• International Journal of Control, Automation, and Systems
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• v.6 no.5
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• pp.639-650
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• 2008

Electricity price forecasting has become an integral part of power system operation and control. In this paper, a wavelet transform (WT) based neural network (NN) model to forecast price profile in a deregulated electricity market has been presented. The historical price data has been decomposed into wavelet domain constitutive sub series using WT and then combined with the other time domain variables to form the set of input variables for the proposed forecasting model. The behavior of the wavelet domain constitutive series has been studied based on statistical analysis. It has been observed that forecasting accuracy can be improved by the use of WT in a forecasting model. Multi-scale analysis from one to seven levels of decomposition has been performed and the empirical evidence suggests that accuracy improvement is highest at third level of decomposition. Forecasting performance of the proposed model has been compared with (i) a heuristic technique, (ii) a simulation model used by Ontario's Independent Electricity System Operator (IESO), (iii) a Multiple Linear Regression (MLR) model, (iv) NN model, (v) Auto Regressive Integrated Moving Average (ARIMA) model, (vi) Dynamic Regression (DR) model, and (vii) Transfer Function (TF) model. Forecasting results show that the performance of the proposed WT based NN model is satisfactory and it can be used by the participants to respond properly as it predicts price before closing of window for submission of initial bids.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.2
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• pp.781-790
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• 2013

This research focuses on development of the conceptual cost estimation models for I.L.M box girder bridge. The current conceptual cost estimation for public construction projects is dependent on governmental average unit price references which has been regarded as inaccurate and unreliable by many experts. Therefore, there have been strong demands for developing a better way of conceptual cost estimating methods. This research has proposed three different conceptual cost estimating method for a P.S.C. girder bridge built with the I.L.M method. Model (I) attempts to seek the proper breakdown of standard works that are accountable for more than 95 percentage in total cost and calculates the amount of standard work's materials from the standard section and volume of I.L.M box girder bridge. Model (II) utilizes a correlation analysis (coefficient over 0.6 or more) between breakdown of standard works and input data that would be considered available information in preliminary design phase. Model(III) obtains conceptual estimating through multiple-regression analysis between the breakdown of standard works and all of input data related to them. In order to validate the clustering of coverage in the preliminary design phase, the variation of I.L.M cost coverage from multiple-regression analysis[model(III)] has been investigated which result in between -3.76% and 11.79%, comparing with AACE(Association for the Advancement of Cost Engineering) which informs its variation between -5% and +15% in the design phase. The model proposed from this research are envisioned to be improved to a great distinct if reliable cost date for P.S.C. girder bridges can be continually collected with reasonable accuracies.

• Land and Housing Review
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• v.12 no.4
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• pp.93-101
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• 2021

As the number of small households (1 to 2 persons per household) in Korea gradually increases, so does the importance of housing supply policies for small households. In response to the increase in small households, the government has been continuously supplying urban housing for these households. Since housing for small households is a sales and rental business similar to apartments and general business facilities, it is important for the building owner to calculate the project's estimated construction duration during the planning stage. Review of literature found a model for estimating the duration of construction of large-scale buildings but not for small-scale buildings such as urban housing for small households. Therefore this study aimed to develop and verify a model for estimating construction duration for urban housing at the planning stage based on multiple regression analysis. Independent variables inputted into the estimation model were building site area, building gross floor area, number of below ground floors, number of above ground floors, number of buildings, and location. The modified coefficient of determination (R_a²) of the model was 0.547. The developed model resulted in a Root Mean Square Error (RMSE) of 171.26 days and a Mean Absolute Percentage Error (MAPE) of 26.53%. The developed estimation model is expected to provide reliable construction duration calculations for small-scale urban residential buildings during the planning stage of a project.

• Journal of Industrial Convergence
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• v.20 no.12
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• pp.179-186
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• 2022

This study aims to develop a predictive model to suggest a used sales price to sellers and buyers when trading used tablet PCs. For model development, we analyzed the real used tablet PC transaction data and additionally collected detailed product information. We developed several predictive models and selected the best predictive model among them. Specifically, we considered a multiple linear regression model using the used sales price as a dependent variable and other variables in the integrated data as independent variables, a multiple linear regression model including interactions, and the models from stepwise variable selection in each model. The model with the best predictive performance was finally selected through cross-validation. Through this study, we can predict the sales price of used tablet PCs and suggest appropriate used sales prices to sellers and buyers.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1256-1263
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• 2022

With the advent of the Fourth Industrial Revolution, the amount of energy used in buildings has been increasing due to changes in the energy use structure caused by the massive spread of information-oriented equipment, climate change and greenhouse gas emissions. For the efficient use of energy, it is necessary to have a plan that can predict and reduce the amount of energy use according to the type of energy source and the use of buildings. To address such issues, this study presents a model embedded in a digital twin that predicts energy use in buildings. The digital twin is a system that can support a solution of urban problems through the process of simulations and analyses based on the data collected via sensors in real-time. To develop the energy use prediction model, energy-related data such as actual room use, power use and gas use were collected. Factors that significantly affect energy use were identified through a correlation analysis and multiple regression analysis based on the collected data. The proof-of-concept prototype was developed with an exhibition facility for performance evaluation and validation. The test results confirm that the error rate of the energy consumption prediction model decreases, and the prediction performance improves as the data is accumulated by comparing the error rates of the model. The energy use prediction model thus predicts future energy use and supports formulating a systematic energy management plan in consideration of characteristics of building spaces such as the purpose and the occupancy time of each room. It is suggested to collect and analyze data from other facilities in the future to develop a general-purpose energy use prediction model.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.9 s.114
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• pp.897-903
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• 2006

The reduction of the vehicle interior noise has been the main interest of noise and vibration harshness (NVH) engineers. The driver's perception on the vehicle noise is affected largely by psychoacoustic characteristic of the noise as well as the SPL. In particular, the heating, ventilation and air conditioning (HVAC) system sound among the vehicle interior noise has been reflected sensitively in psychoacoustics view point. Even though the HVAC noise is not louder than overall noise level, it clearly affects subjective perception to drivers in the way of making to be nervous or annoyed. Therefore, these days a vehicle engineer takes aim at developing sound quality as well as reduction of noise. In this paper, we acquired noises in the HVAC from many vehicles. Through the objective and subjective sound quality (SQ) evaluation with acquiring noises recorded by the vehicle HVAC system, the simple and multiple regression models were obtained for the subjective evaluation 'Pleasant' using the semantic differential method (SDM). The regression procedure also allows you to produce diagnostic statistics to evaluate the regression estimates including appropriation and accuracy. Furthermore, the neural network (NN) model were obtained using three inputs(loudness, sharpness and roughness) of the SQ metrics and one output(subjective 'Pleasant'). Because human's perception is very complex and hard to estimate their pattern, we used NN model. The estimated models were compared with correlations between output indexes of SQ and hearing test results for verification data 'Pleasant'. As a result of application of the SQ indexes, the NN model was shown with the largest correlation of SQ indexes and we found possibilities to predict the SQ metrics.

• The Korean Journal of Applied Statistics
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• v.25 no.2
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• pp.341-350
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• 2012

Composite quantile regression model is considered for iid error case. Since the regression coefficients are the same across different quantiles, composite quantile regression can be used to combine the strength across multiple quantile regression models. For the composite quantile regression, bootstrap method is examined for statistical inference including the selection of the number of quantiles and confidence intervals for the regression coefficients. Feasibility of the bootstrap method is demonstrated through a simulation study.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2009.11a
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• pp.44-44
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• 2009

Fault detection is necessary for yield enhancement and cost reduction in semiconductor manufacturing. Sensory data acquired from the semiconductor processing tool is too large to analyze for the purpose of fault detection and classification(FDC). We studied the techniques of fault detection using statistical method. Multiple regression analysis smoothly detected faults and can be easy made a model. For real-time and fast computing time, the huge data was analyzed by each step. We also considered interaction and critical factors in tool parameters and process.

• Health Policy and Management
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• v.13 no.3
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• pp.129-147
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• 2003

This study is to grasp a trend of profitability classified by characteristics of hospitals and to analyze related factors. Subjects are 145 hospitals which have gotten the standardization audit by Korean Hospital Association during 1998-200l. Profitability was measured in the aspect of operation profit rate with operating margin to gross revenue as proxy variables. Independent variables were classified by general factors (ownership, number of beds, period of establishment, competition), financial factors (liabilities to total assets, current ratio, fixed ratio, total asset turnover, inventories turnover), and factors related to patient treatment (average length of stay, bed occupancy rate, new outpatient ratio, admission ratio of outpatients, number of patients per specialist, personnel costs per adjusted inpatient, administrative costs per adjusted inpatient). Hierarchical multiple regression analysis model was used in this study. As a result of hierarchical multiple regression analyzation of operating margin to gross revenue, adjustive $R^2$ of general factors was relatively more powerful. The factors had significant effect on operating margin to gross revenue were ownership(+), number of beds(+), competition(+), current ratio(+), fixed ratio(+), total asset turnover(+), personnel costs per adjusted inpatient(-).

• Journal of Korean Society for Quality Management
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• v.12 no.2
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• pp.25-32
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• 1984

The purpose of this paper is to compare several forecasting methods for the case of the cement product by the analysis of the forecasting data and by the study of major forecasting methods, which are the Trend Projection, Exponential Smoothing, and Multiple Regression Analysis. As a result, it is thought that the Multiple Regression Analysis is the optimal model for the case of the cement product. In addition, it is important to consider the future circumstances for forecasting, and to improve the level of the forecasting results through the precise analysis of the collected data.