• Journal of Broadcast Engineering
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• v.24 no.6
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• pp.919-927
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• 2019

In this paper, we propose an accurate camera calibration method for acquiring multiview stereoscopic images. Generally, camera calibration is performed by using checkerboard structured patterns. The checkerboard pattern simplifies feature point extraction process and utilizes previously recognized lattice structure, which results in the accurate estimation of relations between the point on 2-dimensional image and the point on 3-dimensional space. Since estimation accuracy of camera parameters is dependent on feature matching, accurate detection of checkerboard corner is crucial. Therefore, in this paper, we propose the method that performs accurate camera calibration method through accurate detection of checkerboard corners. Proposed method detects checkerboard corner candidates by utilizing 1-dimensional gaussian filters with succeeding corner refinement process to remove outliers from corner candidates and accurately detect checkerboard corners in sub-pixel unit. In order to verify the proposed method, we check reprojection errors and camera location estimation results to confirm camera intrinsic parameters and extrinsic parameters estimation accuracy.

• The Transactions of the Korea Information Processing Society
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• v.4 no.10
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• pp.2461-2469
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• 1997

Target detection and recognition problems, in which neural networks are widely used, require translation invariant and real-time processing in addition to the requirements that general pattern recognition problems need. This paper presents a novel architecture that meets the requirements and explains effective methodology to train the network. The proposed neural network is an architectural extension of the shared-weight neural network that is composed of the feature extraction stage followed by the pattern recognition stage. Its feature extraction stage performs correlational operation on the input with a weight kernel, and the entire neural network can be considered a nonlinear correlation filter. Therefore, the output of the proposed neural network is correlational plane with peak values at the location of the target. The architecture of this neural network is suitable for implementing with parallel or distributed computers, and this fact allows the application to the problems which require realtime processing. Net training methodology to overcome the problem caused by unbalance of the number of targets and non-targets is also introduced. To verify the performance, the proposed network is applied to detection and recognition problem of a specific automobile driving around in a parking lot. The results show no false alarms and fast processing enough to track a target that moves as fast as about 190 km per hour.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.59-70
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• 2022

In this paper, we proposed an active fault tolerant control (AFTC) method for the position control of a quadrotor with complete loss of effectiveness of one motor. We obtained the dynamics of a quadrotor using Lagrangian equation without small angle assumption. For detecting the fault on a motor, we designed a fault detection module, which consists of the fault detection and diagnosis (FDD) module and the fault detection and isolation (FDI) module. For the FDD module, we designed a nonlinear observer that observes the states of a quadrotor based on the obtained dynamics. Using the observed states of a quadrotor, we designed residual signals and set the appropriate threshold values of residual signals to detect the fault. Also, we designed an FDI module to identify the fault location using the designed additional conditions. To make a quadrotor track the desired path after detecting the fault of a motor, we designed a fault tolerant controller based on the multiple sliding surface control (MSSC) technique. Finally, through simulations, we verified the effectiveness of the proposed AFTC method for a quadrotor with complete loss of effectiveness of one motor.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.6
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• pp.503-516
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• 2021

Autonomous fire detection and suppression system requires advanced technology for complex detection technology and injection/control technology for accurate hitting by fire location. Also, foam spraying should be included to respond to oil fires. However, when a single spray monitor is used in common, water and foam spray properties appear different, so for accurate fire suppression, research on the spray trajectory and distance will be required. In this study, experimental studies and numerical analysis studies were combined to analyze the foam spray characteristics through the spray monitor developed for the establishment of an autonomous fire extinguishing system. For flow analysis of foam injection, modeling was performed using OpenFOAM analysis software, and the commonly used foaming agent (Aqueous Film-Forming Foam) was applied for foam properties. The injection distance analysis was performed according to the injection pressure and the injection angle according to the form of the foam, and at the same time, the results were verified and presented through the injection experiment.

• Journal of the Korean Geotechnical Society
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• v.39 no.7
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• pp.31-37
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• 2023

Since tunnels are built underground, it is impossible to check visually the location and degree of deterioration of steel ribs. Therefore, in tunnel maintenance, GPR images are generally used to detect steel ribs. While research on GPR image analysis employing artificial neural networks has primarily focused on detecting underground pipes and road damage, there have been limited applications for analyzing tunnel GPR data, specifically for steel rib detection, both internationally and domestically. In this study, a one-step object detection algorithm called YOLO, based on a convolutional neural network, was utilized to automate the localization of steel ribs using GPR data. The performance of the algorithm is then analyzed. Two datasets were employed for the analysis. A dataset comprising 512 original images and another dataset consisting of 2,048 augmented images. The omission rate, which represents the ratio of undetected steel ribs to the total number of steel ribs, was 0.38% for the model using the augmented data, whereas the omission rate for the model using only the original data was 7.18%. Thus, from an automation standpoint, it is more practical to employ an augmented dataset.

• Journal of Advanced Navigation Technology
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• v.26 no.6
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• pp.427-433
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• 2022

Obstacle detection, collision recognition, and avoidance technologies are required the collision avoidance technology for UAVs. In this paper, considering collinear multiple static obstacle, we propose an obstacle detection algorithm using LiDAR and a collision recognition and avoidance algorithm based on CPA. Preprocessing is performed to remove the ground from the LiDAR measurement data before obstacle detection. And we detect and classify obstacles in the preprocessed data using the K-means clustering algorithm. Also, we estimate the absolute positions of detected obstacles using relative navigation and correct the estimated positions using a low-pass filter. For collision avoidance with the detected multiple static obstacle, we use a collision recognition and avoidance algorithm based on CPA. Information of obstacles to be avoided is updated using distance between each obstacle, and collision recognition and avoidance are performed through the updated obstacles information. Finally, through obstacle location estimation, collision recognition, and collision avoidance result analysis in the Gazebo simulation environment, we verified that collision avoidance is performed successfully.

• Composites Research
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• v.36 no.6
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• pp.429-434
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• 2023

In this study, aimed at improving the existing acoustic emission sensor for real time monitoring, a macro-fiber composite (MFC) transducer was employed as the acoustic emission sensor in the gas leak detection system. Prior to implementation, structural analysis was conducted to optimize the MFC's design. Consequently, the flexibility of the MFC facilitated excellent adherence to curved pipes, enabling the reception of acoustic emission (AE) signals without complications. Analysis of AE signals revealed substantial variations in parameter values for both high-pressure and low-pressure leaks. Notably, in the parameters of the Fast Fourier Transform (FFT) graph, the change amounted to 120% to 626% for high-pressure leaks compared to the case without leaks, and approximately 9% to 22% for low-pressure leaks. Furthermore, depending on the distance from the leak site, the magnitude of change in parameters tended to decrease as the distance increased. As the results, in the future, not only will it be possible to detect a leak by detecting the amount of parameter change in the future, but it will also be possible to identify the location of the leak from the amount of change.

• Journal of Internet of Things and Convergence
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• v.10 no.4
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• pp.9-19
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• 2024

In this paper, a real-time public transportation monitoring system is proposed. The proposed system was implemented by developing a public transportation app and utilizing optical sensors, pressure sensors, and an object detection algorithm. Additionally, a bus model was created to verify the system's functionality. The proposed real-time public transportation monitoring system has three key features. First, the app can monitor congestion levels within public transportation by detecting seat occupancy and the total number of passengers based on changes in optical and pressure sensor readings. Second, to prevent errors in the optical sensor that can occur when multiple passengers board or disembark simultaneously, we explored the possibility of using the YOLO object detection algorithm to verify the number of passengers through CCTV footage. Third, convenience is enhanced by displaying occupied seats in different colors on a separate screen. The system also allows users to check their current location, available public transportation options, and remaining time until arrival. Therefore, the proposed system is expected to offer greater convenience to public transportation users.

• Journal of Industrial Convergence
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• v.22 no.10
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• pp.21-28
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• 2024

Personal Mobility devices pose a significant safety risk to users depending on road conditions. With the recent surge in usage, the number of accidents has also increased, highlighting the need for a preventative system. This study aims to design and develop a warning system to enhance the safety of drivers by detecting road obstacles. To achieve this, a dataset is constructed using images of road obstacles, and the YOLOv5 model is trained. The system is based on the Raspberry Pi 4B, which processes video frames captured by a camera in real-time and triggers an LED warning when an obstacle is detected. The Flask framework is used to monitor the obstacle detection status in real time. Additionally, a GPS sensor is utilized to collect the user's location and speed data, and an auditory warning is triggered via a buzzer if the set speed is exceeded. In the future, this system could be expanded to transmit detected road obstacles and GPS information to a server, providing users with real-time road safety information. The results of this study can serve as essential technology for developing such a system.

• Geophysics and Geophysical Exploration
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• v.9 no.4
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• pp.279-290
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• 2006

The borehole radar methods used to tunnel detection are mainly classified into borehole radar reflection, directional antenna, crosshole scanning, and radar tomography methods. In this study, we have investigated the feasibility and limitation of each method to tunnel detection through case studies. In the borehole radar reflection data, there were much more clear diffraction signals of the upper wings than lower wings of the hyperbolas reflected from the tunnel, and their upper and lower wings were spreaded out to more than 10m higher and lower traces from the peaks of the hyperbolas. As the ratio of borehole diameter to antenna length increases, the ringing gets stronger on the data due to the increase in the impedance mismatching between antennas and water in the boreholes. It is also found that the reflection signals from the tunnel could be enhanced using the optimal offset distance between transmitter and receiver antennas. Nevertheless, the borehole radar reflection data could not provide directional information of the reflectors in the subsurface. Direction finding antenna system had a advantage to take a three dimensional location of a tunnel with only one borehole survey even though the cost is still very high and it required very high expertise. The data from crosshole scanning could be a good indicator for tunnel detection and it could give more reliable result when the borehole radar reflection survey is carried out together. The images of the subsurface also can be reconstructed using travel time tomography which could provide the physical property of the medium and would be effective for imaging the underground structure such as tunnels. Based on the results described above, we suggest a cost-effective field procedure for detection of a tunnel using borehole radar techniques; borehole radar reflection survey using dipole antenna can firstly be applied to pick up anomalous regions within the borehole, and crosshole scanning or reflection survey using directional antenna can then be applied only to the anomalous regions to detect the tunnel.