• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.697-702
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• 2008

Mobile display panel is small so that users are often difficult to perceive images clearly. About image we perceive much through colors and therefore we propose color fitting approach for clear perception even on the small and low quality LCD panels. Various color modifications have been studied and used in commercial software packages. For mobile usage, our approach instantly enhances color images by modifying colors in a way to contrast differences of them. The method includes tone enhancements (which contrast dark and bright sides) and color enhancements (which reduce saturation for pure colorants). Based on color theory, our method also modifies color values towards specified complementary and preference colors. We term this color fitting. This approach enables displaying photos, multimedia messages, videos and digital media broadcasting (DMB) for better perception in real-time on mobile devices. Index Terms.) color fitting, visualization on small display, mobile graphics, visual perception.

• The Transactions of the Korea Information Processing Society
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• v.4 no.2
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• pp.401-408
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• 1997

Human gets almist all of his knoweledge from the recognition and the accumulation of input patterns,image or sound,the he gets theough his eyes and through his ears.Among these means,his chracter recognition,an ability that allows him to recognize characters and understand their meanings through visual information, is now applied to a pattern recognition system using neural network in computer. Recurrent neural network is one of those models that reuse the output value in neural network learning.Recently many studies try to apply this recurrent neural network to the classification of static patterns like off-line handwritten characters. But most of their efforts are not so drrdtive until now.This stusy suggests a new type of recurrent neural network for an deedctive classification of the static patterns such as off-line handwritten chracters.Using the new J-E(Jordan-Elman)neural network model that enlarges and combines Jordan Model and Elman Model,this new type is better than those of before in recobnizing the static patterms such as figures and handwritten-characters.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.9B
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• pp.1731-1741
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• 1999

Color images are often corrupted by the noise due to noisy sensors or channel transmission errors. Some filters such as vector media and vector $\alpha$-trimmed mean filter have bee used for color noise removal. In this paper, We propose the fuzzy cluster filters based on the possibilistic c-means clustering, because the possibilistic c-means clustering can get robust memberships in noisy environments. Also, we propose weighted vector $\alpha$-trimmed mean filter to improve the conventional vector $\alpha$-trimmed mean filter. In this filter, the central data are more weighted than the outlying data. In this paper, we implemented the color noise generator to evaluate the performance of the proposed filters in the color noise environments. The NCD measure and visual measure by human observer are used for evaluation the performance of the proposed filters. In the experiment, proposed fuzzy cluster filters in the sense of NCD measure gave the best performance over conventional filters in the mixed noise. Simulation results showed that proposed weighted vector $\alpha$-trimmed mean filters better than the conventional vector $\alpha$-trimmed mean filter in any kinds of noise.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2002.02a
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• pp.494-500
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• 2002

본 연구의 목적은 DGPS와 자이로 센서를 장착하여 무인으로 콤바인을 직선주행, 선회 및 곡선주행, 직진주행 중 1 m 오프셋(offset) 주행시켰을 때, 무인 자율주행의 성능을 분석하고 개선하는데 있다. 콤바인의 무인자율주행 시스템은 콤바인의 현재위치를 인식하고, 주행하고 있는 콤바인의 주행방향을 감지하여 미리 설정된 경로를 따라 자율적으로 주행하며, 주행 중에 장애물이 검출되면 정지할 수 있도록 개발하였다. 콤바인의 무인 자율주행시스템은 DGPS의 입력 신호로부터 콤바인의 현재위치를 결정하고, 자이로 센서의 입력신호로부터 주행방향을 알 수 있다. 또한 장애물의 감지를 위한 초음파 센서, 콤바인의 주행방향을 조정하는 유압 작동부, 좌.우의 조향레버를 조정하는 서보모터 시스템, 마이크로 컴퓨터로 구성된 제어기와 입출력 인터페이스로 구성되어 있다. 콤바인 자율주행시스템의 프로그램은 DGPS 신호, 자이로 센서 등을 수신하는 수신 프로그램, DGPS 신호등으로부터 관련 변수들을 분석하여 콤바인의 조향수준을 결정하고, 유압실린더 등을 제어하는 제어 프로그램과 콤바인의 이동경로를 저장하는 저장 프로그램으로 구성되어 있다. 콤바인의 무인주행 실험결과 RMS 오차는 50 m 직선주행에서 7.52 cm, 20 m 직선주행 후 1 m 오프셋 된 30 m의 직선주행에서 21.85 cm, 20 m 직선주행 후 90$^{\circ}$선회하여 25 m 직선주행에서 7.55 cm, 반지름 23 m의 원주 사분면 곡선주행에서는 25.98 cm로 각각 나타났다. 소 구획의 포장에서 벼는 가로 방향으로 25~30 cm, 세로 방향으로 15~20 cm의 간격으로 심어져 있다. DGPS 신호에 의한 위치 결정을 할 때 자체 오차 10 cm를 고려하여도 콤바인이 직선구간 및 선회구간을 주행하며 수확작업이 가능함을 알 수 있었다. 그러나 곡선구간에서는 최대오차가 65.5 cm로 매우 크게 나타나, 콤바인을 무인 자율주행으로 수확하기에는 어려움이 있는 것으로 나타났다. 실제 포장은 이론적인 완전한 직선보다는 작은 굴곡이 있는 곡선의 형태가 이루어져 있으므로 주행 오차를 감소하기 위해서는 기계시각을 이용하면 보다 정밀한 조향을 이룰 수 있을 것으로 예상된다. 포장에서 DGPS 신호, 자이로 센서 등을 이용한 콤바인의 무인주행 장치는 무인 수확작업을 위한 가능성을 보여주었고, 일부의 센서의 기능을 개선하면 만족한 성능을 나타낼 수 있을 것으로 판단된다.

• Journal of Korea Multimedia Society
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• v.12 no.8
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• pp.1055-1065
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• 2009

High-dynamic-range (HDR) rendering technology changes the range from the broad dynamic range (up to 9 log units) of a luminance, in a real-world scene, to the 8-bit dynamic range which is the common output of a display's dynamic range. One of the techniques, iCAM06 has a superior capacity for making HDR images. iCAM06 is capable of making color appearance predictions of HDR images based on CIECAM02 and incorporating spatial process models in the human visual system (HVS) for contrast enhancement. However there are several problems in the iCAM06, including obscure user controllable factors to be decided. These factors have a serious effect on the output image but users get into difficulty in that they can't find an adequate solution on how to adjust. So a suggested model gives a quantitative formulation for user controllable factors of iCAM06 to find suitable values which corresponds with different viewing conditions, and improves subjective visuality of displayed images for varying illuminations.

• Journal of KIISE:Software and Applications
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• v.33 no.6
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• pp.564-573
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• 2006

Temporal information plays an important role in natural language processing (NLP) applications such as information extraction, discourse analysis, automatic summarization, and question-answering. In the topic detection and tracking (TDT) area, the temporal information often used is the publication date of a message, which is readily available but limited in its usefulness. We developed a relatively simple NLP method of extracting temporal information from Korean news articles, with the goal of improving performance of TDT tasks. To extract temporal information, we make use of finite state automata and a lexicon containing time-revealing vocabulary. Extracted information is converted into a canonicalized representation of a time point or a time duration. We first evaluated the extraction and canonicalization methods for their accuracy and investigated on the extent to which temporal information extracted as such can help TDT tasks. The experimental results show that time information extracted from text indeed helps improve both precision and recall significantly.

• Journal of the Korea Society of Computer and Information
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• v.24 no.10
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• pp.65-70
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• 2019

In recently, ship trajectory data consisting of ship position, speed, course, and so on can be obtained from the Automatic Identification System device with which all ships should be equipped. These data are gathered more than 2GB every day at a crowed sea port and used for analysis of ship traffic statistic and patterns. In this study, we propose a method to process ship trajectory data efficiently with distributed computing resources using MapReduce algorithm. In data preprocessing phase, ship dynamic and static data are integrated into target dataset and filtered out ship trajectory that is not of interest. In mapping phase, we convert ship's position to Geohash code, and assign Geohash and ship MMSI to key and value. In reducing phase, key-value pairs are sorted according to the same key value and counted the ship traffic number in a grid cell. To evaluate the proposed method, we implemented it and compared it with IALA waterway risk assessment program(IWRAP) in their performance. The data processing performance improve 1 to 4 times that of the existing ship trajectory analysis program.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.1-6
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• 2020

Road movement pollutant air environment big data is a link between real-time traffic data such as vehicle type, speed, and load using AVC, VDS, WIM, and DTG, which are always traffic volume survey equipment, and road shape (uphill, downhill, turning section) data using GIS. It consists of traffic flow data. Also, unlike general data, a lot of data per unit time is generated and has various formats. In particular, since about 7.4 million cases/hour or more of large-scale real-time data collected as detailed traffic flow information are collected, stored and processed, a system that can efficiently process data is required. Therefore, in this study, an open source-based data parallel processing performance optimization study is conducted for the visualization of big data in the air environment of road transport pollution.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.183-188
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• 2023

Game object inheritance and multiple components allow for visualization of system architecture, good code reuse, and fast prototyping. On the other hand, objects are more likely to rely on high latency between game objects and components, static casts, and lots of references to things like null pointers. Therefore, It is important to design a game in such a way so that the dependency of objects on multiple classes could be reduced and existing codes could be reused. Therefore, we designed the game to make the classes more modular by applying Dependency Injection and the design patterns proposed by the Gang of Four. Since these dependencies are attributes of the game object and the injection occurs only in the initialization pass, there is little performance degradation or performance penalty in the game loop. Therefore, this paper proposed an efficient design method to effectively reuse APIs in the design and implementation of survival games.

• Journal of KIISE:Software and Applications
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• v.34 no.6
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• pp.549-561
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• 2007

Vision-based scene understanding is to infer and interpret the context of a scene based on the evidences by analyzing the images. A probabilistic approach using Bayesian networks is actively researched, which is favorable for modeling and inferencing cause-and-effects. However, it is difficult to gather meaningful evidences sufficiently and design the model by human because the real situations are dynamic and uncertain. In this paper, we propose a learning method of Bayesian network that reduces the computational complexity and enhances the accuracy by searching an efficient BN structure in spite of insufficient evidences and training data. This method represents the domain knowledge as ontology and builds an efficient hierarchical BN structure under constraint rules that come from the ontology. To evaluate the proposed method, we have collected 90 images in nine types of circumstances. The result of experiments indicates that the proposed method shows good performance in the uncertain environment in spite of few evidences and it takes less time to learn.