• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.5A
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• pp.333-340
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• 2003

In this paper, we propose a new turbo-coded orthogonal frequency division multiplexing (OFDM) transmission scheme that can improve greatly the performance by making all the turbo-coded symbols have the same reliability for OFDM transmission over a frequency selective fading channel. The same reliability, that is, the same fading can be accomplished through multiplexing of turbo-coded symbols using distinct orthogonal codes and spreading over the whole effective subcarriers (hereafter, called as the orthogonal code multiplexing (OCM)). As for the orthogonal code selection, we choose the set of the discrete Fourier transform (DFT) basis sequences, since the code set holds the orthogonality irrespective of the length and also has the equal energy property. We perform computer simulations using the Log-maximum-a-posteriori (Log-MAP) algorithm for iterative decoding in order to assess the performance of the proposed transmission scheme.

• Composites Research
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• v.16 no.1
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• pp.26-33
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• 2003

Utilizing a thermo-acoustic emission (AE) technique, a study on detection and evaluation of microfractures in cross-ply laminate composites was performed. Fiber breakages and matrix fractures formed by a cryogenic cooling at $-191^{\circ}C$ were observed with ultrasonic C-scan, optical and scanning electron microscopy. Those microfractures were monitored in a non-destructive in-situ state as three different types of thermo-AE signals classified on the basis of Fast-Fourier Transform and Short-Time Fourier Transform. Thus, it was concluded that real-time estimation of microfracture processes being formed during cryogenic cooling could be accomplished by monitoring such different types of thermo-AEs in each time-stage and then by analyzing thermo-AE behaviors for the respective AE types on the basis of the AE signal analysis results obtained during thermal heating and cooling load cycles.

• Structural Engineering and Mechanics
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• v.29 no.2
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• pp.135-154
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• 2008

The moving vehicle loads on a bridge deck is one of the most important live loads of bridges. They should be understood, monitored and controlled before the bridge design as well as when the bridge is open for traffic. A MOM-based algorithm (MOMA) is proposed for identifying the timevarying moving vehicle loads from the responses of bridge deck in this paper. It aims at an acceptable solution to the ill-conditioning problem that often exists in the inverse problem of moving force identification. The moving vehicle loads are described as a combination of whole basis functions, such as orthogonal Legendre polynomials or Fourier series, and further estimated by solving the new system equations developed with the basis functions. A number of responses have been combined, some numerical simulations on single axle, two axle and multiple-axle loads, being either constant or timevarying, have been carried out and compared with the existing time domain method (TDM) in this paper. The illustrated results show that the MOMA has higher identification accuracy and robust noise immunity as well as producing an acceptable solution to ill-conditioning cases to some extent when it is used to identify the moving force from bridge responses.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.33-39
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• 2023

Recently, reinforcement learning has been used to improve the communication performance of flying ad-hoc networks (FANETs) and to design mobility models. Mobility model is a key factor for predicting and controlling the movement of unmmaned aerial vehicle (UAVs). In this paper, we designed and analyzed the performance of Q-learning with fourier basis function approximation and Deep-Q Network (DQN) models for optimal path finding in a three-dimensional virtual environment where UAVs operate. The experimental results show that the DQN model is more suitable for optimal path finding than the Q-learning model in a three-dimensional virtual environment.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.12
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• pp.2520-2528
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• 2009

In this paper, we proposed tracking detection methodology using information granulation-based fuzzy radial basis function neural networks (IG-FRBFNN). According to IEC 60112, tracking device is manufactured and utilized for experiment. We consider 12 features that can be used to decide whether tracking phenomenon happened or not. These features are considered by signal processing methods such as filtering, Fast Fourier Transform(FFT) and Wavelet. Such some effective features are used as the inputs of the IG-FRBFNN, the tracking phenomenon is confirmed by using the IG-FRBFNN. The learning of the premise and the consequent part of rules in the IG-FRBFNN is carried out by Fuzzy C-Means (FCM) clustering algorithm and weighted least squares method (WLSE), respectively. Also, Hierarchical Fair Competition-based Parallel Genetic Algorithm (HFC-PGA) is exploited to optimize the IG-FRBFNN. Effective features to be selected and the number of fuzzy rules, the order of polynomial of fuzzy rules, the fuzzification coefficient used in FCM are optimized by the HFC-PGA. Tracking inference engine is implemented by using the LabVIEW and loaded into embedded system. We show the superb performance and feasibility of the tracking detection system through some experiments.

• Journal of The Korean Astronomical Society
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• v.37 no.5
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• pp.349-353
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• 2004

Magnetic fields are an important ingredient of galaxy clusters and are indirectly observed on cluster scales as radio haloes and radio relics. One promising method to shed light on the properties of cluster wide magnetic fields is the analysis of Faraday rotation maps of extended extragalactic radio sources. We developed a Fourier analysis for such Faraday rotation maps in order to determine the magnetic power spectra of cluster fields. In an advanced step, here we apply a Bayesian maximum likelihood method to the RM map of the north lobe of Hydra A on the basis of our Fourier analysis and derive the power spectrum of the cluster magnetic field. For Hydra A, we measure a spectral index of -5/3 over at least one order of magnitude implying Kolmogorov type turbulence. We find a dominant scale of about 3 kpc on which the magnetic power is concentrated, since the magnetic autocorrelation length is ${\lambda}_B = 3 {\pm} 0.5\;kpc$. Furthermore, we investigate the influences of the assumption about the sampling volume (described by a window function) on the magnetic power spectrum. The central magnetic field strength was determined to be ${\~}7{\pm}2{\mu}G$ for the most likely geometries.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.8
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• pp.84-93
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• 2010

This paper describes a method of visualization and fast estimation of parameter in continuous time signal. The parameter estimation method of this paper directly estimate the parameters on the basis of the discrete Fourier transform. Also, this paper present to efficient visualization method of dominant parameters obtained in continuous time signal. The proposed methods are applied to test functions with three dominant modes. The results show that the proposed methods are highly applicable to parameter estimation and visualization in continuous time signal.

• Journal of Power Electronics
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• v.14 no.3
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• pp.564-571
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• 2014

Rotor initial position is an important factor affecting the control performance of electrically excited synchronous motors. This study presents a novel method for estimating rotor initial position based on sliding discrete Fourier transform (sDFT). By injecting an ac excitation into the rotor winding, an induced voltage is generated in stator windings. Through this voltage, the stator flux can be obtained using a pure integral voltage model. Considering the influence from a dc bias and an integral initial value, we adopt the sDFT to extract the fundamental flux component. A quadrant identification model is designed to realize the accurate estimation of the rotor initial position. The sDFT and high-pass filter, DFT, are compared in detail, and the contrast between dc excitation and ac injection is determined. Simulation and experimental results verify that this type of novel method can eliminate the influence of dc bias and other adverse factors, as well as provide a basis for the control of motor drives.

• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.1060-1071
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• 2014

Signals of the Electroencephalogram (EEG) can reflect the electrical background activity of the brain generated by the cerebral cortex nerve cells. This has been the mostly utilized signal, which helps in effective analysis of brain functions by supervised learning methods. In this paper, an approach for improving the accuracy of EEG signal classification is presented to detect epileptic seizures. Moreover, Independent Component Analysis (ICA) is incorporated as a preprocessing step and Short Time Fourier Transform (STFT) is used for denoising the signal adequately. Feature extraction of EEG signals is accomplished on the basis of three parameters namely, Standard Deviation, Correlation Dimension and Lyapunov Exponents. The Artificial Neural Network (ANN) is trained by incorporating Levenberg-Marquardt(LM) training algorithm into the backpropagation algorithm that results in high classification accuracy. Experimental results reveal that the methodology will improve the clinical service of the EEG recording and also provide better decision making in epileptic seizure detection than the existing techniques. The proposed EEG signal classification using feed forward Backpropagation Neural Network performs better than to the EEG signal classification using Adaptive Neuro Fuzzy Inference System (ANFIS) classifier in terms of accuracy, sensitivity, and specificity.

• Bulletin of the Korean Chemical Society
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• v.10 no.2
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• pp.123-128
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• 1989

Calorimetric study in conjunction with Fourier-transform infrared (FTIR) spectroscopic study was carried out on the blends of poly(ethylene oxide) (PEO) with isotactic, atactic and syndiotactic poly(methyl methacrylate) (i-, a-, and s-PMMA). From the differential scanning calorimetric (DSC) measurements, the three types of blends show a depression of the melting temperatures. This indicates that PEO is compatible with i-, a-, and s-PMMA. But the largest melting point depressions of PEO are always found in the blends with s-PMMA. For PEO/a-PMMA and PEO/s-PMMA, the degree of crystallinity as a function of composition deviates substantially from that of the ideal blend in which no interaction between the components exists. The FTIR spectra of all three types of blends are recorded. In order to observe the microstructural changes of PEO in blends, we analyzed the spectra using digital weighted subtraction and addition techniques. It was concluded that the microstructures of PEO are strongly perturbed by the PMMA's. Among these blends PEO microstructure in PEO/s-PMMA blends is most greatly influenced. It indicates that the blending is most preferred with s-PMMA than a- and i-PMMA. It can be explained on the basis of the molecular structure of PMMA's.