• Journal of the Korean Applied Science and Technology
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• v.34 no.4
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• pp.818-826
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• 2017

In accordance with the New and Renewable Energy Supply Statistics, biomass power generation has surged since 2013, and use of wood pellet has the most sharply increased, 696Gwh in 2013, 2,764Gwh in 2014 and 2,512Gwh in 2015. Total domestic wood pellet consumption was 1.48million tons in 2015, of which wood pellets consumed for power generation account for about 1.08million tons, about 73%. In this study, we gained the result that the wood pellet would be consumed 2.61million tons in 2020, 6.85million tons in 2025, 11.39million tons in 2030. We also calculated the optimum biomass power generation, on the premise that the power plant co-fire 50% biomass, and the result was that 2.26million tons of wood pellets should be produced domestically in 2021 to operate the present licensed wood pellet power plant from this study.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.1
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• pp.225-234
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• 2019

Bitcoin is a cryptocurrency with characteristics such as de-centralization and distributed ledger, and these features are maintained through a mining system called "proof of work". In the mining system, mining difficulty is adjusted to keep the block generation time constant. However, Bitcoin's current method to update mining difficulty does not reflect the future hash power, so the block generation time can not be kept constant and the error occurs between designed time and real time. This increases the inconsistency between block generation and real world and causes problems such as not meeting deadlines of transaction and exposing the vulnerability to coin-hopping attack. Previous studies to keep the block generation time constant still have the error. In this paper, we propose a machine-learning based method to reduce the error. By training with the previous hash power, we predict the future hash power and adjust the mining difficulty. Our experimental result shows that the error rate can be reduced by about 36% compared with the current method.

• Journal of the Korean Solar Energy Society
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• v.39 no.3
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• pp.59-66
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• 2019

New and renewable energy forecasts are key technology to reduce the annual operating cost of new and renewable facilities, and accuracy of forecasts is paramount. In this study, we intend to build a model for the prediction of short-term solar power generation for 1 hour to 3 hours. To this end, this study applied two time series technique, ARIMA model without considering seasonality and SARIMA model with considering seasonality, comparing which technique has better predictive accuracy. Comparing predicted errors by MAE measures of solar power generation for 1 hour to 3 hours at four locations, the solar power forecast model using ARIMA was better in terms of predictive accuracy than the solar power forecast model using SARIMA. On the other hand, a comparison of predicted error by RMSE measures resulted in a solar power forecast model using SARIMA being better in terms of predictive accuracy than a solar power forecast model using ARIMA.

• Korean Journal of Construction Engineering and Management
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• v.20 no.6
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• pp.126-131
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• 2019

The estimation of available solar energy at particular locations is critical to find and assess suitable locations of PV sites. The amount of PV power generation is however affected by various geographical factors (e.g., weather), which may make it difficult to identify the complex relationship between affecting factors and power outputs and to apply findings from one study to another in different locations. This study thus undertakes a regression analysis using data collected from 172 PV plants spatially distributed in Korea to identify critical weather conditions and estimate the potential power generation of PV systems. Such data also include solar radiation, precipitation, fine dust, humidity, temperature, cloud amount, sunshine duration, and wind speed. The estimated PV power generation is then compared to the actual PV power generation to evaluate prediction performance. As a result, the proposed model achieves a MAPE of 11.696(%) and an R-squred of 0.979. It is also found that the variables, excluding humidity, are all statistically significant in predicting the efficiency of PV power generation. According, this study may facilitate the understanding of what weather conditions can be considered and the estimation of PV power generation for evaluating and determining suitable locations of PV facilities.

• Journal of Practical Engineering Education
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• v.14 no.2
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• pp.387-392
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• 2022

To participate in global carbon neutrality, the Korean government is also planning to carry out zero-energy building certification for all buildings by 2030 through the enforcement decree of the 'Green Building Support Act'. Accordingly, the government is providing various projects related to solar power generation, which are relatively close to life. In particular, roof-mounted photovoltaic power generation systems are attracting attention in terms of using unused space to produce energy without destroying the environment, but low power generation efficiency compared to other photovoltaic power generation facilities is pointed out as a disadvantage. Therefore, in this paper, to solve this problem, we propose an efficient solar panel angle variable system through research on the solar panel structure for single-axial solar tracking, and also consider the application environment of the roof-mounted solar power generation system. Suggests measures to prevent damage and secondary damage. In addition, it is judged that it is possible to control the solar panel based on ICT convergence and configure the accident prediction safety system to link the project-based education program.

• Journal of The Korean Association For Science Education
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• v.26 no.1
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• pp.88-98
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• 2006

The purpose of this study was to analyze the process of scientists' science knowledge generation by employing four creative scientists as participants. Raw protocols were collected by an intensive interview method and then analyzed by a psychological modelling procedure. The present study showed that the process of knowledge generation divided into the processes of inductive, abductive, and deductive thinking. Furthermore, the inductive process in simple and operative observation was involved in the processes of generating a question, conjecture/prediction, designing an operational method, operation, and simple observation. Also, the abductive process had two components; question generation, and hypothesis generation which consisted of analyzing questions, searching explicans, and constructing hypothesis. Finally, the deductive process involved inventing abstract test methods, inventing abstract criteria, inventing concrete test methods, inventing concrete criteria, collecting results, and evaluating hypotheses and stating conclusions.

• Journal of Platform Technology
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• v.6 no.3
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• pp.47-54
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• 2018

Predictive analysis in the hardware industry can be performed at an appropriate point in time to prevent failure of production facilities and reduce management costs. This helps to perform more efficient and scientific maintenance through automation of failure analysis. Among them, predictive management aims to prevent the occurrence of anomalous state by identifying and improving the abnormal state based on the gathering, analysis, and scientific data management of facilities using information technology and constructing prediction model do. In this study, we made a fault tree through the Relex tool and analyzed the error code of the hardware to study the safety.

• Asian-Australasian Journal of Animal Sciences
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• v.30 no.2
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• pp.149-153
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• 2017

Objective: This study was to determine the relationship between estimated breeding value and phenotype information after farrowing when juvenile selection was made in candidate pigs without phenotype information. Methods: After collecting phenotypic and genomic information for the total number of piglets born by Landrace pigs, selection accuracy between genomic breeding value estimates using genomic information and breeding value estimates of best linear unbiased prediction (BLUP) using conventional pedigree information were compared. Results: Genetic standard deviation (${\sigma}_a$) for the total number of piglets born was 0.91. Since the total number of piglets born for candidate pigs was unknown, the accuracy of the breeding value estimated from pedigree information was 0.080. When genomic information was used, the accuracy of the breeding value was 0.216. Assuming that the replacement rate of sows per year is 100% and generation interval is 1 year, genetic gain per year is 0.346 head when genomic information is used. It is 0.128 when BLUP is used. Conclusion: Genetic gain estimated from single step best linear unbiased prediction (ssBLUP) method is by 2.7 times higher than that the one estimated from BLUP method, i.e., 270% more improvement in efficiency.

• Convergence Security Journal
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• v.6 no.1
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• pp.55-63
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• 2006

In this paper, an algorithm that provisions absolute differentiation of packet delays is proposed with an objective for enhancing quality of service (QoS) in future packet networks. It features an adaptive scheme that compensates the deviation for prediction on the traffic to be arrived continuously. It predicts the traffic to be arrived at the beginning of a time slot and measures the actual arrived traffic at the end of the time slot, and derives the deviation between the two quantity. The deviation is utilized to the delay control operation for the next time slot to offset it. As it compensates the prediction error continuously, it shows superior adaptability to the bursty traffic as well as the constant rate traffic. It is demonstrated through simulation that the algorithm meets the quantitative delay bounds and shows superiority to the traffic fluctuation in comparison with the conventional mechanism.

• Journal of KSNVE
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• v.10 no.3
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• pp.444-450
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• 2000

Wheel-rail noise is normally classified into three catagories : rolling impact and squeal noise. In this paper rolling noise caused by the irregularity between a wheel and a rail is analysed as follows: The irregularity between the wheel and the rail is assumed as linear superposition of sinusoidal profiles. Wheel-rail contact stiffness is linearized by using Hertzian contact theory and then contact force between the wheel and the rail is calculated. vibration of the rail and the wheel is calculated theoretically by receptance method or FEM depending on the geometry of the wheel or the rail for the frequency range of 100-500 Hz important for noise generation. The radiation noise caused by those vibration response is computed by BEM To verify this analysis tools rolling noise is calculated by proposed analysis steps using typical roughness data and these results are compared with experimental rolling noise data. This analysis tools show reasonable results and finally used for the prediction of the Korean high speed train rolling noise.