• The Journal of the Korea Contents Association
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• v.10 no.12
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• pp.84-90
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• 2010

In this paper, we propose a quadratic discriminant analysis based approach for improving the discriminating strength of weak classifiers based on simple Haar-like features that were used in the Viola-Jones object detection framework. Viola and Jones built a strong classifier using a boosted ensemble of weak classifiers. However, their single threshold (or decision boundary) based weak classifier is sub-optimal and too weak for efficient discrimination between object class and background. A quadratic discriminant analysis based approach is presented which leads to hyper-quadric boundary between the object class and background class, thus realizing multiple thresholds based weak classifiers. Experiments carried out for car detection using 1000 positive and 3000 negative images for training, and 500 positive and 500 negative images for testing show that our method yields higher classification performance with fewer classifiers than single threshold based weak classifiers.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.1
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• pp.184-192
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• 2022

Valve internal leakage is caused by damage to the internal parts of the valve, resulting in accidents and shutdowns of the piping system. This study investigated the possibility of a real-time leak detection method using the acoustic emission (AE) signal generated from the piping system during the internal leakage of a butterfly valve. Datasets of raw time-domain AE signals were collected and postprocessed for each operation mode of the valve in a systematic manner to develop a data-driven model for the detection and classification of internal leakage, by applying machine learning algorithms. The aim of this study was to determine whether it is possible to treat leak detection as a classification problem by applying two classification algorithms: support vector machine (SVM) and convolutional neural network (CNN). The results showed different performances for the algorithms and datasets used. The SVM-based binary classification models, based on feature extraction of data, achieved an overall accuracy of 83% to 90%, while in the case of a multiple classification model, the accuracy was reduced to 66%. By contrast, the CNN-based classification model achieved an accuracy of 99.85%, which is superior to those of any other models based on the SVM algorithm. The results revealed that the SVM classification model requires effective feature extraction of the AE signals to improve the accuracy of multi-class classification. Moreover, the CNN-based classification can be a promising approach to detect both leakage and valve opening as long as the performance of the processor does not degrade.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.190-193
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• 2000

In this paper, we investigate the distribution of classification accuracies of multiclass problems in the feature space and analyze performances of the conventional feature extraction algorithms. In order to find the distribution of classification accuracies, we sample the feature space and compute the classification accuracy corresponding to each sampling point. Experimental results showed that there exist much better feature sets that the conventional feature extraction algorithms fail to find. In addition, the distribution of classification accuracies is useful for developing and evaluating the feature extraction algorithm.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.643-646
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• 1999

In this paper, we propose a feature extraction method based on Bhattacharyya distance for multiclass problems. The Bhattacharyya distance provides a valuable information in determining the effectiveness of a feature set and has been used as separability measure for feature selection. Recently, a feature extraction algorithm hat been proposed for two normally distributed classes based on Bhattacharyya distance. In this paper, we propose to expand the previous approach to multiclass cases. Experiment results show that the proposed method compares favorably with the conventional methods.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.647-650
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• 1999

In this Paper, we propose an optimal feature extraction for multiclass problems through proper choice of initial feature vectors. Although numerous feature extraction algorithms have been proposed, those algorithms are not optimal for multiclass problems. Recently, an optimal feature extraction algorithm for multiclass problems has been proposed, which provides a better performance than the conventional feature extraction algorithms. In this paper, we improve the algorithm by choosing good initial feature vectors. As a result, the searching time is significantly reduced. The chance to be stuck in a local minimum is also reduced.

• Proceedings of the IEEK Conference
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• 2001.06a
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• pp.257-260
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• 2001

As today's network infrastructure continues to grow and DiffServ IP networks ate now available to provide various levels of flexible QoS services. DiffServ guarantees good scalability but shows dynamic QoS dependent on network traffic loads. Therefore, in this paper, we investigate the dynamics of DiffSev QoS and present analytical model to estimate the allowable traffic load under the given network conditions.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2001.06a
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• pp.109-112
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• 2001

기존의 K-means 알고리즘은 학습벡터가 단일군집에 할당되는 방법이 crisp 이므로 다른 군집에 할당될 확률을 무시하게 된다. 따라서 군집화 작업과 관련하여 반복적인 코드북 설계 과정에서 각 학습벡터를 다중 군집으로 할당하는 Fuzzy c-means를 사용한다. 또한 Fuzzy c-means 알고리즘의 학습과정에서 구해지는 각 클래스 의 프로토타입에 가중치를 곱하여 다음 학습의 프로토타입으로 사용함으로써 Fuzzy c-means 알고리즘 적용 결과 얻어지는 코트북의 성능을 기존 알고리즘과 비교하여 개선된 Fuzzy c-means 알고리즘을 찾기 위한 근거를 마련한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.340-343
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• 1996

실세계에서 존재하는 대부분의 지식은 다양한 패턴들로 구성되어 있다. 본 논문에서는 사례베이스 추론(Case-Based Reasoning : CBR)에서 다중의 의미를 갖는 불확실한 지식을 쉽게 표현할 수 있는 러프 집합을 이용하여 지식의 함축의 의미를 갖는 지식을 간략화하는 방법을 제안한다. 전문가의 지식 구조를 명확화 하는데는 많은 노력이 필요하고 지식획득의 병목현상이 일어난다. 이러한 문제점을 해결하기 위해 많은 사례의 수를 러프 집합의 성질을 이용하여 사례를 동치 클래스로 분류하여 사례의 수를 감소하므로써 CBR의 기능을 향상시킨다.

• Proceedings of the Korea Information Processing Society Conference
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• 2000.04a
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• pp.692-697
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• 2000

멀티미디어 개인 휴대 통신(MPCS)에서 다중 클래스호에 대한 효율적인 채널할당은 매우 중요하다고 할 수 있다. 본 논문에서는 Image/Voice/Data 호에 대하여 가변적 보호 채널을 할당하는 새로운 방식을 제안하였다. 이러한 방식은 3차원 상태 천이도로 모델링 되며 보호 채널의 크기를 가변적으로 조절함으로써 보다 융통성있는 서비스를 제공하게 되며, 또한 수학적 분석과 시뮬레이션을 통해 비교분석을 수행하였다.

• Korean Journal of Construction Engineering and Management
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• v.18 no.6
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• pp.78-88
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• 2017

With the growing use of BIM in the AEC industry, various new applications are being developed to meet these specific needs. Such developments have increased the importance of Industry Foundation Classes, which is the international standard for sharing BIM data and thus ensuring interoperability. However, mapping individual BIM objects to IFC entities is still a manual task, and is a main cause for errors or omissions during data transfers. This research focused on addressing this issue by applying novelty detection, which is a technique for detecting anomalies in data. By training the algorithm to learn the geometry of IFC entities, misclassifications (i.e., outliers) can be detected automatically. Two IFC classes (ifcWall, ifcDoor) were trained using objects from three BIM models. The results showed that the algorithm was able to correctly identify 141 of 160 outliers. Novelty detection is thus suggested as a competent solution to resolve the mapping issue, mainly due to its ability to create multiple inlier boundaries and ex ante training of element geometry.