• Journal of Korea Water Resources Association
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• v.30 no.1
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• pp.63-73
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• 1997

Finite element model is established for the simulation of groundwater flow due to water curtain around underground oil storage Choleski decomposition method. The symmetric global conductance matrix is solved by vector storage Choleski decomposition method. The model is verified through comparison with the results of electric analogy. For the application of this model to real site, the finite element meshes are constructed according to representative vertical cross and longitudinal sections. In cross-sectional analysis, potential and flow distributions are compared based on the cavern pressure and horizontal water curtain. For longitudinal section, effects between nearly located caverns with or without vertical water curtain are analyzed. These results prove that the established model can be used as a tool for flow analysis around underground caverns.

• Journal of Information Technology Applications and Management
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• v.29 no.3
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• pp.43-55
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• 2022

In this study, with the goal of developing a deep learning-based product recommendation model for effective matching of influencers and products, a deep learning model with a collaborative filtering model combined with generalized matrix decomposition(GMF), a collaborative filtering model based on multi-layer perceptron (MLP), and neural collaborative filtering and generalized matrix Factorization (NeuMF), a hybrid model combining GMP and MLP was developed and tested. In particular, we utilize one-class problem free boosting (OCF-B) method to solve the one-class problem that occurs when training is performed only on positive cases using implicit feedback in the deep learning-based collaborative filtering recommendation model. In relation to model selection based on overall experimental results, the MLP model showed highest performance with weighted average precision, weighted average recall, and f1 score were 0.85 in the model (n=3,000, term=15). This study is meaningful in practice as it attempted to commercialize a deep learning-based recommendation system where influencer's promotion data is being accumulated, pactical personalized recommendation service is not yet commercially applied yet.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.6043-6062
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• 2019

Low-rank matrix decomposition has shown its capability in many applications such as image in-painting, de-noising, background reconstruction and defect detection etc. In this paper, we consider the texture background of rail track images and the sparse foreground of the defects to construct a low-rank matrix decomposition model with block sparsity for defect inspection of rail tracks, which jointly minimizes the nuclear norm and the 2-1 norm. Similar to ADM, an alternative method is proposed in this study to solve the optimization problem. After image decomposition, the defect areas in the resulting low-rank image will form dark stripes that horizontally cross the entire image, indicating the preciselocations of the defects. Finally, a two-stage defect extraction method is proposed to locate the defect areas. The experimental results of the two datasets show that our algorithm achieved better performance compared with other methods.

• The Journal of Natural Sciences
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• v.7
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• pp.27-35
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• 1995

This paper introduces a VLSI (Very Large Scale Integrated Circuit) implementation of the 2-D Discrete Cosine Transform (DCT) with an application to image and video coding. This implementation, which is based upon a state space model, uses both algorithm and data partitioning to achieve high efficiency. With this implementation, the amount of data transfers between the processing elements (PEs) are reduced and all the data transfers are limitted to be local. This system accepts the input as a progressively scanned data stream which reduces the hardware required for the input data control module. With proper ordering of computations, a matrix transposition between two matrix by matrix multiplications, which is required in many 2-D DCT systems based upon a row-column decomposition, can be also removed. The new implementation scheme makes it feasible to implement a single 2-D DCT VLSI chip which can be easily expanded for a larger 2-D DCT by cascading these chips.

• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.36-48
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• 2022

As a valid numerical method to obtain a high-resolution result of a flow field, computational fluid dynamics (CFD) have been widely used to study coolant flow and heat transfer characteristics in fuel rod bundles. However, the time-consuming, iterative calculation of Navier-Stokes equations makes CFD unsuitable for the scenarios that require efficient simulation such as sensitivity analysis and uncertainty quantification. To solve this problem, a reduced-order model (ROM) based on proper orthogonal decomposition (POD) and machine learning (ML) is proposed to simulate the flow field efficiently. Firstly, a validated CFD model to output the flow field data set of the rod bundle is established. Secondly, based on the POD method, the modes and corresponding coefficients of the flow field were extracted. Then, an deep feed-forward neural network, due to its efficiency in approximating arbitrary functions and its ability to handle high-dimensional and strong nonlinear problems, is selected to build a model that maps the non-linear relationship between the mode coefficients and the boundary conditions. A trained surrogate model for modes coefficients prediction is obtained after a certain number of training iterations. Finally, the flow field is reconstructed by combining the product of the POD basis and coefficients. Based on the test dataset, an evaluation of the ROM is carried out. The evaluation results show that the proposed POD-ROM accurately describe the flow status of the fluid field in rod bundles with high resolution in only a few milliseconds.

• Journal of Communications and Networks
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• v.16 no.1
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• pp.92-97
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• 2014

A novel method for extracting frequency slippage signal from radar full pulse sequence is presented. For the radar full pulse sequence received by radar interception receiver, radio frequency (RF) and time of arrival (TOA) of all pulses constitute a two-dimensional information sequence. In a complex and intensive electromagnetic environment, the TOA of pulses is distributed unevenly, randomly, and in a nonstationary manner, preventing existing methods from directly analyzing such time series and effectively extracting certain signal features. This work applies Gaussian noise insertion and structure function to the TOA-RF information sequence respectively such that the equalization of time intervals and correlation processing are accomplished. The components with different frequencies in structure function series are separated using empirical mode decomposition. Additionally, a chaos detection model based on the Duffing equation is introduced to determine the useful component and extract the changing features of RF. Experimental results indicate that the proposed methodology can successfully extract the slippage signal effectively in the case that multiple radar pulse sequences overlap.

• Environmental and Resource Economics Review
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• v.12 no.1
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• pp.91-120
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• 2003

This paper is intended to measure sectoral sustainabilities and inter-industry linkage effects of natural capital depletion of the Korean industries, and to analyze sources of their change over time using the familiar input-output model. The sustainability indicator that we are measuring in this paper is based on the so-called genuine saving concept proposed by the World Bank(1997). We accommodated the concept in the extended analytical framework of Proops et al.(1999) to analyze sectoral sustainabilities of the Korean industries. We decomposed sectoral sustainabilities so measured into their composing factors based on the decomposition method devised by Chung & Rhee (2001). According to our analysis, overall sustainability of the Korean industries has been declined since 1995. In heavy and chemical, transportation, and electricity sectors, their sustainabilities has been gotten worse. Among four major factors influencing the sustainability, change in GDP was the most important followed by changes in savings, industrial structures, and demand patterns.

• Journal of Electrical Engineering and Technology
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• v.7 no.3
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• pp.436-442
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• 2012

This paper develops a design methodology of a decentralized fuzzy load-frequency controller for a large-scale nonlinear power system with valve position limits on governors. The concerned system is locally exactly modeled in Takagi-Sugeno's form. Sufficient design condition for uniform ultimate boundedness of the closed-loop system is derived based on the overlapping decomposition. Convergence of all incremental frequency deviations to zero is also investigated. A simulation result is provided to visualize the effectiveness of the proposed technique.

• International journal of advanced smart convergence
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• v.8 no.2
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• pp.39-46
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• 2019

Human mobility estimation plays a key factor in a lot of promising applications including location-based recommendation systems, urban planning, and disease outbreak control. We study the human mobility estimation problem in the case where recent locations of a person-of-interest are unknown. Since matrix decomposition is used to perform latent semantic analysis of multi-dimensional data, we propose a human location estimation algorithm based on matrix factorization to reconstruct the human movement patterns through the use of information of persons with correlated movements. Specifically, the optimization problem which minimizes the difference between the reconstructed and actual movement data is first formulated. Then, the gradient descent algorithm is applied to adjust parameters which contribute to reconstructed mobility data. The experiment results show that the proposed framework can be used for the prediction of human location and achieves higher predictive accuracy than a baseline model.

• Journal of Korea Trade
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• v.24 no.7
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• pp.93-106
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• 2020

Purpose - This paper aims to examine the environmental effects of South Korean foreign trade, and the changing relationship between industrial "three wastes" emissions and foreign trade. Design/methodology - Based on time series data of South Korean foreign trade and industrial "three wastes" from 2009 to 2019, a VAR model was used to analyze the long-term internal links and dynamic changes between foreign trade and environmental pollution. Findings - Variance decomposition analysis shows that for the three types of pollutants, self-impact contributes the most to the variance decomposition. It follows that South Korean foreign trade has a certain negative impact on the environment, and this impact has a certain sustainability. Originality/value - This paper contributes to the study on the relationship between foreign trade and environmental pollution. It theoretically proposes a coordinated development path for foreign trade development and green development based on the environmental impact of foreign trade, to provide a reference for the development of collaborative promotion.