• 한국통신학회논문지
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• 제42권5호
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• pp.1085-1092
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• 2017

본 논문은 풍력발전예측의 정확도 개선을 위하여 바람의 특성을 반영한 풍력발전량예측 방법을 제안한다. 제안한 방법은 크게 바람의 특성을 추출하는 부분과 발전량을 예측하는 부분으로 구성된다. 바람의 특성을 추출하는 부분은 발전량, 풍향과 풍속의 상관분석을 이용한다. 풍향과 풍속의 상관관계를 근거로 K-means 방법으로 클러스터링하여 특징 벡터를 추출한다. 예측하는 부분은 임의의 실수값을 예측 할 수 있도록 SVM을 일반화 한 SVR을 이용하여 기계학습을 한다. 기계학습은 바람의 특성을 반영한 제안한 방법과 바람의 특성을 반영하지 않은 기존방법을 비교 실험하였다. 또한, 제안한 방법의 정확도와 타당성을 검증하기 위하여 장소가 상이한 제주도 풍력발전단지 3지역에서 수집된 데이터를 사용하였다. 실험결과, 제안한 방법의 오차가 일반적인 풍력발전예측 오차보다 개선되었다.

• Steel and Composite Structures
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• 제21권6호
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• pp.1183-1210
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• 2016

Viscoelastic sandwich structures (VSSs) are widely used in mechanical equipment, but in the service process, they always suffer from aging which affect the whole performance of equipment. Therefore, aging state recognition of VSSs is significant to monitor structural state and ensure the reliability of equipment. However, non-stationary vibration response signals and weak state change characteristics make this task challenging. This paper proposes a novel method for this task based on adaptive second generation wavelet packet transform (ASGWPT) and multiwavelet support vector machine (MWSVM). For obtaining sensitive feature parameters to different structural aging states, the ASGWPT, its wavelet function can adaptively match the frequency spectrum characteristics of inspected vibration response signal, is developed to process the vibration response signals for energy feature extraction. With the aim to improve the classification performance of SVM, based on the kernel method of SVM and multiwavelet theory, multiwavelet kernel functions are constructed, and then MWSVM is developed to classify the different aging states. In order to demonstrate the effectiveness of the proposed method, different aging states of a VSS are created through the hot oxygen accelerated aging of viscoelastic material. The application results show that the proposed method can accurately and automatically recognize the different structural aging states and act as a promising approach to aging state recognition of VSSs. Furthermore, the capability of ASGWPT in processing the vibration response signals for feature extraction is validated by the comparisons with conventional second generation wavelet packet transform, and the performance of MWSVM in classifying the structural aging states is validated by the comparisons with traditional wavelet support vector machine.

• 한국멀티미디어학회논문지
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• 제18권11호
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• pp.1281-1288
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• 2015

Nongroup Cellular Automata(CA) having two trees in the state transition diagram of a CA is suitable for pattern classifier which divides pattern set into two classes. Maji et al. [1] classified patterns by using multiple attractor cellular automata as a pattern classifier with dependency vector. In this paper we propose a method of generation of a pattern classifier using feature vector which is the extension of dependency vector. In addition, we propose methods for finding nonreachable states in the 0-tree of the state transition diagram of TPMACA corresponding to the given feature vector for the analysis of the state transition behavior of the generated pattern classifier.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권8호
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• pp.4041-4059
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• 2019

Key establishment method based on channel reciprocity for time division duplex (TDD) system has earned a vital consideration in the majority of recent research. While most of the cellular systems rely on frequency division duplex (FDD) systems, especially the 5G network, which is not characterized by the channel reciprocity feature. This paper realizes the generation of a group secret key for multi-terminals communicated through a wireless network in FDD mode, by utilizing the nature of the physical layer for the wireless links between them. I consider a new group key generation approach, which using bitwise XOR with a modified pairwise secret key generation approach not based on the channel reciprocity feature. Precisely, this multi-node secret key agreement technique designed for three wireless network topologies: 1) the triangle topology, 2) the multi-terminal star topology, and 3) the multi-node chain topology. Three multi-node secret key agreement protocols suggest for these wireless communication topologies in FDD mode, respectively. I determine the upper bound for the generation rate of the secret key shared among multi-node, for the three multi-terminals topologies, and give numerical cases to expose the achievement of my offered technique.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권4호
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• pp.277-285
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• 2004

We present a new hybrid stereo matching technique in terms of the co-operation of area-based stereo and feature-based stereo. The core of our technique is that feature matching is carried out by the reference of the disparity evaluated by area-based stereo. Since the reference of the disparity can significantly reduce the number of feature matching combinations, feature matching error can be drastically minimized. One requirement of the disparity to be referenced is that it should be reliable to be used in feature matching. To measure the reliability of the disparity, in this paper, we employ the self-consistency of the disunity Our suggested technique is applied to the detection of 3D line segments by 2D line matching using our hybrid stereo matching, which can be efficiently utilized in the generation of the rooftop model from urban imagery. We carry out the experiments on our hybrid stereo matching scheme. We generate synthetic images by photo-realistic simulation on Avenches data set of Ascona aerial images. Experimental results indicate that the extracted 3D line segments have an average error of 0.5m and verify our proposed scheme. In order to apply our method to the generation of 3D model in urban imagery, we carry out Preliminary experiments for rooftop generation. Since occlusions are occurred around the outlines of buildings, we experimentally suggested multi-image hybrid stereo system, based on the fusion of 3D line segments. In terms of the simple domain-specific 3D grouping scheme, we notice that an accurate 3D rooftop model can be generated. In this context, we expect that an extended 3D grouping scheme using our hybrid technique can be efficiently applied to the construction of 3D models with more general types of building rooftops.

• 제어로봇시스템학회논문지
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• 제12권3호
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• pp.255-263
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• 2006

In the last decade, face analysis, e.g. face detection, face recognition, facial expression recognition, is a very lively and expanding research field. As computer animated agents and robots bring a social dimension to human computer interaction, interest in this research field is increasing rapidly. In this paper, we introduce an artificial emotion mimic system which can recognize human facial expressions and also generate the recognized facial expression. In order to recognize human facial expression in real-time, we propose a facial expression classification method that is performed by weak classifiers obtained by using new rectangular feature types. In addition, we make the artificial facial expression using the developed robotic system based on biological observation. Finally, experimental results of facial expression recognition and generation are shown for the validity of our robotic system.

• Journal of Information Processing Systems
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• 제14권6호
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• pp.1318-1330
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• 2018

Video captioning refers to the process of extracting features from a video and generating video captions using the extracted features. This paper introduces a deep neural network model and its learning method for effective video captioning. In this study, visual features as well as semantic features, which effectively express the video, are also used. The visual features of the video are extracted using convolutional neural networks, such as C3D and ResNet, while the semantic features are extracted using a semantic feature extraction network proposed in this paper. Further, an attention-based caption generation network is proposed for effective generation of video captions using the extracted features. The performance and effectiveness of the proposed model is verified through various experiments using two large-scale video benchmarks such as the Microsoft Video Description (MSVD) and the Microsoft Research Video-To-Text (MSR-VTT).

• 전자공학회논문지B
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• 제29B권11호
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• pp.102-110
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• 1992

In this paper, a new method of character recognition is proposed. It uses density information, in addition to positional and directional information generally used, to recognize a character. Four directional feature primitives are extracted from the thinning templates on the observation that the output of the templates have directional property in general. A simple and fast feature extraction scheme is possible. Features are organized from recursive nonary tree(N-tree) that corresponds to normalized character area. Each node of the N-tree has four directional features that are sum of the features of it's nine sub-nodes. Every feature primitive from the templates are added to the corresponding leaf and then summed to the upper nodes successively. Recognition can be accomplished by using appropriate feature level of N-tree. Also, effectiveness of each node's feature vector was tested by experiment. A method to implement the proposed feature vector organization algorithm into hardware is proposed as well. The third generation node, which is 4$\times$4, is used as a unit processing element to extract features, and it was implemented in hardware. As a result, we could observe that it is possible to extract feature vector for real-time processing.

• 정보보호학회논문지
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• 제20권4호
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• pp.63-73
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• 2010

최근 분산 서비스 거부 공격의 근원인 악성 봇 프로그램이 널리 유포되고 있으며 보안이 유지되지 않는 PC를 통하여 악성 봇이 설치된 PC의 수가 기하급수적으로 증가하고 있다. 이를 통한 분산 서비스 거부 공격이 계속적으로 발생하고 있으며 최근 금품을 요구하는 사례도 발견되었다. 따라서 분산 서비스 거부 공격에 대응하기 위한 연구가 필요하며 본 논문에서는 네트워크 패킷 헤더의 속성에 대해 불확실성을 나타내는 척도인 엔트로피를 이용하는, 분산 서비스 거부 공격 탐지에 효과적인 특정 생성 방법을 제안한다. DARPA 2000 데이터셋과 직접 실험을 통해 구성한 분산 서비스 거부 공격 데이터셋에 대해 향상된 엔트로피 수식과 효율적인 엔트로피 계산 기법, 다양한 엔트로피 특징 값을 사용하는 제안 기법을 적용해보고 베이지안 네트워크 분류기를 이용하여 분류함으로써 제안하는 방법이 효과적인지를 검증해 본다.

• 한국멀티미디어학회논문지
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• 제11권2호
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• pp.257-268
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• 2008

멀티미디어 추천시스템은 사용자의 선호도를 분석하여 멀티미디어 상품을 사용자에게 추천하는 시스템이다. 다양한 추천 기법들에서 가장 널리 사용되는 기법은 협동적 여과 방식이다. 그러나 협동적 여과는 정보 부족 문제와 초기 시작 문제가 존재한다. 선호도 정보가 적게 존재하면 유사 사용자 추출이 어려우며, 이러한 문제는 시스템을 처음 사용하는 새로운 사용자에게 더욱 심각한 문제를 발생시킨다. 본 논문에서는 정보 부족 문제를 해결하고 추천 정확도를 향상시키기 위해 사용자와 상품에 대한 속성 생성 기법을 제안한다. 본 논문에서 제안한 기법은 속성의 분포를 이용하여 추가 속성을 생성하고, 추가 속성을 포함한 변형된 데이터를 이용하여 상품을 추천한다. 여러 실험을 통해 제안된 기법의 효과를 확인하였다.