Abstract: An acute kidney injury predicting system and a method thereof are proposed. A processor reads the data to be tested, the detection data, the machine learning algorithm and the risk probability comparison table from a main memory. The processor trains the detection data according to the machine learning algorithm to generate an acute kidney injury prediction model, and inputs the data to be tested into the acute kidney injury prediction model to generate an acute kidney injury characteristic risk probability and a data sequence table. The data sequence table lists the data to be tested in sequence according to a proportion of each of the data to be tested in the acute kidney injury characteristics. The processor selects one of the medical treatment data from the risk probability comparison table according to the acute kidney injury characteristic risk probability.

Abstract: A method for predicting precision of an electrical discharge machine is provided. In the method, plural sets of process data are obtained while the electrical discharge machine processes workpiece samples. Process features are established based on the process data. Each of the workpiece samples with respect to each of at least one measurement item is measured by using a metrology tool, thereby obtaining measurement values of the workpiece samples with respect to each measurement item. A correlation analysis operation is performed to obtain correlation coefficients. At least one key feature is selected from the process features as representative according to the correlation coefficients. The measurement values of the workpiece samples with respect to each measurement item, and the sets of process data corresponding to the key features are used to build a predictive model for predicting the precision of the electrical discharge machine.

Abstract: A computer-implemented method is provided. First, channel data is obtained, and whether the channel data complies with a predetermined condition of an event is determined. If the channel data complies with the predetermined condition, the channel data is recorded into a database according to a sampling frequency and a recording duration. The channel data corresponding to the event is obtained from the database, and is displayed according to a user operation. A replay frequency and stepping time interval corresponding to the user operation are obtained. The recording duration is adjusted according to the replay frequency, and the sampling frequency is adjusted according to the stepping time interval.

Abstract: A method for predicting precision of an electrical discharge machine is provided. In the method, plural sets of process data are obtained while the electrical discharge machine processes workpiece samples. Process features are established based on the process data. Each of the workpiece samples with respect to each of at least one measurement item is measured by using a metrology tool, thereby obtaining measurement values of the workpiece samples with respect to each measurement item. A correlation analysis operation is performed to obtain correlation coefficients. At least one key feature is selected from the process features as representative according to the correlation coefficients. The measurement values of the workpiece samples with respect to each measurement item, and the sets of process data corresponding to the key features are used to build a predictive model for predicting the precision of the electrical discharge machine.

Abstract: A computer-implemented method is provided. First, channel data is obtained, and whether the channel data complies with a predetermined condition of an event is determined. If the channel data complies with the predetermined condition, the channel data is recorded into a database according to a sampling frequency and a recording duration. The channel data corresponding to the event is obtained from the database, and is displayed according to a user operation. A replay frequency and stepping time interval corresponding to the user operation are obtained. The recording duration is adjusted according to the replay frequency, and the sampling frequency is adjusted according to the stepping time interval.