• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.6 no.10
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• pp.373-382
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• 2016

Under the Agricultural technology is influenced from climate that is requisite of seasonal. So this system will cover the problems and develop the agricultural industry as well. So far, the agricultural industry is developing however, it has the points of the weakness because of natural disasters such as wind risk and heavy snow. This paper designs system to change vinyl on the greenhouse. This is a preliminary study for the real-time feedback control of greenhouse. The study developed a wireless IoT sensor system based on authentic technology capacities, to integrate with the protected horticulture Management System. These system was used to evaluate the levels of the snow cover and wind through IoT devices. The existing greenhouse uses the warm water to clear snow or to change methods. This system will recover by changing the vinyl which is covered outside of the greenhouse. The points of the system is changing vinyl to spin pipe. It is contained extra vinyl. The effects of this system are minimized labor protected crops from natural disasters. For this purpose, the study first developed a wireless IoT sensor unit that integrates an MEMS device and wireless communication module. Also, the study developed an operating program that enables real-time response measurement. It will help operational and maintenance greenhouse as a result.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.5-8
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• 2018

Recently, global warming and tropical nights have caused the use of electric fans to explode. Under these circumstances, running the fan for long periods of time is not only a risk of explosion due to overloading the power but also a small amount of electricity and environmental pollution. Therefore, the research was conducted to develop devices that automatically turn fans on and off according to room temperature, reducing the risk of explosion as well as saving energy. This study suggested that electric fans turn on and off automatically and display temperature in mobile applications. The ability to turn on and off allows the fan to turn on itself if the indoor temperature rises above a certain level. Conversely, if the indoor temperature drops below a certain level, the fan should be turned off. Second, the temperature display function checks indoor temperature through mobile applications. The automatic on/off capabilities proposed in this study could contribute to reducing the risk of explosion and saving energy. However, if the indoor temperature rises above a certain temperature even though there is no one inside, the fan can be turned on. The expectation from this study is that the ability of fans to operate and turn off at appropriate temperatures can reduce the risk of explosion, electrical charges, and environmental pollution.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.8
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• pp.171-180
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• 2020

Smart Factory consists of digital automation solutions throughout the production process, including design, development, manufacturing and distribution, and it is an intelligent factory that installs IoT in its internal facilities and machines to collect process data in real time and analyze them so that it can control itself. The smart factory's equipment works in a physical combination of numerous hardware, rather than a virtual character being driven by a single object, such as a game. In other words, for a specific common goal, multiple devices must perform individual actions simultaneously. By taking advantage of the smart factory, which can collect process data in real time, if reinforcement learning is used instead of general machine learning, behavior control can be performed without the required training data. However, in the real world, it is impossible to learn more than tens of millions of iterations due to physical wear and time. Thus, this paper uses simulators to develop grid sortation systems focusing on transport facilities, one of the complex environments in smart factory field, and design cooperative multi-agent-based reinforcement learning to demonstrate efficient behavior control.

• Journal of the Society of Disaster Information
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• v.17 no.4
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• pp.684-693
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• 2021

Purpose: The purpose is to establish a safe facility environment from abnormal voltages such as lightning by developing a smart land resistance measuring device that can acquire real-time land resistance data using Arduino. Method: This paper studied design models and application cases by developing a land resistance acquisition and analysis system with Arduino and a power line communication (PLC) system. Some sites in the wind power generation complex in Gyeongsangnam-do were selected as test beds, and real-time land resistance data applied with new technologies were obtained. The electrode arrangement adopted a smart electrode arrangement using a combination of a Wenner four electrode arrangement and a Schlumberger electrode arrangement. Result: First, the characteristic of this technology is that the depth of smart multi-electrodes is organized differently to reduce the error range of the acquired data even in the stratigraphic structure with specificity between floors. Second, IT convergence technology was applied to enable real-time transmission and reception of information on land resistance data acquired from smart ground electrodes through the Internet of Things. Finally, it is possible to establish a regular management system and analyze big data accumulated in the server to check the trend of changes in various elements, and to model the optimal ground algorithm and ground system design for the IT convergence environment. Conclusion: This technology will reduce surge damage caused by lightning on urban infrastructure underlying the 4th industrial era and design an optimized ground system model to protect the safety and life of users. It is also expected to secure intellectual property rights of pure domestic technology to create jobs and revitalize our industry, which has been stagnant as a pandemic in the post-COVID-19 era.

• Journal of Industrial Convergence
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• v.20 no.3
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• pp.33-40
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• 2022

Recently, IoT-linked services have been used in various environments, and IoT and artificial intelligence technologies are being fused. However, since technologies that process IoT data stably are not fully supported, research is needed for this. In this paper, we propose a processing technique that can optimize IoT data after generating embedded vectors based on machine learning for IoT data. In the proposed technique, for processing efficiency, embedded vectorization is performed based on QR such as index of IoT data, collection location (binary values of X and Y axis coordinates), group index, type, and type. In addition, data generated by various IoT devices are integrated and managed so that load balancing can be performed in the IoT data collection process to asymmetrically link IoT data. The proposed technique processes IoT data to be orthogonalized based on hash so that IoT data can be asymmetrically grouped. In addition, interference between IoT data may be minimized because it is periodically generated and grouped according to IoT data types and characteristics. Future research plans to compare and evaluate proposed techniques in various environments that provide IoT services.

• Journal of the Korea Society of Computer and Information
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• v.24 no.12
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• pp.51-57
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• 2019

In this paper, we analyzed the performance of IoT based smart wearable mine detection device. There are various mine detection methods currently used by the military. Still, in the general field, mine detection is performed by visual detection, probe detection, detector detection, and other detection methods. The detection method by the detector is using a GPR sensor on the detector, which is possible to detect metals, but it is difficult to identify non-metals. It is hard to distinguish whether the area where the detection was performed or not. Also, there is a problem that a lot of human resources and time are wasted, and if the user does not move the sensor at a constant speed or moves too fast, it is difficult to detect landmines accurately. Therefore, we studied the smart wearable mine detection device composed of human body antenna, main microprocessor, smart glasses, body-mounted LCD monitor, wireless data transmission, belt type power supply, black box camera, which is to improve the problem of the error of mine detection using unidirectional ultrasonic sensing signal. Based on the results of this study, we will conduct an experiment to confirm the possibility of detecting underground mines based on the Internet of Things (IoT). This paper consists of an introduction, experimental environment composition, simulation analysis, and conclusion. Introduction introduces the research contents such as mines, mine detectors, and research progress. It consists of large anti-personnel mine, M16A1 fragmented anti-mine, M15 and M19 antitank mines, plastic bottles similar to mines and aluminum cans. Simulation analysis is conducted by using MATLAB to analyze the mine detection device implementation performance, generating and transmitting IoT signals, and analyzing each received signal to verify the detection performance of landmines. Then we will measure the performance through the simulation of IoT-based mine detection algorithm so that we will prove the possibility of IoT-based detection landmine.