• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.34-37
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• 2005

An efficient and robust neural network-based scheme is introduced in this paper to perform automatic cloud detection. Unlike many existing cloud detection schemes which use thresholding and statistical methods, we used the artificial neural network methods, the multi-layer perceptrons (MLP) with back-propagation algorithm and radial basis function (RBF) networks for cloud detection from Geostationary satellite images. We have used a simple scene (a mixed scene containing only cloud and clear sky). The main results show that the neural networks are able to handle complex atmospheric and meteorological phenomena. The experimental results show that two methods performed well, obtaining a classification accuracy reaching over 90 percent. Moreover, the RBF model is the most effective method for the cloud classification.

• Advances in environmental research
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• 제4권4호
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• pp.219-231
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• 2015

In recent years, raising air pollutants has become as a big concern, especially in metropolitan cities such as Tehran. Therefore, forecasting the level of pollutants plays a significant role in air quality management. One of the forecasting tools that can be used is an artificial neural network which is able to model the complicated process of air pollution. In this study, we applied two different methods of artificial neural networks, the Multilayer Perceptron (MLP) and Radial Basis Function (RBF), to predict the hourly air concentrations of toluene in Tehran. Hourly temperature, wind speed, humidity and $NO_x$ were selected as inputs. Both methods had acceptable results; however, the RBF neural network produced better results. The coefficient of determination ($R^2$) between the observed and predicted data was 0.9642 and 0.99 for MLP and RBF neural networks, respectively. The results of the mean bias errors (MBE) were 0.00 and -0.014 for RBF and MLP, respectively which indicate the adequacy of the models. The index of agreement (IA) between the observed and predicted data was 0.999 and 0.994 in the RBF and the MLP, respectively which indicates the efficiency of the models. Finally, sensitivity analysis related to the MLP neural network determined that temperature was the most significant factor in air concentration of toluene in Tehran which may be due to the volatile nature of toluene.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 춘계학술대회 학술발표 논문집
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• pp.67-70
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• 1998

There were many cases to apply artificial intelligence to medicine. In this paper, we present the prediction model of the development of the NIDDM(noninsulin-dependent diabetes mellitus). It is not difficult that doctor diagnose patient as DM(diabetes mellitus). However NIDDM is usually developmented later on 40 years old and symptom appeares gradually. So screening test or prediction model is needed absolutely. Our model predicts development of NIDDM with still normal data 2 year ago. Prediction models developed are both MLP(multilayer perceptron) with backpropagation training and RBFN(radial basis function network). Performance of both models were evaluated with likelihood ratio. MLP was about two and RBFN was about three. We expect that models developed can prevent development of DM and utilize normal data.

• Advances in environmental research
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• 제5권3호
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• pp.153-167
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• 2016

We applied multilayer perceptron (MLP) and radial basis function (RBF) neural network in upstream and downstream water quality stations of the Karaj Reservoir in Iran. For both neural networks, inputs were pH, turbidity, temperature, chlorophyll-a, biochemical oxygen demand (BOD) and nitrate, and the output was dissolved oxygen (DO). We used an MLP neural network with two hidden layers, for upstream station 15 and 33 neurons in the first and second layers respectively, and for the downstream station, 16 and 21 neurons in the first and second hidden layer were used which had minimum amount of errors. For learning process 6-fold cross validation were applied to avoid over fitting. The best results acquired from RBF model, in which the mean bias error (MBE) and root mean squared error (RMSE) were 0.063 and 0.10 for the upstream station. The MBE and RSME were 0.0126 and 0.099 for the downstream station. The coefficient of determination ($R^2$) between the observed data and the predicted data for upstream and downstream stations in the MLP was 0.801 and 0.904, respectively, and in the RBF network were 0.962 and 0.97, respectively. The MLP neural network had acceptable results; however, the results of RBF network were more accurate. A sensitivity analysis for the MLP neural network indicated that temperature was the first parameter, pH the second and nitrate was the last factor affecting the prediction of DO concentrations. The results proved the workability and accuracy of the RBF model in the prediction of the DO.

• 예술인문사회 융합 멀티미디어 논문지
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• 제9권6호
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• pp.757-772
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• 2019

본 논문에서는 아시아 38개 컨테이너항만 들을 대상으로 10년(2007년-2016년)동안의 4개의 투입요소(선석길이, 수심, 총면적, 크레인 수)와 1개의 산출요소(컨테이너화물 처리량)를 이용하여 인공신경망모형(다층퍼셉트론, 방사형기저함수)으로 클러스터링에 영향을 미친 요소들을 파악하였으며, 1단계 교차효율성 메트릭스를 이용한 군집 수를 사회연결망모형과 타부서치모형에 적용하여 클러스터링을 파악하고 효율성을 측정하였다. 또한 2단계효율성 메트릭스모형을 이용한 클러스터링을 파악하고 효율성을 측정하여 1단계 교차효율성 메트릭스에 의한 측정결과와 비교하였다. 주요한 실증분석 결과는 다음과 같다. 첫째, 인공신경망모형에 의해서 측정해 보았을 때, 군집에 영향을 많이 미친 요소별로 제시해 보면 컨테이너화물 처리량, 선석길이와 수심, 총면적, 크레인 수의 순서로 나타났다. 둘째, 사회연결망분석에서는 2단계 교차효율성(Type IV)메트릭스에 의한 군집은 benevolent 와 aggressive 모형에서 매년 동일한 결과를 보였다. 셋째, 클러스터링 후에 1단계 교차효율성 모형에 비해서 사회연결망 모형 분석과 타부서치 모형 분석에서 국내항만들의 효율성이 거의(사회연결망 모형에서 인천항의 경우 제외) 악화되는 것으로 나타났다. 다섯째, 일반적인 투입지향, 규모수확불변하의 CCR모형의 효율성 측정결과와 비교했을 때는 클러스터링이 모든 항만들에 대해서 약 37%이상의 효율성을 증대시켰다. 여섯째, 사회연결망모형과 타부서치모형에 의해서 클러스터링 되는 항만들은 부산항(고베, 오사카, 포트클랑, 탄중 펠파스, 마닐라항), 인천항(사히드 라자히, 광양), 광양항(아카바, 포트 슐탄 카바스, 담만, 크호르 파칸, 인천)으로 나타났다. 한국항만당국은 본 연구에서 이용된 방법을 도입하여 항만개선방안을 마련해야만 한다.