• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.322-327
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• 2007

Researchers in the field of psychology used Electroencephalographic (EEG) to record activities of human brain lot many years. As technology develope, neural basis of functional areas of emotion processing is revealed gradually. So we measure fundamental areas of human brain that controls emotion of human by using EEG. Hands gestures such as shaking and head gesture such as nodding are often used as human body languages for communication with each other, and their recognition is important that it is a useful communication medium between human and computers. Research methods about gesture recognition are used of computer vision. Many researchers study emotion recognition method which uses one of physiological signals and gestures in the existing research. In this paper, we use together physiological signals and gestures for emotion recognition of human. And we select the driver emotion as a specific target. The experimental result shows that using of both physiological signals and gestures gets high recognition rates better than using physiological signals or gestures. Both physiological signals and gestures use Interactive Feature Selection(IFS) for the feature selection whose method is based on a reinforcement learning.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.6
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• pp.646-650
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• 2014

In this paper, in order to avoid the deterioration of the pattern classification performance which results from the curse of dimensionality, we propose a new feature selection method. The newly proposed feature selection method is based on Fuzzy C-Means clustering algorithm which analyzes the data points to divide them into several clusters and the concept of a function with fuzzy numbers. When it comes to the concept of a function where independent variables are fuzzy numbers and a dependent variable is a label of class, a fuzzy number should be related to the only one class label. Therefore, a good feature is a independent variable of a function with fuzzy numbers. Under this assumption, we calculate the goodness of each feature to pattern classification problem. Finally, in order to evaluate the classification ability of the proposed pattern classifier, the machine learning data sets are used.

• Journal of Gifted/Talented Education
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• v.15 no.1
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• pp.37-48
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• 2005

Comparing to the other subject, the relationship among physics contents is strong from the perspective of knowledge order as grades go up. That is, The things already that students learned, are learning and will learn are closed related from grade to grade. We expect students to be proactive and creative in studying physics, which is the goal of 21th century, analyzing their knowledge structure based on the knowledge order through assessment. Especially, using computer system, we provide students with substantial feedback for the assessment as well as objective validity is increased along with speedy and exact process in a bid to help students' mathematical understanding grow. This paper seeks to analyze the data from assessment applying knowledge spaces of the scientifical gifted in selective examination and to applicate on development of evaluation method.

• Journal of Intelligence and Information Systems
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• v.16 no.2
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• pp.95-108
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• 2010

In this paper, we present a hybrid neural network model for dynamic hand gesture recognition. The model consists of two modules, feature extraction module and pattern classification module. We first propose a modified CNN(convolutional Neural Network) a pattern recognition model for the feature extraction module. Then we introduce a weighted fuzzy min-max(WFMM) neural network for the pattern classification module. The data representation proposed in this research is a spatiotemporal template which is based on the motion information of the target object. To minimize the influence caused by the spatial and temporal variation of the feature points, we extend the receptive field of the CNN model to a three-dimensional structure. We discuss the learning capability of the WFMM neural networks in which the weight concept is added to represent the frequency factor in training pattern set. The model can overcome the performance degradation which may be caused by the hyperbox contraction process of conventional FMM neural networks. From the experimental results of human action recognition and dynamic hand gesture recognition for remote-control electric home appliances, the validity of the proposed models is discussed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.146-147
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• 2017

Big data analysis is a technique for effectively analyzing unstructured data such as the Internet, social network services, web documents generated in the mobile environment, e-mail, and social data, as well as well formed structured data in a database. The most big data analysis techniques are data mining, machine learning, natural language processing, and pattern recognition, which were used in existing statistics and computer science. Global research institutes have identified analysis of big data as the most noteworthy new technology since 2011. Therefore, companies in most industries are making efforts to create new value through the application of big data. In this study, we analyzed using the Social Matrics which a big data analysis tool of Daum communications. We analyzed public perceptions of "Internet of things" keyword, one month as of october 8, 2017. The results of the big data analysis are as follows. First, the 1st related search keyword of the keyword of the "Internet of things" has been found to be technology (995). This study suggests theoretical implications based on the results.

• Journal of Korea Water Resources Association
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• v.41 no.10
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• pp.1059-1065
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• 2008

Rainfall data is the most basic input data to analyze the hydrological phenomena and can be missing due to various reasons. In this research, a neural network based model to estimate missing rainfall data as approximate values was developed for 12 rainfall stations in the Soyang river basin to improve existing methods. This approach using neural network has shown to be useful in many applications to deal with complicated natural phenomena and displayed better results compared to the popular offline estimating methods, such as RDS(Reciprocal Distance Squared) method and AMM(Arithmetic Mean Method). Additionally, we proposed automated data reconciliation systems composed of a neural network learning processer to be capable of real-time reconciliation to transmit reliable hydrological data online.

• Communications of Mathematical Education
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• v.23 no.3
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• pp.523-543
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• 2009

We conduct a survey to find out the academic achievement factors for 484 second grade students at two middle schools in Busan city and Kyungsangnam Do, respectively. The survey questionnaire includes home environment and background, students' personal character, relationships with friends, learning attitudes towards improving problem solving, variables related to teaching methods and teachers, the school's computer facilities, mobile class by students levels, private education current situations, etc. Private education current situations are presented according to regions. Based on survey data we perform factor analysis to find major factors affecting mathematics academic achievement. We analyze the characteristics of the major factors. We also propose basic data and implications to mathematics educators and mathematics teachers at middle schools for improving middle school mathematics education quality.

• Journal of Gifted/Talented Education
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• v.20 no.3
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• pp.1027-1037
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• 2010

Comparing to the other subject, the relationship among physics contents is strong from the perspective of knowledge order as grades go up. That is, The things already that students learned, are learning and will learn are closed related from grade to grade. We expect students to be proactive and creative in studying physics, which is the goal of 21th century, analyzing their knowledge structure based on the knowledge order through assessment. Especially, using computer system, we provide students with substantial feedback for the assessment as well as objective validity is increased along with speedy and exact process in a bid to help students' mathematical understanding grow. This paper seeks to analyze the data from assessment applying knowledge spaces of the scientifical gifted in the force and the motion concept to applicate on teaching method.

• Annual Conference of KIPS
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• 2019.05a
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• pp.152-155
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• 2019

클라우드가 대중화되어 많은 모바일 유저들이 자동 백업 기능을 사용하면서 민감한 개인정보가 포함된 사진들이 무분별하게 클라우드에 업로드 되고 있다. 개인정보를 포함한 클라우드가 악의적으로 해킹 될 시, 사진에 포함된 지문, 자동차 번호판, 카드 번호 등이 유출됨에 따라 대량의 개인정보가 유출될 가능성이 크다. 이에 따라 적절한 기준에 맞게 사진 속 개인 정보 유출을 막을 수 있는 기술의 필요성이 대두되고 있다. 현재의 클라우드 시스템의 문제를 해결하고자 본 연구는 모바일 기기에서 클라우드 서버로 사진을 백업하는 과정에서 영역 검출과 블러링의 과정을 제안하고 있다. 클라우드 업로드 과정에서 사진 속의 개인 정보를 검출한 뒤 이를 블러링하여 클라우드에 저장함으로써 악의적인 접근이 행해지더라도 개인정보의 유출을 방지할 수 있다. 머신러닝과 computer vision library등을 이용하여 이미지 내에 민감한 정보를 포함하고 있는 영역을 학습된 모델을 통해 검출한 뒤, OpenCV를 이용하여 블러링처리를 진행한다 사진 속에 포함될 수 있는 생체정보인 지문은 손 영역을 검출한 뒤, 해당 영역을 블러링을 하여 업로드하고 카드번호나 자동차 번호판이 포함된 사진은 영역을 블러링한 뒤, 암호화하여 업로드 된다. 후에 필요에 따라 본인인증을 거친 후 일정기간 열람을 허용하지만 사용되지 않을 경우 삭제되도록 한다. 개인정보 유출로 인한 피해가 꾸준히 증가하고 있는 지금, 사진 속의 개인 정보를 보호하는 기술은 안전한 통신과 더불어 클라우드의 사용을 더 편리하게 할 수 있을 것으로 기대된다.

• Journal of Drive and Control
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• v.13 no.1
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• pp.1-9
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• 2016

Nowadays automatic transmission equipped vehicles prevail in construction and agricultural equipment due to their convenience in driving and operation. Though domestic vehicle manufacturers install imported electronic controlled transmissions at present, overseas products will be replaced by domestic ones in the near future owing to development efforts over the past 10 years. For passenger cars, there are many kinds of shift control algorithms that enhance the shift quality such as feedback and learning control. However, since shift control technologies for heavy duty vehicles are not highly developed, it is possible to improve the shift quality with an organized control method. A feedback control algorithm for neutral-into-gear shift, which is enabled during the inertia phase for the master clutch slip speed to track the slip speed reference, is proposed based on the power transmission structure of TH100. The performance of the feedback shift control is verified by a vehicle test which is implemented with firmware embedded TCU. As the master clutch engages along the predetermined speed trajectory, it can be concluded that the shift quality can be managed by a shift time control parameter. By extending the proposed feedback algorithm for neutral-into-gear shift to gear change and shuttle shift, it is expected that the quality of the shift can be improved.