• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2018년도 학술발표회
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• pp.258-258
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• 2018

In this study, a hydraulic flow model and an error correction model are combined to improve the flood simulation accuracy. First, the hydraulic flow model is calibrated by optimizing the Manning's roughness coefficient that considers spatial and temporal variability. Then, an error correction model were used to correct the systematic errors of the calibrated hydraulic model. The error correction model is developed using Artificial Neural Networks (ANNs) that can estimate the systematic simulation errors of the hydraulic model by considering some state variables as inputs. The input variables are selected using parital mutual information (PMI) technique. It was found that the calibrated hydraulic model can simulate flood water levels with good accuracy. Then, the accuracy of estimated flood levels is improved further by using the error correction model. The method proposed in this study can be used to the flood control and water resources management as it can provide accurate water level eatimation.

• International Journal of Computer Science & Network Security
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• 제21권4호
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• pp.214-222
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• 2021

Recently, the growth of e-commerce in Saudi Arabia has been exponential, bringing new remarkable challenges. A naive approach for product matching and categorization is needed to help consumers choose the right store to purchase a product. This paper presents a machine learning approach for product matching that combines deep learning techniques with standard artificial neural networks (ANNs). Existing methods focused on product matching, whereas our model compares products based on unstructured descriptions. We evaluated our electronics dataset model from three business-to-consumer (B2C) online stores by putting the match products collectively in one dataset. The performance evaluation based on k-mean classifier prediction from three real-world online stores demonstrates that the proposed algorithm outperforms the benchmarked approach by 80% on average F1-measure.

• 한국음향학회:학술대회논문집
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• 한국음향학회 2004년도 춘계학술발표대회 논문집 제23권 1호
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• pp.337-340
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• 2004

한국어 문-음성 합성 시스템(TTS: Text-To-Speech)은 합성음의 자연스러움을 증가시키기 위해 운율 발생 알고리듬을 만들어 시스템에 적용하고 있다. 운율 법칙은 각국의 언어에 대한 언어학적 정보나 자연음에서 구한 운율에 대한 지식을 기반으로 음성 합성 시스템에 적용하고 있다. 그러나 이렇게 구한 운율 법칙이 자연음에 존재하는 모든 운율 법칙을 포함할 수도 없고, 또 추출한 운율 법칙이 틀린 법칙이라면, 합성음의 자연감이나 이해도는 떨어질 것이므로, TTS의 실용화에 장애가 될 수 있다. 이러한 점을 감안하여 본 논문에서는 자연음에 내재하는 운율을 학습할 수 있는 인공 신경망을 이용한 운율발생 신경망을 제안하였다. 훈련단계에서 인공 신경망의 입력 단에 한국어 문장의 음소 열을 차례로 이동시켜 인가하면 입력 단의 중앙에 해당하는 음소의 운율 정보가 출력되도록 훈련시킬 때, 목표 패턴을 이용한 감독학습을 통해, 자연음에 내재하는 운율을 학습하도록 하였다. 평가 단계에서 문장의 음소 열을 입력하고, 추정율을 측정하여 인공 신경망이 한국어 문장에 내재하는 운율을 학습하여 발생시킬 수 있음을 살펴보았다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 하계학술대회 논문집 B
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• pp.485-487
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• 1997

Schemes for detecting and diagnosing faults are presented. Faults are detected when residuals change significantly and thresholds are exceed. Two stage artificial neural networks are applied to diagnose faults. The idealized steady state patterns of residuals are defined and learned by ANNs using back propagation algorithm. The first stage ANN is trained to classify the subsystem in which the various faults are located. The first stage ANN could be also used to detect faults with threshold, checking. The second stage ANNs are trained to discriminate the specific cause of a fault at the subsystem level.

• 한국농공학회논문집
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• 제60권6호
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• pp.43-54
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• 2018

The accurate estimation of reference crop evapotranspiration ($ET_o$) is essential in irrigation water management to assess the time-dependent status of crop water use and irrigation scheduling. The importance of $ET_o$ has resulted in many direct and indirect methods to approximate its value and include pan evaporation, meteorological-based estimations, lysimetry, soil moisture depletion, and soil water balance equations. Artificial neural networks (ANNs) have been intensively implemented for process-based hydrologic modeling due to their superior performance using nonlinear modeling, pattern recognition, and classification. This study adapted two well-known ANN algorithms, Backpropagation neural network (BPNN) and Generalized regression neural network (GRNN), to evaluate their capability to accurately predict $ET_o$ using daily meteorological data. All data were obtained from two automated weather stations (Chupungryeong and Jangsu) located in the Yeongdong-gun (2002-2017) and Jangsu-gun (1988-2017), respectively. Daily $ET_o$ was calculated using the Penman-Monteith equation as the benchmark method. These calculated values of $ET_o$ and corresponding meteorological data were separated into training, validation and test datasets. The performance of each ANN algorithm was evaluated against $ET_o$ calculated from the benchmark method and multiple linear regression (MLR) model. The overall results showed that the BPNN algorithm performed best followed by the MLR and GRNN in a statistical sense and this could contribute to provide valuable information to farmers, water managers and policy makers for effective agricultural water governance.

• Water Engineering Research
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• 제3권2호
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• pp.143-153
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• 2002

As the natural flows in rivers dramatically decrease during drought season in Korea, a deterioration of river water quality is accelerated. Thus, consideration of downstream water quality responding to changes in reservoir release is essential for an integrated watershed management with regards to water quantity and quality. In this study, water quality models based on artificial neural networks (ANNs) method were developed using historical downstream water quality (rm $\NH_3$-N) data obtained from a water treatment plant in Geum river and reservoir release data from Daechung dam. A nonlinear multiple regression model was developed and compared with the ANN models. In the models, the rm NH$_3$-N concentration for next time step is dependent on dam outflow, river water quality data such as pH, alkalinity, temperature, and rm $\NH_3$-N of previous time step. The model parameters were estimated using monthly data from Jan. 1993 to Dec. 1998, then another set of monthly data between Jan. 1999 and Dec. 2000 were used for verification. The predictive performance of the models was evaluated by comparing the statistical characteristics of predicted data with those of observed data. According to the results, the ANN models showed a better performance than the regression model in the applied cases.

• Advances in environmental research
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• 제12권1호
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• pp.17-40
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• 2023

This article presents studies of the adsorption process in a continuous system of fluoride solutions at a concentration of 30 mg/L using a bone char packed in fixed-bed columns, as well as regeneration studies in the same system using HNO₃, HCl and NaOH at 0.01, 0.1 and 1 M. The Thomas Model, Artificial Neural Networks (ANNs), Numerical Integration and Mass Transfer Zone were used for the modeling of asyemmetrical breakthrough curves obtained from the fluoride adsorption on bone char. The maximum adsorption capacity of the breakthrough curves was estimated, and various design parameters of the columns were obtained for the different operating conditions. Results showed that an improvement in the modeling capabilities of the Thomas model can be obtained using ANNs. Moreover, ANNs are useful for determining reasonable and accurate design parameters of packed-bed adsorption columns. This modeling approach can be useful for the process system engineering of dynamic adsorption systems involved in the field of water treatment and purification. It is important to highlight that the obtained results indicate that, when using HCl or HNO₃ at a concentration of 0.1 M, a large number of adsorption-desorption cycles are obtained and, therefore, the highest values of adsorption capacity, which leads to a reduction in operation costs.

• 한국항해항만학회지
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• 제32권1호
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• pp.81-88
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• 2008

컨테이너항만의 물동량 예측은 항만의 개발 및 운영계획을 위해 매우 중요한 과정이다. 일반적으로 회귀분석, ARIMA모형 등의 통계적 방법론을 통해 많은 예측이 이뤄져왔다. 최근의 연구에서는 인공 신경망(ANN)기법을 통한 예측이 이뤄지고 있으며 기존의 선형적인 기법을 대신하고 있다. 본 연구에서는 선형모형과 비선형모형에 강점이 있는 ARIMA모형과 신경망모형을 결합해 보다 효과적인 예측 모형을 개발하고자 한다. 실제 항만의 과거 자료를 통해 모델의 적합성을 측정하였고 항만의 특성에 따라 모형의 적합성이 다양하게 나타났다.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2007년도 추계학술대회 및 제23회 정기총회
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• pp.259-260
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• 2007

컨테이너항만의 물동량 예측은 항만의 계발 및 운영계획을 위해 매우 중요한 과정이다. 일반적으로 회귀분석, ARIMA 등의 통계적 방법론을 통해 많은 예측이 이뤄져왔다. 최근의 연구에서는 인공 신경망(ANN)기법을 통한 예측이 이뤄지고 있으며 기존의 선형적인 기법을 대신하고 있다. 본 연구에서는 선형모델과 비선형모델에 강점이 있는 ARIMA와 신경망 모델을 결합해 보다 효과적인 예측 모델을 개발하고자 한다. 실제 항만의 과거 자료를 통해 모델의 적합성을 측정하였고 항만의 특성에 따라 모형의 적합성이 다양하게 나타났다.

• 산업기술연구
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• 제18권
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• pp.379-384
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• 1998

Conventional short-term load forecasting techniques have limitation in their use on holidays due to dissimilar load behaviors of holidays and insufficiency of pattern data. Thus, a new short-term load forecasting method for special days in anomalous load conditions is proposed in this paper. The proposed method uses two Artificial Neural Networks(ANN); one is for the estimation of load curve, and the other is for the estimation of minimum and maximum value of load. The forecasting procedure is as follows. First, the normalized load curve is estimated by ANN. At next step, minimum and maximum values of load in a special day are estimated by another ANN. Finally, the estimate of load in a whole special day is obtained by combining these two outputs of ANNs. The proposed method shows a good performance, and it may be effectively applied to the practical situations.