• Journal of Institute of Control, Robotics and Systems
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• v.4 no.4
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• pp.448-457
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• 1998

A neural network based control algorithm with fuzzy compensation is proposed for the automated adjustment in the production of electronic end-products. The process of adjustment is to tune the variable devices in order to examine the specified performances of the products ready prior to packing. Camcorder is considered as a target product. The required test and adjustment system is developed. The adjustment system consists of a NNC(neural network controller), a sub-NNC, and an auxiliary algorithm utilizing the fuzzy logic. The neural network is trained by means of errors between the outputs of the real system and the network, as well as on the errors between the changing rate of the outputs. Control algorithm is derived to speed up the learning dynamics and to avoid the local minima at higher energy level, and is able to converge to the global minimum at lower energy level. Many unexpected problems in the application of the real system are resolved by the auxiliary algorithms. As the adjustments of multiple items are related to each other, but the significant effect of performance by any specific item is not observed. The experimental result shows that the proposed method performs very effectively and are advantageous in simple architecture, extracting easily the training data without expertise, adapting to the unstable system that the input-output properties of each products are slightly different, with a wide application to other similar adjustment processes.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.2
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• pp.41-51
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• 2014

This paper describes a method to estimate the noise power using the minimum statistics approach, which was originally proposed for audio processing. The proposed minimum statistics-based method separates a noisy image into multiple frequency bands using the three-level discrete wavelet transform. By assuming that the output of the high-pass filter contains both signal detail and noise, the proposed algorithm extracts the region of pure noise from the high frequency band using an appropriate threshold. The region of pure noise, which is free from the signal detail part and the DC component, is well suited for minimum statistics condition, where the noise power can be extracted easily. The proposed algorithm reduces the computational load significantly through the use of a simple processing architecture without iteration with an estimation accuracy greater than 90% for strong noise at 0 to 40dB SNR of the input image. Furthermore, the well restored image can be obtained using the estimated noise power information in parametric image restoration algorithms, such as the classical parametric Wiener or ForWaRD image restoration filters. The experimental results show that the proposed algorithm can estimate the noise power accurately, and is particularly suitable for fast, low-cost image restoration or enhancement applications.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.8A
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• pp.612-617
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• 2012

Two phase control (PC) schemes using limited feedback are proposed for multiple-input single-output (MISO) systems. One PC scheme cophases channel gains with respect to the first transmit antenna channel gain, and the other PC scheme cophases channel gains by positioning all the channel gains into a fixed sector. We analyze the combined channel gain for both PC schemes, and find that the PC scheme that cophases with respect to the first transmit antenna channel gain provides 1.2 dB power gain over an orthogonal space-time block code (OSTBC) when the number of transmit antennas is four and the number of feedback bits is three.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.3
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• pp.432-440
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• 2015

We investigate BER performance according to the order of spatial and frequency diversity combining in uplink SC-FDMA of SIMO systems. It is found that frequency diversity combining (FDC) after spatial diversity combining (SDC) is better than the reverse order combing in all SNR (Signal to Noise Ratio) range. Also, it is shown that FDC after SDC requires less computational complexity than the reverse order combining.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.423-432
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• 2014

Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is applied for solving Combined Economic Emission Dispatch (CEED) problem with valve-point loading of thermal generators. This CEED problem with valve-point loading is a nonlinear, constrained multi-objective optimization problem, with power balance and generator capacity constraints. The valve-point loading introduce ripples in the input-output characteristics of generating units and make the CEED problem as a nonsmooth optimization problem. To validate its effectiveness of NSGA-II, two benchmark test systems, IEEE 30-bus and IEEE 118-bus systems are considered. To compare the Pareto-front obtained using NSGA-II, reference Pareto-front is generated using multiple runs of Real Coded Genetic Algorithm (RCGA) with weighted sum of objectives. Comparison with other optimization techniques showed the superiority of the NSGA-II approach and confirmed its potential for solving the CEED problem. Numerical results show that NSGA-II algorithm can provide Pareto-front in a single run with good diversity and convergence. An approach based on Technique for Ordering Preferences by Similarity to Ideal Solution (TOPSIS) is applied on non-dominated solutions obtained to determine Best Compromise Solution (BCS).

• Smart Structures and Systems
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• v.29 no.1
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• pp.53-62
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• 2022

The deployment of advanced structural health monitoring (SHM) systems in large-scale civil structures collects large amounts of data. Note that these data may contain multiple types of anomalies (e.g., missing, minor, outlier, etc.) caused by harsh environment, sensor faults, transfer omission and other factors. These anomalies seriously affect the evaluation of structural performance. Therefore, the effective analysis and mining of SHM data is an extremely important task. Inspired by the deep learning paradigm, this study develops a novel generative adversarial network (GAN) and convolutional neural network (CNN)-based data anomaly detection approach for SHM. The framework of the proposed approach includes three modules : (a) A three-channel input is established based on fast Fourier transform (FFT) and Gramian angular field (GAF) method; (b) A GANomaly is introduced and trained to extract features from normal samples alone for class-imbalanced problems; (c) Based on the output of GANomaly, a CNN is employed to distinguish the types of anomalies. In addition, a dataset-oriented method (i.e., multistage sampling) is adopted to obtain the optimal sampling ratios between all different samples. The proposed approach is tested with acceleration data from an SHM system of a long-span bridge. The results show that the proposed approach has a higher accuracy in detecting the multi-pattern anomalies of SHM data.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.3 s.345
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• pp.1-9
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• 2006

In this paper, we propose a MIMO-OFDMA (Multi Input Multi Output-Orthogonal Frequency Division Multiple Access) system based on beamforming for performance improvement of downlink real-time traffic transmission in harsh channel conditions with low CIR (Carrier-to-Interference Ratio). In the proposed system, we first consider the M-GTA-SBA (Modified-Grouped Transmit Antenna-Simple Bit Allocation) using effective CSI (Channel State Information) calculation procedure based on spatial resource grouping, which is adequate for the combination of MRT (Maximum Ratio Transmission) in the transmitter and MRC (Maximum Ratio Combining) in the receiver. In addition, to reduce feedback information for the beamforming, we also apply QEGT (Quantized Equal Gain Transmission) based on quantization of amplitudes and phases of beam weights. Furthermore, considering multi-user environments, we propose the P-SRA (Proposed-Simple Resource Allocation) algorithm for fair and efficient resource allocation. Simulation results reveal that the proposed MIMO-OFDMA system achieves significant improvement of spectral efficiency in low CRI region as compared to a typical open-loop MIMO-OFDMA system using pseudo-orthogonal space time block code and H-ARQ IR (Hybrid-Automatic Repeat Request Incremental Redundancy).

• Journal of Advanced Navigation Technology
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• v.16 no.6
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• pp.988-996
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• 2012

Assuming perfect channel state information (CSI), the conventional interference alignment (IA) algorithm in the uplink cellular system suppresses inter-cell interference (ICI) by aligning ICI to a randomly selected reference vector. However, IA in practice relies on limited feedback between base stations and users, resulting in residual ICI. In this paper, we propose the optimization of the reference vector that minimizes the upper-bound of residual ICI power. Secondly, the iterative IA that designs the direction of transmit and receive filter is proposed to minimize the residual ICI as well as maximize the desired signals. Moreover, we propose the user scheduling method combined with proposed IA schemes which provides the multiuser diversity gain in multi-cell environments. Finally, the performance gain of the proposed IA algorithms compared with the existing IA are analyzed and demonstrated by simulation results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.12C
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• pp.1020-1028
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• 2010

Relay has attracted great attention due to its inherent capability to extend the service coverage and combat shadowing in next generation mobile communication systems. So far, most relay technologies have been developed under the half-duplex (HD) constraint that prevents relays from transmitting and receiving at the same time. Although half-duplex relay (HDR) is easy to implement, it requires partitioning of resource for transmission and reception, reducing the whole system capacity. In this paper, we propose a multinser precoding and power control scheme with sum rate matching for a full-duplex (FD) multiple-input multiple-output (MIMO) relay. Full-duplex relay (FDR) can overcome the drawback of HDR by transmitting and receiving on the same frequency at the same time, while it is crucial to reduce the effect of self-interference that is caused by its own transmitter to its own receiver. The proposed precoding scheme cancels the self-interference of the FDR as well as to support multiuser MIMO. Moreover, we suggest a power allocation scheme for FD MIMO relay with the constraint that the sum rate of the relay's received data streams is equal to that of the relay's transmit data streams.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.6
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• pp.86-93
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• 2009

We investigate lattice-reduction-aided precoding techniques for multi-user multiple-input multiple-output (MIMO) channels. When assuming full knowledge of the channel state information only at the transmitter, a vector perturbation (VP) is a promising precoding scheme that approaches sum capacity and has simple receiver. However, its encoding is nondeterministic polynomial time (NP)-hard problem. Vector perturbation using lattice reduction algorithms can remarkably reduce its encoding complexity. In this paper, we propose a vector perturbation scheme using Seysen's lattice reduction (VP-SLR) with simultaneously reducing primal basis and dual one. Simulation results show that the proposed VP-SLR has better bit error rate (BER) and larger capacity than vector perturbation with Lenstra-Lenstra-Lovasz lattice reduction (VP-LLL) in addition to less encoding complexity.