• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1355-1356
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• 2015

본 논문에서는 Convolution기법 기반 RBFNNs 패턴 분류기를 사용한 2차원 얼굴인식 시스템을 설계한다. 제안된 방법은 특징 추출과 차원축소를 하는 컨볼루션 계층과 부분추출 계층을 교대로 연결하여 2차원 이미지를 1차원의 특징 배열로 만든다. 그 후, 만들어진 1차원의 특징 배열을 RBFNNs 패턴 분류기의 입력으로 사용하여 인식을 수행한다. RBFNNs의 조건부에는 FCM 클러스터링 알고리즘을 사용하며 연결가중치는 1차 선형식을 사용하였다. 또한 최소 자승법(LSE : Least Square Estimation)을 사용하여 다항식의 계수를 추정하였다. 제안된 모델의 성능을 평가하기 위해 CMU PIE Database를 사용한다.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1369-1370
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• 2015

본 논문에서는 얼굴 인식을 위하여 인공 신경망의 일종인 Extreme Learning Machine의 학습 알고리즘을 기반으로 하여 지능형 알고리즘인 퍼지 집합 이론을 이용하여 주변 노이즈에 매우 강한 특성을 보이며 학습 속도가 매우 빠른 새로운 패턴 분류기를 제안한다. 제안된 퍼지 패턴 분류기는 기존 신경회로망의 학습 속도에 비해 매우 빠른 학습 속도를 보이며, 패턴 분류기의 일반화 성능이 우수하다고 알려진 Extreme Learning Machine의 특성을 퍼지 집합 이론과 결합하여 퍼지 패턴 분류기의 일반화 성능을 개선하였다. 제안된 퍼지 패턴 분류기는 얼굴 인식 데이터를 이용하여 성능을 평가 하였다.

• Journal of Multimedia Information System
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• v.4 no.2
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• pp.79-82
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• 2017

Smoke and fire have different shapes and colours. This article suggests a fully connected system which is used two features using Adaboost algorithm for constructing a strong classifier as linear combination. We calculate the local histogram feature by gradient and bin, local binary pattern value, and projection vectors for each cell. According to the histogram magnitude, this paper applied adapted weighting value to improve the recognition rate. To preserve the local region and shape feature which has edge intensity, this paper processed the normalization sequence. For the extracted features, this paper Adaboost algorithm which makes strong classification to classify the objects. Our smoke detection system based on the proposed approach leads to higher detection accuracy than other system.

• Journal of Information Processing Systems
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• v.12 no.3
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• pp.392-409
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• 2016

The aim of this paper is to examine the effectiveness of combining three popular tools used in pattern recognition, which are the Active Appearance Model (AAM), the two-dimensional discrete cosine transform (2D-DCT), and Kernel Fisher Analysis (KFA), for face recognition across age variations. For this purpose, we first used AAM to generate an AAM-based face representation; then, we applied 2D-DCT to get the descriptor of the image; and finally, we used a multiclass KFA for dimension reduction. Classification was made through a K-nearest neighbor classifier, based on Euclidean distance. Our experimental results on face images, which were obtained from the publicly available FG-NET face database, showed that the proposed descriptor worked satisfactorily for both face identification and verification across age progression.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.959-962
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• 1988

This paper describes an automatic visual inspection system for fabric defects based on pattern recognition techniques. The inspection for fabric defects can be separated into three sequences of operations which are the detection of fabric defects[1], the classification of figures of fabric defects, and the classification of fabric defects. Comparing projections of defect-detected images with the predefined complex, the classification accuracy of figures of fabric defects was found to be 95.3 percent. Employing the Bayes classifier using cluster shade in SGLDM and variance in decorrelation method as features, the classification accuracy of regional figure defects was found to be 82.4 percent. Finally, some experimental results for line and dispersed figures of fabric defects are included.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2690-2692
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• 2002

This paper describes a technique for human sensibility evaluation using 10-channel EEG(electroencephalogram). The proposed method uses the linear predictor coefficients as EEG feature parameters and a neural network as sensibility pattern classifier. For subject independent system, multiple templates are stored and the most similar template can be selected. EEG signals corresponding to 4 emotions such as, relaxation, joy, sadness and anger are collected from 5 armature performers. The states of relaxation and joy are considered as positive sensibility and those of sadness and anger as negative. The classification performance using the proposed method is about 72.6%. This will be promising performance in the human sensibility evaluation.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.45 no.1
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• pp.20-27
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• 2008

In this paper, a nonlinear classifier of a feed-forward neural network is implemented on an FPGA chip. The feedforward neural network is implemented in hardware for fast parallel processing. After off line training of neural network, weight values are saved and used to perform forward propagation of neural processing. As an example, AND and XOR digital logic classification is conducted in off line, and then weight values are used in neural network. Experiments are conducted successfully and confirmed that the FPGA neural network hardware works well.

• 유체기계공업학회:학술대회논문집
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• 1999.12a
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• pp.317-323
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• 1999

The necessity of diagnosis of the rotating machinery which is widely used in the industry is increasing. Many research has been conducted to manipulate field vibration signal data for diagnosing the fault of designated machinery. As the pattern recognition tool of that signal, neural network which use usually back-propagation algorithm was used in the diagnosis of rotating machinery. In this paper, self-organizing feature map(SOFM) which is unsupervised learning algorithm is used in the abnormal vibration diagnosis of rotating machinery and then learning vector quantization(LVQ) which is supervised teaming algorithm is used to improve the quality of the classifier decision regions.

• Journal of the Korea Institute of Military Science and Technology
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• v.19 no.2
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• pp.236-243
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• 2016

This paper presents a threat list acquisition method in an engagement area using the support vector machines (SVM). The proposed method consists of track creation, track estimation, track feature extraction, and threat list classification. To classify the threat track robustly, dynamic track estimation and pattern recognition algorithms are used. Dynamic tracks are estimated accurately by approximating a track movement using position, velocity and time. After track estimation, track features are extracted from the track information, and used to classify threat list. Experimental results showed that the threat list acquisition method in the engagement area achieved about 95 % accuracy rate for whole test tracks when using the SVM classifier. In case of improving the real-time process through further studies, it can be expected to apply the fire control systems.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1107-1109
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• 1996

In this paper, we discuss the automatic recognition system of vehicle types and licence plate numbers using artificial neural networks, which will be used as vehicle identifier. We confine to expose the vehicle licence number for violating bus lane and stolen cars. Therefore, the vehicle height, width and distribution profile are used as the feature parameters of vehicle type. This system is composed of two parts: one is an image preprocessor of vehicle images and the other one is a pattern classifier by neural networks. The experimental results show that our method has good results for the recognition of vehicle types and numbers.