• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.203-208
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• 2021

At the manufacturing site, workers have been operating by inputting materials into the manufacturing process and leaving input records according to the work instructions, but product LOT tracking has been not possible due to many omissions. Recently, it is being carried out as a system to automatically input materials using RFID-Tag. In particular, the initial automatic recognition rate was good at 97 percent by automatically generating input information through RACK (TAG) ID and RACK input time analysis, but the automatic recognition rate continues to decrease due to multi-material RACK, TAG loss, and new product input issues. It is expected that it will contribute to increasing speed and yield (normal product ratio) in the overall production process by improving automatic recognition rate and real-time monitoring through the establishment of artificial intelligent smart factory datasets.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.36 no.5
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• pp.93-103
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• 2020

The purpose of this study is to suggest a supervisory way to improve the efficiency of Building Supervision using Deep Learning, especially object detecting technology. Since the establishment of the Building Supervision system in Korea, it has been changed and improved many times systematically, but it is hard to find any improvement in terms of implementing methods. Therefore, the Supervision is until now the area where a lot of money, time and manpower are needed. This might give a room for superficial, formal and documentary supervision that could lead to faulty construction. This study suggests a way of Building Supervision which is more automatic and effective so that it can lead to save the time, effort and money. And the way is to detect the hoop-bars of a column and count the number of it automatically. For this study, we made a hoop-bar detecting network by transfor learnning of YOLOv2 network through MATLAB. Among many training experiments, relatively most accurate network was selected, and this network was able to detect rebar placement in building site pictures with the accuracy of 92.85% for similar images to those used in trainings, and 90% or more for new images at specific distance. It was also able to count the number of hoop-bars. The result showed the possibility of automatic Building Supervision and its efficiency improvement.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.3
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• pp.150-159
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• 2022

This paper presents an implementation of detecting whether a helmet is worn and there is a fall accident through individual image analysis in real-time from extracting the image objects of several workers active in the industrial field. In order to detect image objects of workers, YOLO, a deep learning-based computer vision model, was used, and for whether a helmet is worn or not, the extracted images with 5,000 different helmet learning data images were applied. For whether a fall accident occurred, the position of the head was checked using the Pose real-time body tracking algorithm of Mediapipe, and the movement speed was calculated to determine whether the person fell. In addition, to give reliability to the result of a falling accident, a method to infer the posture of an object by obtaining the size of YOLO's bounding box was proposed and implemented. Finally, Telegram API Bot and Firebase DB server were implemented for notification service to administrators.

• Journal of Positioning, Navigation, and Timing
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• v.1 no.1
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• pp.35-43
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• 2012

The UGV is one of the special field robot developed for mine detection, surveillance and transportation. To achieve successfully the missions of the UGV, the accurate and reliable navigation data should be provided. This paper presents design and implementation of multi-sensor-based open architecture integrated navigation for localization of UGV. The presented architecture hierarchically classifies the integrated system into four layers and data communications between layers are based on the distributed object oriented middleware. The navigation manager determines the navigation mode with the QoS information of each navigation sensor and the integrated filter performs the navigation mode-based data fusion in the filtering process. Also, all navigation variables including the filter parameters and QoS of navigation data can be modified in GUI and consequently, the user can operate the integrated navigation system more usefully. The conventional GPS/INS integrated system does not guarantee the long-term reliability of localization when GPS solution is not available by signal blockage and intentional jamming in outdoor environment. The presented integration algorithm, however, based on the adaptive federated filter structure with FDI algorithm can integrate effectively the output of multi-sensor such as 3D LADAR, vision, odometer, magnetic compass and zero velocity to enhance the accuracy of localization result in the case that GPS is unavailable. The field test was carried out with the UGV and the test results show that the presented integrated navigation system can provide more robust and accurate localization performance than the conventional GPS/INS integrated system in outdoor environments.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.4
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• pp.161-168
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• 2018

High blood pressure is main today's adult disease and existing blood pressure gauge is not possible for real-time blood pressure measurement and remote monitoring. But real-time blood pressure monitoring u-healthcare system makes effect health management. In my paper, for monitoring real-time blood pressure, an architecture of real-time blood pressure monitoring system which consisted of wrist type-blood pressure measurement, smart-phone and u-healthcare server is presented. And the analog circuit architecture which is major core function for pulse wave detection and digital hardware architecture for wrist type-blood pressure measurement is presented. Also for software development to operate this hardware system, UML analysis method and flowcharts and screen design for this software design are showed. Therefore such design method in my paper is expected to be useful for real-time blood pressure monitoring u-healthcare system implementation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.239-241
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• 2022

Currently, in Korea, a multi-seater lane (HOV) and a designated lane system are being implemented to solve traffic congestion. However, in both systems, it is difficult to crack down on cases of violations without permission, so people are required to be assigned to areas that want to crack down. In this process, manpower and budget are inefficiently consumed. To compensate for these shortcomings, we propose the development of an integrated enforcement system through YOLO, a deep learning object recognition model. If the two systems are implemented and integrated using YOLO, they will have advantages in terms of manpower and budget over existing systems because only data learning and system maintenance are considered. In addition, in the case of violations in which it is difficult for the existing unmanned system to crack down, the effect of increasing the crackdown rate through continuous learning can be expected.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.9
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• pp.273-282
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• 2023

Recently, various technologies such as logistics automation and port operations automation with ICT technology are being developed to build smart ports. However, there is a lack of technology development for port safety and safety accident prevention. This paper proposes an AI-based shipping container loading safety management system for the prevention of safety accidents at container loading fields in ports. The system consists of an AI-based shipping container safety accident risk classification and storage function and a real-time safety accident monitoring function. The system monitors the accident risk at the site in real-time and can prevent container collapse accidents. The proposed system is developed as a prototype, and the system is ecaluated by direct application in a port.

• The KIPS Transactions:PartB
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• v.11B no.1
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• pp.45-52
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• 2004

We propose ESS_WMCE. This paper explains the design and implementation of the EDSS running on ESS_WMCE. EDSS is a synchronization error control system for web based multimedia collaboration environment. We have an error detection approach by using hooking method. The technique of an error transmission is a mended model of utilizing an application sharing system. DOORAE is a good framework model for supporting development on application for computer supported cooperated works. It has primitive service functions. Service functions are implemented with an object oriented concept. It is a system that is suitable for detecting and sharing a software error rapidly occurring on web based multimedia collaboration environment by using software techniques. It is able to share an error as well as providing URL synchronization to access shared objects. When an error occurs, this system detects an error by using hooking methods in MS-Windows API(Application Program Interface) function. If an error is found, it is able to provide an error sharing to access shared objects.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.8
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• pp.89-96
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• 2017

This Paper proposes a vehicle detection system and a license plate recognition system from CCTV images installed on public roads. Since the environment of this system acquires the image in the general road environment, the stable condition applied to the existing vehicle entry / exit system is not given, and the input image is distorted and the resolution is irregular. At the same time, the viewing angle of the input image is more wide, so that the computation load is high and the recognition accuracy of the plate is likely to be lowered. In this paper, we propose an improved method to detect and recognize a license plate without a separate input control devices. The vehicle and license plate were detected based on the HOG feature descriptor, and the characters inside the license plate were recognized using the k-NN algorithm. Experimental environment was set up for the roads more than 45m away from the CCTV, Experiments were carried out on an entry vehicle capable of visually identifying license plate and Experimental results show good results of the proposed method.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.5
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• pp.431-437
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• 2006

In this paper vector code correlation(VCC) method and an algorithm to promote the image-processing performance in building an effective measurement system using cameras are described far automatically berthing and controlling the ship equipped with side-thrusters. In order to realize automatic ship berthing, it is indispensable that the berthing assistant system on the ship should continuously trace a target in the berth to measure the distance to the target and the ship attitude, such that we can make the ship move to the specified location. The considered system is made up of 4 apparatuses compounded from a CCD camera, a camera direction controller, a popular PC with a built-in image processing board and a signal conversion unit connected to parallel port of the PC. The object of this paper is to reduce the image-processing time so that the berthing system is able to ensure the safety schedule against risks during approaching to the berth. It could be achieved by composing the vector code image to utilize the gradient of an approximated plane found with the brightness of pixels forming a certain region in an image and verifying the effectiveness on a commonly used PC. From experimental results, it is clear that the proposed method can be applied to the measurement system for automatic ship berthing and has the image-processing time of fourfold as compared with the typical template matching method.