• 대한임베디드공학회논문지
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• 제10권4호
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• pp.207-212
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• 2015

Wi-Fi Direct allows battery powered mobile devices to form a wireless ad hoc network. Since one member of the network called Group Owner (GO) takes the role of managing the network, the network lifetime is mainly affected by the energy efficiency of GO. Hence, Wi-Fi Direct defines power saving schemes that allow GO to save the energy by turning off its radio interface during the periods called absence periods. However, the Wi-Fi Direct specification does not specify how to set parameters (denoted by PS parameters) determining the schedule of the absence periods. In this paper, we therefore propose a novel traffic-aware power saving scheme for Wi-Fi Direct networks. In particular, the proposed scheme estimates the application-level traffic load and adaptively tunes the PS parameters according to the estimated value.

• 한국정보시스템학회지:정보시스템연구
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• 제29권1호
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• pp.267-287
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• 2020

Purpose Online platform companies can increase the spread of content by communicating with users who have diverse preferences through social networks. Previous studies show the mixed effect on the network overlap, and there was a limited examinations for the underlying mechanism. This study expects high academic and practical implications that can be provided by studying on the user's viewership network. The purpose of this research is to examine the effects of network overlap on the users' viewership for creators of user-generated content in YouTube. We explain the direct and in-direct effects through the content sharing and the valence of user ratings. Design/methodology/approach The data contains 45 channels and 4,085 video clips from YouTube. We control the effect of the categories, channel characteristics, and vide clip characteristics on the viewership. PROCESS macro were used to analyze the direct and in-direct effects of network overlap. Findings The analysis results showed that the network overlap directly affect on the users' viewership. The variable decreases the moderators (i.e., content sharing and the valence of user ratings). This result implies that the users can not satisfy their need for uniqueness which is achieved by content sharing and rating in the overlapped network.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2004년도 학술대회지
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• pp.491-493
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• 2004

In this paper, a direct controller for nonlinear plants using a neural network is presented. The controller is composed of an approximate controller and a neural network auxiliary controller. The approximate controller gives the rough control and the neural network controller gives the complementary signal to further reduce the output tracking error. This method does not put too much restriction on the type of nonlinear plant to be controlled. In this method, a RBF neural network is trained and the system has a stable performance for the inputs it has been trained for. Simulation results show that it is very effective and can realize a satisfactory control of the nonlinear system.

• 한국정보전자통신기술학회논문지
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• 제5권1호
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• pp.7-12
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• 2012

In this paper, a direct controller for nonlinear plants using a neural network is presented. The controller is composed of an approximate controller and a neural network auxiliary controller. The approximate controller gives the rough control and the neural network controller gives the complementary signal to further reduce the output tracking error. This method does not put too much restriction on the type of nonlinear plant to be controlled. In this method, a RBF neural network is trained and the system has a stable performance for the inputs it has been trained for. Simulation results show that it is very effective and can realize a satisfactory control of the nonlinear system.

• KDI Journal of Economic Policy
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• 제31권2호
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• pp.87-126
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• 2009

인터넷포털 산업에서 최종사용자시장으로부터 광고주시장으로 작용하는 간접 네트워크 외부성과 최종사용자시장 내의 직접 네트워크 외부성의 크기는 인터넷포털 기업 간 경쟁에 있어서 중요한 경쟁정책적 의미를 가진다. 본 연구는 국내 인터넷포털 산업에서 직 간접 네트워크 외부성의 실재와 크기, 인터넷포털 기업들의 시장력을 실증구조모형을 통해 분석하고 경쟁정책에의 시사점을 제시한다. 본 연구의 분석 결과는 산업 내에 이러한 직 간접 네트워크 외부성이 실재하며, 이로 인해 최종사용자시장 점유율과 광고주시장의 시장력 사이에 '레버리지' 효과가 존재함을 보여준다.

• Smart Structures and Systems
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• 제32권1호
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• pp.49-59
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• 2023

A bridge transportation network supplies products from various source nodes to destination nodes through bridge structures in a target region. However, recent frequent earthquakes have caused damage to bridge structures, resulting in extreme direct damage to the target area as well as indirect damage to other lifeline structures. Therefore, in this study, a surrogate model-based comprehensive framework to estimate the seismic resilience of bridge transportation networks is proposed. For this purpose, total system travel time (TSTT) is introduced for accurate performance indicator of the bridge transportation network, and an artificial neural network (ANN)-based surrogate model is constructed to reduce traffic analysis time for high-dimensional TSTT computation. The proposed framework includes procedures for constructing an ANN-based surrogate model to accelerate network performance computation, as well as conventional procedures such as direct Monte Carlo simulation (MCS) calculation and bridge restoration calculation. To demonstrate the proposed framework, Pohang bridge transportation network is reconstructed based on geographic information system (GIS) data, and an ANN model is constructed with the damage states of the transportation network and TSTT using the representative earthquake epicenter in the target area. For obtaining the seismic resilience curve of the Pohang region, five epicenters are considered, with earthquake magnitudes 6.0 to 8.0, and the direct and indirect damages of the bridge transportation network are evaluated. Thus, it is concluded that the proposed surrogate model-based framework can efficiently evaluate the seismic resilience of a high-dimensional bridge transportation network, and also it can be used for decision-making to minimize damage.

• 한국농촌건축학회논문집
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• 제23권1호
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• pp.35-42
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• 2021

This study analyzed the path that affects the user's walking satisfaction on main street in four local government towns in Jeollanam-do. and as an empirical study to find the direction of improvement toward the main street from the perspective of walking, the results are as follows. First, it was found that the network had a direct (+) effect on walking satisfaction as a main street. In addition, it was analyzed that land use had no direct (+) effect on walking satisfaction on the main street, but had a direct (+) effect on the network. Second, it was analyzed from the fact that the walking environment does not have a direct (+) effect on walking satisfaction, but has a direct (+) effect on the network. and it was analyzed that the street-building relationship had a direct (+) effect on the street landscape, and the street landscape had a direct (+) effect on the walking environment. The study was completed by suggesting implications according to the above research results.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.219-222
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• 1997

Due to the unpredictability and irregularity, the behaviors of chaotic systems are considered as undesirable phenomena to be avoided or controlled. Thus in this paper, to control systems showing chaotic behaviors, a direct adaptive control method using a radial basis function network (RBFN) as an excellent alternative of multi-layered feed-forward networks is presented. Compared with an indirect scheme, a direct one does not need the estimation of the controlled process and gives fast control effects. Through simulations on the two representative continuous-time chaotic systems, Duffing and Lorenz systems, validity of the proposed control scheme is shown.

• Journal of Power Electronics
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• 제5권1호
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• pp.36-44
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• 2005

This paper presents an implementation of high performance control of a reluctance synchronous motor (RSM) using a neural network with a direct torque control. The equivalent circuit in a RSM, which considers iron losses, is theoretically analyzed. Also, the optimal current ratio between torque current and exiting current is analytically derived. In the case of a RSM, unlike an induction motor, torque dynamics can only be maintained by controlling the flux level because torque is directly proportional to the stator current. The neural network is used to efficiently drive the RSM. The TMS320C3l is employed as a control driver to implement complex control algorithms. The experimental results are presented to validate the applicability of the proposed method. The developed control system shows high efficiency and good dynamic response features for a 1.0 [kW] RSM having a 2.57 ratio of d/q.

• 전기학회논문지P
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• 제54권1호
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• pp.20-29
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• 2005

This paper presents an implementation of efficiency optimization of reluctance synchronous motor (RSM) using a neural network (NN) with a direct torque control (DTC). The equipment circuit considered with iron losses in RSM is analyzed theoretically, and the optimal current ratio between torque current and exiting current component are derived analytically. For the RSM driver, torque dynamic can be maintained with DTC using TMS320F2812 DSP Controller even with controlling the flux level because a torque is directly proportional to the stator current unlike induction motor. In order to drive RSM at maximum efficiency and good dynamics response, the Backpropagation Neural Network is adapted. The experimental results are presented to validate the applicability of the proposed method. The developed control system show high efficiency and good dynamic response features with 1.0 [kW] RSM having 2.57 inductance ratio of d/q.