Abstract: A method for predicting clinical severity of a neurological disorder includes steps of: a) identifying, according to a magnetic resonance imaging (MRI) image of a brain, brain image regions each of which contains a respective portion of diffusion index values of a diffusion index, which results from image processing performed on the MRI image; b) for one of the brain image regions, calculating a characteristic parameter based on the respective portion of the diffusion index values; and c) calculating a severity score that represents the clinical severity of the neurological disorder of the brain based on the characteristic parameter of the one of the brain image regions via a prediction model associated with the neurological disorder.

Abstract: A model building system, a quality prediction system, and a quality management system using the same are provided. The quality management system includes the quality prediction system, a measurement device, and a quality inspection device. The quality prediction system includes the model building system and a model application device. The model building system includes a data transformation device and a model training device. The data transformation device transforms raw measured data, including measurement properties of products, to sets of cluster data and multi-dimensional metadata corresponding to the measurement properties. The model training device creates a prediction model according to the cluster data, the quality inspection results, and the multi-dimensional metadata corresponding to a portion of the products, and then modifies structural parameters of the prediction model according to the raw measured data and the quality inspection results corresponding to the other portion of the products.

Abstract: A data analyzing device, a data analyzing method, and a quality improvement system are provided. The data analyzing device includes a combination generating module, a ratio calculation module, and a weighting calculation module. The data analyzing device performs relevance analysis of to-be-analyzed data associated with a production operation procedure to generate relevance information. The ratio calculation module calculates a ratio parameter according to a total outlier product quantity corresponding to risky combinations in the relevance information and a total product quantity in the to-be-analyzed data. The weighting calculation module calculates a weighting parameter according to a number of the risky combinations in the relevance information and a total number of combinations of production control factors in the to-be-analyzed data. A production equipment selectively adjusts settings of the production operation procedure according to the ratio parameter and the weighting parameter.

Abstract: A dispatching method and system based on multiple levels of steady state production rate in working benches are provided. The dispatching method includes the following steps: receiving a plurality of real-time streaming data regarding a plurality of products being produced by a plurality of productive working benches; grouping the production rate values comprised in each real-time streaming data according to a first data binning technique, so as to produce a first steady state production rate value corresponding to each real-time streaming data; grouping the production rate values comprised in each real-time streaming data according to a second data binning technique, so as to produce a second steady state production rate value corresponding to each real-time streaming data; and determining a dispatching message of a to-be-produced product according to a portion of the first steady state production rate values and a portion of the second steady state production rate values.

Abstract: An apparatus and method for determining a target adjustment route for a preset control condition set of a production line are provided. The apparatus establishes at least one candidate adjustment route for the preset control condition set according to the historical control condition sets. Each candidate adjustment route includes at least one adjustment control condition set arranged in an adjustment order. Each adjustment control condition set is one of the historical control condition sets. Within the same candidate adjustment route, the historical yield related values corresponding to the adjustment control condition sets are all greater than the preset yield related value and increase in the adjustment order. Within the same candidate adjustment route, the numbers of the adjustment control condition(s) included in the adjustment sets increase in the adjustment order. The apparatus selects one of the candidate adjustment route(s) as the target adjustment route.

Abstract: An apparatus and method for detecting impact factors for an operating environment. The apparatus generates a detection result for each of the first factors of a plurality of first historical records by analyzing a dissimilarity degree of the plurality of first data corresponding to each first factor. Each detection result is a continuous data type or a discrete data type. The apparatus trains a data type recognition model according to the first historical records and the detection results. The apparatus establishes a basic prediction model by a training set of a plurality of second historical records, generates a comparison set by rearranging the second data corresponding to a specific factor in the training set, establishes a comparison prediction model by the comparison set, and determines a degree of importance of the specific factor by comparing the accuracies of the basic prediction model and the comparison prediction model.

Abstract: An apparatus and method for determining a target adjustment route for a preset control condition set of a production line are provided. The apparatus establishes at least one candidate adjustment route for the preset control condition set according to the historical control condition sets. Each candidate adjustment route includes at least one adjustment control condition set arranged in an adjustment order. Each adjustment control condition set is one of the historical control condition sets. Within the same candidate adjustment route, the historical yield related values corresponding to the adjustment control condition sets are all greater than the preset yield related value and increase in the adjustment order. Within the same candidate adjustment route, the numbers of the adjustment control condition(s) included in the adjustment sets increase in the adjustment order. The apparatus selects one of the candidate adjustment route(s) as the target adjustment route.

Abstract: An apparatus and a method thereof for determining a control condition set of a production line. The apparatus divides several historical control condition sets into several groups, wherein the historical control conditions corresponding to the same control factor are the same in each group. For each group, the apparatus calculates a measurement of central tendency according to the historical yield related values in the group. The apparatus decides a subset of the groups. For each group in the subset, the apparatus calculates a degree of variation and a number regarding the different control conditions between the control condition set and the group. The apparatus calculates weight scores. Based on the measurements of central tendency and the weight scores, the apparatus selects one of the groups as a selected group and assigns the historical control conditions of the selected group as the control conditions of the control condition set.

Abstract: An apparatus and a method thereof for determining a control condition set of a production line. The apparatus divides several historical control condition sets into several groups, wherein the historical control conditions corresponding to the same control factor are the same in each group. For each group, the apparatus calculates a measurement of central tendency according to the historical yield related values in the group. The apparatus decides a subset of the groups. For each group in the subset, the apparatus calculates a degree of variation and a number regarding the different control conditions between the control condition set and the group. The apparatus calculates weight scores. Based on the measurements of central tendency and the weight scores, the apparatus selects one of the groups as a selected group and assigns the historical control conditions of the selected group as the control conditions of the control condition set.

Abstract: A monitoring system includes a first storage device and a processor. The first storage is configured to temporarily store a first iterated value corresponding to first history parameters. The processor is configured to receive first parameters of first products tested by a test machine, generate a second iterated value by performing an iterative calculation of the first parameters and the first iterated value, and update the first iterated value temporarily stored in the first storage device to the second iterated value for follow-up iterative calculations to selectively output an alert information, wherein the second iterated value corresponds to the first parameters and the first history parameters.

Abstract: A dispatching method and system based on multiple levels of steady state production rate in working benches are provided. The dispatching method includes the following steps: receiving a plurality of real-time streaming data regarding a plurality of products being produced by a plurality of productive working benches; grouping the production rate values comprised in each real-time streaming data according to a first data binning technique, so as to produce a first steady state production rate value corresponding to each real-time streaming data; grouping the production rate values comprised in each real-time streaming data according to a second data binning technique, so as to produce a second steady state production rate value corresponding to each real-time streaming data; and determining a dispatching message of a to-be-produced product according to a portion of the first steady state production rate values and a portion of the second steady state production rate values.

Abstract: A stable manufacturing efficiency generating method includes the steps: generating a real-time grouping parameter set according to a plurality of historical efficiency values; grouping a plurality of real-time efficiency values corresponding to a product to generate a plurality of real-time efficiency groups according to the real-time grouping parameter set; selecting one of the real-time efficiency groups having most real-time efficiency values as a stable efficiency group; and generating a stable manufacturing efficiency value according to an average value of the real-time efficiency values in the stable efficiency group.

Abstract: A production performance management device and a production performance management method thereof are provided. The production performance management device includes a connection interface, a storage and a processor. The connection interface receives production data. The storage stores the production data and a production performance management program. The processor runs the production performance management program. The production performance management program defines a main objective, a plurality of measurement indices, a plurality of factors, a custom mapping function between the main objective and the measurement indices and a plurality of specific mapping functions between the measurement indices and the factors, and uses the custom mapping function and the specific mapping functions to decide a factor value of each of the factors from the production data to make a main objective value of the main objective meet a main constraint.

Abstract: A merchandise recommendation method for multiple users used in a merchandise recommendation system including a user database, a merchandise database, a data transmission module, a processing module and a memory is provided. The merchandise recommendation method includes the steps outlined below. The processing module receives participant information and target merchandise information from a remote originator host. The processing module retrieves corresponding user information from the user database according to the participant information. The processing module retrieves corresponding merchandise information from the merchandise database according to the target merchandise information. The processing module analyzes social influence information and preference information included in the user information and analyzes the merchandise information to generate an analysis result. The processing module generates composite merchandise recommendation information according to the analysis result.

Abstract: A performance management system, method and a non-transitory computer readable storage medium are disclosed herein. The performance management system includes a value-driven management module and an integrated control module. The value-driven management module includes a value-driven model configuration unit and a value-driven target configuration unit. The value-driven model configuration unit is configured to configure a value-driven model with a plurality of targets, and the targets respond to a plurality of factors. The value-driven target configuration unit is configured to set a goal value for each of the targets. The integrated control module is configured to monitor a performance of a building in accordance with the goal value for each of the targets and the value-driven model.