• Geophysics and Geophysical Exploration
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• v.5 no.4
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• pp.280-290
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• 2002

Three-dimensional (3-D) electromagnetic (EM) modeling algorithm has been developed using finite element method (FEM) to acquire more efficient interpretation techniques of EM data. When FEM based on nodal elements is applied to EM problem, spurious solutions, so called 'vector parasite', are occurred due to the discontinuity of normal electric fields and may lead the completely erroneous results. Among the methods curing the spurious problem, this study adopts vector element of which basis function has the amplitude and direction. To reduce computational cost and required core memory, complex bi-conjugate gradient (CBCG) method is applied to solving complex symmetric matrix of FEM and point Jacobi method is used to accelerate convergence rate. To verify the developed 3-D EM modeling algorithm, its electric and magnetic field for a layered-earth model are compared with those of layered-earth solution. As we expected, the vector based FEM developed in this study does not cause ny vector parasite problem, while conventional nodal based FEM causes lots of errors due to the discontinuity of field variables. For testing the applicability to high frequencies 100 MHz is used as an operating frequency for the layer structure. Modeled fields calculated from developed code are also well matched with the layered-earth ones for a model with dielectric anomaly as well as conductive anomaly. In a vertical electric dipole source case, however, the discontinuity of field variables causes the conventional nodal based FEM to include a lot of errors due to the vector parasite. Even for the case, the vector based FEM gave almost the same results as the layered-earth solution. The magnetic fields induced by a dielectric anomaly at high frequencies show unique behaviors different from those by a conductive anomaly. Since our 3-D EM modeling code can reflect the effect from a dielectric anomaly as well as a conductive anomaly, it may be a groundwork not only to apply high frequency EM method to the field survey but also to analyze the fold data obtained by high frequency EM method.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.105-122
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• 2019

Dimensionality reduction is one of the methods to handle big data in text mining. For dimensionality reduction, we should consider the density of data, which has a significant influence on the performance of sentence classification. It requires lots of computations for data of higher dimensions. Eventually, it can cause lots of computational cost and overfitting in the model. Thus, the dimension reduction process is necessary to improve the performance of the model. Diverse methods have been proposed from only lessening the noise of data like misspelling or informal text to including semantic and syntactic information. On top of it, the expression and selection of the text features have impacts on the performance of the classifier for sentence classification, which is one of the fields of Natural Language Processing. The common goal of dimension reduction is to find latent space that is representative of raw data from observation space. Existing methods utilize various algorithms for dimensionality reduction, such as feature extraction and feature selection. In addition to these algorithms, word embeddings, learning low-dimensional vector space representations of words, that can capture semantic and syntactic information from data are also utilized. For improving performance, recent studies have suggested methods that the word dictionary is modified according to the positive and negative score of pre-defined words. The basic idea of this study is that similar words have similar vector representations. Once the feature selection algorithm selects the words that are not important, we thought the words that are similar to the selected words also have no impacts on sentence classification. This study proposes two ways to achieve more accurate classification that conduct selective word elimination under specific regulations and construct word embedding based on Word2Vec embedding. To select words having low importance from the text, we use information gain algorithm to measure the importance and cosine similarity to search for similar words. First, we eliminate words that have comparatively low information gain values from the raw text and form word embedding. Second, we select words additionally that are similar to the words that have a low level of information gain values and make word embedding. In the end, these filtered text and word embedding apply to the deep learning models; Convolutional Neural Network and Attention-Based Bidirectional LSTM. This study uses customer reviews on Kindle in Amazon.com, IMDB, and Yelp as datasets, and classify each data using the deep learning models. The reviews got more than five helpful votes, and the ratio of helpful votes was over 70% classified as helpful reviews. Also, Yelp only shows the number of helpful votes. We extracted 100,000 reviews which got more than five helpful votes using a random sampling method among 750,000 reviews. The minimal preprocessing was executed to each dataset, such as removing numbers and special characters from text data. To evaluate the proposed methods, we compared the performances of Word2Vec and GloVe word embeddings, which used all the words. We showed that one of the proposed methods is better than the embeddings with all the words. By removing unimportant words, we can get better performance. However, if we removed too many words, it showed that the performance was lowered. For future research, it is required to consider diverse ways of preprocessing and the in-depth analysis for the co-occurrence of words to measure similarity values among words. Also, we only applied the proposed method with Word2Vec. Other embedding methods such as GloVe, fastText, ELMo can be applied with the proposed methods, and it is possible to identify the possible combinations between word embedding methods and elimination methods.

• Journal of Environmental Impact Assessment
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• v.30 no.3
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• pp.141-154
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• 2021

In this study, the effect of the restoration of Yaksa stream and the construction of an apartment complex by the urban renewal project in the Yaksa district of Chuncheon on air quality in the surrounding area was evaluated using computational fluid dynamics (CFD) model simulations. In orderto compare the impact of the project, wind and pollutant concentration fields were simulated using topographic data in 2011 and 2017, which stand for the periods before and after the urban renewal project, respectively. In the numerical experiments, the scenarios were set to analyze the effect of the construction of the apartment complex and the effect of stream restoration. Wind direction and wind speed data obtained from the Chuncheon Automated Synoptic Observing System (ASOS) were used as the inflow boundary conditions, and the simulation results were weighted according to the frequencies of the eight-directional inflow wind directions. The changes in wind speed and NO_X concentration distribution according to the changes in building and terrain between scenarios were compared. As a result, the concentration of NO_X emitted from the surrounding roads increased by the construction of the apartment complex, and the magnitude of the increase was reduced as the result of including the effect of stream restoration. The concentration of NO_X decreased around the restored stream, while the concentration increased significantly around the constructed apartment complex. The increase in the concentration of NO_X around the apartment complex was more pronounced in the place located in the rear of the wind direction to the apartment complex, and the effect remains up to the height of the building. In conclusion, it was confirmed that the relative arrangement of apartment complex construction and stream restoration in relation to the main wind direction of the target area was one of the major factors in determining the surrounding air quality.

• Korean Chemical Engineering Research
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• v.60 no.4
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• pp.535-543
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• 2022

A CPFD (Computational particle fluid dynamics) model of solar fluidized bed receiver of silicon carbide (SiC: average d_p=123 ㎛) particles was established, and the model was verified by comparing the simulation and experimental results to analyze the effect of particle behavior on the performance of the receiver. The relationship between the heat-absorbing performance and the particles behavior in the receiver was analyzed by simulating their behavior near bed surface, which is difficult to access experimentally. The CPFD simulation results showed good agreement with the experimental values on the solids holdup and its standard deviation under experimental condition in bed and freeboard regions. The local solid holdups near the bed surface, where particles primarily absorb solar heat energy and transfer it to the inside of the bed, showed a non-uniform distribution with a relatively low value at the center related with the bubble behavior in the bed. The local solid holdup increased the axial and radial non-uniformity in the freeboard region with the gas velocity, which explains well that the increase in the RSD (Relative standard deviation) of pressure drop across the freeboard region is responsible for the loss of solar energy reflected by the entrained particles in the particle receiver. The simulation results of local gas and particle velocities with gas velocity confirmed that the local particle behavior in the fluidized bed are closely related to the bubble behavior characterized by the properties of the Geldart B particles. The temperature difference of the fluidizing gas passing through the receiver per irradiance (∆T/I_DNI) was highly correlated with the RSD of the pressure drop across the bed surface and the freeboard regions. The CPFD simulation results can be used to improve the performance of the particle receiver through local particle behavior analysis.

• Archives of design research
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• v.13 no.1
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• pp.209-218
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• 2000

The purpose of this study was to improve the curriculum for industrial design department in the junior colleges. In order to achieve the purpose, two methodologies were carried out. First is job analysis of the industrial designers who have worked in the small & medium manufacturing companies, second is survey for the opinions of professors in the junior colleges. Some results were as follows: 1. The period of junior college for industrial designers is 2 years according to present. But selectively 1 year of advanced course can be established. 2. The practice subjects same as computational formative techniques needed to product development have to be increased. In addition kinds of selection subjects same as foreign language, manufacturing process, new product information and consumer behavior investigation have to be extended. 3. The next subjects need to adjust the title, contents and hours. (1) The need of 3.D related subjects same as computer modeling, computer rendering, 3.D modeling was high. The use of computer is required to design presentation subjects. (2)The need of advertising and sale related subjects same as printing, merchandise, package, typography, photography was low, the need of presentation techniques of new product development was high. (3) The need of field practice, special lecture on practice and reading original texts related subjects was same as at present, but these are not attached importance to form. As the designers feel keenly the necessity of using foreign language, the need of language subject was high.

• Nuclear Medicine and Molecular Imaging
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• v.43 no.5
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• pp.459-467
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• 2009

Purpose: The maximum likelihood-expectation maximization (ML-EM) is the statistical reconstruction algorithm derived from probabilistic model of the emission and detection processes. Although the ML-EM has many advantages in accuracy and utility, the use of the ML-EM is limited due to the computational burden of iterating processing on a CPU (central processing unit). In this study, we developed a parallel computing technique on GPU (graphic processing unit) for ML-EM algorithm. Materials and Methods: Using Geforce 9800 GTX+ graphic card and CUDA (compute unified device architecture) the projection and backprojection in ML-EM algorithm were parallelized by NVIDIA's technology. The time delay on computations for projection, errors between measured and estimated data and backprojection in an iteration were measured. Total time included the latency in data transmission between RAM and GPU memory. Results: The total computation time of the CPU- and GPU-based ML-EM with 32 iterations were 3.83 and 0.26 see, respectively. In this case, the computing speed was improved about 15 times on GPU. When the number of iterations increased into 1024, the CPU- and GPU-based computing took totally 18 min and 8 see, respectively. The improvement was about 135 times and was caused by delay on CPU-based computing after certain iterations. On the other hand, the GPU-based computation provided very small variation on time delay per iteration due to use of shared memory. Conclusion: The GPU-based parallel computation for ML-EM improved significantly the computing speed and stability. The developed GPU-based ML-EM algorithm could be easily modified for some other imaging geometries.

• Journal of Korean Society of Environmental Engineers
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• v.32 no.2
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• pp.165-174
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• 2010

The numerical modeling of a coal gasification reaction occurring in an entrained flow coal gasifier is presented in this study. The purposes of this study are to develop a reliable evaluation method of coal gasifier not only for the basic design but also further system operation optimization using a CFD(Computational Fluid Dynamics) method. The coal gasification reaction consists of a series of reaction processes such as water evaporation, coal devolatilization, heterogeneous char reactions, and coal-off gaseous reaction in two-phase, turbulent and radiation participating media. Both numerical and experimental studies are made for the 1.0 ton/day entrained flow coal gasifier installed in the Korea Institute of Energy Research (KIER). The comprehensive computer program in this study is made basically using commercial CFD program by implementing several subroutines necessary for gasification process, which include Eddy-Breakup model together with the harmonic mean approach for turbulent reaction. Further Lagrangian approach in particle trajectory is adopted with the consideration of turbulent effect caused by the non-linearity of drag force, etc. The program developed is successfully evaluated against experimental data such as profiles of temperature and gaseous species concentration together with the cold gas efficiency. Further intensive investigation has been made in terms of the size distribution of pulverized coal particle, the slurry concentration, and the design parameters of gasifier. These parameters considered in this study are compared and evaluated each other through the calculated syngas production rate and cold gas efficiency, appearing to directly affect gasification performance. Considering the complexity of entrained coal gasification, even if the results of this study looks physically reasonable and consistent in parametric study, more efforts of elaborating modeling together with the systematic evaluation against experimental data are necessary for the development of an reliable design tool using CFD method.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.37-50
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• 2020

Information security has become an important issue in the world. Various information and communication technologies, such as the Internet of Things, big data, cloud, and artificial intelligence, are developing, and the need for information security is increasing. Although the necessity of information security is expanding according to the development of information and communication technology, interest in information security investment is insufficient. In general, measuring the effect of information security investment is difficult, so appropriate investment is not being practice, and organizations are decreasing their information security investment. In addition, since the types and specification of information security measures are diverse, it is difficult to compare and evaluate the information security countermeasures objectively, and there is a lack of decision-making methods about information security investment. To develop the organization, policies and decisions related to information security are essential, and measuring the effect of information security investment is necessary. Therefore, this study proposes a method of constructing an investment portfolio for information security measures using game theory and derives an optimal defence probability. Using the two-person game model, the information security manager and the attacker are assumed to be the game players, and the information security countermeasures and information security threats are assumed as the strategy of the players, respectively. A zero-sum game that the sum of the players' payoffs is zero is assumed, and we derive a solution of a mixed strategy game in which a strategy is selected according to probability distribution among strategies. In the real world, there are various types of information security threats exist, so multiple information security measures should be considered to maintain the appropriate information security level of information systems. We assume that the defence ratio of the information security countermeasures is known, and we derive the optimal solution of the mixed strategy game using linear programming. The contributions of this study are as follows. First, we conduct analysis using real performance data of information security measures. Information security managers of organizations can use the methodology suggested in this study to make practical decisions when establishing investment portfolio for information security countermeasures. Second, the investment weight of information security countermeasures is derived. Since we derive the weight of each information security measure, not just whether or not information security measures have been invested, it is easy to construct an information security investment portfolio in a situation where investment decisions need to be made in consideration of a number of information security countermeasures. Finally, it is possible to find the optimal defence probability after constructing an investment portfolio of information security countermeasures. The information security managers of organizations can measure the specific investment effect by drawing out information security countermeasures that fit the organization's information security investment budget. Also, numerical examples are presented and computational results are analyzed. Based on the performance of various information security countermeasures: Firewall, IPS, and Antivirus, data related to information security measures are collected to construct a portfolio of information security countermeasures. The defence ratio of the information security countermeasures is created using a uniform distribution, and a coverage of performance is derived based on the report of each information security countermeasure. According to numerical examples that considered Firewall, IPS, and Antivirus as information security countermeasures, the investment weights of Firewall, IPS, and Antivirus are optimized to 60.74%, 39.26%, and 0%, respectively. The result shows that the defence probability of the organization is maximized to 83.87%. When the methodology and examples of this study are used in practice, information security managers can consider various types of information security measures, and the appropriate investment level of each measure can be reflected in the organization's budget.