• Journal of the Computational Structural Engineering Institute of Korea
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• v.20 no.3
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• pp.229-237
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• 2007

In this study, an artificial neural network (ANN)-based damage detection algorithm using acceleration signals is developed for real-time alarming locations of damage in beam-type structures. A new ANN-algorithm using output-only acceleration responses is designed tot damage detection in real time. The cross-covariance of two acceleration-signals measured at two different locations is selected as the feature representing the structural condition. Neural networks are trained lot potential loading Patterns and damage scenarios of the target structure for which its actual loadings are unknown. The feasibility and practicality of the proposed method are evaluated from laboratory-model tests on free-free beams for which accelerations were measured before and after several damage cases.

• The Korean Journal of Pain
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• v.28 no.2
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• pp.105-108
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• 2015

Background: The most definitive diagnosis of neck pain caused by facet joints can be obtained through cervical medial branch blocks (CMBBs). However, intravascular injections need to be carefully monitored, as they can increase the risk of false-negative blocks when diagnosing cervical facet joint syndrome. In addition, intravascular injections can cause neurologic deficits such as spinal infarction or cerebral infarction. Digital subtraction angiography (DSA) is a radiological technique that can be used to clearly visualize the blood vessels from surrounding bones or dense soft tissues. The purpose of this study was to compare the rate of detection of intravascular injections during CMBBs using DSA and static images obtained through conventional fluoroscopy. Methods: Seventy-two patients were included, and a total of 178 CMBBs were performed. The respective incidences of intravascular injections during CMBBs using DSA and static images from conventional fluoroscopy were measured. Results: A total of 178 CMBBs were performed on 72 patients. All cases of intravascular injections evidenced by the static images were detected by the DSAs. The detection rate of intravascular injections was higher from DSA images than from static images (10.7% vs. 1.7%, P < 0.001). Conclusions: According to these findings, the use of DSA can improve the detection rate of intravascular injections during CMBBs. The use of DSA may therefore lead to an increase in the diagnostic and therapeutic value of CMBBs. In addition, it can decrease the incidence of potential side effects during CMBBs.

• Smart Media Journal
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• v.6 no.4
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• pp.9-16
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• 2017

The railway point machine is an especially important component that changes the traveling direction of a train. Failure of the point machine may cause a serious railway accident. Therefore, early detection of failures is important for the management of railway condition monitoring systems. In this paper, we propose a noise-robust anomaly detection method in railway condition monitoring systems using sound data. First, we extract feature vectors from the spectrogram image of sound signals and convert it into modulation feature to ensure robust performance, and lastly, use the support vector machine (SVM) as an early anomaly detector of railway point machines. By the experimental results, we confirmed that the proposed method could detect the anomaly conditions of railway point machines with acceptable accuracy even under noisy conditions.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.539-542
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• 2003

A motor is the workhorse of our industry. The issues of preventive and condition-based maintenance, online monitoring, system fault detection, diagnosis, and prognosis are of increasing importance. Different internal motor faults (e.g., inter-turn short circuits, broken bearings, broken rotor bars) along with external motor faults (e.g., phase failure, mechanical overload, blocked rotor) are expected to happen sooner or later. This paper introduces the fault detection technique of induction motors based upon the stator current. The fault motors have rotor bar broken or rotor unbalance defect, respectively. The stator currents are measured by the current meters and stored by the time domain. The time domain is not suitable to represent the current signals, so the frequency domain is applied to display the signals. The Fourier Transformer is used for the conversion of the signal. After the conversion of the signals, the features of the signals have to be extracted by the signal processing methods like a wavelet analysis, a spectrum analysis, etc. The discovered features are entered to the pattern classification model such as a neural network model, a polynomial neural network, a fuzzy inference model, etc. This paper describes the fault detection results that use wavelet decomposition. The wavelet analysis is very useful method for the time and frequency domain each. Also it is powerful method to detect the features in the signals.

• Journal of Broadcast Engineering
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• v.18 no.3
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• pp.393-400
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• 2013

Hand shape recognition is a fundamental technique for implementing natural human-computer interaction. In this paper, we discuss a method for effectively detecting a hand region in Kinect-based hand shape recognition. Since Kinect is a camera that can capture color images and infrared images (or depth images) together, both images can be exploited for the process of detecting a hand region. That is, a hand region can be detected by finding pixels having skin colors or by finding pixels having a specific depth. Therefore, after analyzing the performance of each, we need a method of properly combining both to clearly extract the silhouette of hand region. This is because the hand shape recognition rate depends on the fineness of detected silhouette. Finally, through comparison of hand shape recognition rates resulted from different hand region detection methods in general environments, we propose a high-performance hand region detection method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.22 no.1
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• pp.81-90
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• 2004

Because satellite images include geometry distortions according to photographing conditions and sensor property, and their spatial and radiational resolution and spectrum resolution are different, it is so difficult to make a precise results of analysis. For comparing more than two images, the precise geometric corrections should be preceded because it necessary to eliminate systematic errors due to basic sensor information difference and non-systematic errors due to topographical undulations. In this study, we did sensor modeling using satellite sensor information to make a basic map of change detection for artificial topography. We eliminated the systematic errors which can be occurred in photographing conditions using GCP and DEM data. The Kompsat EOC images relief could be reduced by precise rectification method. Classifying images which was used for change detections by city and forest zone, the accuracy of the matching results are increased by 10% and the positioning accuracies also increased. The result of change detection using basic map could be used for basic data fur GIS application and topographical renovation.

• Journal of the Korean Society for Nondestructive Testing
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• v.25 no.4
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• pp.287-293
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• 2005

Ultrasonic guided waves are gaining increasing attention for the inspection of platelike and rodlike structures. At the same time, inspection methods that do not require contact with the test piece are being developed for advanced applications. This paper capitalizes on recent advances in the areas of guided wave ultrasonics and noncontact ultrasonics to demonstrate a superior method for the nondestructive detection of defects thinning simulating hidden corrosion in thin aluminum plates. The proposed approach uses EMAT(electro-magnetic acoustic transducer) for the noncontact generation and detection of guided waves. Interesting features in the dispersive behavior of selected guided modes are used for the detection of plate thinning. It is shown that mode cutoff measurements provide a qualitative detection of defects thinning. Measurement of the mode group velocity can be also used to quantify depth thinning.

• Korean Journal of Remote Sensing
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• v.34 no.5
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• pp.777-786
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• 2018

GF (Guided Filtering) is a representative image processing technique to effectively remove noise while preserving edge information in the digital image. In this paper, we proposed a unsupervised change detection method for the KOMPSAT-3 satellite image using the GF and evaluated its performance. In order to utilize GF for the unsupervised change detection, cross-sharpened images were generated based on GF, and CVA (Change Vector Analysis) was applied to the generated cross-sharpened images to extract the changed area in the multitemporal satellite imagery. Experimental results using KOMPSAT-3 satellite images showed that the proposed method can be effectively used to detect changed regions compared with CVA results based on existing cross-sharpened images.

• Korean Journal of Microbiology
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• v.47 no.2
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• pp.103-109
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• 2011

Pectobacterium carotovorum subsp. carotovorum is the causative agent of soft rot in crops such as potato and cabbages. Loop-mediated isothermal amplification (LAMP) is a simple DNA amplification method, as well as isothermal PCR technique. In this study, a new method for the rapid detection of Pectobacterium carotovorum subsp. carotovorum was developed using LAMP that named PCC-LAMP. Based on lytic murein transglycolase gene of Pectobacterium carotovorum subsp. carotovorum, a set of four primers for LAMP was designed. The optimal PCC-LAMP reaction temperature was established at $61^{\circ}C$. Under standard conditions, PCC-LAMP amplified $1{\times}10^3$ copies of clone PCC-pBX437 per reaction. Further, this method can also assay directly by SYBR Green I without electrophoresis. Amplification was not detected for five other bacterial species. In conclusion, PCC-LAMP may be a useful method for the detection Pectobacterium carotovorum subsp. carotovorum in the field.

• The Korean Journal of Mycology
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• v.41 no.1
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• pp.52-55
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• 2013

Phoma glomerata (Corda) Wollenw. & Hochapfel is a pathogenic fungus causing spot diseases of plant leaves and fruits. This fungus is important in plant quarantine of seedlings and fruits in Korea. The aim of this study was to develop a sensitive and effective diagnostic method for P. glomerata detection in imported plants. The fungal species-specific PCR primers were designed based on the nucleotide sequences of the translation elongation factor 1 alpha gene and their specificity and sensitivity were tested. The designed primers named as PhoGlo-F and PhoGlo-R amplified specifically a 170 bp sized DNA band of the target gene from the genomic DNA of P. glomerata. No amplicon was produced from genomic DNAs of 16 other Phoma spp. and reference fungal species tested. Moreover, PhoGlo-F/PhoGlo-R primers successfully worked with real-time PCR technique. The detection limit of DNA content by conventional and real-time PCR were 10 pg and 1pg of the genomic DNA of P. glomerata, respectively. We believed that the developed makers would be very useful for P. glomerata detection.