• Journal of Information Technology Services
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• v.7 no.4
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• pp.45-60
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• 2008

Networked enterprise (NE) is an organization of independent companies that collaborate with each other temporary or permanently for accomplishing common goals. The USA and EU have been developing principal concepts, techniques, and solutions to enhance the competitiveness of traditional industries including small-and-medium enterprises (SMEs). In Korea, however, implementation as well as R&D of NE is very few, which we believe comes from lack of understanding on Its meaning and lack of effective information systems for it. This paper is to suggest an Enterprise Architecture (EA) framework or reference model of NE and an Information Technology Architecture (ITA) of NE. The EA framework will help stakeholder of NE (e,g., policy makers, members of NE, IT solution providers, and researchers) understand structural and behavioral characteristics of NE. The ITA will be used as a guideline of developing information systems for NE that is essential for spreading networked business models, The focus of this paper is not on logical-level design but on conceptual-level modeling of NE. As verification of the suggested framework and architecture is still required, so we'll apply them to various manifestations of NE, e.g., dynamic supply chain, vertical integration of extended enterprises, and P2P-style virtual enterprises.

• Smart Structures and Systems
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• v.24 no.6
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• pp.733-744
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• 2019

Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.

• Steel and Composite Structures
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• v.33 no.3
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• pp.389-402
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• 2019

Reinforced concrete walls and buckling restrained braces are effective structural elements that are used to resist seismic loads. In this paper, the behavior of the reinforced concrete walls coupled with buckling restrained braces is investigated. In such a system, there is not any conventional reinforced concrete coupling beam. The coupling action is provided only by buckling restrained braces that dissipate energy and also cause coupling forces in the wall piers. The studied structures are 10-, 20- and 30-story ones designed according to the ASCE, ACI-318 and AISC codes. Wall nonlinear model is then prepared using the fiber elements in PERFORM-3D software. The responses of the systems subjected to the forward directivity near-fault (NF) and ordinary far-fault (FF) ground motions at maximum considered earthquake (MCE) level are studied. The seismic responses of the structures corresponding to the inter-story drift demand, curvature ductility of wall piers, and coupling ratio of the walls are compared. On average, the results show that the inter-story drift ratio for the examined systems subjected to the far-fault events at MCE level is less than allowable value of 3%. Besides, incremental dynamic analysis is used to examine the considered systems. Results of studied systems show that, the taller the structures, the higher the probability of their collapse. Also, for a certain peak ground acceleration of 1 g, the probability of collapse under NF records is more than twice this probability under FF records.

• Earthquakes and Structures
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• v.16 no.4
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• pp.437-453
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• 2019

This study investigates a new optimal placement method for viscous dampers between structures in order to prevent pounding of adjacent structures with different dynamic characteristics under earthquake effects. A relative displacement spectrum is developed in two single degree of freedom system to reveal the critical period ratios for the most risky scenario of collision using El Centro earthquake record (NS). Three different types of viscous damper design, which are classical, stair and X-diagonal model, are considered to prevent pounding on two adjacent building models. The objective function is minimized under the upper and lower limits of the damping coefficient of the damper and a target modal damping ratio. A new algorithm including time history analyses and numerical optimization methods is proposed to find the optimal dampers placement. The proposed design method is tested on two 12-storey adjacent building models. The effects of the type of damper placement on structural models, the critical period ratios of adjacent structures, the permissible relative displacement limit, the mode behavior and the upper limit of damper are investigated in detail. The results of the analyzes show that the proposed method can be used as an effective means of finding the optimum amount and location of the dampers and eliminating the risk of pounding.

• Journal of Aerospace System Engineering
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• v.12 no.6
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• pp.50-57
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• 2018

This paper proposes the use of supports as passive vibration absorber to a composite solar panel for a high-agility satellite. We further defined the dynamic model of the composite solar panel with the help of the Ritz method and verified vibration suppression performance of the support by performing vibration analysis. Finally, this research ensures optimal design of the composite solar panel with the support for maximizing vibration suppression performance in limited mass. The proposed results of the optimal design can be applied in actual structural design of satellites.

• Journal of Korean Association for Spatial Structures
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• v.18 no.4
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• pp.69-80
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• 2018

In this study, an algorithm applying deep learning to the truss structures was proposed. Deep learning is a method of raising the accuracy of machine learning by creating a neural networks in a computer. Neural networks consist of input layers, hidden layers and output layers. Numerous studies have focused on the introduction of neural networks and performed under limited examples and conditions, but this study focused on two- and three-dimensional truss structures to prove the effectiveness of algorithms. and the training phase was divided into training model based on the dataset size and epochs. At these case, a specific data value was selected and the error rate was shown by comparing the actual data value with the predicted value, and the error rate decreases as the data set and the number of hidden layers increases. In consequence, it showed that it is possible to predict the result quickly and accurately without using a numerical analysis program when applying the deep learning technique to the field of structural analysis.

• Steel and Composite Structures
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• v.32 no.2
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• pp.189-198
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• 2019

This paper aims to study the mechanical performance of three-tower four-span suspension bridges with steel truss girders, including the static and dynamic characteristics of the bridge system, and more importantly, the influence of structural parameters including the side-main span ratio, sag-to-span ratio and the girder stiffness on key mechanical indices. For this purpose, the Oujiang River North Estuary Bridge which is a three-tower four-span suspension bridge with two main spans of 800m under construction in China is taken as an example in this study. This will be the first three-tower suspension bridge with steel truss girders in the world. The mechanical performance study and parametric analysis are conducted based on a validated three-dimensional spatial truss finite element model established for the Oujiang River North Estuary Bridge using MIDAS Civil. It is found that a relatively small side-main span ratio seems to be quite appropriate from the perspective of mechanical performance. And decreasing the sag-to-span ratio is an effective way to reduce the horizontal force subjected to the midtower and improve the antiskid safety of the main cable, while the vertical stiffness of the bridge will be reduced. However, the girder stiffness is shown to be of minimal significance on the mechanical performance. The findings from this paper can be used for design of three-tower suspension bridges with steel truss girders.

• East Asian Journal of Business Economics (EAJBE)
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• v.9 no.3
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• pp.71-82
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• 2021

Purpose - The purpose of this study is to examine the intermediary role of job satisfaction between job autonomy and job performance and whether the process was adjusted based on the work context. Research design, data, and methodology - This study was conducted by sample survey method on 334 supervising engineers. Data analysis methods were frequency analysis, confirmatory factor analysis, reliability analysis, correlation analysis, and structural equation model analysis. Result - The results of this study suggest that: (1) after controlling for age, position, and working years, job autonomy had a significant positive impact on job performance, (2) job autonomy can not only directly affect job performance but also indirectly affect performance through job satisfaction, (3) job satisfaction has an intermediary effect on job autonomy and job performance, and (4) the relationship between job autonomy and job satisfaction is moderated by the work context, and the result showed a negative moderating effect. Conclusion - This study suggests that job autonomy significantly improves job performance, and the higher job autonomy a supervising engineer has, the more satisfied they are with their work, thus enriching the precursor research on dynamic changes in job performance. When the working environment is poor, supervisors are more sensitive to the perception of job autonomy and have a stronger impact on job satisfaction and performance.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.303-311
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• 2021

Ambidexterity and leadership agility have become the most researched topics to analyze their application in companies, especially in this dynamic era. Several researchers have analyzed it in large companies. However, only a few have discussed the two topics simultaneously and at the MSME level. This study aims to analyze the relationship between ambidexterity and leadership agility and innovation capability and performance at MSMEs in Yogyakarta and East Java, Indonesia. This study is analyzed by using quantitative methods with SEM (Structural Equation Model) methods. The data in this study is primary data that is obtained through distributing 230 questionnaires to MSME managers in Yogyakarta and East Java, Indonesia. From 230 questionnaires distributed, 200 questionnaires are returned and completed, so the response rate in this study is 86%. The results in this study indicate that ambidexterity and leadership agility have a significant effect on innovation capability and MSME performance. This study also proved that innovation capability has a significant effect on MSME performance. Therefore, it is recommended for MSME managers to develop ambidexterity and leadership agility so they can create innovation and good performance. In the end, this study has provided findings related to the combination of ambidexterity and leadership agility variables.

• Computers and Concrete
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• v.27 no.1
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• pp.29-39
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• 2021

This study aimed to develop a method for the determination of the damaged story in reinforced concrete (RC) structure with ambient vibrations, based on modified jerk energy methodology. The damage was taken as a localized reduction in the stiffness of the structural member. For loading, random white noise excitation was used, and dynamic responses from the finite element model (FEM) of 4 story RC shear frame were extracted at nodal points. The data thus obtained from the structure was used in the damage detection and localization algorithm. In the structure, two damage configurations have been introduced. In the first configuration, damage to the structure was artificially caused by a local reduction in the modulus of elasticity. In the second configuration, the damage was caused, using the Elcentro1940 and Kashmir2005 earthquakes in real-time history. The damage was successfully detected if the frequency drop was greater than 5% and the mode shape correlation remained less than 0.8. The results of the damage were also compared to the performance criteria developed in the Seismostruct software. It is demonstrated that the proposed algorithm has effectively detected the existence of the damage and can locate the damaged story for multiple damage scenarios in the RC structure.