• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.1
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• pp.56-63
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• 2016

In this study, we introduce a design of Fuzzy RBFNNs-based digit recognition system using the incremental-PCA in order to recognize the handwritten digits. The Principal Component Analysis (PCA) is a widely-adopted dimensional reduction algorithm, but it needs high computing overhead for feature extraction in case of using high dimensional images or a large amount of training data. To alleviate such problem, the incremental-PCA is proposed for the computationally efficient processing as well as the incremental learning of high dimensional data in the feature extraction stage. The architecture of Fuzzy Radial Basis Function Neural Networks (RBFNN) consists of three functional modules such as condition, conclusion, and inference part. In the condition part, the input space is partitioned with the use of fuzzy clustering realized by means of the Fuzzy C-Means (FCM) algorithm. Also, it is used instead of gaussian function to consider the characteristic of input data. In the conclusion part, connection weights are used as the extended diverse types in polynomial expression such as constant, linear, quadratic and modified quadratic. Experimental results conducted on the benchmarking MNIST handwritten digit database demonstrate the effectiveness and efficiency of the proposed digit recognition system when compared with other studies.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.1
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• pp.29-37
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• 1999

In general, the information processing capability of a neural network is determined by its architecture and efficient training patterns. However, there is no systematic method for designing neural network and selecting effective training patterns. Evolutionary Algorithms(EAs) are referred to as the methods of population-based optimization. Therefore, EAs are considered as very efficient methods of optimal system design because they can provide much opportunity for obtaining the global optimal solution. In this paper, we propose a new method for finding the optimal structure of neural networks based on competitive co-evolution, which has two different populations. Each population is called the primary population and the secondary population respectively. The former is composed of the architecture of neural network and the latter is composed of training patterns. These two populations co-evolve competitively each other, that is, the training patterns will evolve to become more difficult for learning of neural networks and the architecture of neural networks will evolve to learn this patterns. This method prevents the system from the limitation of the performance by random design of neural networks and inadequate selection of training patterns. In co-evolutionary method, it is difficult to monitor the progress of co-evolution because the fitness of individuals varies dynamically. So, we also introduce the measurement method. The validity and effectiveness of the proposed method are inspected by applying it to the visual servoing of robot manipulators.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.4 no.2
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• pp.111-118
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• 2011

It is not easy to define a boundary between TV and home computer in these day since developing H/W and S/W of computer technology induces that TV is conflated with computer. Coming digital HD broadcasting forces for replacement of TV at home and the trend of TV is became bigger. The evolved TV is able to replace the computer by connecting to the network and people want to do interactions with contents by using the bidirectional communication. Therefore, it is expected to changing the human lifestyle. It is natural that contents for all members of family are needed, since screen of TV become bigger. It is required that the contents should guarantees the accessability of information to the all of family members and the easy interaction with contents. But, the related basic research is not enough to catch the user's eye to induce flow or presence. Our goal of this study is to analyse effects of experiential space perception on performing interactive digital contents. The result shows that users interacted with contents without any difficulty when they met a same dimension and shape of objects as dimension and shape objects in their experiences or learning.

• Proceedings of the CALSEC Conference
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• 1999.07a
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• pp.41-50
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• 1999

최근의 CALS 시스템 구축 가속화 및 전자상거래 활성화 분위기는 정보기술의 전략적 활용의 초점을 개별기업 위주에서 기업간 협력체제 구축과 이를 바탕으로 하는 기업간 전자적 결합(Electronic Integration)으로 옮기고 있다. 우리는 정보기술의 공유를 기반으로 새로운 기업간의 관계를 구축할 수 있으며, 이러한 관계를 전략적으로 활용함으로써 기존 사업에서의 경쟁우위 뿐만 아니라, 새로운 사업기회를 창출할 수 있다. 관련기업간의 데이터 표준화를 통한 정보의 공유는 CALS 시스템 구축의 바탕이 된다고 할 수 있는데, 정보공유의 범위는 산업별 특성과 제품별 특성에 따라 달라질 수밖에 없으며, 관련기업간의 교섭력(Bargaining Power)에 의해서도 영향을 받게된다. CALS 환경에서는 개별기업의 정보전략에 따라 수직적 협력관계에 있는 기업들간의 정보공유 정도와 수평적 협력관계에 있는 기업들간의 정보공유 정도가 결정된다고 할 수 있다. 정보공유의 범위를 설정하는 데에는 기업간에 이루어지는 업무의 성격, 공급-제조-유통으로 연결되는 관련기업간의 구조 등도 크게 영향을 미치는 것으로 파악되고 있다. 이 연구에서는 CALS 시스템 구축과정에서 우리가 고려할 수 있는 정보공유의 범위를 관련업무공유, 시스템공유, 부품 설비공유, 시설공유 등의 관점에서 분석하여 봄으로써, 각 산업별로 진행되고 있는 시범사업들의 시스템 영역의 범위를 재조명해보고자 한다. 이 연구에서 집중적으로 다루게 될 정보공유의 범위에 대한 내용은 CALS 시스템을 개발하려는 기업들의 응용서비스 개발 및 정보전략 구축에 중요한 공헌을 할 수 있을 것으로 기대된다.진" 사업에 대한 표준 설정을 위하여 노스캐롤라이나주 지방보건소의 "보건교육/건강증진" 표준체제를 예로 들었다. 다음으로, 경제적인 효율면에서 볼 때 "보건교육/건강증진" 사업에는 단기 혹은 장기 투자가치가 있는가 하는 것이다. 새로 태어나는 미국 지방 보건소의 "보건교육/건강증진" 활동은 지역 시민 및 그 단체가 광범위하고도 자연 다발적으로 참여할 때만 성공할 수 있다고 결론 지울 수 있다.한 분야별 전문가시스템 개발을 지양하고 MCRDR이론을 기반으로 한 범용성 있는 전문가시스템 개발 툴의 개발에 관한 연구를 소개한다. 후 새로운 지식을 얻는 반복적인 Explanation-based Data Mining Architecture를 제시하였다. 본 연구의 의의로는 데이타 마이닝을 통한 귀납적 지식생성에 있어 귀납적 오류의 발생을 고메인 지식을 통해 설명가능 함을 보임으로 검증하고 아울러 이러한 설명을 통해 연역적으로 새로운 가설지식을 생성시켜 이를 가설검증방식으로 검증함으로써 귀납적 접근과 연역적 접근의 통합 데이타 마이닝 접근을 제시하였다는데 있다.gical learning to give information necessary to improve the making. program and policy decision making, The objectives of the study are to develop the methodology of modeling the socioeconomic evaluation, and build up the practical socioeconomic evaluation model of the HAN projec

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.2
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• pp.334-348
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• 2015

Widespread use of smart devices accompanies increase of use of access point (AP), which enables the connection to the wireless network. If the appropriate security is not served when a user tries to connect the wireless network through an AP, various security problems can arise due to the rogue APs. In this paper, we are going to examine the threat by evil-twin, which is a kind of rogue APs. Most of recent researches for detecting rogue APs utilize the measured time difference, such as round trip time (RTT), between the evil-twin and authorized APs. These methods, however, suffer from the low detection rate in the network congestion. Due to these reasons, in this paper, we suggest a new factor, packet inter-arrival time (PIAT), in order to detect evil-twins. By using both RTT and PIAT as the learning factors for the support vector machine (SVM), we determine the non-linear metric to classify evil-twins and authorized APs. As a result, we can detect evil-twins with the probability of up to 96.5% and at least 89.75% even when the network is congested.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.3
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• pp.197-205
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• 2020

Korean language has the characteristics that the pronunciation of phoneme units such as vowels and consonants are fixed and the pronunciation associated with a notation does not change, so that foreign learners can approach rather easily Korean language. However, when one pronounces words, phrases, or sentences, the pronunciation changes in a manner of a wide variation and complexity at the boundaries of syllables, and the association of notation and pronunciation does not hold any more. Consequently, it is very difficult for foreign learners to study Korean standard pronunciations. Despite these difficulties, it is believed that systematic analysis of pronunciation errors for Korean words is possible according to the advantageous observations that the relationship between Korean notations and pronunciations can be described as a set of firm rules without exceptions unlike other languages including English. In this paper, we propose a visualization framework which shows the differences between standard pronunciations and erratic ones as quantitative measures on the computer screen. Previous researches only show color representation and 3D graphics of speech properties, or an animated view of changing shapes of lips and mouth cavity. Moreover, the features used in the analysis are only point data such as the average of a speech range. In this study, we propose a method which can directly use the time-series data instead of using summary or distorted data. This was realized by using the deep learning-based technique which combines Self-organizing map, variational autoencoder model, and Markov model, and we achieved a superior performance enhancement compared to the method using the point-based data.

• Journal of KIISE
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• v.43 no.1
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• pp.87-95
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• 2016

Recently, due to the development of the Internet and electronic devices, there has been an enormous increase in the amount of available knowledge and information. As this growth has proceeded, studies on large-scale ontological reasoning have been actively carried out. In general, a machine learning program or knowledge engineer measures and provides a degree of confidence for each triple in a large ontology. Yet, the collected ontology data contains specific uncertainty and reasoning such data can cause vagueness in reasoning results. In order to solve the uncertainty issue, we propose an RDFS reasoning approach that utilizes confidence values indicating degrees of uncertainty in the collected data. Unlike conventional reasoning approaches that have not taken into account data uncertainty, by using the in-memory based cluster computing framework Spark, our approach computes confidence values in the data inferred through RDFS-based reasoning by applying methods for uncertainty estimating. As a result, the computed confidence values represent the uncertainty in the inferred data. To evaluate our approach, ontology reasoning was carried out over the LUBM standard benchmark data set with addition arbitrary confidence values to ontology triples. Experimental results indicated that the proposed system is capable of running over the largest data set LUBM3000 in 1179 seconds inferring 350K triples.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.242-250
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• 2019

In this paper, we propose a method to predict the failure of industrial robot using Seq2Seq (Sequence to Sequence) model, which is a model for transforming time series data among Artificial Neural Network models. The proposed method uses the data of the joint current and angular value, which can be measured by the robot itself, without additional sensor for fault diagnosis. After preprocessing the measured data for the model to learn, the Seq2Seq model was trained to convert the current to angle. Abnormal degree for fault diagnosis uses RMSE (Root Mean Squared Error) during unit time between predicted angle and actual angle. The performance evaluation of the proposed method was performed using the test data measured under different conditions of normal and defective condition of the robot. When the Abnormal degree exceed the threshold, it was classified as a fault, and the accuracy of the fault diagnosis was 96.67% from the experiment. The proposed method has the merit that it can perform fault prediction without additional sensor, and it has been confirmed from the experiment that high diagnostic performance and efficiency are available without requiring deep expert knowledge of the robot.

• Journal of the Korea Convergence Society
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• v.10 no.4
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• pp.1-6
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• 2019

Neural networks have been used to predict the direction of stock index movement from past data. The conventional research that predicts the upward or downward movement of the stock index predicts a rise or fall even with small changes in the index. It is highly likely that losses will occur when trading ETFs by use of the prediction. In this paper, a neural network model that predicts the movement direction of the daily KOrea composite Stock Price Index (KOSPI) to reduce ETF trading losses and earn more than a certain amount per trading is presented. The proposed model has outputs that represent rising (change rate in index ${\geq}{\alpha}$), falling (change rate ${\leq}-{\alpha}$) and neutral ($-{\alpha}$ change rate < ${\alpha}$). If the forecast is rising, buy the Leveraged Exchange Traded Fund (ETF); if it is falling, buy the inverse ETF. The hit ratio (HR) of PNN1 implemented in this paper is 0.720 and 0.616 in the learning and the evaluation respectively. ETF trading yields a yield of 8.386 to 16.324 %. The proposed models show the better ETF trading success rate and yield than the neural network models predicting KOSPI.

• Journal of the Korean Chemical Society
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• v.64 no.6
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• pp.379-388
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• 2020

The purpose of this study are to develop the TEP activity for learning experimental apparatus at elementary school and to test the effects of the TEP activity. This study consists of two steps. First through literature research on the difficulties and needs of experimental apparatus education developed the form that how to educate the experimental apparatus at elementary school. Second, applied the TEP activity and figured out the effects as two aspect(knowledge about experimental apparatus and actual using skill during lesson). This worksheet was applied to 3rd grade students in elementary school about 4 experimental apparatuses(Beaker, Electronic scale, Glass rod, Spatula). The results of this study are as follows: There is no specific time to teach what is and how to use experimental apparatus by regular curriculum. So many students and teachers need method and time to learn them. Also they want to lots of opportunities to use them. With that needs given previously, TEP activity developed by 3 steps. 1. Trigger interest 2. Explore experimental apparatus: learned knowledges about experimental apparatus focused on appearance(name, purpose, directions for use, precautions) 3. Practice experimental apparatus: actual using time to acquire skills. After that did the survey of knowledge and observation of students' behavior during usual class to confirm the effects. According to the results, TEP activity helped the students to improve there awareness of the experimental apparatus and actual using skills.