• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.660-665
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• 1993

Neural networks are used in the framework of sensorbased tracking control of robot manipulators. They learn by practice movements the relationship between PSD ( an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. Furthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple backpropagation networks one of which is selected according to which division (corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very fast training and processing implementation required for real time control.

• Journal of the Korean Society of Industry Convergence
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• v.7 no.2
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• pp.167-172
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• 2004

Neural networks are used in the framework of sensor based tracking control of robot manipulators. They learn by practice movements the relationship between PSD(an analog Position Sensitive Detector) sensor readings for target positions and the joint commands to reach them. Using this configuration, the system can track or follow a moving or stationary object in real time. Furthermore, an efficient neural network architecture has been developed for real time learning. This network uses multiple sets of simple back propagation networks one of which is selected according to which division (Corresponding to a cluster of the self-organizing feature map) in data space the current input data belongs to. This lends itself to a very training and processing implementation required for real time control.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.9
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• pp.50-55
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• 2011

This research is to apply the adaptive control of neuron networks for the real-time attitude control of Multi-articulated robot. Multi-articulated robot is expressed with a complicated mathematical model on account of the mechanic, electric non-linearity which each articulation of mechanism has, and includes an unstable factor in time of attitude control. If such a complex expression is included in control operation, it leads to the disadvantage that operation time is lengthened. Thus, if the rapid change of the load or the disturbance is given, it is difficult to fulfill the control of desired performance. In this research we used the response property curve of the robot instead of the activation function of neural network algorithms, so the adaptive control system of neural networks constructed without the information of modeling can perform a real-time control. The proposed adaptive control algorithm generated control signs corresponding to the non-linearity of Multi-articulated robot, which could generate desired motion in real time.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.10B
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• pp.630-638
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• 2005

As real-time media applications based on IP multicast networks spread widely, the end-to-end QoS (quality of service) provisioning for these applications have become very important. To guarantee the end-to-end QoS of multi-party media applications, it is essential to monitor the time-varying status of both network metrics (i.e., delay, jitter and loss) and system metrics (i.e., CPU and memory utilization). In this paper, targeting the multicast-enabled AG (Access Grid) group collaboration tool based on multi-Party real-time media services, a hybrid monitoring scheme that can monitor the status of both multicast network and node system is investigated. It combines active monitoring and passive monitoring approaches to measure multicast network. The active monitoring measures network-layer metrics (i.e., network condition) with probe packets while the passive monitoring checks application-layer metrics (i.e., user traffic condition by analyzing RTCP packets). In addition, it measures node system metrics from system API. By comparing these hybrid results, we attempt to pinpoint the causes of performance degradation and explore corresponding reactions to improve the end-to-end performance. The experimental results show that the proposed hybrid monitoring can provide useful information to coordinate the performance improvement of multi-party real-time media applications.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.1
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• pp.62-69
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• 2013

This paper proposes real-time video transmission system by the car-mounted cameras based on MOST Network. Existing vehicles transmit videos by connecting the car-mounted cameras in the form of analog. However, the increase in the number of car-mounted cameras leads to development of the network to connect the cameras. In this paper, DSP is applied to process MPEG 2 encoding/decoding for real-time video transmission in a short period of time. MediaLB is employed to transfer data stream between DSP and MOST network controller. During this procedure, DSP cannot transport data stream directly from MediaLB. Therefore, FPGA is used to deliver data stream transmitting MediaLB to DSP. MediaLB is designed to streamline hardware/software application development for MOST Network and to support all MOST Network data transportation methods. As seen in this paper, the test results verify that real-time video transmission using proposed system operates in a normal matter.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.1
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• pp.18-28
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• 1993

Processes in the real-time distributed control systems share a network medium to exchange their data. Performance of feedback control loops in the real-time distributed control systems is subject to the network-induced delays from sensor to controller, and from controller to actuator. The network-induced delays are directly dependent upon the data sampling times of control components which share a network medium. In this study, an algorithm of determining data sampling times is developed using the "window concept". where the sampling datafrom the control components dynamically share a limited number of windows. The scheduling algorithm is validated through the aimulation experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.1
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• pp.95-104
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• 1999

In this paper we propose a fast and precise control algorithm for a mobile robot, which aims at the self-tuning control applying two multi-layered neural networks to the structure of computed torque method. Through this algorithm, the nonlinear terms of external disturbance caused by variable task environments and dynamic model errors are estimated and compensated in real time by a long term neural network which has long learning period to extract the non-linearity globally. A short term neural network which has short teaming period is also used for determining optimal gains of PID compensator in order to come over the high frequency disturbance which is not known a priori, as well as to maintain the stability. To justify the global effectiveness of this algorithm where each of the long term and short term neural networks has its own functions, simulations are peformed. This algorithm can also be utilized to come over the serious shortcoming of neural networks, i.e., inefficiency in real time.

• Journal of Korean Society on Water Environment
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• v.20 no.4
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• pp.321-326
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• 2004

The objective of this study is to test the applicability of neural network models to forecast water quality at Naesa and Pyongchang river. Water quality data devided into rainy day and non-rainy day to find characteristics of them. The mean and maximum data of rainy day show higher than those of non-rainy day. And discharge correlate with TOC at Pyongchang river. Neural network model is trained to the correlation of discharge with water quality. As a result, it is convinced that the proposed neural network model can apply to the analysis of real time water quality monitoring.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.11
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• pp.3193-3199
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• 2009

The real-time intelligent home network control system is the system which can control and monitor intelligent home network anytime and anywhere with mobile devices. In this study, to embody the real-time control system for intelligent home network, I designed the sub-module which can control various USN senses with using ZigBee, and organized the GUI environment into the client module to drive by users with mobiles devices.

• Journal of Korea Multimedia Society
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• v.7 no.6
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• pp.814-823
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• 2004

Current video games and game room games are played as a single player mode on the basis of various emulators. With the evolution of data communications and game technology, a new trend in the game industry has made the primary interests of game developers and companies in the game industry be moved toward a multiplayer mode from the traditional single player mode. In this paper, we represent how to implement a network game platform by allowing network modules to be run in conjunction with the current video emulator games. It also suggests a synchronization scheme for real-time game playout and practical mechanism that can support network games to be played with the Peer-to-Peer process using a lobby system.