• 한국CDE학회논문집
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• 제14권6호
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• pp.382-389
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• 2009

Recently, knowledge management has been required in companies as a tool of competitiveness. Companies have constructed Enterprise Resource Planning(ERP) system in order to manage huge knowledge. But, it is not easy to formalize knowledge in organization. We focused on data mining system by genetic programming(GP). Data mining system by genetic programming can be useful tools to derive and extract the necessary information and knowledge from the huge accumulated data. However when we don't have enough amounts of data to perform the learning process of genetic programming, we have to reduce input parameter(s) or increase number of learning or training data. In this study, an enhanced data mining method combining Genetic Programming with Self organizing map, that reduces the number of input parameters, is suggested. Experiment results through a prototype implementation are also discussed.

• International Journal of Contents
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• 제14권4호
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• pp.24-29
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• 2018

EEG-based brain-computer interfaces has focused on explicitly expressed intentions to assist physically impaired patients. For EEG-based-computer interfaces to function effectively, it should be able to understand users' implicit information. Since it is hard to gather EEG signals of human brains, we do not have enough training data which are essential for proper classification performance of implicit intention. In this paper, we improve the subject independent classification of implicit intention through the generation of additional training data. In the first stage, we perform the PCA (principal component analysis) of training data in a bid to remove redundant components in the components within the input data. After the dimension reduction by PCA, we train ICA (independent component analysis) network whose outputs are statistically independent. We can get additional training data by adding Gaussian noises to ICA outputs and projecting them to input data domain. Through simulations with EEG data provided by CNSL, KAIST, we improve the classification performance from 65.05% to 66.69% with Gamma components. The proposed sample generation method can be applied to any machine learning problem with fewer samples.

• 대한원격탐사학회지
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• 제29권6호
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• pp.589-593
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• 2013

Sea Surface Temperature (SST) is the temperature close to the ocean's surface and affects the Earth's atmosphere as an important parameter for the climate circulation and change. The SST from satellite still has biases from the error in specifying retrieval coefficients from either forward modeling or instrumental biases. So in this paper, we performed sensitivity analysis using input parameter of the SST to notice that the SST is most affected among the input parameter. We used Infrared (IR) data from the Communication, Ocean, and Meteorological Satellite (COMS)/Meteorological Imager (MI) from April 2011 to March 2012. We also used the Global Space-based Inter-Calibration System (GSICS) correction to quality of the IR data from COMS. SST was calculated by substituting the input parameters; IR data with or without the GSICS correction. The results of this sensitivity analysis, the SST was sensitive from -0.0403 to 0.2743 K when the IR data were changed by the GSICS corrections.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.2485-2489
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• 2005

This paper introduces a new preprocessing scheme to replace original data of gray scale images with particular ordered data so that performance of lossless compression can be improved more efficiently. As a kind of preprocessing technique to maximize performance of entropy encoder, the proposed method converts the input image data into more compressible form. Before encoding a stream of the input image, the proposed preprocessor counts co-occurrence frequencies for neighboring pixel pairs. Then, it replaces each pair of adjacent gray values with particular ordered numbers based on the investigated co-occurrence frequencies. When compressing ordered image using entropy encoder, we can expect to raise compression rate more highly because of enhanced statistical feature of the input image. In this paper, we show that lossless compression rate increased by up to 37.85% when comparing results from compressing preprocessed and non-preprocessed image data using entropy encoder such as Huffman, Arithmetic encoder.

• 대한전자공학회논문지SD
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• 제41권11호
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• pp.87-95
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• 2004

본 논문에서는 SoC 테스트를 위한 새로운 테스트 데이터 압축 방법을 제안한다. 제안하는 압축 방법은 테스트 데이터 압축을 위해 압축율과 하드웨어 오버헤드를 고려하여 최대 효율을 가지도록 하는데 기초하고 있다. 압축율을 높이기 위해서 본 논문에서는 IR 기법과 MSCIR 압축 코드를 사용하며, 뿐만아니라 이를 위한 사전 작업인 새로운 맵핑 기법 및 테스트 패턴순서 재조합 방법을 제안한다. 기존의 연구와는 달리 CSR 구조를 사용하지 않고 원래의 테스트 데이터를 사용하여 압축하는 방법을 사용한다. 이렇게 함으로써 제안하는 압축 방법은 기존의 연구에 비해 훨씬 높은 압축율을 가지며 낮은 하드웨어 오버헤드의 디컴프레션 구조를 가진다. ISCAS '89 벤치 회로에 대한 기존의 연구와의 비교로서 그 결과를 알 수 있다.

• 한국한의학연구원논문집
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• 제12권2호통권17호
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• pp.85-92
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• 2006

Pulse is one of the basic diagnostic information of TKM(Traditional Korean Medicine). To quantify and standardize pulse diagnosis, we had collected an amount of clinical data from May 2005 by using newly developed pulse analyzer. But there were many noises in pulse wave according to measuring method, environment, operator and condition of patient. So some data can’t be included for analyzing diagnosis. To reduce noises from measuring pulse and to collect reliable pulse wave data, we made the process map of measuring method and applied six sigma project. With this we can improved the method of measuring pulse wave in collecting clinical data. The project follows a disciplined process of five macro phases: define, measure, analyze, improve and control (DMAIC). A process map and C-E diagram are used to identify process input and output variables. The major input variables are selected by using C&E matrix, and process map is developed by analyzing input variables. And the optimum process conditions are going to be controled to avoid in increasing loss of collecting pulse wave data.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 D
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• pp.2580-2582
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• 2004

In this paper, a Neural Network(NN) based approach for classification of two dimensions images. The proposed algorithm is able to apply in the actual industry. The described diagnostic algorithm is presented to defect surface failures on tiles. A way to get data for a digital image process is several kinds of it. The tiles are scanned and the digital images are preprocessed and classified using neural networks. It is important to reduce the amount of input data with problem specific preprocessing. The auto-associative neural network is used for feature generation and selection while the probabilistic neural network is used for classification. The proposed algorithm is evaluated experimentally using one hundred of the real tile images. Sample image data to preprocess have histogram. The histogram is used as input value of probabilistic neural network. Auto-associative neural network compress input data and compressed data is classified using probabilistic neural network. Classified sample images are determined by human state. So it is intervened human subjectivity. But digital image processing and neural network are better than human classification ability. Therefore it is very useful of quality control improvement.

• 디지털산업정보학회논문지
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• 제11권1호
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• pp.97-109
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• 2015

In this paper, we design and implement a two-way automatic communication system using a mobile instant messenger that can provide a customized service through a real-time two-way communication using a mobile instant messaging service. Two-way automatic communication system using a mobile instant messenger can improve the quality and quantity of information in real-time update of the information through the feedback. In addition, since it is communicated by using a mobile Instant Messanger in 1:1 there is no concerns that is recognized as spam, as well as it is possible to provide customized information for each user. Two-way automatic communication system using a mobile instant messanger shows the difference of the speed according to the data input time in typing by hands in result of comparing the time to input a date and the processing speed to search a data. Therefore in category treatment, command processing and natural language processing, Category treatment way is the most excellent in aspect of data processing speed, otherwise in aspect of the total speed to combine the data input time and the processing time, the command processing way is the best method.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2013년도 춘계학술대회
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• pp.613-615
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• 2013

최근 들어 워드 파일을 XML로 변환하여 서비스하기 위한 요구가 많아지고 있다. 이에 본 논문에서는 워드 파일(아래한글, MS-Office)로 입력된 데이터를 XML 파일로 변환하여 사용자가 XML 매핑 파일을 만들어 워드 프로세서에 입력된 데이터를 바로 추출하여 데이터베이스에 저장하는 시스템을 제안한다. 이는, 워드프로세스에 양식을 미리 작성하여 필요한 데이터를 데이터베이스에서 조회하여 워드프로세서 문서를 어플리케이션 프로그램에서 워드 파일을 생성 할 수 있다.

• Journal of Information Processing Systems
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• 제5권1호
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• pp.5-10
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• 2009

Text data has always accounted for a major portion of the world's information. As the volume of information increases exponentially, the portion of text data also increases significantly. Text classification is therefore still an important area of research. LDA is an updated, probabilistic model which has been used in many applications in many other fields. As regards text data, LDA also has many applications, which has been applied various enhancements. However, it seems that no applications take care of the input for LDA. In this paper, we suggest a way to map the input space to a reduced space, which may avoid the unreliability, ambiguity and redundancy of individual terms as descriptors. The purpose of this paper is to show that LDA can be perfectly performed in a "clean and clear" space. Experiments are conducted on 20 News Groups data sets. The results show that the proposed method can boost the classification results when the appropriate choice of rank of the reduced space is determined.