• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.6
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• pp.1-13
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• 2023

Irrigation return flow plays an important role in river flow forecasting, basin water supply planning, and determining irrigation water use. Therefore, accurate calculation of irrigation return flow rate is essential for the rational use and management of water resources. In this study, EPA-SWMM (Environmental Protection Agency-Storm Water Management Model) modeling was used to analyze the irrigation return flow and return flow rate of each intake work using irrigation canal network. As a result of the EPA-SWMM, we tried to estimate the quick return flow and delayed return flow using the water supply, paddy field, drainage, infiltration, precipitation, and evapotranspiration. We selected 9 districts, including pumping stations and weirs, to reflect various characteristics of irrigation water, focusing on the four major rivers (Hangang, Geumgang, Nakdonggang, Yeongsangang, and Seomjingang). We analyzed the irrigation period from May 1, 2021 to September 10, 2021. As a result of estimating the irrigation return flow rate, it varied from approximately 44 to 56%. In the case of the Gokseong Guseong area with the highest return flow rate, it was estimated that the quick return flow was 4,677 10³ m³ and the delayed return flow was 1,473 10³ m³ , with a quick return flow rate of 42.6% and a delayed return flow rate of 13.4%.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.1
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• pp.38-46
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• 2024

In general, EEG signal analysis has been the subject of several studies due to its ability to provide an objective mode of recording brain stimulation, which is widely used in brain-computer interface research with applications in medical diagnosis and rehabilitation engineering. In this study, we developed EEG reception hardware to measure electroencephalograms and implemented a processing system, classifying it into server and data processing. It was conducted as an intermediate-stage research on the implementation of a brain-computer interface using electroencephalograms, and was implemented in the form of predicting the user's arm movements according to measured electroencephalogram data. Electroencephalogram measurements were performed using input from four electrodes through an analog-to-digital converter. After sending this to the server through a communication process, we designed and implemented a system flow in which the server classifies the electroencephalogram input using a convolutional neural network model and displays the results on the user terminal.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.163-177
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• 2024

The fingerprint image quality relies on the clearness of separated ridges by valleys and the uniformity of the separation. The condition of skin still dominate the overall quality of the fingerprint. However, the identification performance of such system is very sensitive to the quality of the captured fingerprint image. Fingerprint image quality analysis and enhancement are useful in improving the performance of fingerprint identification systems. A fuzzy technique is introduced in this paper for both fingerprint image quality analysis and enhancement. First, the quality analysis is performed by extracting four features from a fingerprint image which are the local clarity score (LCS), global clarity score (GCS), ridge_valley thickness ratio (RVTR), and the Global Contrast Factor (GCF). A fuzzy logic technique that uses Mamdani fuzzy rule model is designed. The fuzzy inference system is able to analyse and determinate the fingerprint image type (oily, dry or neutral) based on the extracted feature values and the fuzzy inference rules. The percentages of the test fuzzy inference system for each type is as follow: For dry fingerprint the percentage is 81.33, for oily the percentage is 54.75, and for neutral the percentage is 68.48. Secondly, a fuzzy morphology is applied to enhance the dry and oily fingerprint images. The fuzzy morphology method improves the quality of a fingerprint image, thus improving the performance of the fingerprint identification system significantly. All experimental work which was done for both quality analysis and image enhancement was done using the DB_ITS_2009 database which is a private database collected by the department of electrical engineering, institute of technology Sepuluh Nopember Surabaya, Indonesia. The performance evaluation was done using the Feature Similarity index (FSIM). Where the FSIM is an image quality assessment (IQA) metric, which uses computational models to measure the image quality consistently with subjective evaluations. The new proposed system outperformed the classical system by 900% for the dry fingerprint images and 14% for the oily fingerprint images.

• Korean Chemical Engineering Research
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• v.62 no.3
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• pp.214-224
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• 2024

Valves play an essential role in a chemical plant such as regulating fluid flow and pressure. Therefore, optimal selection of the valve size and type is essential task. Valve size and type have been selected based on theoretical formulas about calculating valve sizing coefficient (Cv). However, this approach has limitations such as requiring expert knowledge and consuming substantial time and costs. Herein, this study developed a model for predicting valve sizes and types using machine learning. We developed models using four algorithms: ANN, Random Forest, XGBoost, and Catboost and model performances were evaluated using NRMSE & R² score for size prediction and F1 score for type prediction. Additionally, a case study was conducted to explore the impact of phases on valve selection, using four datasets: total fluids, liquids, gases, and steam. As a result of the study, for valve size prediction, total fluid, liquid, and gas dataset demonstrated the best performance with Catboost (Based on R², total: 0.99216, liquid: 0.98602, gas: 0.99300. Based on NRMSE, total: 0.04072, liquid: 0.04886, gas: 0.03619) and steam dataset showed the best performance with RandomForest (R²: 0.99028, NRMSE: 0.03493). For valve type prediction, Catboost outperformed all datasets with the highest F1 scores (total: 0.95766, liquids: 0.96264, gases: 0.95770, steam: 1.0000). In Engineering Procurement Construction industry, the proposed fluid-specific machine learning-based model is expected to guide the selection of suitable valves based on given process conditions and facilitate faster decision-making.

• Journal of Digital Convergence
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• v.13 no.11
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• pp.63-70
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• 2015

The fierce competition of mobile messenger services (MMS) allows MMS providers to perform a variety of marketing campaigns and business activities to enhance user loyalty. The applied model in this study is based on Oliver's four-stage loyalty model for the formation processes of user loyalty about MMS. While social network formation and service quality are the key elements of cognitive loyalty, positive mood and negative mood are the key components of affective loyalty in the study. Conative loyalty is captured by commitment. The data of 249 KakaoTalk users at least five times for three months is empirically tested based on the research model using partial least squares. The analysis of test identifies that positive feeling and commitment significantly influences behavioral loyalty, whereas negative feeling plays a significant role in inhibiting behavioral loyalty. The findings of this study show that social network formation and service quality significantly affect only positive feeling. The analysis results reveal several insights that can help MMS managers understand the roles of cognitive, affective, conative, and behavioral loyalty in the MMS environment.

• Journal of Korea Water Resources Association
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• v.53 no.2
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• pp.97-105
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• 2020

Recently, Artificial Neural Network receives attention as a data prediction method. Among these, a Long Shot-term Memory (LSTM) model specialized for time-series data prediction was utilized as a prediction method of hydrological time series data. In this study, the LSTM model was constructed utilizing deep running open source library TensorFlow which provided by Google, to predict inflows of multipurpose dams. We predicted the inflow of the Yongdam Multipurpose Dam which is located in the upper stream of the Geumgang. The hourly flow data of Yongdam Dam from 2006 to 2018 provided by WAMIS was used as the analysis data. Predictive analysis was performed under various of variable condition in order to compare and analyze the prediction accuracy according to four learning parameters of the LSTM model. Root mean square error (RMSE), Mean absolute error (MAE) and Volume error (VE) were calculated and evaluated its accuracy through comparing the predicted and observed inflows. We found that all the models had lower accuracy at high inflow rate and hourly precipitation data (2006~2018) of Yongdam Dam utilized as additional input variables to solve this problem. When the data of rainfall and inflow were utilized together, it was found that the accuracy of the prediction for the high flow rate is improved.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.2
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• pp.215-226
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• 2018

The purpose of this study is to improve the accuracy of the urban runoff and drainage network analysis by using the observed water level in the drainage network. To do this, sensitivity analysis for major parameters of SWMM (Storm Water Management Model) was performed and parameters were calibrated. The sensitivity of the parameters was the order of the roughness of the conduit, the roughness of the impervious area, the width of the watershed, and the roughness of the pervious area. Six types of scenarios were set up according to the number and types of parameter considering four parameters with high sensitivity. These scenarios were applied to the Seocho-3/4/5, Yeoksam, and Nonhyun drainage basins, where the serious flood damage occurred due to the heavy rain on 21 July, 2013. Parameter optimization analysis based on PEST (Parameter ESTimation) model for each scenario was performed by comparing observed water level in the conduits. By analyzing the accuracy of each scenario, more improved simulation results could be obtained, that is, the maximum RMSE (Root Mean Square Error) could be reduced by 2.41cm and the maximum peak error by 13.7%. The results of this study will be helpful to analyze volume of the manhole surcharge and forecast the inundation area more accurately.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.3
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• pp.262-276
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• 2018

Models to predict Leaf Wetness Duration (LWD) were evaluated using the observed meteorological and dew data at the 11 citrus orchards in Jeju, South Korea from 2016 to 2017. The sensitivity and the prediction accuracy were evaluated with four models (i.e., Number of Hours of Relative Humidity (NHRH), Classification And Regression Tree/Stepwise Linear Discriminant (CART/SLD), Penman-Monteith (PM), Deep-learning Neural Network (DNN)). The sensitivity of models was evaluated with rainfall and seasonal changes. When the data in rainy days were excluded from the whole data set, the LWD models had smaller average error (Root Mean Square Error (RMSE) about 1.5hours). The seasonal error of the DNN model had the similar magnitude (RMSE about 3 hours) among all seasons excluding winter. The other models had the greatest error in summer (RMSE about 9.6 hours) and the lowest error in winter (RMSE about 3.3 hours). These models were also evaluated by the statistical error analysis method and the regression analysis method of mean squared deviation. The DNN model had the best performance by statistical error whereas the CART/SLD model had the worst prediction accuracy. The Mean Square Deviation (MSD) is a method of analyzing the linearity of a model with three components: squared bias (SB), nonunity slope (NU), and lack of correlation (LC). Better model performance was determined by lower SB and LC and higher NU. The results of MSD analysis indicated that the DNN model would provide the best performance and followed by the PM, the NHRH and the CART/SLD in order. This result suggested that the machine learning model would be useful to improve the accuracy of agricultural information using meteorological data.

• Journal of Information Technology Services
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• v.14 no.1
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• pp.195-215
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• 2015

Recently, as the number of smart-phone users has been rapidly increased, enterprise managers have a keen interest in business application of social media. Most previous studies have focused on perspective of the individual unit of analysis instead of enterprise level unit. The study is focused on the relationship between the enterprises' recognition and behavioral intention (and use) about social media application. The purpose of this study is to develop the model of small and medium enterprises' social media application, and to find the factors affecting their behavioral intention or use behavior. The moderating effects of four corporate characteristics on the relationship between the enterprises' recognition and behavioral intention are also examined. We surveyed 900 corporate staffs and received 203 responses. After questionnaires with unreliable responses had been excluded, 182 effective samples were used in the final analysis. The findings suggest that Performance Expectation, Social Influence, Facilitating Conditions significantly affect Behavioral Intention of social medea, and Behavioral Intention affects USE. Furthermore, some corporate characteristics have moderating effect on the relationship between recognition of social media and Behavioral Intention.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.6 no.1
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• pp.7-17
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• 1997

During the past decade, there were many well-established theories for the adaptive control of linear systems, but there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of industrial robot control. Neural network computing methods provide one approach to the development of adaptive and learning behavior in robotic system for manufacturing. Computational neural networks have been demonstrated which exhibit capabilities for supervised learning, matching, and generalization for problems on an experimental scale. Supervised learning could improve the efficiency of training and development of robotic systems. In this paper, a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator using digital signal processors is proposed. Digital signal processors, DSPs, are micro-processors that are developed particularly for fast numerical computations involving sums and products of variables. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method. The proposed adaptive-neuro control scheme is illustrated to be an efficient control scheme for implementation of real-time control for SCARA robot with four-axes by experiment.