• Journal of the Society of Naval Architects of Korea
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• v.45 no.2
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• pp.175-185
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• 2008

One of the concerns that arise during navigation in ice-covered waters is the magnitude of ice loads encountered by ships. However, the accurate estimation of ice loads still remains as a rather difficult task in the design of icebreaking vessels. This paper focuses on the development of simple ice load prediction formulas for the icebreaking cargo vessels. The maximum ice loads are expected from unbroken ice sheet and these loads are most likely to be concentrated at the bow area. Published ice load data for icebreaking vessels, from the model tests and also from full-scale sea trials, are collected and then several ice load prediction formulas are compared with these data. Finally, based on collected data, a semi-empirical ice load prediction formula is recommended for the icebreaking cargo vessels.

• Smart Structures and Systems
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• v.24 no.4
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• pp.507-524
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• 2019

A Bayesian dynamic linear model (BDLM) is presented for a data-driven analysis for response prediction and load effect separation of a revolving auditorium structure, where the main loads are self-weight and dead loads, temperature load, and audience load. Analyses are carried out based on the long-term monitoring data for static strains on several key members of the structure. Three improvements are introduced to the ordinary regression BDLM, which are a classificatory regression term to address the temporary audience load effect, improved inference for the variance of observation noise to be updated continuously, and component discount factors for effective load effect separation. The effects of those improvements are evaluated regarding the root mean square errors, standard deviations, and 95% confidence intervals of the predictions. Bayes factors are used for evaluating the probability distributions of the predictions, which are essential to structural condition assessments, such as outlier identification and reliability analysis. The performance of the present BDLM has been successfully verified based on the simulated data and the real data obtained from the structural health monitoring system installed on the revolving structure.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.4C
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• pp.145-151
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• 2009

In general, a drilled shaft embedded in weathered rock has a large load bearing capacity. Therefore, most of the load tests are performed only up to the load level that confirms the pile design load capacity, and stopped much before the ultimate load of the pile is attained. If a reliable ultimate load value can be extracted from the premature load test data, it will be possible to greatly improve economic efficiency as well as pile design quality. The main purpose of this study is to propose a method for judging the reliability of the ultimate load of piles that is obtained from extrapolated load test data. To this aim, ten static load test data of load-displacement curves were obtained from testing of piles to their failures from 3 different field sites. For each load-displacement curve, loading was assumed as 25%, 50%, 60%, 70%, 80%, and 90% of the actual pile bearing capacity. The limited known data were then extrapolated using the hyperbolic function, and the ultimate capacity was re-determined for each extrapolated data by the Davisson method (1972). Statistical analysis was performed on the reliability of the re-evaluated ultimate loads. The results showed that if the ratio of the maximum-available displacement to the predicted displacement exceeds 0.6, the extrapolated ultimate load may be regarded as reliable, having less than a conservative 20% error on average. The applicability of the proposed method of judgment was also verified with static load test data of driven piles.

• Journal of environmental and Sanitary engineering
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• v.14 no.3
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• pp.1-9
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• 1999

In the estimation of pollution load in water basin, a data information has generally used from surveyed data. A Geographic Information System(GIS) was adopted to evaluate the amount of pollution load in Anyang stream basin which is one of the major tributaries in the Han river flows through urban area. The digital maps of administrative boundary, stream network, sub-basin, soil type, and land-use for spatial data as well as attribute data were generated. And the database of sub-basins and pollution source was structured to estimate pollution load in Anyang stream basin by an Arc/Info GIS.As the results of this investigation, the pollution load of Mokgam-chun sub-basin was the highest amount. And that of Hagi-chun sub-basin and the fourth main stream sub-basin were also high amount in Anyang stream basin. In general, it was found that the pollution load generated from the upstream area in Kyunggi province was higher than that from downstream area in Seoul. Because the point and non-point source pollution load played very significant role in the deterioration of the water quality of the Anyang stream, an integrated approach to water quality management should be required for the sub-basins of high pollution load amount.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.1
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• pp.96-102
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• 2018

For efficient operating strategy of electric power system, forecasting of daily peak electric load is an important but difficult problem. Therefore a daily peak electric load forecasting system using a neural network and fuzzy system is presented in this paper. First, original peak load data is interpolated in order to overcome the shortage of data for effective prediction. Next, the prediction of peak load using these interpolated data as input is performed in parallel by a neural network predictor and a fuzzy predictor. The neural network predictor shows better performance at drastic change of peak load, while the fuzzy predictor yields better prediction results in gradual changes. Finally, the superior one of two predictors is selected by the rules based on rough sets at every prediction time. To verify the effectiveness of the proposed method, the computer simulation is performed on peak load data in 2015 provided by KPX.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.1
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• pp.24-31
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• 2012

This study is designed to predict the overall electric power load, to apply the method of time sharing and to reduce simultaneous load factor of electric power when authorized by user entering demand plans and using schedules into the user's interface for a certain period of time. This is about smart grid, which reduces electric power load through simultaneous load factor of electric power reduction system supervision agent. Also, this study has the following characteristics. First, it is the user interface which enables authorized users to enter and send/receive such data as demand plan and using schedule for a certain period of time. Second, it is the database server, which collects, classifies, analyzes, saves and manages demand forecast data for a certain period of time. Third, is the simultaneous load factor of electric power control agent, which controls usage of electric power by getting control signal, which is intended to reduce the simultaneous load factor of electric power by the use of the time sharing control system, form the user interface, which also integrate and compare the data which were gained from the interface and the demand forecast data of the certain period of time.

• Journal of Electrical Engineering and Technology
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• v.2 no.3
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• pp.329-334
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• 2007

In this paper, a new load flow algorithm is proposed on the basis of distributed load modeling in radial distribution networks. Since the correct state data in the distribution power networks is basic for all distribution automation algorithms in the Distribution Automation System (DAS), the distribution networks load flow is essential to obtain the state data. DAS Feeder Remote Terminal Units (FRTUs) are used to measure and acquire the necessary data for load flow calculations. In case studies, the proposed algorithm has been proven to be more accurate than a conventional algorithm; and it has also been tested in a simple radial distribution system.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.19 no.6
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• pp.427-433
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• 2007

Generally, H.V.A.C load capacity in early planning phase can presume with maximum thermal load. Basic data can prove by air conditioning equipment system data analysis at existing building. There are poor and not reliable alternative presentation. In this paper, measured data after use H.V.A.C load calculation K-load program reply choosing standard building and variables simulation. And I founded peak load correlation graph and mode for several kinds of variable and contents of size. I wish that equipment designer is beaconed to produce optimum capacity at building as quantitative through this result.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.37 no.8
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• pp.789-798
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• 2013

The procedures and relevant programs developed for analyzing mechanical load data of wind turbine generator systems, which are obtained through type certification tests, are verified. The following issues according to IEC 61400-13 are covered in the developed programs: data validation, time series analysis, summary load statistics, generation of fatigue load spectra, and estimation of equivalent loads. A capture matrix for normal power production is generated to determine whether the collected data sets are sufficient to carry out fatigue analysis. Fatigue load spectra are obtained through the rainflow counting method using 50 load ranges; finally, equivalent loads are calculated using different S-N curve slopes, m, according to the relevant materials. Case studies are performed using aero-elastic simulation data of the NREL 5 MW baseline wind turbine with a monopile foundation.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.24 no.3
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• pp.212-217
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• 2012

The cooling of data centers has emerged as a significant challenge as the density of IT equipment increased. With the rapid increasing of heat load and cooling system, predictions for electronics power trends have been closely watched. A data center power density projection is needed so that IT organizations can develop data centers with adequate cooling for reasonable lifetimes. This paper will discuss the need for something more than processor and equipment power trend projections which have overestimated the required infrastructure for customers. This projection will use data from a survey of actual enterprise data centers and the ASHRAE projections to formulate a data center server heat load trend projection.