• 한국펄프종이공학회:학술대회논문집
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• 한국펄프종이공학회 2004년도 춘계학술발표논문집
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• pp.86-96
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• 2004

Identification of dynamics of short circulation during grade change operations in paper mills is very important for the effective plant operation. In the present study a prediction method of One Pass Retention(OPR) is proposed based on the neural network. The present method is used to analyze the dynamics of short circulation during grade change. Properties of the product paper largely depend upon the change in the OPR. In the present study the OPR is predicted from the training of the network by using grade change operation data. The results of the prediction are applied to the modeling equation to give flow rates and consistencies of short circulation.

• 한국콘텐츠학회논문지
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• 제14권8호
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• pp.30-39
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• 2014

본 논문에서는 예측영상을 생성하고 예측영상과 원본영상 사이의 차분영상의 히스토그램을 이용하여 워터마크를 삽입하는 알고리즘을 제안한다. 제안하는 알고리즘은 예측영상의 예측성능을 향상시키기 위해 적응적으로 참조 픽셀을 선택하였다. 선택된 참조픽셀은 양선형 보간과 방향성 예측을 통해 나머지 픽셀들을 예측하는데 이용된다. 실험결과 PSNR의 증가와 많은 워터마크 삽입량을 확인할 수 있었다.

• 한국공간구조학회논문집
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• 제6권3호
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• pp.35-41
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• 2006

구조물의 모니터링과 손상 및 진동예측에 많은 센서들이 사용되고 있으며, 압전소자 및 변형게이지는 재료 및 구조물의 손상에 사용되고 있다. 그러나 진동에 대한 실험은 미진한 실정이다. 압전소자는 구조물의 변형되었을 때 로드셀의 경우에서처럼 작용되는 외력을 전기적인 신호로 바꾸어주는 센서이다. 이를 이용하여, 철근 콘크리트 판에서 진동예측을 압전소자의 전압변화로 사용하였다. 본 연구는 판에서 압전소자를 사용하여 진동을 예측하기 위한 기초적 연구이다.

• 자동차안전학회지
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• 제6권2호
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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.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2011년도 학술발표회
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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.

• 한국수자원학회논문집
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• 제36권2호
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• pp.251-261
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• 2003

여러 가지 토양수분의 예측인자에 대한 해상도 문제를 고찰하였다. 다양한 인자에 대한 민감도는 통계적인 분석을 기반으로 논의되었다. 수치지형모형에서 세 가지 흐름 결정 알고리즘의 해상도에 대한 통계적인 분석이 수행되었다. 단방향 흐름알고리즘으로 계산한 상부사면 기여면적은 다른 두 알고리즘(다방향 알고리즘, DEMON)보다 더욱 민감한 것으로 나타났다. 습윤지수의 경우는 해상도나 계산과정의 변화에 상대적으로 민감도가 미소한 것으로 나타났다.

• 한국공간구조학회논문집
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• 제20권4호
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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.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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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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• 제21권4호
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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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• 제1권1호
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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.