• International journal of advanced smart convergence
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• 제9권2호
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• pp.58-67
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• 2020

In this paper, we propose a data-driven-based beam selection scheme for massive multiple-input and multiple-output (MIMO) systems in ultra-dense networks (UDN), which is capable of addressing the problem of high computational cost of conventional coordinated beamforming approaches. We consider highly dense small-cell scenarios with more small cells than mobile stations, in the millimetre-wave band. The analog beam selection for hybrid beamforming is a key issue in realizing millimetre-wave UDN MIMO systems. To reduce the computation complexity for the analog beam selection, in this paper, two deep neural network models are used. The channel samples, channel gains, and radio frequency beamforming vectors between the access points and mobile stations are collected at the central/cloud unit that is connected to all the small-cell access points, and are used to train the networks. The proposed machine-learning-based scheme provides an approach for the effective implementation of massive MIMO system in UDN environment.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 1999년도 학술대회논문집-국제 전기방전 및 플라즈마 심포지엄 Proceedings of 1999 KIIEE Annual Conference-International Symposium of Electrical Discharge and Plasma
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• pp.256-261
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• 1999

Many control techniques have been proposed in order to improve the control performance of discrete-time domain control system. In the position control system using a DC servo motor as control system, the response-characteristic of system is controlled by the I-PD controller. In the I-PD longer if gains of I-PD controller are unsuitable. In this paper, therefore, a expanded I-PD control system is constructed by inserting a pre-compensator at out terminal of I-PD controller. It is implemented by neural network with two hidden layers. From the result of computer simulation in the proposed control algorithm, its usefulness is verified.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 B
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• pp.781-786
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• 1998

Since a gas turbine is still a significant contributor to peak time, it is very important to tune the gains of P. I. D to get a maximum power and stability within permissible limits. In the gas turbine, the main control loop must adjust the fuel flow to ensure the correct output power and frequency. but it is not easy, because the control loop is composed of many subsystems. In this paper we acquire a transfer function based on the operations data of Gun-san gas turbine and study to apply a loop compensation type 2-DOF PID controller tuning by neural-network to control loop of gas turbine to reduce phenomena caused by integral and derivative actions through simulation. We obtained satisfactory results to disturbances of subcontrol loop such as, fuel flow, air flow, turbine extraction temperature.

• 융합신호처리학회 학술대회논문집
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• 한국신호처리시스템학회 2000년도 하계종합학술대회논문집
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• pp.105-108
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• 2000

Many control techniques have been proposed in order to improve the control performance of discrete-time domain control system. In the position control system using a DC servo motor as a driver, the response-characteristic of system is controlled by the PID controller. In the PID control system, the transient response characteristic is more increased and settling time gets longer if gains of PID controller are unsuitable. In this paper, therefore, a expended PID control system is constructed by inserting a pre-compensator at output terminal of PID controller. It is implemented by using the double layers neural network. Form the results of computer simulation in the proposed control algorithm, its usefulness is verified.

• ETRI Journal
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• 제46권4호
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• pp.571-580
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• 2024

A compact triple-band porous electromagnetic bandgap structure-loaded coplanar-waveguide-fed wearable antenna is introduced for applications of wireless body area networks. The porous structure is aimed to create a stopband or bandgap in the electromagnetic spectrum and increase breathability. The holes in the bottom electromagnetic bandgap surface increase the inductance, which in turn increases the bandwidth. The final design resonates at three bands with impedance bandwidths of 264 MHz, 100 MHz, and 153 MHz and maximum gains of 2.18 dBi, 6.75 dBi, and 9.50 dBi at 2.45 GHz, 3.5 GHz, and 5.5 GHz, respectively. In addition, measurements indicate that the proposed design can be deformed up to certain curvature and withstand human tissue loading. Moreover, the specific absorption rate remains within safe levels for humans. Therefore, the proposed antenna can suitably operate in the industrial, scientific, and medical, Bluetooth, Wi-Fi, and WiMAX bands for potential application to wireless body area networks.

• 대한전자공학회논문지TC
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• 제38권8호
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• pp.19-29
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• 2001

최근 인터넷 응용계층, 사용자수와 IP 백본망에서 트래픽의 폭발적인 증가는 전송망에 대해서 새로운 변화를 요구하고 있다. 이에 따라 대용량 WDM시스템들이 광범위하게 설치되고 있으며 이를 인터넷에 효율적으로 운영하기 위하여 IP/WDM을 구현하려는 노력이 진전되고 있다. WDM 전광망의 이용이득을 얻기 위해서는 IP 계층의 라우팅 프로토콜과 함께 전광망의 물리적인 망을 효율적으로 활용하게 해주는 논리적 토폴로지를 형성하고 광망을 제어하는 소프트웨어가 필요하다. 이를 위하여 WDM 전광망에서의 라우팅과 파장할당(RWA)을 효율적으로 수행할 수 있는 알고리듬이 필요하다. 본 논문에서는 WDM 전광망의 논리적인 토폴로지 형성을 위한 두 개의 직관적인 알고리듬을 구현하여 국내 백본망에 적용하였으며, 이들이 잘 동작함을 보였고 이들을 블록킹율등의 측면에서 비교분석하였다.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2000년도 춘계학술대회논문집
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• pp.56-63
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• 2000

Abstract - In this paper, an adaptive mechanism based on immune algorithm is designed and it is applied for the autonomous guided vehicle(AGV) driving. When the immune algorithm is applied to the PID controller, there exists the case that the plant is damaged due to the abrupt change of PID parameters since the parameters are adjusted almost randomly. To solve this problem, a neural network is used to model the plant and the parameter tuning of the model is performed by the immune algorithm. After the PID parameters are determined in this off-line manner, these gains are then applied to the plant for the on-line control using immune adaptive algorithm. Moreover, even though the neural network model may not be accurate enough intially, the weighting parameters are adjusted to be accurate through the on-line fine tuning. The computer simulation for the control of steering and speed of AGV is performed. The results show that the proposed controller has better performances than other conventional controllers.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2004년도 추계 종합학술대회 논문집
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• pp.275-279
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• 2004

본 논문은 한국 전자통신연구원에서 개발하고 있는 IP 기반의 대규모 광역 스토리지 시스템에 관한 논문이다. OASIS는 IP 네트워크 상의 수백~수천 명의 동시 사용자를 효과적으로 지원하고 또한 네트워크 스토리지 서비스를 WAN 범위까지로 확대 시키는 것을 목적으로 하는 스토리지 시스템이다. 이를 위해 IP 기반 스토리지 연결망 기술과 객체 기반 스토리지 기술 그리고 클러스터 기반 서버 구조 기술을 활용한다. 본 시스템은 특히 FTTH, WiBro 등 차세대 통신망의 도입에 따라 점차 현실성을 얻어가는 IP 기반 스토리지 서비스에서 대규모 스토리지 서비스를 위한 목적으로 활용될 수 있다.

• ETRI Journal
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• 제38권6호
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• pp.1052-1063
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• 2016

We propose a millimeter wave (MMW)-based mobile hotspot network (MHN) system for application in high-speed railways that is capable of supporting a peak backhaul link throughput of 1 Gbps per train at 400 km/h. The MHN system can be implemented in subways and high-speed trains to support passengers with smart devices and provide access to the Internet. The proposed system can overcome the inherent high path loss in MMW through system designs and high antenna gains. We present a simulation of the system performance that shows that a fixed beamforming strategy can provide high signal-to-interference-plus-noise-ratio similar to those of an adaptive beamforming strategy, with the exception of 15% of the train path in which the network can use link adaptation with low-order modulation formats or trigger a handover to maintain the connection. We also demonstrate the feasibility of the MHN system using a test bed deployed in Seoul subway line 8. The backhaul link throughput varies instantaneously between 200 Mbps and 500 Mbps depending on the SNR variations while the train is running. During the field trial, the smartphones used could make connections through offloading.

• 대한전기학회논문지:시스템및제어부문D
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• 제49권4호
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• pp.201-212
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• 2000

In this paper, an adaptive mechanism based on immune algorithm is designed and it is applied for the autonomous guided vehicle(AGV) driving. When the immune algorithm is applied to the PID controller, there exists the cast that the plant is damaged due to the abrupt change of PID parameters since the parameters are adjusted almost randomly. To solve this problem, a neural network is used to model the plant and the parameter tuning of the model is performed by the immune algorithm. After the PID parameters are determined in this off-line manner, these gains are then applied to the plant for the on-line control using immune adaptive algorithm. Moreover, even though the neural network model may not be accurate enough intially, the weighting parameters are adjusted to be accurate through the on-line fine tuning. The computer simulation for the control of steering and speed of AGV is performed. The results show that the proposed controller has better performances than other conventional controllers.