• The Journal of the Acoustical Society of Korea
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• 제17권1E호
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• pp.54-65
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• 1998

This study attempts to test the classifying performance of a neural network and thereby examine its applicability to the signals distorted in a shallow water environment. Linear frequency modulated(LFM) signals are simulated by using an acoustic model and also measured through sea experiment. The network is constructed to have three layers and trained on both data sets. To get normalized power spectra as feature vectors, the study considers the three transforms : shot-time Fourier transform (STFT), wavelet transform (WT) and pseudo Wigner-Ville distribution (PWVD). After trained on the simulated signals over water depth, the network gives over 95% performance with the signal to noise ratio (SNR) being up to-10 dB. Among the transforms, the PWVD presents the best performance particularly in a highly noisy condition. The network performs worse with the summer sound speed profile than with the winter profile. It is also expected to present much different performance by the variation of bottom property. When the network is trained on the measured signals, it gives a little better results than that trained on the simulated data. In conclusion, the simulated signals are successfully applied to training a network, and the trained network performs well in classifying the signals distorted by a surrounding environment and corrupted by noise.

• Communications for Statistical Applications and Methods
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• 제10권3호
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• pp.997-1005
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• 2003

We can use the logistic model for categorical data when the response variables are binary data. In this paper we consider the problem of constructing the confidence intervals for logistic model in I${\times}$J${\times}$2 contingency table. These constructions are simplified by applying logit transformation. This transforms the problem to consider linear form which called the logit model. After obtaining the confidence intervals for the logit model, the reverse transform is applied to obtain the confidence intervals for the logistic model.

• 한국통신학회:학술대회논문집
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• 한국통신학회 1984년도 춘계학술발표회논문집
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• pp.13-17
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• 1984

A hybrid version of the Hadamard and center Line Symmetric Haar Transform called H-CLSH is defined and developed. Efficient algorithms for fast computation of the H-CLSH and its inverse are developed. The H-CLSH is applied to digital signal and image processing and its utility and image processing and its utility and effectiveness are compared with Hadamard-Haar discrete transforms on the basis of some standard performance criteria.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2018년도 춘계 종합학술대회 논문집
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• pp.549-550
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• 2018

We have developed a web server for the high throughput annotation of gene. This system processes entire data sets with an automated pipeline of 13 analytic services, then deposits the data into the MySQL database and transforms it into three kinds of reports: preprocessing, assembling and annotation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권10호
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• pp.4198-4213
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• 2020

Data encryption, particularly application-level data encryption, is a common solution to protect data confidentiality and deal with security threats. Application-level encryption is a process in which data is encrypted before being sent to the database. However, cryptography transforms data and makes the query difficult to execute. Various studies have been carried out to find ways in order to implement a searchable encrypted database. In the current paper, we provide a new encrypting method and querying on encrypted data (ZSDB) for different data types. It is worth mentioning that the proposed method is based on secret sharing. ZSDB provides data confidentiality by dividing sensitive data into two parts and using the additional server as Dictionary Server. In addition, it supports required operations on various types of data, especially LIKE operator functioning on string data type. ZSDB dedicates the largest volume of execution tasks on queries to the server. Therefore, the data owner only needs to encrypt and decrypt data.

• 한국콘텐츠학회논문지
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• 제11권6호
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• pp.1-7
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• 2011

KOSPI는 정치 및 경제를 포함한 다양한 요소에 영향을 받는 관계로 정확한 단기 KOSPI 예측 방법론 개발은 매우 어려운 문제로 여겨지고 있다. 본 논문에서는 가중 퍼지소속함수 기반 신경망(NEWFM; neural network with weighted fuzzy membership functions)의 특징 추출기법을 사용하여 5일 동안의 주가 단기추세를 예측하는 방안을 제안한다. 비중복면적 분산 측정법에 의해 중요도가 가장 낮은 특징입력을 하나씩 제거하면서 최소의 특징입력을 선택한다. 특징입력으로써 기술지표를 이용하여 얻은 데이터를 웨이블릿 변환을 이용하여 39개의 계수들을 추출한다. 이들 39개의 특징입력 중 비중복면적 분산측정법에 의해서 추출된 12개의 계수가 사용된다. 제안된 방법에서는 민감도가 72.79%, 특이도가 74.76%, 정확도가 73.84%를 나타낸다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.2029-2032
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• 2004

The quantity of data were rapidly increased recently and caused the data overwhelming. This led to be difficult in searching the required data. The method of eliminating redundant data was needed. One of the efficient methods was Knowledge Discovery in Database (KDD). Generally data can be separate into 2 cases, continuous data and discrete data. This paper describes algorithm that transforms continuous attributes into discrete ones. We present an Improved Class Attribute Interdependence Maximization (ICAIM), which designed to work with supervised data, for discretized process. The algorithm does not require user to predefine the number of intervals. ICAIM improved CAIM by using significant test to determine which interval should be merged to one interval. Our goal is to generate a minimal number of discrete intervals and improve accuracy for classified class. We used iris plant dataset (IRIS) to test this algorithm compare with CAIM algorithm.

• International journal of advanced smart convergence
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• 제6권3호
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• pp.22-28
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• 2017

With the recent development of technologies to manage vast amounts of data, data mining technology has had a major impact on all industries.. Data mining is the process of discovering useful correlations hidden in data, extracting executable information for the future, and using it for decision making. In other words, it is a core process of Knowledge Discovery in data base(KDD) that transforms input data and derives useful information. It extracts information that we did not know until now from a large data base. In the decision tree, c4.5 algorithm was used. In addition, the C4.5 algorithm was used in the decision tree to analyze the difference between frequency and mortality in the region. In this paper, the frequency and mortality of percutaneous coronary intervention for patients with heart disease were divided into regions.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2001년도 춘계학술대회 논문집
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• pp.627-630
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• 2001

XML is suitable to integrate various forms of engineering design data since it possesses the characteristics of both documents and data. In this research a web-based design system has been developed, which integrates various gear design data in the form of XML. The system generates XML document containing gear design data and transforms gear design data in the relational database into XML document form automatically. The XML documents are transmitted to gear modeler agent through SOAP, and then the agent is automatically executed and generates CAD model files and VRML files. The designer can check the generated VRML model of gear immediately in the web service.

• 한국컴퓨터정보학회논문지
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• 제14권11호
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• pp.13-20
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• 2009

편재형 컴퓨팅 환경은 다양한 컴퓨팅 장비의 접속만큼이나 그 응용분야도 다양하다. 이러한 컴퓨팅환경에서 XML은 최적의 데이터 표현이다. W3C 표준인 XML은 XSLT를 이용하여 다른 응용분야에 적절한 데이터 변환이 가능하다. 그러나 XML은 텍스트 기반으로 이진데이터 저장방식에 비해 파일의 크기가 커서 컴퓨팅 파워가 적은 편재형 컴퓨팅 환경에서는 다루기가 어렵다는 단점을 갖고 있다. 본 논문에서는 XML문서의 인코딩 방법에 대하여 살펴보고 자원을 적게 사용하는 편재형 컴퓨팅 환경에서 인코딩 된 EXI 포맷의 XML문서가 다른 응용의 적합한 EXI 포맷 XML 문서으로의 변환방법을 제안한다. 다양한 응용환경 중에서 본 논문에서는 EXI 포맷의 XHTML 문서를 EXI 포맷 VoiceXML로 변환하는 시스템을 제시한다. 이 시스템은 자원이 적은 편재형 컴퓨팅 환경에서 EXI 포맷 XML 문서의 재사용성을 높이고 EXI 포맷의 XML활용에 기여할 수 있다.