• Journal of Korean Association for Spatial Structures
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• v.6 no.3 s.21
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• pp.35-41
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• 2006

Various monitoring sensors have been used for the monitoring, damage and vibration prediction of structures. They have been used for sensing damage in a variety of materials and structures such as piezoelectric materials (PZT) and electric strain gauges. But, many experiments of vibration were not performed. The PZT changes physical force if load cell to electrical signal due to deformation of structure. The voltage change of piezoelectric sensors for plates are used for vibration prediction. In this study, a fundamental study for vibration prediction using piezoelectric sensors are discussed in plates.

• Journal of Auto-vehicle Safety Association
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• v.6 no.2
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• pp.36-42
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• 2014

In order to prevent the sharp edge during the side impact, a cause analysis and CAE based risk prediction were carried out in this study. It was found that sharp edge occurs mainly because of stiffness difference between the major parts and structural stress concentration. It could be improved by directly reinforcing the crack initiation region or by weakening the joints connecting the parts. The fracture criterion based on major in-plain strain was suggested and the risk prediction process for sharp edge prevention was established.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.303-303
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• 2011

효율적인 수자원 관리를 위해서는 미래 수문자료의 예측치에 대한 구간을 추정하여 미래에 관측될 자료에 대한 정보를 얻는 문제는 어렵지만 중요한 부분에 해당한다. 특히 중장기 유량예측은 입력변수의 불확실성이 크므로 확률론적 방법을 적용한 예측이 유리하다. 본 연구에서는 SSARR 모형을 이용하여 현재 유역의 상태에 과거에 재현되었던 강우를 결합한 앙상블 유출시나리오를 생성하였다. 그리고 대청댐 월 유입량에 대한 확률론적 예측방안을 제시하기위하여 과거 시나리오의 관측 ESP(Ensemble Streamflow Prediction)확률 및 Croley방법, PDF-Ratio방법을 한국의 기상예측정보 실정에 맞는 가중치 부여방안으로 적용하여 분석하였다. 2010년도 상반기를 기준으로 각 분석 기법별 정확성을 검증한 결과 Croley, PDF-Ratio 등 기상전망을 가중치로 부여한 확률론적 예측기법의 효용성을 확인하였다.

• Journal of Korea Water Resources Association
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• v.36 no.2
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• pp.251-261
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• 2003

The resolution issue of various soil moisture prediction parameters such as wetness index and curvatures is addressed. The sensitivities of various index are discussed on the base of the statistical aspects. The statistical analysis of three flow determination algorithms on the DEM is performed. The upslope area associated with SFD algorithm appear to more sensitive than the parameters of the other algorithms(MFD, DEMON). The wetness index shows relatively less variation both in resolution and the calculation Procedures.

• Journal of Korean Association for Spatial Structures
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• v.20 no.4
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• pp.111-121
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• 2020

There has been increasing interest in UHPC (Ultra-High Performance Concrete) materials in recent years. Owing to the superior mechanical properties and durability, the UHPC has been widely used for the design of various types of structures. In this paper, machine learning based compressive strength prediction methods of the UHPC are proposed. Various regression-based machine learning models were built to train dataset. For train and validation, 110 data samples collected from the literatures were used. Because the proportion between the compressive strength and its composition is a highly nonlinear, more advanced regression models are demanded to obtain better results. The complex relationship between mixture proportion and concrete compressive strength can be predicted by using the selected regression method.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.135-135
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• 2021

In water resources management, rainfall prediction with high accuracy is still one of controversial issues particularly in countries facing heavy rainfall during wet seasons in the monsoon climate. The aim of this study is to develop an artificial neural network (ANN) for predicting future six months of rainfall data (from April to September 2020) from daily meteorological data (from 1971 to 2019) such as rainfall, temperature, wind speed, and humidity at Seoul, Korea. After normalizing these data, they were trained by using a multilayer perceptron (MLP) as a class of the feedforward ANN with 15,000 neurons. The results show that the proposed method can analyze the relation between meteorological datasets properly and predict rainfall data for future six months in 2020, with an overall accuracy over almost 70% and a root mean square error of 0.0098. This study demonstrates the possibility and potential of MLP's applications to predict future daily rainfall patterns, essential for managing flood risks and protecting water resources.

• Journal of information and communication convergence engineering
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• v.21 no.4
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• pp.294-299
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• 2023

Micro ribonucleic acids (miRNAs) can regulate the protein expression levels of genes in the human body and have recently been reported to be closely related to the cause of disease. Determining the genes related to miRNAs will aid in understanding the mechanisms underlying complex miRNAs. However, the identification of miRNA-related genes through wet experiments (in vivo, traditional methods are time- and cost-consuming). To overcome these problems, recent studies have investigated the prediction of miRNA relevance using deep learning models. This study presents a method for predicting the relationships between miRNAs and genes. First, we reconstruct a negative dataset using the proposed method. We then extracted the feature using an autoencoder, after which the feature vector was concatenated with the original data. Thereafter, the concatenated data were used to train a long short-term memory model. Our model exhibited an area under the curve of 0.9609, outperforming previously reported models trained using the same dataset.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.1
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• pp.17-21
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• 2023

The primary objective of this research is to utilize machine learning models to analyze factors tailored to each dataset for predicting mental health conditions. The study aims to develop appropriate models based on specific datasets, with the goal of accurately predicting mental health states through the analysis of distinct factors present in each dataset. This approach seeks to design more effective strategies for the prevention and intervention of depression, enhancing the quality of mental health services by providing personalized services tailored to individual circumstances. Overall, the research endeavors to advance the development of personalized mental health prediction models through data-driven factor analysis, contributing to the improvement of mental health services on an individualized basis.

• The Journal of Industrial Distribution & Business
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• v.8 no.4
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• pp.47-52
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• 2017

Purpose - This current study will investigate the average financial ratio of top and failed five-star hotels in the Jeju area. A total of 14 financial ratio variables are utilized. This study aims to; first, assess financial ratio of the first-class hotels in Jeju to establishing variables, second, develop distress prediction model for the first-class hotels in Jeju district by using logit analysis and third, evaluate distress prediction capacity for the first-class hotels in Jeju district by using logit analysis. Research design, data, and methodology - The sample was collected from year 2015 and 14 financial ratios of 12 first-class hotels in Jeju district. The results from the samples were analyzed by t-test, and the independent variables were chosen. This was an empirical study where the distress prediction model was evaluated by logit analysis. This current research has focused on critically analyzing and differentiating between the top and failed hotels in the Jeju area by utilizing the 14 financial ratio variables. Results - The verification result of the accuracy estimated by logit analysis has shown to indicate that the distress prediction model's distress prediction capacity was 83.3%. In order to extract the factors that differentiated the top hotels in the Jeju area from the failed hotels among the 14 chosen, the analysis of t-black was utilized by independent variables. Logit analysis was also used in this study. As a result, it was observed that 5 variables were statistically significant and are included in the logit analysis for discernment of top and failed hotels in the Jeju area. Conclusions - The distress prediction press' prediction capability was compared in this research analysis. The distress prediction press prediction capability was shown to range from 75-85% by logit analysis from a previous study. In this current research, the study's prediction capacity was shown to be 83.33%. It was considered a high number and was found to belong to the range of the previous study's prediction capacity range. From a practical perspective, the capacity of the assessment of the distress prediction model in the top and failed hotels in the Jeju area was considered to be a prominent factor in applications of future hotel appraisal.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.2161-2166
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• 2009

It is important for 0-6 hour nowcasting to provide for a high-quality initial condition in a meso-scale atmospheric model by a data assimilation of several observation data. The polarimetric radar data is expected to be assimilated into the forecast model, because the radar has a possibility of measurements of the types, the shapes, and the size distributions of hydrometeors. In this paper, an impact on rainfall prediction of the data assimilation of hydrometeor types (i.e. raindrop, graupel, snowflake, etc.) is evaluated. The observed information of hydrometeor types is estimated using the fuzzy logic algorism. As an implementation, the cloud-resolving nonhydrostatic atmospheric model, CReSS, which has detail microphysical processes, is employed as a forecast model. The local ensemble transform Kalman filter, LETKF, is used as a data assimilation method, which uses an ensemble of short-term forecasts to estimate the flowdependent background error covariance required in data assimilation. A heavy rainfall event occurred in Okinawa in 2008 is chosen as an application. As a result, the rainfall prediction accuracy in the assimilation case of both hydrometeor types and the Doppler velocity and the radar echo is improved by a comparison of the no assimilation case. The effects on rainfall prediction of the assimilation of hydrometeor types appear in longer prediction lead time compared with the effects of the assimilation of radar echo only.