Abstract: A placer and router for an iterative placement and routing of a sorted operation unit graph on a reconfigurable processor is presented as well as a method of operating a placer and router for an iterative placement and routing of a sorted operation unit graph on a reconfigurable processor. The placer and router is configured to receive an architectural specification of the reconfigurable processor and the sorted operation unit graph having an ordered sequence of nodes and edges that interconnect nodes in the ordered sequence of nodes. The placer and router is further configured to iteratively assign nodes of the sorted operation unit graph to locations on the reconfigurable processor followed by an assignment of edges that connect nodes that were assigned in the current iteration and nodes that were assigned in previous iterations to interconnection resources of the reconfigurable processor.

Abstract: A placer and router for an iterative placement and routing of a sorted operation unit graph on a reconfigurable processor is presented as well as a method of operating a placer and router for an iterative placement and routing of a sorted operation unit graph on a reconfigurable processor. The placer and router is configured to receive an architectural specification of the reconfigurable processor and the sorted operation unit graph having an ordered sequence of nodes and edges that interconnect nodes in the ordered sequence of nodes. The placer and router is further configured to provide an assignment of nodes of the sorted operation unit graph to locations on the reconfigurable processor and an assignment of edges of the sorted operation unit graph to physical links and switches of the reconfigurable processor.

Abstract: A sorting tool for determining an ordered sequence of nodes in an operation unit graph for placing and routing the operation unit graph onto a reconfigurable processor is presented as well as a method of operating a sorting tool for determining an ordered sequence of nodes in an operation unit graph for placing and routing the operation unit graph onto a reconfigurable processor. The sorting tool is configured to receive the operation unit graph including a set of unsorted nodes and edges that interconnect nodes in the set of unsorted nodes, determine an ordered sequence of the nodes in the operation unit graph, and provide the ordered sequence of nodes for the placing and routing of the operation unit graph onto the reconfigurable processor.

Abstract: Disclosed herein are a control server and method for controlling a robot using an artificial neural network, and a robot implementing the same. The robot includes a driver, a processor controlling the driver using an artificial neural network-based algorithm model, and a learning processor learning the algorithm model, where the algorithm model includes a first GAN (generative adversarial network) and a second GAN, the processor generates a plurality of real-like-fake images using a first generator included in a learned first GAN, the learning processor learns a second GAN based on a plurality of random texture rendered images and the plurality of real-like-fake images, and the processor generates a canonical image by inputting a real image to a second generator included in a learned second GAN, and controls the driver using the canonical image.

Abstract: Disclosed herein are a control server and method for controlling a robot using an artificial neural network, and a robot implementing the same. The robot includes a driver, a processor controlling the driver using an artificial neural network-based algorithm model, and a learning processor learning the algorithm model, where the algorithm model includes a first GAN (generative adversarial network) and a second GAN, the processor generates a plurality of real-like-fake images using a first generator included in a learned first GAN, the learning processor learns a second GAN based on a plurality of random texture rendered images and the plurality of real-like-fake images, and the processor generates a canonical image by inputting a real image to a second generator included in a learned second GAN, and controls the driver using the canonical image.

Abstract: The present disclosure relates to an image filtering method for detecting an orientation component of an edge and an image recognizing method using the same. The image filtering method includes receiving an original image, generating a plurality of first images by filtering the original image with filters respectively generated along a plurality of channels, generating a second image by selecting a channel having a maximum value for each image unit, from the generated first images, and generating an output image whose edge is detected so as to maintain the consistency of channel by filtering the second image with filters respectively generated along the plurality of channels to generate a plurality of third images and comparing the channel of the second image with the channels of the third images.

Abstract: Provided are a method for learning task skill and a robot using the same. The modeling method for learning a task skill includes: receiving training data for a task to be performed by a learning engine; dividing, by the learning engine, the received training data into segments by using a geometric property of a predetermined probabilistic model; and learning, by the learning engine, a basis skill for the divided segments by modeling each divided segment.

Abstract: The present disclosure relates to an image filtering method for detecting an orientation component of an edge and an image recognizing method using the same. The image filtering method includes receiving an original image, generating a plurality of first images by filtering the original image with filters respectively generated along a plurality of channels, generating a second image by selecting a channel having a maximum value for each image unit, from the generated first images, and generating an output image whose edge is detected so as to maintain the consistency of channel by filtering the second image with filters respectively generated along the plurality of channels to generate a plurality of third images and comparing the channel of the second image with the channels of the third images.

Abstract: Provided are a method for learning task skill and a robot using the same. The modeling method for learning a task skill includes: receiving training data for a task to be performed by a learning engine; dividing, by the learning engine, the received training data into segments by using a geometric property of a predetermined probabilistic model; and learning, by the learning engine, a basis skill for the divided segments by modeling each divided segment.

Abstract: An apparatus to remove periodic disturbances in a recording medium having large eccentricity and deflection mass includes an actuator to change a position of a pickup in response to a combination of first and second compensation signals, an error detector to detect an error between positions actuator and the recording medium, a first compensator to receive the detected error and to output the first compensation signal that the actuator uses to actuate the pickup along the recording medium in upper and lower directions, and right and left directions; and a second compensator to output the second compensation signal from which the disturbance has been removed, to combine the compensated error and previous first and second compensation signals, and to filter the combined result at a predetermined bandwidth.

Abstract: An apparatus to remove periodic disturbances in a recording medium having large eccentricity and deflection mass includes an actuator to change a position of a pickup in response to a combination of first and second compensation signals, an error detector to detect an error between positions actuator and the recording medium, a first compensator to receive the detected error and to output the first compensation signal that the actuator uses to actuate the pickup along the recording medium in upper and lower directions, and right and left directions; and a second compensator to output the second compensation signal from which the disturbance has been removed, to combine the compensated error and previous first and second compensation signals, and to filter the combined result at a predetermined bandwidth.

Abstract: The present invention relates to carbapenem derivatives of Formula I, wherein X is carbonyl or sulfonyl, and a preparation method thereof. The carbapenem derivatives of the invention have excellent antibacterial properties and are thus useful as antibiotics.

Abstract: A fuzzy controller controls a track seeking operation and a track following operation for an actuator in accordance with a position error between a present position value and a reference value. When the actuator is in track seeking operation, the actuator is accelerated or decelerated based on a velocity error value between an actual velocity value of the actuator and a target velocity value determined from a velocity profile according to the position error. In a deceleration control of the actuator, a fuzzy inference value generated based on the velocity error value and its differentiated velocity error value is used for a control of the actuator, thereby reducing an occurrence of overshooting at a target track.