• Journal of Communications and Networks
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• v.14 no.2
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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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• v.26 no.5
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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.

• Journal of Korea Multimedia Society
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• v.21 no.12
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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%.

• Journal of Korean Society on Water Environment
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• v.27 no.1
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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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• v.13 no.1
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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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• 1991.10b
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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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• 1993.10a
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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.

• Proceedings of the KIEE Conference
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• 1996.07b
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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.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.6
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• pp.865-871
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• 2011

DC-DC converter commonly used in photovoltaic systems or fuel cell systems is a boost converter. Among several types of boost converter, the interleaved boost converter with small input and output current ripples is widely used in recent years. Because of small input and output current ripples, the circuit can reduce the size of the input and output capacitors. Thus, instead of conventional electrolytic capacitor, the film capacitor with high reliability can be used and this is the life and reliability of the entire system can be improved. In this paper, the input/output current ripple formulas of the multi-stage interleaved boost converter are derived, and the characteristics in accordance with duty are found out. In order to verify the above mentioned contents, the derived results will make a comparison with the calculated values by using PSIM tool.

• Journal of Advanced Navigation Technology
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• v.14 no.3
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• pp.391-396
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• 2010

This paper presents some important research result comparisons for multi function RF modules which use amplifier or frequency multiplier. By using multiplier, multi function module can be realized amply in comparison to multi band module which has separate block for each frequency band. Some com paring analysis among the switching method between separate amplifier and multiplier, the structure using frequency selective reflector, and the module using the defected ground structure. The multi function module which operates as amplifier or multiplier with input frequency is developed and input frequency suppression and output harmonics suppression can be improve d by using defected ground structure.