• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.2
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• pp.206-215
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• 1994

A method for simulating general characteristics and temperature characteristics of discharging SCR of the capacitor discharge impulse magnetizer-magnetizing fixture system using SPICE is presented. This method has been developed which can aid the design, understanding and inexpensive, time-saving of magnetizing circuit. As the detailed characteristic of magnetizing circuit can be obtained, the efficient design of the magntizing circuit which produce desired magnet will be possible using our SPICE modeling. Especially, computation of the temperature rise of discharging SCR is very important since it gives some indication of thermal characteristic of discharging circuit. It is implemented on a 486 personal computer, and the modeling results are checked against experimental measures. The experimental results have been achived using 305[V] and 607[V] charging voltage, low-energy capacitor discharge impulse magnetizer-magnetizing fixture of air cleaner DC motor.

• JSTS:Journal of Semiconductor Technology and Science
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• v.16 no.3
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• pp.367-374
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• 2016

This paper presents an electro-thermal modeling of an amorphous silicon (a-Si) uncooled microbolometer. This modeling provides a comprehensive solution for simulating the electro-thermal characteristics of the fabricated microbolometer and enables electro-thermal co-simulation between MEMS and CMOS integrated circuits. To validate this model, three types of uncooled microbolometers were fabricated using a post-CMOS surface micromachining process. The simulation results show a maximum discrepancy of 2.6% relative to the experimental results.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.10
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• pp.2028-2037
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• 2009

This paper presents the simulator for dynamic modeling of a MT(micro turbine) during start-up period. The simulator is implemented by modeling a dynamic power of main components of a MT including compressor, combustor and turbine. A modeling for a MT under steady state operation can be accurately built from thermodynamics analysis. But dynamic modeling during start-up period is very difficult because efficiency of main components is very low and the designed value has big error and nonlinear characteristics during start-up. In this paper, new method without using thermodynamics analysis during start-up is proposed for the simulator. The power models of main components are derived from analysis of the experimental operation data by test motoring using a electric starter under constant power output. The simulator is developed using MATLAB/Simulink. For constant power output control, sensorless vector inverter is designed and algorithms for starting from stall and method for controling a output power are proposed. The performance of developed simulator is verified by comparing experimental and simulation start-up results.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.9-15
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• 2023

A common language for modeling software requirements and design in recent years is Unified Modeling Language (UML). Essential principles and rules are provided by UML to help visualize and comprehend complex software systems. It has therefore been incorporated into the curriculum for software engineering courses at several institutions all around the world. However, it is commonly recognized that UML is challenging for beginners to understand, mostly owing to its complexity and ill-defined nature. It is unavoidable that we need to comprehend their preferences and issues considerably better than we do presently to approach the problem of teaching UML to beginner students in an acceptable manner. This paper offers a hint-based approach that can be implemented along with an ordinary lab task. Some keywords are highlighted to indicate class diagram components and make students understand the textual descriptions. The experimental results indicate significant improvement in students' learning skills. Furthermore, the majority of students also positively responded to the survey conducted in the end experimental study.

• Transactions of the Korean Society of Automotive Engineers
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• v.24 no.1
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• pp.53-58
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• 2016

Modeling of engine coolant temperature was conducted for a series hybrid powertrain system. The purpose of this modeling was a simplification of complex heat transfer process inside a engine cooling system in order to apply it to the vehicle powertrain simulation software. A basic modeling concept is based on the energy conservation equation within engine coolant circuit and are composed of heat rejection from engine to coolant, convection heat transfer from an engine surface and a radiator to ambient air. At the final stage, the coolant temperature was summarized as a simple differential equation. Unknown heat transfer coefficients and heat rejection term were defined by theoretical and experimental methods. The calculation result from this modeling showed a reasonable prediction by comparison with the experimental data.

• Clinical and Experimental Pediatrics
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• v.65 no.5
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• pp.239-249
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• 2022

Cells survive and proliferate through complex interactions among diverse molecules across multiomics layers. Conventional experimental approaches for identifying these interactions have built a firm foundation for molecular biology, but their scalability is gradually becoming inadequate compared to the rapid accumulation of multiomics data measured by high-throughput technologies. Therefore, the need for data-driven computational modeling of interactions within cells has been highlighted in recent years. The complexity of multiomics interactions is primarily due to their nonlinearity. That is, their accurate modeling requires intricate conditional dependencies, synergies, or antagonisms between considered genes or proteins, which retard experimental validations. Artificial intelligence (AI) technologies, including deep learning models, are optimal choices for handling complex nonlinear relationships between features that are scalable and produce large amounts of data. Thus, they have great potential for modeling multiomics interactions. Although there exist many AI-driven models for computational biology applications, relatively few explicitly incorporate the prior knowledge within model architectures or training procedures. Such guidance of models by domain knowledge will greatly reduce the amount of data needed to train models and constrain their vast expressive powers to focus on the biologically relevant space. Therefore, it can enhance a model's interpretability, reduce spurious interactions, and prove its validity and utility. Thus, to facilitate further development of knowledge-guided AI technologies for the modeling of multiomics interactions, here we review representative bioinformatics applications of deep learning models for multiomics interactions developed to date by categorizing them by guidance mode.

• Korean Journal of Agricultural Science
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• v.43 no.2
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• pp.163-175
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• 2016

For the past decades, advances in computational devices have propelled mathematical modeling to become an effective tool for solving the black box of complex biological systems because of its prominent analytical power and comprehensive insight. Nevertheless, modeling is still limitedly used in the fields of agriculture and food which generally concentrate on producing experimental data rather than processing them. This study, hence, intends to introduce modeling in terms of its procedure types of structure, formulation, analyses, and software, with reviews of current notable studies from micro to macro scales so as to propose the modeling technique as a novel approach in discerning conundrums in agriculture and food systems. We expect this review to provide an eligible source for researchers who are willing to apply modeling techniques into the unexplored fields related to bio-systems that comprehensively include biology, nutrition, agriculture, food, animal science, and ecology.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1998.04a
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• pp.127-134
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• 1998

The objectives of this paper is to analyze the reinforced concrete structures by using fiber model. In this study, the fiber modeling techniques including modeling of support conditions are studied. In order to verify the modeling techniques, analysis results obtained for reinforced concrete cantilever beam and reinforced concrete T-girder bridge under cyclic loading are compared with experimental results from full scale test. From the comparison, it is shown that the modeling techniques in this study can be well applied to the nonlinear failure analysis of reinforced concrete structures with porper modifications.

• Journal of the Korean Institute of Telematics and Electronics
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• v.23 no.3
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• pp.370-375
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• 1986

In this paper, modeling of the conduction mechanism of ion implanted p-type polycrystalline silicon is studied. From this modeling, the resistivity of p-type polycrystalline and its dependence on dopant concentration are calculated. The proposed modeling whose grain size is about 1450 \ulcorneris shwon to agree well with the experimental result.

• Nuclear Engineering and Technology
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• v.44 no.8
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• pp.889-896
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• 2012

Recent progress in the computational fluid dynamics methods of two- and multiphase phase flows has already started opening up new exciting possibilities for using complete multidimensional models to simulate boiling systems. Combining this new theoretical and computational approach with novel experimental methods should dramatically improve both our understanding of the physics of boiling and the predictive capabilities of models at various scale levels. However, for the multidimensional modeling framework to become an effective predictive tool, it must be complemented with accurate mechanistic closure laws of local boiling mechanisms. Boiling heat transfer has been studied quite extensively before. However, it turns out that the prevailing approach to the analysis of experimental data for both pool boiling and forced-convection boiling has been associated with formulating correlations which normally included several adjustable coefficients rather than based on first principle models of the underlying physical phenomena. One reason for this has been the tendency (driven by practical applications and industrial needs) to formulate single expressions which encompass a broad range of conditions and fluids. This, in turn, makes it difficult to identify various specific factors which can be independently modeled for different situations. The objective of this paper is to present a mechanistic modeling concept for both pool boiling and forced-convection boiling. The proposed approach is based on theoretical first-principle concepts, and uses a minimal number of coefficients which require calibration against experimental data. The proposed models have been validated against experimental data for water and parametrically tested. Model predictions are shown for a broad range of conditions.