• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.12 no.5
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• pp.787-794
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• 2001

In this paper, the development of large-signal model extractor for GaAs FET device through the Monolithic Microwave integrated Circuit(MMIC) is presented. The measurement program controlled by personal computer is developed for the processing of an amount of measured data, and the de-embedding algorithm is added to the program for voltage dropping as attached series resistance on measurement system. The small-signal model parameters are typically consisted of 7 elements that are considered as complexity of large-signal model and its the accuracy of the small-signal model is verified through comparing with measured data as varied bias point. The fitting function model, one of the empirical model, is used for quick simulation. In the process of large-signal model parameter extraction, one-dimensional optimization method is proposed and optimized parameters are extracted. This study can reduce the modeling and measuring time and can secure a suitable model for circuit.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.122-122
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• 2011

수자원 분야에서 기후변화 관련 연구는 치수 측면 보다는 이수 측면에서 주로 이뤄지고 있다. 이는 홍수분석을 위한 시간 단위를 충족시켜주는 전지구 대기순환모형(Global Circulation Model: GCM)의 자료가 드물고, 시간 단위의 GCM 자료라 하더라도 극치값(extreme value) 표현에는 한계가 있기 때문이다. 이를 극복하기 위하여 과거 관측자료의 통계적 특성으로 극치자료의 편의(bias)를 보정하고 시간 단위로 분해하기도 한다. 하지만 이런 통계적 상세화(statistical downscaling)는 미래 기후는 과거자료와 통계적 차이가 유의하지 않음을 가정하고 있어, 미래 기후는 현재와 다를 것이라는 공감대에 는 적합하지 않다. 이와 같은 이유로 타당한 극치수문변수 결과를 얻기 위해서는 시간 단위의 고분해능(high resolution) GCM이나 지역기후모델(regional climate model)과 같은 고해상도의 미래 기후변화 자료가 필요하게 된다. 이에 국립기상연구소에서는 영국 기상청의 통합모델(UM)기반의 지역기후모델(HadGEM3)을 사용하여 50 km 및 12.5 km 격자 단위로 역학적 상세화(dynamic downscaling)를 수행하였다. 본 연구에서는 개발된 HadGEM3-RA 결과의 극치수문변수 검증을 위하여 한강유역의 관측 자료와 다양한 방법으로 비교하였다. 두 자료의 극치값을 GEV (Generalized Extreme Value) 분포에 적합(fitting)시켜 비초과확률별 극치사상과, 특정 임계값(threshold value) 이상의 극치사상 발생확률을 비교하였다. 검토 결과, HadGEM3-RA는 통계적 상세화로 구한 극치값 보다는 작았으나 기존의 지역 기후모델에 비하여 현실성 있는 극치값이 계산되었음을 확인하였다.

• Journal of Korea Water Resources Association
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• v.43 no.11
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• pp.977-984
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• 2010

In order to determine the optimal sediment discharge formula for Hyeongsan river, some statistical approaches have been applied to analyze the simulated results of long-term bed change by HEC-6. The field measurements have been conducted to obtain the data for model calibration and verification such as sediment discharge, bed material, and channel geometry. Several sediment discharge formulae have been verified according to the bias, RMSE, RRMSE, discrepancy ratio, and S/N ratio of bed change along the thalweg. Comparing the formulae, Laursen formula(modified by Copeland) have shown the best performance to simulate the long-term bed change of Hyeongsan river.

• Atmosphere
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• v.28 no.3
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• pp.233-245
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• 2018

This study explores the Subseasonal-to-Seasonal (S2S) prediction skills of the Northern Hemisphere mid-latitude geopotential height in the Global Seasonal forecasting model version 5 (GloSea5) hindcast experiment. The prediction skills are quantitatively verified for the period of 1991~2010 by computing the Anomaly Correlation Coefficient (ACC) and Mean Square Skill Score (MSSS). GloSea5 model shows a higher prediction skill in winter than in summer at most levels regardless of verification methods. Quantitatively, the prediction limit diagnosed with ACC skill of 500 hPa geopotential height, averaged over $30^{\circ}N{\sim}90^{\circ}N$, is 11.0 days in winter, but only 9.1 days in summer. These prediction limits are primarily set by the planetary-scale eddy phase errors. The stratospheric prediction skills are typically higher than the tropospheric skills except in the summer upper-stratosphere where prediction skills are substantially lower than upper-troposphere. The lack of the summer upper-stratospheric prediction skill is caused by zonal mean error, perhaps strongly related to model mean bias in the stratosphere.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.10
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• pp.917-925
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• 2016

Schmitt Trigger logic is a gate level design method to have hysteresis characteristics to improve noise immunity in digital circuits. Dynamic Threshold voltage MOS(DTMOS) Schmitt trigger circuits can improve noise immunity without adding additional transistors but by controlling substrate bias. The performance of DTMOS Schmitt trigger logic has not been verified yet in standard CMOS process through measurement. In this paper, DTMOS Schmitt trigger logic was implemented and verified using Magna $0.18{\mu}m$ MPW process. DTMOS Schmitt trigger buffer, inverter, NAND, NOR and simple digital logic circuits were made for our verification. Hysteresis characteristics, power consumption, and delay were measured and compared with common CMOS logic gates. EM Immunity enhancement was verified through Direct Power Injection(DPI) noise immunity test method. DTMOS Schmitt trigger logics fabricated using CMOS process showed a significantly improved EM Immunity in 10 M~1 GHz frequency range.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.2
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• pp.36-47
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• 2006

The process of dual spin turn maneuver is introduced for attitude acquisition or recovery from flat spin state of a satellite. The physical principle of momentum transfer during dual spin turn is explained clearly. The case studies of special dual spin turn, in addition to the conventional dual spin turn, that are known as an acceptable cases, are performed to investigate the principle of dual spin turn and to provide a physical insight as well as the solution of dual spin turn. This study is done based on case-study simulation, which includes two-state control scheme composed of open-loop maneuver and energy dissipation device. Furthermore, we investigate the stability for the verification of all control cases after implementing two-stage control. We also provide the simulation scenario of flat spin recovery using dual spin turn method as an example.

• Clinical and Experimental Emergency Medicine
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• v.5 no.4
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• pp.264-271
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• 2018

Objective For patients with acute myocardial infarction (AMI), symptoms assessed by emergency medical services (EMS) providers have a critical role in prehospital treatment decisions. The purpose of this study was to evaluate the diagnostic accuracy of EMS provider-assessed cardiac symptoms of AMI. Methods Patients transported by EMS to 4 study hospitals from 2008 to 2012 were included. Using EMS and administrative emergency department databases, patients were stratified according to the presence of EMS-assessed cardiac symptoms and emergency department diagnosis of AMI. Cardiac symptoms were defined as chest pain, dyspnea, palpitations, and syncope. Disproportionate stratified sampling was used, and medical records of sampled patients were reviewed to identify an actual diagnosis of AMI. Using inverse probability weighting, verification bias-corrected diagnostic performance was estimated. Results Overall, 92,353 patients were enrolled in the study. Of these, 13,971 (15.1%) complained of cardiac symptoms to EMS providers. A total of 775 patients were sampled for hospital record review. The sensitivity, specificity, positive predictive value, and negative predictive value of EMS provider-assessed cardiac symptoms for the final diagnosis of AMI was 73.3% (95% confidence interval [CI], 70.8 to 75.7), 85.3% (95% CI, 85.3 to 85.4), 3.9% (95% CI, 3.6 to 4.2), and 99.7% (95% CI, 99.7 to 99.8), respectively. Conclusion We found that EMS provider-assessed cardiac symptoms had moderate sensitivity and high specificity for diagnosis of AMI. EMS policymakers can use these data to evaluate the pertinence of specific prehospital treatment of AMI.

• Journal of Environmental Science International
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• v.30 no.12
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• pp.1053-1065
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• 2021

In this study, the prediction technology of Hydrological Quantitative Precipitation Forecast (HQPF) was improved by optimizing the weather predictors used as input data for machine learning. Results comparison was conducted using bias and Root Mean Square Error (RMSE), which are predictive accuracy verification indicators, based on the heavy rain case on August 21, 2021. By comparing the rainfall simulated using the improved HQPF and the observed accumulated rainfall, it was revealed that all HQPFs (conventional HQPF and improved HQPF 1 and HQPF 2) showed a decrease in rainfall as the lead time increased for the entire grid region. Hence, the difference from the observed rainfall increased. In the accumulated rainfall evaluation due to the reduction of input factors, compared to the existing HQPF, improved HQPF 1 and 2 predicted a larger accumulated rainfall. Furthermore, HQPF 2 used the lowest number of input factors and simulated more accumulated rainfall than that projected by conventional HQPF and HQPF 1. By improving the performance of conventional machine learning despite using lesser variables, the preprocessing period and model execution time can be reduced, thereby contributing to model optimization. As an additional advanced method of HQPF 1 and 2 mentioned above, a simulated analysis of the Local ENsemble prediction System (LENS) ensemble member and low pressure, one of the observed meteorological factors, was analyzed. Based on the results of this study, if we select for the positively performing ensemble members based on the heavy rain characteristics of Korea or apply additional weights differently for each ensemble member, the prediction accuracy is expected to increase.

• Atmosphere
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• v.32 no.3
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• pp.179-189
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• 2022

The purpose of this study is to build and evaluate a high-resolution (50 m) KMAPP (Korea Meteorological Administration Post Processing) reflecting building data. KMAPP uses LDAPS (Local Data Assimilation and Prediction System) data to detail ground wind speed through surface roughness and elevation corrections. During the detailing process, we improved the vegetation roughness data to reflect the impact of city buildings. AWS (Automatic Weather Station) data from a total of 48 locations in the metropolitan area including Seoul in 2019 were used as the observation data used for verification. Sensitivity analysis was conducted by dividing the experiment according to the method of improving the vegetation roughness length. KMAPP has been shown to improve the tendency of LDAPS to over simulate surface wind speeds. Compared to LDAPS, Root Mean Square Error (RMSE) is improved by approximately 23% and Mean Bias Error (MBE) by about 47%. However, there is an error in the roughness length around the Han River or the coastline. Accordingly, the surface roughness length was improved in KMAPP and the building information was reflected. In the sensitivity experiment of improved KMAPP, RMSE was further improved to 6% and MBE to 3%. This study shows that high-resolution KMAPP reflecting building information can improve wind speed accuracy in urban areas.

• Journal of ILASS-Korea
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• v.28 no.1
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• pp.1-9
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• 2023

In the cylinder of gasoline direct injection engines, the spray targeting from injectors is of great significance for fuel consumption and pollutant emissions. The automotive industry is putting a lot of effort into improving injector targeting accuracy. To improve the targeting accuracy of injectors, it is necessary to develop models that can predict the spray targeting positions. When developing spray targeting models, the most used technique is computational fluid dynamics (CFD). Recently, due to the superiority of machine learning in prediction accuracy, the application of machine learning in this field is also receiving constant attention. The purpose of this study is to build a machine learning model that can accurately predict spray targeting based on the design parameters of injectors. To achieve this goal, this study firstly used laser sheet beam visualization equipment to obtain many spray cross-sectional images of injectors with different parameters at different injection pressures and measurement planes. The spray images were processed by MATLAB code to get the targeting coordinates of sprays. A total of four models were used for the prediction of spray targeting coordinates, namely ANN, LSTM, Conv1D and Conv1D & LSTM. Features fed into the machine learning model include injector design parameters, injection conditions, and measurement planes. Labels to be output from the model are spray targeting coordinates. In addition, the spray data of 7 injectors were used for model training, and the spray data of the remaining one injector were used for model performance verification. Finally, the prediction performance of the model was evaluated by R² and RMSE. It is found that the Conv1D&LSTM model has the highest accuracy in predicting the spray targeting coordinates, which can reach 98%. In addition, the prediction bias of the model becomes larger as the distance from the injector tip increases.