Abstract: A lend moving apparatus includes a circuit board including a mounting groove, an image sensor arranged within the mounting groove of the circuit board, and a first epoxy arranged within the mounting groove. The mounting groove includes a first side surface and a second side surface that face each other, and a third side surface and a fourth side surface that face each other. The circuit board includes at least one application groove provided on the first side surface and/or the second side surface of the mounting groove. The at least one application groove includes an opening opened toward the upper surface of the circuit board. At least a portion of the first epoxy is arranged in the at least one application groove.

Abstract: The present disclosure relates to a system for controlling a motor of a vehicle for increasing control accuracy of the motor for driving the vehicle, and an object of the present disclosure is to provide a system for controlling a motor of a vehicle, which may accurately perform a motor control even when a battery voltage (i.e., motor voltage) applied to the motor upon the driving control of the motor is changed.

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: 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: Disclosed is a method for detecting false alarm. The method includes receiving a measured value that is measured when an alarm is generated from a target for monitoring, measuring non-similarity between the measured value that is measured when the alarm is generated and a pre-stored normal pattern, measuring non-similarity between the measured value and pre-stored measured values related to a past false alarm if the non-similarity exceeds a predetermined threshold value and providing the generated alarm to a user if the non-similarity between the measure value and the pre-stored related values related to the past false alarm exceeds the predetermined threshold value.

Abstract: Disclosed is a method for detecting false alarm. The method includes receiving a measured value that is measured when an alarm is generated from a target for monitoring, measuring non-similarity between the measured value that is measured when the alarm is generated and a pre-stored normal pattern, measuring non-similarity between the measured value and pre-stored measured values related to a past false alarm if the non-similarity exceeds a predetermined threshold value and providing the generated alarm to a user if the non-similarity between the measure value and the pre-stored related values related to the past false alarm exceeds the predetermined threshold value.

Abstract: A cluster system and a method for providing service availability. The cluster system includes a first physical server having registered therein an active virtual machine; and a plurality of physical servers, wherein the plurality of physical servers comprises a second physical server having registered therein a standby virtual machine corresponding to the active virtual machine, the active virtual machine failing over to the standby virtual machine when a failure occurs in the first physical server, wherein each of the plurality of physical servers stores post-failure registration information when the failure occurs, wherein the post-failure registration information associates the active virtual machine with a physical server among the plurality of physical servers, and wherein the physical server is different from the second physical server.

Abstract: Provided is a cluster system including a first physical server having registered therein an active virtual machine; and a plurality of physical servers, wherein the plurality of physical servers comprises a second physical server having registered therein a standby virtual machine corresponding to the active virtual machine, the active virtual machine failing over to the standby virtual machine when a failure occurs in the first physical server, wherein each of the plurality of physical servers stores post-failure registration information when the failure occurs, wherein the post-failure registration information associates the active virtual machine with a physical server among the plurality of physical servers, and wherein the physical server is different from the second physical server.