• Title/Summary/Keyword: direct network

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Adaptive Power Saving Protocol Based on Traffic Estimation for Wi-Fi Direct Networks (Wi-Fi Direct 망을 위한 부하량 예측 기반의 전력 절감 기법)

  • Yoo, Hongseok;Park, Yang-Woo;Lee, Chae-Soo;Yun, Tae-Jin
    • IEMEK Journal of Embedded Systems and Applications
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    • v.10 no.4
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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.

The Successful Strategies for YouTube Channels Using the Network Overlap (네트워크 중복을 이용한 유튜브 채널의 성공 전략)

  • Shin, Jin-Hee;Son, Jung-Min
    • The Journal of Information Systems
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    • v.29 no.1
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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.

An Neural Network Direct Controller for Nonlinear Systems

  • Nam Kee Hwan;Bae Cheo Soo;Cho Hyeon Seob;Ra Sang Dong
    • Proceedings of the IEEK Conference
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    • 2004.08c
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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.

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Direct Controller for Nonlinear System Using a Neural Network

  • Bae, Cheol-Soo;Park, Young-Cheol;Nam, Kee-Hwan;Kang, Yong-Seok;Kim, Tae-Woo;Hwang, Suen-Ki;Kim, Hyon-Yul;Kim, Moon-Hwan
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.5 no.1
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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.

Market Power of Internet Portals with Direct and Indirect Network Externality (직·간접 네트워크 외부성하에서 인터넷포털 기업의 시장력 분석)

  • Jin, Yangsoo
    • KDI Journal of Economic Policy
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    • v.31 no.2
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    • pp.87-126
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    • 2009
  • In the internet portal industry, the indirect network externality from portal visitors to advertisers and the direct network externality among portal visitors have important implications for anti-trust policies. This paper examines the existence and the magnitude of the direct/indirect network externality in the Korean internet portal industry and measures its effect on the market power of the internet portals. The results show that the direct/indirect network externality is substantive in the industry hence the market share of a portal in the visitors' side has the 'leverage' effect on its market power in the advertisers' side.

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A surrogate model-based framework for seismic resilience estimation of bridge transportation networks

  • Sungsik Yoon ;Young-Joo Lee
    • Smart Structures and Systems
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    • v.32 no.1
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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.

Directions for Improving the Pedestrian Environment in Main Street of Towns - Targeting Main Street in Four Local Government Towns in Jeollanam-do - (읍소재지 중심가로의 보행환경 개선 방향 - 전라남도 4개 지방정부 읍소재지의 중심가로를 대상으로 -)

  • Park, Sung-Jin;Kang, In-Ho
    • Journal of the Korean Institute of Rural Architecture
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    • v.23 no.1
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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.

Direct adaptive control of chaotic nonlinear systems using a radial basis function network (방사 기저 함수 회로망을 이용한 혼돈 비선형 시스템의 직접 적응 제어)

  • 김근범;박광성;최윤호;박진배
    • 제어로봇시스템학회:학술대회논문집
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    • 1997.10a
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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.

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Direct Torque Control System of a Reluctance Synchronous Motor Using a Neural Network

  • Kim Min-Huei
    • Journal of Power Electronics
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    • v.5 no.1
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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.

A Direct Torque Control System for Reluctance Synchronous Motor Using Neural Network (신경회로망을 이용한 동기 릴럭턴스 전동기의 직접토크제어 시스템)

  • Kim, Min-Huei
    • The Transactions of the Korean Institute of Electrical Engineers P
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    • v.54 no.1
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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.