• Nuclear Engineering and Technology
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• v.35 no.6
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• pp.556-565
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• 2003

A quality assurance safety assessment database, called QUARK (QUality Assurance Program for Radioactive Waste Management in Korea), has been developed to manage both analysis information and parameter database for safety assessment of low- and intermediate-level radioactive waste (LILW) disposal facility in Korea. QUARK is such a tool that serves QA purposes for managing safety assessment information properly and securely. In QUARK, the information is organized and linked to maximize the integrity of information and traceability. QUARK provides guidance to conduct safety assessment analysis, from scenario generation to result analysis, and provides a window to inspect and trace previous safety assessment analysis and parameter values. QUARK also provides default database for safety assessment staff who construct input data files using SAGE(Safety Assessment Groundwater Evaluation), a safety assessment computer code.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.8
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• pp.779-784
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• 2008

This paper presents Kalman filter based system modeling algorithm for autonomous robot systems. State of the robot system is measured using embedded sensor systems and then carried to a host computer via ubiquitous sensor network (USN). We settle a linear state-space motion equation for unknown robot system dynamics and modify a popular Kalman filter algorithm in deriving suitable parameter estimation mechanism. To represent time-delay nature due to network media in system modeling, we construct an augmented state-space model which is mainly composed of original state and estimated parameter vectors. We conduct real-time experiment to test our proposed estimation algorithm where speed state of the constructed robot is used as system observation.

• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.203-223
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• 2022

We propose a Bayesian approach to cumulative logistic regression model for the ordinal response based on the orthogonal principal components via singular value decomposition considering the multicollinearity among predictors. The advantage of the suggested method is considering dimension reduction and parameter estimation simultaneously. To evaluate the performance of the proposed model we conduct a simulation study with considering a high-dimensional and highly correlated explanatory matrix. Also, we fit the suggested method to a real data concerning sprout- and scab-damaged kernels of wheat and compare it to EM based proportional-odds logistic regression model. Compared to EM based methods, we argue that the proposed model works better for the highly correlated high-dimensional data with providing parameter estimates and provides good predictions.

• Journal of Practical Engineering Education
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• v.13 no.2
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• pp.233-242
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• 2021

In this paper, as a liberal arts course for non-majors, we proposed a supervised learning artificial neural network parameter optimization method and a basic education method for activation function to design a basic artificial neural network subject curriculum. For this, a method of finding a parameter optimization solution in a spreadsheet without programming was applied. Through this training method, you can focus on the basic principles of artificial neural network operation and implementation. And, it is possible to increase the interest and educational effect of non-majors through the visualized data of the spreadsheet. The proposed contents consisted of artificial neurons with sigmoid and ReLU activation functions, supervised learning data generation, supervised learning artificial neural network configuration and parameter optimization, supervised learning artificial neural network implementation and performance analysis using spreadsheets, and education satisfaction analysis. In this paper, considering the optimization of negative parameters for the sigmoid neural network and the ReLU neuron artificial neural network, we propose a training method for the four performance analysis results on the parameter optimization of the artificial neural network, and conduct a training satisfaction analysis.

• Journal of Korean Neurosurgical Society
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• v.57 no.3
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• pp.152-158
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• 2015

Objective : The purpose of this study to develop new deep-brain stimulation system for long-term use in animals, in order to develop a variety of neural prostheses. Methods : Our system has two distinguished features, which are the fully implanted system having wearable wireless power transfer and ability to change the parameter of stimulus parameter. It is useful for obtaining a variety of data from a long-term experiment. Results : To validate our system, we performed pre-clinical test in Parkinson's disease-rat models for 4 weeks. Through the in vivo test, we observed the possibility of not only long-term implantation and stability, but also free movement of animals. We confirmed that the electrical stimulation neither caused any side effect nor damaged the electrodes. Conclusion : We proved possibility of our system to conduct the long-term pre-clinical test in variety of parameter, which is available for development of neural prostheses.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.6A
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• pp.661-669
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• 2009

Various kinds of shear connectors such as headed stud, channel, perforated steel plate and others are commonly used to transfer stress and conduct composite performance in steel concrete composite structures, and many researches have been conducted to improve the characteristics of different types of shear connectors. It is focused in this study on the pull-out and pushout performance of steel pipe pile cap with the open type perfobond for the composite connection to the spread footing. A parameter analysis was conducted, using ABAQUS, a nonlinear finite element analysis program, to obtain data for determining the characteristics of the structure and to allow various parametric analyses of steel pipe cap with the open perfobond.

• Journal of Korean Neurosurgical Society
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• v.67 no.1
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• pp.50-59
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• 2024

Objective : This study aimed to conduct a bibliometric analysis on pelvic parameter related research over the last 30 years, analyzing trends, hotspots, and influential works within this field. Methods : A comprehensive Web of Science database search was performed. The search yielded 3249 results, focusing on articles and reviews published from 1992 to 2022 in English. Data was analyzed using CiteSpace and VOSviewer for keyword, authorship, and citation burst analysis, co-citation analysis, and clustering. Results : The number of publications and citations related to pelvic parameters has increased exponentially over the last 30 years. The USA leads in publication count with 1003 articles. Top publishing journals include the European Spine Journal, Spine, and Journal of Neurosurgery: Spine, with significant contributions by Schwab, Lafage V, and Protoptaltis. The most influential articles were identified using centrality and sigma values, indicating their role as key articles within the field. Research hotspots included spinal deformity, total hip arthroplasty, and sagittal alignment. Conclusion : Interest in pelvic parameter related research has grown significantly over the last three decades, indicating its relevance in modern orthopedics. The most influential works within this field have contributed to our understanding of spinal deformity, pelvic incidence, and their relation to total hip arthroplasty. This study provides a comprehensive overview of the trends and influential research in the field of pelvic parameters.

• Journal of Biomedical Engineering Research
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• v.43 no.1
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• pp.11-18
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• 2022

An electromagnetic generator with variable stimulation parameters is required to conduct basic research on magnetic flux density and frequency for pulsed electromagnetic fields (PEMFs). In this study, we design an electromagnetic generator that can conduct basic research by providing parameters optimized for cell and animal experimental conditions through adjustable stimulation parameters. The magnetic core was selected as a solenoid capable of uniform and stable electromagnetic stimulation. The solenoid was designed in consideration of the experimental mouse and cell culture dish insertion. A voltage and current adjustable power supply for variable magnetic flux density was designed. The system was designed to be adjustable in frequency and pulse width and to enable 3-channel output. The reliability of the system and solenoid was evaluated through magnetic flux density, frequency, and pulse width measurements. The measured magnetic flux density was expressed as an image and qualitatively observed. Based on the acquired image, the stimulation area according to the magnetic flux density decrease rate was extracted. The PEMF frequency and pulse width error rates were presented as mean ± SD, and were confirmed to be 0.0928 ± 0.0934% and 0.529 ± 0.527%, respectively. The magnetic flux density decreased as the distance from the center of the solenoid increased, and decreased sharply from 60 mm or more. The length of the magnetic stimulation area according to the degree of magnetic flux density decrease was obtained through the magnetic flux density image. A PEMF generator and stimulation parameter control system suitable for cell and animal models were designed, and system reliability was evaluated.

• Proceedings of the KSME Conference
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• 2007.05b
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• pp.2097-2101
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• 2007

This paper presents an optimal design of a micro evaporator which maximizes the heat transfer coefficient. Number of gaps, spanwise distance and streamwise distance are selected as the geometric design parameters. Mass flow rate of the refrigerant is selected as the non-geometric design parameter. Temperature at the surface of the heater is measured to valuate the heat transfer coefficient. Nine experiments are conducted using $L_9(3^4)$ orthogonal array. Maximum heat transfer coefficient is 640 W/$m^2K$ at the parameters of 2 gaps, 0.2 mm spanwise distance, 1.0 mm streamwise distance and 0.72 g/s mass flow rate. Among the 3 geometric parameters, the spanwise distance is the most sensitive parameter influencing the heat transfer coefficient. We conduct a second stage of experiment to increase the heat transfer coefficient by reselecting the mass flow rate. We concluded that 0.87 g/s is the optimized flow rate for an active micro cooler resulting in a heat transfer coefficient of 651 W/$m^2K$.

• Journal of Korea Multimedia Society
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• v.18 no.7
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• pp.886-894
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• 2015

In this paper, we conduct sensitivity analysis of parameters used for inverse modeling in order to estimate the PM₁₀ emissions from the 16 areas in East Asia accurately. Parameters used in sensitivity analysis are R, the observational error covariance matrix, and B, a priori (background) error covariance matrix. In previous studies, it was used with the predetermined parameter empirically. Such a method, however, has difficulties in estimating an accurate emissions. Therefore, an automatically determining method for the most suitable value of R and B with an error measurement criteria and posteriori emissions accuracy is required. We determined the parameters through a sensitivity analysis, and improved the accuracy of posteriori emissions estimation. Inverse modeling methods used in the emissions estimation are pseudo inverse, NNLS (Nonnegative Least Square), and BA(Bayesian Approach). Pseudo inverse has a small error, but has negative values of emissions. In order to resolve the problem, NNLS is used. It has a unrealistic emissions, too. The problems are resolved with BA(Bayesian Approach). We showed the effectiveness and the accuracy of three methods through case studies.