• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.3
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• pp.123-131
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• 2019

In recent years, a non-contact respiration and heart rates monitoring via IR-UWB radar has been paid much attention to in various applications - patient monitoring, occupancy detection, survivor exploring in disaster area, etc. In this paper, we address a novel approach of real time heart rate estimation using IR-UWB radar. We apply sine fitting and peak detection method for estimating respiration rate and heart rate, respectively. We also deploy two techniques to mitigate the error caused by wrong estimation of respiration rate: a moving average filter and finding the frequency of the highest occurrence. Experimental results show that the algorithm can estimate heart rate in real time when respiration rate is presumed to be estimated accurately.

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.902-909
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• 2020

It is announced a new procedure for the real-time overall hull response monitoring system depends on inclinometer sensor data. The procedure requires a few inclinometer sensors' data, located on the deck. Sensor data is used to obtain curvature values; and curvature values are used to find out displacements or relevant moment values according to pre-calculated moment-curvature diagrams. Numerical studies are demonstrated with reasonable accuracy for the pre-ultimate and the post-ultimate nonlinear behaviors. Elastic, inelastic, and post-collapse structural bending moment capacity determination of the hull has been presented. The proposed inverse engineering technique will be able to see the response of the hull in real-time with high accuracy to manage the course and speed when cruising or control the loading and the unloading process at the port.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1085-1096
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• 2022

The manufacturing industry is continuously pursuing advanced technology and smartization as it converges with innovative technology. Improvement of manufacturing productivity is achieved by monitoring, analyzing, and controlling the facilities and processes of the manufacturing site in real time through a network. In this paper, we proposed a new OPC-UA based digital twin model for smart factory facilities. A testbed system for USB flash drive packaging facility was implemented based on the proposed digital twin model and OPC-UA data communication scheme. Through OPC-UA based digital twin model, equipment and process status information is transmitted and received from PLC to monitoring and control 3D digital models and physical models in real time. The usefulness of the developed digital twin testbed system was evaluated through usability test.

• International Journal of Korean Welding Society
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• v.3 no.1
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• pp.45-50
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• 2003

Welding using lasers can be mass-produced in high speed. In the laser welding, performing real-time monitoring system of the welding quality is very important in enhancing the efficiency of welding. In this study, the plasma and molten metal which are generated during laser welding were measured using the UV sensor and IR sensors. The results of laser welding were classified into five categories such as optimal heat input, little low heat input, low heat input, partial joining due to gap mismatch, and nozzle deviation. Also, a system was formulated which uses the measured signals with a fuzzy pattern recognition method which is used to perform real-time evaluation of the welding quality and the defects which can occur in laser welding.

• International conference on construction engineering and project management
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• 2017.10a
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• pp.252-259
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• 2017

Nowadays, the existing manual method for recording actual progress of the construction site has some drawbacks, such as great reliance on the experience of professional engineers, work-intensive, time consuming and error prone. A method integrating computer vision and BIM(Building Information Modeling) is presented for indoor automatic progress monitoring. The developed method can accurately calculate the engineering quantity of target component in the time-lapse images. Firstly, sample images of on-site target are collected for training the classifier. After the construction images are identified by edge detection and classifier, a voting algorithm based on mathematical geometry and vector operation will divide the target contour. Then, according to the camera calibration principle, the image pixel coordinates are conversed into the real world Coordinate and the real coordinates would be corrected with the help of the geometric information in BIM model. Finally, the actual engineering quantity is calculated.

• International Journal of Contents
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• v.3 no.1
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• pp.24-28
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• 2007

This paper embodies the agent based cinder monitoring system which supports PDA(Personal Digital Assistant). Monitoring system automatically manages data by using data managing agents such as a state managing agent, a location managing agent, a badness managing agent, a circumstances managing agent, etc, and uses a massive data processing agent to manage massive data. The development of agent based data monitoring system for the stable cinder reuse will be an epoch-making method to develop the process mechanized or manual-labored that widely spreads into the real-time automated process.

• International Journal of Contents
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• v.4 no.4
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• pp.7-11
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• 2008

This paper embodies the agent based cinder monitoring system which supports PDA{Personal Digital Assistant). Monitoring system automatically manages data by using data managing agents such as a state managing agent, a location managing agent, a badness managing agent, a circumstances managing agent, etc, and uses a massive data processing agent to manage massive data. The development of agent based data monitoring system for the stable cinder reuse will be an epoch-making method to develop the process mechanized or manual-labored that widely spreads into the real-time automated process.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1243-1244
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• 2022

In recent years, the growing interest in off-site construction has led to factories scaling up their manufacturing and production processes in the construction sector. Consequently, continuous large-scale site monitoring in low-variability environments, such as prefabricated components production plants (precast concrete production), has gained increasing importance. Although many studies on computer vision-based site monitoring have been conducted, challenges for deploying this technology for large-scale field applications still remain. One of the issues is collecting and transmitting vast amounts of video data. Continuous site monitoring systems are based on real-time video data collection and analysis, which requires excessive computational resources and network traffic. In addition, it is difficult to integrate various object information with different sizes and scales into a single scene. Various sizes and types of objects (e.g., workers, heavy equipment, and materials) exist in a plant production environment, and these objects should be detected simultaneously for effective site monitoring. However, with the existing object detection algorithms, it is difficult to simultaneously detect objects with significant differences in size because collecting and training massive amounts of object image data with various scales is necessary. This study thus developed a large-scale site monitoring system using edge computing and a small-object detection system to solve these problems. Edge computing is a distributed information technology architecture wherein the image or video data is processed near the originating source, not on a centralized server or cloud. By inferring information from the AI computing module equipped with CCTVs and communicating only the processed information with the server, it is possible to reduce excessive network traffic. Small-object detection is an innovative method to detect different-sized objects by cropping the raw image and setting the appropriate number of rows and columns for image splitting based on the target object size. This enables the detection of small objects from cropped and magnified images. The detected small objects can then be expressed in the original image. In the inference process, this study used the YOLO-v5 algorithm, known for its fast processing speed and widely used for real-time object detection. This method could effectively detect large and even small objects that were difficult to detect with the existing object detection algorithms. When the large-scale site monitoring system was tested, it performed well in detecting small objects, such as workers in a large-scale view of construction sites, which were inaccurately detected by the existing algorithms. Our next goal is to incorporate various safety monitoring and risk analysis algorithms into this system, such as collision risk estimation, based on the time-to-collision concept, enabling the optimization of safety routes by accumulating workers' paths and inferring the risky areas based on workers' trajectory patterns. Through such developments, this continuous large-scale site monitoring system can guide a construction plant's safety management system more effectively.

• Journal of the Korean Dietetic Association
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• v.20 no.2
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• pp.123-132
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• 2014

This study investigated the effect of food consumption monitoring and real-time communication-based weight control program using a smartphone on weight reduction and maintenance in obese women. This study consisted of two phases: an 8-week weight control program with food consumption monitoring and real-time communication using a smartphone and an 8-week follow-up. Sixteen obese female college students (>30% body fat) participated in this program, and we measured changes in body composition and dietary intake volume. Follow-up analysis focused on weight maintenance after 8 weeks of no contact. Total energy intake (P<0.01), body weight (P<0.001), percent body fat (P<0.001), body fat (P<0.05), and body mass index (P <0.001) decreased significantly after the 8-week weight control program. However, we could not observe total energy intake or body composition regain after the follow-up period. These results suggest that food consumption monitoring and real-time communication using a smartphone can be effective for weight control and maintenance.

• The Journal of the Korea Contents Association
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• v.16 no.2
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• pp.221-230
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• 2016

As structures' long term performances and users' safety have been highlighted, a new maintenance technique using IT has drawn attention around the globe. Therefore, throughout the paper, by analyzing bridge's static and dynamic data using wireless measuring sensor, a "real time smart bridge monitoring system" has developed. Smart bridge monitoring system is consists of three main parts a sensor that can measure major members' movement, a wireless system that informs the data from the sensor, and the database system that analysis the data. In order to test the performance of the system, five different were placed on the Olympic Bridge, Seoul. The power system of the sensors was replaced by self-sustain solar energy system. In order to validate data from the real time smart bridge monitoring system, the data was collected for a week from both wireless system and the wired system and the two data were compared to see the relevance.