• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.396-398
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• 2012

대부분의 실세계 데이터는 시간성을 띄고 있으므로 시간성을 지닌 데이터를 분석할 수 있는 기계 학습 방법론은 매우 중요하다. 이런 관점에서 비디오 데이터는 다양한 모달리티가 결합된 대표적인 시간 데이터 이므로 비디오 데이터를 대상으로 하는 기계 학습 방법은 큰 의미를 갖는다. 본 논문에서는 음성 채널에기반한 비디오 데이터 분석 방법의 예비 연구로 비디오 데이터에 등장하는 화자를 인식할 수 있는 간단한 방법을 소개한다. 제안 방법은 MFCC (Mel-frequency cepstrum coefficients)를 이용하여 인간 음성 특성의 분포를 분석한 후 분석 결과를 신경망에 입력하여 목표한 화자를 인식하는 복합 신경망 모델을 특징으로 한다. 실제 TV 드라마 데이터에서 가우시안 혼합모델, 가우시안 혼합 신경망 모델, 제안 방법의 화자 인식 성능을 비교한 결과 제안 방법이 가장 우수한 인식 성능을 보임을 확인하였다.

• Korea Science and Art Forum
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• v.34
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• pp.133-148
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• 2018

Korea's housing structure is predicted that one-person housing will be the most common type of housing in Korea. Therefore, this study intends to derive contents for designing a one-person housing space considering the life of a rapidly increasing one-person householder. For this purpose, this study objectively derives the social, economic and cultural influencing factors of one-person households through big data analysis, and analyzed the correlation between contents using social network analysis methodology. In this paper, 60 core contents related to one person housing space were derived by applying big data analysis methodology. And through social network analysis, the most influential contents were derived from the space editing and space composition categories. This means that the residential space is an important part of the design idea that can flexibly respond to changes in the user's life. Based on this study, future research will focus on the concept and design methodology of one-person housing space.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.4
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• pp.319-326
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• 2015

Recently in the fields o f stochastic optimal control ( SOC) and reinforcemnet l earning (RL), there have been a great deal of research efforts for the problem of finding data-based sub-optimal control policies. The conventional theory for finding optimal controllers via the value-function-based dynamic programming was established for solving the stochastic optimal control problems with solid theoretical background. However, they can be successfully applied only to extremely simple cases. Hence, the data-based modern approach, which tries to find sub-optimal solutions utilizing relevant data such as the state-transition and reward signals instead of rigorous mathematical analyses, is particularly attractive to practical applications. In this paper, we consider a couple of methods combining the modern SOC strategies and approximate inference together with machine-learning-based data treatment methods. Also, we apply the resultant methods to a variety of application domains including financial engineering, and observe their performance.

• Design Convergence Study
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• v.15 no.6
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• pp.195-214
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• 2016

Predictive analysis is based on a probabilistic learning algorithm called pattern recognition or machine learning. Therefore, if users want to extract more information from the data, they are required high statistical knowledge. In addition, it is difficult to find out data pattern and characteristics of the data. This study conducted statistical data analyses and visual data analyses to supplement prediction analysis's weakness. Through this study, we could find some implications that haven't been found in the previous studies. First, we could find data pattern when adjust data selection according as splitting criteria for the decision tree method. Second, we could find what type of data included in the final prediction model. We found some implications that haven't been found in the previous studies from the results of statistical and visual analyses. In statistical analysis we found relation among the multivariable and deducted prediction model to predict high box office performance. In visualization analysis we proposed visual analysis method with various interactive functions. Finally through this study we verified final prediction model and suggested analysis method extract variety of information from the data.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.1
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• pp.69-74
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• 2005

In this paper, we suggested user assistant soft computing method for 3D effect optimization. In order to maximize 3D effect of image, intervals among cameras have to be set up properly according to distance between cameras and an object. Two data such as interval and distance was obtained to use in neural network as the data for learning. However, if the data for learning was obtained by only human's subjective views, it could be that the obtained data was not optimal for learning because the data had an accidental ewer To obtain optimal data lot learning, we added candidature data to obtained data through data analysis, and then selected the most proper data between the candidature data and the obtained data for learning in neural network. Usually, 3D effect of image was affected by both distance from an object to cameras and an object size. Therefore, we suggested fuzzy inference model which was able to represent two factors like distance and size. Candidature data was added by fuzzy model. In the simulation result, we verified that the mote the obtained data was affected by human's subjective views, the more effective the suggested system was.

• Journal of the Korean Society for information Management
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• v.28 no.4
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• pp.85-97
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• 2011

The purpose of this study was to develop a methodology of user participation evaluation of a scholarly information site in the field of science and technology and to enhance the site by applying a set of testing protocols. Experiments were conducted in a laboratory setting. Data from multiple sources, including eyetracking, search logs and post surveys, were collected and analyzed quantitatively. Based on the results of eyetracking, the contents and images were reorganized after removing unessential elements in the site. The resulting data from the search logs showed that the users were able to finish the tasks more quickly with the revised user interface. The results of the data analysis of post surveys indicated an overall improvement in the revised website compared to the original one.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.456-459
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• 2022

본 연구는 최근 자연어 인공지능 연구 동향에 발맞추어 사전 학습된 언어 인공지능을 활용한 의미론적 분석을 통해 국문 보고서의 가독성을 평가하는 방법론 두 가지를 제안한다. 연구진은 연구 과정에서 사전 학습된 언어 인공지능을 활용해 추가 학습 없이 문장을 임의의 벡터값으로 임베딩하고 이를 통해 1. 의미론적 복잡도 와 2. 내재적 감정 변동성 두 가지 지표를 추출한다. 나아가, 앞서 발견한 두 지표가 국문 보고서의 가독성과 정(+)의 상관관계에 있음을 확인하였다. 본 연구는 통사론적 분석과 레이블링 된 데이터에 크게 의존하던 기존의 가독성 평가 방법론으로 부터 탈피해, 별도의 학습 없이 기존 가독성 지표에 근사한다는 점에서 의미가 있다.

• Journal of Digital Convergence
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• v.14 no.12
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• pp.591-597
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• 2016

Research question and method play important roles in conducting a research in a scientifically valid way. In today's digitalized research environment, social network service (SNS) has rapidly become a new source of big data. While this shift provides new challenges for researchers in Korea, there is little scholarly discussion of how research questions can be framed and what statistical methods can be applied. This article suggests some basic but primary types of example questions for researchers employing social big data analytics. Further, we illustrate the interface of the intended data set specifically for SNS-mediated communication and information exchange behaviors. Lastly, a statistical test known as proper method for social big data is introduced.

• Construction Engineering and Management
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• v.15 no.6
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• pp.39-43
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• 2014

• Journal of the Korea Society of Computer and Information
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• v.11 no.1 s.39
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• pp.11-18
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• 2006

The logical analysis of data(LAD) is a Boolean-logic based data mining tool. A critical step in analyzing data by LAD is the pattern generation stage where useful knowledge and hidden structural information in data is discovered in the form of patterns. A conventional method for pattern generation in LAD is based on term enumeration that renders the generation of higher degree patterns practically impossible. In this paper, we present a novel optimization-based pattern generation methodology and propose two mathematical programming models, a mixed 0-1 integer and linear programming (MILP) formulation and a well-studied set covering problem (SCP) formulation for the generation of optimal and heuristic patterns, respectively. With benchmark datasets, we demonstrate the effectiveness of our models by automatically generating with ease patterns of high complexity that cannot be generated with the conventional approach.