• Journal of Korean Society for Geospatial Information Science
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• v.13 no.3 s.33
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• pp.33-40
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• 2005

The aim of remotely sensed data classification is to produce the best accuracy map of the earth surface assigning each pixel to its appropriate category of the real-world. The classification of satellite multi-spectral image data has become tool for generating ground cover map. Many classification methods exist. In this study, MLC(Maximum Likelihood Classification), ANN(Artificial neural network), SVM(Support Vector Machine), Naive Bayes classifier algorithms are compared using IKONOS image of the part of Dalsung Gun, Daegu area. Two preprocessing methods are performed-PCA(Principal component analysis), ICA(Independent Component Analysis). Boosting algorithms also performed. By the combination of appropriate feature selection pre-processing and classifier, the best results were obtained.

• Journal of Korean Society of Water and Wastewater
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• v.29 no.3
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• pp.415-425
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• 2015

Aging water pipe networks hinder efficient management of important water service indices such as revenue water and leakage ratio due to pipe breakage and malfunctioning of pipe appurtenance. In order to control leakage in water pipe networks, various methods such as the minimum night flow analysis and sound waves method have been used. However, the accuracy and efficiency of detecting water leak by these methods need to be improved due to the increase of water consumption at night. In this study the Principal Component Analysis (PCA) technique was applied to the night water flow data of 426 days collected from a water distribution system in the interval of one hour. Based on the PCA technique, computational algorithms were developed to narrow the time windows for efficient execution of leak detection job. The algorithms were programmed on computer using the MATLAB. The presented techniques are expected to contribute to the efficient management of water pipe networks by providing more effective time windows for the detection of the anomaly of pipe network such as leak or abnormal demand.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2786-2788
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• 2003

본 논문에서는 생체자기신호의 잡음제거 기법 중 PCA(Principal Component Analysis) 알고리즘을 사용하여 효과적으로 노이즈를 제거하기 위한 방법을 제안하였다. 61 채널 SQUID 시스템을 이용하여 심자도 신호를 측정하였고, 40 채널 SQUID 시스템을 이용하여 뇌자도 신호를 측정하였다. 그리고, 측정한 신호 성분들을 제안한 방법을 이용하여 주성분들을 분리하였고, 이들 중에서 노이즈 성분을 추정하여 측정한 신호에서 제거하였다. 이러한 방법을 이용한 결과, 심자도 신호에 존재하는 펄스 노이즈로 인하여 왜곡된 생체 자기 신호의 노이즈를 감소 시킬 수 있었으며, 뇌자도 신호에 존재하는 외부 노이즈 성분을 제거하여 임상 진단에 유용한 데이터를 얻을 수 있었다.

• Journal of Intelligence and Information Systems
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• v.10 no.3
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• pp.105-114
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• 2004

In the past year, the increasing concern about the biometric recognition makes the great activities on the security fields, such as the entrance control or user authentication. In particular, although the features of face recognition, such as user friendly and non-contact made it to be used widely, unhappily it has some disadvantages of low accuracy or low Re-attempts Rates. For this reason, I suggest the new approach to re-classify the classified data of recognition result data to solve the problems. For this study, I will use the typical appearance-based, PCA(Principal Component Analysis) algorithm and verify the performance improvement by adopting the re-classification approach using 200 peoples (10 pictures per one person).

• Korean Journal of Remote Sensing
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• v.35 no.6_3
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• pp.1173-1185
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• 2019

We, for the first time, retrieved sulfur dioxide (SO₂) vertical column density (VCD) in industrial and volcanic areas from TROPOspheric Monitoring Instrument (TROPOMI) using the Principle component analysis(PCA) algorithm. Furthermore, SO₂ VCDs retrieved by the PCA algorithm from TROPOMI raw data were compared with those retrieved by the Differential Optical Absorption Spectroscopy (DOAS) algorithm (TROPOMI Level 2 SO₂ product). In East Asia, where large amounts of SO₂ are released to the surface due to anthropogenic source such as fossil fuels, the mean value of SO₂ VCD retrieved by the PCA (DOAS) algorithm was shown to be 0.05 DU (-0.02 DU). The correlation between SO₂ VCD retrieved by the PCA algorithm and those retrieved by the DOAS algorithm were shown to be low (slope = 0.64; correlation coefficient (R) = 0.51) for cloudy condition. However, with cloud fraction of less than 0.5, the slope and correlation coefficient between the two outputs were increased to 0.68 and 0.61, respectively. It means that the SO₂ retrieval sensitivity to surface is reduced when the cloud fraction is high in both algorithms. Furthermore, the correlation between volcanic SO₂ VCD retrieved by the PCA algorithm and those retrieved by the DOAS algorithm is shown to be high (R = 0.90) for cloudy condition. This good agreement between both data sets for volcanic SO₂ is thought to be due to the higher accuracy of the satellite-based SO₂ VCD retrieval for SO₂ which is mainly distributed in the upper troposphere or lower stratosphere in volcanic region.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.5 s.305
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• pp.55-62
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• 2005

In this paper, we propose the face recognition system using HNMA(Hippocampal Neuron Modeling Algorithm) which can remodel the cerebral cortex and hippocampal neuron as a principle of a man's brain in engineering, then it can learn the feature-vector of the face images very fast and construct the optimized feature each image. The system is composed of two parts. One is feature-extraction and the other is teaming and recognition. In the feature extraction part, it can construct good-classified features applying PCA(Principal Component Analysis) and LDA(Linear Discriminants Analysis) in order. In the learning part, it cm table the features of the image data which are inputted according to the order of hippocampal neuron structure to reaction-pattern according to the adjustment of a good impression in the dentate gyrus region and remove the noise through the associate memory in the CA3 region. In the CA1 region receiving the information of the CA3, it can make long-term memory learned by neuron. Experiments confirm the each recognition rate, that are face changes, pose changes and low quality image. The experimental results show that we can compare a feature extraction and learning method proposed in this paper of any other methods, and we can confirm that the proposed method is superior to existing methods.

• Resources Recycling
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• v.26 no.2
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• pp.46-55
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• 2017

In this study, the design methodology of Radial Basis Function Neural Networks is developed with the aid of Laser Induced Breakdown Spectroscopy and also applied to the practical plastics sorting system. To identify black plastics such as ABS, PP, and PS, RBFNNs classifier as a kind of intelligent algorithms is designed. The dimensionality of the obtained input variables are reduced by using PCA and divided into several groups by using K-means clustering which is a kind of clustering techniques. The entire data is split into training data and test data according to the ratio of 4:1. The 5-fold cross validation method is used to evaluate the performance as well as reliability of the proposed classifier. In case of input variables and clusters equal to 5 respectively, the classification performance of the proposed classifier is obtained as 96.78%. Also, the proposed classifier showed superiority in the viewpoint of classification performance where compared to other classifiers.

• Resources Recycling
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• v.26 no.1
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• pp.69-77
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• 2017

Used small household appliances(small e-waste) consists of a variety of complex materials and components. The small e-waste is mainly composed of plastics and an important potential source of waste plastic. The black plastics, particularly are very difficult to separate by resin type and therefore these are mainly recycled in the form of a mixtures. In the present study, the sorting technologies such as gravity and electro static separation, near-infrared ray(NIR) and IR/Raman optical sorting separation on mixture of black plastics were analyzed and their limitations on sorting process were also investigated. The Laser Induced Breakdown Spectroscopy(LIBS) spectrum of each black plastics was used for identification of black plastics by resin type, and after analyzing the normalization operation, Principal Component Analysis(PCA) was carried out. The spectrum data was optimized through PCA process. In order to improve the identification accuracy and sorting efficiency of black plastics, it is necessary to design a classifier with high efficiency and to improve the performance and reliability of the classifier by applying the field of intelligent algorithms.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.07a
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• pp.160-163
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• 2012

업믹스(Upmix) 기술은 홈시어터와 같은 다채널 스피커 재생 환경에서 콘텐츠의 대부분을 차지하는 스테레오 음원을 다채널 환경에 재생하기 위한 채널 포맷 변환 기술을 말한다. 업믹스를 위한 전처리 단계로서 특정 방향으로 패닝된 주(primary)성분과 잔향 및 배경음과 같은 Ambient 성분을 분리하는 과정이 필요하다. Primary와 Ambient를 분리하기 위한 방법으로 채널 간의 상관도, 적응 필터 및 주성분 분석법(principal component analysis, PCA)이 널리 이용되고 있다. 이에 본 논문에서는 비교적 정확하게 Primary와 Ambient를 분리한다고 알려진 주성분 분석법을 이용하여 신호를 분리해 내고 이 때 주성분 분석법이 가지는 문제점을 해결한 향상된 Primary-Ambient 분리 알고리즘을 제안하였다. 제안된 알고리즘은 분리 성능이 Primary 성분이 패닝된 각도에 영향을 받지 않으며 또한 Primary 성분에 섞인 잔여 Ambient를 제거함으로써 기존의 주성분 분석법 보다 더 정확하게 Primary와 Ambient를 분리 할 수 있고 상관성이 없는 Ambient 특성을 좀 더 정확하게 반영한다.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.4
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• pp.125-132
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• 2021

As the layers of artificial neural network deepens, and the dimension of data used as an input increases, there is a problem of high arithmetic operation requiring a lot of arithmetic operation at a high speed in the learning and recognition of the neural network (NN). Thus, this study proposes a data dimensionality reduction method to reduce the dimension of the input data in the NN. The proposed Line-segment Feature Analysis (LFA) algorithm applies a gradient-based edge detection algorithm using median filters to analyze the line-segment features of the objects existing in an image. Concerning the extracted edge image, the eigenvalues corresponding to eight kinds of line-segment are calculated, using 3×3 or 5×5-sized detection filters consisting of the coefficient values, including [0, 1, 2, 4, 8, 16, 32, 64, and 128]. Two one-dimensional 256-sized data are produced, accumulating the same response values from the eigenvalue calculated with each detection filter, and the two data elements are added up. Two LFA256 data are merged to produce 512-sized LAF512 data. For the performance evaluation of the proposed LFA algorithm to reduce the data dimension for the recognition of handwritten numbers, as a result of a comparative experiment, using the PCA technique and AlexNet model, LFA256 and LFA512 showed a recognition performance respectively of 98.7% and 99%.