• Journal of Communications and Networks
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• 제14권2호
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• pp.199-205
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• 2012

This work investigates the progressive linear precoder design for packet retransmissions in multi-input multi-output (MIMO) systems with multiple codewords and automatic repeat request (ARQ) bundling feedback. Assuming perfect channel state information, a novel progressive linear ARQ precoder is proposed in the perspective of minimizing the packet error rate. We devise the ARQ precoder by combining power loading and sub channel pairing between current retransmission and previous transmissions. Furthermore, we extend the design to the case that the channel estimation error exists. Numerical results show that the proposed scheme can improve the performance of MIMO ARQ systems significantly regardless of the channel estimation error.

• ETRI Journal
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• 제26권5호
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• pp.443-451
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• 2004

Since the publication of Alamouti's famous space-time block code, various quasi-orthogonal space-time block codes (QSTBC) for multi-input multi-output (MIMO) fading channels for more than two transmit antennas have been proposed. It has been shown that these codes cannot achieve full diversity at full rate. In this paper, we present a simple feedback scheme for rich scattering (flat Rayleigh fading) MIMO channels that improves the coding gain and diversity of a QSTBC for 2$^n$ (n=3, 4, ${\cdots}$) transmit antennas. The relevant channel state information is sent back from the receiver to the transmitter quantized to one or two bits per code block. In this way, signal transmission with an improved coding gain and diversity near to the maximum diversity order is achieved. Such high diversity can be exploited with either a maximum-likelihood receiver or low-complexity zero-forcing receiver.

• 한국멀티미디어학회논문지
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• 제21권12호
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• pp.1467-1472
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• 2018

For the problem that the face image of surveillance video cannot be accurately identified due to the low resolution, this paper proposes a low resolution face recognition solution based on convolutional neural network model. Convolutional Neural Networks (CNN) model for multi-scale input The CNN model for multi-scale input is an improvement over the existing "two-step method" in which low-resolution images are up-sampled using a simple bi-cubic interpolation method. Then, the up sampled image and the high-resolution image are mixed as a model training sample. The CNN model learns the common feature space of the high- and low-resolution images, and then measures the feature similarity through the cosine distance. Finally, the recognition result is given. The experiments on the CMU PIE and Extended Yale B datasets show that the accuracy of the model is better than other comparison methods. Compared with the CMDA_BGE algorithm with the highest recognition rate, the accuracy rate is 2.5%~9.9%.

• 한국물환경학회지
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• 제27권1호
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• pp.30-35
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• 2011

2D pollutant transport model was applied to the simulation of contaminant transport in the channel. At first, two kinds of virtual channels having different slopes were designed. The distribution of contaminant, which flows from one of the three drainages to the main channel, was simulated by each 2D model. Concentrations of 745 nodes were converted to input data of neural network model (Multi-perceptron) for training and verification using matrix. The first three cases (Case A-1, A-2, A-3) were used for training Multi-perceptron, the other three cases (Case B-1, B-2, B-3) were used for verification. As a result, Multi-perceptron reasonably divided the cases into the three characteristics which have different contaminant distributions due to the different input point of water pollution source. It can be a useful methodology for the water quality monitoring and backtracking.

• International Journal of Naval Architecture and Ocean Engineering
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• 제13권1호
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• pp.848-857
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• 2021

This paper proposes an efficient approach based on a machine learning technique to predict the local stresses on mooring chain links. Three-link and multi-link finite element analyses were conducted for a target chain link of D107 with steel grade R4; 24,000 and 8000 analyses were performed, respectively. Two serial Artificial Neural Network (ANN) models based on a deep multi-layer perceptron technique were developed. The first ANN model corresponds to multi-link analyses, where the input neurons were the tension force and angle and the output neurons were the interlink angles. The second ANN model corresponds to the three-link analyses with the input neurons of the tension force, interlink angle, and the local stress positions, and the output neurons of the local stress. The predicted local stresses for the untrained cases were reliable compared to the numerical simulation results.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.1828-1832
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• 1991

We study the observer design problem for nonautonomous discrete-time nonlinear systems. We investigate the structure of nonautonomous discrete-time systems which are state equivalent to the nonlinear observer form and characterize their class. Necessary and sufficient conditions for the existence of an input independent (local) diffeomorphism axe derived which transforms multi-input, multi-output nonlinear systems into the nonlinear observer form.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.1213-1218
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• 1993

Variable structure control is applied to the robust output tracking control problem of general nonlinear multi-input multi-output (MIMO) systems. Using the concept of relative degree and minimum phase, input/output(I/O) linearization is undertaken. For I/O the linearized system, a new sliding hyperplanes design method is proposed. In this procedure, we can construct very robust and efficient sliding mode controller for general nonlinear systems of relative degree higher than two. The control results are illustrated by adopting a numerical example.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.1092-1095
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• 1996

In this paper The tuning of PID controller for multi input-output is studied by using fuzzy inference. State of coupling is estimated by fuzzy inference, its results is used for tuning of PID controller to get optimum P,I,D parameter with regard to state of coupling. This method is simulated to Turbo-generating system with $2{\times}2$ multi input-output and made with electronic circuit, its response is very satisfactory.

• 한국전자통신학회논문지
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• 제6권6호
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• pp.865-871
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• 2011

태양광 시스템이나 연료전지 시스템에 많이 사용되는 DC-DC 컨버터는 부스트 컨버터이다. 이 중 입출력 전류 리플이 작은 인터리브드 부스트 컨버터가 최근에 많이 사용되고 있다. 이 회로는 입출력 전류 리플이 작기 때문에 입출력 커패시터의 크기를 줄일 수 있다. 따라서 기존의 전해 커패시터에서 신뢰성이 우수한 필름 커패시터를 사용할 수 있고 이는 전체 시스템의 수명 및 신뢰성을 향상시킬 수 있다. 본 논문에서는 다단 인터리브드 부스트 컨버터의 입출력 전류리플의 수식을 유도하고 듀티에 따른 특성을 알아본다. 위에서 언급한 내용을 확인하기 위해서 PSIM 툴을 이용하여 계산된 값과 비교를 할 것이다.

• 한국항행학회논문지
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• 제14권3호
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• pp.391-396
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• 2010

본 논문에서는 증폭기 및 체배기를 이용한 다기능 RF 모듈의 주요 연구 결과를 비고 제시하였다. 주파수 대역별로 별도의 블록을 갖는 다중대역 모듈에 비해 체배기를 이용하면 간단하게 다기능 모듈 구현이 가능하다. 별개의 증폭기 및 체배기를 스위칭하는 방법, 주파수 선택적 반사기 구조를 이용한 방법, 결함접지구조를 이용한 구조를 비교 제시하였다. 입력 주파수에 따라 증폭기 또는 주파수 체배기로 동작하는 회로로 다기능 모듈을 개발하였으며, 결함접지구조를 이용하여 입력 주파수 억압 및 출력 고조파 억압을 향상시켰다.