公开(公告)号：US20230408987A1

公开(公告)日：2023-12-21

申请号：US17987905

申请日：2022-11-16

Applicant: ASUSTeK COMPUTER INC.

Inventor： Jia-En Lee ,  Shu-Chiao Liao

IPC: G05B13/02

CPC classification number: G05B13/0265

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.

公开(公告)号：US12026075B2

公开(公告)日：2024-07-02

申请号：US18073300

申请日：2022-12-01

Applicant: ASUSTEK COMPUTER INC.

Inventor： Cheng-Wei Gu ,  Shu-Chiao Liao ,  Ming-Hung Chung ,  Yao-Hsun Huang ,  Yuan-I Tseng ,  Hung-Ju Lin

IPC: G06F11/273 ,  G06F1/3296

CPC classification number: G06F11/2733 ,  G06F1/3296

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.

公开(公告)号：US20230341464A1

公开(公告)日：2023-10-26

申请号：US17979860

申请日：2022-11-03

Applicant: ASUSTeK COMPUTER INC.

Inventor： Hung-Ju Lin ,  Yuan-I Tseng ,  Cheng-Wei Gu ,  Shu-Chiao Liao

IPC: G01R31/317 ,  G01R31/28 ,  G06K9/62

CPC classification number: G01R31/31708 ,  G01R31/2841 ,  G06K9/6256

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.