• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.4
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• pp.637-645
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• 2022

Cloud computing has been gaining popularity over decades, and container, a technology that is primarily used in cloud native applications, is also drawing attention. Although container technologies are lighter and more capable than conventional VMs, there are several security threats, such as sharing kernels with host systems or uploading/downloading images from the image registry. one of which can refer to the integrity of container images. In addition, runtime security while the container application is running is very important, and monitoring the behavior of the container application at runtime can help detect abnormal behavior occurring in the container. Therefore, in this paper, first, we implement a signing checker that automatically checks the signature of an image based on the existing Docker Content Trust (DCT) technology to ensure the integrity of the container image. Next, based on falco, an open source project of Cloud Native Computing Foundation(CNCF), we introduce newly created image for the convenience of existing falco image, and propose implementation of docker-compose and package configuration that easily builds a monitoring system.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.104-108
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• 2023

This paper deals with research on innovative systems using Python-based artificial intelligence technology in the field of plant growth monitoring. The importance of monitoring and analyzing the health status and growth environment of plants in real time contributes to improving the efficiency and quality of crop production. This paper proposes a method of processing and analyzing plant image data using computer vision and deep learning technologies. The system was implemented using Python language and the main deep learning framework, TensorFlow, PyTorch. A camera system that monitors plants in real time acquires image data and provides it as input to a deep neural network model. This model was used to determine the growth state of plants, the presence of pests, and nutritional status. The proposed system provides users with information on plant state changes in real time by providing monitoring results in the form of visual or notification. In addition, it is also used to predict future growth conditions or anomalies by building data analysis and prediction models based on the collected data. This paper is about the design and implementation of Python-based plant growth monitoring systems, data processing and analysis methods, and is expected to contribute to important research areas for improving plant production efficiency and reducing resource consumption.

• Progress in Medical Physics
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• v.32 no.2
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• pp.40-49
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• 2021

Purpose: This study aimed to develop a surface-guided radiosurgery system customized for a neurosurgery clinic that could be used as an auxiliary system for improving the accuracy, monitoring the movements of patients while performing hypofractionated radiosurgery, and minimizing the geometric misses. Methods: RGB-D cameras were installed in the treatment room and a monitoring system was constructed to perform a three-dimensional (3D) scan of the body surface of the patient and to express it as a point cloud. This could be used to confirm the exact position of the body of the patient and monitor their movements during radiosurgery. The image from the system was matched with the computed tomography (CT) image, and the positional accuracy was compared and analyzed in relation to the existing system to evaluate the accuracy of the setup. Results: The user interface was configured to register the patient and display the setup image to position the setup location by matching the 3D points on the body of the patient with the CT image. The error rate for the position difference was within 1-mm distance (min, -0.21 mm; max, 0.63 mm). Compared with the existing system, the differences were found to be as follows: x=0.08 mm, y=0.13 mm, and z=0.26 mm. Conclusions: We developed a surface-guided repositioning and monitoring system that can be customized and applied in a radiation surgery environment with an existing linear accelerator. It was confirmed that this system could be easily applied for accurate patient repositioning and inter-treatment motion monitoring.

• Journal of Welding and Joining
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• v.27 no.6
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• pp.9-16
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• 2009

In this paper, the coaxial monitoring system to capture image of weld pool was developed in laser-arc hybrid welding. In order to obtain the reliable image, green laser was used as a illumination system and measuring components such as band pass filter, ND (Neutral Density) filter and shutter speed was designed and optimized. Using this monitoring system, weld pool images were captured according to laser power, welding speed, welding current and interspace between laser and arc through the experiment. ANOVA (Analysis of Variation) was carried out to identify the influence of process variables on bead widths extracted from captured images of monitoring system. Welding speed and current were major factor to affect weld pool.

• International Journal of Advanced Culture Technology
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• v.10 no.2
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• pp.252-259
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• 2022

Artificial intelligence is used in fusion with image processing techniques using cameras. Image processing technology is a technology that processes objects in an image received from a camera in real time, and is used in various fields such as security monitoring and medical image analysis. If such image processing reduces the accuracy of recognition, providing incorrect information to medical image analysis, security monitoring, etc. may cause serious problems. Therefore, this paper uses a mixture of YOLOv4-tiny model and image processing algorithm and uses the COCO dataset for learning. The image processing algorithm performs five image processing methods such as normalization, Gaussian distribution, Otsu algorithm, equalization, and gradient operation. For RGB images, three image processing methods are performed: equalization, Gaussian blur, and gamma correction proceed. Among the nine algorithms applied in this paper, the Equalization and Gaussian Blur model showed the highest object detection accuracy of 96%, and the gamma correction (RGB environment) model showed the highest object detection rate of 89% outdoors (daytime). The image binarization model showed the highest object detection rate at 89% outdoors (night).

• Journal of Biosystems Engineering
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• v.32 no.3
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• pp.173-178
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• 2007

This research was conducted to develop and analyze a wireless monitoring system for judging if sick or dead layers (SDL) exist in multi-tier layers battery (MLB) by machine vision, and to evaluate the performance between a wired monitoring system and it. This study used the AP (Access Point), the RS-285 to RS-232 converter, RS-232 to Ethernet converter, PICBASIC board and upgraded lump image processing method to change wired monitoring system into wireless monitoring system. The system was tested at a pilot farm and farm layer house. Results showed that monitoring judgement success rate at a pilot farm on normal cage (without SDL) was 82.3% and that on abnormal cage (with SDL) was 87.5%, respectively. And communication performance test results showed at farm layer house was $700{\sim}900$ kbps while equipments operated. There were dropped slightly than performance of wired monitoring system, however, the quantity was too small to make a significant difference of performance of the controling system developed for wireless communication.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.26 no.3
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• pp.272-277
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• 2017

CCTV image processing techniques have been developed for safety management in manufacturing sites. However, CCTV growth has become a social problem for video surveillance with regard to privacy. This study aims to manage the safety system efficiently and protect privacy simultaneously. In this study, the CCTV monitoring system is composed of five steps (accident monitoring, detection, notification, management, restoration). De-identified image is observed when we are in a normal situation. De-identified image changes to identified image when it detects an accident. As soon as it detects an accident, the accident information is sent to the safety administrator. Then the administrator could conduct safety measures. Afterward, accumulated accident data could be used for statistical data that could be utilized as analyzing expecting accident.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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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.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.850-852
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• 2004

The usable output of glassware formation production line depends, among other factors, on the weight and shape of a lump of hot molten glass called a gob. In this paper, an automated image processing system for monitoring gob weight and shape on a production line is proposed. Present techniques often rely on manual weighing of gobs on a sample basis by manually controlling the gob feeder. The proposed automated image monitoring system checks the weight and shape of every free-falling gob before it reaches the mould. In this system, the estimated weight of the gob is sufficiently close to its actual weight.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.166-168
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• 2007

This paper is dealing with how to control of a client robot's movement for instructions from a server PC and a wireless andremote control Robot that sends the server information of images for monitoring. To implement this. 802.11x WLAN with TCP/IP socket programming is used to get the driving instructions from the server PC and control movements of the robot such as a forward, backward and directions. As well as this, ARM9 cored PAX255 embedded processor and Linux OS is used for the function transmitting BMP format of 320 ${\times}$ 240 pixel for stopped image data.