Abstract: A system and a method for parameter optimization with adaptive search space and a user interface using the same are provided. The system includes a data acquisition unit, an adaptive adjustment unit and an optimization search unit. The data acquisition unit obtains a set of executed values of several operating parameters and a target parameter. The adaptive adjustment unit includes a parameter space transformer and a search range definer. The parameter space transformer performs a space transformation on a parameter space of the operating parameters according to the executed values. The search range definer defines a parameter search range in a transformed parameter space based on the sets of the executed values. The optimization search unit takes the parameter search range as a limiting condition and takes optimizing the target parameter as a target to search for a set of recommended values of the operating parameters.

Abstract: A dynamic prediction model establishment method, an electric device and a user interface are provided. The dynamic prediction model establishment method includes the following steps. An integration model is established by a processing device according to at least one auxiliary data set. The integration model is modified as a dynamic prediction model by the processing device according to a target data set. A sampling point recommendation information is provided by the processing device according to an error degree or an uncertainty degree between the at least one auxiliary data set and the target data set.

Abstract: An electronic device and a method for training a neural network model are provided. The method includes: obtaining a first neural network model and a first pseudo-labeled data; inputting the first pseudo-labeled data into the first neural network model to obtain a second pseudo-labeled data; determining whether a second pseudo-label corresponding to the second pseudo-labeled data matching a first pseudo-label corresponding to the first pseudo-labeled data; in response to the second pseudo-label matching the first pseudo-label, adding the second pseudo-labeled data to a pseudo-labeled dataset; and training the first neural network model according to the pseudo-labeled dataset.

Abstract: A system and a method for parameter optimization with adaptive search space and a user interface using the same are provided. The system includes a data acquisition unit, an adaptive adjustment unit and an optimization search unit. The data acquisition unit obtains a set of executed values of several operating parameters and a target parameter. The adaptive adjustment unit includes a parameter space transformer and a search range definer. The parameter space transformer performs a space transformation on a parameter space of the operating parameters according to the executed values. The search range definer defines a parameter search range in a transformed parameter space based on the sets of the executed values. The optimization search unit takes the parameter search range as a limiting condition and takes optimizing the target parameter as a target to search for a set of recommended values of the operating parameters.

Abstract: A parameter optimization method includes: a parameter search is performed on an input parameter, an output response value and a target value through a plurality of optimization schemes to search for a plurality of candidate recommended parameters. Each optimization scheme is assigned to a weight value according to user historical decision information. At least one recommended parameter is selected from the candidate recommended parameters according to the weight values. An user interface is provided for a user to input a decision instruction. A new input parameter is selected from the at least one recommended parameter according to the decision instruction; the new input parameter is inputted into the target system; and a new output response value is evaluated whether meets a specification condition. The user historical decision information is updated based on the decision instruction to adjust the weight values.

Abstract: A dynamic prediction model establishment method, an electric device and a user interface are provided. The dynamic prediction model establishment method includes the following steps. An integration model is established by a processing device according to at least one auxiliary data set. The integration model is modified as a dynamic prediction model by the processing device according to a target data set. A sampling point recommendation information is provided by the processing device according to an error degree or an uncertainty degree between the at least one auxiliary data set and the target data set.

Abstract: A tree search-based scheduling method and an electronic apparatus are provided. In the method, multiple order lists are received and a schedule is initialized, wherein each order list includes multiple production operations. In each order list, a first production operation which has not been joined into the schedule yet is selected, such that multiple prior operations are selected. An execution priority of the prior operations is calculated according to multiple dispatching rules, and multiple candidate operations are selected from the prior operations according to the execution priority. Afterwards, the candidate operations are listed as a next operation of the schedule respectively, and a scheduling simulation is performed according to the dispatching rules to obtain multiple scheduling indicators of the candidate operations. Scheduling is performed according to the scheduling indicators.

Abstract: A health assessment method and a health assessment device of a workpiece processing apparatus are disclosed. The health assessment method includes the following steps. Acquire a first sensing data related to the workpiece processing apparatus at an operation stage of the workpiece processing apparatus. Set the first sensing data as a substitution of a first transform model to acquire a virtual workpiece quality. Set the virtual workpiece quality as a substitution of a second transform model to acquire a first virtual apparatus health index.

Abstract: A parameter optimization method includes: a parameter search is performed on an input parameter, an output response value and a target value through a plurality of optimization schemes to search for a plurality of candidate recommended parameters. Each optimization scheme is assigned to a weight value according to user historical decision information. At least one recommended parameter is selected from the candidate recommended parameters according to the weight values. An user interface is provided for a user to input a decision instruction. A new input parameter is selected from the at least one recommended parameter according to the decision instruction; the new input parameter is inputted into the target system; and a new output response value is evaluated whether meets a specification condition. The user historical decision information is updated based on the decision instruction to adjust the weight values.

Abstract: A tree search-based scheduling method and an electronic apparatus are provided. In the method, multiple order lists are received and a schedule is initialized, wherein each order list includes multiple production operations. In each order list, a first production operation which has not been joined into the schedule yet is selected, such that multiple prior operations are selected. An execution priority of the prior operations is calculated according to multiple dispatching rules, and multiple candidate operations are selected from the prior operations according to the execution priority. Afterwards, the candidate operations are listed as a next operation of the schedule respectively, and a scheduling simulation is performed according to the dispatching rules to obtain multiple scheduling indicators of the candidate operations. Scheduling is performed according to the scheduling indicators.

Abstract: An abnormality measuring method and an abnormality measuring apparatus of equipment are provided. The abnormality measuring method includes the following steps: acquiring a feature sequence corresponding to a life cycle according to recipe information and sensing information, wherein the feature sequence includes a plurality of feature subset sequences, and the life cycle is relative to a plurality of process runs; performing repeatedly a life segment analyzing process to acquire a plurality of life segments of the life cycle and each of the plurality of the feature subset sequences corresponding to one of the plurality of life segments; building a corresponding trending distribution of each of the plurality of life segments according to a corresponding feature subset sequence of the life segment; and determining whether to send an alarm message according to a plurality of trending distributions.

Abstract: A breakdown measuring method and a breakdown measuring device are disclosed. The breakdown measuring method includes the following steps: capturing a plurality of process flows, each of the process flows includes at least one recipe step; analyzing a flow attribute corresponding to each of the process flows; capturing sensing data corresponding to the at least one recipe step; generating a local feature in a time interval corresponding to each of the process flows according to the corresponding flow attribute, the corresponding at least one recipe step and the corresponding sensing data; generating a trend distribution according to the local features of the process flows; determining whether to send an alarm information based on the trend distribution.

Abstract: A system and a method for predicting a remaining useful life (RUL) of a component of an equipment are provided. The system for predicting the RUL of the component of the equipment includes a data acquisition unit, a feature capturing unit, a mapping function generating unit, a similarity analyzing unit and a RUL calculating unit. The feature capturing unit obtains an estimation feature according to a real time sensing record, and obtains a plurality of training features according to a set of history sensing records. The similarity analyzing unit obtains k similar features which are similar to the estimation feature according to the training features. The RUL calculating unit obtains at least one of k predicting information via a mapping function according to the k similar features and calculates an estimation RUL according to at least one predicting value.

Abstract: A method for monitoring a manufacturing apparatus includes collecting a control signal, of a controller and a corresponding sensing signal, of the manufacturing apparatus over a predetermined period of time; segmenting the control signal and the corresponding sensing signal to obtain at least one control step; calculating a character of the controller based on at least one of the control signal, the sensing signal, the at least one control step, or information from a user manual; generating, based on the character, a health index indicating health of the controller; and generating a warning signal when the health index exceeds a predetermined threshold.

Abstract: An abnormality measuring method and an abnormality measuring apparatus of equipment are provided. The abnormality measuring method includes the following steps: acquiring a feature sequence corresponding to a life cycle according to recipe information and sensing information, wherein the feature sequence includes a plurality of feature subset sequences, and the life cycle is relative to a plurality of process runs; performing repeatedly a life segment analyzing process to acquire a plurality of life segments of the life cycle and each of the plurality of the feature subset sequences corresponding to one of the plurality of life segments; building a corresponding trending distribution of each of the plurality of life segments according to a corresponding feature subset sequence of the life segment; and determining whether to send an alarm message according to a plurality of trending distributions.

Abstract: A system and a method for predicting a remaining useful life (RUL) of a component of an equipment are provided. The system for predicting the RUL of the component of the equipment includes a data acquisition unit, a feature capturing unit, a mapping function generating unit, a similarity analyzing unit and a RUL calculating unit. The feature capturing unit obtains an estimation feature according to a real time sensing record, and obtains a plurality of training features according to a set of history sensing records. The similarity analyzing unit obtains k similar features which are similar to the estimation feature according to the training features. The RUL calculating unit obtains at least one of k predicting information via a mapping function according to the k similar features and calculates an estimation RUL according to at least one predicting value.

Abstract: A breakdown measuring method and a breakdown measuring device are disclosed. The breakdown measuring method includes the following steps: capturing a plurality of process flows, each of the process flows includes at least one recipe step; analyzing a flow attribute corresponding to each of the process flows; capturing sensing data corresponding to the at least one recipe step; generating a local feature in a time interval corresponding to each of the process flows according to the corresponding flow attribute, the corresponding at least one recipe step and the corresponding sensing data; generating a trend distribution according to the local features of the process flows; determining whether to send an alarm information based on the trend distribution.

Abstract: A method for monitoring a manufacturing apparatus includes collecting a control signal, of a controller and a corresponding sensing signal, of the manufacturing apparatus over a predetermined period of time; segmenting the control signal and the corresponding sensing signal to obtain at least one control step; calculating a character of the controller based on at least one of the control signal, the sensing signal, the at least one control step, or information from a user manual; generating, based on the character, a health index indicating health of the controller; and generating a warning signal when the health index exceeds a predetermined threshold.

Abstract: A health assessment method and a health assessment device of a workpiece processing apparatus are disclosed. The health assessment method includes the following steps. Acquire a first sensing data related to the workpiece processing apparatus at an operation stage of the workpiece processing apparatus. Set the first sensing data as a substitution of a first transform model to acquire a virtual workpiece quality. Set the virtual workpiece quality as a substitution of a second transform model to acquire a first virtual apparatus health index.

Abstract: A language learning system including a storage module, a feature extraction module, and an assessment and diagnosis module is provided. The storage module stores training data and an assessment decision tree generated according to the training data. The feature extraction module extracts pronunciation features of a pronunciation given by a language learner. The assessment and diagnosis module identifies a diagnosis path corresponding to the pronunciation of the language learner in the assessment decision tree and outputs feedback information corresponding to the diagnosis path. Thereby, the language learning system can assess and provide feedback information regarding words, phrases or sentences pronounced by the language learner.