• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.60-64
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• 2007

Increasing using multimedia services as VoIP, Video conference, DMB, IPTV, etc, it is necessary to increase network traffics and develop the mechanism about a flow control for real time traffic transmission. In order to transfer realtime multimedia data, the transfer rate can be control on network state data measuring packet losses of a receiver and delay time of packets through getting periodical feedback RTP/RTCP packet. This paper describes using efficiant flow control on multicast that can reduce errors according to getting feedback tranfer delay and proposes the mechanism that can adapt dynamic change of network. In simulation, the transfer rate can efficiently be control on dynamic change of network and it makes the maximum of the use of a bandwidth and the minimum of packet losses.

• Journal of Broadcast Engineering
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• v.17 no.4
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• pp.596-610
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• 2012

This paper proposes a method to provide the high quality 3D broadcasting service in a mobile broadcasting system. In this method, audio and video data are delivered through a heterogeneous network, consisting of a mobile network as well as a broadcasting network, due to the limited bandwidth of the broadcasting system. However, it is more difficult to synchronize the left and right video frames of a 3D stereoscopic service, which come through different types of networks. The proposed method suggests the use of the offset from the initial timestamp of RTP (Real Time Protocol) to determine the order of frames and to find the pair of a left and a right frame that must be played at the same time. Additionally, a new signaling method is introduced for a mobile device to request a 3D service and to get the initial RTP timestamp.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.6
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• pp.98-107
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• 2011

In this paper, we propose the location determination system of an agent mobile device using the information provided by the WLAN(Wireless LAN) infrastructure. This system is configured as a typical ESS(Extended Service Set)-type WLAN structure with real-time location positioning engine and thru AP(Access Point) controller. The positioning engine collects the information of agent devices using SNMP(Small Network Management Protocol) thru AP controller and utilize those information as Cell ID. for LBS(Location Based Service). In the result of a real office environment implementation, the average success rate of inter-AP roaming is measured to 62.5% and the duration time of the device information update within the AP is average of 11 second of time, which means this system is adaptable to the location based service of above average accuracy but somewhat less urgency.

• The KIPS Transactions:PartC
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• v.16C no.3
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• pp.373-382
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• 2009

we present more efficient method of a medium access for real-time ubiquitous sensor networks. Proposed MAC protocol is like the randomized CSMA/CA protocol, but unlike previous legacy protocols, it does not use a time-varying contention window from which a node randomly picks a transmission slot. To reduce the latency for the delivery of event reports from sensor nodes, we carefully decide to select a fixed-size contention window with non-uniform probability distribution of transmitting in each slot. We show that the proposed method can offer up to several times latency reduction compared to legacy of IEEE 802.11 as the size of the sensor network scales up to 256 nodes using a widely-used network simulation package, NS-2. We finally show that proposed MAC scheme comes close to meet bounds on the best latency being achieved by a decentralized CSMA-based MAC protocol for real-time ubiquitous sensor networks which is sensitive to latency.

• International Journal of Advanced Culture Technology
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• v.10 no.1
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• pp.274-283
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• 2022

Korea is facing a number difficulties arising from rising housing prices. As 'housing' takes the lion's share in personal assets, many difficulties are expected to arise from fluctuating housing prices. The purpose of this study is creating housing price prediction model to prevent such risks and induce reasonable real estate purchases. This study made many attempts for understanding real estate instability and creating appropriate housing price prediction model. This study predicted and validated housing prices by using the LSTM technique - a type of Artificial Intelligence deep learning technology. LSTM is a network in which cell state and hidden state are recursively calculated in a structure which added cell state, which is conveyor belt role, to the existing RNN's hidden state. The real sale prices of apartments in autonomous districts ranging from January 2006 to December 2019 were collected through the Ministry of Land, Infrastructure, and Transport's real sale price open system and basic apartment and commercial district information were collected through the Public Data Portal and the Seoul Metropolitan City Data. The collected real sale price data were scaled based on monthly average sale price and a total of 168 data were organized by preprocessing respective data based on address. In order to predict prices, the LSTM implementation process was conducted by setting training period as 29 months (April 2015 to August 2017), validation period as 13 months (September 2017 to September 2018), and test period as 13 months (December 2018 to December 2019) according to time series data set. As a result of this study for predicting 'prices', there have been the following results. Firstly, this study obtained 76 percent of prediction similarity. We tried to design a prediction model of real estate transaction price with the LSTM Model based on AI and Bigdata. The final prediction model was created by collecting time series data, which identified the fact that 76 percent model can be made. This validated that predicting rate of return through the LSTM method can gain reliability.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.553-558
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• 2003

The boundary estimation problem is used to estimate the shape of organic depend on the phase of the cardiac cycle or interested in the detection of the location and size of anomalies with resistivity values different from the background tissues such as nuclear reactor. And we can use the method to solve the optimal solution such as modified Newton raphson, kalman filter, extended kalman filter, etc. But, this method consumes much time and is sensitive to the initial value and noise in the estimation of the unknown shape. In the paper, we propose that multi-layer neural networks estimate the boundary of the unknown object using Fourier coefficient. This method can be used at the real time estimation and have strong characteristics at the noise and initial value. It uses voltage change; difference the homogeneous voltage to the non-homogeneous voltage, and change of Fourier coefficient change to train multi-layer neural network. After train, we can have real time estimation using this method.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.652-657
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• 2004

As the real-time multimedia applications through Internet increase, the bandwidth available to TCP connections is oppressed by the UDP traffic, result in the performance of overall system is extremely deteriorated. Therefore, developing a new transmission protocol is necessary. The TCP-friendly algorithm is an example meeting this necessity. The TCP-friendly (TFRC) is an UDP-based protocol that controls the transmission rate based on the available round transmission time (RTT) and the packet loss rate (PLR). In the data transmission processing, transmission rate is determined based on the conditions of the previous transmission period. If the one-step ahead predicted values of the control factors are available, the performance will be improved significantly. This paper proposes a prediction model of transmission rate control factors that will be used for the transmission rate control, which improves the performance of the networks. The model developed through this research is predicting one-step ahead variables of RTT and PLR. A multiplayer perceptron neural network is used as the prediction model and Levenberg-Marquardt algorithm is used for the training. The values of RTT and PLR were collected using TFRC protocol in the real system. The obtained prediction model is validated using new data set and the results show that the obtained model predicts the factors accurately.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.11
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• pp.1201-1209
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• 1991

Generally, the conventional tracking algorithms are very limited in the practical applications because of its exponential increase in the required computation time for the number of targets being tracked. Therefore, in this paper, a new real-time moving target tracking system is proposed, which is based on the neural networks with massive parallel processing capabilities. Through the theoretical and experimental results, the target tracking system based on neural network algorithm is analyzed to be computationally independent of the number of objects being tracked and performs the optimized tracking through its massive parallel computation and learning capabilities. And this system also has massive matched filtering effects because the moving target data can be compactly stored in the interconnection weights by learning. Accordingly, a possibility of the proposed neural network target tracking system can be suggested to the fields of real-time application.

• The Journal of the Korea Contents Association
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• v.10 no.6
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• pp.106-114
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• 2010

Recently, with the development of network technology, the web-based remote control systems and the real time remote control systems that do not have any restrictions at any time and space are many developed in the several industry FA field. In this paper, we developed the graphic editor screen of the client PC (PDA) with wireless network and the real time remote control system that can access the industry's server and can control the transmission of data for the FA of iron and steel industry field. Implementation of this system brought on the several effects of the decrease of the worker's movement, the control of quick equipments, a step-up in production efficiency, and the decrease of maintenance expense and so on in administration side of industry field.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.537-540
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• 2000

The Distribution System consist of many tie-line switches and sectionalizing switches, operated a radial type. When an outage occurs in Distribution System, outage areas are isolated by system switches, has to restored as soon as possible. At this time, system operator have to get a information about network topology for service restoration of outage areas. Therefore, the searching result of restorative topology has to fast computation time and reliable result topology for to restore a electric service to outage areas, equal to optimal switching operation problem. So, the problem can be defined as combinatorial optimization problem. The service restoration problem is so important problem which have outage area minimization, outage loss minimization. Many researcher is applying to the service restoration problem with various techniques. In this paper, advanced Dijkstra algorithm is applied to searching a restoration topology, is so efficient to searching a shortest path in graph type network. Additionally, fuzzy rules and operator are applied to overcome a fuzziness of correlation with input data. The present technique has superior results which are fast computation time and searching results than previous researches, demonstrated by example distribution model system which has 3 feeders, 26 buses. For a application capability to real distribution system, additionally demonstrated by real distribution system of KEPCO(Korea Electric Power Corporation) which has 8 feeders and 140 buses.