• Structural Engineering and Mechanics
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• v.57 no.6
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• pp.1125-1142
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• 2016

Tunneling is one of the challenging tasks in civil engineering because it involves a variety of decision making and engineering judgment based on knowledge and experience. One of the challenges is to construct tunnels in risky areas under shallow overburden. In order to prevent the collapse of ceilings and walls of a large tunnels, in such conditions, either a sequential excavation method (SEM) or ground reinforcing method, or a combination of both, can be utilized. This research deals with the numerical modeling of L-profiles and pipe fore-poling pre-support systems in the adit tunnel in northwestern Iran. The first part of the adit tunnel has been drilled in alluvial material with very weak geotechnical parameters. Despite applying an SEM in constructing this tunnel, analyzing the results of numerical modeling done using FLAC3D, as well as observations during drilling, indicate the tunnel instability. To improve operational safety and to prevent collapse, pre-support systems, including pipe fore-poling and L-profiles were designed and implemented. The results of the numerical modeling coupled with monitoring during operation, as well as the results of instrumentation, indicate the efficacy of both these methods in tunnel collapse prevention. Moreover, the results of modeling using FLAC3D and SECTION BUILDER suggest a double angle with equal legs ($2L100{\times}100{\times}10mm$) in both box profile and tee array as an alternative section to pipe fore-poling system while neither $L80{\times}80{\times}8mm$ nor $2L80{\times}80{\times}8mm$ can sustain the axial and shear stresses exerted on pipe fore-poling system.

• Journal of Korean Society for Geospatial Information Science
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• v.16 no.2
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• pp.49-56
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• 2008

Image registration is one of the critical techniques of image mosaic which has many applications such as generating panoramas, video monitoring, image rendering and reconstruction, etc. The fundamental tasks of image registration are point features extraction and tracking which take much computation time. KLT(Kanade-Lucas-Tomasi) feature tracker has proposed for extracting and tracking features through image sequences. The aim of this study is to demonstrate the usage of effective and robust KLT feature detector and tracker for an image registration using the sequence image frames captured by UAV video camera. In result, by using iterative implementation of the KLT tracker, the features extracted from the first frame of image sequences could be successfully tracked through all frames. The process of feature tracking in the various frames with rotation, translation and small scaling could be improved by a careful choice of the process condition and KLT pyramid implementation.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.12
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• pp.201-209
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• 2009

In power plant operations, sensor values often exhibit erroneous values due to their failures or the intrusions of various noises. However, most of the power plant monitoring and fault diagnosis systems perform their tasks based on the assumptions that the collected sensor values are correct all the times. These assumptions, which are not valid, often lead to serious consequences such as power plant trips. In this paper, we propose a power plant sensor value validation technique that can utilize the relationships existing among the sensor values as the sensor redundancy. The proposed technique is applied to the flow meters installed along boiler feed water systems of a typical tubular type boiler thermal power plant and shows a good potential of future applications.

• Journal of Veterinary Science
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• v.22 no.4
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• pp.48.1-48.15
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• 2021

Background: Porcine epidemic diarrhea virus (PEDV) is a swine enteropathogenic coronavirus that has devastated the swine industry in South Korea over the last 30 years. The lack of an effective method to control the endemics has led to a surge in PEDV recurrences in affected farms throughout the country. Objectives: In the first step toward establishing systematic monitoring of and active control measures over the swine populations, we constructed an assessment model that evaluates the status of (1) biosecurity, (2) herd immunity, and (3) virus circulation in each of the PEDV-infected farms. Methods: A total of 13 farrow-to-finish pig farms with a history of acute PEDV infection on Jeju Island were chosen for this study. The potential risk of the recurrence in these farms was estimated through on-site data collection and laboratory examination. Results: Overall, the data indicated that a considerable number of the PEDV-infected farms had lax biosecurity, achieved incomplete protective immunity in the sows despite multi-dose vaccination, and served as incubators of the circulating virus; thus, they face an increased risk of recurrent outbreaks. Intriguingly, our results suggest that after an outbreak, a farm requires proactive tasks, including reinforcing biosecurity, conducting serological and virus monitoring to check the sows' immunity and to identify the animals exposed to PEDV, and improving the vaccination scheme and disinfection practices if needed. Conclusions: The present study highlights the significance of coordinated PEDV management in infected farms to reduce the risk of recurrence and further contribute towards the national eradication of PEDV.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.12
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• pp.1611-1617
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• 2018

The body temperature of livestock is directly related to the health of livestock such that it changes immediately when there exists health problem. Accordingly, the monitoring of livestock's temperature is one of most important tasks in farm management. However, the temperature of livestock is usually measured using skin-attached sensor which is significantly affected by the outside temperature and the condition of attachment which results in the inaccurate measurement of temperature. Herein we have proposed new scheme which estimates the body core temperature of cow based on measured data from neck-attached smart sensor. Especially, we have considered both schemes which estimate the exact temperature and which detect the unusually high temperature based on machine learning. We have found that the occurrence of high temperature can be detected accurately. The proposed scheme can be used in monitoring of health condition of cow and improving the efficiency of farm management.

• Journal of Sensor Science and Technology
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• v.31 no.6
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• pp.423-427
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• 2022

A pressure sensor that mimics the sensing ability of human skin has emerged as high-profile technology because it shows remarkable applications in numerous fields such as robotics, human health monitoring, and artificial prosthetics. Whereas recent pressure sensors have achieved high sensitivity similar to that of human skin, they still show limited detection bandwidth. Moreover, once these e-skin are fabricated, their sensitivity and stiffness are fixed; therefore, they can be used for only limited applications. Our study proposes a new adaptive pressure sensor built with uniform gallium microdroplet-elastomer composite. Based on the phase transition of gallium microdroplets, the proposed sensor undergoes mode transformation, enabling it to have a higher sensitivity and wider detection bandwidth compared with those of human skin. In addition, we succeeded in extending a single adaptive pressure sensor to sensor arrays based on its high uniformity, reproducibility, and large-scale manufacturability. Finally, we designed an adaptive e-skin with the sensor array and demonstrated its applications on health monitoring tasks including blood pulse and body weight measurements.

• Journal of KIBIM
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• v.12 no.4
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• pp.61-69
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• 2022

Earthmoving operations account for approximately 25% of construction cost, generally executed prior to the construction of buildings and structures with heavy equipment. For the successful completion of earthwork projects, it is crucial to constantly monitor earthwork equipment (e.g., trucks), estimate productivity, and optimize the construction process and equipment on a construction site. Traditional methods however require time-consuming and painstaking tasks for the manual observations of the ongoing field operations. This study proposed the use of a GPS sensor embedded in a smartphone for the tracking and visualization of equipment locations, which are in turn used for the estimation and simulation of cycle times and production rates of ongoing earthwork. This approach is implemented into a digital platform enabling real-time data collection and simulation, particularly in a 2D (e.g., maps) or 3D (e.g., point clouds) virtual environment where the spatial and temporal flows of trucks are visualized. In the case study, the digital platform is applied for an earthmoving operation at the site development work of commercial factories. The results demonstrate that the production rates of various equipment usage scenarios (e.g., the different numbers of trucks) can be estimated through simulation, and then, the optimal number of tucks for the equipment fleet can be determined, thus supporting the practical potential of real-time sensing and simulation for onsite equipment management.

• Smart Structures and Systems
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• v.31 no.4
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• pp.325-334
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• 2023

Computer vision-based damage detection enables non-contact, efficient and low-cost bridge health monitoring, which reduces the need for labor-intensive manual inspection or that for a large number of on-site sensing instruments. By leveraging recent semantic segmentation approaches, we can detect regions of critical structural components and identify damages at pixel level on images. However, existing methods perform poorly when detecting small and thin damages (e.g., cracks); the problem is exacerbated by imbalanced samples. To this end, we incorporate domain knowledge to introduce a hierarchical semantic segmentation framework that imposes a hierarchical semantic relationship between component categories and damage types. For instance, certain types of concrete cracks are only present on bridge columns, and therefore the noncolumn region may be masked out when detecting such damages. In this way, the damage detection model focuses on extracting features from relevant structural components and avoid those from irrelevant regions. We also utilize multi-scale augmentation to preserve contextual information of each image, without losing the ability to handle small and/or thin damages. In addition, our framework employs an importance sampling, where images with rare components are sampled more often, to address sample imbalance. We evaluated our framework on a public synthetic dataset that consists of 2,000 railway bridges. Our framework achieves a 0.836 mean intersection over union (IoU) for structural component segmentation and a 0.483 mean IoU for damage segmentation. Our results have in total 5% and 18% improvements for the structural component segmentation and damage segmentation tasks, respectively, compared to the best-performing baseline model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1706-1727
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• 2023

Under Water Sensor Networks (UWSN) has gained attraction among various communities for its potential applications like acoustic monitoring, 3D mapping, tsunami detection, oil spill monitoring, and target tracking. Unlike terrestrial sensor networks, it performs an acoustic mode of communication to carry out collaborative tasks. Typically, surface sink nodes are deployed for aggregating acoustic phenomena collected from the underwater sensors through the multi-hop path. In this context, UWSN is constrained by factors such as lower bandwidth, high propagation delay, and limited battery power. Also, the vulnerabilities to compromise the aquatic environment are in growing numbers. The paper proposes an Energy-Efficient standalone Intrusion Detection System (EEIDS) to entail the acoustic environment against malicious attacks and improve the network lifetime. In EEIDS, attributes such as node ID, residual energy, and depth value are verified for forwarding the data packets in a secured path and stabilizing the nodes' energy levels. Initially, for each node, three agents are modeled to perform the assigned responsibilities. For instance, ID agent verifies the node's authentication of the node, EN agent checks for the residual energy of the node, and D agent substantiates the depth value of each node. Next, the classification of normal and malevolent nodes is performed by determining the score for each node. Furthermore, the proposed system utilizes the sheep-flock heredity algorithm to validate the input attributes using the optimized probability values stored in the training dataset. This assists in finding out the best-fit motes in the UWSN. Significantly, the proposed system detects and isolates the malicious nodes with tampered credentials and nodes with lower residual energy in minimal time. The parameters such as the time taken for malicious node detection, network lifetime, energy consumption, and delivery ratio are investigated using simulation tools. Comparison results show that the proposed EEIDS outperforms the existing acoustic security systems.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.20 no.4
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• pp.248-255
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• 2010

Airborne nanoparticle concentrations in three metrics (particle surface area concentration, particle number concentration, and particle mass concentrations) were measured by task in a laboratory making carbon nanotubes (CNTs) films using three direct reading instruments. Because of the conducted other researcher's experiment before the tasks, airborne nanoparticle surface area and number concentrations are the highest at the first time conducted weighing and mixing by sonication task, respectively. Because of the mist generated during mixing by sonication, the highest airborne nanoparticle surface area and PM1 concentrations were measured in the task among the total. Nanoparticle surface area concentrations at the researchers' breathing zones had high correlation (r=0.93, p<0.01) with those measured at an area in the laboratory. This result indicates that nanoparticles generated during the experiment contaminated the whole room air. When the experiment performed all the fume hoods weren't operated and making CNTs films task were conducted in the out of the fume hoods. In conclusion, researchers performing making CNTs film experiments were exposed to airborne nanoparticles generated during the experiment without adequate controls. We recommend that adequate controls should be implemented so that workers' exposures to airborne nanoparticle are limited to minimum levels.