• Journal of the Korean Society for Precision Engineering
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• v.13 no.9
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• pp.138-148
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• 1996

In moving structures such as robots and feeders of production lines, vibrations may not be ignored. Recently it becomes a big problem to control the vibration in a motion because moving structures are in higher speed, larger size and lighter weight. In this study a nonlinear system was model- led and identified by using neural networks and the vibration in motions was controlled actively by using a neural network controller. To investigate vilidity of this method, an experimental apparatus was made and tested. The model was composed of a DC servomotor, a carrier and a flexible plate. Its motion was measured by a gap sensor and an encoder. Trapezoidal, cycloid and trapecloid type trajectories were used in this exper- riment. Computer simulations and experiments weredone for each trajectory.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.691-694
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• 2000

In this paper, a motion control algorithm is proposed by using neural network system, which makes a robot arm successfully avoid unexpected obstacle when the robot is moving from the start to the goal position. During the motion, if there is an obstacle the vision system recognizes it. And in every time the optimization-algorithm quickly chooses a motion among the possible motions of robot. The proposed algorithm has a good avoidance characteristic in simulation.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.05a
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• pp.319-320
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• 2012

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.169-177
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• 2014

It is essential to support the image and video analysis technology such as motion detection since the DVR and NVR storage were adopted in the real time visual surveillance system. Especially the network camera would be popular as a video input device. The traditional CCTV that supports analog video data get be replaced by the network camera. In this paper, we present the design and implementation of video surveillance system that provides the real time motion detection by the video storage server. The mobile application also has been implemented in order to provides the retrieval functionality of image analysis results. We develop the video analysis server with open source library OpenCV and implement the daemon process for video input processing and real-time image analysis in our video surveillance system.

• Journal of the Korea Computer Graphics Society
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• v.27 no.5
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• pp.73-79
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• 2021

Recently, in the field of computer animation, a method for generating motion using deep learning has been studied away from conventional finite-state machines or graph-based methods. The expressiveness of the network required for learning motions is more influenced by the diversity of motion contained in it than by the simple length of motion to be learned. This study aims to find an efficient network structure when the types of motions to be learned are diverse. In this paper, we train and compare three types of networks: basic fully-connected structure, mixture of experts structure that uses multiple fully-connected layers in parallel, recurrent neural network which is widely used to deal with seq2seq, and transformer structure used for sequence-type data processing in the natural language processing field.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.379-380
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• 2018

Previoisly presented "Implementation to pupil motion recognition system using convolution neural network".is improved. Using OpenCV, face and eye areas are detected, and then configure the neural network using Numpy. This pupil motion recognition system is based on the Numpy for configuring and calculating the neural network. This system is implemented on DE1-SOC.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.4
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• pp.294-300
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• 2009

The neural network with radial basis function is introduced for position tracking control of AC servo drive with the existence of system uncertainties. An adaptive robust term is applied to overcome the external disturbances. The proposed controller is implemented on a high performance digital signal processing DSP TMS320C6713-300. The stability and the convergence of the system are proved by Lyapunov theory. The validity and robustness of the controller are verified through simulation and experimental results

• Journal of Internet Computing and Services
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• v.8 no.3
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• pp.119-129
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• 2007

To recognition a human motion, in this paper, we propose a neural approach using silhouettes in video frames captured by two cameras placed at the front and side of the human body. To extract features of the silhouettes for motion estimation, the proposed system computes both global and local features and then groups these features into static and dynamic features depending on whether features are in a static frame. Extracted features are in a static frame. Extracted features are used to train a RBF network. The neural system uses static features as the input of the neural network and dynamic features as additional features for recognition. In this paper, the proposed method was applied to movement education for young children. The basic movements for such education consist of locomotor movements, such as walking, jumping, and hopping, and non-locomotor movements, including bending, stretching, balancing and turning. The system demonstrated the effectiveness of motion recognition for movement education generated by the proposed neural network. The proposed system dan be extended to the system for movement education which develops the spatial sense of young children.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.129.2-129
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• 2001

Visual effects are important cues for providing occupant s with virtual reality in a vehicle simulator which imitates real driving. The viewpoint of an occupant is sensitively dependent upon the occupant´s posture, therefore, the total body motion must be considered in a graphic simulator. A real time simulation is required for the dynamic analysis of complex human body motion. This study attempts to apply a neural network to the motion analysis in various driving situations. A full car of medium-sized vehicles was selected and modeled, and then analyzed using ADAMS in such driving conditions as bump-pass and acceleration. A multibody system analysis software, MADYMO, was used in the motion analysis of an adult male dummy in the seated position. Position data of the head were collected as inputs to the viewpoint movement. Based on these data, a back- propagation neural network was ...

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.9A
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• pp.1411-1418
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• 1999

In this paper, we propose a fast motion estimation algorithm using motion prediction and neural network. Considering that the motion vectors have high spatial correlation, the motion vector of current block is predicted by those of neighboring blocks. The codebook of motion vector is designed by Kohonen self-organizing feature map(KSFM) learning algorithm which has a fast learning speed and 2-D adaptive chararteristics. Since the similar codevectors are closely located in the 2-D codebook the motion is progressively estimated from the predicted codevector in the codebook. Computer simulation results show that the proposed method has a good performance with reduced computational complexity.