• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1345-1346
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• 2015

본 논문에서는 보행자 및 배경 이미지로부터 HOG-PCA 특징을 추출하고 다항식 기반 RBFNNs(Radial Basis Function Neural Network) 패턴분류기과 최적화 알고리즘을 이용하여 보행자를 검출하는 시스템 설계를 제안한다. 입력 영상으로부터 보행자를 검출하기 위해 전처리 과정에서 HOG(Histogram of oriented gradient) 알고리즘을 통해 특징을 추출한다. 추출된 특징은 고차원이므로 패턴분류기 분류 시 많은 연산과 처리속도가 따른다. 이를 개선하고자 PCA (Principal Components Analysis)을 사용하여 저차원으로의 차원 축소한다. 본 논문에서 제안하는 분류기는 pRBFNNs 패턴분류기의 효율적인 학습을 위해 최적화 알고리즘인 PSO(Particle Swarm Optimization)을 사용하여 구조 및 파라미터를 최적화시켜 모델의 성능을 향상시킨다. 사용된 데이터로는 보행자 검출에 널리 사용되는 INRIA2005_person data set에서 보행자와 배경 영상을 각각 1200장을 학습 데이터, 검증 데이터로 구성하여 분류기를 설계하고 테스트 이미지를 설계된 최적의 분류기를 이용하여 보행자를 검출하고 검출률을 확인한다.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.688-690
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• 2004

최근 전자화된 문서 영상을 효율적으로 관리하고 검색하기 위한 문서구조분석 방법과 문서의 자동 분류에 관한 많은 연구가 발표되고 있다. 본 논문에서는 NMF(non-negative matrix factorization) 알고리즘을 사용하여 폰트를 자동으로 분류하는 방법을 제안한다. 제안된 방법은 폰트의 구분 특징들이 공간적으로 국부성을 가지는 부분으로 표현될 수 있다는 가정을 바탕으로, 전체의 폰트 이미지들로부터 각 폰트들의 구분 특징인 부분을 학습하고, 학습된 부분들을 특징으로 사용하여 폰트를 분류하는 방법이다. 학습된 폰트의 특징들은 계층적 군집화 알고리즘을 이용하여 템플릿을 생성하고, 테스트 패턴을 분류하기 위하여 템플릿 패턴과의 EMD(earth mover's distance)를 사용한다. 실험결과에서 폰트 이미지들의 공간적으로 국부적인 특징들이 조사되고, 그 특징들의 폰트 식별을 위한 적절성을 보였다. 제안된 방법이 기존의 문자인식. 문서 검색 시스템들의 전처리기로 사용되면. 그 시스템들의 성능을 향상시킬 것으로 기대된다.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2011.04a
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• pp.18-21
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• 2011

비정형의 대공간 구조물은 대량의 부재가 규칙성을 가지고 공간상에서 반복되는 패턴을 가지는 특징이 있다. 이러한 특징은 기존의 구조해석 모델러로부터의 접근을 어렵게 하는 반면, 관계성과 규칙성을 논리적으로 모델링함으로써 최종 모델을 생성해내는 파라메트릭 모델링 방법에는 매우 적합하다. 본 연구에서는 이전의 연구를 통해 개발된 파라메트릭 구조해석 모델링 툴인 STRAUTO을 이용해 전형적인 비정형 대공간 구조물인 용인시민체육공원 주경기장의 모델링에 파라메트릭 모델링 기법을 직접 적용해 봄으로써 이 새로운 접근법의 적합성과 효율성을 검토해 보았다.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.5
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• pp.197-202
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• 2022

IoT (Internet of Things) devices are being attacked by malware due to many security vulnerabilities, such as the use of weak IDs/passwords and unauthenticated firmware updates. However, due to the diversity of CPU architectures, it is difficult to set up a malware analysis environment and design features. In this paper, we design time series features using the byte sequence of executable files to represent independent features of CPU architectures, and analyze them using recurrent neural networks. The proposed feature is a fixed-length time series pattern extracted from the byte sequence by calculating partial entropy and applying linear interpolation. Temporary changes in the extracted feature are analyzed by RNN and LSTM. In the experiment, the IoT malware detection showed high performance, while low performance was analyzed in the malware family classification. When the entropy patterns for each malware family were compared visually, the Tsunami and Gafgyt families showed similar patterns, resulting in low performance. LSTM is more suitable than RNN for learning temporal changes in the proposed malware features.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.3
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• pp.645-648
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• 2008

In this paper, we analyze inner- and far-field emitted field pattern by more exactly calculating modes formed from a finite periodic dielectric structure(FPDS). It is assumed that TE-modes are generated in FPDS, and the fields in each layer are determined by proper boundary conditions. Consequently, the fields generate modes in the FPDS and the number of modes depends on its structural characteristics. In this work, the modes betwween dielectric layers and their field patterns are calculated in a specific frequency. In addition. far field patterns are given by using FFT of the calculated modes.

• Korean Journal of Optics and Photonics
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• v.8 no.3
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• pp.241-244
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• 1997

A speckle pattern from an optical fiber is used for a reference beam in writing and reading a volume hologram. The photorefractive volume hologram with this scheme shows good spatial selectivity for spatial(shift) multiplexing because the speckle pattern in writing and reading a hologram looses correlation with a small spatial shift. This scheme has the insensitivity to axial movement. The data storage system with this scheme will have a high storage density and a good stability in operation. We theoretically analyze the diffracted beam from a volume hologram recorded with the speckle pattern from the optical fiber. Experimental results are presented and compared with numerical analysis.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.42 no.2 s.302
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• pp.39-48
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• 2005

This paper proposes a novel approach to recognize nine kinds of motion for a multifunction myoelectric hand, acquiring four channel EMG signals from electrodes placed on the forearm. To analyze EMG with properties of nonstationary signal, time-frequency features are extracted by wavelet packet transform. For dimensionality reduction and nonlinear mapping of the features, we also propose a feature projection composed of PCA and SOFM. The dimensionality reduction by PCA simplifies the structure of the classifier, and reduces processing time for the pattern recognition. The nonlinear mapping by SOFM transforms the PCA-reduced features to a new feature space with high class separability. Finally a multilayer neural network is employed as the pattern classifier. From experimental results, we show that the proposed method enhances the recognition accuracy, and makes it possible to implement a real-time pattern recognition.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.05a
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• pp.157-157
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• 2004

최근 탄소나노튜브는 역학적으로 견고하고 화학적 안정성이 뛰어나며 열전도도가 높고 속이 비어 있다는 특성 때문에 다양한 분야에 응용될 수 있을 뿐만 아니라 기능 또한 뛰어나다. 특히 구조적으로 매우 큰 aspect ratio를 가지고 있기 때문에 탄소나노튜브는 국소적으로 상당한 전계 증가를 보이고 비교적 낮은 전압에서도 다량의 전계방출 전류를 생성하는 특징을 가지고 있다 그래서, 탄소나노튜브를 전계 방출원으로 사용하기 위해서는 균일하게 수직 배열된 탄소나노튜브를 성장시키는 기술을 요구한다.(중략)

• Journal of Korea Multimedia Society
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• v.7 no.1
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• pp.35-43
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• 2004

In this paper, we proposed a vehicle recognition and electronic toll collection system with detection and classification of vehicle identification mark and emblem as well as recognition of vehicle license plate to unman toll fee collection system or incoming/outcoming vehicles to an institution. In the proposed algorithm, we first process pre-processing step such as noise reduction and thinning from the rear side input image of vehicle and detect vehicle mark, emblem and license plate region using intensity variation informations, template masking and labeling operation. And then, we classify the detected vehicle features regions into vehicle mark and emblem as well as recognize characters and numbers of vehicle license plate using hybrid and seven segment pattern vector. To show the efficiency of the proposed algorithm, we tested it on real vehicle images of implemented vehicle recognition system in highway toll gate and found that the proposed method shows good feature detection/classification performance regardless of irregular environment conditions as well as noise, size, and location of vehicles. And also, the proposed algorithm may be utilized for catching criminal vehicles, unmanned toll collection system, and unmanned checking incoming/outcoming vehicles to an institution.

• Journal of KIISE:Software and Applications
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• v.36 no.8
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• pp.633-641
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• 2009

In this paper, we proposed a new pattern classifier which can be incrementally learned, be added new class in learning time, and handle with analog data. Proposed pattern classifier has hierarchical structure and the classification rate is improved by using different metric for each levels. Proposed model is based on the Gaussian ARTMAP which is an artificial neural network model for the pattern classification. We hierarchically constructed the Gaussian ARTMAP and proposed the Principal Component Emphasis(P.C.E) method to be learned different features in each levels. And we defined new metric based on the P.C.E. P.C.E is a method that discards dimensions whose variation are small, that represents common attributes in the class. And remains dimensions whose variation are large. In the learning process, if input pattern is misclassified, P.C.E are performed and the modified pattern is learned in sub network. Experimental results indicate that Hierarchical Gaussian ARTMAP yield better classification result than the other pattern recognition algorithms on variable data set including real applicable problem.