• 한국환경과학회지
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• 제17권9호
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• pp.1039-1052
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• 2008

The SWAT model developed by the USDA-Agricultural Research service for the prediction of rainfall run-off, sediment, and chemical yields in a basin was applied to Jeju Island watershed to estimate the amount of runoff. The research outcomes revealed that the estimated amount of runoff for the long term on 2 water-sheds showed fairly good performance by the long-term daily runoff simulation. The watershed of Chunmi river located the eastern region in Jeju Island, after calibrations of direct runoff data of 2 surveys, showed the similar values to the existing watershed average runoff rate as 22% of average direct runoff rate for the applied period. The watershed of Oaedo river located the northern region showed $R^2$ of 0.93, RMSE of 14.92 and ME of 0.70 as the result of calibrations by runoff data in the occurrence of 7 rainfalls.

• Computers and Concrete
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• 제23권4호
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• pp.255-265
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• 2019

The potential of using genetic programming to predict engineering data has caught the attention of researchers in recent years. The present paper utilized weighted genetic programming (WGP), a derivative model of genetic programming (GP), to model the compressive strength of concrete. The calculation results of Abrams' laws, which are used as the design codes for calculating the compressive strength of concrete, were treated as the inputs for the genetic programming model. Therefore, knowledge of the Abrams' laws, which is not a factor of influence on common data-based learning approaches, was considered to be a potential factor affecting genetic programming models. Significant outcomes of this work include: 1) the employed design codes positively affected the prediction accuracy of modeling the compressive strength of concrete; 2) a new equation was suggested to replace the design code for predicting concrete strength; and 3) common data-based learning approaches were evolved into knowledge-based learning approaches using historical data and design codes.

• Communications for Statistical Applications and Methods
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• 제23권4호
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• pp.297-319
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• 2016

Football match predictions are of great interest to fans and sports press. In the last few years it has been the focus of several studies. In this paper, we propose the Poisson regression model in order to football match outcomes. We applied the proposed methodology to two national competitions: the 2012-2013 English Premier League and the 2015 Brazilian Football League. The number of goals scored by each team in a match is assumed to follow Poisson distribution, whose average reflects the strength of the attack, defense and the home team advantage. Inferences about all unknown quantities involved are made using a Bayesian approach. We calculate the probabilities of win, draw and loss for each match using a simulation procedure. Besides, also using simulation, the probability of a team qualifying for continental tournaments, being crowned champion or relegated to the second division is obtained.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.36-36
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• 2023

Sedimentation is a natural process that occurs in all reservoirs. Sedimentation problem reduces the storage capacity of the reservoir and limits its ability to provide water for various uses, such as irrigation, hydropower generation, and flood control. Therefore, predicting reservoir sedimentation is important for ensuring the efficient operation and sedimentation management of a reservoir and . In this study, the HECRAS model was applied to predict longitudinal distribution of deposited sediment in the Pleikrong reservoir to 2050. Different scenarios was considered: (i) no climate change, (ii) climate change (under two emissions scenarios, RCP4.5 and RCP8.5), and (iii) climate change and land use change (followed land use planning of the watershed). The computation results with different scenarios were analyses and compared. The results show that the reservoir reduced storage volume's rate and sedimentation proceed toward to the dam in the case of climate change is faster than in the case of no climate change. Analyses also indicates that following the land used planning could also improve the long-term problem of the reservoir sedimentation. The outcomes of this study will be helpful for a sustainable plan of sediment management for the Pleikrong reservoir.

• International Journal of Naval Architecture and Ocean Engineering
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• 제6권3호
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• pp.700-714
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• 2014

This study aims to identify the relations between seakeeping characteristics and hull form parameters for YTU Gulet series with cruiser stern. Seakeeping analyses are carried out by means of a computer software which is based on the strip theory and statistical short term response prediction method. Multiple regression analysis is used for numerical assessment through a computer software. RMS heave-pitch motions and absolute vertical accelerations on passenger saloon for Sea State 3 at head waves are investigated for this purpose. It is well known that while ship weight and the ratios of main dimensions are the primary factors on ship motions, other hull form parameters ($C_P$, $C_{WP}$, $C_{VP}$, etc.) are the secondary factors. In this study, to have an idea of geometric properties on ship motions of gulets three different regression models are developed. The obtained outcomes provide practical predictions of seakeeping behavior of gulets with a high level of accuracy that would be useful during the concept design stage.

• 한국안전학회지
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• 제38권1호
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• pp.1-8
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• 2023

It is extremely important to design safety-guaranteed industrial processes because such process determine the ultimate outcomes of industrial activities, including worker safety. Application of artificial intelligence (AI) in industrial safety involves modeling industrial safety systems by using vast amounts of safety-related data, accident prediction, and accident prevention based on predictions. As a preliminary step toward realizing AI-based industrial safety in Korea, this study discusses neural network-based modeling of industrial safety systems. The input variables that are the most discriminatory relative to the output variables of industrial safety processes are selected using two information-theoretic measures, namely entropy and cross entropy. Normalized frequency and severity of industrial accidents are selected as the output variables. Our simulation results confirm the effectiveness of the proposed neural network model and, therefore, the feasibility of extending the model to include more input and output variables.

• 한국해양공학회지
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• 제37권5호
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• pp.181-189
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• 2023

The fuel consumption of marine diesel engines holds paramount importance in contemporary maritime transportation and shapes energy efficiency strategies of ocean-going vessels. Nonetheless, a noticeable gap in knowledge prevails concerning the influence of ship hull conditions and propeller roughness on fuel consumption. This study bridges this gap by utilizing artificial intelligence techniques in Matlab, particularly convolutional neural networks (CNNs) to comprehensively investigate these factors. We propose a time-series prediction model that was built on numerical simulations and aimed at forecasting ship hull and propeller conditions. The model's accuracy was validated through a meticulous comparison of predictions with actual ship-hull and propeller conditions. Furthermore, we executed a comparative analysis juxtaposing predictive outcomes with navigational environmental factors encompassing wind speed, wave height, and ship loading conditions by the fuzzy clustering method. This research's significance lies in its pivotal role as a foundation for fostering a more intricate understanding of energy consumption within the realm of maritime transport.

• Clinical and Experimental Pediatrics
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• 제52권9호
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• pp.964-970
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• 2009

Neonatal seizures are generally not only brief and subtle but also not easily recognized and are usually untreated. In sick neonates, seizures are frequently not manifested clinically but are detected only by electroencephalography (subclinical EEG seizures). This phenomenon of electroclinical dissociation is fairly common in neonates. On the other hand, neonates frequently show clinical behaviors such as stiffening, apnea, or autonomic manifestations that mimic seizures, which is usually associated with underlying encephalopathy and non-epileptic seizures. Therefore, it might be difficult to confirm the diagnosis of neonatal seizures. Early recognition of neonatal seizures is important to minimize poor neurodevelopmental outcomes, including cognitive, behavioral, and learning disabilities, as well as the development of postnatal epilepsy. EEG is a reliable tool in the determination of neonatal seizures. Continuous EEG monitoring is essential for the identification of seizures, evaluation of treatment efficacy, and prediction of the neurodevelopmental outcome. However, there is not yet a wide consensus on the optimal "standard" lead montage for the continuous EEG monitoring.

• Journal of Chest Surgery
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• 제53권4호
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• pp.178-183
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• 2020

During esophagectomy and esophagogastrostomy, the prediction of anastomotic leakage relies on the operating surgeon's tactile or visual diagnosis. Therefore, anastomotic leaks are relatively unpredictable, and new intraoperative evaluation methods or tools are essential. A fluorescence imaging system enables visualization over a wide region of interest, and provides intuitive information on perfusion intraoperatively. Surgeons can choose the best anastomotic site of the gastric tube based on fluorescence images in real time during surgery. This technology provides better surgical outcomes when used with an optimal injection dose and timing of indocyanine green.

• Steel and Composite Structures
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• 제31권5호
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• pp.427-435
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• 2019

The estimation of moment and rotation in steel rack connections could be significantly helpful parameters for designers and constructors in the initial designing and construction phases. Accordingly, Extreme Learning Machine (ELM) has been optimized to estimate the moment and rotation in steel rack connection based on variable input characteristics as beam depth, column thickness, connector depth, moment and loading. The prediction and estimating of ELM has been juxtaposed with genetic programming (GP) and artificial neural networks (ANNs) methods. Test outcomes have indicated a surpass in accuracy predicting and the capability of generalization in ELM approach than GP or ANN. Therefore, the application of ELM has been basically promised as an alternative way to estimate the moment and rotation of steel rack connection. Further particulars are presented in details in results and discussion.