• Transactions of the Korean Society of Automotive Engineers
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• v.7 no.7
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• pp.32-41
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• 1999

In this study, a new knocking control algorithm was developed using the knock threshold value auto-correction algorithm. This algorithm uses the Fast Fourier Transform9FFT) method by measuring cylinder block vibration signals of a 1498 cc four-cylinder spark ignition engine. The experimental results show the proposed knock control algorithm provides improved performance compared to existing methods. The results also show that the proposed FFT algorithm provides real-time adjustment of the knock threshold value.

• Journal of the Optical Society of Korea
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• v.20 no.2
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• pp.257-262
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• 2016

A two-dimensional optical code division multiple access (OCDMA) en/decoder composed of four double-ring resonator add/drop filters and three delay waveguides is designed, and a transfer matrix method combined with fast Fourier transform is implemented to provide numerical simulations for the en/decoder. The auto-correlation peak level over the maximum cross-correlation level is larger than 3 at the center of the correctly decoded pulse for most of wavelength hopping and spectral phase code combinations, which assures the BER lower than 10^-3 which corresponds to the forward error correction limit.

• Korean Journal of Remote Sensing
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• v.27 no.3
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• pp.353-357
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• 2011

Although the intensity-hue-saturation (IHS) method for pan-sharpening has a spectral distortion problem, it is a popular method in the remote sensing community and has been used as a standard procedure in many commercial packages due to its fast computing and easy implementation. Recently, IHS-like approaches have tried to overcome the spectral distortion problem inherited from the IHS method itself and yielded a good result. In this paper, a similar IHS-like method with least squares for WorldView-2 pan-sharpening is presented. In particular, unlike the previous methods with three or four-band multispectral images for pan-sharpening, six bands of WorldView-2 multispectral image located within the range of panchromatic spectral radiance responses are considered in order to reduce the spectral distortion during the merging process. As a result, the new approach provides a satisfactory result, both visually and quantitatively. Furthermore, this shows great value in spectral fidelity of WorldView-2 eight-band multispectral imagery.

• Journal of the Korean Society of Safety
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• v.28 no.1
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• pp.57-62
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• 2013

Aerodynamic characteristics of cross section shape is an important parameter for the wind response and structural stability of long span bridges. Numerical simulation methods have been introduced to estimate the aerodynamic characteristics for more detailed flow analysis and cost saving in place of existing wind tunnel experiment. In this study, the computational fluid dynamics(CFD) simulation and large eddy simulation( LES) technique were used to estimate lift, drag and moment coefficients of four cross sections. The Strouhal numbers were also determined by the fast Fourier transform of time series of the lift coefficient. The values from simulations and references were in a good agreement with average difference of 16.7% in coefficients and 8.5% in the Strouhal numbers. The success of the simulations is expected to attribute to the practical use of numerical estimation in construction engineering and wind load analysis.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.1
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• pp.16-21
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• 1999

The analysis of distribution line faults is essential to the proper protections of the power system. A high impedance fault test, which was carried in Korean electric power systems, it was found that a arcing phenomenon occurred during the high level portion of conductor voltage in each cycle. In this paper, we propose a new method for detection of high impedance faults, which uses the arcing fault current difference during high voltage and low voltage portion of conductor voltage waveform. To extract this difference, we diveded one cycle fault current into equal spanned four data windows according to the magnitude of voltage waveform and applied fast fourier transform(FFT) to each data window. The frequency spectrum of current wavefrom in each portion are used as the inputs of neural network and is trained to detect high impedance faults. The proposed method shows improved accuracy when applied to staged fault data and fault-like load.

• Wind and Structures
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• v.38 no.1
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• pp.1-13
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• 2024

In order to effectively simulate nonstationary stochastic turbulent wind fields, four separation hybrid (SEP-H) models are proposed in the present study. Based on the assumption that the lateral turbulence component at one single-point is uncorrelated with the longitudinal and vertical turbulence components, the fluctuating wind is separated into 2nV-1D and nV1D nonstationary stochastic vector processes. The first process can be expressed as double proper orthogonal decomposition (DPOD) or proper orthogonal decomposition and spectral representation method (POD-SRM), and the second process can be expressed as POD or SRM. On this basis, four SEP-H models of nonstationary stochastic turbulent wind fields are developed. In addition, the orthogonal random variables in the SEP-H models are presented as random orthogonal functions of elementary random variables. Meanwhile, the number theoretical method (NTM) is conveniently adopted to select representative points set of the elementary random variables. The POD-FFT (Fast Fourier transform) technique is introduced in frequency to give full play to the computational efficiency of the SEP-H models. Finally, taking a long-span bridge as the engineering background, the SEP-H models are compared with the dimension-reduction DPOD (DR-DPOD) model to verify the effectiveness and superiority of the proposed models.

• Geophysics and Geophysical Exploration
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• v.7 no.1
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• pp.45-50
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• 2004

This paper describes a method of detecting formation boundaries, and permeable fractures, from frequency-domain Stoneley wave logs. Field data sets were collected between the depths of 330 and 360 m in well EE-4 in the Higashi-Hachimantai geothermal field, using a monopole acoustic logging tool with a source central frequency of 15 kHz. Stoneley wave amplitude spectra were calculated by performing a fast Fourier transform on the waveforms, and the spectra were then collected into a frequency-depth distribution of Stoneley wave amplitudes. The frequency-domain Stoneley wave log shows four main characteristic peaks at frequencies 6.5, 8.8, 12, and 13.3 kHz. The magnitudes of the Stoneley wave at these four frequencies are affected by formation properties. The Stoneley wave at higher frequencies (12 and 13.3 kHz) has higher amplitudes in hard formations than in soft formations, while the wave at lower frequencies (6.5 and 8.8 kHz) has higher amplitudes in soft formations than in hard formations. The correlation of the frequency-domain Stoneley wave log with the logs of lithology, degree of welding, and P-wave velocity is excellent, with all of them showing similar discontinuities at the depths of formation boundaries. It is obvious from these facts that the frequency-domain Stoneley wave log provides useful clues for detecting formation boundaries. The frequency-domain Stoneley wave logs are also applicable to the detection of a single permeable fracture. The procedure uses the Stoneley wave spectral amplitude logs at the four frequencies, and weighting functions. The optimally weighted sum of the four Stoneley wave spectral amplitudes becomes almost constant at all depths, except at the depth of a permeable fracture. The assumptions that underlie this procedure are that the energy of the Stoneley wave is conserved in continuous media, but that attenuation of the Stoneley wave may occur at a permeable fracture. This attenuation may take place at anyone of the four characteristic Stoneley wave frequencies. We think our multispectral approach is the only reliable method for the detection of permeable fractures.

• Journal of Sensor Science and Technology
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• v.31 no.1
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• pp.51-56
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• 2022

In this study an efficient method for detecting and monitoring engine misfiring, focusing on minute speed changes in the crankshaft is proposed., Its validity is verified using various misfiring cases. Typically, the crankshaft speed fluctuates around the normal value depending on the engine misfiring status. Even a minute speed change in the crankshaft can be estimated by measuring the rotation time of each tooth of the 118-tooth flywheel attached to the crankshaft with a 2-MHz timer. Therefore, a speed pattern for an in-line six-cylinder engine consists of 236 tooth rotation speeds corresponding to the two rotations of the crankshaft, in which all the cylinders complete four-stroke cycle. FFT analysis can reduce the number of components of a speed pattern from 236 to just four major components: - fundamental frequency_(f), 2f, 3f, 6f., - This makes the comparison of the misfiring cases simpler and faster. In the experiment, five engine status cases (one normal firing and, four misfiring cases) were simulated. While the 6f component was the largest for the normal case, the f component increased as misfiring occurred one, two apart, and two consecutive times. The 3D FFT pattern comprising the ratio of f, 2f, and 3f, 6f showed that the distance between the misfiring and normal states was larger

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.4
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• pp.426-435
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• 1999

A high impedance fault(HIF) is one of the serious problems facing the electric utility industry today. Because of the high impedance of a downed conductor under some conditions these faults are not easily detected by over-current based protection devices and can cause fires and personal hazard. In this paper a new method for detection of HIF which uses adaptive neuro-fuzzy inference system (ANFIS) is proposed. Since arcing fault current shows different changes during high and low voltage portion of conductor voltage waveform we firstly divided one cycle of fault current into equal spanned four data windows according to the mangnitude of conductor voltage. Fast fourier transform(FFT) is applied to each data window and the frequency spectrum of current waveform are chosen asinputs of ANFIS after input selection method is preprocessed. Using staged fault and normal data ANFIS is trained to discriminate between normal and HIF status by hybrid learning algorithm. This algorithm adapted gradient descent and least square method and shows rapid convergence speed and improved convergence error. The proposed method represent good performance when applied to staged fault data and HIFLL(high impedance like load)such as arc-welder.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.6
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• pp.1060-1069
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• 2016

In this paper, we introduce the fingerprint recognition system based on Radial Basis Function Neural Network(RBFNN). Fingerprints are classified as four types(Whole, Arch, Right roof, Left roof). The preprocessing methods such as fast fourier transform, normalization, calculation of ridge's direction, filtering with gabor filter, binarization and rotation algorithm, are used in order to extract the features on fingerprint images and then those features are considered as the inputs of the network. RBFNN uses Fuzzy C-Means(FCM) clustering in the hidden layer and polynomial functions such as linear, quadratic, and modified quadratic are defined as connection weights of the network. Particle Swarm Optimization (PSO) algorithm optimizes a number of essential parameters needed to improve the accuracy of RBFNN. Those optimized parameters include the number of clusters and the fuzzification coefficient used in the FCM algorithm, and the orders of polynomial of networks. The performance evaluation of the proposed fingerprint recognition system is illustrated with the use of fingerprint data sets that are collected through Anguli program.