• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.9 no.3
• /
• pp.97-106
• /
• 1989

This paper is on the modeling of two-dimensional groundwater flow, which is the first step of the development of Dynamic System Model for groundwater flow and pollutant transport in subsurface porous media. The particular features of the model are its versatility and flexibility to deal with as many real-world problems as possible. Points as well as distributed sources/sinks are included to represent recharges/pumping and rainfall infiltrations. All sources/sinks can be transient or steady state. Prescribed hydraulic head on the Dirichlet boundaries and fluxes on Neumann or Cauchy boundaries can be time-dependent or constant. Sources/sinks strength over each element and node, hydraulic head at each Dirichlet boundary node and flux at each boundary segment can vary independently of each other. Either completely confined or completely unconfined aquifers, or partially confined and partially unconfined aquifers can be dealt with effectively. Discretization of a compound region with very irregular curved boundaries is made easy by including both quadrilateral and triangular elements in the formulation. Large-field problems can be solved efficiently by including a pointwise iterative solution strategy as an optional alternative to the direct elimination solution methed for the matrix equation approximating the partial differential equation of groundwater flow. The model also includes transient flow through confining leaky aquifers lying above and/or below the aquifer of interest. The model is verified against three simple cases to which analytical solutions are available. The groundwater flow model shall be combined with the model of pollutant transport in subsurface porous media. Then the combined model, with the applications of the Eigenvalue technique and the Dynamic system theory, shall be improved to the Dynamic System Model which can simulate the real groundwater flow and the pollutant transport accurately and effectively for the analyses and predictions.

• Journal of the Korean Vacuum Society
• /
• v.17 no.3
• /
• pp.175-182
• /
• 2008

This study is concerned with the evaluation of the corrosion resistance of coated semiconductor equipment parts with various processes. To select the appropriate basis for evaluation, replacement parts were observed during the semiconductor manufacturing process. This study also ran a dry corrosion test using $Al_2O_3$, which is mostly used as a coating material. This test quantitatively measured the efficiency of coated parts. Surface morphology, leakage current and breakdown voltage were also evaluated. This study showed that a dry corrosion process led to the drop of electrical properties, for example, the leakage current increase and the dielectric strength decrease. The surface morphology test displayed that surface damage is largely dependent on the exposure time to corrosive environments. By using the values that changed during the corrosion process, it may be possible to contrive a method to evaluate the efficiency of coated parts with various processes.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.22 no.4
• /
• pp.220-227
• /
• 2021

PCT (Power Cable Tunnel) and UT (Utility Tunnel), which are non-transport underground infrastructures, are mostly RC (Reinforced Concrete) structures, and their durability decreases due to the deterioration caused by carbonation over time. In particular, since the rate of carbonation varies by use and region, a predictive model based on actual carbonation data is required for individual maintenance. In this study, a carbonation prediction model was developed for non-transport underground infrastructures, such as PCT and UT. A carbonation prediction model was developed using multiple regression analysis and deep neural network techniques based on the actual data obtained from a safety inspection. The structures, region, measurement location, construction method, measurement member, and concrete strength were selected as independent variables to determine the dependent variable carbonation rate coefficient in multiple regression analysis. The adjusted coefficient of determination (Ra2) of the multiple regression model was found to be 0.67. The coefficient of determination (R2) of the model for predicting the carbonation of non-transport underground infrastructures using a deep neural network was 0.82, which was superior to the comparative prediction model. These results are expected to help determine the optimal timing for repair on carbonation and preventive maintenance methodology for PCT and UT.

• Applied Chemistry for Engineering
• /
• v.35 no.5
• /
• pp.423-428
• /
• 2024

Microneedles with micron-sized needle arrays are an emerging technology for the transdermal administration of active pharmaceutical ingredients with minimally invasive pain. Over the past decade, although various additive manufacturing technologies have been employed for precise fabrication of microneedles, these methods are often limited by material compatibility and bioavailability, in addition to being time-consuming and costly. In here, we compare the resolution of Polyjet and DLP-SLA 3D printing methods for the precise fabrication of biodegradable PCLDA/PEGDA microneedles. To enhance the structural accuracy of the microneedles from both printing methods, we evaluate the 3D printing conditions, including 3D printing angle and needle height and diameter. Molds for microneedles are fabricated using optimized 3D printing methods, and subsequent replica molding processes are employed to fabricate the polymeric microneedles with sharp need tips. Finally, we use photocurable PCLDA and PEGDA for biodegradable and biocompatible microneedles, and their mechanical properties as PCLDA concentrations are analyzed to assess the strength required for skin insertion. This study has demonstrated the efficient and low-cost fabrication of high-resolution microneedles for transdermal drug delivery.

• Economic and Environmental Geology
• /
• v.45 no.6
• /
• pp.661-672
• /
• 2012

Lab scale experiments to investigate the dissolution reaction among supercritical $CO_2$-sandstone-groundwater by using sandstones from Gyeongsang basin were performed. High pressurized cell system (100 bar and $50^{\circ}C$) was designed to create supercritical $CO_2$ in the cell, simulating the sub-surface $CO_2$ storage site. The first-order dissolution coefficient ($k_d$) of the sandstone was calculated by measuring the change of the weight of thin section or the concentration of ions dissolved in groundwater at the reaction time intervals. For 30 days of the supercritical $CO_2$-sandstone-groundwater reaction, physical properties of sandstone cores in Gyeongsang basin were measured to investigate the effect of supercritical $CO_2$ on the sandstone. The weight change of sandstone cores was also measured to calculate the dissolution coefficient and the dissolution time of 1 g per unit area (1 $cm^2$) of each sandstone was quantitatively predicted. For the experiment using thin sections, mass of $Ca^{2+}$ and $Na^+$ dissolved in groundwater increased, suggesting that plagioclase and calcite of the sandstone would be significantly dissolved when it contacts with supercritical $CO_2$ and groundwater at $CO_2$ sequestration sites. 0.66% of the original thin sec-tion mass for the sandstone were dissolved after 30 days reaction. The average porosity for C sandstones was 8.183% and it increased to 8.789% after 30 days of the reaction. The average dry density, seismic velocity, and 1-D compression strength of sandstones decreased and these results were dependent on the porosity increase by the dissolution during the reaction. By using the first-order dissolution coefficient, the average time to dissolve 1 g of B and C sandstones per unit area (1 $cm^2$) was calculated as 1,532 years and 329 years, respectively. From results, it was investigated that the physical property change of sandstones at Gyeongsang basin would rapidly occur when the supercritical $CO_2$ was injected into $CO_2$ sequestration sites.

• Journal of Intelligence and Information Systems
• /
• v.24 no.1
• /
• pp.167-181
• /
• 2018

Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN(Convolutional Neural Network), which is known as the effective solution for recognizing and classifying images or voices, has been popularly applied to classification and prediction problems. In this study, we investigate the way to apply CNN in business problem solving. Specifically, this study propose to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. As mentioned, CNN has strength in interpreting images. Thus, the model proposed in this study adopts CNN as the binary classifier that predicts stock market direction (upward or downward) by using time series graphs as its inputs. That is, our proposal is to build a machine learning algorithm that mimics an experts called 'technical analysts' who examine the graph of past price movement, and predict future financial price movements. Our proposed model named 'CNN-FG(Convolutional Neural Network using Fluctuation Graph)' consists of five steps. In the first step, it divides the dataset into the intervals of 5 days. And then, it creates time series graphs for the divided dataset in step 2. The size of the image in which the graph is drawn is $40(pixels){\times}40(pixels)$, and the graph of each independent variable was drawn using different colors. In step 3, the model converts the images into the matrices. Each image is converted into the combination of three matrices in order to express the value of the color using R(red), G(green), and B(blue) scale. In the next step, it splits the dataset of the graph images into training and validation datasets. We used 80% of the total dataset as the training dataset, and the remaining 20% as the validation dataset. And then, CNN classifiers are trained using the images of training dataset in the final step. Regarding the parameters of CNN-FG, we adopted two convolution filters ($5{\times}5{\times}6$ and $5{\times}5{\times}9$) in the convolution layer. In the pooling layer, $2{\times}2$ max pooling filter was used. The numbers of the nodes in two hidden layers were set to, respectively, 900 and 32, and the number of the nodes in the output layer was set to 2(one is for the prediction of upward trend, and the other one is for downward trend). Activation functions for the convolution layer and the hidden layer were set to ReLU(Rectified Linear Unit), and one for the output layer set to Softmax function. To validate our model - CNN-FG, we applied it to the prediction of KOSPI200 for 2,026 days in eight years (from 2009 to 2016). To match the proportions of the two groups in the independent variable (i.e. tomorrow's stock market movement), we selected 1,950 samples by applying random sampling. Finally, we built the training dataset using 80% of the total dataset (1,560 samples), and the validation dataset using 20% (390 samples). The dependent variables of the experimental dataset included twelve technical indicators popularly been used in the previous studies. They include Stochastic %K, Stochastic %D, Momentum, ROC(rate of change), LW %R(Larry William's %R), A/D oscillator(accumulation/distribution oscillator), OSCP(price oscillator), CCI(commodity channel index), and so on. To confirm the superiority of CNN-FG, we compared its prediction accuracy with the ones of other classification models. Experimental results showed that CNN-FG outperforms LOGIT(logistic regression), ANN(artificial neural network), and SVM(support vector machine) with the statistical significance. These empirical results imply that converting time series business data into graphs and building CNN-based classification models using these graphs can be effective from the perspective of prediction accuracy. Thus, this paper sheds a light on how to apply deep learning techniques to the domain of business problem solving.

• Asia pacific journal of information systems
• /
• v.20 no.3
• /
• pp.139-166
• /
• 2010

The supply chain management (SCM) systems have emerged as strong managerial tools for manufacturing firms in enhancing competitive strength. Despite of large investments in the SCM systems, many companies are not fully realizing the promised benefits from the systems. A review of literature on adoption, implementation and success factor of IOS (inter-organization systems), EDI (electronic data interchange) systems, shows that this issue has been examined from multiple theoretic perspectives. And many researchers have attempted to identify the factors which influence the success of system implementation. However, the existing studies have two drawbacks in revealing the determinants of systems implementation success. First, previous researches raise questions as to the appropriateness of research subjects selected. Most SCM systems are operating in the form of private industrial networks, where the participants of the systems consist of two distinct groups: focus companies and vendors. The focus companies are the primary actors in developing and operating the systems, while vendors are passive participants which are connected to the system in order to supply raw materials and parts to the focus companies. Under the circumstance, there are three ways in selecting the research subjects; focus companies only, vendors only, or two parties grouped together. It is hard to find researches that use the focus companies exclusively as the subjects probably due to the insufficient sample size for statistic analysis. Most researches have been conducted using the data collected from both groups. We argue that the SCM success factors cannot be correctly indentified in this case. The focus companies and the vendors are in different positions in many areas regarding the system implementation: firm size, managerial resources, bargaining power, organizational maturity, and etc. There are no obvious reasons to believe that the success factors of the two groups are identical. Grouping the two groups also raises questions on measuring the system success. The benefits from utilizing the systems may not be commonly distributed to the two groups. One group's benefits might be realized at the expenses of the other group considering the situation where vendors participating in SCM systems are under continuous pressures from the focus companies with respect to prices, quality, and delivery time. Therefore, by combining the system outcomes of both groups we cannot measure the system benefits obtained by each group correctly. Second, the measures of system success adopted in the previous researches have shortcoming in measuring the SCM success. User satisfaction, system utilization, and user attitudes toward the systems are most commonly used success measures in the existing studies. These measures have been developed as proxy variables in the studies of decision support systems (DSS) where the contribution of the systems to the organization performance is very difficult to measure. Unlike the DSS, the SCM systems have more specific goals, such as cost saving, inventory reduction, quality improvement, rapid time, and higher customer service. We maintain that more specific measures can be developed instead of proxy variables in order to measure the system benefits correctly. The purpose of this study is to find the determinants of SCM systems success in the perspective of vendor companies. In developing the research model, we have focused on selecting the success factors appropriate for the vendors through reviewing past researches and on developing more accurate success measures. The variables can be classified into following: technological, organizational, and environmental factors on the basis of TOE (Technology-Organization-Environment) framework. The model consists of three independent variables (competition intensity, top management support, and information system maturity), one mediating variable (collaboration), one moderating variable (government support), and a dependent variable (system success). The systems success measures have been developed to reflect the operational benefits of the SCM systems; improvement in planning and analysis capabilities, faster throughput, cost reduction, task integration, and improved product and customer service. The model has been validated using the survey data collected from 122 vendors participating in the SCM systems in Korea. To test for mediation, one should estimate the hierarchical regression analysis on the collaboration. And moderating effect analysis should estimate the moderated multiple regression, examines the effect of the government support. The result shows that information system maturity and top management support are the most important determinants of SCM system success. Supply chain technologies that standardize data formats and enhance information sharing may be adopted by supply chain leader organization because of the influence of focal company in the private industrial networks in order to streamline transactions and improve inter-organization communication. Specially, the need to develop and sustain an information system maturity will provide the focus and purpose to successfully overcome information system obstacles and resistance to innovation diffusion within the supply chain network organization. The support of top management will help focus efforts toward the realization of inter-organizational benefits and lend credibility to functional managers responsible for its implementation. The active involvement, vision, and direction of high level executives provide the impetus needed to sustain the implementation of SCM. The quality of collaboration relationships also is positively related to outcome variable. Collaboration variable is found to have a mediation effect between on influencing factors and implementation success. Higher levels of inter-organizational collaboration behaviors such as shared planning and flexibility in coordinating activities were found to be strongly linked to the vendors trust in the supply chain network. Government support moderates the effect of the IS maturity, competitive intensity, top management support on collaboration and implementation success of SCM. In general, the vendor companies face substantially greater risks in SCM implementation than the larger companies do because of severe constraints on financial and human resources and limited education on SCM systems. Besides resources, Vendors generally lack computer experience and do not have sufficient internal SCM expertise. For these reasons, government supports may establish requirements for firms doing business with the government or provide incentives to adopt, implementation SCM or practices. Government support provides significant improvements in implementation success of SCM when IS maturity, competitive intensity, top management support and collaboration are low. The environmental characteristic of competition intensity has no direct effect on vendor perspective of SCM system success. But, vendors facing above average competition intensity will have a greater need for changing technology. This suggests that companies trying to implement SCM systems should set up compatible supply chain networks and a high-quality collaboration relationship for implementation and performance.