• Journal of Korea Multimedia Society
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• v.22 no.5
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• pp.613-625
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• 2019

The rise of the Internet of Things (IoT) devices have greatly influenced many industries and one of them is healthcare where wearable devices started to track all your daily activities for better health monitoring accuracy and even down to tracking daily food intake in some cases. With the amounts of data that are being tracked and shared between from these devices, questions were raised on how to uphold user's data privacy when data is shared between these IoT devices and third party. With the blockchain platforms started to mature since its inception, the technology can be implemented according to a variety of use case scenarios. In this paper, we present a system architecture based on the healthcare system and IoT network by leveraging on multiple blockchain networks as the medium in between that should enable users to have direct authority on data accessibility of their shared data. We provide proof of concept implementation and highlight the results from our testing to show how the efficiency and scalability of the healthcare system improved without having a significant impact on the performance of the Electronic Medical Record (EMR) that mostly affected by the previous solution since these solutions directly connected to a public blockchain network and which resulted in significant delays and high cost of operation when a large amount of data or complicated functions are involved.

• Journal of Science and Technology Studies
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• v.19 no.2
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• pp.207-249
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• 2019

This study aims to show the dynamics behind official historical descriptions, tracking the development of information system for Seoul Olympic Games. Shortly after the decision was made to host the Seoul Olympics, the South Korean government planned to import a computer system used in the previous Olympics. In response, scientists at Electronic Data Processing Center of KAIST put much effort into persuading the authorities to take the initiative in their own development, and they had to prove their capabilities by developing a computer system that worked safely in the National Sports Festival. With the development of the Olympic information system taking shape, various disputes arose among the host ministries, participating organizations and researchers. As a result, the end product resulted in a modularity of four different systems developed by different agencies, rather than a single system. For this reason, no organization claimed ownership or directly inherited the technical performance of the Olympic computer system, although it was praised as a "gold medal outside the arena." Nevertheless, the technology, hardware and manpower accumulated during the development process have since spread to various areas, laying the groundwork for the development of South Korea's information and communication technology.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.3
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• pp.187-194
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• 2019

The off-axis Infra-Red(IR) seeker is mounted on the nose cone side of the anti-air high speed missile to alleviate thermal shield effect due to aerodynamic heating. The seeker output can not be regarded as the Line-of-Sight(LOS) rate any more as missile's roll motion to keep the target tracking is associated. In this paper, we propose a method to calculate the LOS rate for off-axis seeker on a 2-axis gimbal. Firstly, true LOS rate equations are analytically derived but not implementable because boresight error rate is not measurable. And then the first order lag approximation to obtain boresight error rate is proposed. The proposed LOS rate calculation method can compensate the coupling effect by considering the rotations of missile and gimbal. The performance of the proposed method is verified via full nonlinear 6-DOF(Degree of Freedom) simulations.

• The Journal of the Korea Contents Association
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• v.19 no.5
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• pp.417-425
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• 2019

Storage snapshot technology allows to preserve data at a specific point in time, and recover and access data at a desired point in time. It is an essential technology for storage protection application. Existing snapshot methods have some problems in that they dependent on storage hardware vendor, file system or virtual block device. In this paper, we propose a new snapshot method for solving the problems and creating snapshots on-line. The proposed snapshot method uses a method of extracting the log records of update operations at the virtual file system layer to enable the snapshot method to operate independently on file systems, virtual block devices, and storage hardwares. In addition, the proposed snapshot mehod creates and manages snapshots for directories and files without interruption to the storage service. Finally, through experiments we measure the snapshot creation time and the performance degradation caused by the snapshot.

• The Journal of Korean Institute of Information Technology
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• v.17 no.9
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• pp.91-97
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• 2019

When a radar tracks the warhead of a ballistic missile, decoys of a ballistic missile put a heavy burden on the radar resource management tracking the targets. To reduce this burden, it is necessary to be able to separate the signal of the warhead from the received dynamic radar cross section (RCS) signal on the radar. In this paper, we propose the method of separating the dynamic RCS of each target from the received signal by the Hough transform which extracts straight lines from the image. The micro motion of the targets was implemented using a 3D CAD model of the warhead and decoys. Then, we calculated the dynamic RCS from the 3D CAD model having micromotion and verified the performance by applying the proposed algorithm. Simulation results show that the proposed method can separate the signals of the warhead and decoys at the signal-to-noise ratio (SNR) of 10dB.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.672-678
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• 2020

Ultra-high-speed images that accurately depict the minute movements of objects have become common as low-cost and high-performance cameras that can film at high speeds have emerged. In this paper, the proposed method removes unexpected noise contained in images after input at high speed, and then extracts an area of interest that can represent personal information, such as skin areas, from the image in which noise has been removed. In this paper, noise generated by abnormal electrical signals is removed by applying bilateral filters. A color model created through pre-learning is then used to extract the area of interest that represents the personal information contained within the image. Experimental results show that the introduced algorithms remove noise from high-speed images and then extract the area of interest robustly. The approach presented in this paper is expected to be useful in various applications related to computer vision, such as image preprocessing, noise elimination, tracking and monitoring of target areas, etc.

• Journal of IKEEE
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• v.26 no.3
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• pp.364-372
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• 2022

This paper proposes the design and implementation results for human and object classification systems utilizing frequency modulated continuous wave (FMCW) radar sensor. Such a system requires the process of radar sensor signal processing for multi-target detection and the process of deep learning for the classification of human and object. Since deep learning requires such a great amount of computation and data processing, the lightweight process is utmost essential. Therefore, binary neural network (BNN) structure was adopted, operating convolution neural network (CNN) computation in a binary condition. In addition, for the real-time operation, a hardware accelerator was implemented and verified via FPGA platform. Based on performance evaluation and verified results, it is confirmed that the accuracy for multi-target classification of 90.5%, reduced memory usage by 96.87% compared to CNN and the run time of 5ms are achieved.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.39-46
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• 2021

In this paper, we propose a method for the motion recognition method required in the industrial field in mixed reality. In industrial sites, movements (grasping, lifting, and carrying) are required throughout the upper full-body, from trunk movements to arm movements. In this paper, we use a method composed of sensors and wearable devices that are not vision-based such as Kinect without using heavy motion capture equipment. We used two IMU sensors for the trunk and shoulder movement, and used Myo arm band for the arm movements. Real-time data coming from a total of 4 are fused to enable motion recognition for the entire upper body area. As an experimental method, a sensor was attached to the actual clothes, and objects were manipulated through synchronization. As a result, the method using the synchronization method has no errors in large and small operations. Finally, through the performance evaluation, the average result was 50 frames for single-handed operation on the HoloLens and 60 frames for both-handed operation.

• Journal of Broadcast Engineering
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• v.26 no.6
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• pp.738-747
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• 2021

Today, with the improvement of deep learning technology, computer vision areas such as image classification, object detection, object segmentation, and object tracking have shown remarkable improvements. Various applications such as intelligent surveillance, robots, Internet of Things, and autonomous vehicles in combination with deep learning technology are being applied to actual industries. Accordingly, the requirement of an efficient compression method for video data is necessary for machine consumption as well as for human consumption. In this paper, we propose an object-based compression of thermal infrared images for machine vision. The input image is divided into object and background parts based on the object detection results to achieve efficient image compression and high neural network performance. The separated images are encoded in different compression ratios. The experimental result shows that the proposed method has superior compression efficiency with a maximum BD-rate value of -19.83% to the whole image compression done with VVC.

• Smart Structures and Systems
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• v.31 no.4
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• pp.393-407
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• 2023

To assess structural condition in a non-destructive manner, computer vision-based structural health monitoring (SHM) has become a focus. Compared to traditional contact-type sensors, the advantages of computer vision-based measurement systems include lower installation costs and broader measurement areas. In this study, we propose a novel computer vision-based vibration measurement and coarse-to-fine damage assessment method for truss bridges. First, a deep learning model FairMOT is introduced to track the regions of interest (ROIs) that include joints to enhance the automation performance compared with traditional target tracking algorithms. To calculate the displacement of the tracked ROIs accurately, a normalized cross-correlation method is adopted to fine-tune the offset, while the Harris corner matching is utilized to correct the vibration displacement errors caused by the non-parallel between the truss plane and the image plane. Then, based on the advantages of the stochastic damage locating vector (SDLV) and Bayesian inference-based stochastic model updating (BI-SMU), they are combined to achieve the coarse-to-fine localization of the truss bridge's damaged elements. Finally, the severity quantification of the damaged components is performed by the BI-SMU. The experiment results show that the proposed method can accurately recognize the vibration displacement and evaluate the structural damage.