• Earthquakes and Structures
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• v.17 no.2
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• pp.175-189
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• 2019

The aim of the study is to show the results of complex shaking table experimental investigation focused on the response of two models of cylindrical steel tanks under mining tremors and moderate earthquakes, including the aspects of diagnosis of structural damage. Firstly, the impact and the sweep-sine tests have been carried out, so as to determine the dynamic properties of models filled with different levels of liquid. Then, the models have been subjected to seismic and paraseismic excitations. Finally, one fully filled structure has been tested after introducing two different types of damages, so as to verify the method of damage diagnosis. The results of the impact and the sweep-sine tests show that filling the models with liquid leads to substantial reduction in natural frequencies, due to gradually increasing overall mass. Moreover, the results of sweep-sine tests clearly indicate that the increase in the liquid level results in significant increase in the damping structural ratio, which is the effect of damping properties of liquid due to its sloshing. The results of seismic and paraseismic tests indicate that filling the tank with liquid leads initially to considerable reduction in values of acceleration (damping effect of liquid sloshing); however, beyond a certain level of water filling, this regularity is inverted and acceleration values increase (effect of increasing total mass of the structure). Moreover, comparison of the responses under mining tremors and moderate earthquakes indicate that the power amplification factor of the mining tremors may be larger than the seismic power amplification factor. Finally, the results of damage diagnosis of fully filled steel tank model indicate that the forms of the Fourier spectra, together with the frequency and power spectral density values, can be directly related to the specific type of structural damage. They show a decrease in the natural frequencies for the model with unscrewed support bolts (global type of damage), while cutting the welds (local type of damage) has resulted in significant increase in values of the power spectral density for higher vibration modes.

• Structural Engineering and Mechanics
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• v.71 no.1
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• pp.57-63
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• 2019

During the last two decades, much joint research regarding vibration based methods has been done, leading to developing various algorithms and techniques. These algorithms and techniques can be divided into modal methods and signal methods. Although modal methods have been widely used for health monitoring and damage detection, signal methods due to higher efficiency have received considerable attention in various fields, including aerospace, mechanical and civil engineering. Signal-based methods are derived directly from the recorded responses through signal processing algorithms to detect damage. According to different signal processing techniques, signal-based methods can be divided into three categories including time domain methods, frequency domain methods, and time-frequency domain methods. The frequency domain methods are well-known and interest in using them has increased in recent years. To determine dynamic behaviours, to identify systems and to detect damages of bridges, different methods and algorithms have been proposed by researchers. In this study, a new algorithm to detect seismic damage in the bridge's piers is suggested. To evaluate the algorithm, an analytical model of a bridge with simple spans is used. Based on the algorithm, before and after damage, the bridge is excited by a sine force, and the piers' responses are measured. The dynamic specifications of the bridge are extracted by Power Spectral Density function. In addition, the Least Square Method is used to detect damage in the bridge's piers. The results indicate that the proposed algorithm can identify the seismic damage effectively. The algorithm is output-only method and measuring the excitation force is not needed. Moreover, the proposed approach does not need numerical models.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.1
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• pp.1-9
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• 2023

The skin structure of sensor pod mounted on the exterior of aircraft can be exposed to the acoustic dynamic load and static load such as aerodynamic pressure and inertial load during flight. Fatigue life prediction of structural model under acoustic load should be performed and also differential stiffness of model modified by static load should be considered. The acoustic noise test spectrum of MIL-STD-810G was applied to the structural model and the stress response power spectral density (PSD) was calculated. The frequency response analysis was performed with or without prestress induced by inplane static load, and the response spectrum was compared. Time series data was generated using the calculated PSD, and the time and frequency domain fatigue life were predicted and compared. The variation of stress response spectrum due to static load and predicted fatigue life according to the different structural model considering mean stress were examined and decreasing fatigue life was observed in the model with prestress of compressive static load.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.84-90
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• 2022

Machine tool state monitoring is a process that automatically detects the states of machine. In the manufacturing process, the efficiency of machining and the quality of the product are affected by the condition of the tool. Wear and broken tools can cause more serious problems in process performance and lower product quality. Therefore, it is necessary to develop a system to prevent tool wear and damage during the process so that the tool can be replaced in a timely manner. This paper proposes a method for diagnosing five tool states using a deep learning-based hierarchical convolutional neural network to change tools at the right time. The one-dimensional acoustic signal generated when the machine cuts the workpiece is converted into a frequency-based power spectral density two-dimensional image and use as an input for a convolutional neural network. The learning model diagnoses five tool states through three hierarchical steps. The proposed method showed high accuracy compared to the conventional method. In addition, it will be able to be utilized in a smart factory fault diagnosis system that can monitor various machine tools through real-time connecting.

• Journal of Korea Water Resources Association
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• v.34 no.6
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• pp.735-743
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• 2001

CN procedure has been proven to be useful method for evaluating the effects of changes in land-use and treatment on hydrology. In this study, the use of Landsat multi-spectral image was investigated for analyzing the land-use distribution. From the Landsat data, forest areas were classified according to the density of trees. Watershed CN's were calculated to analyze the effects of the density of trees and soil cover types on direct runoff. According to the results, the density of trees had a little effect while soil cover types had a large effect on CN, From the comparison of estimated runoffs from CN method with observed runoffs, detailed soil cover map provides improved results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.647-669
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• 2016

Multiple attribute for decision making including user preference will increase the complexity of route selection process. Various approaches have been proposed to solve the optimal route selection problem. In this paper, multi attribute decision making (MADM) algorithms such as Simple Additive Weighting (SAW), Weighted Product Method (WPM), Analytic Hierarchy Process (AHP) method and Total Order Preference by Similarity to the Ideal Solution (TOPSIS) methods have been proposed for acoustic signature based optimal route selection to facilitate user with better quality of service. The traffic density state conditions (very low, low, below medium, medium, above medium, high and very high) on the road segment is the occurrence and mixture weightings of traffic noise signals (Tyre, Engine, Air Turbulence, Exhaust, and Honks etc) is considered as one of the attribute in decision making process. The short-term spectral envelope features of the cumulative acoustic signals are extracted using Mel-Frequency Cepstral Coefficients (MFCC) and Adaptive Neuro-Fuzzy Classifier (ANFC) is used to model seven traffic density states. Simple point method and AHP has been used for calculation of weights of decision parameters. Numerical results show that WPM, AHP and TOPSIS provide similar performance.

• Wind and Structures
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• v.6 no.6
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• pp.437-450
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• 2003

In this paper, the structural random response due to the turbulent boundary layer excitation is investigated. Using the mode shapes and natural frequencies of an undamped structural operator, a fully analytical model has been assembled. The auto and cross-spectral densities of kinematic quantities are so determined through exact analytical expansions. In order to reduce the computational costs associated with the needed number of modes, it has been tested an innovative methodology based on a scaling procedure. In fact, by using a reduced spatial domain and defining accordingly an augmented artificial damping, it is possible to get the same energy response with reduced computational costs. The item to be checked was the power spectral density of the displacement response for a flexural simply supported beam; the very simple structure was selected just to highlight the main characteristics of the technique. In principle, it can be applied successfully to any quantity derived from the modal operators. The criterion and the rule of scaling the domain are also presented, investigated and discussed. The obtained results are encouraging and they allow thinking successfully to the definition of procedure that could represent a bridge between modal and energy methods.

• Journal of The Korean Astronomical Society
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• v.36 no.spc1
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• pp.29-36
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• 2003

Quiescent solar radiation, at microwave spectral regime, is dominated by gyroresonant and thermal Bremsstrahlung radiations from hot electrons residing in solar active region corona. These radiations are known to provide excellent diagnostics on the coronal temperature, density, and magnetic field, provided that spatially resolved spectra are available from observations. In this paper we present an imaging spectroscopy implemented for a bipolar active region, AR 7912, using the multifrequency interferometric data from the Owens Valley Solar Array (OVSA), as processed with a new imaging technique, so-called Spatio-Spectral Maximum Entropy Method (SSMEM). From the microwave maps at 26 frequencies in the range of 1.2-12.4 GHz at both right- and left-circular polarizations, we construct spatially resolved brightness spectra in every reconstructed pixel of about 2 arcsec interval. These spectra allowed us to determine 2-D distribution of electron temperature, magnetic field of coronal base, and emission measure at the coronal base above the active region. We briefly compare the present result with existing studies of the coronal active regions.

• Journal of Biomedical Engineering Research
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• v.28 no.6
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• pp.750-755
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• 2007

The linear SEF (Spectral Edge Frequency) parameter and spectrum analysis method can not reflect the non-linear of EEG. This method can not contribute to acquire real time analysis and obtain a high confidence in the clinic due to low discrimination. To solve the problems, the development of a new index is carried out using the bispectrum analyzing the EEG including the non-linear characteristic. At the bispectrum analysis of the 2 dimension, the most significant's power spectrum density peaks appeared much at the specific area in awake and anesthesia state. Because many peaks are showed at the specific area in the frequency coordinate, these points are used to create the new index. Range of the index is 0-100. At the anesthesia, the index is 20-50 and at the awake, the index is 90-60. New index can discriminate the awake and anesthesia state.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.8
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• pp.1951-1962
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• 1993

The creation process of a typical machined surface is treated here as a dynamic system. An investigation is carried out to establish a relationship between the characteristics of cutting force fluctuations that cause vibration response of the tool-workpiece system and the formation of surface in face cutting by sintered carbide cutting tool. Cutting force is measured and analyzed in frequency domain. The power spectral densities of cutting force give a useful information in surface generation and it can be used to find out the control factor of surface roughness. The terms, PSD ratio & Normalized spindle frequency PSD, are defined and when the value of power in spindle frequency is absolutely little but relatively large, it is obtained high accuracy surface roughness. The aim of this research is to find surface profile by measured and analyzed cutting force signals. The simulation of surface generation gives the comprechension of its mechanism and help to predict and control the surface quality. In this study, it is suggested what informations about surface generation can be acquired from the cuttuing force signal and an way of generating a better surface.