• Journal of the Korean Society of Radiology
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• v.17 no.5
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• pp.633-640
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• 2023

With the recent development of static and dynamic modulated brachytherapy methods in brachytherapy, which use radiation shielding to modulate the dose distribution to deliver the dose, the amount of parameters and data required for dose calculation in inverse treatment planning and treatment plan optimization algorithms suitable for new directional beam intensity modulated brachytherapy is increasing. Although intensity-modulated brachytherapy enables accurate dose delivery of radiation, the increased amount of parameters and data increases the elapsed time required for dose calculation. In this study, a GPU-based CUDA-accelerated dose calculation algorithm was constructed to reduce the increase in dose calculation elapsed time. The acceleration of the calculation process was achieved by parallelizing the calculation of the system matrix of the volume of interest and the dose calculation. The developed algorithms were all performed in the same computing environment with an Intel (3.7 GHz, 6-core) CPU and a single NVIDIA GTX 1080ti graphics card, and the dose calculation time was evaluated by measuring only the dose calculation time, excluding the additional time required for loading data from disk and preprocessing operations. The results showed that the accelerated algorithm reduced the dose calculation time by about 30 times compared to the CPU-only calculation. The accelerated dose calculation algorithm can be expected to speed up treatment planning when new treatment plans need to be created to account for daily variations in applicator movement, such as in adaptive radiotherapy, or when dose calculation needs to account for changing parameters, such as in dynamically modulated brachytherapy.

• Geomechanics and Engineering
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• v.35 no.2
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• pp.121-133
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• 2023

The demand for cement and limestone crushed materials has increased many folds due to the tremendous increase in construction activities in Pakistan during the past few decades. The number of cement production industries has increased correspondingly, and so the rock-blasting operations at the limestone quarry sites. However, the safety procedures warranted at these sites for the blast-induced ground vibrations (BIGV) have not been adequately developed and/or implemented. Proper prediction and monitoring of BIGV are necessary to ensure the safety of structures in the vicinity of these quarry sites. In this paper, an attempt has been made to predict BIGV using artificial neural network (ANN) at three selected limestone quarries of Pakistan. The ANN has been developed in Python using Keras with sequential model and dense layers. The hyper parameters and neurons in each of the activation layers has been optimized using randomized and grid search method. The input parameters for the model include distance, a maximum charge per delay (MCPD), depth of hole, burden, spacing, and number of blast holes, whereas, peak particle velocity (PPV) is taken as the only output parameter. A total of 110 blast vibrations datasets were recorded from three different limestone quarries. The dataset has been divided into 85% for neural network training, and 15% for testing of the network. A five-layer ANN is trained with Rectified Linear Unit (ReLU) activation function, Adam optimization algorithm with a learning rate of 0.001, and batch size of 32 with the topology of 6-32-32-256-1. The blast datasets were utilized to compare the performance of ANN, multivariate regression analysis (MVRA), and empirical predictors. The performance was evaluated using the coefficient of determination (R²), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and root mean squared error (RMSE)for predicted and measured PPV. To determine the relative influence of each parameter on the PPV, sensitivity analyses were performed for all input parameters. The analyses reveal that ANN performs superior than MVRA and other empirical predictors, andthat83% PPV is affected by distance and MCPD while hole depth, number of blast holes, burden and spacing contribute for the remaining 17%. This research provides valuable insights into improving safety measures and ensuring the structural integrity of buildings near limestone quarry sites.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.33 no.6
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• pp.288-293
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• 2023

To optimize the process parameters of laser powder bed fusion process to fabricate the high density STS316L alloy, the effect of laser power, scanning speed and hatching distance on the relative density was studied. Tensile properties of additively manufactured STS316L alloy using optimized parameters was also evaluated according to the build direction. As a result of additive manufacturing process under the energy density of 55.6 J/mm³, 83.3 J/mm³ and 111.1 J/mm³, high density STS316L specimens was suitably fabricated when the energy density, power and scan speed were 83.3 J/mm³, 225 W and 1000 mm/s, respectively. The yield strength, ultimate tensile strength, and elongation of STS316L specimens in direction perpendicular to the build direction, show the most competitive values. Anisotropic shape of the pores and the lack of fusion defects probably caused strain localization which result in deterioration of tensile properties.

• Progress in Medical Physics
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• v.24 no.2
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• pp.108-118
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• 2013

Currently, an arterial spin labeling (ASL) magnetic resonance imaging (MRI) technique does not routinely used in clinical studies to measure perfusion in brain because optimization of imaging protocol is required to obtain optimal perfusion signals. Therefore, the objective of this study was to investigate changes of perfusion-weighed signal intensities with varying several parameters on a pulsed arterial spin labeling MRI technique obtained from a 3T MRI system. We especially evaluated alternations of ASL-MRI signal intensities on special brain areas, including in brain tissues and lobes. The signal targeting with alternating radiofrequency (STAR) pulsed ASL method was scanned on five normal subjects (mean age: 36 years, range: 29~41 years) on a 3T MRI system. Four parameters were evaluated with varying: 1) the labeling gap, 2) the labeling delay time, 3) the labeling thickness, and 4) the slice scan order. Signal intensities were obtained from the perfusion-weighted imaging on the gray and white matters and brain lobes of the frontal, parietal, temporal, and occipital areas. The results of this study were summarized: 1) Perfusion-weighted signal intensities were decreased with increasing the labeling gap in the bilateral gray matter areas and were least affected on the parietal lobe, but most affected on the occipital lobe. 2) Perfusion-weighted signal intensities were decreased with increasing the labeling delay time until 400 ms, but increased up to 1,000 ms in the bilateral gray matter areas. 3) Perfusion-weighted signal intensities were increased with increasing the labeling thickness until 120 mm in both the gray and white matter. 4) Perfusion-weighted signal intensities were higher descending scans than asending scans in both the gray and white matter. We investigated changes of perfusion-weighted signal intensities with varying several parameters in the STAR ASL method. It should require having protocol optimization processing before applying in patients. It has limitations to apply the ASL method in the white matter on a 3T MRI system.

• Journal of Korean Society of Disaster and Security
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• v.15 no.1
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• pp.1-12
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• 2022

An automatic optimization technique is used to estimate the optimal parameters of the hydrologic model, and different hydrologic response results can be provided depending on objective functions. In this study, the parameters of the event-based rainfall-runoff model were estimated using various objective functions, the reproducibility of the hydrograph according to the objective functions was evaluated, and appropriate objective functions were proposed. As the rainfall-runoff model, the storage function model(SFM), which is a lumped hydrologic model used for runoff simulation in the current Korean flood forecasting system, was selected. In order to evaluate the reproducibility of the hydrograph for each objective function, 9 rainfall events were selected for the Cheoncheon basin, which is the upstream basin of Yongdam Dam, and widely-used 7 objective functions were selected for parameter estimation of the SFM for each rainfall event. Then, the reproducibility of the simulated hydrograph using the optimal parameter sets based on the different objective functions was analyzed. As a result, RMSE, NSE, and RSR, which include the error square term in the objective function, showed the highest accuracy for all rainfall events except for Event 7. In addition, in the case of PBIAS and VE, which include an error term compared to the observed flow, it also showed relatively stable reproducibility of the hydrograph. However, in the case of MIA, which adjusts parameters sensitive to high flow and low flow simultaneously, the hydrograph reproducibility performance was found to be very low.

• Information Systems Review
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• v.16 no.3
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• pp.161-177
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• 2014

Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.

• Journal of the Korean Applied Science and Technology
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• v.36 no.4
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• pp.1243-1252
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• 2019

The purpose of this study is to investigate the optimum conditions of biomass pretreatment with potassium hydroxide (KOH) for efficient utilization of cellulose, hemicellulose and lignin from Miscanthus. The optimization of variables was performed by response surface methodology (RSM). The variation ranges of the parameters for the RSM were potassium hydroxide 0.2~0.8 M, reaction temperature 110~190℃ and reaction time 10~90 min. The optimum conditions of alkali pretreatment from Miscanthus were determined as follows: concentration of KOH 0.47 M, reaction temperature 134℃ and reaction time 65 min. At the optimum conditions, the yield of cellulose from the solid fraction after pretreatment was predicted to be 95% by model prediction. Finally, 66.1 ± 1.1% of cellulose were obtained by verification experiment under the optimum conditions. The order contents of solid extraction were hemicellulose 26.4 ± 0.4%, lignin 3.7 ± 0.1% and ash 0.5 ± 0.04%. The yield of ethanol concentration of 96% was obtained using separated saccharification and fermentation.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1150-1158
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• 2010

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problem. First, in preprocessing part, we use a CCD camera to obtain a picture frame in real-time. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. We use an AdaBoost algorithm proposed by Viola and Jones, which is exploited for the detection of facial image area between face and non-facial image area. As the feature extraction algorithm, PCA method is used. In this study, the PCA method, which is a feature extraction algorithm, is used to carry out the dimension reduction of facial image area formed by high-dimensional information. Secondly, we use pRBFNNs to identify the ID by recognizing unique pattern of each person. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as three kinds of polynomials such as constant, linear, and quadratic. Coefficients of connection weight identified with back-propagation using gradient descent method. The output of pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of the Particle Swarm Optimization. The proposed pRBFNNs are applied to real-time face recognition system and then demonstrated from the viewpoint of output performance and recognition rate.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.3 s.357
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• pp.7-17
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• 2007

An analysis and design theory of an aperture-coupled cavity-fed back-to-back microstrip directional coupler is presented for the efficient and optimized design. For this purpose, an equivalent network is developed, and simple but accurate calculations of circuit element values are described. Design equations of the coupler are derived based on the equivalent circuit. In order to determine various structural design parameters, the evolutionary hybrid optimization method based on the genetic algorithm and Nelder-Mead method is invoked. As a validation check of the proposed theory and optimized design method, a 10 dB directional coupler was designed and fabricated. The measured coupling was 10.3 dB at 3 GHz, and the return loss and isolation were 31.8 dB and 30.5 dB, respectively. The directional coupler also showed very good quadrature phase characteristics. Good agreements between the measured and the design specifications fully validate the proposed network analysis and design procedure.

• Journal of the Korean Geotechnical Society
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• v.18 no.2
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• pp.161-170
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• 2002

This paper describes a systematic way of simultaneously identifying the ambient pore pressure and the rigidity index $(=G/s_u)$ of soil by applying an optimization technique to the early part of piezocone dissipation test result. An analytical solution developed by Randolph & Wroth(1979) was implemented in normalized from to express the build-up and dissipation of excess pore pressures around a piezocone as a function of the rigidity index. An ambient pore pressure and optimal rigidity index were determined by minimizing the differences between theoretical and measured excess pore pressure curves using optimization technique. The effectiveness of the proposed back-analysis method was examined against the well-documented performance of piezocone dissipation tests(Tanaka & Sakagami, 1989), from the viewpoints of proper determination of selected target parameters and saving of test duration. It is shown that the proposed back-analysis method can evaluate properly the ambient pore pressure and the rigidity index by using only the early phase of the dissipation test data. Also, it is shown that the proposed back-analysis method permits the horizontal coefficient of consolidation to be identified rationally. Consideration for strain level of back-analyzed rigidity index shows that it corresponds to at least intermediate to large strain level.