• Earthquakes and Structures
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• v.13 no.4
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• pp.337-351
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• 2017

In the present study a capacity spectrum method based on constant ductility inelastic spectra to estimate the seismic performance of structures equipped with elastic viscous dampers is presented. As the definition of the structures' effective damping, due to the damping system, is necessary, an alternative method to specify the effective damping ratio ${\xi}eff$ is presented. Moreover, damping reduction factors (B) are introduced to generate high damping elastic demand spectra. Given the elastic spectra for damping ratio ${\xi}eff$, the performance point of the structure can be obtained by relationships that relate the strength demand reduction factor (R) with the ductility demand factor (${\mu}$). As such expressions that link the above quantities, known as R - ${\mu}$ - Τ relationships, for different damping levels are presented. Moreover, corrective factors (Bv) for the pseudo-velocity spectra calculation are reported for different levels of damping and ductility in order to calculate with accuracy the values of the viscous dampers velocities. Finally, to evaluate the results of the proposed method, the whole process is applied to a four-storey reinforced concrete frame structure and to a six-storey steel structure, both equipped with elastic viscous dampers.

• Journal of Korea Multimedia Society
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• v.9 no.12
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• pp.1542-1551
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• 2006

In the Grid Database, some replicas will have more requests from the clients than others. A fast consistency algorithm has been presented to satisfy the high demand nodes in a shorter period of time. But it has poor performance in multiple regions of high demand for forming the island of locally consistent replicas. Then, a leader election method is proposed, whereas it needs much additional cost for periodic leader election, information storage, and message passing, Also, false leader can be created. In this paper, we propose a tree-based algorithm for replica update propagation. Leader replicas with high demand are considered as the roots of trees which are interconnected. All the other replicas are sorted and considered as nodes of the trees. Once an update occurs at any replica, it need be transmitted to the leader replicas first. Every node that receives the update propagates it to its children in the tree. The update propagation is optimized by cost reduction for fixed propagation schedule. And it is also flexible for the dynamic model in which the demand conditions change with time.

• Environmental and Resource Economics Review
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• v.28 no.3
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• pp.437-465
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• 2019

In this study, we propose a new method to forecast long-term energy demand in Korea. Based on Chang et al. (2016), which models the time varying long-run relationship between electricity demand and GDP with a function coefficient panel model, we design several schemes to retain objectivity of the forecasting model. First, we select the bandwidth parameters for the income coefficient based on the out-of-sample forecasting performance. Second, we extend the income coefficient using the functional principal component analysis method. Third, we proposed a method to reflect the elasticity change patterns inherent in Korea. In the empirical analysis part, we forecasts the long-term energy demand in Korea using the proposed method to show that the proposed method generates more stable long term forecasts than the existing methods.

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.2
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• pp.11-22
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• 2007

This paper is to discuss analytical techniques to estimate demand sizes and volumes that determine optimal locations for multiple facilities for a given services. While demand size estimation is a core part of location modeling to enhance solution quality and practical applicability, the estimation method has been used in limited and restrict parts such as a single population centroid in a given larger census boundary area or small theoretical application experiments(e.s. census track and enumeration district). Therefore, this paper strives to develop an analytical estimation method of demand size that converts area based demand data to point based population weighted centroids. This method is free to spatial boundary units and more robust to estimate accurate demand volumes regardless of geographic boundaries. To improve the estimation accuracy, this paper uses house weighted value to the population centroid calculation process. Then the population weighted centroids are converted to individual demand points on a grid formated surface area. In turn, the population weighted centroids, demand points and network distance measures are operated into location-allocation models to examine their roles to enhance solution quality and applicability of GIS location models. Finally, this paper demonstrates the robustness of the weighted estimation method with the application of location-allocation models.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.5
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• pp.82-96
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• 2020

The Demand Responsive Transit (DRT) system is the flexible public transport service that determines the route and schedule of the service vehicles according to users' requests. With increasing importance of public transport systems in urban areas, the development of stable and fast routing algorithms for DRT has become the goal of many researches over the past decades. In this study, a new heuristic method is proposed to generate fast and efficient routes for multiple vehicles using demand clustering and destination demand priority searching method considering the imbalance of users' origin and destination demands. The proposed algorithm is tested in various demand distribution scenarios including random, concentration and directed cases. The result shows that the proposed method reduce the drop of service ratio due to an increase in demand density and save computation time compared to other algorithms. In addition, compared to other clustering-based algorithms, the walking cost of the passengers is significantly reduced, but the detour time and in-vehicle travel time of the passenger is increased due to the detour burden.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.10
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• pp.1056-1061
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• 2009

This paper proposes a short-term water demand forecasting algorithm based on kalman filtering with data mining for sustainable water supply and effective energy saving. The proposed algorithm utilizes a mining method of water supply data and a decision tree method with special days like Chuseok. And the parameters of MLAR (Multi Linear Auto Regression) model are estimated by Kalman filtering algorithm. Thus, we can achieve the practicality of the proposed forecasting algorithm through the good results applied to actual operation data.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.28-34
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• 2009

In this study firstly we survey the calculation method and the characteristics of the way of estimating CBL(Customer BaseLine Load) that is important calculation tool for DRP internationally. Also we analyze the power consumption pattern using the 15 minutes load profiles of about 120,000 customers in domestic. Based on this pattern, we provide the CBL calculation method that can be utilized in DRP to save the cost, and analyze the accuracy of the CBL calculation proposed in this paper through the simulation.

• Earthquakes and Structures
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• v.26 no.2
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• pp.117-130
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• 2024

Probabilistic model of seismic demand is the main tool used for seismic demand estimation, which is a fundamental component of the new performance-based design method. This model seeks to mathematically relate the seismic demand parameter and the ground motion intensity measure. This study is intended to use Bayesian analysis to evaluate the accuracy of the seismic demand estimation of Steel moment resisting frames (SMRFs) through a completely Bayesian method in statistical calculations. In this study, two types of intensity measures (earthquake intensity-related indices such as magnitude and distance and intensity indices related to ground motion and spectral response including peak ground acceleration (PGA) and spectral acceleration (SA)) have been used to form the models. In addition, an extensive database consisting of sixty accelerograms was used for time-series analysis, and the target structures included five SMRFs of three, six, nine, twelve and fifteen stories. The results of this study showed that for low-rise frames, first mode spectral acceleration index is sufficient to accurately estimate demand. However, for high-rise frames, two parameters should be used to increase the accuracy. In addition, adding the product of the square of earthquake magnitude multiplied by distance to the model can significantly increase the accuracy of seismic demand estimation.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.4
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• pp.247-252
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• 2013

Korea gas corporation (KOGAS) is responsible for the whole sale of natural gas in the domestic market. It is important to forecast the daily demand of city gas for supply and demand control, and delivery management. Since there is the autoregressive characteristic in the daily gas demand, we introduce a modified autoregressive model as the first step. The daily gas demand also has a close connection with the outdoor temperature. Accordingly, our second proposed model is a temperature-based model. Those two models, however, do not meet the requirement for forecasting performances. To produce acceptable forecasting performances, we develop a weighted average model which compounds the autoregressive model and the temperature model. To examine our proposed methods, the forecasting results are provided. We confirm that our method can forecast the daily city gas demand accurately with reasonable performances.

• International journal of advanced smart convergence
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• v.10 no.4
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• pp.59-65
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• 2021

Electricity contributes to the development of the economy. Therefore, forecasting electricity demand plays an important role in the development of the electricity industry in particular and the economy in general. This study aims to provide a precise model for long-term electricity demand forecast in the residential sector by using three independent variables include: Population, Electricity price, Average annual income per capita; and the dependent variable is yearly electricity consumption. Based on the support of Multiple variable regression, the proposed method established a model with variables that relate to the forecast by ignoring variables that do not affect lead to forecasting errors. The proposed forecasting model was validated using historical data from Vietnam in the period 2013 and 2020. To illustrate the application of the proposed methodology, we presents a five-year demand forecast for the residential sector in Vietnam. When demand forecasts are performed using the predicted variables, the R square value measures model fit is up to 99.6% and overall accuracy (MAPE) of around 0.92% is obtained over the period 2018-2020. The proposed model indicates the population's impact on total national electricity demand.