• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.663-667
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• 2004

This paper presents new pruning and learning methods for the fuzzy rule-based classifier. The structure of the proposed classifier is framed from the fuzzy sets in the premise part of the rule and the Bayesian classifier in the consequent part. For the simplicity of the model structure, the unnecessary features for each fuzzy rule are eliminated through the iterative pruning algorithm. The quality of the feature is measured by the proposed correctness method, which is defined as the ratio of the fuzzy values for a set of the feature values on the decision region to one for all feature values. For the improvement of the classification performance, the parameters of the proposed classifier are finely adjusted by using the gradient descent method so that the misclassified feature vectors are correctly re-categorized. The cost function is determined as the squared-error between the classifier output for the correct class and the sum of the maximum output for the rest and a positive scalar. Then, the learning rules are derived from forming the gradient. Finally, the fuzzy rule-based classifier is tested on two data sets and is found to demonstrate an excellent performance.

• Journal of Nutrition and Health
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• 제27권9호
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• pp.901-909
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• 1994

The effects of sardine oil(high in eicosapentaenoic acid : EPA) and tuna oil(high in docosahexaenoic acid : DHA, also high in EPA) on fatty acid composition of brain and learning ability were evaluated in male weanling Sprague-Dawley rats and compared with the effects of corn oil and beef tallow. Animals assigned by randomized block design to one of the four experimental diet groups containing dietary lipid at 15%(w/w) level were given ad libitum for 7 weeks. Food intake and body weight gain of the fish oil groups were significantly lower than those of the corn oil and beef tallow groups. However, brain weights of the groups were not significantly different. In the brain fatty acid composition, the corn oil group showed high concentrations of n-6 fatty acids, the fish oil groups of n-3 fatty acids, and the beef tallow group of saturated fatty acids. Brain EPA and DHA contents of the fish oil groups showed significantly higher than the other groups while the brain ratio of saturated/monounsaturated/polyunsaturated fatty acid was controlled in a narrow range. In a maze test, the fish oil groups appeared to arrive at the goal faster than the corn oil and beef tallow groups. It explained that EPA in diets might efficiently convert to DHA resulting in DHA accumulation in brain tissue and might increase the learning performance as DHA did.

• 컴퓨터교육학회논문지
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• 제8권5호
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• pp.73-83
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• 2005

현재 초등학생들의 인터넷 이용률은 급격히 증가하고 있는 반면, 아이디와 비밀번호를 무분별하게 관리하고 있으며, 그에 따른 정보 유출의 심각성을 전혀 인식하지 못하고 있다. 본 논문에서는 초등학생 대상으로 개인정보 관리의 중요성을 인식시키기 위한 교수학습 방법을 설계하였다. 설계한 교수학습 방법은 역할놀이 모형을 기반으로 하였으며 초등학교 3학년을 대상으로 현장 수업에 적용하였다. 수업한 결과를 분석하여 설계한 교수학습 방법이 기존의 강의법보다 더 효과가 있음을 보인다.

• Journal of Multimedia Information System
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• 제7권1호
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• pp.11-16
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• 2020

Deep Learning has become the most important technology in the field of artificial intelligence machine learning, with its high performance overwhelming existing methods in various applications. In this paper, an interactive window service based on object recognition technology is proposed. The main goal is to implement an object recognition technology using this deep learning technology to remove the existing eye tracking technology, which requires users to wear eye tracking devices themselves, and to implement an eye tracking technology that uses only usual cameras to track users' eye. We design an interactive system based on efficient eye detection and pupil tracking method that can verify the user's eye movement. To estimate the view-direction of user's eye, we initialize to make the reference (origin) coordinate. Then the view direction is estimated from the extracted eye pupils from the origin coordinate. Also, we propose a blink detection technique based on the eye apply ratio (EAR). With the extracted view direction and eye action, we provide some augmented information of interest without the existing complex and expensive eye-tracking systems with various service topics and situations. For verification, the user guiding service is implemented as a proto-type model with the school map to inform the location information of the desired location or building.

• 수산해양교육연구
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• 제22권4호
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• pp.576-588
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• 2010

The goal of this research was to survey a method to reinforce the efficient political function for lifelong learning when Busan metropolitan city promotes lifelong policy. To achieve this goal, the survey to reinforce the political function for lifelong education was carried out on persons in charge of lifelong education in Busan metropolitan city. The results are as follows: First, they showed high degree of perception on lifelong policy and business. Second, on a question about how much the tasks to promote lifelong education presented by our nation are needed, they answered that concrete agenda for an aging society is needed and a necessary institute to invigorate it is lifelong learning institute. Third, they answered that the policy which is necessary to be implemented as soon as possible is the development education for vocational competency, and the ratio of the perception which requires to hire education experts for life long education was high.

• International Journal of Human Ecology
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• 제11권2호
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• pp.93-101
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• 2010

This study finds methods to activate culinary education by surveying the level of recognition for culinary practice from the culinary teachers of vocational high schools. The number of individuals surveyed is 103. Data is verified by using SPSS 14.0 (SPSS Inc, Chicago, USA). The results of the survey on recognition for culinary education showed that learning requirements are very high as well as that theoretical education and trial demonstrations are necessary to enhance the effects of culinary practice education. Desired teaching learning materials were cooking materials by a certified technician. Their desired supplementary data for enhancing practical techniques were practical demonstrations, various materials and language instruction to learn other culinary practices. It is reported that there was increasing work other than learning time and complication for preparing practice. In addition, they desire more websites for cooking need to be built. Plans for activating culinary education are as follow: First, how to enhance the professional abilities of culinary teachers. Second, to show the necessity of theoretical education, video-based education, and culinary practice demonstrations. Third, to show the necessity to increase the ratio for culinary practice classes. Fourth, to display various teaching and learning materials. Fifth, to enhance websites for culinary data. Sixth, to provide opportunities to augment a sense of achievement.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권10호
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• pp.3867-3886
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• 2015

This article investigates the problem of opportunistic spectrum access in dynamic environment, in which the signal-to-noise ratio (SNR) is time-varying. Different from existing work on continuous feedback, we consider more practical scenarios in which the transmitter receives an Acknowledgment (ACK) if the received SNR is larger than the required threshold, and otherwise a Non-Acknowledgment (NACK). That is, the feedback is discrete. Several applications with different threshold values are also considered in this work. The channel selection problem is formulated as a non-cooperative game, and subsequently it is proved to be a potential game, which has at least one pure strategy Nash equilibrium. Following this, a multi-agent Q-learning algorithm is proposed to converge to Nash equilibria of the game. Furthermore, opportunistic spectrum access with multiple discrete feedbacks is also investigated. Finally, the simulation results verify that the proposed multi-agent Q-learning algorithm is applicable to both situations with binary feedback and multiple discrete feedbacks.

• 디지털산업정보학회논문지
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• 제17권4호
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• pp.53-62
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• 2021

Deep learning models such as convolutional neural networks and recurrent neual networks process a huge amounts of data, so they require a lot of storage and consume a lot of time and power due to memory access. Recently, research is being conducted to reduce memory usage and access by compressing data using the feature that many of deep learning data are highly sparse and localized. In this paper, we propose a compression-decompression method of storing only the non-zero data and the location information of the non-zero data excluding zero data. In order to make the location information of non-zero data, the matrix data is divided into sections uniformly. And whether there is non-zero data in the corresponding section is indicated. In this case, section division is not executed only once, but repeatedly executed, and location information is stored in each step. Therefore, it can be properly compressed according to the ratio and distribution of zero data. In addition, we propose a hardware structure that enables compression and decompression without complex operations. It was designed and verified with Verilog, and it was confirmed that it can be used in hardware deep learning accelerators.

• 한국물환경학회지
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• 제37권5호
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• pp.335-343
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• 2021

Water quality prediction is essential for the proper management of water supply systems. Increased suspended sediment concentration (SSC) has various effects on water supply systems such as increased treatment cost and consequently, there have been various efforts to develop a model for predicting SSC. However, SSC is affected by both the natural and anthropogenic environment, making it challenging to predict SSC. Recently, advanced machine learning models have increasingly been used for water quality prediction. This study developed an ensemble machine learning model to predict SSC using the XGBoost (XGB) algorithm. The observed discharge (Q) and SSC in two fields monitoring stations were used to develop the model. The input variables were clustered in two groups with low and high ranges of Q using the k-means clustering algorithm. Then each group of data was separately used to optimize XGB (Model 1). The model performance was compared with that of the XGB model using the entire data (Model 2). The models were evaluated by mean squared error-ob servation standard deviation ratio (RSR) and root mean squared error. The RSR were 0.51 and 0.57 in the two monitoring stations for Model 2, respectively, while the model performance improved to RSR 0.46 and 0.55, respectively, for Model 1.

• 한국군사과학기술학회지
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• 제22권2호
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• pp.278-286
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• 2019

Conventional methods for selecting jamming techniques in electronic warfare are based on libraries in which a list of jamming techniques for radar signals is recorded. However, the choice of jamming techniques by the library is limited when modified signals are received. In this paper, we propose a method to predict the jamming technique for radar signals by using deep learning methods. Long short-term memory(LSTM) is a deep running method which is effective for learning the time dependent relationship in sequential data. In order to determine the optimal LSTM model structure for jamming technique prediction, we test the learning parameter values that should be selected, such as the number of LSTM layers, the number of fully-connected layers, optimization methods, the size of the mini batch, and dropout ratio. Experimental results demonstrate the competent performance of the LSTM model in predicting the jamming technique for radar signals.