Acer Inc.

Acer Medical Inc.

Wistron Corporation

National Yang Ming Chiao Tung University

National Health Research Institutes

Taipei Veterans General Hospital

Acer Cyber Security Incorporated

Chang Gung Memorial Hospital, Keelung

Chun-Hsien Li

Tsung-Hsien Tsai

Jun-Hong Chen

Hao-Min Cheng

Yun-Hsuan Chan

Wei-Ting Wang

Chien-Hung Li

Chiung-Ying Huang

Yin-Hsong Hsu

Ming-Kung Sun

Yueh-Yarng Tsai

Pin-Cyuan Lin

Zong-Cyuan Jhang

Ting-Fen Tsai

Liang-Kung Chen

Sheng-Wei Chu

Chia-Chiung Liu

Chi-Hsiao Yeh

Yin-Hao Lee

Te-Chung Huang

Tsung-Han Yang

Andy Ho

Szu-Chieh Wang

Chen-Huan Chen

Li-Ning Peng

Chia-Shang Yuan

Chun I Yeh

Che-Wen Ku

Jun-Mein Wu

The disclosure provides a recommended method for replacing equipment and an electronic device. A plurality of reference electricity consumptions of an equipment are estimated by using an electricity consumption prediction model based on equipment operation information of the equipment in a plurality of unit periods. A plurality of actual electricity consumptions of the equipment in the multiple unit periods are obtained. An abnormal electricity consumption time interval is determined by comparing the actual electricity consumptions with the reference electricity consumptions. A replacement index and an equipment energy efficiency of the equipment in the abnormal electricity consumption time interval are determined according to the equipment operation information of the equipment in the abnormal electricity consumption time interval. In response to determining that the replacement index and the equipment energy efficiency meet a replacement condition, a recommended equipment list including at least one recommended equipment is provided.

Recommended method for replacing equipment and electronic device

An energy-saving prediction method for factory electricity consumption and an electronic apparatus are provided. The method includes the following steps. A reference electricity consumption amount in a unit period is predicted based on factory actual operation information in the unit period by using an electricity consumption prediction model. An actual electricity consumption amount in the unit period is acquired. The reference electricity consumption amount and the actual electricity consumption amount are displayed on an electricity consumption reference interface. A function is provided according to a first difference value between the reference electricity consumption amount and the actual electricity consumption amount.

Energy-saving prediction method for factory electricity consumption and electronic apparatus

An electronic device and a method for predicting a blockage of a coronary artery are provided. The method includes: obtaining multiple pieces of electrocardiogram (ECG) data respectively corresponding to a coronary artery set; generating multiple first probabilities corresponding to the multiple pieces of electrocardiogram data respectively according to the multiple pieces of electrocardiogram data and a first phase model, generating a first determined result according to the multiple first probabilities, and selecting a first data subset corresponding to a first probability subset from the multiple pieces of electrocardiogram data in response to each one in the first data subset of the multiple first probabilities being greater than a first threshold; generating multiple second probabilities corresponding to the first data subset according to the first data subset and a second phase model, and generating a second determined result according to the multiple second probabilities.

Electronic device and method for predicting blockage of coronary artery

An electronic device and a method for selecting a feature of an electrocardiogram (ECG) are provided. The method includes: obtaining the ECG; performing a first pre-processing on the ECG to generate a first ECG; marking multiple extreme points corresponding to at least one type of wave on the first ECG; calculating a first feature value corresponding to a first feature according to the multiple extreme points of the at least one type of wave, generating a first performance index corresponding to a machine learning model according to the first feature value, and determining whether to select the first feature according to the first performance index; and outputting the first feature in response to selecting the first feature.

Electronic device and method for selecting feature of electrocardiogram

A method and a system for mental index prediction are provided. The method includes the following steps. A plurality of images of a subject person are obtained. A plurality of emotion tags of the subject person in the images are analyzed. A plurality of integrated emotion tags in a plurality of predetermined time periods are calculated according to the emotion tags respectively corresponding to the images. A plurality of preferred features are determined according to the integrated emotion tags. A mental index prediction model is established according to the preferred features to predict a mental index according to the emotional index prediction model.

Method and system for mental index prediction

A feature identifying method and an electronic device are provided. The method includes: obtaining a plurality of physiological information obtained by measuring a subject at a plurality of time points in one day; converting the plurality of physiological information into a plurality of correlation features respectively; establishing a plurality of first risk prediction models according to the plurality of correlation features, and identifying at least one first correlation feature from the plurality of correlation features according to the plurality of first risk prediction models; establishing a plurality of second risk prediction models according to the at least one first correlation feature, and identifying, according to the plurality of second risk prediction models, at least one second correlation feature capable of predicting a specific disease from the at least one first correlation feature; and outputting the at least one second correlation feature.

Feature identifying method and electronic device

The disclosure provides a disease classification method and a disease classification device. The disease classification method includes: inputting samples into a first stage model and obtaining a first stage determination result; inputting first samples determined positive by the first stage model into a second stage high specificity model to obtain second samples determined to be positive and third samples determined to be negative and rule in the second samples; inputting fourth samples determined negative by the first stage model into a second stage high sensitivity model to obtain fifth samples determined to be positive and sixth samples determined to be negative and rule out the sixth samples; obtaining a second stage determination result of the second and sixth samples; and inputting the third and fifth samples not ruled in or ruled out into a third stage model and obtaining a third stage determination result of the third and fifth samples.

Disease classification method and disease classification device

A teammate recommendation method for a multiplayer online game is proposed. The teammate recommendation method includes the steps of: determining one or more characteristic values for each of a plurality of players based on past game performance information of the players; dividing the players into a plurality of groups based on the characteristic values of the players; determining a Key Performance Indicator (KPI) for each of a plurality of combinations of the groups based on teammate information and team ranking of each game in the past game performance information; selecting one of the combinations, which includes a first group that the first player belongs to, based on the KPIs of the combinations; and recommending one or more second players based on the selected combination.

Teammate recommendation method for a multiplayer online game

A physiological status evaluation method and a physiological status evaluation apparatus are provided. The method includes the following: obtaining original electrocardiogram data of a user by an electrocardiogram detection apparatus; converting the original electrocardiogram data into digital integration data; obtaining a plurality of physiological characteristic parameters according to the digital integration data; filtering the physiological characteristic parameters for at least one notable characteristic parameter through at least one filter model, where decision importance of the at least one notable characteristic parameter in a decision process of the at least one filter model is greater than a threshold; building a prediction model according to the at least one notable characteristic parameter; and evaluating a physiological status of the user through the prediction model.

Physiological status evaluation method and physiological status evaluation apparatus

A system of training behavior labeling model is provided. Specifically, a processing unit inputs each data of a training data set into a plurality of learning modules to establish a plurality of labeling models. The processing unit obtains a plurality of second labeling information corresponding to each data of a verification data set and generates a behavior labeling result according to the second labeling information corresponding to each data of the verification data set. The processing unit obtains a labeling change value according to the behavior labeling result and first labeling information corresponding to each data of the verification data set. The processing unit, if determining that the labeling change value is greater than a change threshold, updates the first labeling information according to the behavior labeling results, exchanges the training data set and the verification data set and reestablishes the labeling models.

System and method of training behavior labeling model

A method for detecting abnormality adapted to detect abnormal operations of an operating system is provided. The method includes: calculating a safe range of usage of the operating system during one or more time periods according to a historical data stream; calculating abnormal ratios corresponding to the one or more time periods according to a current data stream and the safe range of usage; selecting one or more abnormal time periods from the one or more time periods according to a threshold and the abnormal ratios; calculating an abnormal indicator for each of the one or more abnormal time periods according to the historical data stream and the current data stream; and ranking the one or more abnormal time periods according to the abnormal indicator(s).

Method and device for detecting abnormal operation of operating system

A method and a system for recommending a teammate for a team game are provided. The method includes the following: searching game data, where the game data includes a plurality of game evaluation parameters; choosing a target parameter from the game evaluation parameters, where a correlation between the target parameter and at least one game experience index is greater than a correlation between remaining parameters and the at least one game experience index; automatically selecting a target role position according to the target parameter and a first game role position of a chosen game role; automatically selecting a target game role according to the target role position; and recommending a player to be teamed up who plans to use the target game role to play the game as a teammate according to the target game role.

Method and system for recommending teammate for team game

Growing community of inventors

Taipei, Taiwan

Chun-Hsien Li