• Fire Science and Engineering
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• v.33 no.1
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• pp.85-91
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• 2019

This study evaluated the fire detection performance of an automatic fire extinguishing system for road tunnels, which combines flame wavelength detection technology with flame image detection technology. This fusion technique to improve the fire detection capability can reduce the damage caused by the fire suppression by locating the fire source in the fire and discharging the pressurized water only at the fire source. Experiments were conducted to determine the position of a fire source when a $70cm{\times}70cm$ target was placed at a distance of 15 m, 20 m, 25 m, 30 m, and 35 m, respectively, in a situation where there is a flame and smoke in a tunnel. The performance of the ultraviolet and triple wavelength infrared (IR3) sensors was attenuated due to the interference of thick smoke. In addition when the flame was blocked by thick smoke, the image sensor sensed the smoke and emitted a fire signal.

• Composites Research
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• v.31 no.6
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• pp.429-434
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• 2018

We investigate strain transmissions of a surface bonded distributed optical fiber sensor considering strain variation according to positions. We first derive a strain transmission ratio depending on a wavelength of a strain distribution of the host structure from an analysis model. The strain transmission ratio is compared with numerical results obtained from the finite element method using ABAQUS. We find that the analytical results agree well with the numerical results. The strain transmission ratio is a function of a wavelength, i.e. the strain transmission ratio decreases (increases) as the wavelength of the host strain decreases (increases). Therefore, if an arbitrary strain distribution containing various wavelengths is given to a host structure, a distorted strain distribution will be observed in the distributed optical fiber sensor compare to that of the host structure, because each wavelength shows different strain transmission ratio. The strain transmission ratio derived in this study will be useful for accurately identifying the host strain distribution based on the signal of a distributed optical fiber sensor.

• Journal of Microbiology and Biotechnology
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• v.29 no.10
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• pp.1522-1542
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• 2019

To adapt to environmental changes and to maintain cellular homeostasis, microorganisms adjust the intracellular concentrations of biochemical compounds, including metal ions; these are essential for the catalytic function of many enzymes in cells, but excessive amounts of essential metals and heavy metals cause cellular damage. Metal-responsive transcriptional regulators play pivotal roles in metal uptake, pumping out, sequestration, and oxidation or reduction to a less toxic status via regulating the expression of the detoxification-related genes. The sensory and regulatory functions of the metalloregulators have made them as attractive biological parts for synthetic biology, and the exceptional sensitivity and selectivity of metalloregulators toward metal ions have been used in heavy metal biosensors to cope with prevalent heavy metal contamination. Due to their importance, substantial efforts have been made to characterize heavy metal-responsive transcriptional regulators and to develop heavy metal-sensing biosensors. In this review, we summarize the biochemical data for the two major metalloregulator families, SmtB/ArsR and MerR, to describe their metal-binding sites, specific chelating chemistry, and conformational changes. Based on our understanding of the regulatory mechanisms, previously developed metal biosensors are examined to point out their limitations, such as high background noise and a lack of well-characterized biological parts. We discuss several strategies to improve the functionality of the metal biosensors, such as reducing the background noise and amplifying the output signal. From the perspective of making heavy metal biosensors, we suggest that the characterization of novel metalloregulators and the fabrication of exquisitely designed genetic circuits will be required.

• The Journal of Korean Institute of Information Technology
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• v.17 no.5
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• pp.47-55
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• 2019

As the damage caused by GPS jamming has been increased, researches for detecting and preventing GPS jamming is being actively studied. This paper deals with a GPS jamming detection method using multiple GPS receiving channels and three-types machine learning techniques. Proposed multiple GPS channels consist of commercial GPS receiver with no anti-jamming function, receiver with just anti-noise jamming function and receiver with anti-noise and anti-spoofing jamming function. This system enables user to identify the characteristics of the jamming signals by comparing the coordinates received at each receiver. In this paper, The five types of jamming signals with different signal characteristics were entered to the system and three kinds of machine learning methods(AB: Adaptive Boosting, SVM: Support Vector Machine, DT: Decision Tree) were applied to perform jamming detection test. The results showed that the DT technique has the best performance with a detection rate of 96.9% when the single machine learning technique was applied. And it is confirmed that DT technique is more effective for GPS jamming detection than the binary classifier techniques because it has low ambiguity and simple hardware. It was also confirmed that SVM could be used only if additional solutions to ambiguity problem are applied.

• Annals of Clinical Neurophysiology
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• v.23 no.2
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• pp.108-116
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• 2021

Background: Nitrous oxide (N₂O) is used in surgery and dentistry for its anesthetic and analgesic effects. However, neurological and psychiatric manifestations of N₂O abuse have been increasingly reported among Korean adults. The aim of this study was to demonstrate laboratory findings of N₂O abuse in Korean patients. Methods: Patients diagnosed with N₂O-induced neuropathy or myelopathy from August 2018 to December 2019 were enrolled. Their clinical presentations and laboratory and imaging findings were analyzed. Results: Sensory changes and limb weakness were present in nine of the enrolled patients. The laboratory findings revealed that seven patients had high homocysteine levels and five had high methylmalonic acid levels in their blood. Nerve conductions studies indicated that axonal neuropathy was present in four cases and longer F-wave and Hoffman's-reflex latencies were present in two cases. Signal changes in cervical spine imaging occurred in five patients, while two had normal results. Conclusions: Chronic N₂O abuse can cause neurological damage or psychiatric problems. Because N₂O is illegal for recreational use in Korea, patients tend to hide their history of use. Even though the spinal imaging results were normal, clinicians should consider the possibility of N₂O use, and further electrophysiological tests should be applied for precise evaluations.

• Journal of Animal Science and Technology
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• v.64 no.1
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• pp.123-134
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• 2022

Toll-like receptors (TLRs), as a part of innate immunity, plays an important role in detecting pathogenic molecular patterns (PAMPs) which are structural components or product of pathogens and initiate host defense systems or innate immunity. Precise negative feedback regulations of TLR signaling are important in maintaining homeostasis to prevent tissue damage by uncontrolled inflammation during innate immune responses. In this study, we identified and characterized the function of the pancreatic progenitor cell differentiation and proliferation factor (PPDPF) as a negative regulator for TLR signal-mediated inflammation in chicken. Bioinformatics analysis showed that the structure of chicken PPDPF evolutionarily conserved amino acid sequences with domains, i.e., SH3 binding sites and CDC-like kinase 2 (CLK2) binding sites, suggesting that relevant signaling pathways might contribute to suppression of inflammation. Our results showed that stimulation with polyinosinic:polycytidylic acids (Poly [I:C]), a synthetic agonist for TLR3 signaling, increased the mRNA expression of PPDPF in chicken fibroblasts DF-1 but not in chicken macrophage-like cells HD11. In addition, the expression of pro-inflammatory genes stimulated by Poly(I:C) were reduced in DF-1 cells which overexpress PPDPF. Future studies warrant to reveal the molecular mechanisms responsible for the anti-inflammatory capacity of PPDPF in chicken as well as a potential target for controlling viral resistance.

• Smart Structures and Systems
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• v.29 no.1
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• pp.181-193
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• 2022

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.

• Smart Structures and Systems
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• v.29 no.1
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• pp.77-91
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• 2022

Various monitoring systems have been implemented in civil infrastructure to ensure structural safety and integrity. In long-term monitoring, these systems generate a large amount of data, where anomalies are not unusual and can pose unique challenges for structural health monitoring applications, such as system identification and damage detection. Therefore, developing efficient techniques is quite essential to recognize the anomalies in monitoring data. In this study, several machine learning techniques are explored and implemented to detect and classify various types of data anomalies. A field dataset, which consists of one month long acceleration data obtained from a long-span cable-stayed bridge in China, is employed to examine the machine learning techniques for automated data anomaly detection. These techniques include the statistic-based pattern recognition network, spectrogram-based convolutional neural network, image-based time history convolutional neural network, image-based time-frequency hybrid convolution neural network (GoogLeNet), and proposed ensemble neural network model. The ensemble model deliberately combines different machine learning models to enhance anomaly classification performance. The results show that all these techniques can successfully detect and classify six types of data anomalies (i.e., missing, minor, outlier, square, trend, drift). Moreover, both image-based time history convolutional neural network and GoogLeNet are further investigated for the capability of autonomous online anomaly classification and found to effectively classify anomalies with decent performance. As seen in comparison with accuracy, the proposed ensemble neural network model outperforms the other three machine learning techniques. This study also evaluates the proposed ensemble neural network model to a blind test dataset. As found in the results, this ensemble model is effective for data anomaly detection and applicable for the signal characteristics changing over time.

• Smart Structures and Systems
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• v.31 no.5
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• pp.469-483
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• 2023

The dynamic characteristics of wind turbine blades are usually monitored by contact sensors with the disadvantages of high cost, difficult installation, easy damage to the structure, and difficult signal transmission. In view of the above problems, based on computer vision technology and the improved YOLOv5 (You Only Look Once v5) deep learning model, a non-contact dynamic characteristic monitoring method for wind turbine blade is proposed. First, the original YOLOv5l model of the CSP (Cross Stage Partial) structure is improved by introducing the CSP2_2 structure, which reduce the number of residual components to better the network training speed. On this basis, combined with the Deep sort algorithm, the accuracy of structural displacement monitoring is mended. Secondly, for the disadvantage that the deep learning sample dataset is difficult to collect, the blender software is used to model the wind turbine structure with conditions, illuminations and other practical engineering similar environments changed. In addition, incorporated with the image expansion technology, a modeling-based dataset augmentation method is proposed. Finally, the feasibility of the proposed algorithm is verified by experiments followed by the analytical procedure about the influence of YOLOv5 models, lighting conditions and angles on the recognition results. The results show that the improved YOLOv5 deep learning model not only perform well compared with many other YOLOv5 models, but also has high accuracy in vibration monitoring in different environments. The method can accurately identify the dynamic characteristics of wind turbine blades, and therefore can provide a reference for evaluating the condition of wind turbine blades.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.3
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• pp.571-578
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• 2018

The deteriorated pipline often causes the unexpected leakage and crack. Negligence and late maintenance leads the enormous damage for gas and water resource. This paper proposes early fault detection and diagnosis algorithm for pipeline using acoustic emission (AE) signals. Early fault detection method for pipeline compares the frequency amplitude of the spectrum to that of the spectrum in normal condition. Larger amplitude of the spectrum indicates abnormal condition. Early fault diagnosis algorithm uses support vector machines (SVM), which is trained for normal and abnormal conditions to diagnose the measured AE signal from the target pipeline. In the experiment, a pipeline testbed is constructed similarly to real industrial pipeline. Normal, 5mm cracked, 10mm holed pipelines are installed and tested in this study. The proposed fault detection and diagnosis technique is validated as an efficient approach to detect early faulty condition of pipeline.