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: 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: A feature extraction method for photoplethysmography includes obtaining a photoplethysmography (PPG) signal; calculating a first-order derivative photoplethysmography (FDPPG) signal, a second-order derivative photoplethysmography (SDPPG) signal and a third-order derivative photoplethysmography (TDPPG) signal from the PPG signal, wherein the SDPPG signal has multiple feature points; and performing a feature extraction operation that exploits the property of the TDPPG signal to impute the missing feature points of the SDPPG signal. Moreover, the feature extraction operation further includes: resolving the ambiguous feature points of the SDPPG signal.

Abstract: An online reading processing system and method for providing interactive messages to users at end computer devices are provided, which include storing online reading information in a data storage medium; setting the online reading information with a head mark and a tail mark of at least one expert-marked key range by a setting module; reading the information after being set and hiding the head mark and the tail mark thereof; receiving the key range marked by the user; determining whether the user-marked key range covers the head mark and the tail mark so as to form interactive messages according the determination, thereby solving the drawback of failing to provide appropriate feedback or assessment according to users' behaviors as encountered in the prior techniques, and also increasing online reading interaction and enjoyment.

Abstract: An audio/video interaction method whereby users interact with an output audio/video file is provided. The method includes obtaining input behaviors performed by a user on an audio/video file to serve as behavior information and storing the same in a behavior database thereof; and comparing predetermined behavior patterns in a database with the user's behavior information to obtain a feedback information and further analyzing the behavior information to generate corresponding statistical data, thereby providing feedback information upon the output of the audio/video file or providing the statistical data when the output thereof is discontinued.

Abstract: An online reading processing system and method for providing interactive messages to users at end computer devices are provided, which include storing online reading information in a data storage medium; setting the online reading information with a head mark and a tail mark of at least one expert-marked key range by a setting module; reading the information after being set and hiding the head mark and the tail mark thereof; receiving the key range marked by the user; determining whether the user-marked key range covers the head mark and the tail mark so as to form interactive messages according the determination, thereby solving the drawback of failing to provide appropriate feedback or assessment according to users' behaviors as encountered in the prior techniques, and also increasing online reading interaction and enjoyment.

Abstract: A high-concentration photovoltaic system is provided with a central monitor and control system. The central monitor and control system includes a computing unit, a main data base server, a database/web server, three multi-port serial servers, a plurality of switches and a plurality of communication interfaces. With the central monitor and control system, instant monitor, control and inquiry are possible.