• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.7-10
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• 1998

In implementing real time video on demand(VOD), the increase of user on internet causes a network traffic congestion. In this paper, we programmed a CGI able to login in VOD home for limiting the number of user in solving the problem, and also applied and adaptive multimedia synchronization technique for controlling video and audio data in a network. In addition, a real time multimedia player was designed and implemented in a personal computer operating at Window95/98/NT.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.119.3-119
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• 2001

When building a network-based real-time control system, a network-induced delay time should be surly considered for real time schedulability to be guaranteed. The network delay time on end-to-end communication has been analyzed theoretically and modeled mathematically from many previous works. There also exist any other delay element not considered before. In this paper, the remote feedback control system using the CAN protocol is proposed to control three axes´ manipulator arm and the application layer of CAN is modeled to analyze the delay elements defined by three types of time delay: Software delay time, Controller delay time, and Access delay time, in details. The analyzed results are used as an important component to determine PID gains of the proposed system. The effect of the delay time on the control performance is evaluated by com paring the response characteristics of the control system through simulation.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.6
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• pp.688-692
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• 2010

Wireless sensor network system collects and analyzes real-time data that have been requested by the many application nodes. This paper has constructed a sensor network cluster with various elements in the Gunsan City area of Jeollabuk-do, S.korea. The purpose of this paper is to utilize the constructed system in order to illustrate the real-time data in a diagram and analyze it to deduce the change ratio. The resulting analysis contents allow simple data interpretation by illustrating the data in change ratio by time, space, and motional directions. This analytical method will offer great benefit to those users using the wireless sensor network.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.26 no.10
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• pp.89-94
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• 2012

This research is to apply the control of neuron networks for the real-time walking 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 walking 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. This paper proposes a new mode to implement a neural network controller by installing a real object for controlling and an algorithm for this, which can replace the existing method of implementing a neural network controller by utilizing activation function at the output node. The proposed control algorithm generated control signs corresponding to the non-linearity of Multi-articulated robot, which could generate desired motion in real time.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1805-1810
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• 1991

This paper derives the upper and the lower bound of the mean cycle time and the mean service time of the class 6 and the class 4, within which the minimum utilization constrain of the class 4 is guaranteed. Also, derived are conditions under which the token bus network is stable or unstable. These bounds and stable conditions are represented in terms of the high priority token hold time, the token rotation time and the arrival rate and the total station number etc. This paper suggest a parameter tuning algorithm in a partially symmetric token bus network with two classes, which maximizes the token rotation time for a suitable high priority token hold time and at the same time meets the stability condition of the network, the real time constraint and the minimum utilization constraint of the class 4.

• The Journal of the Acoustical Society of Korea
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• v.41 no.1
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• pp.30-37
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• 2022

This paper compares and evaluates model performance from two perspectives according to the learning target and network structure for training Deep Neural Network (DNN)-based speech enhancement models in the frequency domain. In this case, spectrum mapping and Time-Frequency (T-F) masking techniques were used as learning targets, and a real network and a complex network were used for the network structure. The performance of the speech enhancement model was evaluated through two objective evaluation metrics: Perceptual Evaluation of Speech Quality (PESQ) and Short-Time Objective Intelligibility (STOI) depending on the scale of the dataset. Test results show the appropriate size of the training data differs depending on the type of networks and the type of dataset. In addition, they show that, in some cases, using a real network may be a more realistic solution if the number of total parameters is considered because the real network shows relatively higher performance than the complex network depending on the size of the data and the learning target.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.488-491
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• 1997

The PMGS(Plant Model Generating System) was developed based on modular modeling method and fluid network calculation concept. Fluid network calculation is used as a method of real-time computation of fluid network, and the module which has a topology with node and branch is defined to take advantages of modular modeling. Also, the database which have a shared memory as an instance is designed to manage simulation data in real-time. The applicability of the PMGS was examined implementing the HRSG(Heat Recovery Steam Generator) control logic on DCS.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.112-117
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• 1992

The Real-time Distributed Control Systems(RDCS) consist of several distributed control processes which share a network medium to exchange their data. Performance of feedback control loops in the RDCS 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 the control components which share a network medium. In this study, a scheduling algorithm of determining data sampling times is developed using the window concept, where the sampling data from the control components dynamically share a limited number of windows.

• Journal of the Korean Professional Engineers Association
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• v.42 no.6
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• pp.36-41
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• 2009

Smart Grid is grafting IT(information technology) techniques on existing electric power network, supplier and the consumer to do real-time exchange of information lead to both direction and energy efficiency optimization, it is a next potential electric power network method. Because of applying various distributed electric power sources, the electric power network system will follow in size and it is dispersive and it will operate independently, and it become the intelligent electric power network, which in consumer demand reacts at real-time, because of using various sensors. In this article explain concept, features, and contemporary background of Smart Grid, and describe improve reliability of the electric power quality.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.11
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• pp.1482-1484
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• 2019

We present a method for transmitting 3D object information in real time in a telepresence system. Three-dimensional object information consists of a large amount of point cloud data, which requires high performance computing power and ultra-wideband network transmission environment to process and transmit such a large amount of data in real time. In this paper, multiple users can transmit object motion and facial expression information in real time even in small network bands by using GANs (Generative Adversarial Networks), a non-supervised learning machine learning algorithm, for real-time transmission of 3D point cloud data. In particular, we propose the creation of an object similar to the original using only the feature information of 3D objects using conditional GANs.