• Information Systems Review
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• v.19 no.2
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• pp.21-35
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• 2017

The advancement of IT and the "Fourth Industrial Revolution" blurred the boundary between industries. The importance of strategic cooperation between enterprises is emphasized. IT companies must consider their existing business areas and create new territories to drive changes in the industry. They must also secure their competitive edge and manage economic costs to enable them to compete with their global counterparts. By utilizing their resources effectively, these firms can create value through inter-firm cooperation. This study analyzes the collaborative network of global IT companies using social network analysis and examines the effect of this network on firm performance. Collaborative linkages and betweenness centrality, which represent the bridging position of a firm in a network, significantly affect firm performance. This result highlights the importance of the structural position of a firm in a cooperative network of IT companies. This study also characterizes clusters in a network of IT companies. Most of these clusters comprise a combination of IT companies in diverse IT industries. These clusters suggest that these companies engage in multilateral cooperation without boundaries to maximize their business capabilities. This study offers practical implications for establishing a cooperative strategy and framework that can capture business trends in the IT industry from a macroscopic view. This study also visualizes collaborative networks in a multifaceted way using social network analysis to provide researchers and business practitioners with an informative viewpoint.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.41-51
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• 2024

In this paper, we explore the enhancement of target detection accuracy in the guided weapon using deep learning object detection on infrared (IR) images. Due to the characteristics of IR images being influenced by factors such as time and temperature, it's crucial to ensure a consistent representation of object features in various environments when training the model. A simple way to address this is by emphasizing the features of target objects and reducing noise within the infrared images through appropriate pre-processing techniques. However, in previous studies, there has not been sufficient discussion on pre-processing methods in learning deep learning models based on infrared images. In this paper, we aim to investigate the impact of image pre-processing techniques on infrared image-based training for object detection. To achieve this, we analyze the pre-processing results on infrared images that utilized global or local information from the video and the image. In addition, in order to confirm the impact of images converted by each pre-processing technique on object detector training, we learn the YOLOX target detector for images processed by various pre-processing methods and analyze them. In particular, the results of the experiments using the CLAHE (Contrast Limited Adaptive Histogram Equalization) shows the highest detection accuracy with a mean average precision (mAP) of 81.9%.

• Journal of Advanced Research in Ocean Engineering
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• v.1 no.2
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• pp.85-93
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• 2015

The nonlinear time-domain analysis method was implemented to carry out a series of integrated simulations for a deep-water crane vessel system composed of four sub components, including a floating vessel, lifted equipment, hoisting cable and dynamic positioning (hereinafter DP) system. The analysis of the coupled dynamics consists of the crane vessel and equipment connected using the crane wire, and the DP is modeled according to the wind, wave and current conditions. The DP systems were numerically implemented using a classical PD feedback controller, and various simulations of the deepwater installation were conducted using different conditions in order to evaluate the global performance of the floating crane vessel combined with the DP system.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2012.05a
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• pp.127-128
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• 2012

Exhaustion of fossil fuels and continued high oil prices, global warming, climate change and to respond to the development and use of alternative energy technologies is expanding rapidly throughout the world. Recently, character of domestic building is appearing by along with economic growth, high-rise, large size, congestion. For this reason, the amount of electrical energy used in a building is increasing. In this study, the applicability of PV modules that are used as roofing and efficiency analysis, and more from the building of BIPV modules built using the activation of alternative energy sources in Korea are aimed want done.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.04a
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• pp.45-54
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• 2003

This paper deal with the GPS performance for determining the ocean tidal loading components(M$_2$, N$_2$, S$_2$, K$_2$) and the availability of permanent GPS stations(CHJU, KANR) established in Korea. We determined the ocean tidal loading components from GPS observation by spectrum analysis and compared to that from global ocean tidal models(GOT00.2, FES99, CRS4.0, NAO99). Through this study, we have a sense that amplitude and phase lags of ocean tidal loading components from observed GPS data was almost equal to value calculated in ocean tide models. The diurnal ocean tide loading constituents are not considered, because unmodeled troposhere effects increase the noise level near the diurnal frequency band and prevent us from obtaining significant results.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.528-530
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• 1998

This paper addresses analysis and design of a fuzzy model-based-controller for the control of uncertain SISO nonlinear systems. In the design procedure, we represent the nonlinear system by using a Takagi-Sugeno fuzzy model and construct a global fuzzy logic controller via parallel distributed compensation and sliding mode control. Unlike other parallel distributed controllers. this globally stable fuzzy controller is designed without finding a common positive definite matrix for a set of Lyapunov equations, and has good tracking performance. Furthermore, stability analysis is conducted not for the fuzzy model but for the real underlying nonlinear system. A simulation is included for the control of the Duffing forced-oscillation system, to show the effectiveness and feasibility of the proposed fuzzy control method.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.185-189
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• 1998

This paper addresses analysis and design of a fuzzy model-based-controller for the control of uncertain SISO nonlinear systems. In the design procedure, we represent the nonlinear system by using a Takagi-Sugeno fuzzy model and construct a global fuzzy logic controller via parallel distributed compensation and sliding mode control. Unlike other parallel distributed controllers, this globally stable fuzzy controller is designed without finding a common positive definite matrix for a set of Lyapunov equations, and has good tracking performance. The stability analysis is conducted not for the fuzzy model but for the real underlying nonlinear system. Furthermore, the proposed method can be applied to partially known uncertain nonlinear systems. A numerical simulation is performed for the control of an inverted pendulum, to show the effectiveness and feasibility of the proposed fuzzy control method.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.9
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• pp.898-904
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• 2006

Ionospheric storms, caused by the interaction between Solar and geomagnetic activities, may degrade the differential GNSS(Global Navigation Satellite Systems) performance significantly, and the importance of the ionospheric storm research is growing for the GBAS(Ground-Based Augmentation System) and SBAS(Satellite-Based Augmentation System) development. In order to support Korean GNSS augmentation system development, a software tool for analyzing the regional ionosphere is being developed and its preliminary results are discussed. After brief description of the ionosphere and ionospheric storm, the research topics on the GBAS applications are discussed. The need for ionospheric spatial gradient analysis is described and some results on the ionospheric spatial gradient during recent storm periods are discussed.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.677-680
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• 2003

In this paper, the analysis of practical end-to-end delay in worst case is performed for distributed control system considering the implementation of the system. The control system delay is composed of the delay caused by multi-task scheduling of operating system, the delay caused by network communication, and the delay caused by the asynchronous between them. Through simulation tests based on CAN(Controller Area Network), the proposed end-to-end delay in worst case is validated. Additionally, online clock synchronization algorithm is proposed here for the control system. Through another simulation test, the online algorithm is proved to have better performance than offline one in the view of network bandwidth utilization.

• Journal of the Korean Society of Industry Convergence
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• v.3 no.4
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• pp.361-367
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• 2000

This paper proposes an efficient feature extraction of the image data using nonlinear principal component analysis neural networks of a new learning algorithm. The proposed method is a learning algorithm with momentum for reflecting the past trends. It is to get the better performance by restraining an oscillation due to converge the global optimum. The proposed algorithm has been applied to the cancer image of $256{\times}256$ pixels and the coin image of $128{\times}128$ pixels respectively. The simulation results show that the proposed algorithm has better performances of the convergence and the nonlinear feature extraction, in comparison with those using the backpropagation and the conventional nonlinear PCA neural networks.