• Wind and Structures
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• 제33권6호
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• pp.423-435
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• 2021

Although existing algorithms can predict wind speed using historical observation data, for engineering feasibility, most use moment methods and probability density functions to estimate fitted parameters. However, extreme wind speed prediction accuracy for long-term return periods is not always dependent on how the optimized frequency distribution curves are obtained; long-term return periods emphasize general distribution effects rather than marginal distributions, which are closely related to potential extreme values. Moreover, there are different wind speed parent sample types; how to theoretically select the proper extreme value distribution is uncertain. The influence of different sampling time intervals has not been evaluated in the fitting process. To overcome these shortcomings, updated steps are introduced, involving parameter sensitivity analysis for different sampling time intervals. The extreme value prediction accuracy of unknown parent samples is also discussed. Probability analysis of mean wind is combined with estimation of the probability plot correlation coefficient and the maximum likelihood method; an iterative estimation algorithm is proposed. With the updated steps and comparison using a Monte Carlo simulation, a fitting policy suitable for different parent distributions is proposed; its feasibility is demonstrated in extreme wind speed evaluations at Longhua and Chuansha meteorological stations in Shanghai, China.

• 대한공간정보학회지
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• 제19권3호
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• pp.33-40
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• 2011

본 연구에서는 제주지역의 산사태가능성을 분석하기 위하여 사람의 발길이 많은 사라봉, 별도봉 지역과 송악산 지역의 지형 및 토질공학적 사면 붕괴 유발 인자들을 이용하여 로지스틱회귀분석기법과 인공신경망기법을 GIS기법과 결합하여 예측지도를 작성하고 비교분석하였다. 산사태 예측지도를 작성하기 위해서 산사태 발생에 영향을 주는 사면경사, 고도, 건조밀도, 투수계수, 간극율을 선택하였으며 선정된 지역을 대상으로 실시한 야외조사와 토양물성시험 결과를 정리한 후 이를 토대로 GIS기법을 적용하여 각 레이어별 주제도를 작성하였다. 생성된 주제도를 각각 로지스틱회귀분석기법과 인공신경망기법으로 작성하여 비교분석한 결과 사면경사와 간극율의 경중률이 가장 높게 나타났고, 예측지도는 로지스틱회귀분석기법이 더욱 정확한 결과를 나타내었으며, 도로변과 산책로를 중심으로 산사태 발생가능성이 높게 분포하고 있음을 알 수 있었다.

• Nuclear Engineering and Technology
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• 제52권11호
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• pp.2471-2478
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• 2020

Pumps are essential machinery in the various industries. With the development of high-speed and large-scale pumps, especially high energy density, high requirements have been imposed on the vibration and noise performance of pumps, and cavitation is an important source of vibration and noise excitation in pumps, so it is necessary to improve pumps cavitation performance. The modern pump optimization design method mainly adopts parameterization and artificial intelligence coupling optimization, which requires direct correlation between geometric parameters and pump performance. The existing cavitation performance calculation method is difficult to be integrated into multi-objective automatic coupling optimization. Therefore, a fast prediction method for pump cavitation performance is urgently needed. This paper proposes a novel cavitation prediction method based on impeller pressure isosurface at single-phase media. When the cavitation occurs, the area of pressure isosurface S_iso increases linearly with the NPSH_a decrease. This demonstrates that with the development of cavitation, the variation law of the head with the NPSH_a and the variation law of the head with the area of pressure isosurface are consistent. Therefore, the area of pressure isosurface S_iso can be used to predict cavitation performance. For a certain impeller blade, since the area ratio R_s is proportional to the area of pressure isosurface S_iso, the cavitation performance can be predicted by the R_s. In this paper, a new cavitation performance prediction method is proposed, and the feasibility of this method is demonstrated in combination with experiments, which will greatly accelerate the pump hydraulic optimization design.

• Journal of Mechanical Science and Technology
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• 제19권6호
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• pp.1229-1242
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• 2005

When natural rubber is used for a long period of time, it becomes aged; it usually becomes hardened and loses its damping capability. This aging process affects not only the material property but also the (fatigue) life of natural rubber. In this paper the aging effects on the material property and the fatigue life were experimentally investigated. In addition, several fatigue life prediction equations for natural rubber were proposed. In order to investigate the aging effects on the material property, the load-stretch ratio curves were plotted from the results of the tensile test, the compression test and the simple shear test for virgin and heat-aged rubber specimens. Rubber specimens were heat-aged in an oven at a temperature ranging from $50^{\circ}C$ to $90^{\circ}C$ for a period ranging from 2 days to 16 days. In order to investigate the aging effects on the fatigue life, fatigue tests were conducted for differently heat-aged hourglass-shaped and simple shear specimens. Moreover, finite element simulations were conducted for the specimens to calculate physical quantities occurring in the specimens such as the maximum value of the effective stress, the strain energy density, the first invariant of the Cauchy-Green deformation tensor and the maximum principal nominal strain. Then, four fatigue life prediction equations based on one of the physical quantities could be obtained by fitting the equations to the test data. Finally, the fatigue life of a rubber bush used in an automobile was predicted by using the prediction equations, and it was compared with the test data of the bush to evaluate the reliability of those equations.

• Journal of Radiation Protection and Research
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• 제46권4호
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• pp.204-212
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• 2021

Background: Identification of radioisotopes for plastic scintillation detectors is challenging because their spectra have poor energy resolutions and lack photo peaks. To overcome this weakness, many researchers have conducted radioisotope identification studies using machine learning algorithms; however, the effect of data normalization on radioisotope identification has not been addressed yet. Furthermore, studies on machine learning-based radioisotope identifiers for plastic scintillation detectors are limited. Materials and Methods: In this study, machine learning-based radioisotope identifiers were implemented, and their performances according to data normalization methods were compared. Eight classes of radioisotopes consisting of combinations of ²²Na, ⁶⁰Co, and ¹³⁷Cs, and the background, were defined. The training set was generated by the random sampling technique based on probabilistic density functions acquired by experiments and simulations, and test set was acquired by experiments. Support vector machine (SVM), artificial neural network (ANN), and convolutional neural network (CNN) were implemented as radioisotope identifiers with six data normalization methods, and trained using the generated training set. Results and Discussion: The implemented identifiers were evaluated by test sets acquired by experiments with and without gain shifts to confirm the robustness of the identifiers against the gain shift effect. Among the three machine learning-based radioisotope identifiers, prediction accuracy followed the order SVM > ANN > CNN, while the training time followed the order SVM > ANN > CNN. Conclusion: The prediction accuracy for the combined test sets was highest with the SVM. The CNN exhibited a minimum variation in prediction accuracy for each class, even though it had the lowest prediction accuracy for the combined test sets among three identifiers. The SVM exhibited the highest prediction accuracy for the combined test sets, and its training time was the shortest among three identifiers.

• Journal of Preventive Medicine and Public Health
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• 제49권6호
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• pp.367-375
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• 2016

Objectives: The established theory that breast density is an independent predictor of breast cancer risk is based on studies targeting white women in the West. More Asian women than Western women have dense breasts, but the incidence of breast cancer is lower among Asian women. This meta-analysis investigated the association between breast density in mammography and breast cancer risk in Asian women. Methods: PubMed and Scopus were searched, and the final date of publication was set as December 31, 2015. The effect size in each article was calculated using the interval-collapse method. Summary effect sizes (sESs) and 95% confidence intervals (CIs) were calculated by conducting a meta-analysis applying a random effect model. To investigate the dose-response relationship, random effect dose-response meta-regression (RE-DRMR) was conducted. Results: Six analytical epidemiology studies in total were selected, including one cohort study and five case-control studies. A total of 17 datasets were constructed by type of breast density index and menopausal status. In analyzing the subgroups of premenopausal vs. postmenopausal women, the percent density (PD) index was confirmed to be associated with a significantly elevated risk for breast cancer (sES, 2.21; 95% CI, 1.52 to 3.21; $I^2=50.0%$). The RE-DRMR results showed that the risk of breast cancer increased 1.73 times for each 25% increase in PD in postmenopausal women (95% CI, 1.20 to 2.47). Conclusions: In Asian women, breast cancer risk increased with breast density measured using the PD index, regardless of menopausal status. We propose the further development of a breast cancer risk prediction model based on the application of PD in Asian women.

• 공업화학
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• 제18권6호
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• pp.636-642
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• 2007

다양한 크기를 가진 다성분 입자의 충전분율을 정확하게 예측할 수 있는 소프트웨어 프로그램을 개발하였다. 이를 위하여 다양한 크기의 염소산암모늄(Ammonium perchlorate, AP)과 알루미늄(Aluminum, Al) 입자와 혼합물에 대한 충전분율 측정실험을 수행하였다. 실험에 의해 얻어진 충전분율은 개발된 프로그램에 의해 예측된 값과 비교하였다. 크기가 다른 2종류 입자의 혼합 충전의 경우 상대오차가 0.25~13.13%, 3종류 입자의 혼합 충전의 경우 0.13~10.01%로 나타나 실험값과 잘 일치하였다. 목표 충전분율을 얻기 위한 각 구성입자의 질량분율 contour를 프로그램을 통하여 구할 수 있으며 이를 통하여 충전시스템을 최적화 할 수 있다.

• 한국농림기상학회지
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• 제9권4호
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• pp.217-227
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• 2007

서부양벚과실파리(Rhagolettis indifferens Curran)은 미국 북서부지역 및 California 북부에서 재배되는 단체리(Prunus avium)에 가장 큰 피해를 주는 해충이다. 체리의 수출을 위한 식물검역에서 Zero Tolerance의 규제를 받고 있는 이 해충의 방제를 위해 농가에서는 월동 후 우화 시점부터 지속적으로 약제를 살포하고 있으며, 살충제 처리의 적기를 예측할 수 있는 모형이 절실히 필요한 실정이다. 본 연구는 서부양벚과실파리를 대상으로 월동 후 성충의 우화 및 발생시기, 유충의 밀도변화, 번데기의 용화시기 및 밀도변화 등을 정량적으로 추적하여 개체군 밀도의 경시적 변동과 월동 후 우화시기를 예측하는 모형 검정의 기초자료로 사용코자 수행하였다. 이를 위하여 황색끈끈이트랩, 우화케이지, 용화트랩 등을 이용하여 실제 과원에서 각 태별 발생경과를 경시적으로 조사하였으며, 체리 과실이 달리는 시기부터 일정 간격으로 과실을 수거하여 시기별 과실 내부의 유충 수를 조사하였고, 실내에서 용화케이지를 이용하여 용화시기를 조사하였다. 그 결과 월동 성충의 우화는 5월 중순에 시작하여 6월 초순에 정점에 도달하였고, 6월 중순부터 7월 상순까지 과실 당 1마리 이상의 유충이 존재하였다. 7월 중순에 번데기의 수가 정점에 도달하였으며, 월동 중에 토양습도 등의 조건에 따른 번데기의 발육속도 및 생존율을 측정하기 위해 대량의 번데기를 확보하였다. 이 연구에서 얻어진 자료에서 나타난 과실파리의 월동 후 개체군 밀도변동과 용화시기를 Song et al.(2003)의 모형에서 예측한 결과와 비교한 결과 모형에 의해 예측된 발생일과 실측 발생일과는 1$\sim$2일 차이로 매우 정확하게 예측이 되었다. 이로 미루어 볼 때 본 연구에서 획득한 포장 실측자료는 누적 우화일, 유충의 발육단계, 산란일 등 다른 중요한 생물학적 사건을 예측하는 모형의 정확도 검정에도 잘 활용될 수 있을 것으로 생각된다.

• 한국지반환경공학회 논문집
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• 제24권4호
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• pp.23-29
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• 2023

지반함몰의 주요 발생원인은 지하매설물의 손상으로 알려져 있다. 지반함몰은 상·하수관의 손상으로 인한 물길 형성에 따른 지반 내 토립자의 이동으로 공동이 형성되어 상부지반이 붕괴되는 메커니즘을 보이고 있다. 따라서 지반함몰은 지하매설물의 밀집도가 높은 도심지를 중심으로 발생하고 있으며, 사고 발생 시 인명 및 경제적 피해를 야기하므로 사고에 대한 대비가 반드시 필요하다. 이에 따라 지반함몰 위험을 예측하기 위한 연구가 꾸준히 수행되고 있으며, 본 연구에서는 ○○시의 2개 구를 대상으로 지반함몰 위험도 예측 모델을 제시하고자 하였다. 대상 지역의 지하매설물 속성 데이터(활용년수, 관직경)와 지하매설물 밀집도, 지반함몰 이력 데이터를 활용하여 데이터셋을 구축하고 전처리를 수행한 뒤, 기계학습 모델에 적용하여 최적의 평가지표가 도출되는 모델을 선정하였으며, 선정된 모델의 신뢰도를 평가하고 모델에서 도출되는 지반함몰 위험도 예측 시 활용된 영향인자의 중요도를 제시하고자 하였다.

• 대한기계학회논문집B
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• 제28권6호
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• pp.665-671
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• 2004

A mathematical model is presented to predict the frost properties and heat and mass transfer within the frost layer formed on a cold plate. The model consists of the laminar flow equations for air-side and the empirical correlation of local frost density. The correlation of local frost density used in this study is obtained from various experimental conditions by considering frosting parameters. The numerical results are compared with experimental data to validate the model, and agree well with experimental data within a maximum error of 9%.