• Journal of the Korean Society for Nondestructive Testing
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• v.29 no.5
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• pp.466-472
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• 2009

An acoustic leak monitoring system(ALMS) using acoustic emission(AE) technique was applied for leakage detection of nuclear power plant's pipeline which is operated in high temperature and pressure condition. Since this system only monitors the existence of leak using the root mean square(RMS) value of raw signal from AE sensor, the difficulty occurs when the characteristics of leak size and shape need to be evaluated. In this study, dual monitoring system using AE sensor and accelerometer was introduced in order to solve this problem. In addition, artificial neural network(ANN) with Levenberg.Marquardt(LM) training algorithm was also applied due to rapid training rate and gave the reliable classification performance. The input parameters of this ANN were extracted from varying signal received from experimental conditions such as the fluid pressure inside pipe, the shape and size of the leak area. Additional experiments were also carried out and with different objective which is to study the generation and characteristic of lamb and surface wave according to the pipe thickness.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.5
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• pp.118-123
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• 2004

The compressor has one of important roles in refrigeration cycle and it determines refrigeration efficiency and quality This paper aims to monitor rotary compressors for room air conditioners by using Acoustic Emission(AE) technique. The reliability of rotary compressors has been evaluated through visual inspection on them after long term test. This paper describes methods for acquisition and processing AE raw signal to monitor the state of rotary compressor. A detecting method of abnormal compressor in real time is suggested and special-purpose monitoring system which can be applied to automatic manufacturing line is developed using one-chip microprocessor at low cost.

• The Journal of Pediatrics of Korean Medicine
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• v.28 no.3
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• pp.47-58
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• 2014

Objectives GyejigaChulBuTang (GCBT) is a prescription used to treat acute and chronic arthritis in Korea, China, and Japan. This study assessed the anti-inflammatory and anti-oxidant activities of GCBT on lipopolysaccharide (LPS)-stimulated RAW264.7 macrophage cells. Methods Raw264.7 cells were pretreated with or without GCBT for 1 hour prior to incubation with LPS. Anti-inflammatory activity of GCBT was evaluated with reference to gene expression and production levels of proinflammatory cytokines ($TNF{\alpha}$, IL-$1{\beta}$, IL-6, GM-CSF and $INF{\gamma}$) and inflammatory mediators (iNOS, COX-2, NO and $PGE_2$). In addition, intracellular ROS generation and signal transduction of MAPK family, PI3K/Akt and $I{\kappa}B{\alpha}/NF{\kappa}B$ was investigated. Results Prior treatment with GCBT inhibited elevation of $TNF{\alpha}$, IL-$1{\beta}$, IL-6, GM-CSF, $INF{\gamma}$, NO and $PGE_2$, together with their cognate mRNAs in a dose-dependent manner. Intracellular ROS contents were similarly reduced. These effects were due to inhibition of LPS-induced phosphorylation of MAPK family, PI3K/Akt and $I{\kappa}B{\alpha}$ as well as nuclear translocation of $NF{\kappa}B$. Conclusions GCBT suppresses pro-inflammatory mediators. GCBT has potential in the treatment of juvenile rheumatoid arthritis associated with inflammation.

• 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.

• Journal of Life Science
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• v.31 no.11
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• pp.987-994
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• 2021

Our previous studies have reported that antimicrobial peptides (AMPs) derived from the larvae of white-spotted flower chafer (Protaetia brevitarsis seulensis) exert anti-inflammatory and neuroprotective activities. This study explored the anti-inflammatory effects of protaetiamycine 9 (CVLKKAYFLTNLKLRG-NH₂), a novel AMP, derived from P. b. seulensis against lipopolysaccharide (LPS)-mediated inflammatory response in RAW264.7 macrophage cells. Protaetiamycine 9 (25, 50, 75, and 100 ㎍/ml) did not cause cytotoxic effects against RAW264.7 cells. The RAW264.7 cells were pre-treated with various concentrations of protaetiamycine 9 (25-100 ㎍/ml) for 1 hr and then exposed to LPS (100 ng/ml) for 24 hr. Protaetiamycine 9 treatments decreased the LPS-induced secretion of inflammatory mediators, such as nitric oxide (NO), in a dose-dependent manner. Protaetiamycine 9 (25-100 ㎍/ml) effectively downregulated the LPS-induced increase in mRNA and the protein expression of inducible NO synthase (iNOS) and cyclooxygenase-2 (COX-2), which are involved in the production of inflammatory mediators. Protaetiamycine 9 also suppressed the production and gene expression of pro-inflammatory cytokines, including interleukin (IL)-6 and IL-1β, compared to the presence of LPS alone. Furthermore, protaetiamycine 9 inhibited the degradation of inhibitory kappa B alpha (IκB-α) and the phosphorylation of mitogen-activated protein kinases (MAPKs), such as extracellular signal-regulated kinase (ERK), c-Jun N-terminal kinase (JNK), and p38. In conclusion, these results suggest that protaetiamycine 9 exhibits LPS-mediated inflammatory responses by blocking IκB-α degradation and MAPK phosphorylation.

• Natural Product Sciences
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• v.13 no.4
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• pp.283-288
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• 2007

Acanthopanacis Cortex (AC) has been popularly used as an herbal medicine for medical treatment of rheumatoid arthritis, insomnia, impotence and diabetes. Here, we investigated immunostimulating effects of the aqueous extract of AC on macrophage. We studied nitric oxide (NO) and tumor necrosis factor (TNF)-${\alpha}$ release in response to AC treatment, as they are important secretory products of macrophage. AC alone induce the NO and TNF-${\alpha}$ production. AC increase c-Jun NH2-terminal kinase 1/2 (JNK) and extracellular signal-regulated kinase (ERK) phosphorylation but does not p38 activation in RAW 264.7 cells. Also AC resulted in the enhanced cell-surface expression of CD80 and CD14. In addition, AC resulted in enhanced T cell-stimulatory capacity and increased T cell secretion of interferon (IFN)-gamma. After feeding with AC to mouse for 10 days, the change of $CD28^+$ and $CD40^+$ population was analyzed. AC increased $CD28^+$ population in splenocytes in vivo. These studies indicate that AC induces macrophage activation and suggest the possible use of AC in macrophage-based immunotherapies.

• Journal of Life Science
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• v.31 no.10
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• pp.898-904
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• 2021

Locusta migratoria is a widespread locust species in many parts of the world and is considered an alternative source for the production of protein for value-added ingredients. We previously identified putative antimicrobial peptides derived from L. migratoria through an in silico analysis of its transcriptome. However, its anti-inflammatory effect has not been studied. In this study, we investigated the anti-inflammatory activities of the antimicrobial peptide locustacin (KTHILSFFPSFLPLFLKK-NH₂) derived from L. migratoria on lipopolysaccharide (LPS)-stimulated RAW264.7 macrophage cells. Locustacin (50, 100, and 200 ㎍/ml) significantly reduced the production of nitric oxide (NO) in LPS-stimulated macrophages without any cytotoxicity. Locustacin also inhibited the mRNA and protein expression of pro-inflammatory mediators, such as inducible NO synthase and cyclooxygenase-2, in contrast to the presence of LPS alone. Locustacin decreased the release of LPS-induced pro-inflammatory cytokines, including interleukin (IL)-6 and IL-1β, and their gene expression in a dose-dependent manner. Furthermore, locustacin (100 and/or 200 ㎍/ml) inhibited phosphorylation levels of extracellular signal regulated kinase, p38, and c-Jun N-terminal kinase. Locustacin also suppressed the degradation of inhibitory kappa B alpha, which was considered to be an inhibitor of nuclear factor kappa B (NF-κB). Collectively, these results demonstrate that locustacin can exert anti-inflammatory effects through the inhibition of mitogen-activated protein kinase (MAPK) phosphorylation, activation of NF-κB, and downstream inflammatory mediators in LPS-stimulated macrophage cells.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.14-21
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• 2020

In order to provide confidential services between two communicating parties, block data encryption using a symmetric secret key is applied. A power analysis attack on a cryptosystem is a side channel-analysis method that can extract a secret key by measuring the power consumption traces of the crypto device. In this paper, we propose an attack model that can recover the secret key using a power analysis attack based on a deep learning convolutional neural network (CNN) algorithm. Considering that the CNN algorithm is suitable for image analysis, we particularly adopt the recurrence plot (RP) signal processing method, which transforms the one-dimensional power trace into two-dimensional data. As a result of executing the proposed CNN attack model on an XMEGA128 experimental board that implemented the AES-128 encryption algorithm, we recovered the secret key with 22.23% accuracy using raw power consumption traces, and obtained 97.93% accuracy using power traces on which we applied the RP processing method.

• Journal of the Korean Society for Nondestructive Testing
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• v.23 no.3
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• pp.227-236
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• 2003

Leaks in underground pipelines can cause social, environmental and economical problems. One of relevant countermeasures against leaks is to find and repair of leak points of the pipes. Leak noise is a good source to identify the location of leak points of the pipelines. Although there have been several methods to detect the leak location with leak noise, such as listening rods, hydrophones or ground microphones, they have not been so efficient tools. In this paper, acoustic emission (AE) sensors and accelermeters are used to detect leak locations which could provide all easier and move efficient method. Filtering, signal processing and algorithm of raw input data from sensors for the detection of leak location are described. A 120m-long pipeline system for experiment is installed and the results with the system show that the algorithm with the AE sensors and accelerometers offers accurate pinpointing of leaks. Theoretical analysis of sound wave propagation speed of water in underground pipes, which is critically important in leak locating, is also described.