• 대한안전경영과학회지
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• 제2권3호
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• pp.171-184
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• 2000

We may be given a set of observations with the classes or clusters. The aim of this article is to provide an up-to-date review of different approaches to classification, compare their performance on a wide range of challenging data-sets. In this paper, machine learning algorithm classifiers based on CART, C4.5, CAL5, FACT, QUEST and statistical discriminant analysis are compared on various datasets in classification error rate and algorithms.

• 한국정보처리학회논문지
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• 제7권6호
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• pp.1867-1873
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• 2000

함수근사는 과학과 고학부야에서 공범위하게 응용된다. 시포트 벡터 기계(support vector machine, SVM)는 원래 분류를 위해 계안되어져 문자인식, 얼굴인식 등의 응용분야에서 좋은 결과를 보여주고 있다. 최근 SVM이론 함수근사로 확장되어 많이 활용되려 하고 있다. 그러나 함수근사를 위한 SVM 알고리즘은 QP(quadratic proramming)문제와 관련되어있어 계산에 시간이 걸리며 QP를 위한 패키지가 있어야 한다. 본 논문에서는 함수근사를 위해 커널-애더트론 알고리즘을 이용한 SVM을 제안하고 QP를 이용한 SVM과 성능을 비교하고자 한다.

• 한국통신학회논문지
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• 제35권8A호
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• pp.760-767
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• 2010

For complex channel blind equalization, this study presents the performance and characteristics of two complex blind information theoretic learning algorithms (ITL) which are based on minimization of Euclidian distance (ED) between probability density functions compared to constant modulus algorithm which is based on mean squared error (MSE) criterion. The complex-valued ED algorithm employing constant modulus error and the complex-valued ED algorithm using a self-generated symbol set are analyzed to have the fact that the cost function of the latter forces the output signal to have correct symbol values and compensate amplitude and phase distortion simultaneously without any phase compensation process. Simulation results through MSE convergence and constellation comparison for severely distorted complex channels show significantly enhanced performance of symbol-point concentration with no phase rotation.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.26.6-26
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• 2001

In this paper we introduce a new algorithm that enables radial basis function network(RBFN) to be structured automatically and guarantees the stability of the RBFN. Because this new algorithm is efficient and also have the advantage of fast computational speed we adopt this algorithm as online learning scheme for uncertain nonlinear dynamical systems. Based on the fact that a 3-layered RBFN can represent a specific nonlinear function reasonably well by linearly combining a set of nonlinear and localized basis functions, we show that this RBFN can identify the nonlinear system very well without knowing the information of the system in advance.

• Journal of Information Processing Systems
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• 제18권3호
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• pp.428-442
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• 2022

A reliable automatic passenger counting (APC) system is a key point in transportation related to the efficient scheduling and management of transport routes. In this study, we introduce a lightweight head detection network using deep learning applicable to an embedded system. Currently, object detection algorithms using deep learning have been found to be successful. However, these algorithms essentially need a graphics processing unit (GPU) to make them performable in real-time. So, we modify a Tiny-YOLOv3 network using certain techniques to speed up the proposed network and to make it more accurate in a non-GPU environment. Finally, we introduce an APC system, which is performable in real-time on embedded systems, using the proposed head detection algorithm. We implement and test the proposed APC system on a Samsung ARTIK 710 board. The experimental results on three public head datasets reflect the detection accuracy and efficiency of the proposed head detection network against Tiny-YOLOv3. Moreover, to test the proposed APC system, we measured the accuracy and recognition speed by repeating 50 instances of entering and 50 instances of exiting. These experimental results showed 99% accuracy and a 0.041-second recognition speed despite the fact that only the CPU was used.

• 전기학회논문지
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• 제66권7호
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• pp.1072-1077
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• 2017

Mean-reverting analysis refers to a way of estimating the underlining tendency after new data has evoked the variation in the equilibrium state. In this paper, we propose a new method to interpret the specular portraits of Premature Ventricular Contraction(PVC) arrhythmia by applying K-means unsupervised learning algorithm on electrocardiogram(ECG) data. Aiming at this purpose, we applied a mean-reverting model to analyse Heart Rate Variability(HRV) in terms of the modified poincare plot by considering PVC rhythm as the component of disrupting the homeostasis state. Based on our experimental tests on MIT-BIH ECG database, we can find the fact that the specular patterns portraited by K-means clustering on mean-reverting HRV data can be more clearly visible and the Euclidean metric can be used to identify the discrepancy between the normal sinus rhythm and PVC beats by the relative distance among cluster-centroids.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권10호
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• pp.5112-5128
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• 2016

Tracking human body is an important problem in computer vision field. Tracking failures caused by occlusion can lead to wrong rectification of the target position. In this paper, a robust human tracking algorithm is proposed to address the problem of occlusion, rotation and improve the tracking accuracy. It is based on Tracking-Learning-Detection framework. The key auxiliary information is used in the framework which motivated by the fact that a tracking target is usually embedded in the context that provides useful information. First, face localization method is utilized to find key face location information. Second, the relative position relationship is established between the auxiliary information and the target location. With the relevant model, the key face information will get the current target position when a target has disappeared. Thus, the target can be stably tracked even when it is partially or fully occluded. Experiments are conducted in various challenging videos. In conjunction with online update, the results demonstrate that the proposed method outperforms the traditional TLD algorithm, and it has a relatively better tracking performance than other state-of-the-art methods.

• 한국컴퓨터정보학회논문지
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• 제22권5호
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• pp.65-72
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• 2017

Premature Ventricular Contraction(PVC) arrhythmia is most common abnormal-heart rhythm that may increase mortal risk of a cardiac patient. Thus, it is very important issue to identify the specular portraits of PVC pattern especially from the patient. In this paper, we propose a new method to extract the characteristics of PVC pattern by applying K-means machine learning algorithm on Heart Rate Variability depicted in Poinecare plot. For the quantitative analysis to distinguish the trend of cluster patterns between normal sinus rhythm and PVC beat, the Euclidean distance measure was sought between the clusters. Experimental simulations on MIT-BIH arrhythmia database draw the fact that the distance measure on the cluster is valid for differentiating the pattern-traits of PVC beats. Therefore, we proposed a method that can offer the simple remedy to identify the attributes of PVC beats in terms of K-means clusters especially in the long-period Electrocardiogram(ECG).

• 한국재난정보학회 논문집
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• 제18권3호
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• pp.433-443
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• 2022

연구목적: 본 논문에서는 스마트 디바이스의 높은 보급률을 활용하여 근감소증을 추정 및 예측하는 딥러닝 알고리즘을 제안과 연구를 수행한다. 연구방법: 딥러닝 학습을 위해 스마트 디바이스에 내장된 관성센서를 활용하여 실험 데이터를 수집하였다. 데이터를 수집하는 테스트용 어플리케이션 구현하여 '정상'과 '비정상'걸음과 '달리기', '낙상', '스쿼트' 자세의 5 가지 상태를 구분하여 데이터를 수집하였다. 연구결과: LSTM, CNN, RNN model 사용 시 예측 정확도를 분석했고 CNN-LSTM 융합형 모델을 활용하여 이진분류 정확도 99.87%, 다중 분류 92.30%의 정확도를 보였다. 결론: 근감소증이 있는 사람의 경우 걸음걸이의 이상이 생긴다는 점에 착안하여 스마트 디바이스를 활용한 연구를 진행하였다. 본 연구를 활용하여 근감소증으로 인해 생기는 재난안전을 강화 할 수 있을 것이다.

• 한국정보과학회논문지:소프트웨어및응용
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• 제31권7호
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• pp.949-958
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• 2004

본 연구는 대용량 훈련 데이타를 사용하는 얼굴 검출 분류기의 학습과정에서 새로운 데이터의 추가 학습이 가능한 새로운 방법을 제안한다. 추가되는 데이타로부터 새로운 정보를 학습하여 이미 습득된 기존의 지식을 갱신하는 것이 점진적 학습의 목표이다. 이러한 학습 기법에 기반한 분류기의 설계에서는 최종 분류기가 전체 훈련 데이타 집합의 특성을 반영하는 것이 매우 중요한 문제이다. 제안하는 알고리즘은 최적화된 최종 분류기 획득을 위하여 훈련 집합의 전역적인 특성을 대표하는 검증집합을 생성하고, 이 집단 내에서의 분류성능을 기준으로 중간단계 분류기들의 가중치를 결정한다. 각 중간단계 분류기는 개변 데이타 집합의 학습 결과로써 가중치 기반 결합 방식에 의해 최종 분류기로 구성된다. 반복적인 실험을 통해, 제안한 알고리즘을 사용하여 학습한 얼굴 검출 분류기의 성능이 AdaBoost 및 Learn＋＋기반의 분류기보다 우수한 검출 성능을 보임을 확인하였다.