• Smart Structures and Systems
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• v.32 no.1
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• pp.23-35
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• 2023

Steel bolted joint is an important part of steel structure, and its damage directly affects the bearing capacity and durability of steel structure. Currently, the existing research mainly focuses on the identification of corroded bolts and corroded bolts respectively, and there are few studies on multiple states. A detection framework of corroded and loosened bolts is proposed in this study, and the innovations can be summarized as follows: (i) Vision Transformer (ViT) is introduced to replace the third and fourth C3 module of you-only-look-once version 5s (YOLOv5s) algorithm, which increases the attention weights of feature channels and the feature extraction capability. (ii) Three states of the steel bolts are considered, including corroded bolt, bolt missing and clean bolt. (iii) Line segment detector (LSD) is introduced for bolt rotation angle calculation, which realizes bolt looseness detection. The improved YOLOv5s model was validated on the dataset, and the mean average precision (mAP) was increased from 0.902 to 0.952. In terms of a lab-scale joint, the performance of the LSD algorithm and the Hough transform was compared from different perspective angles. The error value of bolt loosening angle of the LSD algorithm is controlled within 1.09%, less than 8.91% of the Hough transform. Furthermore, the proposed framework was applied to fullscale joints of a steel bridge in China. Synthetic images of loosened bolts were successfully identified and the multiple states were well detected. Therefore, the proposed framework can be alternative of monitoring steel bolted joints for management department.

• Journal of Advanced Navigation Technology
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• v.27 no.5
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• pp.635-642
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• 2023

With the development of artificial intelligence technology, drones are evolving beyond simple remote control tools into intelligent drones that perform missions autonomously. The importance of drones is gradually gaining attention due to the use of drones in overseas military conflicts and the analysis of the future operational environment in Korea. AMAD is proposed for the rapid development of intelligent drones. In order to develop intelligent software based on AMAD, an integrated development environment (IDE) that supports users with functions such as debugging, performance evaluation, and monitoring is essential. In this paper, we define the concepts of the development environment required for intelligent software development and describe the results of reflecting them in the design of the IDE and AMAD's agents, SVI and MPD, which are interfaced with the IDE.

• Journal of the Korean Electrochemical Society
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• v.27 no.1
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• pp.15-31
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• 2024

The antimalarial drug hydroxychloroquine sulphate (HCQ) has taken much attention during the first COVID-19 pandemic phase for the treatment of severe acute respiratory infection (SARI) patients. Hence it is interest to study the electrochemical properties and photocatalytic degradation of the HCQ drug. Copper oxide (CuO) nanoparticles, graphene oxide (GO) and CuO/GO NC (nanocomposite) modified carbon paste electrodes (MCPE) are used for the detection of HCQ in an aqueous medium. Electrochemical behaviour of HCQ (20 μM) was observed using CuO/MCPE, GO/MCPE and CuO/GO NC/MCPE in 0.1 M phosphate buffer at pH 7 with a scan rate of 20 to 120 mV s^-1 by cyclic voltammetry (CV). Differential pulse voltammetry (DPV) of HCQ was performed for 0.6 to 16 μM HCQ. The CuO/GO NC/MCPE showed a reasonably good sensitivity of 0.33 to 0.44 μA μM cm^-2 with LOD of 69 to 92 nM for HCQ. Furthermore, the CuO/GO NC was used as a catalyst for the photodegradation of HCQ by monitoring its UV-Vis absorption spectra. About 98% was degraded in about 34 min under visible light and after 4 cycles it was 87%. The improved photocatalytic activity may be attributed to decrease in bandgap energy and enhanced ability for the electrons to migrate. Thus, CuO/GO NC showed good results for both sensing and degradation applications as well as reproducibility.

• The Mathematical Education
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• v.63 no.2
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• pp.273-294
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• 2024

Recently, interest in Edu-Tech, which applies new technologies to the educational field, is growing. Edu-Tech is now being naturally used in schools, allowing both teachers and students to adapt to these changes. Particularly, there's significant attention on using Edu-Tech to bridge the educational gap through various teaching and learning strategies. This study focuses on the importance of self-directed task management by students for supplementary learning. It developed and utilized a math learning platform that enables teachers to easily provide and manage necessary tasks for students. Initially, the study developed "Sussam-MathTeacher" a problem-based learning application for middle school students, aimed at enhancing problem-solving abilities. This platform operates as a task management system, allowing teachers to assign or recommend problems to either the entire class or individual students. It aims to improve students' problem-solving abilities through a process that includes presenting necessary tasks, monitoring their own progress in solving problems, and self-assessing growth. Through this study, students demonstrated improved problem-solving skills by tackling tasks suited to their levels using "Sussam" highlighting the critical role of teachers in the digital educational environment.

• Laboraroty Animal Research
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• v.35
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• pp.31-38
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• 2019

Although the number of prescriptions and dependence on sleeping pills are increasing, the associations with unexpected abnormal behaviors and metabolic diseases caused by the overuse of sleeping pills are not well understood. In particular, such as abnormal eating-behavior and the occurrence of metabolic disorders caused by psychological unstable states are reported. For this reason, herbal medicine, which has not had such side effects in recent years, is attracting attention as an alternative medicine/food for sleeping inducer. We have used ethanol extracts from Passiflora incarnata (PI) to steadily obtain positive effects on sleep and brain microenvironment. However, as mentioned earlier, sleep-inducing efficacy can only be used safely if the behavioral and metabolic abnormalities do not appear. Thus, in this study, we used Phenomaster equipment to continuously monitor the movement, feeding, water consumption, gas changes, etc. in C57BL/6 mice at a dose of 500 mg/kg/day for 5 consecutive days with PI extract group compared with the control group. Before sacrifice, differences in body composition of mice were also compared. Monitoring of 24 h/5 days through the equipment showed no change in PI-treated group in anything except for significant decrease in blood melatonin levels and activity after PI administration. Taken together, the statistically insignificance of any behavioral and metabolic phenomenon produced by repeated treatment of PI are not only expected to have an accurate sleep effect, but are also free of side effects of the prescribed sleeping pills. This study has given us greater confidence in the safety of the PI extracts we use for sleep-inducer.

• The Korean journal of internal medicine
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• v.39 no.1
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• pp.7-24
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• 2024

Post-tuberculosis lung disease (PTLD) is emerging as a significant area of global interest. As the number of patients surviving tuberculosis (TB) increases, the subsequent long-term repercussions have drawn increased attention due to their profound clinical and socioeconomic impacts. A primary obstacle to its comprehensive study has been its marked heterogeneity. The disease presents a spectrum of clinical manifestations which encompass tracheobronchial stenosis, bronchiectasis, granulomas with fibrosis, cavitation with associated aspergillosis, chronic pleural diseases, and small airway diseases-all persistent consequences of PTLD. The spectrum of symptoms a patient may experience varies based on the severity of the initial infection and the efficacy of the treatment received. As a result, the long-term management of PTLD necessitates a detailed and specific approach, addressing each manifestation individually-a tailored strategy. In the immediate aftermath (0-12 months after anti-TB chemotherapy), there should be an emphasis on monitoring for relapse, tracheobronchial stenosis, and smoking cessation. Subsequent management should focus on addressing hemoptysis, managing infection including aspergillosis, and TB-associated chronic obstructive pulmonary disease or restrictive lung function. There remains a vast expanse of knowledge to be discovered in PTLD. This review emphasizes the pressing need for comprehensive, consolidated guidelines for management of patients with PTLD.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1293-1293
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• 2024

Virtual reality (VR) is increasingly utilized in the construction industry for diverse applications. Immersive virtual reality (IVR) offers practical experiences and educational opportunities for workers, enhancing productivity and safety. Efforts to optimize IVR involve analyzing biometric responses to monitor concentration, assess learning efficiency, and deliver personalized content. However, IVR faces challenges such as high production costs and prolonged production periods. Additionally, integrating biometric response recording into IVR experiences requires separate modules, further extending production timelines. To address these challenges, an integrated platform is necessary to streamline IVR production, user experience, and biometric response setup and recording. This study introduces such a platform designed to enhance the efficacy of IVR experiences through real-time biometric response analysis. The proposed platform comprises three main processes: (i) IVR content production using Unity; (ii) biometric response definition; and (iii) IVR content experience accompanied by generated logs for biometric responses. Firstly, IVR content production using Unity involves the development of IVR environments and scenarios. The platform incorporates diverse 3D models, including urban landscapes, building elements, and furniture, as the basis for IVR environments. Scenarios are constructed by integrating events into these environments, triggered by conditions such as reaching specific locations, the passage of time, or user interactions. Upon event activation, participants are presented with description UIs, quiz UIs, or route guidance, facilitating engagement and progression through interaction. Secondly, biometric responses encompass eye tracking and EEG. Eye tracking captures pupil diameter and fixation status on Areas of Interest (AOI), defined during IVR content production. EEG recording options include signals from each channel by default, as well as frequency-specific signals and EEG metrics such as attention, stress, fatigue, valence, and arousal. The platform supports the addition of new EEG metrics, enhancing customization and recording capabilities. Lastly, IVR content can be experienced alongside generated logs for biometric responses. The dataset enables monitoring and evaluation of participants' learning performance during IVR experiences, with the potential to enhance worker safety and productivity through immersive practical training and education.

• Journal of Sensor Science and Technology
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• v.33 no.5
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• pp.274-287
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• 2024

Chemiresistors play a crucial role in numerous research fields, including environmental monitoring, healthcare, and industrial safety, owing to their ability to detect and quantify gases with high sensitivity and specificity. This review provides a comprehensive overview of the recent advancements in photoactivated chemiresistors and emphasizes their potential for the development of highly sensitive, selective, and low-power gas sensors. This study explores a range of structural configurations of sensing materials, from zero-dimensional quantum dots to three-dimensional, porous nanostructures and examines the impact of these designs on the photoactivity, gas interactions, and overall sensor performance-including gas responses and recovery rates. Particular focus is placed on metal-oxide semiconductors and the integration of ultraviolet micro-light emitting diodes, which have gained attention as key components for next-generation sensing technologies owing to their superior photoactivity and energy efficiency. By addressing existing technical challenges, such as limited sensitivity, particularly at room temperature (~22℃), this paper outlines future research directions, highlighting the potential of photoactivated chemiresistors in developing high-performance, ultralow-power gas sensors for the Internet of Things and other advanced applications.

• Journal of Veterinary Science
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• v.25 no.5
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• pp.65.1-65.11
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• 2024

Importance: Recent developments in genetic analytical techniques have enabled the comprehensive analysis of gastrointestinal symbiotic bacteria as a screening tool for animal health conditions, especially the endangered gibbons at the National Wildlife Rescue Centre (NWRC). Objective: High-throughput sequencing based on 16S ribosomal RNA genes was used to determine the baseline gut bacterial composition and identify potential pathogenic bacteria among three endangered gibbons housed in the NWRC. Methods: Feces were collected from 14 individuals (Hylobates lar, n = 9; Hylobates agilis, n = 4; and Symphalangus syndactylus, n = 1) from March to November 2022. Amplicon sequencing were conducted by targeting V3-V4 region. Results: The fecal microbial community of the study gibbons was dominated by Bacteroidetes and Firmicutes (phylum level), Prevotellaceae and Lachnospiraceae/Muribaculaceae (family level), and Prevotella (and its subgroups) (genera level). This trend suggests that the microbial community composition of the study gibbons differed insignificantly from previously reported conspecific or closely related gibbon species. Conclusions and Relevance: This study showed no serious health problems that require immediate attention. However, relatively low alpha diversity and few potential bacteria related to gastrointestinal diseases and streptococcal infections were detected. Information on microbial composition is essential as a guideline to sustain a healthy gut condition of captive gibbons in NWRC, especially before releasing this primate back into the wild or semi-wild environment. Further enhanced husbandry environments in the NWRC are expected through continuous health monitoring and increase diversity of the gut microbiota through diet diversification.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.