• 한국컴퓨터정보학회논문지
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• 제23권11호
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• pp.51-57
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• 2018

In this paper, we studied about the extraction of the parameter and implementation of speechreading system to recognize the Korean 8 vowel. Face features are detected by amplifying, reducing the image value and making a comparison between the image value which is represented for various value in various color space. The eyes position, the nose position, the inner boundary of lip, the outer boundary of upper lip and the outer line of the tooth is found to the feature and using the analysis the area of inner lip, the hight and width of inner lip, the outer line length of the tooth rate about a inner mouth area and the distance between the nose and outer boundary of upper lip are used for the parameter. 2400 data are gathered and analyzed. Based on this analysis, the neural net is constructed and the recognition experiments are performed. In the experiment, 5 normal persons were sampled. The observational error between samples was corrected using normalization method. The experiment show very encouraging result about the usefulness of the parameter.

• International Journal of Computer Science & Network Security
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• 제24권2호
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• pp.150-154
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• 2024

In recent years, there are extensive researches on the applications of machine learning to the automation and decision support for medical experts during disease detection. However, the performance of machine learning still needs improvement so that machine learning model produces result that is more accurate and reliable for disease detection. Selecting the hyper-parameter that could produce the possible maximum classification accuracy on medical dataset is the most challenging task in developing decision support systems with machine learning algorithms for medical dataset classification. Moreover, selecting the features that best characterizes a disease is another challenge in developing machine-learning model with better classification accuracy. In this study, we have proposed an optimized decision tree model for heart disease classification by using heart disease dataset collected from kaggle data repository. The proposed model is evaluated and experimental test reveals that the performance of decision tree improves when an optimal number of features are used for training. Overall, the accuracy of the proposed decision tree model is 98.2% for heart disease classification.

• 한국정보과학회논문지:소프트웨어및응용
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• 제28권9호
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• pp.649-661
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• 2001

기존의 경계검출 방법은 디지털 영상에서 특정한 위치의 경계 존재 유무를 평가하기 위하여, 경계 신호를 강화하고 잡음의 영향을 줄이기 위한 여러 종류의 파라미터를 사용한다. 그 중 대표적인 것은 평활화 기능으로 가우스 함수를 많이 사용한다. 그러나, 평활화 함수는 그 크기에 따라 경계의 위치에 변화를 가져오기 때문에 특징 계산 등의 후처리 연산에 많은 오류를 전파하게 된다. 본 논문에서는 이러한 경계 검출과정의 오류를 줄일 수 있는 최적의 파라미터 평가를 퍼지 경계 표현을 이용하여 제안한다. 퍼지 경계 표현은 특정한 위치에 경계의 가능성 정도를 멤버십으로 부여하는 표현 방법으로서, 경계의 위치가 불확실하거나 밝기 변화가 이상적인 경계와 다를 경우에는 그의 애매성을 퍼지 멤버십으로 표현한다. 이러한 경계의 퍼지 표현을 이용하여 기존의 경계 검출기를 사용하여 검출된 경계에 대한 존재의 모호성 및 위치의 모호성을 평가하고, 최적의 파라미터 값을 영상의 종류에 따라 자동적으로 선택할 수 있는 측정값을 제안한다.

• 한국컴퓨터정보학회논문지
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• 제10권6호
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• pp.17-26
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• 2005

본 논문에서는 통계학의 선형순위통계량(linear rank statistics)에 기초한 3가지 비모수 검정법 즉. Wilcoxon 검정법, Median 검정법 그리고 Van der Waerden 검정법을 이용하여 에지를 검출하고자 한다. 5$\times$5 윈도우상에서 중심픽셀의 에지여부는 에지-높이 모수(edge-height parameter)를 사용한 모형 하에서 두 영역간의 유의한 차이가 있는지를 검정함으로서 결정한다. 영상실험에서 통계적 방법들 간의 에지검출 성능은 에지맵(edge map)을 통한 정성적인 비교와 객관적인 척도 하에서 정량적인 비교를 통하여 분석하였다.

• 대한의용생체공학회:의공학회지
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• 제10권2호
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• pp.185-190
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• 1989

In this paper, the discriminant analysis was performed on parameter for detection of cardiac arrythmia. The parameters used for discriminant analysis was two group. One group consist of 05 width and Heart rate, and the other Morphology and Heart Rate. For this study, we designed data acquisition system for EKG signals. The parameters pre-processed by this system was heart rate, 05 width and Morphology. And then, we analyzed the discriminancy of two group and extracted the quantity of discriminancy. The analysis results showed first that the group with morphology and heart rate is better discriminant than with 05 width and heart rate : next, that it can quantify the discriminany of each group of diseases.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.2010-2014
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• 2005

Object detection and parameter estimation in point cloud data is a relevant subject to robotics, reverse engineering, computer vision, and sport mechanics. In this paper a software is presented for fully-automatic object detection and parameter estimation in unordered, incomplete and error-contaminated point cloud with a large number of data points. The software consists of three algorithmic modules each for object identification, point segmentation, and model fitting. The newly developed algorithms for orthogonal distance fitting (ODF) play a fundamental role in each of the three modules. The ODF algorithms estimate the model parameters by minimizing the square sum of the shortest distances between the model feature and the measurement points. Curvature analysis of the local quadric surfaces fitted to small patches of point cloud provides the necessary seed information for automatic model selection, point segmentation, and model fitting. The performance of the software on a variety of point cloud data will be demonstrated live.

• 한국통신학회논문지
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• 제21권3호
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• pp.721-730
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• 1996

In this paper, a new test criterion for binary decision problems is proposed. The integrated power flunction over a parameter interval is first itroduced as an extension of the power function. The concept of the most integrated powerful (MIP) test based on the integrated power function is then introduced. The MIP criterion is to masimize the value of the integrated power function in any paricular parameter interval. As an applicationof the MIP test, the known signal detection problem is considered. The test statistic of the MIP detector for known signals is obtained and an approximation to the MIP test statistic is also considered.

• 제어로봇시스템학회논문지
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• 제3권3호
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• pp.244-251
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• 1997

In this paper, we propose a fault diagnosis algorithm to detect and isolate multiple faults in a system. The proposed fault diagnosis algorithm is based on a multiple fault classifier which consists of two ART2 NN(adaptive resonance theory2 neural network) modules and the algorithm is composed of three main parts - parameter estimation, fault detection and isolation. When a change in the system occurs, estimated parameters go through a transition zone in which residuals between the system output and the estimated output cross the threshold, and in this zone, estimated parameters are transferred to the multiple faults classifier for fault isolation. From the computer simulation results, it is verified that when the proposed diagnosis algorithm is performed successfully, it detects and isolates faults in the position control system of a DC motor.

• 대한전자공학회논문지
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• 제23권6호
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• pp.874-880
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• 1986

A new diagnastic method for detection and location of faults in a linear time-invariant system is proposed. The fault detection algorithm is formulated in a signal space, while the fault location algorithm with estimation is done in a parameter space. In a way different from the conventional approach, the method of fault location with estimation is studied to apply the new concept to establish the models with an unknown parameter under the assumption of 1-fold fault. According to computer simulation, the proposed diagnostic method is effective as an algorithm for fault diagnosis of industdrial process controllers.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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• pp.766-771
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• 2002

Automatic change detection based on the vector-to-raster comparison is hard especially in high-resolution image. This paper proposes a method to designate roads in high-resolution image in sequential manner using the information from vector map in which Hough transform is used for reliability. By its linearity, the road of urban areas in a vector map can be easily parameterized. Following some pre-processing to remove undesirable objects, we obtain the edge map of raster image. Then the edge map is transformed to a parameter space to find the selected road from vector map. The comparison is done in the parameter space to find the best matching. The set of parameters of a road from vector map is treated as the constraints to do matching. After designating the road, we may overlay it on the raster image for precise monitoring. The results can be used for detection of changes in road object in a semi-automatic fashion.