• Journal of Advanced Navigation Technology
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• v.13 no.2
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• pp.208-213
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• 2009

This paper presents an adaptive call admission control(CAC) algorithm based on a target handoff call dropping probability in mobile wireless environments. This method uses a neural network for predicting and reserving the bandwidth demands for handoff calls and new calls. The amount of bandwidth to be reserved is adaptively adjusted by a target value of handoff call dropping probability(CDP). That is, if the handoff CDP exceeds the a target CDP value, the bandwidth to be reserved should be increased to reduce the handoff dropping probability below a target value. The proposed method is intended to prevent from increasing handoff call dropping probability when bandwidth to be reserved is not enough for handoff calls due to an uncertain prediction. Our simulations compare the handoff CDP in proposed CAC with that of an existing CAC. Results show that the proposed method sustains handoff call dropping probability below our target value.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.4
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• pp.587-593
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• 2018

The current road traffic noise prediction programs of Korea, which are widely used, are based upon foreign prediction model. Thus, it is necessary to verify foreign prediction models to find out whether they are suitable for the domestic road traffic environment. In addition, an analysis and an in-depth study on the main factors should be conducted in advance as the influence factors on the occurrence of traffic noise vary for each prediction model. Therefore, this study examined the influence factors and the existing prediction models used to forecast road traffic noise. Also, analyzed their relationship with the factors influencing the noise generated by driving vehicles through multiple regression analysis using a prediction model, taking into consideration of the traffic environment and the road geometric structure. In addition, this study will apply experimental values to the existing road traffic noise prediction model (NIER, RLS-90) and the deducted road traffic noise prediction model. As a result, the order of the absolute value sum of the errors are NIER, RLS-90, model value. Through comparison and verification, developed models are to be analyzed for providing basic research results for future study on road traffic noise prediction modeling.

• Journal of the Korean Wood Science and Technology
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• v.44 no.6
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• pp.864-871
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• 2016

This study was conducted to compare and analyze gross calorific values from measurement methods of lignocellulosic biomass and calculation data from calorific value prediction models based on the elemental content. The deviation of Liriodendron tulipifera (LT) and Populus euramericana (PE) was shown 7.7 cal/g and 7.4 cal/g respectively in palletization method, which are within repeatability limit 28.8 cal/g of ISO FDIS 18125. In the case of Thailand charcoal (TC), nontreatment method and palletization method was satisfied with repeatability limit as 22.8 cal/g and 8.8 cal/g respectively. Seowon charcoal (SC) was shown deviation of 11.4 cal/g in nontreatment method, because the density and chemical affinity of sample increases as the carbon content increases from heat treatment at high temperature in the case of TC and SC. In addition, after applying the elemental content of each of these samples to the calorific value prediction models, the study found that Model Equation (3) was relatively consistent with measured calorific values of all these lignocellulosic biomass. Thus, study about the correlation between the density and size of particle should be conducted in order to select the measurement method for a wide range of solid biofuels in the future.

• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.857-865
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• 2013

Extreme rainfall causes heavy losses in human life and properties. Hence many works have been done to predict extreme rainfall by using extreme value distributions. In this study, we use a generalized extreme value distribution to derive the posterior predictive density with hierarchical Bayesian approach based on the data of Seoul area from 1973 to 2010. It becomes clear that the probability of the extreme rainfall is increasing for last 20 years in Seoul area and the model proposed works relatively well for both point prediction and predictive interval approach.

• Journal of the Korean Society for Railway
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• v.15 no.3
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• pp.278-285
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• 2012

The Service Availability (SA) in the viewpoint of passenger is used as the key performance indicator (KPI) of quality of service in Light Rail Transit (LRT) Public-Private Partnerships projects. But there are many disputes on the target value of SA because of the lack of experience in SA. The target value of SA should be set at an early stage of the project to be specified on the system specifications and operation plan. Therefore, this paper developed the quantitative prediction model of SA to set the reasonably achievable target value of SA at an early stage of the LRT project. Also this paper analyzed the relationship and differentiation of SA and Train Punctuality (TP) that is mostly compared with SA.

• Journal of Animal Science and Technology
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• v.62 no.4
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• pp.438-448
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• 2020

This study was performed to increase the accuracy of genomic estimated breeding value (GEBV) predictions for domestic pigs using single-breed and admixed reference populations (single-breed of Berkshire pigs [BS] with cross breed of Korean native pigs and Landrace pigs [CB]). The principal component analysis (PCA), linkage disequilibrium (LD), and genome-wide association study (GWAS) were performed to analyze the population structure prior to genomic prediction. Reference and test population data sets were randomly sampled 10 times each and precision accuracy was analyzed according to the size of the reference population (100, 200, 300, or 400 animals). For the BS population, prediction accuracy was higher for all economically important traits with larger reference population size. Prediction accuracy was ranged from -0.05 to 0.003, for all traits except carcass weight (CWT), when CB was used as the reference population and BS as the test. The accuracy of CB for backfat thickness (BF) and shear force (SF) using admixed population as reference increased with reference population size, while the results for CWT and muscle pH at 24 hours after slaughter (pH) were equivocal with respect to the relationship between accuracy and reference population size, although overall accuracy was similar to that using the BS as the reference.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.64-72
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• 2016

In this paper, we utilize a Gaussian process to predict the power consumption in the air-conditioning system. As the power consumption in the air-conditioning system takes a form of a time-series and the prediction of the power consumption becomes very important from the perspective of the efficient energy management, it is worth to investigate the time-series model for the prediction of the power consumption. To this end, we apply the Gaussian process to predict the power consumption, in which the Gaussian process provides a prior probability to every possible function and higher probabilities are given to functions that are more likely consistent with the empirical data. We also discuss how to estimate the hyper-parameters, which are parameters in the covariance function of the Gaussian process model. We estimated the hyper-parameters with two different methods (marginal likelihood and leave-one-out cross validation) and obtained a model that pertinently describes the data and the results are more or less independent of the estimation method of hyper-parameters. We validated the prediction results by the error analysis of the mean relative error and the mean absolute error. The mean relative error analysis showed that about 3.4% of the predicted value came from the error, and the mean absolute error analysis confirmed that the error in within the standard deviation of the predicted value. We also adopt the non-parametric Wilcoxon's sign-rank test to assess the fitness of the proposed model and found that the null hypothesis of uniformity was accepted under the significance level of 5%. These results can be applied to a more elaborate control of the power consumption in the air-conditioning system.

• Journal of the Korean Society for Railway
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• v.20 no.4
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• pp.474-481
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• 2017

When designing photovoltaic power plants in Korea, the prediction of photovoltaic power generation at the design phase is carried out using PVSyst, PVWatts (Overseas power generation prediction software), and overseas weather data even if the test site is a domestic site. In this paper, for a comparative study to predict power generation using weather information, domestic photovoltaic power plants in two regions were selected as target sites. PVsyst, which is a commercial power generation forecasting program, was used to compare the accuracy between the predicted value of power generation (obtained using overseas weather information (Meteonorm 7.1, NASA-SSE)) and the predicted value of power generation obtained by the Korea Meteorological Administration (KMA). In addition, we have studied ways to improve the prediction of power generation through comparative analysis of meteorological data. Finally, we proposed a revised solar power generation prediction model that considers climatic factors by considering the actual generation amount.

• Journal of the Society of Disaster Information
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• v.9 no.3
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• pp.324-331
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• 2013

Epoxy asphalt concrete is used to reduce dead load and to increase durability on long-span steel bridge overlay. The strength development properties of epoxy asphalt concrete are affected by time and temperature because epoxy asphalt is two-phase reactive materials. The strength development of epoxy asphalt concrete should be predicted precisely to decide traffic opening time. Based on this background in mind, the prediction model for traffic opening time for epoxy asphalt pavement was proposed in this research. The developed model using nonlinear curve fitting revealed R2 value of 0.943 while the R2 value of the existing model using chemical kinetics was 0.806. An improved precise prediction result is to be obtained when the prediction model uses accurate temperature data of pavement.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.175-181
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• 2021

This paper analyzes the artificial intelligence-based approach for short-term energy consumption prediction. In this paper, we employ the reinforcement learning algorithms to improve the limitation of the supervised learning algorithms which usually utilize to the short-term energy consumption prediction technologies. The supervised learning algorithm-based approaches have high complexity because the approaches require contextual information as well as energy consumption data for sufficient performance. We propose a deep reinforcement learning algorithm based on multi-agent to predict energy consumption only with energy consumption data for improving the complexity of data and learning models. The proposed scheme is simulated using public energy consumption data and confirmed the performance. The proposed scheme can predict a similar value to the actual value except for the outlier data.