• Structural Engineering and Mechanics
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• v.11 no.4
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• pp.423-442
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• 2001

This paper presents a method of elastic-plastic analysis for planar steel frames that provides the accuracy of distributed plasticity methods with the computational efficiency that is greater than that of distributed plasticity methods but less than that of plastic-hinge based methods. This method accounts for the effect of spread of plasticity accurately without discretization through the cross-section of a beam-column element, which is achieved by the following procedures. First, nonlinear equations describing the relationships between generalized stresses and strains of the cross-section are derived analytically. Next, nonlinear force-deformation relationships for the beam-column element are obtained through lengthwise integration of the generalized strains. Elastic-plastic flexibility coefficients are then calculated by differentiating the above element force-deformation relationships. Finally, an elastic-plastic stiffness matrix is obtained by making use of the flexibility-stiffness transformation. Adding the conventional geometric stiffness matrix to the elastic-plastic stiffness matrix results in the tangent stiffness matrix, which can readily be used to evaluate the load carrying capacity of steel frames following standard nonlinear analysis procedures. The accuracy of the proposed method is verified by several examples that are sensitive to the effect of spread of plasticity.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.31 no.7 s.262
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• pp.620-627
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• 2007

In this paper, we adapt a modified internal energy non-equilibrium first-order extrapolation thermal boundary condition to the thermal lattice Boltzmann model (TLBM). This model is the double populations approach to simulate hydrodynamic and thermal fields. The bounce-back boundary condition which is a traditional boundary condition of lattice Boltzmann method has only a first order in numerical accuracy at the boundary and numerical instability. A non-equilibrium first-order extrapolation boundary condition has been verified to be of better numerical stability than the bounce-back boundary condition and this boundary condition is proved to be of second-order accuracy for the flat boundaries. The two-dimensional natural convection flow in a square cavity with Pr=0.71 and various Rayleigh numbers are simulated. The results are found to be in good agreement with those of previous studies.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.1
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• pp.81-88
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• 2014

The researcher of this study applied PCA on aerial hyper-spectral sensor and selectively combined bands which contain high amount of information, creating five types of PCA images. By applying Spectral Angle Mapping-supervised classification technique on each type of image, classification process was carried out and accuracy was evaluated. The test result showed that the amount of information contained in the first band of PCA-transformation image was 76.74% and the second accumulated band contained 98.40%, suggesting that most of information were contained in the first and the second PCA components. Quantitative classification accuracy evaluation of each type of image showed that total accuracy, producer's accuracy and user's accuracy had similar patterns. What drew the researcher's attention was the fact that the first and the second bands of the PCA-transformation image had the highest accuracy according to the classification accuracy although it was believed that more than four bands of PCA-transformation image should be contained in order to secure accuracy when doing the qualitative classification accuracy.

• Structural Engineering and Mechanics
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• v.34 no.6
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• pp.779-799
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• 2010

In order to consider high-order effects on the actual limit state function, a new response surface method is proposed for structural reliability analysis by the use of high-order approximation concept in this study. Hermite polynomials are used to determine the highest orders of input random variables, and the sampling points for the determination of highest orders are located on Gaussian points of Gauss-Hermite integration. The cross terms between two random variables, only in case that their corresponding percent contributions to the total variation of limit state function are significant, will be added to the response surface function to improve the approximation accuracy. As a result, significant reduction in computational cost is achieved with this strategy. Due to the addition of cross terms, the additional sampling points, laid on two-dimensional Gaussian points off axis on the plane of two significant variables, are required to determine the coefficients of the approximated limit state function. All available sampling points are employed to construct the final response surface function. Then, Monte Carlo Simulation is carried out on the final approximation response surface function to estimate the failure probability. Due to the use of high order polynomial, the proposed method is more accurate than the traditional second-order or linear response surface method. It also provides much more efficient solutions than the available high-order response surface method with less loss in accuracy. The efficiency and the accuracy of the proposed method compared with those of various response surface methods available are illustrated by five numerical examples.

• Journal of Korea Planning Association
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• v.54 no.3
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• pp.106-118
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• 2019

In order to develop pedestrian navigation service that provides optimal pedestrian routes based on pedestrian satisfaction levels, it is required to develop a prediction model that can estimate a pedestrian's satisfaction level given a certain condition. Thus, the aim of the present study is to develop a pedestrian satisfaction prediction model based on three machine learning algorithms: Logistic Regression, Random Forest, and Artificial Neural Network models. The 2009, 2012, 2013, 2014, and 2015 Pedestrian Satisfaction Survey Data in Seoul, Korea are used to train and test the machine learning models. As a result, the Random Forest model shows the best prediction performance among the three (Accuracy: 0.798, Recall: 0.906, Precision: 0.842, F1 Score: 0.873, AUC: 0.795). The performance of Artificial Neural Network is the second (Accuracy: 0.773, Recall: 0.917, Precision: 0.811, F1 Score: 0.868, AUC: 0.738) and Logistic Regression model's performance follows the second (Accuracy: 0.764, Recall: 1.000, Precision: 0.764, F1 Score: 0.868, AUC: 0.575). The precision score of the Random Forest model implies that approximately 84.2% of pedestrians may be satisfied if they walk the areas, suggested by the Random Forest model.

• The Journal of Fisheries Business Administration
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• v.41 no.1
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• pp.25-52
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• 2010

The purpose of this paper is to suggest major strategies and necessary new projects for the medium- and long-term development of the Korean Fisheries Outlook Project. To suggest the Korean Fisheries Outlook Center with the above purpose, this paper employs Analytic Hierarchy Process analysis based on surveys obtained by special groups related with the KFOP. The survey is broadly composed of two goals; the medium- and long-term development directions and setting up of new furtherance projects. Each goal has upper and lower strategies respectively. The first goal, the medium- and long-term development directions, has four factors as upper strategies. The upper strategies are composed of accuracy, efficiency, timeliness, and political effectiveness of the fisheries outlook information. In addition, each upper strategy has three lower strategies respectively. For example, accuracy of the fisheries outlook information includes strength of data collection function, strength of satellite photography function, and strength of data analysis function. The second goal, setting up of new furtherance projects, has three factors as upper strategies. The upper strategies consist of accuracy promotion of outlook information using high-technique, field expansion of outlook species, and strength of analyzing function on oversea fisheries information. Each upper strategy has three lower strategies respectively. For instant, accuracy promotion of outlook information using high-technique has strength of information analysis function covered from production to consumption, strength of satellite information function, and structure of forecasting model on demand and supply by outlook species. The above upper and lower strategies were analytically drawn out through insightful interviews with special groups such as officials of the government, presidents of the producer and distributor groups, and researchers of the Korea Maritime Institute and other research institutes. As a result of AHP analysis, first, priorities of upper strategies with the medium- and long-term development directions are analyzed as accuracy, timeliness, political effectiveness, and efficiency in order. Also, priorities of all lower strategies reflecting priorities of upper strategies are examined as includes strength of data collection function on the fisheries outlook information, delivery of rapid information on outlook products for all people interested, strength of data analysis function on fisheries outlook information, strength of consumption outlook function on fish products, and strength of early warning system for domestic fish products in order. Second, priorities of upper strategies with the setting up of new furtherance projects are analyzed as accuracy promotion of outlook information using high-technique, field expansion of outlook species, and strength of analysis function on oversea fisheries information in order. In addition, priorities of all lower strategies reflecting priorities of upper strategies are examined as building up of forecasting model on demand and supply by outlook species, strength of information analysis function covering all steps from production to consumption, expansion of consumption outlook for consumers, strength of movement analysis function of oversea farming industry, and outlook expansion of farming species.

• 한국전산유체공학회:학술대회논문집
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• 1999.05a
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• pp.137-143
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• 1999

The unsteady transonic viscous flow has been analyzed over an oscillatory airfoil. The CSCM(Conservative Supra Characteristic Method) upwind flux difference splitting method and the iterative time marching scheme having first order accuracy in time and second to third order accuracy in space was applied on dynamic meshes. A steady flow field of Mach number 0.7 has been calculated for the verification of unsteady algorithm. The time-accurate unsteady calculations have been done for NACA 0012 airfoil oscillating around quarter chord about freestream Mach number 0.6 on dynamic meshes. The results have been compared with the AGARD Case 3 experimental data. The periodic characteristics have been compared with the experimental results.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.200-206
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• 2021

Fraud in e-commerce transaction increased in the last decade especially with the increasing number of online stores and the lockdown that forced more people to pay for services and groceries online using their credit card. Several machine learning methods were proposed to detect fraudulent transaction. Neural networks showed promising results, but it has some few drawbacks that can be overcome using optimization methods. There are two categories of learning optimization methods, first-order methods which utilizes gradient information to construct the next training iteration whereas, and second-order methods which derivatives use Hessian to calculate the iteration based on the optimization trajectory. There also some training refinements procedures that aims to potentially enhance the original accuracy while possibly reduce the model size. This paper investigate the performance of several NN models in detecting fraud in e-commerce transaction. The backpropagation model which is classified as first learning algorithm achieved the best accuracy 96% among all the models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2348-2360
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• 2023

As science and technology evolve, object detection of moving objects has been widely used in the context of machine learning and artificial intelligence. Traditional moving object detection algorithms, however, are characterized by relatively poor real-time performance and low accuracy in detecting moving objects. To tackle this issue, this manuscript proposes a modified Kalman filter algorithm, which aims to expand the equations of the system with the Taylor series first, ignoring the higher order terms of the second order and above, when the nonlinear system is close to the linear form, then it uses standard Kalman filter algorithms to measure the situation of the system. which can not only detect moving objects accurately but also has better real-time performance and can be employed to predict the trajectory of moving objects. Meanwhile, the accuracy and real-time performance of the algorithm were experimentally verified.

• Journal of Positioning, Navigation, and Timing
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• v.4 no.1
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• pp.33-41
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• 2015

Compact Network Real-Time Kinematic (RTK) is a method that combines compact RTK and network RTK, and it can effectively reduce the time and spatial de-correlation errors. A network RTK user receives multiple correction information generated from reference stations that constitute a network, calculates correction information that is appropriate for one's own position through a proper combination method, and uses the information for the estimation of the position. This combination method is classified depending on the method for modeling the GPS error elements included in correction information, and the user position accuracy is affected by the accuracy of this modeling. Among the GPS error elements included in correction information, tropospheric delay is generally eliminated using a tropospheric model, and a combination method is then applied. In the case of a tropospheric model, the estimation accuracy varies depending on the meteorological condition, and thus eliminating the tropospheric delay of correction information using a tropospheric model is limited to a certain extent. In this study, correction information modeling accuracy performances were compared focusing on the Low-Order Surface Model (LSM), which models the GPS error elements included in correction information using a low-order surface, and a modified LSM method that considers tropospheric delay characteristics depending on altitude. Both of the two methods model GPS error elements in relation to altitude, but the second method reflects the characteristics of actual tropospheric delay depending on altitude. In this study, the final residual errors of user measurements were compared and analyzed using the correction information generated by the various methods mentioned above. For the performance comparison and analysis, various GPS actual measurement data were collected. The results indicated that the modified LSM method that considers actual tropospheric characteristics showed improved performance in terms of user measurement residual error and position domain residual error.