• 한국전기전자재료학회논문지
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• 제31권7호
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• pp.462-468
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• 2018

The piezoelectric energy harvesting characteristics of a trapezoidal cantilever generator with lead zirconate titanate (PZT) laminate were investigated with various Ag inner electrodes. The piezoelectric mode of operation was a transverse mode by using a planar electrode pattern. The piezoelectric cantilever generator was fabricated using trapezoidal cofired-PZT/Ag laminates by five specimens of 2, 3, 4, 7, and 13 layers of Ag. As the number of Ag electrodes increased, impedance and output voltage at resonant frequency significantly decreased, and capacitance and output current showed an increasing tendency. A maximum output power density of $7.60mW/cm^3$ was realized for the specimen with seven Ag layers in the optimal condition of acceleration (1.2 g) and resistive load ($600{\Omega}$), which corresponds to a normalized power factor of $5.28mW/g^2{\cdot}cm^3$.

• 한국전자파학회논문지
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• 제26권10호
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• pp.907-913
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• 2015

본 논문은 회로 시뮬레이션과 전자기 시뮬레이션을 병행하여 디지털 스위칭 회로가 실장된 PCB 상의 근접 자계 분포를 예측하는 방법을 제시한다. 제안 방법은 스위칭 회로의 신호 및 전원 포트를 정현 전원으로 구동하여 규격화된 근접 자계 분포를 구하고, 이 결과를 실제 스위칭 회로에 의한 전류의 주파수 스펙트럼으로 가중하여 근접 자계를 예측한다. 예측 방법론을 검증하기 위해 링 발진기와 출력 버퍼로 구성된 스위칭 집적 회로를 제작하고, 칩-온-보드(Chip On Board, 칩-온-보드) 형태로 평가하였다. 자계 프로브를 이용하여 PCB상에서 표면 자계 분포를 측정하였으며, 시뮬레이션 결과와 비교하였다. 측정 결과와 시뮬레이션 계산 결과는 5차 하모닉 주파수까지 10 dB 이내로 일치함을 확인하였다.

• 농업과학연구
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• 제15권2호
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• pp.164-173
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• 1988

This study is to analyse marketing and industrial structure of meat processing industry and to examine it's current situation related to agriculture. For this purpose 1. This paper surveys the history of meat processing industry, and analyses current situation of meat processing industry, based upon economic statistic data. 2. For the research of marketing structure of meat processing products, this paper not only ciassifies into three catagories; the supply of raw meat, main marketing organization, and path, but measures magnitude of Marketing Bill and Farmer's Share practically through statistic data and an on-the-spot survey. 3. This study also attempt to explain the relation of meat processing industry and the other industry and role of meat processing industry is Korean economy by the use of input-output table. The results of the study are as follows; 1. The meat processing industry in Korea produces low quality, and expensive raw meat with limited quality, inefficiency of marketing structure, and unrelated livestock and meat processing industry. 2. Korea market structure of meat processing products has been changed into oligopoly from monopoly by a new corporation entered into monopoly and the size of meat processing market firms has been normalized. 3. Meat processing industry is very important considering with its high back-linkage-effect. In order to develop meat processing industry and marketing, it is essential that operation of intergrated meat market center, meat market center should be efficiently operated. The efficient utilization of domestic resource for raw meat and development of processing technique have to be required, by means of the governmental support.

• 환경영향평가
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• 제29권4호
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• pp.272-285
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• 2020

본 연구에서는 3차원 기상 및 대기질 모델의 입출력 자료를 평가하는 데 필요한 통계 검증지표를 선별하고, 선정된 검증지표의 기준치를 조사하여 그 결과를 요약하였다. 여러 국내외 문헌과 최근 논문 검토를 통해 최종 선정된 통계 검증지표는 MB (Mean Bias), ME (Mean Error), MNB (Mean Normalized Bias Error), MNE (Mean Absolute Gross Error), RMSE (Root Mean Square Error), IOA (Index of Agreement), R (Correlation Coefficient), FE (Fractional Error), FB (Fractional Bias)로 총 9가지이며, 국내외 문헌을 통해 그 기준치를 확인하였다. 그 결과, 기상모델의 경우 대부분 MB와 ME가 주요 지표로 사용되어 왔고, 대기질 모델 결과는 NMB와 NME 지표가 주로 사용되었으며, 그 기준치의 차이를 분석하였다. 아울러 이들 통계 검증지표값을 이용하여 모델 예측 결과를 효과적으로 비교하기 위한 표출 도식으로 축구 도식, 테일러 도식, Q-Q (Quantile-Quantile) 도식의 장단점을 분석하였다. 나아가 본 연구 결과를 기반으로 우리나라의 산악지역의 특수성 등이 잘 고려된 통계 검증지표의 기준치 설정 등의 추가연구가 효과적으로 진행될 수 있기를 기대한다.

• Geomechanics and Engineering
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• 제37권5호
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• pp.475-498
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• 2024

The prediction of the susceptibility of soil to liquefaction using a limited set of parameters, particularly when dealing with highly unbalanced databases is a challenging problem. The current study focuses on different ensemble learning classification algorithms using highly unbalanced databases of results from in-situ tests; standard penetration test (SPT), shear wave velocity (V_s) test, and cone penetration test (CPT). The input parameters for these datasets consist of earthquake intensity parameters, strong ground motion parameters, and in-situ soil testing parameters. liquefaction index serving as the binary output parameter. After a rigorous comparison with existing literature, extreme gradient boosting (XGBoost), bagging, and random forest (RF) emerge as the most efficient models for liquefaction instance classification across different datasets. Notably, for SPT and V_s-based models, XGBoost exhibits superior performance, followed by Light gradient boosting machine (LightGBM) and Bagging, while for CPT-based models, Bagging ranks highest, followed by Gradient boosting and random forest, with CPT-based models demonstrating lower G_mean(error), rendering them preferable for soil liquefaction susceptibility prediction. Key parameters influencing model performance include internal friction angle of soil (ϕ) and percentage of fines less than 75 µ (F₇₅) for SPT and V_s data and normalized average cone tip resistance (q_c) and peak horizontal ground acceleration (a_max) for CPT data. It was also observed that the addition of V_s measurement to SPT data increased the efficiency of the prediction in comparison to only SPT data. Furthermore, to enhance usability, a graphical user interface (GUI) for seamless classification operations based on provided input parameters was proposed.