• Clean Technology
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• v.16 no.3
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• pp.213-219
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• 2010

Vortex tube is the device that can separate small particles from the compressed gas, as well as compressed gas into hot and cold gas. Due to energy and particle separation ability, a vortex tube can be used as the main component of the $CO_2$ absorption device. In this study, experimental approach has been performed to analyze the energy separation characteristics of the vortex tube. To obtain the preliminary design data, energy separation characteristics of the vortex tube has been tested for orifice diameter, nozzle area ratio, and tube length. As a result, the orifice diameter is the major factor of the vortex tube design. The nozzle area ratio and tube length have a minor effect on the energy separation performance. For Dc=0.6D, AR=0.14~0.16, and L=16D, maximum energy separation has been occurred. The result from this study can be used as the basic design data of the $100Nm^3$/hr class vortex tube applied to the $CO_2$ absorption device. Compared with the $CO_2$ absorption process containing an absorption tower, the process with a vortex tube is expected to have a huge advantage of saving the installation space and the operating cost.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.241-265
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• 2023

A corporate insolvency prediction model serves as a vital tool for objectively monitoring the financial condition of companies. It enables timely warnings, facilitates responsive actions, and supports the formulation of effective management strategies to mitigate bankruptcy risks and enhance performance. Investors and financial institutions utilize default prediction models to minimize financial losses. As the interest in utilizing artificial intelligence (AI) technology for corporate insolvency prediction grows, extensive research has been conducted in this domain. However, there is an increasing demand for explainable AI models in corporate insolvency prediction, emphasizing interpretability and reliability. The SHAP (SHapley Additive exPlanations) technique has gained significant popularity and has demonstrated strong performance in various applications. Nonetheless, it has limitations such as computational cost, processing time, and scalability concerns based on the number of variables. This study introduces a novel approach to variable selection that reduces the number of variables by averaging SHAP values from bootstrapped data subsets instead of using the entire dataset. This technique aims to improve computational efficiency while maintaining excellent predictive performance. To obtain classification results, we aim to train random forest, XGBoost, and C5.0 models using carefully selected variables with high interpretability. The classification accuracy of the ensemble model, generated through soft voting as the goal of high-performance model design, is compared with the individual models. The study leverages data from 1,698 Korean light industrial companies and employs bootstrapping to create distinct data groups. Logistic Regression is employed to calculate SHAP values for each data group, and their averages are computed to derive the final SHAP values. The proposed model enhances interpretability and aims to achieve superior predictive performance.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.3
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• pp.88-103
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• 2023

With the recent advancement of 4th Industrial Revolution technology, transportation systems are generating large amounts of mobility data related to the individual movement trajectories of vehicles and people. There are many constraints on utilizing mobility data containing personal information. Thus, in South Korea, the processing and generation of pseudonymized information and the analysis and utilization of this information have been managed in a dual manner by applying separate agencies and technologies through the revision of the Data 3 Act and the enactment of the Data Basic Act. However, this dual approach fails to securely support the entire data lifecycle and suffers from inefficiencies in terms of processing time and cost. Therefore, to compensate for the problems of the existing Expert Data Combination System and Data Safety Zone, this study proposes an Integrated Data Safety Zone Framework that integrates and unifies the process of generating, processing, analyzing, and utilizing mobility data. The integrated process for data processing was redesigned, and common requirements and core technologies were derived. The result is an architecture for a next-generation Integrated Data Safety Zone system that can manage and utilize the entire life cycle of mobility data at one stop.

• Journal of the Microelectronics and Packaging Society
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• v.30 no.2
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• pp.65-70
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• 2023

Nanoimprint lithography (NIL) has attracted much attention due to its process simplicity, excellent patternability, process scalability, high productivity, and low processing cost for pattern formation. However, the pattern size that can be implemented on metal materials through conventional NIL technologies is generally limited to the micro level. Here, we introduce a novel hard imprint lithography method, extreme-pressure imprint lithography (EPIL), for the direct nano-to-microscale pattern formation on the surfaces of metal substrates with various thicknesses. The EPIL process allows reliable nanoscopic patterning on diverse surfaces, such as polymers, metals, and ceramics, without the use of ultraviolet (UV) light, laser, imprint resist, or electrical pulse. Micro/nano molds fabricated by laser micromachining and conventional photolithography are utilized for the nanopatterning of Al substrates through precise plastic deformation by applying high load or pressure at room temperature. We demonstrate micro/nanoscale pattern formation on the Al substrates with various thicknesses from 20 ㎛ to 100 mm. Moreover, we also show how to obtain controllable pattern structures on the surface of metallic materials via the versatile EPIL technique. We expect that this imprint lithography-based new approach will be applied to other emerging nanofabrication methods for various device applications with complex geometries on the surface of metallic materials.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5B
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• pp.575-589
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• 2008

In the present work, we investigate the hydrodynamic behavior of a turbulent bore, such as tsunami bore and tidal bore, generated by the removal of a gate with water impounded on one side. The bore generation system is similar to that used in a general dam-break problem. In order to the numerical simulation of the formation and propagation of a bore, we consider the incompressible flows of two immiscible fluids, liquid and gas, governed by the Navier-Stokes equations. The interface tracking between two fluids is achieved by the volume-of-fluid (VOF) technique and the M-type cubic interpolated propagation (MCIP) scheme is used to solve the Navier-Stokes equations. The MCIP method is a low diffusive and stable scheme and is generally extended the original one-dimensional CIP to higher dimensions, using a fractional step technique. Further, large eddy simulation (LES) closure scheme, a cost-effective approach to turbulence simulation, is used to predict the evolution of quantities associated with turbulence. In order to verify the applicability of the developed numerical model to the bore simulation, laboratory experiments are performed in a wave tank. Comparisons are made between the numerical results by the present model and the experimental data and good agreement is achieved.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4D
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• pp.671-678
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• 2006

In this study, to describe vehicle-structure dynamic interaction phenomena with 1/4 vehicle model, nonlinear Hertzian contact spring and nonlinear contact damper are adopted. The external loads acting on 1/4 vehicle model are selfweight of vehicle and geometry information of running surface. The constraint equation on contact surface is implemented by the Penalty method with stabilization and the reaction from constraint violation. To describe pitching motion of various vehicles two types of the displacement constraint equations are exerted to connect between car bodies and between bogie frames, i.e., the rigid body connection and the rigid body connection with pin, respectively. For the time integration of dynamic equations of vehicles and structure Newmark time integration scheme is adopted. To reduce the error caused by inadequate time step size, adaptive time-stepping technique is also adopted. Thus, it is expected that more versatile dynamic interaction phenomena can be described by this approach and it can be applied to various railway dynamic problems with low computational cost.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.4A
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• pp.281-290
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• 2009

In this study, an analysis technique of hybrid girder considering nonlinearity of steel-concrete contact surface is presented. Steel-concrete hybrid girder shows partial-interaction behavior due to the deformation of shear connectors, slip and detachment at the interface, and cracks under the applied loads. Therefore, the partial-interaction approach becomes more reasonable. Contact surface is modeled by interface element and analyzed nonlinearly because of cost of time and effort to detailed model and analysis. Steel and Concrete are modeled considering non-linearity of materials. Material property of contact surface is obtained from push-out test and input to interface element. For the constitutive models, Drucker-Prager and smeared cracking model are used for concrete in compression and tension, respectively, and a von-Mises model is used for steel. This analysis technique is verified by comparing it with test results. Using verified analysis technique, various analyses are performed with different parameters such as nonlinear material property of interface element and prestress. The results are compared with linear analysis result and analysis result with the assumption of full-interaction.

• Geophysics and Geophysical Exploration
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• v.26 no.4
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• pp.211-228
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• 2023

Ground-penetrating radar (GPR) surveys are commonly used to monitor embankments, which is a nondestructive geophysical method. The results of GPR surveys can be complex, depending on the situation, and data processing and interpretation are subject to expert experiences, potentially resulting in false detection. Additionally, this process is time-intensive. Consequently, various studies have been undertaken to detect cavities in GPR survey data using deep learning methods. Deep-learning-based approaches require abundant data for training, but GPR field survey data are often scarce due to cost and other factors constaining field studies. Therefore, in this study, a deep- learning-based model was developed for embankment GPR survey cavity detection using data augmentation strategies. A dataset was constructed by collecting survey data over several years from the same embankment. A you look only once (YOLO) model, commonly used in computer vision for object detection, was employed for this purpose. By comparing and analyzing various strategies, the optimal data augmentation approach was determined. After initial model development, a stepwise process was employed, including box clustering, transfer learning, self-ensemble, and model ensemble techniques, to enhance the final model performance. The model performance was evaluated, with the results demonstrating its effectiveness in detecting cavities in embankment GPR survey data.

• Information Systems Review
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• v.25 no.4
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• pp.27-45
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• 2023

The importance of information security has grown alongside the development of information and communication technology. However, companies struggle to select suitable countermeasures within their limited budgets. Sönmez and Kılıç (2021) proposed a model using AHP and mixed integer programming to determine the optimal investment combination for mitigating information security breaches. However, their model had limitations: 1) a lack of objective measurement for countermeasure efficacy against security threats, 2) unrealistic scenarios where risk reduction surpassed pre-investment levels, and 3) cost duplication when using a single countermeasure for multiple threats. This paper enhances the model by objectively quantifying countermeasure efficacy using the beta probability distribution. It also resolves unrealistic scenarios and the issue of duplicating investments for a single countermeasure. An empirical analysis was conducted on domestic SMEs to determine investment budgets and risk levels. The improved model outperformed Sönmez and Kılıç's (2021) optimization model. By employing the proposed effectiveness measurement approach, difficulty to evaluate countermeasures can be quantified. Utilizing the improved optimization model allows for deriving an optimal investment portfolio for each countermeasure within a fixed budget, considering information security costs, quantities, and effectiveness. This aids in securing the information security budget and effectively addressing information security threats.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.185-205
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• 2023

As the tourism industry recovers post the COVID-19 pandemic, an increasing number of tourists are utilizing various platforms to leave reviews. However, amidst the vast amount of data, finding useful information remains challenging, often leading to time and cost inefficiencies in selecting travel destinations. Despite ongoing research, there are limitations due to the absence of ratings or the presence of different rating formats across platforms. Moreover, inconsistencies between ratings and the content of reviews pose challenges in developing recommendation models. To address these issues, this study utilized 7,104 reviews of tourist spots in Jeju Island to develop a specialized satisfaction index for Jeju tourist attractions and employed this index to construct a 'Rating Prediction Model.' To validate the model's performance, we predicted the ratings of 700 experimental data points using both the developed model and an LSTM approach. The proposed model demonstrated superior performance with a weighted accuracy of 73.87%, which is approximately 4.67% higher than that of the LSTM. The results of this study are expected to resolve the discrepancies between ratings and review contents, standardize ratings in reviews without ratings or in various formats, and provide reliable rating indicators applicable across all areas of travel in different domains.