• 한국산업정보학회논문지
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• 제6권2호
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• pp.1-6
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• 2001

본 논문에서는 구간 데이터 식별을 위한 구간 신경망의 학습 알고리즘을 제안한다. 제안된 기법은 각 데이터의 속성치가 구간으로 주어져 있는 경우의 패턴 식별 문제에 적용된다. 먼저, 구간 입력 벡터를 다루기 위한 구간 신경망의 구조를 제안하고, 평가 함수를 이용하여 학습 알고리즘을 도출한다. 평가 함수는 구간 신경 망으로부터의 구간 출력과 대응하는 목표 출력을 이용하여 정의된다. 마지막으로 컴퓨터 시뮬레이션에 의해 제안 기법의 구간 데이터 식별 능력을 나타내고, 통상의 역전파 신경망을 이용 기법과 비교한다.

• Journal of the Korean Data and Information Science Society
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• 제25권6호
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• pp.1521-1530
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• 2014

The ROC analysis is considered for multiple class diagnosis. There exist many criteria to find optimal thresholds and measure the accuracy of diagnostic tests for k dimensional ROC analysis. In this paper, we proposed a diagnostic accuracy measure called the correct classification simple rate, which is defined as the summation of true rates for each classification distribution and expressed as a function of summation of sequential true rates for two consecutive distributions. This measure does not weight accuracy across categories by the category prevalence and is comparable across populations for multiple class diagnosis. It is found that this accuracy measure does not only have a relationship with Kolmogorov - Smirnov statistics, but also can be represented as a linear function of some optimal threshold criteria. With these facts, the suggested measure could be applied to test for comparing multiple distributions.

• Journal of Electrical Engineering and Technology
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• 제13권4호
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• pp.1715-1723
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• 2018

This work investigates the performance of fully complex- valued radial basis function network(FC-RBF) and complex extreme learning machine (CELM) based neural approaches for classification of power quality disturbances. This work engages the use of S-Transform to extract the features relating to single and combined power quality disturbances. The performance of the classifiers are compared with their real valued counterparts namely extreme learning machine(ELM) and support vector machine(SVM) in terms of convergence and classification ability. The results signify the suitability of complex valued classifiers for power quality disturbance classification.

• 센서학회지
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• 제24권6호
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• pp.368-372
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• 2015

In this paper, we proposed a post-processing technique for improving classification performance of electronic nose (E-Nose) system which may be occurred drift signals from sensor array. An adaptive radial basis function network using stochastic gradient (SG) and singular value decomposition (SVD) is applied to process signals from sensor array. Due to drift from sensor's aging and poisoning problems, the final classification results may be showed bias and fluctuations. The predicted classification results with drift are quantized to determine which identification level each class is on. To mitigate sharp fluctuations moving-averaging (MA) technique is applied to quantized identification results. Finally, quantization and some edge correction process are used to decide levels of the fluctuation-smoothed identification results. The proposed technique has been indicated that E-Nose system was shown correct odor identification results even if drift occurred in sensor array. It has been confirmed throughout the experimental works. The enhancements have produced a very robust odor identification capability which can compensate for decision errors induced from drift effects with sensor array in electronic nose system.

• 대한기계학회논문집A
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• 제20권4호
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• pp.1309-1319
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• 1996

The research for the classification of the natural defects in welding zone is performd using the neuro-pattern recognition technology. The signal pattern recognition package including the user's defined function is developed to perform the digital signal processing, feature extraction, feature selection and classifier selection, The neural network classifier and the statistical classifiers such as the linear discriminant function classifier and the empirical Bayesian calssifier are compared and discussed. The neuro-pattern recognition technique is applied to the classificaiton of such natural defects as root crack, incomplete penetration, lack of fusion, slag inclusion, porosity, etc. If appropriately learned, the neural network classifier is concluded to be better than the statistical classifiers in the classification of the natural welding defects.

• ETRI Journal
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• 제43권1호
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• pp.82-94
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• 2021

In this work, six voiced/unvoiced speech classifiers based on the autocorrelation function (ACF), average magnitude difference function (AMDF), cepstrum, weighted ACF (WACF), zero crossing rate and energy of the signal (ZCR-E), and neural networks (NNs) have been simulated and implemented in real time using the TMS320C6713 DSP starter kit. These speech classifiers have been integrated into a linear-predictive-coding-based speech analysis-synthesis system and their performance has been compared in terms of the percentage of the voiced/unvoiced classification accuracy, speech quality, and computation time. The results of the percentage of the voiced/unvoiced classification accuracy and speech quality show that the NN-based speech classifier performs better than the ACF-, AMDF-, cepstrum-, WACF- and ZCR-E-based speech classifiers for both clean and noisy environments. The computation time results show that the AMDF-based speech classifier is computationally simple, and thus its computation time is less than that of other speech classifiers, while that of the NN-based speech classifier is greater compared with other classifiers.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 춘계학술대회
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• pp.344-345
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• 2021

본 논문에서는 메이크업의 분류 및 종류를 현대에 알맞게 새롭게 작성해 보았다. 그리고 메이크업의 정확한 의미를 새롭게 지정하였다. 메이크업은 외부의 다양한 환경 변화로부터 피부를 보호해준다. 또한, 우리 피부의 많은 결점을 감춰주고 아름답게 해주는 미화 기능을 갖게 한다.

• 한국주거학회:학술대회논문집
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• 한국주거학회 2008년도 춘계학술발표대회 논문집
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• pp.245-250
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• 2008

The city means not only a geographical area as a role of the spatial boundary, but also a socio-economic place to communicate with each part. It requires various functions to get the income and activities. But city has a limitation of a available land to provide the required function and a sufficient space to supply the activity. Therefore, the development trend of city has not been to spread to the horizontal area any more and considered the vertical area. In addition, various functions put into a massive building because many people want to solve the daily requirements without spending time and cost. In this paper, it aimed at classifying the mixed-use building into function, circulation, relation between the public and private space and the building shape to provide the design information according to the development constraints such as land area, functional limitation, accessibility, etc. The classification of the mixed-use buildings is divided into four areas. The data are collected with foreign countries in Japan, Europe and US.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.1326-1328
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• 2003

An elliptical basis function (EBF) network is proposed in this study for the classification of remotely sensed images. Though similar in structure, the EBF network differs from the well-known radial basis function (RBF) network by incorporating full covariance matrices and uses the expectation-maximization (EM) algorithm to estimate the basis functions. Since remotely sensed data often take on mixture -density distributions in the feature space, the proposed network not only possesses the advantage of the RBF mechanism but also utilizes the EM algorithm to compute the maximum likelihood estimates of the mean vectors and covariance matrices of a Gaussian mixture distribution in the training phase. Experimental results show that the EM-based EBF network is faster in training, more accurate, and simpler in structure.

• 한국인터넷방송통신학회논문지
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• 제24권1호
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• pp.49-54
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• 2024

데이터 불균형은 분류 문제에서 흔히 마주치는 문제로, 데이터셋 내의 클래스간 샘플 수의 현저한 차이에서 기인한다. 이러한 데이터 불균형은 일반적으로 분류 모델에서 과적합, 과소적합, 성능 지표의 오해 등의 문제를 야기한다. 이를 해결하기 위한 방법으로는 Resampling, Augmentation, 규제 기법, 손실 함수 조정 등이 있다. 본 논문에서는 손실 함수 조정에 대해 다루며 특히, 불균형 문제를 가진 Multi-Class 블랙박스 동영상 데이터에서 여러 구성의 손실 함수(Cross Entropy, Balanced Cross Entropy, 두 가지 Focal Loss 설정: 𝛼 = 1 및 𝛼 = Balanced, Asymmetric Loss)의 성능을 I3D, R3D_18 모델을 활용하여 비교하였다.