• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.1
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• pp.184-192
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• 2022

Valve internal leakage is caused by damage to the internal parts of the valve, resulting in accidents and shutdowns of the piping system. This study investigated the possibility of a real-time leak detection method using the acoustic emission (AE) signal generated from the piping system during the internal leakage of a butterfly valve. Datasets of raw time-domain AE signals were collected and postprocessed for each operation mode of the valve in a systematic manner to develop a data-driven model for the detection and classification of internal leakage, by applying machine learning algorithms. The aim of this study was to determine whether it is possible to treat leak detection as a classification problem by applying two classification algorithms: support vector machine (SVM) and convolutional neural network (CNN). The results showed different performances for the algorithms and datasets used. The SVM-based binary classification models, based on feature extraction of data, achieved an overall accuracy of 83% to 90%, while in the case of a multiple classification model, the accuracy was reduced to 66%. By contrast, the CNN-based classification model achieved an accuracy of 99.85%, which is superior to those of any other models based on the SVM algorithm. The results revealed that the SVM classification model requires effective feature extraction of the AE signals to improve the accuracy of multi-class classification. Moreover, the CNN-based classification can be a promising approach to detect both leakage and valve opening as long as the performance of the processor does not degrade.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.225-233
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• 2024

Active sonar transmits sound waves to detect covertly maneuvering underwater objects and detects the signals reflected back from the target. However, in addition to the target's echo, the active sonar's received signal is mixed with seafloor, sea surface reverberation, biological noise, and other noise, making target recognition difficult. Conventional techniques for detecting signals above a threshold not only cause false detections or miss targets depending on the set threshold, but also have the problem of having to set an appropriate threshold for various underwater environments. To overcome this, research has been conducted on automatic calculation of threshold values through techniques such as Constant False Alarm Rate (CFAR) and application of advanced tracking filters and association techniques, but there are limitations in environments where a significant number of detections occur. As deep learning technology has recently developed, efforts have been made to apply it in the field of underwater target detection, but it is very difficult to acquire active sonar data for discriminator learning, so not only is the data rare, but there are only a very small number of targets and a relatively large number of non-targets. There are difficulties due to the imbalance of data. In this paper, the image of the energy distribution of the detection signal is used, and a classifier is learned in a way that takes into account the imbalance of the data to distinguish between targets and non-targets and added to the existing technique. Through the proposed technique, target misclassification was minimized and non-targets were eliminated, making target recognition easier for active sonar operators. And the effectiveness of the proposed technique was verified through sea experiment data obtained in the East Sea.

• The Journal of the Acoustical Society of Korea
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• v.25 no.7
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• pp.346-355
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• 2006

Acoustic data from a shallow water experiment in the East Sea of Korea (MAPLE IV) is Processed to investigate the Performance of matched-field geo-acoustic inversion and source localization. The receiver array consists of two legs as in an L-shape. one vertical and the other horizontal lying on the seabed. Narrowband multi-tone CW source was towed along a slightly inclined bathymetry track. The matched-field geo-acoustic inversion includes comparisons between three processing techniques. all based on the Bartlett processor as; (1) the coherent processing of the data from the full array, (2) the incoherent Product of each output from both the horizontal and vertical arrays, and (3) the cross correlation between the horizontal and vertical arrays. as well as processing each array leg separately. To verify the inversion results. matched-field source localization for low level source signal components were performed using the same Processors used at the inversion stage.

• Investigative Magnetic Resonance Imaging
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• v.9 no.1
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• pp.30-35
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• 2005

Purpose : The purpose of this study is to observe the blood oxygen level dependent (BOLD) contrast changes due to the reaction of human brain at a gustatory sense in response to a salty-taste stimulation. Materials and Methods : Twelve healthy, non-smoking, right-handed male subjects (mean age: 25.6, range: 23-28 years) participated in this salty-taste stimulus functional magnetic resonance (fMRI) study. MRI scans were performed with 1.57 GE Signa, using a multi-slice GE-EPI sequence according to a blood-oxy-gen-level dependent (BOLD) experiment paradigm. Scan parameters included matrix size $128\times128$, FOV 250 mm, TR 5000 msec, TE 60 msec, TH/GAP 5/2 mm. Sequential data acquisitions were carried out for 42 measurements with a repetition time of 5 sec for each taste-stimulus experiments. Analysis of fMRI data was carried out using SPM99 implemented in Matlab. NaCl solution $(3\%)$ was used as a salty stimulus. The task paradigm consisted of alternating rest-stimulus cycles (30-second rest, 15-second stimulus) for 210 seconds. During the stimulus period, NaCl-solution was presented to the subject's mouth through plastic tubes as a bolus of delivered every 5 sec using -processor controlled auto-syringe pump. Results : Insula, frontal opercular taste cortex, amygdala and orbitofrontal cortex (OFC) were activated by a salty-taste stimulation $(NaCl,\;3\%)$ in the fMRI experiments. And dosolateral prefrontal cortex (DLPFC) was also significantly responded to salty-taste stimuli. Activation areas of the right side hemisphere were more superior to the left side hemisphere. Conclusion : The results of this study well correspond to the fact that both insula, amygdala, OFC, DLPFC areas are established as taste cortical areas by neuronal recordings in primates. Authors found that laboratory-developed auto-syringe pump is suitable for gustatory fMRI study. Further research in this field will accelerate to inquire into the mechanism of higher order gustatory process.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.449-461
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• 2021

Analysis Ready Data (ARD) for optical satellite images represents a pre-processed product by applying spectral characteristics and viewing parameters for each sensor. The atmospheric correction is one of the fundamental and complicated topics, which helps to produce Top-of-Atmosphere (TOA) and Top-of-Canopy (TOC) reflectance from multi-spectral image sets. Most remote sensing software provides algorithms or processing schemes dedicated to those corrections of the Landsat-8 OLI sensors. Furthermore, Google Earth Engine (GEE), provides direct access to Landsat reflectance products, USGS-based ARD (USGS-ARD), on the cloud environment. We implemented the Orfeo ToolBox (OTB) atmospheric correction extension, an open-source remote sensing software for manipulating and analyzing high-resolution satellite images. This is the first tool because OTB has not provided calibration modules for any Landsat sensors. Using this extension software, we conducted the absolute atmospheric correction on the Landsat-8 OLI images of Railroad Valley, United States (RVUS) to validate their reflectance products using reflectance data sets of RVUS in the RadCalNet portal. The results showed that the reflectance products using the OTB extension for Landsat revealed a difference by less than 5% compared to RadCalNet RVUS data. In addition, we performed a comparative analysis with reflectance products obtained from other open-source tools such as a QGIS semi-automatic classification plugin and SAGA, besides USGS-ARD products. The reflectance products by the OTB extension showed a high consistency to those of USGS-ARD within the acceptable level in the measurement data range of the RadCalNet RVUS, compared to those of the other two open-source tools. In this study, the verification of the atmospheric calibration processor in OTB extension was carried out, and it proved the application possibility for other satellite sensors in the Compact Advanced Satellite (CAS)-500 or new optical satellites.