• Journal of Korean Society for Quality Management
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• v.51 no.4
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• pp.497-514
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• 2023

Purpose: This study aims to estimate the demand for various public parking lots in Seoul by clustering similar demand types of parking lots and predicting the demand for new public parking lots. Methods: We examined real-time parking information data and used time series clustering analysis to cluster public parking lots with similar demand patterns. We also performed various regression analyses of parking demand based on diverse heterogeneous data that affect parking demand and proposed a parking demand prediction model. Results: As a result of cluster analysis, 68 public parking lots in Seoul were clustered into four types with similar demand patterns. We also identified key variables impacting parking demand and obtained a precise model for predicting parking demands. Conclusion: The proposed prediction model can be used to improve the efficiency and publicity of public parking lots in Seoul, and can be used as a basis for constructing new public parking lots that meet the actual demand. Future research could include studies on demand estimation models for each type of parking lot, and studies on the impact of parking lot usage patterns on demand.

• Journal of Integrative Natural Science
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• v.12 no.1
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• pp.20-23
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• 2019

Demand response is usually operated through using the power rates and incentives. Demand management based on power charges is the most rational and efficient demand management method, and such methods include rolling base charges with peak time, sliding scaling charges depending on time, sliding scaling charges depending on seasons, and nighttime power charges. Search for other methods to stimulate resources on demand by actively deriving the demand reaction of loads to increase the energy efficiency of loads. In this paper, ESS algorithm for saving energy based on predicting the amount of solar power generation that can be used for buildings with small loads not under electrical grid.

• Management Science and Financial Engineering
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• v.19 no.1
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• pp.1-10
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• 2013

Hierarchical forecasting strategy does not always outperform direct forecasting strategy. The performance generally depends on demand features. This research guides the use of the alternative forecasting strategies according to demand features. This paper developed and evaluated various classification models such as logistic regression (LR), artificial neural networks (ANN), decision trees (DT), boosted trees (BT), and random forests (RF) for predicting the relative performance of the alternative forecasting strategies for the South Korean navy's spare parts demand which has non-normal characteristics. ANN minimized classification errors and inventory costs, whereas LR minimized the Brier scores and the sum of forecasting errors.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.442-443
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• 2022

As the recent trend of factories has changed from analog to smart factory, there are various functions that conveniently use smart factory. This paper introduces various techniques for predicting demand within smart factories among the functions of smart factories.

• Structural Engineering and Mechanics
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• v.70 no.3
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• pp.289-301
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• 2019

Hysteretic energy is defined as energy dissipated through inelastic deformations during a ground motion by the system. It includes frequency content and duration of ground motion as two remarkable parameters, while these characteristics are not seen in displacement spectrum. Since maximum displacement individually cannot be the appropriate criterion for damage assessment, hysteretic energy has been evaluated in this research as a more comprehensive seismic demand parameter. An innovative methodology has been proposed to establish a new equivalent linear model to estimate hysteretic energy spectrum for bilinear SDOF models under two different sets of earthquake excitations. Error minimization has been defined in the space of equivalent linearization concept, which resulted in equivalent damping and equivalent period as representative parameters of the linear model. Nonlinear regression analysis was carried out for predicting these equivalent parameter as a function of ductility. The results also indicate differences between seismic demand characteristics of far-field and near-field ground motions, which are not identified by most of previous equations presented for predicting seismic energy. The main advantage of the proposed model is its independency on parameters related to earthquake and response characteristics, which has led to more efficiency as well as simplicity. The capability of providing a practical energy based seismic performance evaluation is another outstanding feature of the proposed model.

• The Journal of Bigdata
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• v.5 no.1
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• pp.227-239
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• 2020

This paper introduces a method for predicting the maximum power demand despite communication errors in industrial sites. Due to the recent policy of de-nuclearization in Korea, the price of electricity is inevitable, and the amount of electricity used and maximum load management for the management of power demand are becoming important issues. Accordingly, it is important to predict and manage peak power. However, problems such as loss and modulation of measured power data occur at industrial sites due to noise generated by various facilities and sensors. It is difficult to predict the exact value when measured effective power data are lost. The study presents a model for predicting and correcting anomalies and missing values when measured effective power data are lost. The models used in this study are expected to be useful in predicting peak power demand in the event of communication errors at industrial sites.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.39-48
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• 2020

Recently, the study and development of environment-friendly energy technique have increased in worldwide due to environmental pollution and energy resources problems. In vehicle industry, the development of electric vehicle(EV) is now on progress, and also, many other governments support the study and development and make an effort for EV to become widely available. In addition, though they strive to construct the EV infra such as a charge station for EV, the techniques related to managing charge demand and peak power are not enough. The standard of EV communication has been already established as ISO/IEC 15118, however, most of implemented EVs and EV charge stations do not support any communication between each of them. In this paper, an improved slow charge scheme for non-communication EVs is proposed and designed by using predicting charge demand. The proposed scheme consists of distributed charge model and charge demand prediction. The distributed charge model is designed to manage to distribute charge power depending on available charge power and charge demand. The charge demand prediction is designed to be used in the distributed charge model. The proposed scheme is based on the collected data which were from EV slow charge station in business building during the past 1 year. The system-level simulation results show that the waiting time of EV and the charge fee of the proposed scheme are better than those of the conventional scheme.

• Journal of Korea Port Economic Association
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• v.37 no.1
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• pp.71-85
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• 2021

A variety of methods have been proposed through a number of studies on sophisticated demand forecasting models that can reduce logistics costs. These studies mainly determine the applicable demand forecasting model based on the pattern of demand quantity and try to judge the accuracy of the model through statistical verification. Demand patterns can be broadly divided into regularity and irregularity. A regular pattern means that the order is regular and the order quantity is constant. In this case, predicting demand mainly through regression model or time series model was used. However, this demand is called "intermittent demand" when irregular and fluctuating amount of order quantity is large, and there is a high possibility of error in demand prediction with existing regression model or time series model. For items that show intermittent demand, predicting demand is mainly done using Croston or HOLTS. In this study, we analyze the demand patterns of various items of air cargo with intermittent patterns and apply the most appropriate model to predict and verify the demand. In this process, intermittent optimal demand forecasting model of air cargo is proposed by analyzing the fit of various models of air cargo by item and region.

• International Journal of Concrete Structures and Materials
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• v.10 no.4
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• pp.513-525
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• 2016

The resistance-demand approach has emerged as an effective approach for determining the shear capacity of reinforced concrete beams. This approach is based on the fact that both the shear resistance and shear demand are correlated with flexural tensile strain from compatibility and equilibrium requirements. The basic shear strength, under a given loading is determined from the intersection of the demand and resistance curves. This paper verifies the applicability of resistance-demand procedure for predicting the shear capacity of high strength concrete beams without web reinforcement. A total of 18 beams were constructed and tested in four-point bending up to failure. The test variables included the longitudinal reinforcement ratio, the shear span to depth ratio, and the beam depth. The shear capacity of the beams was predicted using the proposed procedure and compared with the experimental values. The results of the comparison showed good prediction capability and can be useful to design practice.

• Journal of Information Technology Applications and Management
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• v.26 no.2
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• pp.61-73
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• 2019

In 2019, 5G mobile communication technology will be commercialized. From the viewpoint of technological innovation, 5G service can be applied to other industries or developed further. Therefore, it is important to measure the demand of the Internet of things (IoT) because it is predicted to be commercialized widely in the 5G era and its demand hugely effects on the economic value of 5G industry. In this paper, we applied Bayesian method on regression model to find out the demand of 5G IoT service, wearable service in particular. As a result, we confirmed that the Bayesian regression model is closer to the actual value than the existing regression model. These findings can be utilized for predicting future demand of new industries.