• Journal of the Earthquake Engineering Society of Korea
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• v.5 no.4
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• pp.27-38
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• 2001

A mechanical lumped parameter model is proposed for the dynamic modeling of a semi-infinite reservoir. A semi-analytic transmitting boundary is derived for a semi-infinite 2-D reservoir of constant depth. The characteristics of the solution are examined in both frequency and time domains. Mass, damping and spring coefficients of the mechanical model are obtained to preserve the major features of the solution such as eigenfrequencies and the shapes of Bessel functions that appear as kernels in the convolution integrals. The lumped parameter model in its final form consists of two masses, a spring and two dampers for each eigenfrequency. Application examples demonstrated that the new lumped parameter model could be used for the time domain analysis of dam-reservoir systems.

• Structural Engineering and Mechanics
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• v.66 no.2
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• pp.185-193
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• 2018

In this study, low- and high-frequency structure behaviors were identified and a systematic analysis procedure was proposed using noisy GPS data from a 165-m-high tower in ${\dot{I}}stanbul$, Turkey. The raw GPS data contained long- and short-periodic position changes and noisy signals at different frequencies. To extract the significant results from this complex dataset, the general structure and components of the GPS signal were modeled and analyzed in the time and frequency domains. Uncontrolled jumps and deviations involving the signal in the time domain were pre-filtered. Then, the signal was converted to the frequency domain after applying low- and high-pass filters, and the frequency and periodic component values were calculated. The spectrum of the tower motion obtained from the filtered GPS data had dominant peaks at a low frequency of $1.15572{\times}10-4Hz$ and a high frequency of 0.16624 Hz, consistent with two equivalent GPS datasets. Then, the signal was reconstructed using inverse Fourier transform with the dominant low frequency values to obtain filtered and interpretable clean signals. With the proposed sequence, processing of noisy data collected from the GPS receivers mounted very close to the structure is effective in revealing the basic behaviors and features of buildings.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.40-42
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• 2009

This paper presents an algorithm, which is based on Otsu's method, for accurate and robust endpoint detection for speech recognition under noisy environments. The features are extracted in time domain, and then an optimal threshold is selected by minimizing the discriminant criterion, so as to maximize the separability of the speech part and environment part. The simulation results show that the method play a good performance in detection accuracy.

• Transactions on Electrical and Electronic Materials
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• v.7 no.5
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• pp.252-256
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• 2006

When a partial discharge takes place at the stator of a generator, the electrical pulse will propagate along the stator bars and the capacitor chains formed by the end part of the stator winds. On the first path, the pulse propagates as a travel wave at slow speed. On the second path, the pulse propagates at quick speed. Based on the data of the experiments on a real 50 MW steam generator, the author has found the pulses can propagate by magnetic field of the stator winding. It was studied that how to locating the partial discharge by signals coming from the different paths, including the features of signals on the two paths at time domain and frequency domain, the measurement frequency rang of the signals, the blind area, the advantage and disadvantage of this method.

• Journal of Electrical Engineering and Technology
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• v.2 no.3
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• pp.353-357
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• 2007

The bearing diagnostics method is presented in this paper using fuzzy inference based on vibration data. Both time-domain and frequency-domain features are used as input data for bearing fault detection. The Adaptive Network based Fuzzy Inference System (ANFIS) and Genetic Algorithm (GA) have been proposed to select the fuzzy model input and output parameters. Training results give the optimized fuzzy inference system for bearing diagnosis based on measured vibration data. The result is also tested with other sets of bearing data to illustrate the reliability of the chosen model.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.11 no.3
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• pp.337-344
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• 2000

The wavelet analysis technique is applied in the spectral domain to efficiently represent the multi-scale features of the impedance matrices. In this scheme, the 2-D quadtree decomposition (applying the wavelet transform to only the part of the matrix) method often used in image processing area is applied for a sparse moment matrix. CG(Conjugate-Gradient) method is also applied for saving memory and computation time of wavelet transformed moment matrix. Numerical examples show that for rectangular cylinder case the non-zero elements of the transformed moment matrix grows only as O($N^{1.6}$).

• Journal of Korean Institute of Industrial Engineers
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• v.32 no.2
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• pp.133-140
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• 2006

Large signal type data sets are difficult to classify, especially if the data sets are non-stationary. In this paper, large signal type and non-stationary data sets are wavelet transformed so that distinct features of the data are extracted in wavelet domain rather than time domain. For the classification of the data, a few wavelet coefficients representing class properties are employed for statistical classification methods : Linear Discriminant Analysis, Quadratic Discriminant Analysis, Neural Network etc. The application of our wavelet-based feature selection method to a mass spectrometry data set for ovarian cancer diagnosis resulted in 100% classification accuracy.

• Journal of Multimedia Information System
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• v.3 no.2
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• pp.1-8
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• 2016

In this paper, we propose a method to automatically diagnose various diseases. The input data consists of electrocardiograph (ECG) recordings. We extract R-to-R interval (RRI) signals from ECG recordings, which are preprocessed to remove trends and ectopic beats, and to keep the signal stationary. After that, we perform some prospective analysis to extract time-domain parameters, frequency-domain parameters, and nonlinear parameters of the signal. Those parameters are unique for each disease and can be used as the statistical symptoms for each disease. Then, we perform feature selection to improve the performance of the diagnosis classifier. We utilize the selected features to diagnose various diseases using machine learning. We subsequently measure the performance of the machine learning classifier to make sure that it will not misdiagnose the diseases. The first two steps, which are R-to-R extraction and preprocessing, have been successfully implemented with satisfactory results.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.670-676
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• 2014

The objective of this research is to utilize the ultrasonic method to analyze the property of partial discharge (PD) which is generated by the winding of the insulation stator in the generator. Therefore, a PD measurement system is built based on ultrasonic and virtual instruments. Three types of PD models (internal PD model, surface PD model and slot PD model) have been constructed. With the analysis of these experimental results, this research has identified the ultrasonic signals of the discharges which were produced by three types of PD models. This analysis shows the different features among these PD types. Both the time domain and frequency domain of the ultrasonic signals are obviously different. In addition, an experiment based on a large rotating machine has been done to analyze ultrasonic noises. The result indicates that the ultrasonic noises can be wiped off by the filters and algorithms. The application of this system is convenient for the detection of early signs of insulation failure, which is an effective method for diagnosis of insulation faults.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.212-220
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• 2023

Ontologies are knowledge containers in which information about a specified domain can be shared and reused. An event happens within a specific time and place and in which some actors engage and show specific action features. The fact is that several ontology models are based on events called Event-Based Models, where the event is an individual entity or concept connected with other entities to describe the underlying ontology because the event can be composed of spatiotemporal extents. However, current event-based ontologies are inadequate to bridge the gap between spatiotemporal extents and participants to describe a specific domain event. This paper reviews, describes, and compares the existing event-based ontologies. The paper compares and contrasts various ways of representing the events and how they have been modelled, constructed, and integrated with the ontologies. The primary criterion for comparison is based on the events' ability to represent spatial and temporal extent and the participants in the event.