• Journal of Korea Water Resources Association
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• v.46 no.12
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• pp.1141-1155
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• 2013

A number of studies have been performed to analyze climate change impacts of water resources system. In this study, a coordinated dam operation is compared with an existing operation strategy for coping with projected future runoff scenarios. GCMs (Global Circulation Models) and the LARS-WG downscaling method was used to project future climate scenarios. The water balance model called abcd was employed to estimate future runoff scenarios. The existing dam operation comes from the national dam construction guideline, which is called the "level-operation method." The alternative coordinated dam operation are constructed as a linear programming using New York City rule for refill and drawdown seasons. The results of annual total inflow in future is projected to decrease to 72.81% for Andong dam basin and 65.65% for Imha dam basin. As a result of applying future runoff scenarios into the dam operation model, the reliability of coordinated dam operation, 62.22%, is higher than the reliability of single dam operation, 46.55%. Especially, the difference gets larger as the reliability is low because of lack of water. Therefore, the coordinated operation in the Andong & Imha dams are identified as more appropriate alternative than the existing single operation to respond to water-level change caused by climate change.

• Journal of the Korean Society of Hazard Mitigation
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• v.7 no.3
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• pp.19-31
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• 2007

Humidity and strain were estimated for understanding the relation between humidity change by self-desiccation and shrinkage in high-performance concrete with low water binder ratio and containing fly ash and blast furnace slag. Internal humidity change and shrinkage strain were about 10%, 10%, 7%, 11%, 11% and $320{\times}10^{-6}$, $270{\times}10^{-6}$, $231{\times}10^{-6}$, $371{\times}10^{-6}$, $350{\times}10^{-6}$ respectively on OPC30, O30F10, O30F20, O30G40, O30G50 and from the results, fly ash made humidity change and strain decrease but slag increase comparing with ordinary portland cement. Considering only relation internal humidity and shrinkage by self-desiccation, humidity change and shrinkage represented the strong linear relation regardless of mineral admixture. For specifying the relation on internal humidity change and autogenous shrinkage strain, shrinkage model was established which is driven by capillary pressure in pore water and surface energy in hydrates on the assumption of a single network and extended meniscus in pore system of concrete. This model and experimental results had a similar tendency so it would be concluded that the internal humidity change by self-desiccation in HPC originated in small pores less than 20nm, therefore controlling plan on autogenous shrinkage might be focused on surface tension of water and degree of saturation in small pore.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.3
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• pp.73-82
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• 2017

As of December 2016, the number of registered automobiles in Korea exceeds 21million. As a result, greenhouse gas emission by transportation sector are increasing every year. It was concluded that the development of the driving strategy considering the driving behavior and the road conditions, which are known to affect the fuel efficiency and the greenhouse gas emissions, could be the most effective fuel economy improvement. Therefore, this study aims to develop a fuel efficient driving strategy in a complex linear section with uphill and curved sections. The road topography was designed according to 'Rules about the Road Structure & Facilities Standards'. Various scenarios were selected. After generating the speed profile, it was applied to the Comprehensive Modal Emission Model and fuel consumption was calculated. The scenarios with the lowest fuel consumption were selected. After that, the fuel consumption of the manual driver's driving record and the selected optimal driving strategy were compared and analyzed for verification. As a result of the analysis, the developed optimal driving strategy reduces fuel consumption by 21.2% on average compared to driving by manual drivers.

• Journal of Environmental Policy
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• v.8 no.4
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• pp.75-94
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• 2009

The process of obtaining third-party financing contacts was analyzed via a two-stage game model: a "signaling game" for the first stage,and a "principal-agent model" for the second stage. The two-stage game was solved by a process of backward induction. In the second stage game, the optimal effort level of the energy saving company (ESCO), the optimal compensation scheme of the energy user, and the optimal payoffs for both parties were derived for each subgame. The optimal solutions forthe different subgames were then compared with each other. Our main finding was that if there is some restriction on ESCO's revenue (e.g. a progressive sales tax) that causes ESCO's revenue toincrease at a decreasing rate, then the optimal sharing ratio is uniquely determined at a level of strictly less than one under a linear compensation scheme, i.e. a unique balance exists. Subgames have a unique equilibrium arrived at separately for each situation,. Within this equilibrium, energy users accept energy audit proposals from H-type ESCOs with high levels of technology, but reject proposals from L-type ESCOs with low levels of technology. While L-type ESCOs cannot attain profits in the third-party financing market, H-type ESCOS can pocket the price differential between L-type and H-type audit fees. Accordingly, revenues in an H-type ESCO equilibrium increase not only in line with the technology of the ESCO inquestion, but also faster than in an L-type equilibrium due to more advanced technology. At the same time, energy users receive some positive payoff by allowing ESCOs to perform third-party financing tasks within their existing energy system without incurring any extra costs.

• Journal of the Korea Concrete Institute
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• v.20 no.2
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• pp.131-138
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• 2008

Humidity and strain were estimated for understanding the relation between humidity change by self-desiccation and shrinkage in high-performance concrete with low water binder ratio. Internal humidity change and shrinkage strain were about 10%, 4% and $320\times10^{-6}$, $120\times10^{-6}$ respectively on concrete with water binder ratio 0.3, 0.4 and from the results, humidity change and shrinkage represented the strong linear relation regardless of mixture. For specifying the relation on internal humidity change and autogenous shrinkage strain, shrinkage model was established which is driven by capillary pressure in pore water and surface energy in hydrates on the assumption of a single network and extended meniscus in pore system of concrete. This model and experimental results had a similar tendency so it would be concluded that the internal humidity change by self-desiccation in HPC originated in small pores less than 20 nm, therefore controlling plan on autogenous shrinkage might be focused on surface tension of water and degree of saturation in small pore.

• Journal of the Korean earth science society
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• v.29 no.7
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• pp.579-585
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• 2008

The time-dependent evolution of disk mass for outburst limit cycle in a black hole microquasar is calculated based on the non-linear hydrodynamic model of thermally unstable accretion disk. The physical parameters such as black hole mass, disk size and mass transfer rate are adopted to reproduce the historical 1975 outburst observed in a prototype black hole X-ray nova A0620-00. The time-dependent effect of irradiation from the central hot region to the disk is considered in two ways: direct irradiation and indirect irradiation reflected from hot accretion flow above the disk. The accretion disk thermal instability model can account for the bolometric luminosity appropriate to typical characteristics of system luminosity observed in X-ray transients during the whole cycle of the outburst evolution. The maximum mass of the accretion disk, ${\sim}4.03{\times}10^{24}g$, is achieved at the ignition of an outburst, and the minimum value, ${\sim}8.54{\times}10^{23}g$, is reached during the cooling decay to quiescence. The disk mass varies ${\sim}5$ times during outburst limit cycle.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.7
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• pp.1694-1704
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• 2015

In this paper, a robust trajectory tracking control method of a wheeled mobile robot is newly proposed combining the PDC and the ISMC. The PDC is a relatively simple and easy control method for nonlinear system compared to the other non-linear control methods. And the ISMC can have robust and stable control characteristics against model uncertainties and disturbances from the initial time by placing the states on the sliding plane with desired nominal dynamics. Therefore, the proposed PDC+ISMC trajectory tracking control method shows robust trajectory tracking performance in spite of external disturbance. The tracking performance of the proposed method is verified through simulations. Even though the disturbance increases, the proposed method keeps the performance of the PDC method when there is no disturbance. However, the PDC trajectory tracking control method has increasing tracking error unlike the proposed method when the disturbance increases.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.707-725
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• 2021

Energy management systems (EnMS) contribute to sustainable energy saving and greenhouse gas reduction by emphasizing the role of energy management in production-oriented economies. Although understanding the methods used to measure energy performance is a key factor in constructing successful EnMS, few attempts have been made to examine these methods, their applicability, and their utility in practice. To fill this research gap, this study aimed to deepen the understanding of energy performance measures by focusing on four energy performance indicators (EnPIs) proposed by ISO 50006, namely the measured energy value, ratio between measured values, linear regression model, and nonlinear regression model. This paper presents policy and managerial implications to facilitate the effective use of these measures. An analytic hierarchy process (AHP) analysis was conducted with 41 experts to analyze the preference for EnPIs and their key selection criteria by the industry sector, and organization and user type. The findings suggest that the most preferred EnPI is the ratio between the measured values followed by the measured energy value. The ease of use was considered to be most important while choosing EnPIs.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.1
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• pp.1-7
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• 2022

As a power transmission element, the timing belt is a toothed transmission belt that takes advantages of V-belts and gears. It has characteristics of non-slip and low noise. It is used as a power transmission device when transmitting power from a rotating shaft or linear motion in a mechanism. Rotation can be accurately transmitted through a belt pulley with grooves like a gear and a timing belt with grooves to precisely match with the belt pulley. In particular, in the mechanism in which the timing belt is used for the output shaft, the dynamic characteristics including the rigidity of the timing belt determine the transmission characteristics of the system, so its importance increases. In this paper, a stiffness reinforced belt that can be applied to a timing belt with a limited range of motion to increase its stiffness is proposed. To study the dynamic characteristics of the stiffness reinforced belt, the equation of motion for the stiffness reinforced belt was established, and a simulation model for the stiffness reinforced belt was created and analyzed. In order to confirm the analysis results of the motion equation and simulation model, a 1-axis rotation experimental equipment using a stiffness reinforcing belt was developed and the experiment was conducted. Through motion equations, simulation models, and experiment results, it was confirmed that the stiffness and dynamic characteristics of the timing belt could be improved by applying the proposed stiffness reinforcement belt.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.