• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.6A
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• pp.550-557
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• 2007

We predict a context of various structures by using Bayesian Networks Algorithms, Three-Dimensional Structures Algorithms and Genetic Algorithms. However, these algorithms have unavoidable problems when providing a context-aware service in reality due to a lack of practicality and the delay of process time in real-time environment. As far as context-aware system for specific purpose is concerned, it is very hard to be sure about the accuracy and reliability of prediction. This paper focuses on reasoning and prediction technology which provides a stochastic mechanism for context information by incorporating various context information data. The objective of this paper is to provide optimum services to users by suggesting an intellectual reasoning and prediction based on hierarchical context information. Thus, we propose a design of user location prediction algorithm using sequential matching with n-size flexible window scheme by taking user's habit or behavior into consideration. This algorithm improves average 5.10% than traditional algorithms in the accuracy and reliability of prediction using the Flexible Window Scheme.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2006.07a
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• pp.293-293
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• 2006

• Journal of the Korea Institute of Military Science and Technology
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• v.24 no.1
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• pp.61-69
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• 2021

Recently, autonomous navigation technology is actively being developed due to the increasing demand of an unmanned surface vehicle(USV). Local planning is essential for the USV to safely reach its destination along paths. the dynamic window approach(DWA) algorithm is a well-known navigation scheme as a local path planning. However, the existing DWA algorithm does not consider path line tracking, and the fixed weight coefficient of the evaluation function, which is a core part, cannot provide flexible path planning for all situations. Therefore, in this paper, we propose a new DWA algorithm that can follow path lines in all situations. Fixed weight coefficients were trained using reinforcement learning(RL) which has been actively studied recently. We implemented the simulation and compared the existing DWA algorithm with the DWA algorithm proposed in this paper. As a result, we confirmed the effectiveness of the proposed algorithm.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1997.06a
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• pp.131-136
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• 1997

We present a method to estimate a dense and sharp depth map using multiple cameras for the application to flexible video production. A key issue for obtaining sharp depth map is how to overcome the harmful influence of occlusion. Thus, we first propose to selectively use the depth information from multiple cameras. With a simple sort and discard technique, we resolve the occlusion problem considerably at a slight sacrifice of noise tolerance. However, boundary overreach of more textured area to less textured area at object boundaries still remains to be solved. We observed that the amount of boundary overreach is less than half the size of the matching window and, unlike usual stereo matching, the boundary overreach with the proposed occlusion-overcoming method shows very abrupt transition. Based on these observations, we propose a hierarchical estimation scheme that attempts to reduce boundary overreach such that edges of the depth map coincide with object boundaries on the one hand, and to reduce noisy estimates due to insufficient size of matching window on the other hand. We show the hierarchical method can produce a sharp depth map for a variety of images.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.5
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• pp.943-950
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• 2005

Recently, many of researches on stream transmission for satisfying each of different real-time transmission condition of the multimedia data that demands various service quality through high-speed networks have been studied actively. In this paper, we design a scheme that discriminately reserves the network resources for the transmission of each multimedia application and propose a bandwidth allocation scheme for improving the utilization ratio of free resources. And we also propose a pipelining scheme for providing flexible real-time transmission. The proposed schemes can be used to support a real-time transmission by applying feedback transmission control method based on receiving buffer for guaranteeing the synchronization conditions requested by the multimedia data. Moreover, we propose a transmission control scheme that can take the amount of network resources down to the minimum amount within the range of permissible error-range under the guarantee with no quality degradation simultaneously when the bottleneck is caused by the network congestion. Finally, we propose a dynamic bandwidth smoothing scheme that can smooth the maximum bandwidth to the demand of each video steam for giving continuous transmission to the delay sensitive video steam between senders and receivers.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.6B
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• pp.375-381
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• 2007

This paper focuses on the prediction algorithm among the context-awareness technologies. With a representative algorithm, Bayesian Networks, it is difficult to realize a context-aware as well as to decrease process time in real-time environment. Moreover, it is also hard to be sure about the accuracy and reliability of prediction. One of the simplest algorithms is the sequential matching algorithm. We use it by adding the proposed Cache Scheme. It is adequate for a context-aware service adapting user's habit and reducing the processing time by average 48.7% in this paper. Thus, we propose a design method of user location prediction algorithm that uses sequential matching with the cache scheme by taking user's habit or behavior into consideration. The novel approach will be dealt in a different way compared to the conventional prediction algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.3
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• pp.587-594
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• 2005

In the paper, after we propose a viterbi decoder which has multiple buffering and parallel processing decoding scheme through expanding time-divided imput signal, and map a FPGA, we implement a channel coding system together with PC-based software. Continuous input signal is buffered as order of decoding length and is parallel decoded using a high speed cell for viterbi decoding. Output data rate increases linearly with the cell formed the viterbi decoder, and flexible operation can be satisfied by programming controller and modifying input buffer. The tell for viterbi decoder consists of HD block for calculating hamming distance, CM block for calculating value in each state, TB block for trace-back operation, and LIFO. The implemented cell of viterbi decoder used 351 LAB(Logic Arrary Block) and stably operated in maximum 139MHz in APEX20KC EP20K600CB652-7 FPGA of ALTERA. The whole viterbi decoder including viterbi decoding cells, input/output buffers, and a controller occupied the hardware resource of $23\%$ and has the output data rate of 1Gbps.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.4B
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• pp.354-361
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• 2011

Increasing interest and technological advances in smart home has led to active research on context-awareness service and prediction algorithms such as Bayesian Networks, Tree-Dimensional Structures and Genetic prediction algorithms. Context-awareness service presents that providing automatic customized service regarding individual user's pattern surely helps users improve the quality of life. However, it is difficult to implement context-awareness service because the problems are that handling coincidence with context information and exceptional cases have to consider. To overcome this problem, we proposes an Intelligent Sequential Matching Algorithm(ISMA), models context-awareness service using Timed Petri-net(TPN) which is petri-net to have time factor. The example scenario illustrates the effectiveness of the Timed Petri-net model and our proposed algorithm improves average 4~6% than traditional in the accuracy and reliability of prediction.