• 대한교통학회:학술대회논문집
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• 대한교통학회 2000년도 제37회 학술발표회논문집
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• pp.25-47
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• 2000

In route guidance systems fastest-path routing has typically been adopted because of its simplicity. However, empirical studies on route choice behavior have shown that drivers use numerous criteria in choosing a route. The objective of this study is to develop computationally efficient algorithms for identifying a manageable subset of the nondominated (i.e. Pareto optimal) paths for real-time vehicle routing which reflect the drivers' preferences and route choice behaviors. We propose two pruning algorithms that reduce the search area based on a context-dependent linear utility function and thus reduce the computation time. The basic notion of the proposed approach is that ⅰ) enumerating all nondominated paths is computationally too expensive, ⅱ) obtaining a stable mathematical representation of the drivers' utility function is theoretically difficult and impractical, and ⅲ) obtaining optimal path given a nonlinear utility function is a NP-hard problem. Consequently, a heuristic two-stage strategy which identifies multiple routes and then select the near-optimal path may be effective and practical. As the first stage, we utilize the relaxation based pruning technique based on an entropy model to recognize and discard most of the nondominated paths that do not reflect the drivers' preference and/or the context-dependency of the preference. In addition, to make sure that paths identified are dissimilar in terms of links used, the number of shared links between routes is limited. We test the proposed algorithms in a large real-life traffic network and show that the algorithms reduce CPU time significantly compared with conventional multi-criteria shortest path algorithms while the attributes of the routes identified reflect drivers' preferences and generic route choice behaviors well.

• Journal of Positioning, Navigation, and Timing
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• 제7권4호
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• pp.267-275
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• 2018

The reference stations in a satellite-based augmentation system (SBAS) collect raw data from global navigation satellite system (GNSS) to generate correction and integrity information. The multipath signals degrade GNSS raw data quality and have adverse effects on the SBAS performance. The currently operating SBASs (WAAS and EGNOS, etc.) survey existing commercial equipment to perform multipath assessment around the antennas. For the multi-path assessment, signal power of GNSS and multipath at the MEDLL receiver of NovAtel were estimated and the results were replicated by a ratio of signal power estimated at NovAtel Multipath Assessment Tool (MAT). However, the same experiment environment used in existing systems cannot be configured in reference stations in Korean augmentation satellite system (KASS) due to the discontinued model of MAT and MEDLL receivers used in the existing systems. This paper proposes a test environment for multipath assessment around the antennas in KASS Multipath Assessment Tool (K-MAT) for multipath assessment. K-MAT estimates a multipath error contained in the code pseudorange using linear combination between the measurements and replicates the results through polar plot and histogram for multipath assessment using the estimated values.

• 한국학교ㆍ지역보건교육학회지
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• 제20권2호
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• pp.109-124
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• 2019

Purpose: The purpose of this study was to compare the happiness level of one-person households according to gender in Korea and identify factors influencing householders' happiness. Methods: This was a descriptive correlational study design using the 2017 Community Health Survey data. The participants were 8,724 male and 16,810 female individuals in one-person households. Complex samples descriptive statistics, cross analysis, general linear model, and multi-order regression were conducted to identify the health status, health behavior, and factors influencing happiness. Results: The mean score of happiness was higher in female than male individuals. The main factors of happiness of male householders were suicide thought experience, subjective health level, and experience of having necessary medical services. The main factors of happiness of female householders were suicide thought experience, household income, depression experience. Conclusion: It is necessary to develop and implement nursing interventions and policies according to priorities for the happiness of one-person householders.

• 한국정보시스템학회지:정보시스템연구
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• 제30권2호
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• pp.165-188
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• 2021

Purpose This study was conducted to identify the multidimensional role of institutional mechanisms in the linear relationship of satisfaction, trust and repurchase intention, which are used as an important concept in the research of e-commerce. To this end, a research model was proposed by combining concepts which are the concept of perceived effectiveness of institutional mechanisms for overall e-commerce environment(e.g., PEEIM) and the concep of perceived effectiveness of institutional structures(e.g., PEIS) of a specific marketplace based on the social cognitive theory. Design/methodology/approach This study was conducted by dividing the data into two groups to identify institutional mechanisms and trust-building relationships according to the institutional contexts inherent in e-commerce. The institutional contexts were set up for the top two online companies and the bottom two online companies according to the results of the open market brand assessment from 2018 to 2019 in South Korea. Findings The result of this study found that PEIS had a direct impact on trust in both high and low groups respectively whereas PEEIM presented different paradoxical results in high and low groups. In the relationship between the satisfaction and the trust in the vendor of the high group, PEEIM showed negative moderating effects but in the relationship between the trust and the repurchase intention of the low group PEEIM showed positive moderating effects.

• 한국지진공학회논문집
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• 제25권1호
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• pp.1-9
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• 2021

Most commercial buildings among existing RC buildings in Korea have a multi-story wall-frame structure where RC shear wall is commonly used as its core at stairways or elevators. The members of the existing middle and low-rise wall-frame buildings are likely arranged in ordinary details considering building occupancy, and the importance and difficulty of member design. This is because there are few limitations, considerations, and financial burdens on the code for designing members with ordinary details. Compared with the intermediate or unique details, the ductility and overstrength are insufficient. Furthermore, the behavior of the member can be shear-dominated. Since shear failure in vertical members can cause a collapse of the entire structure, nonlinear characteristics such as shear strength and stiffness deterioration should be adequately reflected in the analysis model. With this background, an 8-story RC wall-frame building was designed as a building frame system with ordinary shear walls, and the effect of reflecting the shear failure mode of columns and walls on the collapse mechanism was investigated. As a result, the shear failure mode effect on the collapse mechanism was evident in walls, not columns. Consequently, it is recommended that the shear behavior characteristics of walls are explicitly considered in the analysis of wall-frame buildings with ordinary details.

• International Journal of Computer Science & Network Security
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• 제22권2호
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• pp.232-240
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• 2022

To fulfill user expectations, the rapid evolution of software techniques and approaches has necessitated reliable and flawless software operations. Aging prediction in the software under operation is becoming a basic and unavoidable requirement for ensuring the systems' availability, reliability, and operations. In this paper, an improved evolutionary computing-driven extreme learning scheme (ECD-ELM) has been suggested for object-oriented software aging prediction. To perform aging prediction, we employed a variety of metrics, including program size, McCube complexity metrics, Halstead metrics, runtime failure event metrics, and some unique aging-related metrics (ARM). In our suggested paradigm, extracting OOP software metrics is done after pre-processing, which includes outlier detection and normalization. This technique improved our proposed system's ability to deal with instances with unbalanced biases and metrics. Further, different dimensional reduction and feature selection algorithms such as principal component analysis (PCA), linear discriminant analysis (LDA), and T-Test analysis have been applied. We have suggested a single hidden layer multi-feed forward neural network (SL-MFNN) based ELM, where an adaptive genetic algorithm (AGA) has been applied to estimate the weight and bias parameters for ELM learning. Unlike the traditional neural networks model, the implementation of GA-based ELM with LDA feature selection has outperformed other aging prediction approaches in terms of prediction accuracy, precision, recall, and F-measure. The results affirm that the implementation of outlier detection, normalization of imbalanced metrics, LDA-based feature selection, and GA-based ELM can be the reliable solution for object-oriented software aging prediction.

• 반도체디스플레이기술학회지
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• 제20권4호
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• pp.38-43
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• 2021

Permanent magnet linear synchronous motor (PMLSM) is suitable for use in cleanroom environments and have advantages such as high speed, high thrust, and high precision. If the stators are arranged in the entire moving path of the mover, there is a problem in that the installation cost increases. To solve this problem, discontinuous armature arrangement PMLSM has been proposed. In this case, the mover receives a greater detent force in the section where the stator is not arranged. When a large detent force occurs, it appears as a ripple component of the thrust during PMLSM operation. If the shape of the stator is changed to reduce the detent force, the characteristics of the back EMF are changed. Therefore, in this paper, the detent force and the harmonic components of back EMF were reduced through multi-purpose shape optimization. To this end, the FEA model was constructed and main effect analysis was performed on the major shape variables affecting each objective function. Then, the optimal shape that minimizes the objective function was derived through the response surface analysis method.

• Nuclear Engineering and Technology
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• 제54권4호
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• pp.1382-1393
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• 2022

This paper investigates the seismic behavior of an electrical cabinet considering the influence of equipment-anchor-interaction (EAI) that is generally not taken into consideration in a decoupled analysis. The hysteresis behavior of an anchor bolt in concrete was thereby considered to highlight this interaction effect. To this end, the experimental behavior of an anchor bolt under reversed cyclic loading was taken from the recently developed literature, and a numerical model for the anchor hysteresis was developed using the component approach. The hysteresis properties were then used to calibrate the multi-linear link element that is implemented as a boundary condition for the cabinet incorporating the EAI. To highlight this EAI further, the nonlinear time history analysis was performed for a cabinet considering the hysteresis behavior comparative to a fixed boundary condition. Additionally, the influence on the seismic fragility was evaluated for the operational and structural condition of the cabinet. The numerical analysis considering the anchor hysteresis manifests that the in-cabinet response spectra (ICRS) are significantly amplified with the corresponding reduction in the seismic capacity of 25% and 15% for an operational and structural safety condition under the selected protocols. Considering the fixed boundary condition over a realistic hysteresis behavior of the anchor bolt is more likely to overestimate the seismic capacity of the cabinet in a seismic qualification procedure.

• Structural Engineering and Mechanics
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• 제85권6호
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• pp.765-780
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• 2023

In this work, a novel combined logarithmic, secant and tangential 2D and quasi-3D refined higher order shear deformation theory is proposed to examine the buckling analysis of simply supported uniform functionally graded plates under uniaxial and biaxial loading. The proposed formulations contain a reduced number of variables compared to others similar solutions. The combined function employed in this study ensures automatically the zero-transverse shear stresses at the free surfaces of the structure. Various models of the material distributions are considered (linear, quadratic, cubic inverse quadratic and power-law). The differentials stability equations are derived via virtual work principle with including the stretching effect. The Navier's approach is applied to solve the governing equations which satisfying the boundary conditions. Several comparative and parametric studies are performed to illustrates the validity and efficacity of the proposed model and the various factors influencing the critical buckling load of thick FG plate.

• 한국공간구조학회논문집
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• 제23권3호
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• pp.87-94
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• 2023

Chloride is one of the most common threats to reinforced concrete (RC) durability. Alkaline environment of concrete makes a passive layer on the surface of reinforcement bars that prevents the bar from corrosion. However, when the chloride concentration amount at the reinforcement bar reaches a certain level, deterioration of the passive protection layer occurs, causing corrosion and ultimately reducing the structure's safety and durability. Therefore, understanding the chloride diffusion and its prediction are important to evaluate the safety and durability of RC structure. In this study, the chloride diffusion coefficient is predicted by machine learning techniques. Various machine learning techniques such as multiple linear regression, decision tree, random forest, support vector machine, artificial neural networks, extreme gradient boosting annd k-nearest neighbor were used and accuracy of there models were compared. In order to evaluate the accuracy, root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2) were used as prediction performance indices. The k-fold cross-validation procedure was used to estimate the performance of machine learning models when making predictions on data not used during training. Grid search was applied to hyperparameter optimization. It has been shown from numerical simulation that ensemble learning methods such as random forest and extreme gradient boosting successfully predicted the chloride diffusion coefficient and artificial neural networks also provided accurate result.