• Ocean Science Journal
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• 제43권4호
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• pp.195-208
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• 2008

This paper evaluates whether a thermodynamic ocean-carbon model can be used to predict the monthly mean global fields of the surface-water partial pressure of $CO_2$ ($pCO_{2SEA}$) from sea surface salinity (SSS), temperature (SST), and/or nitrate ($NO_3$) concentration using previously published regional total inorganic carbon ($C_T$) and total alkalinity ($A_T$) algorithms. The obtained $pCO_{2SEA}$ values and their amplitudes of seasonal variability are in good agreement with multi-year observations undertaken at the sites of the Bermuda Atlantic Timeseries Study (BATS) ($31^{\circ}50'N$, $60^{\circ}10'W$) and the Hawaiian Ocean Time-series (HOT) ($22^{\circ}45'N$, $158^{\circ}00'W$). By contrast, the empirical models predicted $C_T$ less accurately at the Kyodo western North Pacific Ocean Time-series (KNOT) site ($44^{\circ}N$, $155^{\circ}E$) than at the BATS and HOT sites, resulting in greater uncertainties in $pCO_{2SEA}$ predictions. Our analysis indicates that the previously published empirical $C_T$ and $A_T$ models provide reasonable predictions of seasonal variations in surface-water $pCO_{2SEA}$ within the (sub) tropical oceans based on changes in SSS and SST; however, in high-latitude oceans where ocean biology affects $C_T$ to a significant degree, improved $C_T$ algorithms are required to capture the full biological effect on $C_T$ with greater accuracy and in turn improve the accuracy of predictions of $pCO_{2SEA}$.

• Steel and Composite Structures
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• 제14권2호
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• pp.173-190
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• 2013

The major objective of this paper is to evaluate the behavior and ultimate resisting capacity of circular CFT columns. To consider the confinement effect, proper material models with respect to the confinement pressure are selected. A fiber section approach is adopted to simulate the nonlinear stress distribution along the section depth. Material nonlinearity due to the cracking of concrete and the yielding of the surrounding steel tube, as well as geometric nonlinearity due to the P-${\Delta}$ effect, are taken into account. The validity of the proposed numerical analysis model is established by comparing the analytical predictions with the results from previous experimental studies about pure bending and eccentric axial loading. Numerical predictions using an unconfined material model were also compared to investigate the confinement effects on various loading combinations. The ultimate resisting capacities predicted by the proposed numerical model and the design guidelines in Eurocode 4 are compared to evaluate the existing design recommendation.

• Journal of Astronomy and Space Sciences
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• 제14권1호
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• pp.158-165
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• 1997

The Sun interference event predictions for the KOMPSAT TT&C station were performed to analyze the frequency of the event and the impact on the TT&C link. The KOMPSAT orbit was propagated including only J2 geopotential term for maintaining the Sun-synchronism and no other perturbations were included. Local time of ascending node of the KOMPSAT satellite was set to 10h50m00s. The TT&C station was assumed to locate in Taejon and have 9 meter antenna for S-band link. One year of simulation from 1999/07/01 were performed out of 3 year of mission lifetime of KOMPSAT satellite. Total four times of Sun interference events were occurred during 1 year of simulation and those lasted about 50 seconds altogether. The C/N degradation of the TT&C system was calculated about 4dB. The Sun interference event of 50 seconds of year are 0.0076 percents of the S-band contact time when the 30 minute of contact time is assumed in a day.

• Structural Engineering and Mechanics
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• 제66권2호
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• pp.151-160
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• 2018

With the aim to evaluate the fatigue damage accumulation and predict the residual life of engineering components under variable amplitude loadings, this paper proposed a new strain energy-based damage accumulation model by considering both effects of mean stress and load interaction on fatigue life in a low cycle fatigue (LCF) regime. Moreover, an integrated procedure is elaborated for facilitating its application based on S-N curve and loading conditions. Eight experimental datasets of aluminum alloys and steels are utilized for model validation and comparison. Through comparing experimental results with model predictions by the proposed, Miner's rule, damaged stress model (DSM) and damaged energy model (DEM), results show that the proposed one provides more accurate predictions than others, which can be extended for further application under multi-level stress loadings.

• Geomechanics and Engineering
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• 제31권5호
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• pp.453-460
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• 2022

It is essential for geotechnical engineers to conduct studies and make predictions about the stability of slopes, since collapse of a slope may result in catastrophic events. The Gaussian process regression (GPR) approach was carried out for the purpose of predicting the factor of safety (FOS) of the slopes in the study that was presented here. The model makes use of a total of 327 slope cases from Iran, each of which has a unique combination of geometric and shear strength parameters that were analyzed by PLAXIS software in order to determine their FOS. The K-fold (K = 5) technique of cross-validation (CV) was used in order to conduct an analysis of the accuracy of the models' predictions. In conclusion, the GPR model showed excellent ability in the prediction of FOS of slope stability, with an R² value of 0.8355, RMSE value of 0.1372, and MAPE value of 6.6389%, respectively. According to the results of the sensitivity analysis, the characteristics (friction angle) and (unit weight) are, in descending order, the most effective, the next most effective, and the least effective parameters for determining slope stability.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1996년도 추계학술발표회논문집(1)
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• pp.230-235
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• 1996

The new concept of dry area formation based on Poisson distribution of active nucleation sites and the concept of the critical active site density is presented. A simple statistical model is developed to predict the change of slope of the boiling curve up to critical heat flux (CHF) quantitatively. The predictions by the present model are in good agreement with the experimental data. Also it turns out that the present model well explains the mechanism on how the surface wettability influences CHF.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 ITC-CSCC -2
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• pp.1067-1071
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• 2000

This paper presents a new 3-D ray tracing technique based on the image theory with newly defined ray tubes. The proposed method can be applied to indoor environments with arbitrary building layouts and has high computational efficiency compared to the precedent methods resorting to the ray launching scheme. Its predictions are in good agreement with the measurements

• Steel and Composite Structures
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• 제48권4호
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• pp.461-474
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• 2023

Concrete-filled steel tubes are among the most efficient compressive structural members because the strength of the concrete is enhanced given that the surrounding steel tube confines the concrete laterally and the steel tube is restrained with regard to inward deformation due to the concrete existing inside. Accurate estimations of the ultimate compressive strength of CFT are important for efficient designs of CFT members. In this study, an analytical procedure that directly formulates the interaction between the concrete and steel tube by considering the nonlinear Poisson effect and stress-strain curve of the concrete including the confinement effect is proposed. The failure stress of concrete and von-Mises failure yield criterion of steel were used to consider multi-dimensional stresses. To verify the prediction capabilities of the proposed analytical procedure, 99 circular CFT experimental data instances from other studies were used for a comparison with AISC, Eurocode 4, and other researchers' predictions. From the comparison, it was revealed that the proposed procedure more accurately predicted the ultimate compressive strength of a circular CFT regardless of the range of the design variables, in this case the concrete compressive strength, yield strength of the steel tube and diameter relative to the thickness ratio of the tube.

• 한국지구물리탐사학회:학술대회논문집
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• 한국지구물리탐사학회 2003년도 Proceedings of the international symposium on the fusion technology
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• pp.176-183
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• 2003

This paper presents a neural network based approach to disseminating information relating to experimental and field observations in engineering. Although the methodology is generic and can be applied to many areas of engineering science, attention is focussed here solely on geotechnical engineering applications. Field data relating to the settlement of foundations presented by Burland and Burbidge (1985) which led to their well known equation for calculation of settlement, now included in most text books, is re-visited. A part of the data, chosen randomly, is used to train an Artificial Neural Network (ANN), which relates foundation settlement to various causes as identified by the authors. Predictions are made for situations for which data were not used in training. These indicate sufficient accuracy when compared to the original field data. Accuracy of predictions is further improved when all the data are included in the training set. The finally trained ANN is shown to represent these data more accurately than the Burland and Burbidge equation. Based on the above heuristic example, an ANN is presented as an alternative to developing equations and design rules in geotechnical engineering practice. Significant advantages are shown to arise by using this methodology. Ease of updating the ANN, as and when additional data becomes available, being the most important one. Loss of transparency, however, seems to be the main disadvantage.

• International Journal of Computer Science & Network Security
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• 제24권3호
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• pp.113-124
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• 2024

One of the goals of the Saudi Arabia 2030 vision is to ensure full employment of its citizens. Recruitment of graduates depends on the quality of skills that they may have gained during their study. Hence, the quality of education and ensuring that graduates have sufficient knowledge about the in-demand skills of the market are necessary. However, IT graduates are usually not aware of whether they are suitable for recruitment or not. This study builds a prediction model that can be deployed on the web, where users can input variables to generate predictions. Furthermore, it provides data-driven recommendations of the in-demand skills in the Saudi IT labor market to overcome the unemployment problem. Data were collected from two online job portals: LinkedIn and Bayt.com. Three machine learning algorithms, namely, Support Vector Machine, k-Nearest Neighbor, and Naïve Bayes were used to build the model. Furthermore, descriptive and data analysis methods were employed herein to evaluate the existing gap. Results showed that there existed a gap between labor market employers' expectations of Saudi workers and the skills that the workers were equipped with from their educational institutions. Planned collaboration between industry and education providers is required to narrow down this gap.