• JSTS:Journal of Semiconductor Technology and Science
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• v.15 no.5
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• pp.463-469
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• 2015

A vibration-based energy harvester and its equivalent circuit models have been reported. Most models predict voltage signals at harmonic excitation. However, vibrations in a natural environment are unpredictable in frequency and amplitude. In this paper, we propose a realistic equivalent circuit model of a frequency-up-converting impact-based piezoelectric energy harvester. It can describe the behavior of the harvester in a real environment where the frequency and the amplitude of the excitation vary arbitrarily. The simulation results of the model were compared with experimental data and showed good agreement. The proposed model can predict both the impact response and long term response in a non-harmonic excitation. The model is also very useful to analyze the performance of energy conversion circuitry with the harvester.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2002.10a
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• pp.179-182
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• 2002

Rapid Prototyping (RP) technologies provide the ability to fabricate initial prototypes from various model materials. Stratasys' Fused Deposition Modeling (FDM) is a typical RP process that can fabricate prototypes out of plastic materials, and the parts made from FDM were often used as load-carrying elements. Because FDM deposits materials in about $300\mutextrm{m}$ thin filament with designated orientation, parts made from FDM show anisotropic material properties. This paper proposes an analytic model to predict the tensile strength of FDM parts. Applying the Classical Lamination Theory, which was developed for laminated composite materials, a computer code was implemented. Tsai-Wu failure criterion was added to the code to predict the failure of the FDM parts. The tensile strengths predicted by the analytic model were compared with experimental data. The data and prediction agreed reasonably well to prove the validity of the model. In addition, a web-based advisory service was developed to provide to strength prediction and design rules for FDM parts.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.1009-1012
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• 1999

An analytical performance model that can predict the performance of a superscalar processor employing multiple branch prediction is introduced. The model is based on the conditional independence probability and the basic block size of instructions, with the degree of multiple branch prediction, the fetch rate, and the window size of a superscalar architecture. Trace driven simulation is performed for the subset of SPEC integer benchmarks, and the measured IPCs are compared with the results derived from the model. As the result, our analytic model could predict the performance of the superscalar processor using multiple branch prediction within 6.6 percent on the average.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.11
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• pp.1144-1150
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• 2009

Appropriate vibration model is required to predict in advance the vibration level of a large vehicle which carries sensitive electronic/mechanical equipments and drives often on the unpaved and/or off-road conditions. Central tire inflation system(CTIS) is recently adopted to improve the mobile operation of military and bulletproof vehicles. In this paper, full vehicle model(FVM) having 11 degrees of freedom and equipped with CTIS has been developed for a large vehicle which has $8\times8$ wheels$\times$driving wheels. Usability of the developed model is validated via road tests for three different modes (i.e. highway, country, and mud/sand/snow modes) and for various velocity conditions. The developed FVM can be used to predict the vibration level of the large vehicle as well as to determine the driving velocity criterion for different road conditions.

• Journal of Information Technology Applications and Management
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• v.24 no.1
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• pp.111-128
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• 2017

Existing studies for recommender have focused on recommending an appropriate item based on the customer preference. However, it has not yet been studied actively to recommend purchase timing for the repurchase product despite of its importance. This study aims to propose MLP and RNN models based on the only simple purchase history data to predict the timing of customer repurchase and compare performances in the perspective of prediction accuracy and quality. As an experiment result, RNN model showed outstanding performance compared to MLP model. The proposed model can be used to develop CRM system which can offer SMS or app based promotion to the customer at the right time. This model also can be used to increase sales for repurchase product business by balancing the level of order as well as inducing repurchase of customer.

• Journal of Korean Society for Atmospheric Environment
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• v.13 no.3
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• pp.231-241
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• 1997

A tracer model was applied in the Far East Asia to investigate the dry deposition rates of air pollutants on Korean Peninsula originated from different countries including China and Japan. Wind direction was chosen to predict the maximum deposition rates and SO$_2$ was chosen as a tracer to estimate the source strength. Model simulation shows that inflow, deposition and airborne ratios of China-originated SO$_2$ were 50%, 8% and 30%, respectively, at most. Also it was found that deposition, outbounded and airborne ratios of Korea-originated SO$_2$ were 15~77%, 8~75%, and 3~30%, respectively Model simulation also shows that inflow, deposition and airborne ratios of Kyushu-originated SO$_2$ were, 30~45%, 8~14％ and 20~25%, respectively. This study shows that tracer model can be applied on the estimation of air pollutants partitioning in regional scale and that more sophisticated modules and schemes can be developed and applied to better predict the transboundary amounts of air pollutants in this region.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.8 no.3
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• pp.13-20
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• 2009

Development of a dynamic engine model is essential to predict and analyze of dynamic characteristics from a natural gas engine. Reducing the harmful exhaust emissions can be accomplished by a precise air-fuel ratio control. In this paper, the dynamic engine model was proposed and included mixture formation and intake process because the dynamic characteristics can be affected by the mixture components such as an air and a gaseous fuel. The air mass flow, the partial pressure ratio, and the gas constant are changed by variations of the components in the mixture formation and intake process. The dynamic engine model is applied to the natural gas engine for validation test. Experimental results show that the dynamic engine model is effective to predict the dynamic characteristics of the natural gas engine.

• Environmental Engineering Research
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• v.10 no.2
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• pp.62-70
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• 2005

Pollution loading of Combined Sewer Overflows (CSOs) is frequently over the capacity of a wastewater treatment plant (WWTP) receiving the water. The objectives of this study are to investigate water quality of CSOs in Anmyun-ueup, Tean province and to apply Storm Water Management Model to predict flow rate and water quality of the CSOs. The capacity of a local WWTP was also estimated according to rainfall duration and intensity. Eleven water quality parameters were analyzed to characterize overflows. SWMM model was applied to predict the flow rate and pollutant load of CSOs during rain event. Overall, profile of the flow and pollutant load predicted by the model well followed the observed data. Based on model prediction and observed data, CSOs frequently occurs in the study area, even with light precipitation or short rainfall duration. Model analysis also indicated that the local WWTP’s capacity was short to cover the CSOs.

• Journal of Information Technology Services
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• v.18 no.1
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• pp.173-186
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• 2019

Recently, as the amount of fine dust has risen rapidly, our interest is increasing day by day. It is virtually impossible to remove fine dust. However, it is best to predict the concentration of fine dust and minimize exposure to it. In this study, we developed a mathematical model that can predict the concentration of fine dust using various information related to the weather and air quality, which is provided in real time in 'Air Korea (http://www.airkorea.or.kr/)' and 'Weather Data Open Portal (https://data.kma.go.kr/).' In the mathematical model, various domestic seasonal variables and atmospheric state variables are extracted by multiple regression analysis. The parameters that have significant influence on the fine dust concentration are extracted, and using ANN (Artificial Neural Network) and SVM (Support Vector Machine), which are machine learning techniques, we proposed a prediction model. The proposed model can verify its effectiveness by using past dust and weather big data.

• Journal of ILASS-Korea
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• v.28 no.3
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• pp.119-125
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• 2023

In this research, a network model was developed to predict combustion instability in an annular gas turbine combustor (GT24) for power generation. The model consisted of various acoustic elements such as several ducts and area changes which could represent a real combustor with a complex geometry, applied mass, momentum, and energy equations to each element. In addition, a one-dimensional network model through a cylindrical coordinate system has been proposed to predict various acoustic modes. As a result of the analysis, the key resonant frequencies such as longitudinal, circumferential, and complex modes were derived from the EV combustor of GT24, and the reliability of the current model was verified through comparison with the 3D Helmholtz solver.