• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2010년도 정기 학술대회
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• pp.268-271
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• 2010

In this paper, forward and reverse analysis is introduced in order to estimate the elastic-plastic properties from a power-law hardening bulk specimen materials with one simple spherical indentation impression test. In order to verify the reliability of the reverse analysis, we have simulated about a large range of materials that essentially cover all engineering materials, using ABAQUS(6.91) program. Then, we could obtained the fitting functions and plastic parameters from the numerical analysis results. Next, through the procedure of reverse analysis we can obtain the yield stress and power-law exponent. Finally, obtain good agreement between the result from reverse analysis and initial input data from experiment.

• 반도체디스플레이기술학회지
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• 제8권1호
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• pp.19-25
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• 2009

Abstract JPEG2000 achieve quality scalability through the rate control method used in the encoding process, which embeds quality layers to the code-stream. This architecture might raise two drawbacks. First, when the coding process finishes, the number and bit-rates of quality layers are fixed, causing a lack of quality scalability to code-stream encoded with a single or few quality layers. Second, in Post compression rate distortion (PCRD) the bit streams after the truncation points discarded. Therefore, computational power for the discarded bit streams is wasted. For solving of problem, through bit rate control, there are many researches. Each proposed algorithms have specially target feature that is improved performance like reducing computational power. Research results have strength and weakness. For the mean time, research contents are reviewed and compared, so we proposed research direction in the future.

• Journal of Information Processing Systems
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• 제8권3호
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• pp.483-494
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• 2012

Many recent events, such as terrorist attacks, exposed defects in most sophisticated security systems. Therefore, it is necessary to improve security data systems based on the body or behavioral characteristics, often called biometrics. Together with the growing interest in the development of human and computer interface and biometric identification, human face recognition has become an active research area. Face recognition appears to offer several advantages over other biometric methods. Nowadays, Principal Component Analysis (PCA) has been widely adopted for the face recognition algorithm. Yet still, PCA has limitations such as poor discriminatory power and large computational load. This paper proposes a novel algorithm for face recognition using a mid band frequency component of partial information which is used for PCA representation. Because the human face has even symmetry, half of a face is sufficient for face recognition. This partial information saves storage and computation time. In comparison with the traditional use of PCA, the proposed method gives better recognition accuracy and discriminatory power. Furthermore, the proposed method reduces the computational load and storage significantly.

• Journal of Power Electronics
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• 제18권1호
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• pp.129-136
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• 2018

An accurate state-of-charge (SOC) estimation ensures the reliable and efficient operation of a lithium-ion battery management system. On the basis of a combined electrochemical model, this study adopts the forgetting factor least squares algorithm to identify battery parameters and eliminate the influence of test conditions. Then, it implements online SOC estimation with high accuracy and low run time by utilizing the low computational complexity of the unscented Kalman filter (UKF) and the rapid convergence of a particle filter (PF). The PF algorithm is adopted to decrease convergence time when the initial error is large; otherwise, the UKF algorithm is used to approximate the actual SOC with low computational complexity. The effect of the number of sampling particles in the PF is also evaluated. Finally, experimental results are used to verify the superiority of the combined method over other individual algorithms.

• 동력기계공학회지
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• 제2권1호
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• pp.39-44
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• 1998

Computational grids used in the numerical simulation of multi staged turbomachinery flow fields are generated. A multiblock structure simplifies the creation of structured H-grids about complex turbomachinery geometries and facilitate the creation of a grid for multi-row topologies. The numerical algorithm adopts the combination of the algebraic and elliptic method to create the internal grids efficiently and quickly. The input module is made of the results of the preliminary design, i.e., flow-path, aerodynamic conditions along the spanwise direction, and the blade profile data. The final grids generated from each module of the system are used as the preprocessor for the performance prediction of the single row cascades and the flow simulation inside the multi staegd blade passage. Application to low pressure compressor of industrial gas turbine engines was demonstrated to be very reliable and practical in support of design activities.

• Structural Engineering and Mechanics
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• 제73권4호
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• pp.463-479
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• 2020

This paper is aimed to address a simultaneous optimization of the size, shape, and topology of steel lattice towers through a combination of the radial basis function (RBF) neural networks and the artificial bee colony (ABC) metaheuristic algorithm to reduce the computational time because mere metaheuristic optimization algorithms require much time for calculations. To verify the results, use has been made of the CIGRE Tower and a 132 kV transmission towers as numerical examples both based on the design requirements of the ASCE10-97, and the size, shape, and topology have been optimized (in both cases) once by the RBF neural network and once by the MSTOWER analyzer. A comparison of the results shows that the neural network-based method has been able to yield acceptable results through much less computational time.

• Nuclear Engineering and Technology
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• 제50권8호
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• pp.1402-1411
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• 2018

Nuclear-related facilities can be detrimentally affected by ground vibrations due to the collapse of adjacent cooling towers in nuclear power plants. To reduce this hazard risk, a design-oriented acceleration response spectrum (ARS) was proposed to predict the dynamic responses of nuclear-related facilities subjected to ground vibrations. For this purpose, 20 computational cases were performed based on cooling tower-soil numerical models developed in previous studies. This resulted in about 2664 ground vibration records to build a basic database and five complementary databases with consideration of primary factors that influence ground vibrations. Afterwards, these databases were applied to generate the design-oriented ARS using a response spectrum analysis approach. The proposed design-oriented ARS covers a wide range of natural periods up to 6 s and consists of an ascending portion, a plateau, and two connected descending portions. Spectral parameters were formulated based on statistical analysis. The spectrum was verified by comparing the representative acceleration magnitudes obtained from the design-oriented ARS with those from computational cases using cooling tower-soil numerical models with reasonable consistency.

• ETRI Journal
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• 제42권6호
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• pp.846-858
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• 2020

The non-orthogonal multiple access (NOMA) technique offers throughput improvement to meet the demands of the future generation of wireless communication networks. The objective of this work is to further improve the throughput by including an underlay cognitive radio network with an existing multi-carrier NOMA network, using cooperative communication. The throughput is maximized by optimal resource allocation, namely, power allocation, subcarrier assignment, relay selection, user pairing, and subcarrier pairing. Optimal power allocation to the primary and secondary users is accomplished in a way that target rate constraints of the primary users are not affected. The throughput maximization is a combinatorial optimization problem, and the computational complexity increases as the number of users and/or subcarriers in the network increases. To this end, to reduce the computational complexity, a dynamic network resource allocation algorithm is proposed for combinatorial optimization. The simulation results show that the proposed network improves the throughput.

• Journal of information and communication convergence engineering
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• 제21권1호
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• pp.82-89
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• 2023

Various machine-learning models may yield high predictive power for massive time series for time series prediction. However, these models are prone to instability in terms of computational cost because of the high dimensionality of the feature space and nonoptimized hyperparameter settings. Considering the potential risk that model training with a high-dimensional feature set can be time-consuming, we evaluate a feature-importance-based feature selection method to derive a tradeoff between predictive power and computational cost for time series prediction. We used two machine learning techniques for performance evaluation to generate prediction models from a retail sales dataset. First, we ranked the features using impurity- and Local Interpretable Model-agnostic Explanations (LIME) -based feature importance measures in the prediction models. Then, the recursive feature elimination method was applied to eliminate unimportant features sequentially. Consequently, we obtained a subset of features that could lead to reduced model training time while preserving acceptable model performance.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 A
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• pp.46-48
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• 2000

Continuation power flow has been developed to remove the ill-condition problem caused by singularity of power flow Jacobian at and near steady-state voltage instability point in conventional power flow. When solving large-scale power transmission systems, an alternative strategy for improving computational efficiency and reducing computer storage requirements is the decoupled power flow method, which makes use of an approximate version of the Newton-Raphson procedure. This paper presents a technique to improve the speed of continuation power flow system using decoupled power flow method.