• The Journal of the Acoustical Society of Korea
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• v.43 no.3
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• pp.285-292
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• 2024

When calculating sonar detection probability, underwater acoustic uncertainty is assumed to be normal distributed with a standard deviation of 8 dB to 9 dB. However, due to the variability in experimental areas and ocean environmental conditions, predicting detection performance requires accounting for underwater acoustic uncertainty based on ocean experimental data. In this study, underwater acoustic uncertainty was determined using measured mid-frequency (2.3 kHz, 3 kHz) noise level and transmission loss data collected in the shallow water of the East Sea. After calculating the predictable probability of detection reflecting underwater acoustic uncertainty based on ocean experimental data, we compared it with the conventional detection probability results, as well as the predictable probability of detection results considering the uncertainty of the Rayleigh distribution and a negatively skewed distribution. As a result, we confirmed that differences in the detection area occur depending on each underwater acoustic uncertainty.

• Structural Engineering and Mechanics
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• v.91 no.1
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• pp.39-47
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• 2024

Bridge deterioration shows the change of bridge condition during its operation, and predicting bridge deterioration is important for implementing predictive protection and planning future maintenance. However, in practical application, the raw inspection data of bridges are not continuous, which has a greater impact on the accuracy of the prediction results. Therefore, two kinds of bridge deterioration models are established in this paper: one is based on the traditional regression theory, combined with the distribution fitting theory to preprocess the data, which solves the problem of irregular distribution and incomplete quantity of raw data. Secondly, based on the theory of Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN), the network is trained using the raw inspection data, which can realize the prediction of the future deterioration of bridges through the historical data. And the inspection data of 60 prestressed concrete box girder bridges in Xiamen, China are used as an example for validation and comparative analysis, and the results show that both deterioration models can predict the deterioration of prestressed concrete box girder bridges. The regression model shows that the bridge deteriorates gradually, while the LSTM-RNN model shows that the bridge keeps great condition during the first 5 years and degrades rapidly from 5 years to 15 years. Based on the current inspection database, the LSTM-RNN model performs better than the regression model because it has smaller prediction error. With the continuous improvement of the database, the results of this study can be extended to other bridge types or other degradation factors can be introduced to improve the accuracy and usefulness of the deterioration model.

• Journal of Advanced Marine Engineering and Technology
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• v.37 no.3
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• pp.282-290
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• 2013

A primary air pollutant as an indicator of air quality released from incomplete combustion is Carbon monoxide. A study of the distributions of CO concentration with no heat source in a tunnel model closed at left end side is simulated with a commercial CFD code. The tunnel model is used to investigate the CO concentration distributions at three Reynolds numbers of 990, 1970, and 3290. which are computed by the inlet velocities of 0.3, 0.6 and 1.0 m/s. The CFD predictive approaches can be useful for a better design to analyze the distributions of CO concentrations. In the case of the tunnel model closed at left end side alone, the concentration changes of x/H=-5 and -2.5 have the similar laminar characteristics like the case of the tunnel model closed at both end sides expecially at low values of Reynolds number. Irregular average CO concentration variations at Re=1790 are considered that the transition from laminar to turbulent flow occurs even in three different tunnel models.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.4
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• pp.37-43
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• 2019

The research aims to study the relationship between export performance and stock return of Vietnamese fishery companies. To conduct this study, quarterly data was collected for period from 2010-2018 of 13 fishery companies listing in Ho Chi Minh Stock Exchange (HOSE) and Ha Noi Stock Exchange (HNX). The export performance was measured by export intensity, export growth and export market coverage. In addition, interest rate, exchange rate, GDP, firm size, profitability, and financial leverage were considered as the control variables in the research model. Panel data analysis with Generalized Least Squares model was employed to estimate the predictive regression. The findings indicated that export intensity and export growth have a significant and positive relationship with stock returns. However, export market coverage has not a significant relationship with stock return at the 0.05 level. Profitability, financial leverage, and exchange rate have a positive relationship, while interest rate and GDP have no relation to stock return at the 0.05 significance level. The findings imply that investors should consider the export intensity instead of export growth and export market coverage as selecting stock of fishery exports firms to invest; managers should increase export intensity to increase company's stock price or firm market value.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.1
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• pp.259-268
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• 2020

The purpose of this study is to examine the predictive effects of intuition, business analytic, networking capabilities on innovation performance. The data was collected using a cross-sectional quantitative survey. A total of 292 useable responses were collected from Thai Processed Food Exporters (TPFE). The findings also indicated that the hypothesized relationships between the independent and dependent variables fit the empirical data. Specifically, it is revealed that strategic intuition, business analytic capabilities, network-based capabilities and dynamic capabilities had a direct effect on dynamic strategy. They also had statistically significant direct and indirect effects on dynamic performance. Based on the results of the correlation test, the researchers developed a dynamic capability model for the development of the dynamic performance of the operators, which included concepts, principles, methods, tools and guidelines. Furthermore, the impacts of intuition, business analytic, networking capabilities on dynamic strategy are also examined in this study. It makes a considerable contribution to the existing literature on dynamic strategy of TPFE, particularly in regards to explaining the performance.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.20 no.5
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• pp.1661-1670
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• 1996

Incompressible flow over a backward-facing step is computed by low Reynolds number turbulence models in order to compare with direct simulation results. In this study, selected low Reynolds number 1st and 2nd (Algebraic Stress Model : ASM) moment closure turbulence models are adopted and compared with each other. Each turbulence model predicts different flow characteristics, different re-attachment point, velocity profiles and Reynolds stress distribution etc. Results by .kappa.-.epsilon. turbulence models indicate that predicted re-attachment lengths are shorter than those by standard model. Turbulent intensity and eddy viscosity by low Reynolds number .kappa.-.epsilon. models are still greater than DNS results. The results by algebraic stress model (ASM) are more reasonable than those by .kappa.-.epsilon. models. The convective scheme is QUICK (Quadratic Upstream Interpolation for Convective Kinematics) and SIMPLE algorithm is adopted. Reynolds number based on step height and inlet free stream velocity is 5100.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.151-154
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• 2008

Information on the area and spatial distribution of crop fields is needed for biomass production, arrangement of water resources, trace gas emission estimates, and food security. The present study aims to monitor crops status during the growing season by estimating its aboveground biomass and leaf area index (LAI) from field reflectance taken with a hand-held radiometer. Field reflectance values were collected over specific spectral bandwidths using a handheld radiometer(LI-1800). A methodology is described to use spectral reflectance as indicators of the vegetative status in crop cultures. Two vegetation indices were derived from these spectral measurements. In this paper, first we analyze each spectral reflectance characteristics of vegetation in the order of growth stage. Vegetation indices (NDVI, GNDVI) were calculated from crop reflectance. And assess the nature of relationships between LAI and VI, as measured by the in situ NDVI and GNDVI. Among the two VI, NDVI showed predictive ability across a wider range of LAI than did GNDVI. Specific objectives were to determine the relative accuracy of these two vegetation indices for predicting LAI. The results of this study indicated that the NDVI and GNDVI could potentially be applied to monitor crop agriculture on a timely and frequent basis.

• Earthquakes and Structures
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• v.14 no.3
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• pp.203-214
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• 2018

Predictive demand and collapse fragility functions are two essential components of the probabilistic seismic demand analysis that are commonly developed based on statistics with enormous, costly and time consuming data gathering. Although this approach might be justified for research purposes, it is not appealing for practical applications because of its computational cost. Thus, in this paper, Bayesian regression-based demand and collapse models are proposed to eliminate the need of time-consuming analyses. The demand model developed in the form of linear equation predicts overall maximum inter-story drift of the lowto mid-rise regular steel moment resisting frames (SMRFs), while the collapse model mathematically expressed by lognormal cumulative distribution function provides collapse occurrence probability for a given spectral acceleration at the fundamental period of the structure. Next, as an application, the proposed demand and collapse functions are implemented in a seismic fragility analysis to develop fragility and consequently seismic demand curves of three example buildings. The accuracy provided by utilization of the proposed models, with considering computation reduction, are compared with those directly obtained from Incremental Dynamic analysis, which is a computer-intensive procedure.

• Transactions of Materials Processing
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• v.25 no.3
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• pp.182-188
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• 2016

Flexibly-reconfigurable roll forming (FRRF) is a novel sheet metal forming technology conducive to producing multi-curvature surfaces by controlling the strain distribution along longitudinal direction. In FRRF, a sheet metal is shaped into the desired curvature by using reconfigurable rollers and gaps between the rollers. As FRRF technology and equipment are under development, a simulation model corresponding to the physical FRRF would aid in investigating how the shape of a sheet varies with input parameters. To facilitate the investigation, the current study exploits regression analysis to construct a predictive model for the longitudinal curvature of the sheet. Variables considered as input parameters are sheet compression ratio, radius of curvature in the transverse direction, and initial blank width. Samples were generated by a three-level, three-factor full factorial design, and both convex and saddle curvatures are represented by a quadratic regression model with two-factor interactions. The fitted quadratic equations were verified numerically with R-squared values and root mean square errors.

• Journal of the Korean Solar Energy Society
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• v.29 no.2
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• pp.22-30
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• 2009

In order to clarify predictive accuracy for the wind resource predicted by running WindPRO(Ver. 2.5) which is software for wind farm design developed by EMD from Denmark, an investigation was carried out at the northeast region of Jeju island. The Hangwon, Susan and Hoichun sites of Jeju island were selected for this study. The measurement period of wind at the sites was for one year. As a result, when the sites had different energy roses, though the two Wind Statistics made by STATGEN module were used for the prediction, it was difficult to exactly predict the energy rose at a given site. On the other hand, when the two Wind Statistics were used to predict the average wind speed, the wind power density and the annual energy production, the relative error was under ${\pm}20%$ which improved more than that when using only one Wind Statistics.