage data and the deep learning model, a health indicator for determining the health state of the under-test power supply.

Abstract: A signal abnormality detection system and a method thereof are provided. The signal abnormality detection system includes a signal sensor and a computing device. The signal sensor generates a sample signal to be tested through sensing. The computing device is signal-connected to the signal sensor to receive the sample signal to be tested, perform a correction on the sample signal to be tested, and perform a time-frequency transform on a one-dimensional signal generated after the correction to generate a two-dimensional time-frequency signal. The computing device reconstructs the two-dimensional time-frequency signal by using an abnormality detection model to calculate a reconstructed difference value. The computing device performs comparison to determine whether the reconstructed difference value is greater than a detection threshold to determine whether the sample signal to be tested is an abnormal sample.

Abstract: The disclosure provides a signal detection method. The signal detection method includes: collecting initial data; pre-processing the initial data to obtain an original signal; reconstructing the original signal by using an optimized deep learning model, to generate a reconstructed signal; and comparing the original signal with the reconstructed signal, to determine whether there is an abnormality in the original signal. The disclosure further provides an electronic device using the signal detection method.

Abstract: A stamp including a casing, a spring supported actuating member moved in and out of the casing, a stamp plate moved with the casing relative to the actuating member and having a semi-circular gear at one end meshed with a longitudinal rack inside the actuating member, and an ink pad mounted inside the actuating member and disposed in contact with the stamping face of the stamp plate, wherein when the actuating member is pressed on a workpiece, the stamp plate is moved out of the bottom open end of the actuating member and turned through a 180.degree. angle, causing the stamping face to be stamped on the workpiece.