• Journal of Sensor Science and Technology
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• v.27 no.3
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• pp.199-203
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• 2018

In this study, we develop a methane sensor module that can detect low concentrations below 5,000 ppm and measure up to the detection limit of 50 ppm with the NDIR method, with a long lifetime and high accuracy. Methane ($CH_4$) is one of a representative greenhouse gas, which is very explosive. Thus, it is important to quickly and accurately measure methane concentration in the air. To adjust the methane sensor for industrial field applications, a NDIR-based small sensor was implemented and characterized, where its volume was $4cm{\times}4cm{\times}2cm$ and its response time ($T_{90}$) was less than 30 sec. These results demonstrate that the proposed sensor is commercially available for low-concentration measurement, low volume, and fast response application, such as IoT sensor nodes and portable devices.

• Journal of Korea Multimedia Society
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• v.23 no.5
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• pp.673-682
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• 2020

Buildings are becoming more and more high-rise and large-scale in recent years, so in the event of a disaster such as a fire, enormous human and economic damage is expected. Therefore, management, security, and fire control are essential for large buildings in the city. Because these large buildings are very complex outside and inside, they need a three-dimensional control based on 3D modeling rather than a simple flat-oriented control. To do this, this paper designed and implemented a building control system based on 3D modeling. Specifically, we designed a 3D building / facility editing module for 3D modeling of buildings, a 3D based control module for building control, and a linkage module that connects information such as firefighting equipment, electrical equipment and IoT equipment. Based on this design, a building control system based on 3D modeling was implemented.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.7
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• pp.262-269
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• 2020

Recently, many attempts have been made to reduce the time required for payment in various shopping environments. In addition, for the Fourth Industrial Revolution era, artificial intelligence is advancing, and Internet of Things (IoT) devices are becoming more compact and cheaper. So, by integrating these two technologies, access to building an unmanned environment to save people time has become easier. In this paper, we propose a smart shopping cart system based on low-cost IoT equipment and deep-learning object-detection technology. The proposed smart cart system consists of a camera for real-time product detection, an ultrasonic sensor that acts as a trigger, a weight sensor to determine whether a product is put into or taken out of the shopping cart, an application for smartphones that provides a user interface for a virtual shopping cart, and a deep learning server where learned product data are stored. Communication between each module is through Transmission Control Protocol/Internet Protocol, a Hypertext Transmission Protocol network, a You Only Look Once darknet library, and an object detection system used by the server to recognize products. The user can check a list of items put into the smart cart via the smartphone app, and can automatically pay for them. The smart cart system proposed in this paper can be applied to unmanned stores with high cost-effectiveness.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.6
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• pp.672-680
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• 2019

This paper proposes "A Smart Farm Environment Optimization and Yield Prediction Platform based on IoT and Deep Learning" which gathers bio-sensor data from farms, diagnoses the diseases of growing crops, and predicts the year's harvest. The platform collects all the information currently available such as weather and soil microbes, optimizes the farm environment so that the crops can grow well, diagnoses the crop's diseases by using the leaves of the crops being grown on the farm, and predicts this year's harvest by using all the information on the farm. The result shows that the average accuracy of the AEOM is about 15% higher than that of the RF and about 8% higher than the GBD. Although data increases, the accuracy is reduced less than that of the RF or GBD. The linear regression shows that the slope of accuracy is -3.641E-4 for the ReLU, -4.0710E-4 for the Sigmoid, and -7.4534E-4 for the step function. Therefore, as the amount of test data increases, the ReLU is more accurate than the other two activation functions. This paper is a platform for managing the entire farm and, if introduced to actual farms, will greatly contribute to the development of smart farms in Korea.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.335-338
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• 2017

Presently, the collaboration hardware system and software technology that promoted commercializing ICBMS for integrated system visibility evaluation. This variation will move on the next pervasive period that mixed with cultural and technology convergence. There is possibility for the period system can invoke unpredictable confusing blank state. The blank state systems have ecosystem characteristics that are supplied, maintained and operated through the complex interactions of technology and culture. Using universal middleware can support the life-cycle model and increase the visibility of complex systems and prepare for confusing situations. In this study, based on universal middleware, data and service dynamic standardized modules were evaluated to support stable system visibility platform. The system visibility module consists of Intelligent Pervasive Cloud module, Memorial Service module and Life Cycler connection module. each module reflects various requirements of system visibility requested by external system. In addition, the analysis results are supported by various network application service standards through platform independent system and architecture.

• Journal of the Korean Solar Energy Society
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• v.38 no.3
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• pp.21-28
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• 2018

The objects of PV (Photovoltaic) monitoring system is to reduce the loss of system and operation and maintenance costs. In case of PV plants with configured of centralized inverter type, only 1 PV module might be caused a large loss in the PV plant. For this reason, the monitoring technology of PV module-level that find out the location of the fault module and reduce the system losses is interested. In this paper, a fault diagnosis algorithm are proposed using thermal and electrical characteristics of PV modules under failure. In addition, the monitoring system applied with proposed algorithm was constructed. The wireless sensor using LoRa chip was designed to be able to connect with IoT device in the future. The characteristics of PV module by shading is not failure but it is treated as a temporary failure. In the monitoring system, it is possible to diagnose whether or not failure of bypass diode inside the junction box. The fault diagnosis algorithm are developed on considering a situation such as communication error of wireless sensor and empirical performance evaluation are currently conducting.

• Journal of Sensor Science and Technology
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• v.24 no.5
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• pp.331-341
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• 2015

In this paper, real-time water quality monitoring system of small water supply facilities based on IEEE 802.15.4e-2012 DSME MAC and IEEE 802.15.4g-2012 PHY standard is presented, which is capable to acquire for highly reliable water quality information in the wide outdoor areas for effective water quality management of small water quality facilities is distributed in the long distance and remote areas. Previously, Long distance transmission is difficult in most water quality sensor module is using RS-485 protocol. But with this system, even in harsh outdoor environment, it is possible to establish a radio wave sensor in a wide area network, and not only water quality sensor shall be connected to the wireless system, but also wireless integrated management system shall provide more effective way of management of the numerous small water supply facilities spread throughout the community, so that the administrator can remotely monitor the data of water turbidity, pH, residual chlorine in the water-supply, water-level, and generate alarm to cope with risks. The management of small water facilities is done by residents will be very effective to notice water quality information of small water facilities to residents.

• Journal of Korea Society of Industrial Information Systems
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• v.21 no.6
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• pp.71-80
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• 2016

Along with the increase in the standard of living, the interest and awareness level of the public about environmental issues have also gradually increased. Recently, time spent on indoor activities have increased and the need for awareness and demand on methods in improving the quality of an indoor environment has been recognized. The purpose of this study is to sense the environment information through real-time monitoring of the room environment by using the IoT based technology and to maintain and improve the indoor environment to an appropriate level through the developed interlocking controller device. This provides an automatic control system in improving the indoor environment at home and other small areas like general offices in place of the traditional passive interior environmental improvement actions or large-scale facility control system. A status analysis with the relative reference values of the air quality and CO2, temperature and humidity and illumination and noise was applied through transmitting a control signal to automatic control module in accordance with the nine indoor environment standards set to improve the indoor environment.

• Journal of Convergence for Information Technology
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• v.10 no.10
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• pp.47-52
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• 2020

The general flatbed printer system is composed of a PC and a dedicated S/W, which is inconvenient to use. In the end, there is a need for a technology that can easily and conveniently use various types of printing through simplification, smartization, etc. of a flatbed printer system configuration. That is, there is an increasing demand for multi-dimensional printer capable of printing on various types of materials with one printer and capable of printing various types of products. Therefore, in this paper, we developed a flatbed printer system capable of multi-dimensional printing using Head Encoder/Trigger control. To this end, we developed a flatbed printer that connects the internal module of the flatbed printer with an input type detection sensor and controls all operating states by the head encoder and head trigger signals of the printer through separate main controllers. Through this, the development and diffusion of IoT technology will expand the printer control of the smart environment to the developed form throughout the industry. It is expected to contribute to the development of the 3D printing industry in the future.

• 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.