• Journal of Sensor Science and Technology
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• v.27 no.5
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• pp.352-356
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• 2018

This paper relates to a technology for monitoring a liquefied gas storage tank in the special gas field where demand is increasing owing to the continuous growth of related fields such as the semiconductor, display, and ICT convergence electronics industries. We have proposed a system for real - time monitoring using wireless sensor network technology, and implemented a system consisting of a sensor unit, transmitter module, and receiver module to be attached to a liquefied gas storage tank. The system was applied to LCO2 tanks among various liquefied gas storage tanks to verify the feasibility. The storage tanks employed in the experiments has capacities of 16,179 l and was 1,920 mm in inner diameter. Furthermore, the density was 1.03 g/l. The measured data were compared with reference data on the remaining gas level versus the $CO_2$ height of the surface, expressed using a conventional water meter, provided by an existing storage tank supplier. The experimental results show that the data is similar to the standard data provided by the tank supplier, and has a high accuracy and reliability within an error range of 0.03%.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.16 no.5
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• pp.31-39
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• 2017

Research in the machine tool industry has focused on ICT-based smart machines rather than hardware technologies related to machine tools. Real-time tool-status monitoring is representative of this type of technology and has become important for measuring sensors during cutting processes. In this paper, we studied several research areas and used a round bar to conduct fundamental research into the axial displacement of the main spindle of a tool when it was subjected to a machining load. We were able to use the gap sensor to detect the axial displacement indirectly by using grooves with various shapes on the round bar and sensing the gaps between the grooves. We then determined the optimal groove shape for monitoring the tool state.

• Journal of the Korean Institute of Gas
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• v.26 no.4
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• pp.41-57
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• 2022

The global demand for carbon neutrality in response to climate change is in a situation where it is necessary to prepare countermeasures for carbon trade barriers for some countries, including Korea, which is classified as an export-led economic structure and greenhouse gas exporter. Therefore, digital transformation, which is one of the predictable ways for the carbon-neutral transition model to be applied, should be introduced early. By applying digital technology to industrial gas manufacturing facilities used in one of the major industries, high-tech manufacturing industry, and hydrogen gas facilities, which are emerging as eco-friendly energy, abnormal detection, and diagnosis services are provided with cloud-based predictive diagnosis monitoring technology including operating knowledge. Here are the trends. Small and medium-sized companies that are in the blind spot of carbon-neutral implementation by confirming the direction of abnormal diagnosis predictive monitoring through optimization, augmented reality technology, IoT and AI knowledge inference, etc., rather than simply monitoring real-time facility status It can be seen that it is possible to disseminate technologies such as consensus knowledge in the engineering domain and predictive diagnostic monitoring that match the economic feasibility and efficiency of the technology. It is hoped that it will be used as a way to seek countermeasures against carbon emission trade barriers based on the highest level of ICT technology.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.480-490
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• 2018

Recently, The MRV (monitoring, reporting and verification) regulation, which measures, reports and verifies the emission gas of vessel to head for member countries of Europe Union (EU), is being implemented. As part this reason, we develop a system that estimates simple fuel consumption and recommends optimized stop-over ports of vessel, to calculate amount of carbon emission. To do this, we analyze fuel, distance and time consumption between port and the other port based on stored voyage data for over 10 years of real-ship, and implement a simple fuel consumption estimation module using analyzed result. Also, we design and implement the optimized route recommendation algorithm, existing navigation route display function including comparison with the optimized routes and user custom route plan function. Therefore, we expect the developed system is helpful when makes a navigation route and so on by reference indexes and we anticipate the system to have a sense for future research which learns and predicts for accuracy result.

• Journal of the Society of Disaster Information
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• v.15 no.1
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• pp.39-48
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• 2019

Purpose: This study aims at developing and implementing Smart Safety Management System based on IoT/ICT Convergence for safety of high-risk groups working at disaster or industrial field. Its functions are as follows. Method: We will develop three devices for keeping the safety of high-risk jobs: Sensor of inactivity, Lora based Refitting technology for communication between high-risk workers, and Lora Gateway for monitoring entire situations. Then we will test three devices in respect of their functions, and propose their applicabilities in the field. Results: The system can send and receive safety tags and danger signals by which sensor technology can detect dangerous state of workers. And its command terminal was developed by low-power wireless communication technology and LoRa Gateway, which can fulfill the lifting functions between safety tags. And, furthermore, the command terminal can monitor dangerous situations of disaster sites in real time and can perform the preemptive rescues. Conclusion: This study proves the functional efficacy of Smart Safety Management System for worker safety in various high-risk occupational groups, and also suggests ways to secure worker safety in disaster area and various high risk industrial sites.

• Journal of Digital Contents Society
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• v.18 no.6
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• pp.1175-1182
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• 2017

Recent trend in the manufacturing industry is focused on the convergence with IoT and Big Data, by emergence of the 4th Industrial Revolution. To realize a smart factory, the proposed system based on MTConnect technology collects and integrates various status information of machines from many production facilities including heterogeneous devices. Also it can distribute the acquisited status of heterogeneous manufacturing machines to the remote devices. As a key technology of a flexible automated production line, the proposed system can provide much possibility to manage important information such as error detection and processing state management in the unmanned automation line.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.511-513
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• 2022

Digital Health Care is the convergence of ICT and (non)medical technology, emphasizing the importance of prevent and monitoring health management in terms of new challenging medical paradigm: predictive, preventive, personalized and participatory. Beyond the limited medical industry of long-term care insurance, it is emerging that AI, IoT, Big Data related new services with new technologies in the 4^th revolution era. It is also noted that business field based on test bed is emergent; Caring Robot, wearable devices need to be launched in the market. Diverse service is possible with Big Data and AI etc.

• KIISE Transactions on Computing Practices
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• v.23 no.2
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• pp.85-96
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• 2017

IoT-Cloud service, integration of Internet of Things (IoT) and Cloud, is becoming a critical model for realizing creative and futuristic application services. Since IoT machines have little computing capacity, it is effective to attaching public Cloud resources for realizing IoT-Cloud service. Furthermore, utilizing containers and adopting a microservice architecture for developing IoT-Cloud service are useful for effective realization. The quality of microservice based IoT-Cloud service is affected by service function chaining which inter-connects each functions. For example, an issue with some of the functions or a bottleneck of inter-connection can degrade the service quality. To ensure functionality of the entire service, various test procedures considering various service environments are required to improve the service continuously. Hence in this paper, we introduce experimental realization of continuous integration based on DevOps and employ application performance monitoring for Node.js based IoT-Cloud service. Then we discuss its effectiveness.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.536-546
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• 2022

A society will lose a lot of something in this field when the forest fire broke out. If a forest fire can be detected in advance, damage caused by the spread of forest fires can be prevented early. So, we studied how to detect forest fires using CCTV currently installed. In this paper, we present a deep learning-based model through efficient image data construction for monitoring forest fire smoke, which is unstructured data, based on the deep learning model YOLOv5. Through this study, we conducted a study to accurately detect forest fire smoke, one of the amorphous objects of various forms, in YOLOv5. In this paper, we introduce a method of self-learning by producing insufficient data on its own to increase accuracy for unstructured object recognition. The method presented in this paper constructs a dataset with a fixed labelling position for images containing objects that can be extracted from the original image, through the original image and a model that learned from it. In addition, by training the deep learning model, the performance(mAP) was improved, and the errors occurred by detecting objects other than the learning object were reduced, compared to the model in which only the original image was learned.