• 한국정보디스플레이학회:학술대회논문집
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• 한국정보디스플레이학회 2008년도 International Meeting on Information Display
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• pp.1499-1502
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• 2008

This paper proposes an efficient data compression for the conventional method of reducing EMI in a 10 bit LCD timing controller (TCON). In addition, we develop a new method to reduce EMI in a LCD TCON through repeated data on adjacent blocks. The novel technique reduced EMI by 10 dB for a 52" FHD 10it LCD TV.

• Journal of the Korean Data and Information Science Society
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• 제18권4호
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• pp.1057-1064
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• 2007

From the view of natural language process, quantitative linguistic analysis is a linguistic study relying on statistical methods, and is a mathematical linguistics in an attempt to discover various linguistic characters by interpreting linguistic facts quantitatively through statistical methods. In this study, I would like to introduce a quantitative linguistic analysis method utilizing a computer and statistical methods on literary works. I also try to introduce a use of SynKDP, a synthesized Korean data process, and show the relations between distribution of linguistic unit elements which are used by the hero in a novel #Sassinamjunggi# and theme analysis on literary works.

• Journal of Information Processing Systems
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• 제14권3호
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• pp.563-568
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• 2018

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권2호
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• pp.520-541
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• 2023

In CRNs, SS is of utmost significance. Every CR user generates a sensing report during the training phase beneath various circumstances, and depending on a collective process, either communicates or remains silent. In the training stage, the fusion centre combines the local judgments made by CR users by a majority vote, and then returns a final conclusion to every CR user. Enough data regarding the environment, including the activity of PU and every CR's response to that activity, is acquired and sensing classes are created during the training stage. Every CR user compares their most recent sensing report to the previous sensing classes during the classification stage, and distance vectors are generated. The posterior probability of every sensing class is derived on the basis of quantitative data, and the sensing report is then classified as either signifying the presence or absence of PU. The ISVM technique is utilized to compute the quantitative variables necessary to compute the posterior probability. Here, the iterations of SVM are tuned by novel GO-PSA by combining GOA and PSO. Novel GO-PSA is developed since it overcomes the problem of computational complexity, returns minimum error, and also saves time when compared with various state-of-the-art algorithms. The dependability of every CR user is taken into consideration as these local choices are then integrated at the fusion centre utilizing an innovative decision combination technique. Depending on the collective choice, the CR users will then communicate or remain silent.

• 대한교통학회지
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• 제33권4호
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• pp.417-429
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• 2015

본 연구에서는 고속도로에서 수집되는 차량검지기 자료를 대상으로 자료의 품질에 영향을 미치는 요인을 분석하여, 신뢰성 있는 교통자료의 관리 및 활용을 위한 교통자료 품질 통합평가지표를 개발하였다. 이를 위하여 국내외 자료품질평가에 대한 현황 조사 및 분석을 수행하여 시사점을 도출하였다. 품질관리지표는 기존의 고속도로 검지자료 품질평가에 사용되고 있는 평가지표인 완전성과 유효성뿐만 아니라, 이를 수정 보완한 지표들을 제시하였으며, 교통자료의 특성을 반영한 시공간 일관성 지표와 결측심각성 지표를 신규지표로 제시하였다. 또한, 개별 품질 평가지표들을 통합적으로 관리할 수 있는 통합 평가지표를 개발하고 평가 프레임워크를 제시하였다. 통합 품질평가지표는 쌍대비교를 통한 설문조사 방법으로 가중치를 산출하는 AHP기법과 자료의 변동성을 고려하여 가중치를 산출하는 엔트로피방법을 통합하는 혼합가중치 산출 방안을 적용하여 도출하였다. 본 연구의 결과물은 교통자료의 효율적 관리를 가능하게 하고, 교통자료의 품질을 높일 수 있는 품질관리체계의 중요 구성요소로 활용될 것으로 기대된다.

• 한국산학기술학회논문지
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• 제20권6호
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• pp.113-125
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• 2019

본 연구는 중국 IP산업에서 웹소설드라마 분석을 통해 소비자 대중심리를 알아보고 웹소설 파생상품의 제작방향을 파악하기 위한 연구이다. 이를 위해 중국 웹소설드라마 특성이 파생상품의 수용태도와 행동의도에 어떠한 영향을 미치는지를 분석하였다. 자료수집은 2018.10.15일~10.22일까지 중국 현지인들을 대상으로 수집한 설문조사 자료 612부를 표본으로 삼았다. 통계처리는 데이터 코딩(data coding)과 데이터 크리닝(data cleaning)과정을 거쳐, SPSS v. 21.0 통계 패키지 프로그램을 활용하여 분석하였다. 분석방법은 빈도분석, 탐색적 요인분석, 독립표본 t-test, 일원변량분석(One way ANOVA), 사후검정 Duncan test, 상관관계분석, 다중회귀분석을 실시하였다. 연구결과는 다음과 같다. 첫째, 웹소설드라마 특성인 화제성, 공감성, 흥미성이 파생상품 수용태도인 인지적인 태도 및 정서적인 태도에 유의적인 상관관계를 이루고 있다. 동시에 공감성이 화제성 및 흥미성보다 더 유의적인 영향을 미친 것으로 나타났다. 둘째, 화제성, 공감성, 흥미성으로 구성된 웹소설드라마 특성이 행동의도에 미치는 상관관계에서 웹소설드라마 모든 특성이 행동의도에 긍정적인 영향을 미쳤다. 특히 흥미성 및 화제성보다 공감성이 구매의도 및 추천의도에 더 유의미한 영향을 미친 것이다. 셋째, 정서적 태도가 구매의도 및 추천의도에 미치는 영향은 인지적 태도가 구매의도 및 추천의도에 미치는 영향보다 더 긍정적인 것으로 나타났다. 연구결과는 중국 웹소설의 파생상품 제작방향 설정을 위한 기초자료이자 제작지침으로서 유용한 의미를 갖는다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권1호
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• pp.77-92
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• 2020

When automatic speech recognition (ASR) is provided as a cloud service, it is easy to collect voice and application domain data from users. Harnessing these data will facilitate the provision of more personalized services. In this paper, we demonstrate our transfer learning-based knowledge service that built with the user-generated data collected through our novel system that deliveries personalized ASR service. First, we discuss the motivation, challenges, and prospects of building up such a knowledge-based service-oriented system. Second, we present a Quadruple Transfer Learning (QTL) method that can learn a classification model from a source domain and transfer it to a target domain. Third, we provide an overview architecture of our novel system that collects voice data from mobile users, labels the data via crowdsourcing, utilises these collected user-generated data to train different machine learning models, and delivers the personalised real-time cloud services. Finally, we use the E-Book data collected from our system to train classification models and apply them in the smart TV domain, and the experimental results show that our QTL method is effective in two classification tasks, which confirms that the knowledge transfer provides a value-added service for the upper-layer mobile applications in different domains.

• International Journal of Computer Science & Network Security
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• 제22권6호
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• pp.230-240
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• 2022

Sharing of online videos via internet is an emerging and important concept in different types of applications like surveillance and video mobile search in different web related applications. So there is need to manage personalized web video retrieval system necessary to explore relevant videos and it helps to peoples who are searching for efficient video relates to specific big data content. To evaluate this process, attributes/features with reduction of dimensionality are computed from videos to explore discriminative aspects of scene in video based on shape, histogram, and texture, annotation of object, co-ordination, color and contour data. Dimensionality reduction is mainly depends on extraction of feature and selection of feature in multi labeled data retrieval from multimedia related data. Many of the researchers are implemented different techniques/approaches to reduce dimensionality based on visual features of video data. But all the techniques have disadvantages and advantages in reduction of dimensionality with advanced features in video retrieval. In this research, we present a Novel Intent based Dimension Reduction Semi-Supervised Learning Approach (NIDRSLA) that examine the reduction of dimensionality with explore exact and fast video retrieval based on different visual features. For dimensionality reduction, NIDRSLA learns the matrix of projection by increasing the dependence between enlarged data and projected space features. Proposed approach also addressed the aforementioned issue (i.e. Segmentation of video with frame selection using low level features and high level features) with efficient object annotation for video representation. Experiments performed on synthetic data set, it demonstrate the efficiency of proposed approach with traditional state-of-the-art video retrieval methodologies.

• 전기학회논문지
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• 제57권6호
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• pp.1058-1062
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• 2008

In clinical decision support system(CDSS), unlike rule-based expert method, appropriate data-driven machine learning method can easily provide the information of individual feature(clinical test) for disease classification. However, currently developed methods focus on the improvement of the classification accuracy for diagnosis. With the analysis of feature importance in classification, one may infer the novel clinical test sets which highly differentiate the specific diseases or disease states. In this background, we introduce a novel CDSS that integrate a classifier and feature selection module together. Random forest algorithm is applied for the classifier and the feature importance measure. The system selects the significant clinical tests discriminating the diseases by examining the classification error during backward elimination of the features. The superior performance of random forest algorithm in clinical classification was assessed against artificial neural network and decision tree algorithm by using breast cancer, diabetes and heart disease data in UCI Machine Learning Repository. The test with the same data sets shows that the proposed system can successfully select the significant clinical test set for each disease.

• 한국전자통신학회논문지
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• 제8권3호
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• pp.465-470
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• 2013

Dempster-Shafe 증거이론은 다중센서 데이터융합을 위한 좋은 계산방법을 제공해준다. 이때 기본확률배정 함수가 절대적으로 필요하다. 본 논문에서는 신호를 평가하여 기본확률배정함수를 계산하고 결정하는 방법을 제안한다. 센서들이 보내온 신호를 구간별로 변화율을 평가하고 이 평가를 기초로 기본확률배정함수를 정하도록 한다. 센서들이 감지하여 보고한 신호들은 상황발생 요인과 관련 있는데, 시간간격에 따라서 변화하는 신호값의 추이를 평가하였다. 센서가 감지한 신호의 변화는 상황구성 및 병화와 밀접한 관련이 있으므로 신호값의 변화를 평가하는 것은 상황추론에 도움이 되는 것이었다. 이것을 기본확률배정함수 결정에 포함함으로써 사전정보가 없는 경우에 대해서도 상황추론이 가능할 수 있음을 보였다.