• International Journal of Aeronautical and Space Sciences
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• 제18권2호
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• pp.222-235
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• 2017

In this study, finite element modeling was used to predict the material properties of tri-axially braided textile composite. The model was made based on an experimental test specimen which was also used to compare the final results. The full interlacing of tows was geometrically modelled, from which repeating parts that make up the whole braid called unit cells were identified based on the geometric and mechanical property periodicity. In order to simulate the repeating nature of the unit cell, periodic boundary conditions were applied. For validation of the method, a reference model was analyzed for which a very good agreement was obtained. Material property calculation was done by simulating uniaxial and pure shear tests on the unit cell. The comparison of these results with that of experimental test results showed an excellent agreement. Finally, parametric study on the effect of number of plies, stacking type (symmetric/anti-symmetric) and stacking phase shift was conducted.

• Journal of Electrical Engineering and Technology
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• 제12권3호
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• pp.1053-1063
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• 2017

Power prediction is critical to improve power efficiency in Smart Grids. Markov chain provides a useful tool for power prediction. With careful investigation of practical power datasets, we find an interesting phenomenon that the stochastic property of practical power datasets does not follow the Markov features. This mismatch affects the prediction accuracy if directly using Markov prediction methods. In this paper, we innovatively propose a spatial transform based data processing to alleviate this inconsistency. Furthermore, we propose an enhanced power prediction method, named by Spatial Mapping Markov-Difference (SMMD), to guarantee the prediction accuracy. In particular, SMMD adopts a second prediction adjustment based on the differential data to reduce the stochastic error. Experimental results validate that the proposed SMMD achieves an improvement in terms of the prediction accuracy with respect to state-of-the-art solutions.

• 한국연초학회지
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• 제29권2호
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• pp.74-79
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• 2007

This study was conducted to evaluate the prediction of sensory property of smoke from leaf chemical property and characterize leaf chemical components for the best tobacco taste's leaves in flue-cured tobacco. For analytical and sensory evaluations, one hundred and forty grades were used. The major leaf chemical components to predict the sensory property of smoke were nicotine for impact, irritation and off taste & odor, and total sugar/nicotine ratio for tobacco taste. Within ${\pm}20%$ range of difference, the predictable probabilities of sensory property of smoke form leaf chemical property were 80.0% for off taste & odor and $91.4{\sim}96.4%$ for impact, irritation and tobacco taste. As a result of K-means cluster analysis on the basis of tobacco taste, the desirable leaf chemical component contents were $2.77{\sim}3.55%$ in nicotine and $5.1{\sim}6.9$ in total sugar/nicotine ratio. This study suggest that the some regression equations may be useful to predict the sensory property of tobacco smoke from a few selected leaf chemical components in flue-cured tobacco and to select the flue-cured tobacco leaves for enhance the tobacco taste of cigarette.

• 한국빅데이터학회지
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• 제6권1호
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• pp.133-144
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• 2021

본 논문은 서울시에 존재하는 건물을 대상으로 화재 발생 시 재산 피해액, 화재 발생 여부를 예측하여 건물별 화재 위험도를 도출하였다. 본 연구는 건물의 특성뿐 아니라 해당 건물이 속한 행정동의 특성 및 소방 시설 접근성과 같은 변수를 활용하였다는 점에서 기존 선행연구와의 차이점을 지닌다. 앙상블 보팅(Ensemble Voting) 기법을 활용해 서로 다른 알고리즘을 병합했으며, 이를 통해 재산 피해액과 화재 발생 여부를 예측하고 변수 중요도를 추출하여 화재 위험도를 산출하는 방향으로 연구를 진행하였다. 구축된 모델을 사용하여 서울시에 존재하는 300개 건물을 대상으로 적용한 결과, 화재 위험도 1등급의 경우 건물 내 세대 수가 많으며, 관할 119안전센터가 가장 멀리 위치하는 등 화재 발생 시 그 규모를 키울 수 있는 요인들이 많은 것으로 나타났다. 반면 5등급의 경우, 주변 건물 수나 사업체 수는 많지만, 관할 119안전센터가 가장 가까이 위치해 있어 화재에 적절히 대응할 수 있는 건물들로 나타났다.

• Journal of the Korean Data and Information Science Society
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• 제11권2호
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• pp.173-179
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• 2000

Interval prediction based on the empirical distribution function for the class of time series with time varying coefficients is discussed. To this end, strong mixing property of the model is shown and results due to Fotopoulos et. al.(1994) are employed. A simulation study is presented to assess the accuracy of the proposed interval predictor.

• 폴리머
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• 제38권6호
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• pp.708-713
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• 2014

대부분의 플라스틱 제품은 사출성형을 통해 생산된다. 사출성형에서 성형수축은 피할 수 없으며 이는 제품에 휨이나 뒤틀림을 유발하여 제품의 치수정밀도를 떨어뜨리는 요인으로 작용한다. 사출성형 시 발생하는 휨이나 뒤틀림은 성형조건이나 제품의 형상에도 영향을 받지만 수지의 물성에 따라서도 다양하게 나타난다. 본 연구에서는 제품의 휨을 제어하기 위해 폴리카보네이트를 유리섬유로 보강하여 물성을 예측하였으며, 이를 이용하여 사출성형해석을 실시하였다. 사출성형해석을 통해 유리섬유로 보강된 수지에서 제품의 휨이 감소하는 것을 확인할 수 있었다. 본 연구방법의 타당성과 신뢰성을 검증하기 위하여 사출실험을 실시하여 수지의 물성에 따른 휨 값을 분석하였으며 해석과 실험에서 유사한 경향의 휨이 발생하는 것을 관찰할 수 있었다. 결론적으로 본 연구에서 수행한 바와 같이 해석 프로그램을 통해 수지의 물성을 설계하고 이를 통한 휨의 제어가 가능함을 확인할 수 있었다.

• Computers and Concrete
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• 제12권4호
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• pp.377-392
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• 2013

This paper investigates prediction models estimating the hydration properties of concrete, such as the compressive strength, the splitting tensile strength, the elastic modulus,and the autogenous shrinkage. A prediction model is suggested on the basis of an equation that is formulated to predict the compressive strength. Based on the assumption that the apparent activation energy is a characteristic property of concrete, a prediction model for the compressive strength is applied to hydration-related properties. The hydration properties predicted by the model are compared with experimental results, and it is concluded that the prediction model properly estimates the splitting tensile strength, elastic modulus, and autogenous shrinkage as well as the compressive strength of concrete.

• Journal of Electrical Engineering and Technology
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• 제12권3호
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• pp.989-995
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• 2017

As is known to all that the output of wind power generation has a character of randomness and volatility because of the influence of natural environment conditions. At present, the research of wind power prediction mainly focuses on point forecasting, which can hardly describe its uncertainty, leading to the fact that its application in practice is low. In this paper, a wind power range prediction model based on the multiple output property of BP neural network is built, and the optimization criterion considering the information of predicted intervals is proposed. Then, improved Particle Swarm Optimization (PSO) algorithm is used to optimize the model. The simulation results of a practical example show that the proposed wind power range prediction model can effectively forecast the output power interval, and provide power grid dispatcher with decision.

• ETRI Journal
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• 제34권5호
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• pp.753-758
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• 2012

As the next-generation video coding standard, High Efficiency Video Coding (HEVC) has adopted advanced coding tools despite the increase in computational complexity. In this paper, we propose a selective bi-prediction method to reduce the encoding complexity of HEVC. The proposed method evaluates the statistical property of the sum of absolute differences in the motion estimation process and determines whether bi-prediction is performed. A performance comparison of the complexity reduction is provided to show the effectiveness of the proposed method compared to the HEVC test model version 4.0. On average, 50% of the bi-prediction time can be reduced by the proposed method, while maintaining a negligible bit increment and a minimal loss of image quality.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 심포지엄 논문집 정보 및 제어부문
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• pp.12-14
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• 2006

This paper presents a novel scene change detection method using intra prediction mode and edge direction in H.264/AVC. When scene change occurs, there are less temporal correlation between frames, most of macro-blocks encoded in intra mode. Using this property, the method calculates the percentage of intra mode blocks in each predictive frame in order to get candidates of scene change frame. To further find scene change, we obtain edge histogram of each candidates by using eight prediction direction of intra prediction mode in H.264/AVC. We detect scene change frames with $\iota^1$-norm of edge histograms. The experimental results show that the method is efficient and robust.