• Geomechanics and Engineering
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• 제19권1호
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• pp.79-91
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• 2019

Renewed interest in the long-term pile foundations has been driven by the increase in offshore wind turbine installation to generate renewable energy. A monopile subjected to repetitive loads experiences an evolution of displacements, pile rotation, and stress redistribution along the embedded portion of the pile. However, it is not fully understood how the embedded pile interacts with the surrounding soil elements based on different pile geometries. This study investigates the long-term soil response around offshore monopiles using finite element method. The semi-empirical numerical approach is adopted to account for the fundamental features of volumetric strain (terminal void ratio) and shear strain (shakedown and ratcheting), the strain accumulation rate, and stress obliquity. The model is tested with different strain boundary conditions and stress obliquity by relaxing four model parameters. The parametric study includes pile diameter, embedded length, and moment arm distance from the surface. Numerical results indicate that different pile geometries produce a distinct evolution of lateral displacement and stress. In particular, the repetitive lateral load increases the global lateral load resistance. Further analysis provides insight into the propagation of the shear localization from the pile tip to the ground surface.

• Nuclear Engineering and Technology
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• 제51권1호
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• pp.155-162
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• 2019

To reduce the secondary damage caused by leakage accidents in plant piping systems, a constant surveillance system is necessary. To ensure leaks are promptly addressed, the surveillance system should be able to detect not only the leak itself, but also the location of the leak. Recently, research to develop new methods has been conducted using cameras to detect leakage and to estimate the location of leakage. However, existing methods solely estimate whether a leak exists or not, or only provide two-dimensional coordinates of the leakage location. In this paper, a method using multiple cameras to detect leakage and estimate the three-dimensional coordinates of the leakage location is presented. Leakage is detected by each camera using MADI(Moving Average Differential Image) and histogram analysis. The two-dimensional leakage location is estimated using the detected leakage area. The three-dimensional leakage location is subsequently estimated based on the two-dimensional leakage location. To achieve this, the coordinates (x, z) for the leakage are calculated for a horizontal section (XZ plane) in the monitoring area. Then, the y-coordinate of leakage is calculated using a vertical section from each camera. The method proposed in this paper could accurately estimate the three-dimensional location of a leak using multiple cameras.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권9호
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• pp.4408-4428
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• 2019

Due to the rapid advancement in smart phones, numerous new specifications are developed for variety of applications ranging from health monitoring to navigations and tracking. The word indoor navigation means location identification, however, where GPS signals are not available, accurate indoor localization is a challenging task due to variation in the received signals which directly affect distance estimation process. This paper proposes a hybrid approach which integrates fingerprinting based K-Nearest Neighbors (K-NN) and lateration based MinMax position estimation technique. The novel idea behind this hybrid approach is to use Euclidian distance formulation for distance estimates instead of indoor radio channel modeling which is used to convert the received signal to distance estimates. Due to unpredictable behavior of the received signal, modeling indoor environment for distance estimates is a challenging task which ultimately results in distance estimation error and hence affects position estimation process. Our proposed idea is indoor position estimation technique using Bluetooth enabled smart phones which is independent of the radio channels. Experimental results conclude that, our proposed hybrid approach performs better in terms of mean error compared to Trilateration, MinMax, K-NN, and existing Hybrid approach.

• 식품과학과 산업
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• 제53권4호
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• pp.397-409
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• 2020

Coffee, which has spread from Ethiopia to Arabia, Europe and then to Korea, has become the most beloved beverage among today's Korean people. After liberation, instant coffee was first introduced to Korea through the U.S. military, and coffee became popular in earnest. In the 1970s, Dongsuh Foods led localization of coffee by mass-producing instant and regular coffee, and in the 1990s, coffee shops replaced teahouses. After the 1997 financial crisis, office workers made coffee on their own as companies downsized on secretarial staff, leading to a further growth in instant coffee mix market. In 1999, the first foreign brand Starbucks was introduced to Korea and the culture of takeout espresso coffee took off. As consumers' preferences of coffee constantly evolve, the demand for high-quality specialty coffee has emerged, individual roasters have grown in order to meet the demand, and a viral marketing through SNS has been used as a growth engine. In 2020, the spread of coronavirus(COVID-19) is affecting the global coffee market. As many offices, coffee shops, and restaurants practice social distancing, out-of-home sales such as coffee shops have decreased, whereas sales for a takeout coffee and home-café products have increased.

• 대한원격탐사학회지
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• 제38권1호
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• pp.1-20
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• 2022

As unmanned aerial vehicle (UAV) technology grew in popularity over the years, it was introduced for air quality monitoring. This can easily be used to estimate the sidewalk emission concentration by calculating road traffic emission factors of different vehicle types. These calculations require a simulation of the spread of pollutants from one or more sources given for estimation. For this purpose, a Gaussian plume dispersion model was developed based on the US EPA Motor Vehicle Emissions Simulator (MOVES), which provides an accurate estimate of fuel consumption and pollutant emissions from vehicles under a wide range of user-defined conditions. This paper describes a methodology for estimating emission concentration on the sidewalk emitted by different types of vehicles. This line source considers vehicle parameters, wind speed and direction, and pollutant concentration using a UAV equipped with a monocular camera. All were sampled over an hourly interval. In this article, the YOLOv5 deep learning model is developed, vehicle tracking is used through Deep SORT (Simple Online and Realtime Tracking), vehicle localization using a homography transformation matrix to locate each vehicle and calculate the parameters of speed and acceleration, and ultimately a Gaussian plume dispersion model was developed to estimate the CO, NOx concentrations at a sidewalk point. The results demonstrate that these estimated pollutants values are good to give a fast and reasonable indication for any near road receptor point using a cheap UAV without installing air monitoring stations along the road.

• 한국공간구조학회논문집
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• 제23권3호
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• pp.57-69
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• 2023

In this study, in order to establish a strategy for developing an fire following earthquake risk assessment method that can utilize domestic public databases(building datas, etc.), the method of calculating the ignition and fire-spread among the fire following earthquake risk assessment methodologies proposed by past researchers is investigated After investigating and analyzing the methodology used in the HAZUS-MH earthquake model in the United States and the fire following earthquake risk assessment methodology in Japan, based on this, a database such as a domestic building data utilized to an fire following earthquake risk assessment method suitable for domestic circumstances (planned) was suggested.

• 센서학회지
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• 제32권2호
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• pp.118-123
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• 2023

This study presents sensor system for autonomous mobile robot capable of floor-to-floor self-navigation. The robot was modified using the Turtlebot3 hardware platform and ROS2 (robot operating system 2). The robot utilized the Navigation2 package to estimate and calibrate the moving path acquiring a map with SLAM (simultaneous localization and mapping). For elevator boarding, ultrasonic sensor data and threshold distance are compared to determine whether the elevator door is open. The current floor information of the elevator is determined using image processing results of the ceiling-fixed camera capturing the elevator LCD (liquid crystal display)/LED (light emitting diode). To realize seamless communication at any spot in the building, the LoRa (long-range) communication module was installed on the self-navigating autonomous mobile robot to support the robot in deciding if the elevator door is open, when to get off the elevator, and how to reach at the destination.

• 로봇학회논문지
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• 제18권1호
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• pp.72-81
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• 2023

We introduce tightly-coupled GNSS-LiDAR-Inertial state estimator, which is capable of SLAM (Simultaneously Localization and Mapping) and autonomous driving. Long term drift is one of the main sources of estimation error, and some LiDAR SLAM framework utilize loop closure to overcome this error. However, when loop closing event happens, one's current state could change abruptly and pose some safety issues on drivers. Directly utilizing GNSS (Global Navigation Satellite System) positioning information could help alleviating this problem, but accurate information is not always available and inaccurate vertical positioning issues still exist. We thus propose our method which tightly couples raw GNSS measurements into LiDAR-Inertial SLAM framework which can handle satellite positioning information regardless of its uncertainty. Also, with NLOS (Non-light-of-sight) satellite signal handling, we can estimate our states more smoothly and accurately. With several autonomous driving tests on AGV (Autonomous Ground Vehicle), we verified that our method can be applied to real-world problem.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권5호
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• pp.1597-1610
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• 2022

At present, the main method of high-speed train chassis detection is using computer vision technology to extract keypoints from two related chassis images firstly, then matching these keypoints to find the pixel-level correspondence between these two images, finally, detection and other steps are performed. The quality and accuracy of image matching are very important for subsequent defect detection. Current traditional matching methods are difficult to meet the actual requirements for the generalization of complex scenes such as weather, illumination, and seasonal changes. Therefore, it is of great significance to study the high-speed train image matching method based on deep learning. This paper establishes a high-speed train chassis image matching dataset, including random perspective changes and optical distortion, to simulate the changes in the actual working environment of the high-speed rail system as much as possible. This work designs a convolutional neural network to intensively extract keypoints, so as to alleviate the problems of current methods. With multi-level features, on the one hand, the network restores low-level details, thereby improving the localization accuracy of keypoints, on the other hand, the network can generate robust keypoint descriptors. Detailed experiments show the huge improvement of the proposed network over traditional methods.

• Computers and Concrete
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• 제31권2호
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• pp.151-161
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• 2023

This paper presents an investigation of variables that cause spurious cracking in numerical modeling of concrete fracture. Spurious cracks appear due to the approximate nature of numerical modeling. They overestimate the dissipated energy, leading to divergent results with mesh refinement. This paper is limited to quasi-static loading regime, homogeneous models, cracking as the only nonlinear mode of deformation and cracking only due to tensile loading. Under these conditions, some variables that can be related to spurious cracking are: mesh alignment, ductility, crack band width, structure size, mesh refinement and load increment size. Case studies illustrate the effect of each variable and convergence analyses demonstrate that, after all, load-increment size is the most important variable. Theoretically, a sufficiently small load increment is able to eliminate or at least alleviate the detrimental influence of the other variables. Such load-increment size might be prohibitively small, rendering the simulation unfeasible. Hence, this paper proposes two alternatives. First, it is proposed an algorithm that automatically find such small load increment size automatically, which not necessarily avoid large computations. Then, it is proposed a double simulation technique, in which the crack is forced to propagate through the localization zone.