• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.8
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• pp.1752-1758
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• 2010

A FM-CW radar is used for the various purposes as a remote sensing device since it has the advantages of the relatively simple implementation and the low probability of signal interception. A FM-CW radar uses the same frequency modulated continuous wave for both transmission and demodulation. Therefore, the received beat frequency represents the range and Doppler information of targets. However, using the conventional FFT method, the degree of accuracy and resolution in the spectrum estimation can be seriously degraded in the detection and tracking of fast moving targets because of the short dwell time. Therefore, in this paper, the model parameter estimation methods called as an autoregressive method is applied to overcome these problems and showed that the improved accuracy and resolution can be obtained for the target range and velocity estimation.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.5
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• pp.315-321
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• 2023

Long-span bridges are flexible structures with low natural frequencies and damping ratios, making them susceptible to vibrational serviceability problems. However, the current design guideline of South Korea assumes a uniform threshold of wind speed or vibrational amplitude to assess the occurrence of harmful vibrations, potentially overlooking the complex vibrational patterns observed in long-span bridges. In this study, we propose a pointwise vortex-induced vibration (VIV) detection method using a deep-learning-based signalsegmentation model. Departing from conventional supervised methods of data acquisition and manual labeling, we synthesize training data by generating sinusoidal waves with an envelope to accurately represent VIV. A Fourier synchrosqueezed transform is leveraged to extract time-frequency features, which serve as input data for training a bidirectional long short-term memory model. The effectiveness of the model trained on synthetic VIV data is demonstrated through a comparison with its counterpart trained on manually labeled real datasets from an actual cable-supported bridge.

• Journal of the Korean Geotechnical Society
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• v.32 no.8
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• pp.5-14
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• 2016

The leakage of water under sewer pipelines is one of main sources of sinkholes in urban areas. We performed laboratory model tests to investigate the presence of cavities using impact-echo method, which is a nondestructive test method. To simulate a concrete sewer pipe, a thin concrete plate was built and placed over container filled with sand. The cavity was modeled as an extruded polystyrene foam box. Two sets of tests were performed, one over sand and the other on cavity. A new impact device was developed to apply a consistent high frequency impact load on the concrete plate, thereby increasing the reliability of the test procedure. The frequency and transient characteristics of the measured reflected waveforms were analyzed via fast Fourier transform and short time Fourier spectrum. It was shown that the shapes of Fourier spectra are very similar to one another, and therefore cannot be used to predict the presence of cavity. A new index, termed resonance duration, is defined to record the time of vibration exceeding a prescribed intensity. The results showed that the resonance duration is a more effective parameter for predicting the presence of a cavity. A value of the resonance period was proposed to estimate the presence of cavity. Further studies using various soil types and field tests are warranted to validate the proposed approach.

• The Journal of the Acoustical Society of Korea
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• v.38 no.1
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• pp.106-113
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• 2019

In this paper, we propose an algorithm to classify the received active pulse when the active sonar system is operated as a non-cooperative mode. The proposed algorithm uses CNN (Convolutional Neural Networks) which shows good performance in various fields. As an input of CNN, time frequency analysis data which performs STFT (Short Time Fourier Transform) of the received signal is used. The CNN used in this paper consists of two convolution and pulling layers. We designed a database based neural network and a pulse feature based neural network according to the output layer design. To verify the performance of the algorithm, the data of 3110 CW (Continuous Wave) pulses and LFM (Linear Frequency Modulated) pulses received from the actual ocean were processed to construct training data and test data. As a result of simulation, the database based neural network showed 99.9 % accuracy and the feature based neural network showed about 96 % accuracy when allowing 2 pixel error.

• Korean Journal of Materials Research
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• v.33 no.7
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• pp.315-322
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• 2023

In the present work, we address the new route for the green synthesis of manganese dioxide (MnO₂) by an innovative method named the solution plasma process (SPP). The reaction mechanism of both colloidal and nanostructured MnO₂ was investigated. Firstly, colloidal MnO₂ was synthesized by plasma discharging in KMnO₄ aqueous solution without any additives such as reducing agents, acids, or base chemicals. As a function of the discharge time, the purple color solution of MnO₄^- (oxidation state +7) was changed to the brown color of MnO₂ (oxidation state +4) and then light yellow of Mn²⁺ (oxidation state +2). Based on the UV-vis analysis we found the optimal discharging time for the synthesis of stable colloidal MnO₂ and also reaction mechanism was verified by optical emission spectroscopy (OES) analysis. Secondly, MnO₂ nanoparticles were synthesized by SPP with a small amount of reducing sugar. The precipitation of brown color was observed after 8 min of plasma discharge and then completely separated into colorless solution and precipitation. It was confirmed layered type of nanoporous birnessite-MnO₂ by X-ray powder diffraction (XRD), fourier-transform infrared spectroscopy (FT-IR), and electron microscopes. The most important merits of this approach are environmentally friendly process within a short time compared to the conventional method. Moreover, the morphology and the microstructure could be controllable by discharge conditions for the appropriate potential applications, such as secondary batteries, supercapacitors, adsorbents, and catalysts.

• Journal of the korean academy of Pediatric Dentistry
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• v.31 no.2
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• pp.273-279
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• 2004

Recently, newly developed single high-intensity LED curing lights for composite resins are claimed to have a higher intensity than previous LED curing lights and to results in optimal properties and short curing time. The purpose of this study was to determine the curing effectiveness of the curing units and to evaluate the relationship between the degree of polymerization and distance from curing light tip end to resin surface. One composite resin was tested(Filtek Z250). Thin film specimens were cured with a LED curing unit(Elipar Freelight 2, 10s), Plasma Arc curing unit(Flipo, 6s), Halogen curing light(XL3000, 20s) at four curing light tip to the resin surface(0mm, 2mm, 4mm, 6mm). Degree of conversion of composite resins were determined by a Fourier Transform Infrared Spectrometer(FTIR). From the present study, the following results were obtained. 1. In all curing units, relative light intensity was significantly decreased according to the increase of distance of light tip to the resin surface(p<0.05). LED curing units showed a higher percentile decrease in intensity than other curing units. 2. In all curing units, degree of conversion was decreased as increase of the distance but no statistically significant difference(p>0.05) except between 4mm and 6mm(p<0.05). 3. When comparing degree of conversion of light curing units at each distance(0mm, 2mm, 4mm, 6mm), LED curing light had a higher degree of conversion than plasma arc and halogen curing lights at 0, 2, 4mm(p<0.05). At 6mm, there was a no significant difference among the curing units(p>0.05).

• Smart Structures and Systems
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• v.29 no.4
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• pp.625-640
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• 2022

Maglev rail joints are vital components serving as connections between the adjacent F-type rail sections in maglev guideway. Damage to maglev rail joints such as bolt looseness may result in rough suspension gap fluctuation, failure of suspension control, and even sudden clash between the electromagnets and F-type rail. The condition monitoring of maglev rail joints is therefore highly desirable to maintain safe operation of maglev. In this connection, an online damage detection approach based on three-dimensional (3D) convolutional neural network (CNN) and time-frequency characterization is developed for simultaneous detection of multiple damage of maglev rail joints in this paper. The training and testing data used for condition evaluation of maglev rail joints consist of two months of acceleration recordings, which were acquired in-situ from different rail joints by an integrated online monitoring system during a maglev train running on a test line. Short-time Fourier transform (STFT) method is applied to transform the raw monitoring data into time-frequency spectrograms (TFS). Three CNN architectures, i.e., small-sized CNN (S-CNN), middle-sized CNN (M-CNN), and large-sized CNN (L-CNN), are configured for trial calculation and the M-CNN model with excellent prediction accuracy and high computational efficiency is finally optioned for multiple damage detection of maglev rail joints. Results show that the rail joints in three different conditions (bolt-looseness-caused rail step, misalignment-caused lateral dislocation, and normal condition) are successfully identified by the proposed approach, even when using data collected from rail joints from which no data were used in the CNN training. The capability of the proposed method is further examined by using the data collected after the loosed bolts have been replaced. In addition, by comparison with the results of CNN using frequency spectrum and traditional neural network using TFS, the proposed TFS-CNN framework is proven more accurate and robust for multiple damage detection of maglev rail joints.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.374-379
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• 2007

This paper focuses on the improvement of fault detection algorithm in FRTU(feeder remote terminal unit) on the feeder of distribution power system. FRTU is applied to fault detection schemes for phase fault and ground fault. Especially, cold load pickup and inrush restraint functions distinguish the fault current from the normal load current. FRTU shows FI(Fault Indicator) when the fault current is over pickup value or inrush current. STFT(Short Time Fourier Transform) analysis provides the frequency and time Information. FCM(Fuzzy C-Mean clustering) algorithm extracts characteristics of harmonics. The neural network system as a fault detector was trained to distinguish the inruih current from the fault status by a gradient descent method. In this paper, fault detection is improved by using FCM and neural network. The result data were measured in actual 22.9kV distribution power system.

• Nuclear Engineering and Technology
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• v.5 no.4
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• pp.265-278
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• 1973

The incoherent neutron scattering cross section of molecular liquids is analyzed using a damping function model for correlation functions of molecular translations and rotations. The present approach is different from recent works in that the scattering function is evaluated directly, not through the intermediate scattering function. The damping fuction is determined from a simple relation between its long-wavelength limit and the generalized frequency distribution function, and translation-rotation couplings are assumed to be neglected. A physical model is used for the translational motions of center-of-mass of a molecule, including properly its short-time and long-time behaviors. A simple model for the rotational motions is suggested which relates the damping function to the Fourier transform of the dipole correlation function, or equivalently, the infrared vibrational absorption spectrum. Theoretical absolute scattering intensities are computed for liquid methane and shown to be in satisfactory agreement with both thermal and cold neutron measurements.

• Structural Engineering and Mechanics
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• v.83 no.6
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• pp.709-727
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• 2022

Fastening systems have a significant role in the response of railway slab track systems. Although experimental tests indicate nonlinear behavior of fastening systems, they have been simulated as a linear spring-dashpot element in the available literature. In this paper, the influence of the nonlinear behavior of fastening systems on the slab track response was investigated. In this regard, a nonlinear model of vehicle/slab track interaction, including two commonly used fastening systems (i.e., RFFS and RWFS), was developed. The time history of excitation frequency of the fastening system was derived using the short time Fourier transform. The model was validated, using the results of a comprehensive field test carried out in this study. The frequency response of the track was studied to evaluate the effect of excitation frequency on the railway track response. The results obtained from the model were compared with those of the conventional linear model of vehicle/slab track interaction. The effects of vehicle speed, axle load, pad stiffness, fastening preload on the difference between the outputs obtained from the linear and nonlinear models were investigated through a parametric study. It was shown that the difference between the results obtained from linear and nonlinear models is up to 38 and 18 percent for RWFS and RFFS, respectively. Based on the outcomes obtained, a nonlinear to linear correction factor as a function of vehicle speed, vehicle axle load, pad stiffness and preload was derived. It was shown that consideration of the correction factor compensates the errors caused by the assumption of linear behavior for the fastening systems in the currently used vehicle track interaction models.