• Journal of the Korean Society for Railway
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• v.6 no.2
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• pp.81-86
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• 2003

This study proposes the neural network simulator for the integrity evaluation of weld zone in railway rails. For these purposes, the ultrasonic signals for defects(crack) of weld zone in frames are acquired in the type of time series data and echo strength. The detection of the natural defects in railway truck is performed using the characteristics of echodynamic pattern in ultrasonic signal. And then their applications evaluated feature extraction based on the time-frequency-attractor domain(peak to peak, rise time, rise slope, fall time, fall slope, pulse duration, power spectrum, and bandwidth) and attractor characteristics (fractal dimension and attractor quadrant) etc. The constructed neural network simulator agrees fairly well with the measured results of test block(defect location, beam propagation distance, echo strength, etc). The Proposed neural network simulator in this study can be used for the integrity evaluation of weld zone in railway rails.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.6
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• pp.241-246
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• 2021

In this paper, we developed the evaluation system for the active noise control so that the algorithms can be easily evaluated in real-time on the system. We implemented the active noise controller based on a single-chip with only additional op-amps for signal conditioning because the TMS320C280049 MCU includes almost all necessary peripherals for the active noise controller. Due to the difficulty in testing algorithms on embedded-type hardware unlike in computer simulation, we also developed GUI-based evaluation software which makes it simple to test algorithms on the hardware. Using the GUI software, we can optimize the parameters of the algorithms with ease in a specific noise environment because the parameters can be adjusted in real-time when the algorithm is running on the hardware.

• ETRI Journal
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• v.36 no.5
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• pp.772-782
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• 2014

Two-microphone binary mask speech enhancement (2mBMSE) has been of particular interest in recent literature and has shown promising results. Current 2mBMSE systems rely on spatial cues of speech and noise sources. Although these cues are helpful for directional noise sources, they lose their efficiency in diffuse noise fields. We propose a new system that is effective in both directional and diffuse noise conditions. The system exploits two features. The first determines whether a given time-frequency (T-F) unit of the input spectrum is dominated by a diffuse or directional source. A diffuse signal is certainly a noise signal, but a directional signal could correspond to a noise or speech source. The second feature discriminates between T-F units dominated by speech or directional noise signals. Speech enhancement is performed using a binary mask, calculated based on the proposed features. In both directional and diffuse noise fields, the proposed system segregates speech T-F units with hit rates above 85%. It outperforms previous solutions in terms of signal-to-noise ratio and perceptual evaluation of speech quality improvement, especially in diffuse noise conditions.

• International Journal of Control, Automation, and Systems
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• v.6 no.6
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• pp.818-827
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• 2008

In this paper, we propose a new noise estimation and reduction algorithm for stationary and nonstationary noisy environments. This approach uses an algorithm that classifies the speech and noise signal contributions in time-frequency bins. It relies on the ratio of the normalized standard deviation of the noisy power spectrum in time-frequency bins to its average. If the ratio is greater than an adaptive estimator, speech is considered to be present. The propose method uses an auto control parameter for an adaptive estimator to work well in highly nonstationary noisy environments. The auto control parameter is controlled by a linear function using a posteriori signal to noise ratio(SNR) according to the increase or the decrease of the noise level. The estimated clean speech power spectrum is obtained by a modified gain function and the updated noisy power spectrum of the time-frequency bin. This new algorithm has the advantages of much more simplicity and light computational load for estimating the stationary and nonstationary noise environments. The proposed algorithm is superior to conventional methods. To evaluate the algorithm's performance, we test it using the NOIZEUS database, and use the segment signal-to-noise ratio(SNR) and ITU-T P.835 as evaluation criteria.

• International journal of advanced smart convergence
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• v.3 no.1
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• pp.20-24
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• 2014

To compare body signal, was designed the F-B function system on the body movement for the comfortable state. To detect subject of the normal state, was decided on the base of physical signal in the body movement. There are to detect the condition of Vision, Vestibular, Somatosensory and CNS. Vision condition was verified a variation of greater average (Vi-${\Phi}_{AVG-AVG}$) was presented slightly greater at $17.424{\pm}9.65$ unit. Vestibular condition was identified a variation of slightly greater average (Ve-${\Phi}_{AVG-AVG}$) was presented at $9.068{\pm}1.478$ unit. Somatosensory condition was checked a variation of smaller average (So-${\Phi}_{AVG-AVG}$) was presented slightly smaller at $2.79{\pm}0.419$ unit. CNS condition was confirmed a variation of diminutive smaller average (C-${\Phi}_{AVG-AVG}$) was presented slightly larger at $0.557{\pm}0.153$ unit. As the model depends on the F-B function system of body movement, average values of these perturbation were computed F-B function comparison data. These systems will be to infer a data algorithm and a data signal processing system for the evaluation of the stability.

• Journal of Korean Society of Transportation
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• v.12 no.1
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• pp.55-73
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• 1994

The objective of this research is to evaluate the pedestrian signal time involving green and flashing green times. The minimum pedestrian green indication should give time for pedestrian to start crossing safely, and the flashing green indication should give time to complete the crossing. An average pedestrian crossing speed of 1.1(m/s) was estimated by analyzing the field data which was slower than the 1.2(m/s) currently used. Furthermore, the study proposed that design speed for the flashing green time should be slow speed for considerations pedestrian safety, not the average speed. The 0.78-1.01(m/s) of pedestrian speed was estimated at the elementary school areas that indicated 0.2(m/s) slower than the other areas. The pedestrian starting time (perception/reaction time) and time headway from front to back of herd was estimated to determine minimum pedestrian green time. the pedestrian starting time was estimated to determine minimum pedestrian green time. The pedestrian starting time was ranged 2.52-4.29 seconds. The time interval between the pedestrian rows was found to be 1.25-1.86 seconds, which declines as the pedestrian rows increases, The equation to calculate the pedestrian signal, which declines as the pedestrian rows increases. The equation to calculate the pedestrian signal time is proposed using the pedestrian starting time, the time interval between the pedestrian rows, and pedestrian crossing speed given area types (commercial, business, mixed, and elementary school areas), number of both-directional pedestrians for a cycle, crosswalk length and width.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.12
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• pp.1544-1553
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• 1999

Recently, ultrasonic testing techniques have been widely used in the evaluation of the quality of metal. In this experiment, six heat-treated temperature of specimen have been considered : 0, 1200, 1250, 1300, 1350 and 1387$^{\circ}C$. As heat-treated temperature increases, the grain size of stainless steel also increases and then, eventually make it destroy. In this paper, a pattern recognition method is proposed to identify the heat-treated temperature of metals by evidence accumulation based on artificial intelligence with multiple feature parameters; difference absolute mean value(DAMV), variance(VAR), mean frequency(MEANF), auto regressive model coefficient(ARC), linear cepstrum coefficient(LCC) and adaptive cepstrum vector(ACV). The grain signal pattern recognition is carried out through the evidence accumulation procedure using the distances measured with reference parameters. Especially ACV is superior to the other parameters. The results (96% successful pattern classification) are presented to support the feasibility of the suggested approach for ultrasonic grain signal pattern recognition.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.1 s.178
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• pp.64-71
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• 2006

Ultraprecision machining and MEMS technology have been taken more and more important position in machining of microparts. Micro endmilling is one of the prominent technology that has wide spectrum of application field ranging from macro parts to micro products. Also, the method of micro-grooving using micro endmill is used widely owing to many merit, but has problems of precision and quality of products due to tool wear and tool fracture. This investigation deals with state monitoring using acoustic emission(AE) signal in the micro-grooving. Characteristic evaluation of AE raw signal, AE hit and frequency analysis for condition monitoring is presented. Also, the feature extraction of AE signal directly related to machining process is executed. Then, the distinctive micro endmill state according to the each tool condition is classified by the fuzzy C-means algorithm.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.9
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• pp.1103-1109
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• 2011

This research quantitatively confirmed an acoustical anisotropy that exists in a pure titanium plate from the signal of ultrasonic flow detection and suggested a new way to evaluate the acoustical anisotropy by inputting acquired characteristic of ultrasound signal into the neutral network. Using the fact with the suggested method that the characteristic of ultrasound signal is shown differently depending on the pure titanium plate's rolling direction, the neural network was constructed by extracting the characteristic that can decide each direction of $0^{\circ}$, $45^{\circ}$, and $90^{\circ}$ with waveform analysis program. As a result of inputting the characteristic of ultrasound signal acquired from a random rolling direction into the neural network that was built like this, it showed a pattern recognition rate higher than 95% on directions of $0^{\circ}$, $45^{\circ}$, $90^{\circ}$.

• International Journal of Aeronautical and Space Sciences
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• v.17 no.4
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• pp.467-475
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• 2016

Ultrasonic propagation imaging (UPI) has shown great potential for detection of impairments in complex structures and can be used in wide range of non-destructive evaluation and structural health monitoring applications. The software implementation of such algorithms showed a tendency in time-consumption with increment in scan area because the processor shares its resources with a number of programs running at the same time. This issue was addressed by using field programmable gate arrays (FPGA) that is a dedicated processing solution and used for high speed signal processing algorithms. For this purpose, we need an independent and flexible block of logic which can be used with continuously evolvable hardware based on FPGA. In this paper, we developed an FPGA-based ultrasonic propagation imaging system, where FPGA functions for both data acquisition system and real-time ultrasonic signal processing. The developed UPI system using FPGA board provides better cost-effectiveness and resolution than digitizers, and much faster signal processing time than CPU which was tested using basic ultrasonic propagation algorithms such as ultrasonic wave propagation imaging and multi-directional adjacent wave subtraction. Finally, a comparison of results for processing time between a CPU-based UPI system and the novel FPGA-based system were presented to justify the objective of this research.