• Journal of Korean Society of Water and Wastewater
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• v.21 no.5
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• pp.601-607
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• 2007

In order to maintain constant residual chlorine in sedimentation basin, It is necessary to develop real time prediction model of residual chlorine considering water treatment plant data such as water qualities, weather, and plant operation conditions. Based on the operation data acquired from K water treatment plant, prediction models of residual chlorine in sediment basin were accomplished. The input parameters applied in the models were water temperature, turbidity, pH, conductivity, flow rate, alkalinity and pre-chlorination dosage. The multiple regression models were established with linear and non-linear model with 5,448 data set. The corelation coefficient (R) for the linear and non-linear model were 0.39 and 0.374, respectively. It shows low correlation coefficient, that is, these multiple regression models can not represent the residual chlorine with the input parameters which varies independently with time changes related to weather condition. Artificial neural network models are applied with three different conditions. Input parameters are consisted of water quality data observed in water treatment process based on the structure of auto-regressive model type, considering a time lag. The artificial neural network models have better ability to predict residual chlorine at sediment basin than conventional linear and nonlinear multi-regression models. The determination coefficients of each model in verification process were shown as 0.742, 0.754, and 0.869, respectively. Consequently, comparing the results of each model, neural network can simulate the residual chlorine in sedimentation basin better than mathematical regression models in terms of prediction performance. This results are expected to contribute into automation control of water treatment processes.

• Asia-Pacific Journal of Atmospheric Sciences
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• v.54 no.4
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• pp.611-622
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• 2018

Observed climate data are processed under the assumption that their time series are stationary, as in multi-step temperature and precipitation prediction, which usually leads to low prediction accuracy. If a climate system model is based on a single prediction model, the prediction results contain significant uncertainty. In order to overcome this drawback, this study uses a method that integrates ensemble prediction and a stepwise regression model based on a mean-valued generation function. In addition, it utilizes empirical mode decomposition (EMD), which is a new method of handling time series. First, a non-stationary time series is decomposed into a series of intrinsic mode functions (IMFs), which are stationary and multi-scale. Then, a different prediction model is constructed for each component of the IMF using numerical ensemble prediction combined with stepwise regression analysis. Finally, the results are fit to a linear regression model, and a short-term climate prediction system is established using the Visual Studio development platform. The model is validated using temperature data from February 1957 to 2005 from 88 weather stations in Guangxi, China. The results show that compared to single-model prediction methods, the EMD and ensemble prediction model is more effective for forecasting climate change and abrupt climate shifts when using historical data for multi-step prediction.

• Advances in Computational Design
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• v.7 no.2
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• pp.113-128
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• 2022

Water consumption is strongly affected by numerous factors, such as population, climatic, geographic, and socio-economic factors. Therefore, the implementation of a reliable predictive model of water consumption pattern is challenging task. This study investigates the performance of predictive models based on multi-layer perceptron (MLP), multiple linear regression (MLR), and support vector regression (SVR). To understand the significant factors affecting water consumption, the stepwise regression (SW) procedure is used in MLR to obtain suitable variables. Then, this study also implements three predictive models based on these significant variables (e.g., SWMLR, SWMLP, and SWSVR). Annual data of water consumption in Thailand during 2006 - 2015 were compiled and categorized by provinces and distributors. By comparing the predictive performance of models with all variables, the results demonstrate that the MLP models outperformed the MLR and SVR models. As compared to the models with selected variables, the predictive capability of SWMLP was superior to SWMLR and SWSVR. Therefore, the SWMLP still provided satisfactory results with the minimum number of explanatory variables which in turn reduced the computation time and other resources required while performing the predictive task. It can be concluded that the MLP exhibited the best result and can be utilized as a reliable water demand predictive model for both of all variables and selected variables cases. These findings support important implications and serve as a feasible water consumption predictive model and can be used for water resources management to produce sufficient tap water to meet the demand in each province of Thailand.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.2
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• pp.138-145
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• 2016

This paper proposes an improved tabu search method for subset selection in multiple linear regression models. Variable selection is a vital combinatorial optimization problem in multivariate statistics. The selection of the optimal subset of variables is necessary in order to reliably construct a multiple linear regression model. Its applications widely range from machine learning, timeseries prediction, and multi-class classification to noise detection. Since this problem has NP-complete nature, it becomes more difficult to find the optimal solution as the number of variables increases. Two typical metaheuristic methods have been developed to tackle the problem: the tabu search algorithm and hybrid genetic and simulated annealing algorithm. However, these two methods have shortcomings. The tabu search method requires a large amount of computing time, and the hybrid algorithm produces a less accurate solution. To overcome the shortcomings of these methods, we propose an improved tabu search algorithm to reduce moves of the neighborhood and to adopt an effective move search strategy. To evaluate the performance of the proposed method, comparative studies are performed on small literature data sets and on large simulation data sets. Computational results show that the proposed method outperforms two metaheuristic methods in terms of the computing time and solution quality.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.1
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• pp.45-50
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• 2012

This paper on analysis of roundness error after boring turning of Al-6061 materials with CNC lathe. Experiment applying turning parameters is based on experimental design method. A design and analysis of experiments is conducted to study the effects of these parameters on the roundness error using the S/N ratio and analysis of ANOVA. Multiple linear regression analysis is applied to compare experimental with predicted data in consideration of roundness error. To fixation pressure and the opening which are a turning parameter, the cutting depth and feed speed respected the objective attainment of dissertation and to be applied the result they investigated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.3039-3056
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• 2020

Beaches have many risk factors that cause various accidents, such as drifting and drowning, these accidents have many risk factors. To analyze them, in this paper, we identify beach risk factors, and define the criteria and correlation for each risk factor. Then, we generate new risk factors based on Fuzzy theory, and define Situation Awareness for each time. Finally, we propose a beach risk assessment and prediction model based on linear regression using the calculated risk result and pre-defined risk factors. We use national public data of the Korea Meteorological Administration (KMA), and the Korea Hydrographic and Oceanographic Agency (KHOA). The results of the experiment showed the prediction accuracy of beach risk to be 0.90%, and the prediction accuracy of drifting and drowning accidents to be 0.89% and 0.86%, respectively. Also, through factor correlation analysis and risk factor assessment, the influence of each of the factors on beach risk can be confirmed. In conclusion, we confirmed that our proposed model can assess and predict beach risks.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.7
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• pp.601-608
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• 2015

The static aeroelastic analysis and optimization of flexible wings are conducted for steady state conditions while both aerodynamic and structural parameters can be used as optimization variables. The system of multidisciplinary design optimization as a robust methodology to couple commercial codes for a static aeroelastic optimization purpose to yield a convenient adaptation to engineering applications is developed. Aspect ratio, taper ratio, sweepback angle are chosen as optimization variables and the skin thickness of the wing. The real-coded adaptive range multi-objective genetic algorithm code, which represents the global multi-objective optimization algorithm, was used to control the optimization process. The support vector regression(SVR) is applied for optimization, in order to reduce the time of computation. For this multi-objective design optimization problem, numerical results show that several useful Pareto optimal designs exist for the flexible wing.

• Journal of Korea Game Society
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• v.15 no.4
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• pp.59-68
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• 2015

The progress in Information & Communication Technology enables data scientists to analyze big data for identifying peoples' daily lives and tacit preferences. A variety of industries already aware the potential usefulness of analyzing big data. However limited use of big data has been performed in game industry. In this research, we adopt data mining technique to analyze data gathered from a strategic simulation game. Decision Tree, Random Forest, Multi-class SVM, and Linear Regression techniques are used to find the most important variables to users' game levels. We provide practical guides for game design and usability based on the analyzed results.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.12
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• pp.103-107
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• 2014

Together with housings, general buildings and industrial facilities, multi-purpose complexes are equipped with various and special equipment. They are often used by many unspecified people, which causes an increase in annual electricity consumption. Because of this, a great amount of money has been spent for electric charge, far more in excess of the budget, so a reasonable electricity rate needs to be estimated. In this study, we surveyed the power consumption, average power use, and annual electricity bill of multi-purpose complexes in the past five years. To see the general tendency of the survey, we conducted a statistical analysis with such parameters as average, maximum, and minimum values. Through regression analysis, we could see the trend of the survey in linear way. Based on the survey, we have developed an electric-rate calculation program to estimate the next year's budget on electricity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.9
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• pp.2512-2545
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• 2024

One of the biggest challenges for the medical professionals is spotting cardiovascular issues in the earliest stages. Around the world, Cardiovascular Diseases (CVD) are a major cause of death for almost 18 million people each year. Heart disease is therefore a serious concern that needs to be treated. The numerous elements that affect health, such as excessive blood pressure, elevated cholesterol, aberrant pulse rate, and many other factors, might make it challenging to detect heart disease. Consequently, early disease detection and the development of effective treatments can benefit greatly from the field of artificial intelligence. The purpose of this work is to develop a new IoT based healthcare monitoring framework for the prediction of CVD using machine learning algorithm. Here, the data preprocessing has been performed to create the normalized dataset for improving classification. Then, an Artificial Gorilla Troop Optimization (AGTO) algorithm is deployed to choose the most pertinent features from the normalized dataset. Moreover, the Multi-Linear Regression Classification (MLRC) model is also implemented for accurately categorizing the medical information as whether healthy or CVD affected. The results of the proposed AGTO-MLRC mechanism is validated and compared using the popular benchmarking datasets.