• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권5호
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• pp.2142-2161
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• 2018

Distance dynamics model is an excellent model for uncovering the community structure of a complex network. However, the model has poor robustness. To improve the robustness, we design an enhanced distance dynamics model based on Ego-Leader and propose a corresponding community detection algorithm, called E-Attractor. The main contributions of E-Attractor are as follows. First, to get rid of sensitive parameter ${\lambda}$, Ego-Leader is introduced into the distance dynamics model to determine the influence of an exclusive neighbor on the distance. Second, based on top-k Ego-Leader, we design an enhanced distance dynamics model. In contrast to the traditional model, enhanced model has better robustness for all networks. Extensive experiments show that E-Attractor has good performance relative to several state-of-the-art algorithms.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2014년도 추계학술발표대회
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• pp.837-840
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• 2014

Interactions between genes have long been recognized and studied by many researchers, and they formed a large-scale interaction networks. In systems biology, it has been a challenge to investigate the factors to determine network dynamics. Here, we create a new network called an effectiveness network by calculating thy dynamical effectiveness from a node to another node. We found that robust nodes tend to have smaller number of edges than non-robust nodes. This implies that hub nodes are likely to affect the network robustness.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 추계학술대회 학술발표 논문집
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• pp.195-198
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• 2000

Sliding mode control method is popularly used for robustness to distrurbance and variance of systems internal parameter. However, one of the serious problem of this method is Chattering which occurs in neighborhood of sliding manifold. Another problem is that we cannot expect robustness before system starts sliding mode. A new tuning method of sliding manifold which changes the parameter of sliding manifold dynamically using Wavelet Neural Network is proposed in this paper. We can expect the better performance in sliding mode control by the wavelet neural networks excellent property of approximating arbitrary function for multi-resolution analysis and decrease chattering drastically.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.420-420
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• 2000

In this paper, we propose an adaptive controller using RBFN(radial basis function network) for robot manipulators The structure of the proposed controller consists of a RBFN and VSC-1 ike control. RBFN is used in order to approximate かon system, and VSC-like control to guarantee robustness On the basis of the Lyapunov stability theorem, we guarantee the stability for the total system. And the learning law of RBFN is established by the Lyapunov method, Finally, we apply the proposed controller to tracking control for a 2 link SCARA type robot manipulator.

• ETRI Journal
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• 제42권3호
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• pp.411-419
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• 2020

In real-world intelligent transportation systems, accuracy in vehicle license plate detection and recognition is considered quite critical. Many algorithms have been proposed for still images, but their accuracy on actual videos is not satisfactory. This stems from several problematic conditions in videos, such as vehicle motion blur, variety in viewpoints, outliers, and the lack of publicly available video datasets. In this study, we focus on these challenges and propose a license plate detection and recognition scheme for videos based on a temporal matching prior network. Specifically, to improve the robustness of detection and recognition accuracy in the presence of motion blur and outliers, forward and bidirectional matching priors between consecutive frames are properly combined with layer structures specifically designed for plate detection. We also built our own video dataset for the deep training of the proposed network. During network training, we perform data augmentation based on image rotation to increase robustness regarding the various viewpoints in videos.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2002년도 춘계합동학술대회 논문집
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• pp.121-126
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• 2002

We proposed the design of SVC adaptive fuzzy logic controller(AFLC) using Tabu search and neural network. We tuned the gains of input-output variables of fuzzy logic controller(FLC) and weights of neural network using Tabu search. Neural network was used for adaptively tuning the output gain of FLC. The weights of neural network was learned from the back propagation algorithm in real-time. To evaluate the usefulness of AFLC, we applied the proposed method to single-machine infinite system. AFLC showed the better control performance than PD controller and GAFLC[8] for. three-phase fault in nominal load which had used when tuning AFLC. To show the robustness of AFLC, we applied the proposed method to disturbances such as three-phase fault in heavy and light load. AFLC showed the better robustness than PD controller and GAFLC[8].

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 1996년도 International Conference on Agricultural Machinery Engineering Proceedings
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• pp.780-791
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• 1996

A hybrid image processing system which automatically distinguished lean tissues in the image of a complex beef cut surface and generated the lean tissue contour has been developed. Because of the in homegeneous distribution and fuzzy pattern of fat and lean tissue on the beef cut, conventional image segmentation and contour generation algorithm suffer from a heavy computing requirement, algorithm complexity and poor robustness. The proposed system utilizes an artificial neural network enhance the robustness of processing. The system is composed of pre-network , network and post-network processing stages. At the pre-network stage, gray level images of beef cuts were segmented and resized to be adequate to the network input. Features such as fat and bone were enhanced and the enhanced input image was converted tot he grid pattern image, whose grid was formed as 4 X4 pixel size. at the network stage, the normalized gray value of each grid image was taken as the network input. Th pre-trained network generated the grid image output of the isolated lean tissue. A training scheme of the network and the separating performance were presented and analyzed. The developed hybrid system showed the feasibility of the human like robust object segmentation and contour generation for the complex , fuzzy and irregular image.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권1호
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• pp.366-381
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• 2020

Steganography has been successfully employed in various applications, e.g., copyright control of materials, smart identity cards, video error correction during transmission, etc. Deep learning-based steganography models can hide information adaptively through network learning, and they draw much more attention. However, the capacity, security, and robustness of the existing deep learning-based steganography models are still not fully satisfactory. In this paper, three models for different cases, i.e., a basic model, a secure model, a secure and robust model, have been proposed for different cases. In the basic model, the functions of high-capacity secret information hiding and extraction have been realized through an encoding network and a decoding network respectively. The high-capacity steganography is implemented by hiding a secret image into a carrier image having the same resolution with the help of concat operations, InceptionBlock and convolutional layers. Moreover, the secret image is hidden into the channel B of carrier image only to resolve the problem of color distortion. In the secure model, to enhance the security of the basic model, a steganalysis network has been added into the basic model to form an adversarial network. In the secure and robust model, an attack network has been inserted into the secure model to improve its robustness further. The experimental results have demonstrated that the proposed secure model and the secure and robust model have an overall better performance than some existing high-capacity deep learning-based steganography models. The secure model performs best in invisibility and security. The secure and robust model is the most robust against some attacks.

• 전자공학회논문지B
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• 제31B권8호
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• pp.211-218
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• 1994

In this paper we develope a hybrid learning rule to improve the robustness of multi-layer Perceptions. In most neural networks the activation of a neuron is deternined by a nonlinear transformation of the weighted sum of inputs to the neurons. Investigating the behaviour of activations of hidden layer neurons a new learning algorithm is developed for improved robustness for multi-layer Perceptrons. Unlike other methods which reduce the network complexity by putting restrictions on synaptic weights our method based on error-backpropagation increases the complexity of the underlying proplem by imposing it saturation requirement on hidden layer neurons. We also found that the additional gradient-descent term for the requirement corresponds to the Hebbian rule and our algorithm incorporates the Hebbian learning rule into the error back-propagation rule. Computer simulation demonstrates fast learning convergence as well as improved robustness for classification and hetero-association of patterns.

• 한국건설관리학회논문집
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• 제19권3호
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• pp.33-42
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• 2018

손상된 라이프라인 시스템의 공공서비스 제공 지연 예측은 지진 대응 체계 마련의 첫 단계이다. 그러나 라이프라인 시스템의 서비스제공가능도는 개별 구조물의 물리적 손상뿐만 아니라 인접한 구조물들로부터의 피해파급에 의해 변동될 수 있다. 이에 본 연구는 라이프라인 시스템의 기능 저하를 유발하는 공통원인피해와 연쇄피해의 발생 확률을 추론하기 위해 베이지안 모형을 작성하고 피해의 인과관계를 고려하여 최종 수요자 중심의 네트워크 강건성을 평가하는 방안을 제시하였다. 또한 완화대책에 따른 네트워크 강건성을 분석하기 위해 국내 대구경북지역의 전력 및 상수도 시스템을 대상으로 지진 규모에 따른 공공서비스의 공급 지연 확률을 예측하였다. 그 결과 사례 지역의 경우 안정적인 전력과 상수 수급을 위해 라이프라인 네트워크를 구성하는 노드들 간 피해파급을 저감하는 것이 효과적임을 확인하였다. 본 연구는 지진 피해 진단의 다양한 불확실성 간 인과관계를 도식화하였다는 데에 의의가 있으며, 지속 가능한 공공서비스 확보를 위한 지역단위 대책 수립을 지원할 수 있을 것으로 기대된다.