• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.3
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• pp.41-48
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• 2012

In this paper, we propose a method of edge detection for noisy image. The proposed method uses a progressive filter for noise reduction and a Sobel operator for edge detection. The progressive filter combines a median filter and a modified rational filter. The proposed method for noise reduction adjusts rational filter direction according to an edge in the image which is obtained by median filtering. Our method effectively attenuates the noise while preserving the image details. Edge detection is performed by a Sobel operator. This operator can be implemented by integer operation and is therefore relatively fast. Our proposed method not only preserves edge, but also reduces noise in uniform region. Thus, edge detection is well performed. Our proposed method could improve results using further developed Sobel operator. Experimental results show that our proposed method has better edge detection with correct positions than those by existing median and rational filtering methods for noisy image.

• Progress in Medical Physics
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• v.33 no.2
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• pp.11-24
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• 2022

X-ray detection has widely been applied in medical diagnostics, security screening, nondestructive testing in the industry, etc. Medical X-ray imaging procedures require digital flat detectors operating with low doses to reduce radiation health risks. Recently, metal halide perovskites (MHPs) have shown great potential in high-performance X-ray detection because of their attractive properties, such as strong X-ray absorption, high mobility-lifetime product, tunable bandgap, low-temperature fabrication, near-unity photoluminescence quantum yields, and fast photoresponse. In this paper, we review and introduce the development status of new perovskite X-ray detectors and imaging, which have emerged as a new promising high-sensitivity X-ray detection technology. We discuss the latest progress and future perspective of MHP-based X-ray detection in medical imaging. Finally, we compare the conventional detection methods with quantum-enhanced detection, pointing out the challenges and perspectives for future research directions toward perovskite-based X-ray applications.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.123-130
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• 2022

During the past decades, detection of gear defects remains as a major problem, especially when the gears are subject to non-stationary phenomena. The idea of this paper is to mixture a multilevel wavelet transform with a fast EMD decomposition in order to early detect gear defects. The sensitivity of a kurtosis is used as an indicator of gears defect burn. When the gear is damaged, the appearance of a crack on the gear tooth disrupts the signal. This is due to the presence of periodic pulses. Nevertheless, the existence of background noise induced by the random excitation can have an impact on the values of these temporal indicators. The denoising of these signals by multilevel wavelet transform improves the sensitivity of these indicators and increases the reliability of the investigation. Finally, a defect diagnosis result can be obtained after the fast transformation of the EMD. The proposed approach consists in applying a multi-resolution wavelet analysis with variable decomposition levels related to the severity of gear faults, then a fast EMD is used to early detect faults. The proposed mixed methods are evaluated on vibratory signals from the test bench, CETIM. The obtained results have shown the occurrence of a teeth defect on gear on the 5th and 8th day. This result agrees with the report of the appraisal made on this gear system.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.505-510
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• 2024

Since rolling contact fatigue cracks can always occur on the rail surface, which is the contact surface between wheels and rails, railway rails require thorough inspection and diagnosis to thoroughly inspect the condition of the cracks and prevent breakage. Recent detailed guidelines on the performance evaluation of track facilities present the requirements for methods and procedures for track performance evaluation. However, diagnosing and grading rail surface damage mainly relies on external inspection (visual inspection), which inevitably relies on qualitative evaluation based on the subjective judgment of the inspector. Therefore, in this study, we conducted a deep learning model study for rail surface defect detection using Fast R-CNN. After building a dataset of rail surface defect images, the model was tested. The performance evaluation results of the deep learning model showed that mAP was 94.9%. Because Fast R-CNN has a high crack detection effect, it is believed that using this model can efficiently identify rail surface defects.

• International Journal of Oral Biology
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• v.38 no.4
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• pp.169-173
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• 2013

HyPer is the genetically encoded biosensor of intracellular hydrogen peroxide ($H_2O_2$), the most stable of the reactive oxygen species (ROS) generated by living cells. HyPer has a high sensitivity and specificity for detecting intracellular $H_2O_2$ by confocal laser microscopy. However, it was not known whether high speed ratiometric imaging of $H_2O_2$ by HyPer is possible. We thus investigated the sensitivity of HyPer in detecting changes to the intracellular $H_2O_2$ levels in HEK293 and PC12 cells using a microfluorometer imaging system. Increase in the HyPer ratio were clearly evident on stimulations of more than $100{\mu}M$ $H_2O_2$ and fast changes in the HyPer ratio were observed on ratiometric fluorescent images after $H_2O_2$ treatment. These results suggest that HyPer is a potent biosensor of the fast temporal production of intracellular $H_2O_2$.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.3B
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• pp.130-137
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• 2005

Wireless communication environment is changing rapidly as we use new wireless communication technology such as WiBro to access high speed Internet. As a result, reliable data transmission using TCP is also expected to increase. Since TCP assumes that it is used in wired network, TCP suffers significant performance degradation over wireless network where packet losses are related to non-congestion loss. Especially RTO imposes a great performance degradation of TCP. In this paper, we analyze the loss recovery probabilities based on previous researches, and use simulation results of our algorithm to show that it prevents performance degradation by quickly detecting and recovery losses without RTO during fast recovery.

• Journal of the Korean Society of Tobacco Science
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• v.36 no.1
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• pp.26-33
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• 2014

Simultaneous determination of crop protection agents(CPAs) in food are done with multi-residue methods, which are composed of sample clean-up, concentration, chromatographic separation and detection. Stir Bar Sorptive Extraction(SBSE) technique is used for sample preparation of various analytes in several fields. The aim of this study was to develop a sensitive and fast method based on SBSE followed by thermal desorption - gas chromatography - mass spectrometry(TD - GC/MS) to determine CPAs in tobacco sample. For the analysis of tobacco sample prior to the SBSE method, solvent extraction or ultrasound-assisted solvent extraction was performed. methanol was used as the extraction solvent. The extract was then diluted with water. Finally, the sample was subjected to SBSE. A method for fast screening of crop protection agents in tobacco using SBSE-TD - GC/MS has been developed. About 17 CPAs including organochlorine, organophosphorous and others were identified and quantified. This method showed good linearity and high sensitivity for most of the target CPAs. The method was applied to the determination of CPAs at ng/mL levels in tobacco sample. This method is simple, rapid and may be applied in detection of other components.

• The Transactions of the Korean Institute of Power Electronics
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• v.20 no.1
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• pp.72-80
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• 2015

In distributed generation systems, a grid-connected inverter should operate with synchronization to grid voltage. Considering that synchronization requires the phase angle of grid voltage, a phase locked loop (PLL) scheme is often used. The synchronous reference frame phase locked loop (SRF-PLL) is generally known to provide reasonable performance under ideal grid voltage. However, this scheme indicates performance degradation under the harmonic distorted or unbalanced grid voltage condition. To overcome this limitation, this paper proposes a phase and harmonic detection method of grid voltage using fast Fourier transform (FFT). To reduce the calculation time of FFT algorithm, minimum sampling data is taken from the voltage measurement to determine the phase angle and the magnitude of harmonic components. An experimental test setup for a grid-connected inverter system has been constructed. By comparative simulations and experiments under various abnormal grid voltage conditions, the proposed scheme has been proven to effectively track the phase angle of the grid voltage.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2184-2186
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• 2001

The research presented in this paper focuses on a method for the detection of High Impedance Fault(HIF). The method will use the fast wavelet transform and neural network system. HIF on the multi-grounded three-phase four-wires primary distribution power system cannot be detected effectively by existing over current sensing devices. These paper describes the application of fast wavelet transform to the various HIF data. These data were measured in actual 22.9kV distribution system. Wavelet transform analysis gives the frequency and time-scale information. The neural network system as a fault detector was trained to discriminate HIF from the normal status by a gradient descent method. The proposed method performed very well by proving the right state when it was applied staged fault data and normal load mimics HIF, such as arc-welder.

• ETRI Journal
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• v.43 no.4
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• pp.603-616
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• 2021

Over the last few years, autonomous vehicles have progressed very rapidly. The odometry technique that estimates displacement from consecutive sensor inputs is an essential technique for autonomous driving. In this article, we propose a fast, robust, and accurate odometry technique. The proposed technique is light detection and ranging (LiDAR)-based direct odometry, which uses a spherical range image (SRI) that projects a three-dimensional point cloud onto a two-dimensional spherical image plane. Direct odometry is developed in a vision-based method, and a fast execution speed can be expected. However, applying LiDAR data is difficult because of the sparsity. To solve this problem, we propose an SRI generation method and mathematical analysis, two key point sampling methods using SRI to increase precision and robustness, and a fast optimization method. The proposed technique was tested with the KITTI dataset and real environments. Evaluation results yielded a translation error of 0.69%, a rotation error of 0.0031°/m in the KITTI training dataset, and an execution time of 17 ms. The results demonstrated high precision comparable with state-of-the-art and remarkably higher speed than conventional techniques.