• 응용통계연구
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• 제35권1호
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• pp.93-104
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• 2022

S&P 500과 RUSSELL 2000, DJIA, Nasdaq 100 4가지 미국 주가지수의 실현변동성(realized volatility, RV)을 예측하는데 있어서 사람들의 관심 지표로 삼을 수 있는 인터넷 검색량(search volume, SV) 지수와 내재변동성(implied volatility, IV)를 이용하여 LSTM 딥러닝(deep learning) 방법으로 RV의 예측력을 높이고자하였다. SV을 이용한 LSTM 방법의 실현변동성 예측력이 기존의 기본적인 vector autoregressive (VAR) 모형, vector error correction (VEC)보다 우수하였다. 또한, 최근 제안된 RV와 IV의 공적분 관계를 이용한 vector error correction heterogeneous autoregressive (VECHAR) 모형보다도 전반적으로 예측력이 더 높음을 확인하였다.

• 국제강구조저널
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• 제18권5호
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• pp.1541-1559
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• 2018

This paper addresses the dynamic loading characteristics of the shock tube onto sandwich steel beams as an efficient and accurate alternative to time consuming and complicated fluid structure interaction using finite element modeling. The corrugated sandwich steel beam consists of top and bottom flat substrates of steel 1018 and corrugated cores of steel 1008. The corrugated core layers are arranged with non-uniform thicknesses thus making sandwich beam graded. This sandwich beam is analogous to a steel beam with web and flanges. Substrates correspond to flanges and cores to web. The stress-strain relations of steel 1018 at high strain rates are measured using the split-Hopkinson pressure. Both carbon steels are assumed to follow bilinear strain hardening and strain rate-dependence. The present finite element modeling procedure with an improved dynamic impulse loading assumption is validated with a set of shock tube experiments, and it provides excellent correlation based on Russell error estimation with the test results. Four corrugated graded steel core arrangements are taken into account for core design parameters in order to maximize mitigation of blast load effects onto the structure. In addition, numerical study of four corrugated steel core placed in a reverse order is done using the validated finite element model. The dynamic behavior of the reversed steel core arrangement is compared with the normal core arrangement for deflections, contact force between support and specimen and plastic energy absorption.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1511-1511
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• 2001

Energy content, expressed as calories per gram, is an important part of the evaluation and marketing of foods in developed countries. Currently accepted methods of measurement of energy by U.S. food labeling legislation include measurement of gross calories by bomb calorimetry with an adjustment for undigested protein and by calculation using specific factors for the energy values of protein, carbohydrate less the amount of insoluble dietary fiber, and total fat. The ability of NIRS to predict the energy value of diverse, processed and unprocessed cereal food products was investigated. NIR spectra of cereal products were obtained with an NIR Systems monochromator and the wavelength range used for analysis was 1104-2494 nm. Gross energy of the foods was measured by oxygen bomb calorimetry (Parr Manual No. 120) and expressed as calories per gram (CPGI, range 4.05-5.49 cal/g). Energy value was adjusted for undigested protein (CPG2, range 3.99-5.38 cal/g) and undigested protein and insoluble dietary fiber (CPG3, range 2.42-5.35 cal/g). Using a multivariate analysis software package (ISI International, Inc.) partial least squares models were developed for the prediction of energy content. The standard error of cross validation and multiple coefficient of determination for CPGI using modified partial least squares regression (n=127) was 0.060 cal/g and 0.95, respectively, and the standard error of performance, coefficient of determination, bias and slope using an independent validation set (n=59) were 0.057 cal/g, 0.98, -0.027 cal/g and 1.05 respectively. The PLS loading for factor 1 (Pearson correlation coefficient 0.92) had significant absorption peaks correlated to C-H stretch groups in lipid at 1722/1764 nm and 2304/2346 nm and O-H groups in carbohydrate at 1434 and 2076 nm. Thus the model appeared to be predominantly influenced by lipid and carbohydrate. Models for CPG2 and CPG3 showed similar trends with standard errors of performance, using the independent validation set, of 0.058 and 0.088 cal/g, respectively, and coefficients of determination of 0.96. Thus NIRS provides a rapid and efficient method of predicting energy content of diverse cereal foods.

• 대한기계학회논문집B
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• 제27권3호
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• pp.326-333
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• 2003

In the measurement using DMA and CPC in series, there is some time delay for particles classified in DMA to detect in CPC. During this time, the DMA time-response changes due to the velocity profile of sampling tube and the diffusion of particles in the volume that exists between the DMA exit and the detector of ultra-fine CPC. This is called mixing effect. In the accelerated measurement methods like the TSI -SMPS, the size distribution is obtained from the correlation between the time-varying electrical potential of the DMA and the corresponding particle concentrations sampled in DMA. If the DMA time -response changes during this delay time, this can cause the error of a size distribution measured by this accelerated technique. The kernel function considering this mixing effect using the residence time distribution is proposed by Russell et al. In this study, we obtained a size distribution using this kernel to compare to the result obtained by the commercial accelerated measurement system, TSI -SMPS for verification and considered the errors that result from the mixing effect with the geometric mean diameters of originally sampled particles, using virtually calculated responses obtained with this kernel as input data.