• Journal of Internet of Things and Convergence
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• v.5 no.2
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• pp.73-79
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• 2019

Research is underway on services and systems that provide real-time alerts for suffocating gases and potentially explosive materials, but currently smart bend type services are lacking. This study supports real-time identification of explosion hazards due to static electricity in the workplace and immediate elimination of accident occurrence factors, real-time monitoring of worker status and workplace hazards (oxygen, hazardous chemical concentration), and immediate warning and data in case of danger. We propose a method of establishing an accident prevention system through analysis. In this way, various accidents that may occur in industrial sites are monitored using IoT-based intelligent sensor nodes, wireless network technology, data processing middleware, and integrated control system, and real-time risk information at the industrial sites is prevented and accidents are prevented. By supporting a safe working environment, the company can significantly reduce costs compared to post-procurement costs.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.3
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• pp.153-161
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• 2020

Due to the recent development of the 4th industrial revolution, Smart Factories that organically link various technologies such as AI, IoT, Cloud, and Big Data are increasing. Based on this, in the industrial environment where the internal process is controlled automatically, high availability should be secured against the loss caused when the internal process of the Smart Factory is stopped due to the determinism and malicious attack necessary to control the device such as PLC. The research and analysis of industrial network equipment and security equipment used in various industries can improve the efficiency and usability of industrial control systems in national infrastructure and can provide important feedback to build related infrastructure. Therefore, we compared industrial network equipment and security equipment in this paper in a variety of ways and expect to be used as a roadmap for developing technologies for industrial network equipment and industrial security equipment based on the results of this paper.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.1005-1018
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• 2017

This paper proposes an App Visualization (AppV) based on IoT Self-diagnosis Micro Control Unit (ISMCU) for accident prevention. It collects a current status of a vehicle through a sensor, visualizes it on a smart phone and prevents vehicles from accident. The AppV consists of 5 components. First, a Sensor Layer (SL) judges noxious gas from a current vehicle and a driver's driving habit by collecting data from various sensors such as an Accelerator Position Sensor, an O2 sensor, an Oil Pressure Sensor, etc. and computing the concentration of the CO collected by a semiconductor gas sensor. Second, a Wireless Sensor Communication Layer (WSCL) supports Zigbee, Wi-Fi, and Bluetooth protocol so that it may transfer the sensor data collected in the SL to ISMCU and the data in the ISMCU to a Mobile. Third, an ISMCU integrates the transferred sensor information and transfers the integrated result to a Mobile. Fourth, a Mobile App Block Programming Tool (MABPT) is an independent App generation tool that changes to visual data just the vehicle information which drivers want from a smart phone. Fifth, an Embedded Module (EM) records the data collected through a Smart Phone real time in a Cloud Server. Therefore, because the AppV checks a vehicle' fault and bad driving habits that are not known from sensors and performs self-diagnosis through a mobile, it can reduce time and cost spending on accidents caused by a vehicle's fault and noxious gas emitted to the outside.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.1013-1014
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• 2015

Regardless of the strict enforcement of prevention education in accordance with the school facility management standard, safety accidents that lead to human and physical damages occur in current educational facilities because of teenagers with very low sense of responsibility and insensitivity toward fire and facility safety. To ensure educational facility safety, technology that will enable a fast work process and easy confirmation of electronic blueprints and related documents about the educational facility through smart devices at the site by various means is needed. This paper proposes a system design linked to the National Education Information System (NEIS) that uses the document conversion function, high efficiency resolution, and Internet of Things (IoT) to inspect and control the educational facility in the event of a safety accident through the Educational Facility Resource Management System (EFRMS) that manages the electronic blueprints, and various educational facility documents through various smart devices.

• International journal of advanced smart convergence
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• v.4 no.2
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• pp.94-102
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• 2015

Optical camera communication (OCC) is an extension of Visible Light Communication. Different from traditional visible light communication, optical camera communications is an almost no additional cost technology by taking the advantage of build-in camera in devices. It was became a candidate for communication protocol for IoT. Camera module can be easy attached to IoT device, because it is small and flexible. Furthermore almost smartphone equip one or two camera for both back and font side with high quality and resolution. It can be utilized for receiving the data from LED or positioning. Actually, OCC combines illumination and communication. It can supply communication for special areas or environment where do not allow Radio frequency such as hospital, airplane etc. There are many concept and experiment be proposed. In this paper we proposed utilizing Android smart-phone camera for receiver and introduce new approach in modulation scheme for LED at transmitter. It also show how Manchester coding can be used encode bits while at the same time being successfully decoded by Android smart-phone camera. We introduce new data frame format for easy decoded and can be achieve high bit rate. This format can be easy to adapt to performance limit of Android operator or embedded system.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.5
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• pp.277-286
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• 2019

In this paper, we propose an efficient hyperplane generation technique to classify human activity from combination of events and sequence information obtained from multiple-event sensors. By generating hyperplane efficiently, our machine learning algorithm classify with less memory and run time than the LSVM (Linear Support Vector Machine) for embedded system. Because the fact that light weight and high speed algorithm is one of the most critical issue in the IoT, the study can be applied to smart home to predict human activity and provide related services. Our approach is based on reducing numbers of hyperplanes and utilizing robust string comparing algorithm. The proposed method results in reduction of memory consumption compared to the conventional ML (Machine Learning) algorithms; 252 times to LSVM and 34,033 times to LSTM (Long Short-Term Memory), although accuracy is decreased slightly. Thus our method showed outstanding performance on accuracy per hyperplane; 240 times to LSVM and 30,520 times to LSTM. The binarized image is then divided into groups, where each groups are converted to binary number, in order to reduce the number of comparison done in runtime process. The binary numbers are then converted to string. The test data is evaluated by converting to string and measuring similarity between hyperplanes using Levenshtein algorithm, which is a robust dynamic string comparing algorithm. This technique reduces runtime and enables the proposed algorithm to become 27% faster than LSVM, and 90% faster than LSTM.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.23-28
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• 2023

The installation rate of AMI(: Advanced Metering Infrastructure) capable of automatic electricity measurement is less than 43% nationwide and 10.5% in rural areas, which is very poor. Therefore, for the smart grid, automatic information recording of the watt-hour meter is required. For this purpose, it is necessary to develop a system capable of remote meter reading and use control by improving the existing watt-hour meter. In this paper, in order to enable the AMI function of the existing electricity meter, the remote meter reading and control technology of the existing electricity meter for AMI, the core of the smart grid, was developed using IoT and AI. The main research content was to recognize numbers using Tensorflow and Open-cv to convert it into a power meter sensing device for SG. We confirmed and checked the performance using the protyope system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.112-114
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• 2022

This paper proposes an E-Commerce service that supports large quantities of food waste sales generated by collective residences, including apartments, to consumers in urban areas, such as livestock farmers, through online transactions. Unlike general E-Commerce, the proposed service uses a smart food waste bin equipped with an IoT-based sensor and communication module to automatically collect the location information of each apartment and the amount of food waste to be displayed in a specialized E-Commerce platform. The key of this system is to provide information and sell it to consumers. The smart food waste bin periodically delivers its current capacity and location using a weight sensor, GPS sensor and LoRa communication module to a cloud-based database to be used in web or mobile applications. The proposed E-Commerce service is expected to help resolve the food waste disposal problem and revitalize the local economy by linking with a service that delivers food waste from each apartment to a nearby location where the buyer is located.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.6
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• pp.839-849
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• 2017

Recently, the role of the kitchen has evolved from the space for previous survival to the space that shows the present life and culture. Along with these changes, the use of IoT technology is spreading. As a result, the development and diffusion of new smart devices in the kitchen is being achieved. The user experience for using these smart devices is also becoming important. For a natural interaction between a user and a computer, better interactions can be expected based on context awareness. This paper examines the Natural User Interface (NUI) that does not touch the device based on the user interface (UI) of the smart device used in the kitchen. In this method, we use the image processing technology to recognize the user's hand gesture using the camera attached to the device and apply the recognized hand shape to the interface. The gestures used in this study are proposed to gesture according to the user's context and situation, and 5 kinds of gestures are classified and used in the interface.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.546-549
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• 2022

The installation rate of AMI (advanced metering infrastructure) capable of automatic electricity measurement is less than 43% nationwide and 10.5% in rural areas, which is very poor. Therefore, for the smart grid, automatic information recording of the watt-hour meter is required. For this purpose, it is necessary to develop a system capable of remote meter reading and use control by improving the existing watt-hour meter. In this paper, in order to enable the AMI function of the existing electricity meter, the remote meter reading and control technology of the existing electricity meter for AMI, the core of the smart grid, was developed using IoT and AI. The main research content was to recognize numbers using Tensorflow and Open-cv to convert it into a power meter sensing device for SG. We confirmed and checked the performance using the protyope system.