• Asia pacific journal of information systems
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• v.32 no.1
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• pp.151-167
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• 2022

This paper is motivated by social influence theory implying the multilevel nature of knowledge management (KM) in an organization. Organizational knowledge is generated and distributed by individuals from different groups across organizational boundaries. Its transfers are supported by IS/IT practice, i.e., the individual and collective use of the technology available in the organization. I propose a multilevel perspective to explain how IS/IT practice supports multilevel KM capabilities to manage organizational knowledge successfully and how the effectiveness of multilevel KM capabilities expands into the improvement of multilevel task-related organizational performance. The multilevel KM theory extends the knowledge-based view of the firm by describing the dynamic process through which strategic values of knowledge are generated by IS/IT practice across the organizational levels. This paper also discusses multilevel insights on the strategic value of organizational learning based on the social context of organizations.

• Journal of Distribution Science
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• v.22 no.9
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• pp.65-72
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• 2024

Purpose: This study aims to empirically analyze the influence of supply chain agility and flexibility on supply chain robustness and logistics performance, addressing a research gap in the context of dynamic business environments. Research design, data and methodology: The study examines causal relationships between supply chain agility, flexibility, robustness, and logistics performance among businesses in South Korea. Data were collected through a survey of 300 workers in supply chain-related departments. A structural equation model was employed for hypothesis testing. Results: The empirical analysis shows that supply chain agility and flexibility positively and significantly influence supply chain robustness, which in turn has a significant positive impact on logistics performance. Conclusions: This study contributes by providing empirical evidence on the importance of supply chain agility, flexibility, and robustness in enhancing logistics performance. The findings suggest prioritizing the development of these capabilities for competitive advantage. Further research on the interrelationships between various supply chain capabilities and their impact on performance outcomes is highlighted.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.65-68
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• 1996

Robotic manipulators have become increasingly important in the field of flexible automation. High speed and high-precision trajectory tracking are indispensable capabilities for their versatile application. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. This paper presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. The TMS32OC31 is used in implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme, the networks introduced are neural nets with dynamic neurons, whose dynamics are distributed over all the, network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure, fast in computation, and suitable for implementation of real-time, control. Performance of the neural controller is illustrated by simulation and experimental results for a SCARA robot.

• Journal of Information Technology Applications and Management
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• v.26 no.5
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• pp.57-65
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• 2019

Recommendation Systems are information technologies that E-commerce merchants have adopted so that online shoppers can receive suggestions on items that might be interesting or complementing to their purchased items. These systems stipulate valuable assistance to the user's purchasing decisions, and provide quality of push service. Traditionally, Recommendation Systems have been designed using a centralized system, but information service is growing vast with a rapid and strong scalability. The next generation of information technology such as Cloud Computing and Big Data Environment has handled massive data and is able to support enormous processing power. Nevertheless, analytic technologies are lacking the different capabilities when processing big data. Accordingly, we are trying to design a conceptual service model with a proposed new algorithm and user adaptation on dynamic recommendation service for big data environment.

• 전기의세계
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• v.23 no.1
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• pp.73-78
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• 1974

Digital simulation algorithms and program for multimachine dynamic stability have been developed which represent the effects of machines much more complety than have been available previously. Emphasis is given to the savings of the memory spaces required, thus making it possible to use a small computer with limited capacity of core storage (without auxiliary storage). Both d- and q- aris quantities are fully represented, and the speed-governing and voltage-regulating system available are ertensive, thus allowing a very close approximation to any physical system. Facilities for dynamic and nonlinear loads are also included. The computational algorithms and program developed have been shown to be extensive and complete, and are very desirable features minimizing memory spaces for stability calculations. The capabilities have been demonstrated by several case studies for an actual power system of 44 generators, 22 loads and 33 buses. About 13-K words of memory spaces have been required for the case studies on the basis of two words per real variable and a word per integer variable.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.04a
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• pp.255-260
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• 2001

This paper presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. Robotic manipulators have become increasingly important in the field of flexible automation. High speed and high-precision trajectory tracking are indispensable capabilities for their versatile application. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. The TMS320C31 is used in implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme, the networks introduced are neural nets with dynamic neurons, whose dynamics are distributed over all the network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Performance of the neural controller is illustrated by simulation and experimental results for a SCARA robot.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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• pp.1877-1882
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• 2000

In this paper, an integrated system for dynamic analysis and optimal design of engine mount systems is presented. The system can simulate static and dynamic behaviors of engine mount systems and optimize design parameters such as mount stiffness, mounting locations with desired design targets of frequency or displacement. A FF-engine with an automatic transmission is used to demonstrate the analysis and optimal design capabilities of the proposed design system.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.113-118
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• 1999

This paper presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. Robotic manipulators have become increasingly important n the field of flexible automation. High speed and high-precision trajectory tracking are indispensable capabilities for their versatile application. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. The TMS320C31 is used in implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme, the networks introduced are neural nets with dynamic neurons, whose dynamics are distributed over all the network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Performance of the neural controller is illustrated by simulation and experimental results for a SCARA robot.

• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.2
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• pp.167-174
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• 2014

In this paper, we address a task allocation problem for a robot team that performs a search and attack mission. The robots are limited in sensing and communication capabilities, and carry different types of resources that are used to attack a target. The environment is uncertain and dynamic where no prior information about targets is given and dynamic events unpredictably happen. The goal of robot team is to collect total utilities as much as possible by destroying targets in a mission horizon. To solve the problem, we propose a distributed task allocation framework incorporated with effective resource management based on resource welfare. The framework we propose enables the robot team to retain more robots available by balancing resources among robots, and respond smoothly to dynamic events, which results in system performance improvement.

• The Journal of Korean Physical Therapy
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• v.23 no.4
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• pp.37-44
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• 2011

Purpose: The aim of this study was to investigate the effects of an exercise program using a virtual reality game and a gait exercise program using a treadmill on % maximum voluntary isometric contraction (%MVIC) and static and dynamic balancing capabilities. Methods: A total of 26 elderly women were included in the study. 13 women were assigned to an exercise program using a virtual reality game, and 13 to a gait exercise program using a treadmill. The subjects performed the exercise for 40 min per session, three sessions per week, for eight weeks. Results: The %MVIC of the vastus medialis was significantly increased from $28.91{\pm}2.03%$ to $32.98{\pm}2.6%$ in the virtual reality game exercise group (p<0.00). The %MVIC of the vastus lateralis was significantly increased from $27.17{\pm}1.93%$ to $31.50{\pm}2.18%$ (p<0.00) in the gait exercise program group. The whole path length with both feet on the floor and eyes open was significantly decreased from $1570.92{\pm}820.6mm$ to $1343.62{\pm}242.41mm$ (p<0.00). The whole path length with both feet on the floor and eyes closed was significantly decreased from $1819.85{\pm}361.14mm$ to $1581.05{\pm}285.11mm$ (p<0.00). The length of a functional reach was significantly increased from $25.2{\pm}4.23cm$ to $27.68{\pm}4.04cm$ (p<0.00). Conclusion: The exercise program using a virtual reality game is effective for improving the %MVIC and static and dynamic balancing capabilities in elderly women aged 65 years and more.