• 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.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.741-747
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• 2022

Fatigue lowers an individual's ability and makes it difficult to perform work. As fatigue accumulates, concentration decreases and thus the possibility of causing a safety accident increases. Awareness of fatigue is subjective, but it is necessary to quantitatively measure the level of fatigue in the actual field. In previous studies, it was proposed to measure the level of fatigue by expert judgment by adding objective indicators such as bio-signal analysis to subjective evaluations such as multidisciplinary fatigue scales. However this method is difficult to evaluate fatigue in real time in daily life. This paper is a study on the fatigue classification model that determines the fatigue level of workers in real time using speech data recorded in the field. Machine learning models such as logistic classification, support vector machine, and random forest are trained using speech data collected in the field. The performance evaluation showed good performance with accuracy of 0.677 to 0.758, of which logistic classification showed the best performance. From the experimental results, it can be seen that it is possible to classify the fatigue level using speech signals.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.171-176
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• 2022

The communication signal for quick cut-off specification is defined as "design to support fast cut-off requirements of all applicable photovoltaic(PV) systems" in NEC 2014, NEC 2017 or the corresponding UL standard regardless of the system configuration. On the other hand, if you look at the domestic regulations related to new and renewable energy, the standards, regulations, and guidelines set by each institution are general, or only the parts necessary for the institution are being established and operated. There are many insufficient points to apply these things to photovoltaic facilities, and there are cases where excessive facilities are installed according to the design, inspection standards of supervisors and inspection agencies, and the skill level of inspectors. The internationally accepted IEC standards deal with various facility standards in detail. In each European country, there are separate facility regulations based on IEC. In particular, the performance and safety of devices are dealt with in detail, and in the case of 'NEC Article 690' applied in North America such as the United States, each item is described in detail. Therefore, in this paper, we will look at the details of the PV shutdown technology that is currently used and applied internationally.

• Journal of Medicine and Life Science
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• v.18 no.3
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• pp.41-48
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• 2021

The prevalence of attention deficit hyperactivity disorder (ADHD), a developmental neuropsychiatric disorder, is high among children and adolescents. The pathogenesis of ADHD is mediated with genetic, biological, and environmental factors. Most therapeutic drugs for ADHD have so far targeted biological causes, primarily by regulating catecholaminergic neurotransmitters. However, ADHD drugs that are clinically treated have various problems in their addictiveness and drug stability; thus, it is recommended that efficacy and safety should be secured through simultaneous prescription of multiple drugs rather than a single drug treatment. Accordingly, it is necessary to develop drugs that newly target pathogenic mechanisms of ADHD. In this study, we attempt to confirm the possibility of developing new drugs by reviewing dopamine-related developmental mechanisms of neurons and their correlation with ADHD. Histone deacetylase inhibitors (HDACi) can regulate the concentration of intracellular dopamine in neurons by expressing vesicular monoamine transporter 2 and inducing the exocytosis of neurotransmitters to the synaptic cleft, thereby promoting the development of neurons and signal transmission. This cellular modulation of HDACi is expected to treat ADHD by regulating endogenous catecholamines such as dopamine. Although studies are still in the preclinical stage, HDAC inhibitors clearly have potential as a therapeutic agent with low addictiveness and high efficacy for ADHD treatment.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.393-399
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• 2023

Upper limb amputees wear an upper limb prosthesis for both aesthetic purposes and functional necessity, and in particular, in the case of amputee with both hands, it is essential to wear a myoelectric prosthetic hand capable of gripping action. The prosthetic hand operated by the EMG signal of the remaining muscles is a public insurance benefit item of the Industrial Accident Compensation Insurance, and test method standards are needed to be developed for the safety of the user and the effectiveness of the product performance. In this study, we developed systems for measuring the gripping force of myoelectric hand prosthesis by a load cell and for durability test of the prosthesis over repeated use with a proximity sensor, and propose a test method standard. Since the international test method standard has not yet been established, it is expected that Korea will be able to play a leading role in this standardization field in the future.

• Journal of Veterinary Science
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• v.24 no.4
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• pp.56.1-56.13
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• 2023

Background: Cold atmospheric plasma is a novel innovative approach for wound care, and it is currently underrepresented in veterinary medicine. Objectives: To investigate the efficacy and safety of using cold atmospheric microwave plasma (CAMP) as an adjunct therapy for wound healing in dogs and cats. Methods: Wound healing outcomes were retrospectively analyzed using clinical records of client-owned dogs and cats who were first managed through standard wound care alone (pre-CAMP period) and subsequently via CAMP therapy (CAMP period). The degree of wound healing was estimated based on wound size and a modified wound scoring system. Results: Of the 27 acute and chronic wounds included in the analysis, 81.48% showed complete healing after the administration of CAMP as an adjunct therapy to standard care. Most wounds achieved complete healing in < 5 weeks. Compared with the pre-CAMP period, the rate of wound healing significantly increased every week in the CAMP period in terms of in wound size (first week, p < 0.001; second week, p = 0.012; third week, p < 0.001) and wound score (first week, p < 0.001; second week, p < 0.001; third week, p = 0.001). No adverse events were noted except for mild discomfort and transient erythema. Conclusions: CAMP is a well-tolerated therapeutic option with immense potential to support the treatment of wounds of diverse etiology in small animal practice. Further research is warranted to establish specific criteria for CAMP treatment according to wound characteristics.

• Journal of Information Technology Services
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• v.22 no.6
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• pp.87-102
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• 2023

Existing child car seats do not have a monitoring means for the driver or guardian to effectively recognize the status of whether the seat belt of car seat is fastened and whether the ISOFIX of the car seat is fastened to the inside device of the vehicle. In this paper, we propose a smart car seat system which can monitor in real time, whether the seat belt of a child seated in the car seat is fastened and whether the ISOFIX of the car seat is fastened. The proposed system has been developed with a prototype, in which a Hall sensor, magnet, Bluetooth, and display device are used to detect whether these are fastened and to display the detection results. The prototype system provides the detection results as texts and alarm signal to the display for driver or guardian' smartphone in the car in motion. With functional tests of the prototype system, it was confirmed that the detection functions are properly operated, and the detection results were transmitted to the display device and smartphone via Bluetooth within 0.5 seconds. It is expected that the development system can effectively prevent safety accidents of child car seats.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.1-10
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• 2024

The research on predicting the routes of ships, which constitute the majority of maritime transportation, can detect potential hazards at sea in advance and prevent accidents. Unlike roads, there is no distinct signal system at sea, and traffic management is challenging, making ship route prediction essential for maritime safety. However, the time intervals of the ship route datasets are irregular due to communication disruptions. This study presents a method to adjust the time intervals of data using appropriate interpolation techniques for ship route prediction. Additionally, a deep learning model for predicting ship routes has been developed. This model is an LSTM model that predicts the future GPS coordinates of ships by understanding their movement patterns through real-time route information contained in AIS data. This paper presents a data preprocessing method using linear interpolation and a suitable deep learning model for ship route prediction. The experimental results demonstrate the effectiveness of the proposed method with an MSE of 0.0131 and an Accuracy of 0.9467.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.36 no.5
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• pp.847-856
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• 2016

Recently, with a high level of economic growth, rapid urbanization, population, environment and housing problems were accompanied in Korea. In particular, the traffic problem has become a serious social problem. As the current transportation policy has been carried out, concentrating on traffic flow, in 2015, death rate for pedestrians while walking (1,795 persons) is 38.8% compared to entire death rate in car accident (4,621 persons), so there is need to solve it. Although, crosswalk should make pedestrian cross it safely, it has been made on the basis of the width of road without exact standard for current width of the crosswalk and the location of stop line. Moreover, in the area around many campuses or commercial facilities, crosswalks are set with not considering pedestrian passage, but designed uniformly. Therefore, the purpose of this study is to estimate reasonable dimension of crosswalk considering pedestrian traffic and walking speed and it makes the accident rate lower in the crosswalk, which has a lot of problems including decisions of vehicle traffic signal time, lack of pedestrian's signal timing, pedestrian's crossing of long-distance. The following are the methodology of the study. Firstly, for crosswalk calculation of specifications, examination relating existing regulations and researches dealing with crosswalk, pedestrians and stop line is needed. After analyzing problems of current width of crosswalk and stop line, present the methodology to calculation of specifications and basing on these things, calculation of specifications for crosswalk will be decided. In conclusion, the calculation of specification and improvement of stop line for crosswalk laid out in this study are expected to be utilized as base data in case of establishing relevant safety facilities and standards.