• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.885-888
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• 2017

In this paper, a new strategy for estimating building temperature based on the modified resistance capacitance (R - C) network thermal dynamic model is proposed. The proposed method gives accurate indoor temperature estimation using minimum variance finite impulse response filter. Our study is clarified by the experimental validation of the proposed indoor temperature estimation method. This experiment scenario environment is composed of a demand response (DR) server and home energy management system (HEMS) in a test bed.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1481-1483
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• 1999

Power distribution system planning for maximum customer satisfaction and system efficiency requires accurate forecast of future demand in service area. Spatial load forecasting method provides a more accurate estimation of both magnitudes and location of future electrical load. This method considers the causes of load growth due to addition of customers and per capita consumption among customers by land use (residential, commercial and industrial). So the land-use study and it's preference for small area is quite important. This paper proposes land-use preference estimation method based on fuzzy logic. Fuzzy logic is applied to computing preference scores for each land-use and by these scores the customer growth is allocated in service area. An simulation example is used to illustrate the proposed method.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.938-942
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• 1996

Despite the increasing demand for improved product design, a limited number of works have been reported in the field of sheet metal forming. In the present study, introducing the Taguchi method, an optimal and robust combination of parameters is found and a data base management system and nomograms are utilized for knowledge acquisition. The developed system is applied to a deep drawing process, Through the present study, it is shown that the developed system is useful for the design and the formability estimation of sheet metal forming processes.

• Physical Therapy Korea
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• v.5 no.1
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• pp.1-16
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• 1998

This study analysed national data of manpower supply for physical therapists in Korea. Based on the comparative analysis results of the future demand and supply, as of May 1998, it is estimated that there was already an oversupply of physical therapists in Korea. This oversupply is expected to continue even though there would be an increase in hospital beds, rehabilitation facilities for the elderly, and nursing homes. Thus it would be desirable to cut down the number of students admitted to physical therapy schools each year. Our estimation shows that the Ministry of Health and Welfare must take measures to reduce the supply of physical therapist as soon as possible.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.559-567
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• 2018

In this paper, we propose a new load forecasting method for smart air conditioning (A/C) based on the modified thermodynamics of indoor temperature and the unbiased finite memory estimator (UFME). Based on modified first-order thermodynamics, the dynamic behavior of indoor temperature can be described by the time-domain state-space model, and an accurate estimate of indoor temperature can be achieved by the proposed UFME. In addition, a reliable A/C load forecast can be obtained using the proposed method. Our study involves the experimental validation of the proposed A/C load forecasting method and communication construction between DR server and HEMS in a test bed. Through experimental data sets, the effectiveness of the proposed estimation method is validated.

• Journal of Industrial Technology
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• v.30 no.A
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• pp.3-8
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• 2010

Recently, There have been many attempts to optimize energy usage in buildings and houses using Information Technology(IT) and the typical implementation can be found in Intelligent Building and Zero Energy Building. These kinds of buildings need to forecast the building loads, estimate the capacity requirement for power supply, and decide the capacity of the main transformer of the building. Currently, the capacity of the main transformer has been decided just using typical load estimation method not considering the load characteristics and patterns. In this paper, we propose a new load estimation method considering the load characteristics and patterns of the builiding. The proposed method was applied to actual mail center and verified the feasibility of application to actual design of buildings.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2013.05a
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• pp.238-239
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• 2013

A system to efficiently utilize construction information has been in demand as a result of the changing society where information and knowledge leads to added value. Consequently, the government established a comprehensive information network system for construction industries to manage and utilize construction information which is the currently in use. However, this system is oriented toward management and business performance estimation for engineering and does not integrate a management system for design, construction technology, and construction engineering services. Therefore, this study proposes to develop object technology and management system of construction services and its evaluation to provide such information in real time.

• IE interfaces
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• v.16 no.1
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• pp.16-26
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• 2003

In Korea, cooling power load, which occupies about 20% of peak load in 2000 and fluctuates depending on the popular usage of air conditioning systems, has been recently the focus of the load management. The first work of KEPCO (Korea Electric Power Corporation) to regulate cooling load as low as possible was to estimate its approximate scale and to develop the indirect methods to estimate it from the available time series data for the average hourly loads. However, KEPCO would like to have their methods improved both theoretically and practically. In this paper, we analyze their current indirect methods and detect their faults to design better indirect estimation methods. Under one of the assumptions of "no cooling load in April or May", the linear relationship between basic loads and GDP's, and the normalized seasonal factors of the Winters' multiplicative seasonal model, we provide ten indirect estimation methods in total and suggest the estimated cooling load(1988-1999) based on our various indirect methods.

• Earthquakes and Structures
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• v.16 no.1
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• pp.69-82
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• 2019

As the residual displacement and/or drift demands are commonly used for seismic assessment of buildings, the estimation of these values play a very critical role through earthquake design philosophy. The residual displacement estimation of fixed base structures has been the topic of numerous researches up to now, but the effect of soil flexibility is almost always omitted. In this study, residual displacement demands are investigated for SDOF systems with period range of 0.1-3.0 s for near-field and far-field ground motions for both fixed and interacting cases. The elastoplastic model is used to represent non-degrading structures. Based on time history analyses, a new simple yet effective equation is proposed for residual displacement demand of any system whether fixed base or interacting as a function of structural period, lateral strength ratio and spectral displacement.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.373-376
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• 2021

Deep neural networks have achieved almost human-level results in various tasks and have become popular in the broad artificial intelligence domains. Uncertainty estimation is an on-demand task caused by the black-box point estimation behavior of deep learning. The deep ensemble provides increased accuracy and estimated uncertainty; however, linearly increasing the size makes the deep ensemble unfeasible for memory-intensive tasks. To address this problem, we used model pruning and quantization with a deep ensemble and analyzed the effect in the context of uncertainty metrics. We empirically showed that the ensemble members' disagreement increases with pruning, making models sparser by zeroing irrelevant parameters. Increased disagreement implies increased uncertainty, which helps in making more robust predictions. Accordingly, an energy-efficient compressed deep ensemble is appropriate for memory-intensive and uncertainty-aware tasks.