• 전자공학회논문지 IE
• /
• 제45권2호
• /
• pp.34-39
• /
• 2008

본 논문은 통계 기법인 판별 분석 함수를 이용하여 효율적으로 3차원 모델을 검색하는 시스템을 구현하였다. 제안한 방법은 판별분석 함수를 이용하여 색인으로 검색하는 기법으로, 색인의 생성은 Osada의 D2방법에 의해 추출된 128개의 특징벡터에 통계치(범위, 최소값, 평균, 표준편차, 왜도, 척도)를 변수로 판별분석 함수의 값을 색인 값으로 생성하였다. 쿼리 모델 검색 시 1차 검색으로 쿼리와 저장된 클래스(동종의 모델 그룹)의 색인을 비교하여 상위 2%이내(98% 이상)의 클래스를 추출하여 추출된 클래스에 속하는 모델만을 검색하였다. 이 방법은 검색시간을 단축시키는 효율적인 검색 기법임을 구현을 통해 알 수 있었다. 제안한 방법은 기존의 방법(Osada)보다 3차원 모델 검색 시간을 57%로 단축시켰으며, 쿼리 모델 검색 시 유사모델이 최초로 발견되는 정확도(pecision)가 0.362로 기존의 방법보다 44.8%의 효율이 있었음을 알 수 있었다.

• Earthquakes and Structures
• /
• 제18권1호
• /
• pp.73-82
• /
• 2020

With the coal mining depth increasing, both stress and gas pressure rapidly enhance, causing coal and gas outburst risk to become more complex and severe. The conventional method for prediction of coal and gas outburst adopts one prediction index and corresponding critical value to forecast and cannot reflect all the factors impacting coal and gas outburst, thus it is characteristic of false and missing forecasts and poor accuracy. For the reason, based on analyses of both the prediction indicators and the factors impacting coal and gas outburst at the test site, this work carefully selected 6 prediction indicators such as the index of gas desorption from drill cuttings Δh₂, the amount of drill cuttings S, gas content W, the gas initial diffusion velocity index ΔP, the intensity of electromagnetic radiation E and its number of pulse N, constructed the Bayes discriminant analysis (BDA) index system, studied the BDA-based multi-index comprehensive model for forecast of coal and gas outburst risk, and used the established discriminant model to conduct coal and gas outburst prediction. Results showed that the BDA - based multi-index comprehensive model for prediction of coal and gas outburst has an 100% of prediction accuracy, without wrong and omitted predictions, can also accurately forecast the outburst risk even for the low indicators outburst. The prediction method set up by this study has a broad application prospect in the prediction of coal and gas outburst risk.

• Wind and Structures
• /
• 제36권6호
• /
• pp.393-404
• /
• 2023

Skewness and kurtosis are important higher-order statistics for simulating non-Gaussian wind pressure series on low-rise buildings, but their predictions are less studied in comparison with those of the low order statistics as mean and rms. The distribution gradients of skewness and kurtosis on roofs are evidently higher than those of mean and rms, which increases their prediction difficulty. The conventional artificial neural networks (ANNs) used for predicting mean and rms show unsatisfactory accuracy in predicting skewness and kurtosis owing to the limited capacity of shallow learning of ANNs. In this work, the deep neural networks (DNNs) model with the ability of deep learning is introduced to predict the skewness and kurtosis on a low-rise building. For obtaining the optimal generalization of the DNNs model, the hyper parameters are automatically determined by Bayesian Optimization (BO). Moreover, for providing a benchmark for future studies on predicting higher order statistics, the data sets for training and testing the DNNs model are extracted from the internationally open NIST-UWO database, and the prediction errors of all taps are comprehensively quantified by various error metrices. The results show that the prediction accuracy in this study is apparently better than that in the literature, since the correlation coefficient between the predicted and experimental results is 0.99 and 0.75 in this paper and the literature respectively. In the untrained cornering wind direction, the distributions of skewness and kurtosis are well captured by DNNs on the whole building including the roof corner with strong non-normality, and the correlation coefficients between the predicted and experimental results are 0.99 and 0.95 for skewness and kurtosis respectively.

• 한국축산시설환경학회지
• /
• 제21권1호
• /
• pp.1-8
• /
• 2015

This study was conducted to investigate the dispersion prediction of odor from Hanwoo and dairy farms. Gaussian Plume model used in considering of farm size, wind velocity, atmospheric stability and threshold odor unit to prediction of odor dispersion based on the survey on current state of odor emission and control from 9 site of Hanwoo and 9 site of dairy farms. Farm size, wind velocity and atmospheric stability were affected the distance of odor dispersion, showed longer distance in cases of large farm, low wind velocity and stable atmospheric condition. We will suggestion the adjusted distance of odor dispersion according to farm size was estimated to 50~100 m in Hanwoo farm and 50~150 m in dairy farm when apply the 3OU, 5 m/s wind velocity and stable atmospheric condition.

• 대한산업공학회지
• /
• 제34권1호
• /
• pp.98-107
• /
• 2008

The purpose of this paper is to present an optimization scheme that aims at minimizing the expected cost per unittime. This study considers a linear connected-(r, s)-ouI-of-(m, n):f lattice system whose components are orderedlike the elements of a linear (m, n)-matrix. We assume that all components are in the state 1 (operating) or 0(failed) and identical and s-independent. The system fails whenever at least one connected (r, s)-submatrix offailed components occurs. To find the optimal threshold of maintenance intervention, we use a simulatedannealing(SA) algorithm for the cost optimization procedure. The expected cost per unit time is obtained byMonte Carlo simulation. We also has made sensitivity analysis to the different cost parameters. In this study,utility maintenance model is constructed so that minimize the expense under full equipment policy throughcomparison for the full equipment policy and preventive maintenance policy. The full equipment cycle and unitcost rate are acquired by simulated annealing algorithm. The SA algorithm is appeared to converge fast inmulti-component system that is suitable to optimization decision problem.

• 인터넷정보학회논문지
• /
• 제11권2호
• /
• pp.109-127
• /
• 2010

네트워크 중심전(NCW)의 근간은 군사력을 네트워크로 연결함으로써 분산되어 있지만 집중되어 있는 것처럼 운용하고, 공간적이나 시간적인 요소가 주는 제한사항을 극복한다는 것이다. NCW 개념 하에서는 정보기술이나 물리적인 장비보다 도 인지 사회적 영역이 더욱 큰 비중을 차지하고 있다. 본 연구에서는 인지 사회적 영역에 중점을 두고 사용자 관점에서 효과적인 NCW 수행을 위한 영향요인에 대해 고찰하였다. 연구결과 NCW 수행에 긍정적인 영향을 미치는 요인들을 도출할 수 있었는데 서구의 NCW를 이론적으로는 수용하되 우리의 여건과 현실에 맞는 한국적 NCW 체계를 구축하여야 한다.

• 대한원격탐사학회:학술대회논문집
• /
• 대한원격탐사학회 2007년도 Proceedings of ISRS 2007
• /
• pp.153-155
• /
• 2007

Korea Meteorological Administration (KMA) has operationally produced Atmospheric Motion Vector (AMV) from the consecutive MTSAT-1R satellite image dataset. Comparing with radiosonde data, our current AMV scheme shows more than 10 m/s RMSE. Therefore we need to improve continuously its accuracy. Many AMV producers have stated that the bad performance of the Height Assignment (HA) algorithm is the main reason of degrading the accuracy of AMV. The uncertainties in AMV HA can occur in the algorithm itself, used NWP profiles, and the performance of Radiative Transfer Model (RTM) etc. This study introduces currently operated AMV HA schemes and the impacts of NWP profile data and RTM that these schemes use were investigated. Finally we analyzed the relationship between vectors by vector tracking and heights assigned to each vector by using collocated wind profile dataset with radiosonde data. This study is a preliminary work to improve the accuracy of AMV by removing or decreasing the uncertainties in AMV estimation.

• 한국축산시설환경학회지
• /
• 제20권2호
• /
• pp.41-48
• /
• 2014

This study was conducted to investigate the dispersion prediction of odor from swine farms in Korea. Gaussian Plume model used in considering of farm size, wind velocity, atmospheric stability and threshold odor unit to prediction of odor dispersion based on the survey on current state of odor emission and control from 48 site of swine farms. Farm size, wind velocity and atmospheric stability were affected the distance of odor dispersion, showed longer distance in cases of large farm, low wind velocity and stable atmospheric condition. We will suggestion the adjusted distance of odor dispersion according to farm size was estimated to 180 m in small farm and 320 m in large farm when apply the 3 OU, 5 m/s wind velocity and stable atmospheric condition.

• 한국방재학회 논문집
• /
• 제3권2호
• /
• pp.139-145
• /
• 2003

본 연구에서는 다양한 크기의 원환에 따른 균등원통교각 주위의 세굴심을 분석하여 세굴심 감소효과 및 원환의 적정 크기를 제시하였다. 이를 위하여 수리모형실험을 실시하였으며, 교각에 원환을 설치한 경우 원환을 하상 위보다 하상 아래에 설치하는 것이 더욱 효과적이었으며, 원환의 크기가 클수록 세굴심 감소 효과가 뚜렷하였다. 교각 직경에 대한 원환 직경의 비(W/D)가 2인 경우 약 67% 세굴심이 감소하였으며, 퇴적고는 약 70%정도 증가하였다. 세굴심 감소 효과, 세굴공 및 퇴적고의 분석 결과를 토대로 W/D=2인 원환을 최적 크기로 제안하였다.

• Smart Structures and Systems
• /
• 제2권2호
• /
• pp.189-208
• /
• 2006

Monitoring large-scale civil engineering structures such as offshore platforms and high-large buildings requires a large number of sensors of different types. Innovative sensor data information technologies are very extremely important for data transmission, storage and retrieval of large volume sensor data generated from large sensor networks. How to obtain the optimal sensor set and placement is more and more concerned by researchers in vibration-based SHM. In this paper, a method of determining the sensor location which aims to extract the dynamic parameter effectively is presented. The method selects the number and place of sensor being installed on or in structure by through the tolerance domain statistical inference algorithm combined with second order sensitivity technology. The method proposal first finds and determines the sub-set sensors from the theoretic measure point derived from analytical model by the statistical tolerance domain procedure under the principle of modal effective independence. The second step is to judge whether the sorted out measured point set has sensitive to the dynamic change of structure by utilizing second order characteristic value sensitivity analysis. A 76-high-building benchmark mode and an offshore platform structure sensor optimal selection are demonstrated and result shows that the method is available and feasible.