• Nuclear Engineering and Technology
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• v.49 no.6
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• pp.1219-1225
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• 2017

In this work, a scalable algorithm for model calibration in nuclear engineering applications is presented and tested. The algorithm relies on the construction of surrogate models to replace the original model within the region of interest. These surrogate models can be constructed efficiently via reduced order modeling and subspace analysis. Once constructed, these surrogate models can be used to perform computationally expensive mathematical analyses. This work proposes a surrogate based model calibration algorithm. The proposed algorithm is used to calibrate various neutronics and thermal-hydraulics parameters. The virtual environment for reactor applications-core simulator (VERA-CS) is used to simulate a three-dimensional core depletion problem. The proposed algorithm is then used to construct a reduced order model (a surrogate) which is then used in a Bayesian approach to calibrate the neutronics and thermal-hydraulics parameters. The algorithm is tested and the benefits of data assimilation and calibration are highlighted in an uncertainty quantification study and requantification after the calibration process. Results showed that the proposed algorithm could help to reduce the uncertainty in key reactor attributes based on experimental and operational data.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06c
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• pp.390-394
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• 2010

본 논문에서는 영상인식에서 널리 사용되는 지역적 특징인 SIFT와 부분공간분석에 의한 차원축소방법의 결합을 통하여 얼굴을 인식하는 방법을 제안한다. 기존의 SIFT기반 영상인식 방법에서는 추출된 키 포인트 각각에 대하여 계산된 특징기술자들을 개별적으로 비교하여 얻어지는 유사도를 바탕으로 인식을 수행하는데 반해, 본 논문에서 제안하는 접근법은 SIFT의 특징기술자를 명도 값으로 표현된 얼굴 영상을 여려 변형에 강건한 형태로 표현되도록 변환하는 표현방식으로 본다. SIFT기반의 특징기술자에 의해 표현된 얼굴 영상을 부분공간분석법에 의해 저차원의 특징벡터로 다시 표현되고, 이 특징벡터를 이용하여 얼굴인식을 수행한다. 잘 알려진 벤치마크 데이터인 AR 데이터베이스에 대한 실험을 통해 제안한 방법이 조명 변화와 가려짐에 강인한 인식 결과를 보여줄 뿐 아니라, 기존의 SIFT 기반의 얼굴 인식 방법에 비하여 우수한 처리 속도를 보임을 확인하였다.

• Computational Structural Engineering : An International Journal
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• v.1 no.1
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• pp.31-38
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• 2001

In the case of the non-proportionally damped system such as the soil-structure interaction system, the structural control system and composite structures, the eigenproblem with the damping matrix should be necessarily performed to obtain the exact dynamic response. However, most of the eigenvalue analysis methods such as the subspace iteration method and the Lanczos method may miss some eigenvalues in the required ones. Therefore, the eigenvalue analysis method must include a technique to check the missed eigenvalues to become the practical tools. In the case of the undamped or proportionally damped system the missed eigenvalues can easily be checked by using the well-known Sturm sequence property, while in the case of the non-proportionally damped system a checking technique has not been developed yet. In this paper, a technique of checking the missed eigenvalues for the eigenproblem with the damping matrix is proposed by applying the argument principle. To verify the effectiveness of the proposed method, two numerical examples are considered.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3946-3965
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• 2018

Network anomaly detection in Software Defined Networking, especially the detection of DDoS attack, has been given great attention in recent years. It is convenient to build the Traffic Matrix from a global view in SDN. However, the monitoring and management of high-volume feature-rich traffic in large networks brings significant challenges. In this paper, we propose a moving window Principal Components Analysis based anomaly detection and mitigation approach to map data onto a low-dimensional subspace and keep monitoring the network state in real-time. Once the anomaly is detected, the controller will install the defense flow table rules onto the corresponding data plane switches to mitigate the attack. Furthermore, we evaluate our approach with experiments. The Receiver Operating Characteristic curves show that our approach performs well in both detection probability and false alarm probability compared with the entropy-based approach. In addition, the mitigation effect is impressive that our approach can prevent most of the attacking traffic. At last, we evaluate the overhead of the system, including the detection delay and utilization of CPU, which is not excessive. Our anomaly detection approach is lightweight and effective.

• ETRI Journal
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• v.35 no.2
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• pp.193-199
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• 2013

This paper addresses nonnegative independent component analysis (NICA), with the aim to realize the blind separation of nonnegative well-grounded independent source signals, which arises in many practical applications but is hardly ever explored. Recently, Bertrand and Moonen presented a multiplicative NICA (M-NICA) algorithm using multiplicative update and subspace projection. Based on the principle of the mutual correlation minimization, we propose another novel cost function to evaluate the diagonalization level of the correlation matrix, and apply the multiplicative exponentiated gradient (EG) descent update to it to maintain nonnegativity. An efficient approach referred to as the EG-NICA algorithm is derived and its validity is confirmed by numerous simulations conducted on different types of source signals. Results show that the separation performance of the proposed EG-NICA algorithm is superior to that of the previous M-NICA algorithm, with a better unmixing accuracy. In addition, its convergence speed is adjustable by an appropriate user-defined learning rate.

• Structural Engineering and Mechanics
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• v.57 no.5
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• pp.921-936
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• 2016

In this study, a model order reduction technique is applied to solve the transient responses of submerged floating tunnel (SFT) from Mokpo to Jeju under seismic excitations. Because the SFT is a very long structure as well as a transient response analysis requires large amount of computational resources, the model order reduction is mandatory in the design stage of the SFT. Thus, we apply a model order reduction based on Krylov subspace to the simplified finite element model of the SFT. The responses of the reduced order model are compared with those of the full order model and also are verified by referring a previous work. In conclusion, the computational resources are dramatically reduced with an acceptable accuracy by using the model order reduction, which eventually is useful for designing the full-scale model of SFTs.

• Smart Structures and Systems
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• v.23 no.5
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• pp.405-420
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• 2019

In this study, vibration characteristics of offshore wind turbine tower (WTT) with gravity-based foundation (GBF) are identified from dynamic responses under wave-induced excitations. The following approaches are implemented to achieve the objective. Firstly, the operational modal analysis methods such as frequency domain decomposition (FDD) and stochastic subspace identification (SSI) are selected to estimate modal parameters from output-only dynamic responses. Secondly, a GBF WTT model composed of superstructure, substructure and foundation is simulated as a case study by using a structural analysis program, MIDAS FEA. Thirdly, wave pressures acting on the WTT structure are established by nonlinear regular waves which are simulated from a computational fluid software, Flow 3D. Wave-induced acceleration responses of the target structure are analyzed by applying the simulated wave pressures to the GBF WTT model. Finally, modal parameters such as natural frequencies and mode shapes are estimated from the output-only acceleration responses and compared with the results from free vibration analysis. The effect of wave height and period on modal parameter extraction is also investigated for the mode identification of the GBF WTT.

• Structural Engineering and Mechanics
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• v.16 no.3
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• pp.259-274
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• 2003

In this paper, a novel numerical solution technique, the differential cubature method is employed to study the buckling problems of thick plates with arbitrary quadrilateral planforms and non-uniform boundary constraints based on the first order shear deformation theory. By using this method, the governing differential equations at each discrete point are transformed into sets of linear homogeneous algebraic equations. Boundary conditions are implemented through discrete grid points by constraining displacements, bending moments and rotations of the plate. Detailed formulation and implementation of this method are presented. The buckling parameters are calculated through solving a standard eigenvalue problem by subspace iterative method. Convergence and comparison studies are carried out to verify the reliability and accuracy of the numerical solutions. The applicability, efficiency, and simplicity of the present method are demonstrated through solving several sample plate buckling problems with various mixed boundary constraints. It is shown that the differential cubature method yields comparable numerical solutions with 2.77-times less degrees of freedom than the differential quadrature element method and 2-times less degrees of freedom than the energy method. Due to the lack of published solutions for buckling of thick rectangular plates with mixed edge conditions, the present solutions may serve as benchmark values for further studies in the future.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2511-2520
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• 2018

In this study, Multi-Radial Basis Function Support Vector Machine (Multi-RBF SVM) classifier is introduced based on a composite kernel function. In the proposed multi-RBF support vector machine classifier, the input space is divided into several local subsets considered for extremely nonlinear classification tasks. Each local subset is expressed as nonlinear classification subspace and mapped into feature space by using kernel function. The composite kernel function employs the dual RBF structure. By capturing the nonlinear distribution knowledge of local subsets, the training data is mapped into higher feature space, then Multi-SVM classifier is realized by using the composite kernel function through optimization procedure similar to conventional SVM classifier. The original training data set is partitioned by using some unsupervised learning methods such as clustering methods. In this study, three types of clustering method are considered such as Affinity propagation (AP), Hard C-Mean (HCM) and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA). Experimental results on benchmark machine learning datasets show that the proposed method improves the classification performance efficiently.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.6
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• pp.47-53
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• 2004

This paper proposes an algerian for determining robot location using appearance-based paradigm. First, this algorithm compresses the image set using Principal Component Analysis(PCA) to obtain a low-dimensional subspace, called the eigenspace, and it makes a manifold that represent a continuous-appearance function. Neural network is employed to estimate the location of the mobile robot from the coefficients of the eigenspace. Then, Kalman filtering scheme is used for the fine estimation of the robot location. The algorithm has been implemented and tested on a mobile robot system. It is shown that the robot location is estimated accurately in several trials.