• Applied Biological Chemistry
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• v.43 no.3
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• pp.218-224
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• 2000

The efforts were made to optimite ethanol extraction from persimmon leaf with the time of extraction$(1.5{\sim}2.5\;hrs)$, the temperature of extraction$(70{\sim}90^{\circ}C)$, and the concentration of ethanol$(0{\sim}40%)$ as three primary variables together with several functional characteristics of persimmon leaf as reaction variables. The conditions of extraction was best fitted by using response surface methodology through the center synthesis plan, and the optimal conditions of extraction were established. The contents of soluble solid and soluble tannin went up as the concentration of ethanol went up and the temperature of extraction went down, and the turbidity went down as the concentration of ethanol went down. Electron donation ability was hardly affected by the extraction temperature and had the tendency to go up as the concentration of ethanol went up. The inhibitory activity of xanthine oxidase(XOase) had the tendency to go up as both the concentration of ethanol and the temperature of extraction went up. The inhibitory activity of angiotensin converting enzyme(ACE), the significance of which still was not recognized, showed the maximum when the concentration of ethanol was 27%. In result, the optimal conditions of extraction was the extraction time of two hours, the extraction temperature of $75{\sim}81^{\circ}C$, and the ethanol concentration of $33{\sim}35%$.

• The Korean Journal of Applied Statistics
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• v.32 no.5
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• pp.671-681
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• 2019

Studies on predicting arrhythmia using machine learning have been actively conducted with increasing number of arrhythmia patients. Existing studies have predicted arrhythmia based on multivariate data of feature variables extracted from RR interval data at a specific time point. In this study, we consider that the pattern of the heart state changes with time can be important information for the arrhythmia prediction. Therefore, we investigate the usefulness of predicting the arrhythmia with multivariate time series data obtained by extracting and accumulating the multivariate vectors of the feature variables at various time points. When considering 1-nearest neighbor classification method and its ensemble for comparison, it is confirmed that the multivariate time series data based method can have better classification performance than the multivariate data based method if we select an appropriate time series distance function.

• Korean Chemical Engineering Research
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• v.48 no.4
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• pp.475-482
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• 2010

In this work, soft sensor based on image anlaysis is proposed for quantitatively estimating the visual appearance of manufactured products and is applied to quality monitoring. The methodology consists of three steps; (1) textural feature extraction from product images using wavelet transform, (2) numerical estimation of the product appearance through projection of the textural features on subspace, and (3) use of latent variables of textural features (i.e., numerical estimates of product appearance). The focus of this approach is on the consistent and quantitative estimation of continuous variations in visual appearance rather than on classification into discrete classes. This approach is illustrated through the application to the estimation and monitoring of the appearance of engineered stone countertops.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.301-304
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• 2004

자바의 특징 중에 한 가지는 자바 가상 기계를 기반으로 하고 있게 때문에 특정한 하드웨어나 운영체제에 영향을 받지 않고 독립적으로 수행이 가능하다는 것이다. 하지만 자바 언어로 개발된 애플리케이션은 C나 C++등 다른 언어로 작성한 프로그램에 비하여 실행이 매우 느리다는 단점을 가지게 된다. 이는 자바 가상 기계 에서 바이트코드가 인터프리터 방식으로 사용되기 때문이다. 이러한 단점을 보안하기 위하여 여러 가지 최적화 기법이 적용되고 있다. 본 논문에서는 이러한 방법으로써 바이트코드를 3주소형태 코드로 변환하는 변환기 설계에 대해서 제안할 것이다. 바이트코드에서 스택을 사용하지 않는 3주소형태 코드로의 변환하기 위하여 크게 세 단계를 걸친다. 첫째, 스택에 대한 명백한 참조를 가진 타입화된 스택기반의 중간표현을 생성한다. 둘째, 생성된 코드에서 타입에 대한 정보를 추출하고 추출된 정보를 저장하는 기억장소를 할당하여 추출된 정보를 저장시킨다. 셋째, 스택을 대신할 타입이 없는 지역변수를 생성하여 각각의 변수에 알맞은 타입을 분배함으로써 타입화되고 명백한 3주소형태 코드를 생성한다. 이러한 방식으로 스택기반 언어에서 발생하는 문제점을 해결한다.

• Journal of Korea Multimedia Society
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• v.2 no.2
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• pp.129-136
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• 1999

A linear feature often provide sufficient information for image understanding and coding. An objective of the research reported in this paper is to develop and analyze the reliable methods of extracting lines in gray scale images. The Hough Transform is known as one of the optimal paradigms to detect or identify the linear features by transforming edges in images into peaks in parameter space. The scheme proposed here uses the fuzzy gradient direction model and weights the gradient magnitudes for deciding the voting values to be accumulated in parameter space. This leads to significant computational savings by restricting the transform to within some support region of the observed gradient direction which can be considered as a fuzzy variable and produces robust results.

• Journal of the Korean Institute of Telematics and Electronics
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• v.7 no.3
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• pp.5-11
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• 1970

Some of the coding method have been discussed by extracting characteristics from vowels and consonants of Korean characters. given letters were sampled through 3$\times$5 mesh and also constituted first matrix system which taken subpatterns of vertical Conponent as variables and then, characteristics of the letters are extracted from the second matrix system expresses by common characteristics which are combined-with first one. Single coding was obtained by scanning the characteristic pattern. a good agree between theoretical values and their measurements and the reproducing of all vowels and consonants of Korean chasacters about coding were certified on the display designed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.10B
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• pp.1390-1398
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• 2001

시선 위치 추적이란 사용자가 모니터 상의 어느 지점을 쳐다보고 있는 지를 파악해 내는 기술이다. 시선 위치를 파악하기 위해 본 논문에서는 2차원 카메라 영상으로부터 얼굴 영역 및 얼굴 특징점을 추출한다. 초기에 모니터상의 3 지점을 쳐다볼 때 얼굴 특징점들은 움직임의 변화를 나타내며, 이로부터 카메라 보정 및 매개변수 추정 방법을 이용하여 얼굴특징점의 3차원 위치를 추정한다. 이후 사용자가 모니터 상의 또 다른 지점을 쳐다볼 때 얼굴 특징점의 변화된 3차원 위치는 3차원 움직임 추정방법 및 아핀변환을 이용하여 구해낸다. 이로부터 변화된 얼굴 특징점 및 이러한 얼굴 특징점으로 구성된 얼굴평면이 구해지며, 이러한 평면의 법선으로부터 모니터 상의 시선위치를 구할 수 있다. 실험 결과 19인치 모니터를 사용하여 모니터와 사용자까지의 거리를 50∼70cm정도 유지하였을 때 약 2.08인치의 시선위치에러 성능을 얻었다. 이 결과는 Rikert의 논문에서 나타낸 시선위치추적 성능(5.08cm 에러)과 비슷한 결과를 나타낸다. 그러나 Rikert의 방법은 모니터와 사용자 얼굴까지의 거리는 항상 고정시켜야 한다는 단점이 있으며, 얼굴의 자연스러운 움직임(회전 및 이동)이 발생하는 경우 시선위치추적 에러가 증가되는 문제점이 있다. 동시에 그들의 방법은 사용자 얼굴의 뒤 배경에 복잡한 물체가 없는 것으로 제한조건을 두고 있으며 처리 시간이 상당히 오래 걸리는 문제점이 있다. 그러나 본 논문에서 제안하는 시선 위치 추적 방법은 배경이 복잡한 사무실 환경에서도 사용가능하며, 약 3초 이내의 처리 시간(200MHz Pentium PC)이 소요됨을 알 수 있었다.

• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.1-9
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• 2021

Traditionally, most malicious codes have been analyzed using feature information extracted by domain experts. However, this feature-based analysis method depends on the analyst's capabilities and has limitations in detecting variant malicious codes that have modified existing malicious codes. In this study, we propose a ResNet-Variational AutoEncder-based variant malware classification method that can classify a family of variant malware without domain expert intervention. The Variational AutoEncoder network has the characteristics of creating new data within a normal distribution and understanding the characteristics of the data well in the learning process of training data provided as input values. In this study, important features of malicious code could be extracted by extracting latent variables in the learning process of Variational AutoEncoder. In addition, transfer learning was performed to better learn the characteristics of the training data and increase the efficiency of learning. The learning parameters of the ResNet-152 model pre-trained with the ImageNet Dataset were transferred to the learning parameters of the Encoder Network. The ResNet-Variational AutoEncoder that performed transfer learning showed higher performance than the existing Variational AutoEncoder and provided learning efficiency. Meanwhile, an ensemble model, Stacking Classifier, was used as a method for classifying variant malicious codes. As a result of learning the Stacking Classifier based on the characteristic data of the variant malware extracted by the Encoder Network of the ResNet-VAE model, an accuracy of 98.66% and an F1-Score of 98.68 were obtained.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.8
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• pp.232-237
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• 2013

A newly developed fall detection algorithm using the HMM (Hidden Markov Model) extracted from the video is introduced. To distinguish between the fall from personal difference fall pattern or the normal activities of daily living (ADL), HMM machine learning algorithm is used. For getting fall feature vector of video, the motion vector from the optical flow is applied to the PCA (Principal Component Analysis). The combination of the angle, ratio of long-short axis, velocity from results of PCA make the new fall feature parameters. These parameters were applied to the HMM and the results were compared and analyzed. Among the newly proposed various kinds of fall parameters, the angle of movement showed the best results. The results show that this parameter can distinguish various types of fall from ADLs with 91.5% sensitivity and 88.01% specificity.

• The Transactions of the Korea Information Processing Society
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• v.7 no.11
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• pp.3408-3416
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• 2000

This paper proposes an efficient method for improving the performance of a general regression neural network by using the feature to the independent variables as the center for partern-layer neurons. The adaptive principal component analysis is applied for extracting, efficiently the fcarures by reducing the dimension of given independent variables. In can acluevc a supertor property of the principal component analysis that converts input data into set of statistically independent features and the general regression neuralnetwork, espedtively. The proposed general regression neural network has been applied to regress the Solow's economy(2-independent variable set) and the wie elephone(1-independent vanable set). The simulation results show that the proposed meural networks have better performances of the regressionfor the lest data, in comparison with those using the means or the weighted means of independent variables. Also,it is affected less by the number of neurons and the scope of the smoothing factor.