Abstract: Provided is a method of managing a target process. The method performed by a process management apparatus includes: generating a reference pattern indicating a normal state based on reference observed data on a process factor measured while the target process is maintained in the normal state; obtaining observed data on the process factor measured for a specified observation period; calculating a dissimilarity between the reference pattern and the observed data; and constructing a regression tree for the target process by using the observed data and the dissimilarity, wherein the process factor is set as an independent variable of the regression tree, and the dissimilarity is set as a dependent variable of the regression tree.

Abstract: A method for clustering based on unsupervised learning according to an embodiment of the invention enables clustering for newly generated patterns and is robust against noise, and does not require tagging for training data. According to one or more embodiments, noise is accurately removed using three-dimensional stacked spatial auto-correlation, and multivariate spatial probability distribution values and polar coordinate system spatial probability distribution values are used as learning features for clustering model generation, making them robust to noise, rotation, and fine unusual shapes. In addition, clusters resulting from clustering are classified into multi-level clusters, and stochastic automatic evaluation of normal/defect clusters is possible only with measurement data without a label.

Abstract: A method for clustering based on unsupervised learning according to an embodiment of the invention enables clustering for newly generated patterns and is robust against noise, and does not require tagging for training data. According to one or more embodiments, noise is accurately removed using three-dimensional stacked spatial auto-correlation, and multivariate spatial probability distribution values and polar coordinate system spatial probability distribution values are used as learning features for clustering model generation, making them robust to noise, rotation, and fine unusual shapes. In addition, clusters resulting from clustering are classified into multi-level clusters, and stochastic automatic evaluation of normal/defect clusters is possible only with measurement data without a label.

Abstract: A method for clustering based on unsupervised learning according to an embodiment of the invention enables clustering for newly generated patterns and is robust against noise, and does not require tagging for training data. According to one or more embodiments of the invention, noise is accurately removed using three-dimensional stacked spatial auto-correlation, and multivariate spatial probability distribution values and polar coordinate system spatial probability distribution values are used as learning features for clustering model generation, making them robust to noise, rotation, and fine unusual shapes. In addition, clusters resulting from clustering are classified into multi-level clusters, and stochastic automatic evaluation of normal/defect clusters is possible only with measurement data without a label.

Abstract: A method performed by a computing device for clustering an image according to an embodiment of the present disclosure includes performing a first clustering on feature vectors of the plurality of images, and performing a second clustering for feature vectors belonging to some clusters that do not satisfy a reference score among clusters formed as a result of the first clustering, wherein a clustering parameter of the second clustering and a clustering parameter of the first clustering are different from each other.

Abstract: Provided is a method of managing a target process. The method performed by a process management apparatus includes: generating a reference pattern indicating a normal state based on reference observed data on a process factor measured while the target process is maintained in the normal state; obtaining observed data on the process factor measured for a specified observation period; calculating a dissimilarity between the reference pattern and the observed data; and constructing a regression tree for the target process by using the observed data and the dissimilarity, wherein the process factor is set as an independent variable of the regression tree, and the dissimilarity is set as a dependent variable of the regression tree.

Abstract: Provided is a method of managing a target process. The method performed by a process management apparatus includes: generating a reference pattern indicating a normal state based on reference observed data on a process factor measured while the target process is maintained in the normal state; obtaining observed data on the process factor measured for a specified observation period; calculating a dissimilarity between the reference pattern and the observed data; and constructing a regression tree for the target process by using the observed data and the dissimilarity, wherein the process factor is set as an independent variable of the regression tree, and the dissimilarity is set as a dependent variable of the regression tree.

Abstract: A content scheduling method is provided. The content scheduling method, which is performed by a content scheduling apparatus, comprises acquiring a total play count of target content, determining a plurality of weight values of the target content with respect to a plurality of time slots, each weight value of the plurality of weight values indicating a first preference for the target content with respect to each time slot of the plurality of time slots, generating a linear programming model using the acquired total play count and the plurality of weight values and determining, via a processor, a play count of the target content in the each time slot of the plurality of time slots based on the linear programming model.

Abstract: A method for clustering based on unsupervised learning according to an embodiment of the invention enables clustering for newly generated patterns and is robust against noise, and does not require tagging for training data. According to one or more embodiments of the invention, noise is accurately removed using three-dimensional stacked spatial auto-correlation, and multivariate spatial probability distribution values and polar coordinate system spatial probability distribution values are used as learning features for clustering model generation, making them robust to noise, rotation, and fine unusual shapes. In addition, clusters resulting from clustering are classified into multi-level clusters, and stochastic automatic evaluation of normal/defect clusters is possible only with measurement data without a label.

Abstract: Provided is a method of managing a target process. The method performed by a process management apparatus includes: generating a reference pattern indicating a normal state based on reference observed data on a process factor measured while the target process is maintained in the normal state; obtaining observed data on the process factor measured for a specified observation period; calculating a dissimilarity between the reference pattern and the observed data; and constructing a regression tree for the target process by using the observed data and the dissimilarity, wherein the process factor is set as an independent variable of the regression tree, and the dissimilarity is set as a dependent variable of the regression tree.

Abstract: Provided is a content scheduling method performed by a content scheduling apparatus for a plurality of content reproducing apparatuses located at different geographical locations. The method comprises obtaining a total play count of target content, determining a plurality of weight values of the target content, each weight value of the plurality of weight values indicating a preference for the target content with respect to each content reproduction apparatus of the plurality of content reproduction apparatuses, wherein the plurality of weight values are different for at least some of the plurality of content reproduction apparatuses, generating a linear programming model based on the total play count and the plurality of weight values and determining, via a processor, a play count of the target content in each time slot of a plurality of time slots for the each content reproduction apparatus based on the linear programming model.

Abstract: A content scheduling method is provided. The content scheduling method, which is performed by a content scheduling apparatus, comprises acquiring a total play count of target content, determining a plurality of weight values of the target content with respect to a plurality of time slots, each weight value of the plurality of weight values indicating a first preference for the target content with respect to each time slot of the plurality of time slots, generating a linear programming model using the acquired total play count and the plurality of weight values and determining, via a processor, a play count of the target content in the each time slot of the plurality of time slots based on the linear programming model.

Abstract: Disclosed are an apparatus and method for modeling a three-dimensional (3D) shape. The apparatus for modeling a 3D shape includes a stacker configured to generate a skinned 3D model by forming an empty space inside a target 3D model and stack a plurality of 3D unit objects in the empty space; an influence calculator configured to calculate an influence of a change in a characteristic value of each of the plurality of 3D unit objects on mechanical properties of the target 3D model; a grouper configured to group the plurality of 3D unit objects into a plurality of groups according to the calculated influence; and a characteristic value calculator configured to determine an optimal characteristic value of the 3D unit object for each of the plurality of groups.