• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.10
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• pp.1275-1281
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• 2019

The automatic modulation recognition of a radio signal is a major task of an intelligent receiver, with various civilian and military applications. In this paper, we propose a method to recognize the modulation of radio signals in wireless communication based on the deep neural network. We classify the modulation pattern of radio signal by using the LSTM model, which can catch the long-term pattern for the sequential data as the input data of the deep neural network. The amplitude and phase of the modulated signal, the in-phase carrier, and the quadrature-phase carrier are used as input data in the LSTM model. In order to verify the performance of the proposed learning method, we use a large dataset for training and test, including the ten types of modulation signal under various signal-to-noise ratios.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.253-259
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• 2021

In line with the modern society where artificial intelligence has spread throughout society, the Ministry of Education decided to provide AI education in kindergartens, elementary, middle and high school classes in 2025, and develop related learning materials and textbooks from 2021. Korea currently does not have state-led AI education for kindergarten and elementary school, so there is no systematic teaching material. Therefore, this study designed and presented textbooks for pattern analysis-based computational thinking development based on the GPS curriculum, a kindergarten SW curriculum studied by Y. S. Jeong and S. E. Lim (2020). Class procedures for using the textbooks were classified as introduction activities, development activities, and organization activities. Supplementary explanations were presented by presenting textbooks and teaching aids along with explanations of each activity. In order for this study to help AI education conducted in 2025, research must be conducted to demonstrate its effectiveness through actual application in the future.

• The KIPS Transactions:PartD
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• v.9D no.3
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• pp.365-380
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• 2002

Temporal data mining, the incorporation of temporal semantics to existing data mining techniques, refers to a set of techniques for discovering implicit and useful temporal knowledge from large quantities of temporal data. Temporal knowledge, expressible in the form of rules, is knowledge with temporal semantics and relationships, such as cyclic pattern, calendric pattern, trends, etc. There are many examples of temporal data, including patient histories, purchaser histories, and web log that it can discover useful temporal knowledge from. Many studies on data mining have been pursued and some of them have involved issues of temporal data mining for discovering temporal knowledge from temporal data, such as sequential pattern, similar time sequence, cyclic and temporal association rules, etc. However, all of the works treated data in database at best as data series in chronological order and did not consider temporal semantics and temporal relationships containing data. In order to solve this problem, we propose a theoretical framework for temporal data mining. This paper surveys the work to date and explores the issues involved in temporal data mining. We then define a model for temporal data mining and suggest SQL-like mining language with ability to express the task of temporal mining and show architecture of temporal mining system.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.85-107
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• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.

• Smart Media Journal
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• v.1 no.1
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• pp.36-41
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• 2012

Feature selection is the important technique of selecting a subset of relevant features for building robust pattern recognition systems. Various methods have been studied for feature selection from sequential search algorithms to stochastic algorithms. In this work, we adopted a Quantum-inspired Genetic Algorithm (QGA) which is based on the concept and principles of quantum computing such as Q-bits and superposition of state for feature selection. The performance of QGA is compared to that of the Conventional Genetic Algorithm (CGA) with respect to the classification rates and the number of selected features. The experimental result using UCI data sets shows that QGA is superior to CGA.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.1-6
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• 2006

촉감 제시 방법은 다른 사람에게 방해를 주지 않고 은밀하게 정보를 전달할 수 있는 장점이 있으며, 특히 시각 혹은 청각 장애인에게는 반드시 필요한 정보 전달의 수단이다. 또한 촉감을 이용한 정보의 전달은 시청각을 이용한 정보전달의 방법을 보완하거나 때로는 대체할 수도 있다. 본 논문에서는 웨어러블, 모바일, 또는 유비쿼터스 컴퓨팅 환경에서 사용할 수 있는 착용 가능한 진동촉감 제시 장치를 제안한다. 이 진동촉감 제시 장치는 25개의 진동모터를 $5{\times}5$의 형태로 배열하여 문자, 숫자뿐만 아니라 다양하고 복잡한 패턴을 표시할 수 있다. 코인형 진동모터 각각을 스펀지로 감싸고 푹신푹신한 재질의 패드에 세워서 배열하여 진동의 퍼짐을 최소화하고 사람의 글씨 쓰는 순서에 따라 진동모터를 순차적으로 구동시키는 새로운 추적모드를 제안하여 사용자의 문자 및 숫자 인식률을 크게 향상시켰다. 사용자 성능 평가에서는 사용자의 발등에 영문 알파벳을 표시하여 86.7%의 인식률을 얻었다. 또한 진동촉감 제시 장치를 이용하여 핸드폰에서의 발신자 정보표시를 한다거나 네비게이션 시스템에 적용할 수 있는 등의 유용한 응용분야를 제시하였다.

• Journal of Information Technology and Architecture
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• v.11 no.1
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• pp.25-32
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• 2014

Web mining techniques are often used to discover useful patterns from data log generated by Web servers for the purpose of web usage analysis. Yet traditional Web mining techniques do not reflect sufficiently sequential properties of Web log data. To address such weakness, we introduce a framework for analyzing Web access log data by using process mining techniques. To illustrate the proposed framework, we show the analysis of Web access log in a campus information system based on the framework and discuss the implication of the analysis result.

• Journal of Digital Convergence
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• v.12 no.12
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• pp.249-255
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• 2014

Recently, creation and sharing of information make progress actively through the SNS(Social Network Service) such as twitter, facebook and so on. It is necessary to extract the knowledge from aggregated information and data mining is one of the knowledge based approach. Especially, emotion analysis is a recent subdiscipline of text classification, which is concerned with massive collective intelligence from an opinion, policy, propensity and sentiment. In this paper, We propose the emotion prediction method, which extracts the significant key words and related key words from SNS paragraph, then predicts the emotion using these extracted emotion features.

• The Transactions of the Korea Information Processing Society
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• v.3 no.5
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• pp.1138-1146
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• 1996

Fuzzy logic ignores some informations in the reasoning process. Neural networks are powerful tools for the pattern processing. However, to model human knowledges, besides pattern processing capability, the logical reasoning capability is equally important. Another new neural network called neural logic network is able to do the logical reasoning. Because the fuzzy logical reasoning, we construct fuzzy inference net-work based on the neural logic network, extending the existing rule-inferencing network. And the traditional propagation rule is modified. For the search strategies to find out the belief value of a conclusion in the fuzzy inference network, we conduct a simulation to evaluate the search cost for searching sequentially and searching by means of priorities.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.08a
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• pp.142-142
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• 2010

태양광 발전산업에서 현재 주류인 결정 실리콘 태양전지의 변환효율은 꾸준히 향상되고 있으나, 태양전지의 가격이 매년 서서히 하강되고 있는 실정에서 결정질 실리콘 가격의 상승 등으로 부가가치 창출에 어려움이 있으며, 생산 원가를 낮출 수 있는 태양전지 제조기술로는 2세대 태양전지로 불리는 박막형이 현재의 대안이며, 특히 에너지 변환 효율과 생산 원가에서 장점이 있는 것이 CIGS 박막 태양전지로 판단된다. 화합물반도체 베이스인 CIGS 박막 태양전지는 연구실에서는 세계적으로 20.3% 높은 효율을 보고하고 있으며, 모듈급에서도 13% 효율로 생산이 시작되고 있다. 국내에서도 연구실 규모 뿐만 아니라 대면적(모듈급) CIGS 박막 태양전지 증착용 장비, 제조공정 등의 기술개발이 진행되고 있다. CIGS를 광흡수층으로 하는 CIGS 박막 태양전지의 구조는 여러 층의 단위박막(하부전극, 광흡수층, 버퍼층, 앞면 투명전극, 반사방지막)을 순차적으로 형성시켜 만든다. 이 중에 하부전극은 Mo 재료을 스퍼터링 방법으로 증착하여 주로 사용한다. 하부전극은 0.24 Ohm/cm2 정도의 전기적 특성이 요구되며, 주상조직으로 성장하여야 하며, 기판과의 밀착성이 좋아야하고 또한 레이저 패턴시 기판에서 잘 떨어져야 하는 특성을 동시에 가져야 한다. 그리고 CIGS 박막 내에서 Na 도핑을 어떻게 제어할 것인지도 고려해야한다. 본 연구에서는 대면적(모듈급) CIGS 박막 태양전지에서 요구되는 하부전극 Mo 박막의 특성과 기술적 이슈들에 대해서 연구결과들을 논하고자 한다.