• 한국가정과교육학회지
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• 제2권1호
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• pp.1-13
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• 1990

The purpose of this study is to investigate the degree of learning interest in the curriculum of home economics for the middle and high school girls in korea and Japan, and to obtain the basic guidance for the improvement of the effect of home economics education. In this study korea and Japanese textbooks were compared and the questionaires were administered to 290 middle school girls and 270 high school girls in Chong-Ju, korea, and 261 middle school girls and 248 high school girls in Tokyo, Japan. The obtained data were analyzed by percentile and $\chi$$\^$2/-test. In comparision of the degree of interest in hand sewing and machine sewing korean middle school girls showed higher interest than the high school girls, on the other hand, in Japan, the high school girls were more interested. In the unit of making a simple clothes, the middle school girls of both countries were more interested than the high school girls, and the degree of interest of Japanese girls was higher than that of korean girls.

• 전기학회논문지
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• 제64권1호
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• pp.99-106
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• 2015

In this study, we propose Radial basis function Neural Network(RBFNN) using Recursive Weighted Least Square Estimation(RWLSE) to effectively deal with big data class meteorological radar data. In the condition part of the RBFNN, Fuzzy C-Means(FCM) clustering is used to obtain fitness values taking into account characteristics of input data, and connection weights are defined as linear polynomial function in the conclusion part. The coefficients of the polynomial function are estimated by using RWLSE in order to cope with big data. As recursive learning technique, RWLSE which is based on WLSE is carried out to efficiently process big data. This study is experimented with both widely used some Machine Learning (ML) dataset and big data obtained from meteorological radar to evaluate the performance of the proposed classifier. The meteorological radar data as big data consists of precipitation echo and non-precipitation echo, and the proposed classifier is used to efficiently classify these echoes.

• 대한임베디드공학회논문지
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• 제11권3호
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• pp.163-171
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• 2016

Lane detection is a widely researched topic. Although simple road detection is easily achieved by previous methods, lane detection becomes very difficult in several complex cases involving noisy edges. To address this, we use a Convolution neural network (CNN) for image enhancement. CNN is a deep learning method that has been very successfully applied in object detection and recognition. In this paper, we introduce a robust lane detection method based on a CNN combined with random sample consensus (RANSAC) algorithm. Initially, we calculate edges in an image using a hat shaped kernel, then we detect lanes using the CNN combined with the RANSAC. In the training process of the CNN, input data consists of edge images and target data is images that have real white color lanes on an otherwise black background. The CNN structure consists of 8 layers with 3 convolutional layers, 2 subsampling layers and multi-layer perceptron (MLP) of 3 fully-connected layers. Convolutional and subsampling layers are hierarchically arranged to form a deep structure. Our proposed lane detection algorithm successfully eliminates noise lines and was found to perform better than other formal line detection algorithms such as RANSAC

• 한국의류학회지
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• 제44권2호
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• pp.354-368
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• 2020

Accurate and versatile image data-sets are essential for fashion AI research and AI-based fashion businesses based on a systematic attribute classification system. This study constructs a color and texture attribute hierarchical classification system by collecting fashion item images and analyzing the metadata of fashion items described by consumers. Essential dimensions to explain color and texture attributes were extracted; in addition, attribute values for each dimension were constructed based on metadata and previous studies. This hierarchical classification system satisfies consistency, exclusiveness, inclusiveness, and flexibility. The image tagging to confirm the usefulness of the proposed classification system indicated that the contents of attributes of the same image differ depending on the annotator that require a clear standard for distinguishing differences between the properties. This classification system will improve the reliability of the training data for machine learning, by providing standardized criteria for tasks such as tagging and annotating of fashion items.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권3호
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• pp.976-990
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• 2020

The k-means algorithm is widely used in academia and industry due to easy and simple implementation, enabling fast learning for complex datasets. However, k-means struggles to classify datasets without prior knowledge of specific domains. We proposed the repulsive k-means (RK-means) algorithm in a previous study to improve the k-means algorithm, using the repulsive force concept, which allows deleting unnecessary cluster centroids. Accordingly, the RK-means enables to classifying of a dataset without domain knowledge. However, three main problems remain. The RK-means algorithm includes a cluster repulsive force offset, for clusters confined in other clusters, which can cause cluster locking; we were unable to prove RK-means provided optimal convergence in the previous study; and RK-means shown better performance only normalize term and weight. Therefore, this paper proposes the advanced RK-means (ARK-means) algorithm to resolve the RK-means problems. We establish an initialization strategy for deploying cluster centroids and define a metric for the ARK-means algorithm. Finally, we redefine the mass and normalize terms to close to the general dataset. We show ARK-means feasibility experimentally using blob and iris datasets. Experiment results verify the proposed ARK-means algorithm provides better performance than k-means, k'-means, and RK-means.

• Journal of Electrical Engineering and Technology
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• 제8권3호
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• pp.478-489
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• 2013

The controllability and stability of power systems can be increased by Flexible AC Transmission Devices (FACTs). One of the FACTs devices is Interline Power-Flow Controller (IPFC) by which the voltage stability, dynamic stability and transient stability of power systems can be improved. In the present paper, the convenient operation and control of IPFC for transient stability improvement are considered. Considering that the system's Lyapunov energy function is a relevant tool to study the stability affair. IPFC energy function optimization has been used in order to access the maximum of transient stability margin. In order to control IPFC, a Brain Emotional Learning Based Intelligent Controller (BELBIC) and PI controller have been used. The utilization of the new controller is based on the emotion-processing mechanism in the brain and is essentially an action selection, which is based on sensory inputs and emotional cues. This intelligent control is based on the limbic system of the mammalian brain. Simulation confirms the ability of BELBIC controller compared with conventional PI controller. The designing results have been studied by the simulation of a single-machine system with infinite bus (SMIB) and another standard 9-buses system (Anderson and Fouad, 1977).

• 한국공작기계학회논문집
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• 제18권2호
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• pp.170-177
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• 2009

The objective of this study is to evaluate an acoustic emission (AE) source characterization and fracture behavior of the SM45C steel by using back-propagation neural network (BPN). In previous research Ref. [8] about k-nearest neighbor classifier (k-NNC) continuity, we used K-means clustering method as an unsupervised learning method for obtaining multi-variate AE main data sets, such as AE counts, energy, amplitude, risetime, duration and counts to peak. Similarly, we applied k-NNC and BPN as a supervised learning method for obtaining multi-variate AE working data sets. According to the error of convergence for determinant criterion Wilk's ${\lambda}$, heuristic criteria D&B(Rij) and Tou values are discussed. As a result, in k-NNC before fracture signal is detected or when fracture signal is detected, showed that produce some empty classes in BPN. And we confirmed that could save trouble in AE signal processing if suitable error of convergence or acceptable encoding error give to BPN.

• 한국멀티미디어학회논문지
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• 제6권5호
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• pp.824-830
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• 2003

메모리 기반 추론에서 기억공간의 효율적인 사용과 분류성능의 향상을 위하여 제안되었던 RPA(Recursive Partition Averaging)알고리즘은 대상 패턴 공간을 분할 한 후 대표 패턴을 추출하여 분류 기준 패턴으로 사용한다. 이 기법은 구성된 초월 평면상에서 단순히 대표패턴을 추출하여 분류 성능 저하의 원인이 되는 단점을 가지고 있었다. 여기에서는 기존 RPA의 단점을 보완하기 위해 FPD (Feature-based Population Densimeter)를 이용한 OHC (Optimized Hyperrectangle Calving) 알고리즘을 제안한다. 제안된 알고리즘은 RPA분할 종료 후 OHC를 이용하여 초월 평면을 최적화한 후 패턴 평균 기법을 적용하여 학습 결과를 산출한다. 제안된 알고리즘은 k-NN분류기에서 필요로 하는 메모리 공간의 40%정도를 사용하며 분류에 있어서도 RPA보다 우수한 인식 성능을 보이고 있다. 또한 저장된 패턴의 감소로 인하여, 실제 분류에 소요되는 시간비교에 있어서도 k-NN보다 월등히 우수한 성능을 보이고 있다.

• 지식경영연구
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• 제19권1호
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• pp.97-118
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• 2018

The finance-investment industry is currently focusing on research related to artificial intelligence and big data, moving beyond conventional theories of financial engineering. However, the case of equity optimization portfolio by using an artificial intelligence, big data, and its performance is rarely realized in practice. Thus, the purpose of this study is to propose process improvements in equity selection, information analysis, and portfolio composition, and lastly an improvement in portfolio returns, with the case of an equity optimization model based on quantitative research by an artificial intelligence. This paper is an empirical study of the portfolio based on an artificial intelligence technology of "D" asset management, which is the largest domestic active-quant-fiduciary management in accordance with the purpose of this paper. This study will apply artificial intelligence to finance, analyzing financial and demand-supply information and automating factor-selection and weight of equity through machine learning based on the artificial neural network. Also, the learning the process for the composition of portfolio optimization and its performance by applying genetic algorithms to models will be documented. This study posits a model that the asset management industry can achieve, with continuous and stable excess performance, low costs and high efficiency in the process of investment.

• 한국정보처리학회논문지
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• 제6권6호
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• pp.1571-1580
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• 1999

데이터 마이닝 또는 기계학습을 위한 무감독 학습 알고리즘인 개념적 클러스터링을 이용하여 계층적 구조를 유도해낼 때 자료를 처리하는 순서에 따라 서로 다른 결과에 도달하는 양상을 보인다. 이 순서 바이어스 문제를 해결하는 방안으로 먼저 주어진 자료 세트에 분류를 시행하여 초기 분류를 형성한다. 이 분류를 통해 최종 분류의 가능한 클래스 수를 예측하고 이 정보에 기반하여 자료 분석과 중심 정렬을 통해 자료 처리 순서를 새로이 결정한다. 재배열된 자료 세트에 ITERATE 분류 과정을 적용해 새로운 분류를 생성한다. 본 논문에서는 이 과정을 반복하여 안정적이고 최적의 분류 점수를 갖도록 하는 알고리즘 REIT를 제안하였다. 이 알고리즘을 여러 자료 세트에 적용하고 순서 바이어스의 영향을 최소화하는지 여부를 실험을 통해 비교 분석하였다.