• Journal of the Korea Society of Computer and Information
• /
• v.19 no.9
• /
• pp.55-63
• /
• 2014

Up to date, numerous studies on wireless sensor networks have been performed to overcome the Energy-Constraint of the sensor system. Existing schemes for estimating the residual energy have considered only voltage of sensor system. However battery performance in the real is affected by temperature and load. In this paper we introduce more accurate scheme, for the use in wireless sensor node, based on the interpolation of lookup tables which allow for temperature and load characteristics, as well as battery voltage.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.65 no.6
• /
• pp.969-978
• /
• 2016

Around the world are increasing the demand for ESS. Currently, the domestic is expected to benefit by operating ESS. In the domestic, it is expected to benefit from operations of the installed ESS because of the introduction of ESS less capacity. However ESS capacity to the maximum profit occurs is unknown. ESS is different from the charge-discharge characteristics and the reserve to replace, depending on the application. Therefore, it should be established in accordance with the ESS optimal capacity according to the purpose used because it can maximize the quality and efficiency of the electric energy. To the ESS optimal capacity estimation by the purpose used, It should compare the investment cost caused by ESS facility installation and operation cost caused by operating ESS. In this paper, the operation mathematical model for estimating marginal operation costs established. In operation mathematical model, operating cost is considered fuel cost and no-load cost start-up cost. Because no-load cost and start-up cost are not related to cost and power plant output, there are expressed an integer variable costs as a step function.

• Wind and Structures
• /
• v.32 no.3
• /
• pp.193-204
• /
• 2021

The estimation of the extreme wind load (effect) under a mean recurrence interval (MRI) is an important task in the wind-resistant design for the structure. It can be predicted by either first-order method or full-order method, depending on the accuracy and complexity requirement. Although the first-order method with the consideration of wind directionality has been proposed, less work has been done on the full-order method, especially with the wind directionality. In this study, the full-order method considering the wind directionality is proposed based on multivariate joint probability distribution. Meanwhile, considering two wind directions, the difference of the corresponding results based on the first-order method and full-order method is analyzed. Finally, based on the measured wind speed data, the discrepancy between these two methods is investigated. Results show that the difference between two approaches is not obvious under larger MRIs while the underestimation caused by the first-order method can be larger than 15% under smaller MRIs. Overall, the first-order method is sufficient to estimate the extreme wind load (effect).

• Geotechnical Engineering
• /
• v.3 no.3
• /
• pp.31-48
• /
• 1987

The vertical pressure due to soil prism load and surface surcharge load acts on buried pipe, and occasionally uplifting force due to earthquake or differential settlement acts on it. In this paper, study was performed to estimate the pressure acting on the buried pipe due to soil prism load through analyzing Marston-Spangler theory by new method. And loading tests on the buried flexible pipe were performed to study on the response of the pipe due to surface surcharge load. Also, through the estimation of uplifting resistance theory and uplifting test for buried pipe, the method to determine the maximum uplifting resistance of buried pipe was proposed.

• Computers and Concrete
• /
• v.33 no.6
• /
• pp.739-754
• /
• 2024

This study presents an innovative AI-driven approach to assess the ultimate axial load in Double-Skinned Profiled Steel sheet Composite Walls (DPSCWs). Utilizing a dataset of 80 entries, seven input parameters were employed, and various AI techniques, including Linear Regression, Polynomial Regression, Support Vector Regression, Decision Tree Regression, Decision Tree with AdaBoost Regression, Random Forest Regression, Gradient Boost Regression Tree, Elastic Net Regression, Ridge Regression, and LASSO Regression, were evaluated. Decision Tree Regression and Random Forest Regression emerged as the most accurate models. The top three performing models were integrated into a hybrid approach, excelling in accurately estimating DPSCWs' ultimate axial load. This adaptable hybrid model outperforms traditional methods, reducing errors in complex scenarios. The validated Artificial Neural Network (ANN) model showcases less than 1% error, enhancing reliability. Correlation analysis highlights robust predictions, emphasizing the importance of steel sheet thickness. The study contributes insights for predicting DPSCW strength in civil engineering, suggesting optimization and database expansion. The research advances precise load capacity estimation, empowering engineers to enhance construction safety and explore further machine learning applications in structural engineering.

• Journal of Wetlands Research
• /
• v.6 no.3
• /
• pp.47-54
• /
• 2004

A hydraulically and hydrologically based estimation method of pollutant delivery load for water quality modeling is proposed. The proposed method works on grid basis and routes overland flows from one cell to the next following the maximum downslope directions. The method is able to consider spatially-varied data of source pollutant, topography, land slopes, soil characteristics, land use and aspects, which can be extracted from geographic information systems (GIS) and from digital elevation models (DEMs). Because of this feature, the proposed method can be expected to be used for evaluating the impacts of various practices on watershed management for water quality.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.13 no.4
• /
• pp.120-126
• /
• 1999

;The consicErations for reminder life of transmission line is gradually higher. It is requisite for investigation of ACSR's life to test tensile load of ACSR as a fundamental data. It is vary important to analysis correlations between results of tensile load testing and elasped years. Estimation of ACSR's life can be obtained by statistics processing using mechanical experirrental results. It is a general method to use regression analysis as a statistics processing technique. In this paper, we did experiment on tensile strength of ACSR by using a new and old ACSR as sample experirrental materials. The limit of life estimation is decided by basic line using twenty percentage reduction of rate tensile strength. This basic line is like to results of Canada Ontario Hydro-research. There are $95[\textrm{mm}^2]$, $97[\textrm{mm}^2]$, $120[\textrm{mm}^2]$, $240[\textrm{mm}^2]$ ACSRs which are experimented on this study. 1be life estimation of these ACSR is presented by table 1 to be obtained through the linear regression and nonlinear regression analysis. SPSS and statistics toolbox of matlab is used for analysis.lysis.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.28 no.6
• /
• pp.890-898
• /
• 2022

Dispersion of suspended sediment, caused by coastal and marine development, is a key item in assessing marine environmental impact as it adversely affects marine life by increasing the level of turbidity and decreasing the amount of sunlight in seawater. However, its estimation has not been reliable because of the absence of a standard for the data measurement and divergent approaches to the impact assessment. In this study, we examined the estimation models from 58 Marine Environmental Impact Statements (MEISs, 2012-2014) to identify the gaps in the assessment and devise ways of improving the estimation. We developed four index items-grid system; unit load, particle size, and settling velocit-to evaluate their reliability in the estimation. The mean reliability score of each index was overall low-25 for grid system, 60 for unit load, 34 for particle size, and 17 for settling velocity. To ensure high reliability, it is important to develop a standard guideline that defines precise measurement of suspended sediment for unit load and settling velocity by particle size, followed by a grid system with compatible size for modelling. This can improve the estimation and thus underlie coherent impact assessment of suspended sediment dispersion on marine environment.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.31 no.5
• /
• pp.562-569
• /
• 2007

This paper presents plastic limit loads and approximate J estimates for axial through-wall cracked pipe bends under internal pressure and in-plane bending. Geometric variables associated with a crack and pipe bend are systematically varied, and three possible crack locations (intrados, extrados and crown) in pipe bends are considered. Based on small strain finite element limit analyses using elastic-perfectly plastic materials, effect of bend and crack geometries on plastic limit loads for axial through-wall cracked pipe bends under internal pressure and in-plane bending are quantified, and closed-form limit solutions are given. Based on proposed limit load solutions, a J estimation scheme for axial through-wall cracked pipe bends under internal pressure and in-plane bending is proposed based on reference stress approach.

• Korean Journal of Artificial Intelligence
• /
• v.12 no.1
• /
• pp.17-24
• /
• 2024

This paper introduces a novel approach to time-series estimation for energy load forecasting within Virtual Power Plant (VPP) systems, leveraging advanced artificial intelligence (AI) algorithms, namely Long Short-Term Memory (LSTM) and Seasonal Autoregressive Integrated Moving Average (SARIMA). Virtual power plants, which integrate diverse microgrids managed by Energy Management Systems (EMS), require precise forecasting techniques to balance energy supply and demand efficiently. The paper introduces a hybrid-method forecasting model combining a parametric-based statistical technique and an AI algorithm. The LSTM algorithm is particularly employed to discern pattern correlations over fixed intervals, crucial for predicting accurate future energy loads. SARIMA is applied to generate time-series forecasts, accounting for non-stationary and seasonal variations. The forecasting model incorporates a broad spectrum of distributed energy resources, including renewable energy sources and conventional power plants. Data spanning a decade, sourced from the Korea Power Exchange (KPX) Electrical Power Statistical Information System (EPSIS), were utilized to validate the model. The proposed hybrid LSTM-SARIMA model with parameter sets (1, 1, 1, 12) and (2, 1, 1, 12) demonstrated a high fidelity to the actual observed data. Thus, it is concluded that the optimized system notably surpasses traditional forecasting methods, indicating that this model offers a viable solution for EMS to enhance short-term load forecasting.