• The Journal of the Korea Contents Association
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• v.7 no.6
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• pp.44-51
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• 2007

For real-time recognizing of model objects in remote position a new Neural Networks algorithm is proposed. The proposed neural networks technique is the real time computation methods through the inter-node diffusion. In the networks, a node corresponds to a state in the quantized input space. Each node is composed of a processing unit and fixed weights from its neighbor nodes as well as its input terminal. The most reliable algorithm derived for real time recognition of objects, is a dynamic programming based algorithm based on sequence matching techniques that would process the data as it arrives and could therefore provide continuously updated neighbor information estimates. Through several simulation experiments, real time reconstruction of the nonlinear image information is processed. 1-D LIPN hardware has been composed and various experiments with static and dynamic signals have been implemented.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.5
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• pp.1113-1118
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• 2013

Real-time video communication technology of hybrid Web-based using JavaScript and HTML5 today is on the rise. Therefore, in this paper, we have studied the real-time videoo communication technology using a Web browser that supports HTML5. It has been designed and implemented a system based on HTML5 and JavaScript, allowing for video communication of real-time, such as you would using the HTTP/WebSockets and RTC API through the Web servers in the Web browser between different each other, utilizing a Web browser was. It is available in a usable terminal equipment of a Web browser PC or notebook, smart pad, such as a smart phone, which is available without installation, such as a plug-in register or another. In all environments the network is connected, it is available in all terminals use the Web browser is enabled, which will be utilized in the field of video communication in a restricted environment.

• The Journal of Society for e-Business Studies
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• v.18 no.3
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• pp.107-123
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• 2013

In a wireless sensor network environment, it is required to ensure anonymity by keeping sensor nodes' identifiers not being revealed and to support real-time authentication, lightweight authentication and synchronization. In particular, there exist possibilities of location information leakage by others, privacy interference and security vulnerability when it comes to wireless telecommunications. Anonymity has been an importance issue in wired and wireless network environment, so that it has been studied in wide range. The sensor nodes are interconnected among them based on wireless network. In terms of the sensor node, the researchers have been emphasizing on its calculating performance limit, storage device limit, and smaller power source. To improve of biometric-based D. He scheme, this study proposes a real-time authentication protocol using Unique Random Sequence Code(URSC) and variable identifier for enhancing network performance and retaining anonymity provision.

• Journal of The Korean Association of Information Education
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• v.5 no.3
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• pp.374-379
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• 2001

Recently, R & D about Network Monitoring System is needed to be effective network management and to improve computer education. In this work, we propose a real time network monitoring system named NetCop to improve network management and effective use. This system is designed to show any screen to one or more students in interactive learning environment. The system also provides useful functions such as monitoring students' PCs and chat. In additions, our NetCop can provide more effective learning process to students. Finally, we expect that our system can achieve high network performance and provide high educational quality to managers.

• The Transactions of the Korea Information Processing Society
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• v.7 no.12
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• pp.3896-3909
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• 2000

The MPEG Video Service of hilving il property of continuity and large cilPilcity needs lilrge network capilcity. Because most of users have a heterogeneous network environment, it's not efficient way that all users have same size of video data to provide them with real time video service. Therefore, for the pUl1Xlse of an efficient and appropriate utilization of network resources, it requires to develop and deploy a new scalable transmission technique in consideration of respective network environment and individual clients computing power. The purpose of this paper is to develop a technology that can adjust the amount of dilta transmitted as an M1'EG video stream according to its gi yen communication bandwidth, and a technique that can reflect dynamic bilndwidth while playing a video stream. For this purpose, we propose a TFS (Temporal-Fidelity Scaling) technique that splits the MPEG video stream into various substream according to picture type or resolution. Those methods proposed her can filcilitilte an effective use of network resources, and provide multimedia MPEG video services in real- time with respect to individual client computing environment

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.05a
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• pp.414-418
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• 2003

VoIP is a core technology used to transmit both voice and data in an integrated packet' form. Within this technology, SIP is the signaling protocol used for 'real time' call services; particularly those where H323 is used. Yet, when considering the needs of mobile users, it is essential we integrate VoIp within the mobile technology so the mobile host is able to receive the 'packet' transported and by, and connected to, any available internet-address. For all this to occur, we need to improve Network Delay by reducing transmission problems associated with mobile services. If we are to obtain an optimal service then we must reduce any network delays which may arise from joining Mobile IP and VoIp services. This paper, therefore, considers how, unlike previous research, these delays may be improved through the use of the signaling technology\ulcorner SIP. It also considers how this research may be introduced into current wired and wireless integrated services enabling them to use the IP 'packet'.

• Journal of the Korean Society of Industry Convergence
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• v.19 no.1
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• pp.1-9
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• 2016

In this paper, we propose a new learning control scheme for various walk motion control of biped robot with same learning-base by neural network. We show that learning control algorithm based on the neural network is significantly more attractive intelligent controller design than previous traditional forms of control systems. A multi layer back propagation neural network identification is simulated to obtain a dynamic model of biped robot. Once the neural network has learned, the other neural network control is designed for various trajectory tracking control with same learning-base. The biped robots have been received increased attention due to several properties such as its human like mobility and the high-order dynamic equation. These properties enable the biped robots to perform the dangerous works instead of human beings. Thus, the stable walking control of the biped robots is a fundamentally hot issue and has been studied by many researchers. However, legged locomotion, it is difficult to control the biped robots. Besides, unlike the robot manipulator, the biped robot has an uncontrollable degree of freedom playing a dominant role for the stability of their locomotion in the biped robot dynamics. From the simulation and experiments the reliability of iterative learning control was illustrated.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.12
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• pp.885-893
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• 2006

The Bayesian network(BN) has emerged in recent years as a powerful technique for handling uncertainty iii complex domains. Parameter learning of BN to find the most proper network from given data set has been investigated to decrease the time and effort for designing BN. Off-line learning needs much time and effort to gather the enough data and since there are uncertainties in real world, it is hard to get the complete data. In this paper, we propose an online learning method of Bayesian network parameters from incomplete data. It provides higher flexibility through learning from incomplete data and higher adaptability on environments through online learning. The results of comparison with Voting EM algorithm proposed by Cohen at el. confirm that the proposed method has the same performance in complete data set and higher performance in incomplete data set, comparing with Voting EM algorithm.

• Journal of Advanced Navigation Technology
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• v.24 no.6
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• pp.540-548
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• 2020

In this paper, the performances of virtual reference station (VRS) and flächen korrektur parameter (FKP) based Network real time kinematics (RTK) according to baseline length were compared and analyzed. We applied the VRS and FKP corrections for each baseline length obtained from National Geographic Information Institute Network RTK services to an FKP-supported commercial receiver and analyzed the RTK results in the range and position domains. In the case of VRS, RTK performance was degraded due to the spatial error, which increase in proportion of the baseline length. On the other hand, FKP compensates for spatial errors by using the gradients of dispersive and non-dispersive errors, so it showed stable RTK performance compared to VRS even if the baseline length increases up to 130 km. However, in the case of long baseline of 150 km or more, integer ambiguities were incorrectly fixed due to the decrease in the performance of the FKP corrections.

• Electronics and Telecommunications Trends
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• v.36 no.4
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• pp.145-153
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• 2021

Data imputation is a crucial issue in data analysis because quality data are highly correlated with the performance of AI models. Particularly, it is difficult to collect quality time-series data for uncertain situations (for example, electricity blackout, delays for network conditions). Thus, it is necessary to research effective methods of time-series data imputation. Many studies on time-series data imputation can be divided into 5 parts, including statistical based, matrix-based, regression-based, deep learning (RNN and GAN) based methodologies. This study reviews and organizes these methodologies. Recently, deep learning-based imputation methods are developed and show excellent performance. However, it is associated to some computational problems that make it difficult to use in real-time system. Thus, the direction of future work is to develop low computational but high-performance imputation methods for application in the real field.