• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.5
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• pp.799-806
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• 2020

The rapid growth of smartphone-to-Internet of Things (IoT) connections and the explosive demand for data usage centered on mobile video are increasing day by day, and this increase in data usage creates many problems in the IP system. In a full-based environment, in which information requesters focus on information providers to receive information from specific servers, problems arise with bottlenecks and large data processing. To address this problem, CCN networking technology, a future network technology, has emerged as an alternative to CCN networking technology, which reduces bottlenecks that occur when requesting popular content through caching of intermediate nodes and increases network efficiency, and can be applied to military information and communication networks to address the problem of traffic concentration and the use of various surveillance equipment in full-based networks, such as scientific monitoring systems, and to provide more efficient content.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.4
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• pp.461-466
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• 2016

In this paper, a capsule body shaped surveillance/monitoring device is developed. The device includes a camera and GPS module to transmit live video data and real time GPS coordinates respectively using the Intel Edison module. A control application is developed for the smart phones and tablets to wirelessly view the live video stream and location of the capsule device and also to switch between the multiple capsule devices installed at different locations. The coordination between the developed device and the smart phone / tablet is done using the wireless function of the Intel Edison module.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.2
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• pp.91-98
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• 2019

Accidents of worker that occur in isolated places are difficult to rescue, unlike general construction accidents. There is a problem of communication limitation when an accident occurs in an isolated place. Also, it is difficult to search the accident place due to the absence of CCTV. In order to solve these problems, this paper proposes a device that combines IoT technology with a safety helmet, which must be worn in the workplace. The proposed device additionally designs and implements a real-time PPG(Photoplethysmography) sensor, body temperature sensor, accelerometer sensor and a camera sensor on the helmet. The proposed helmet system allows the user and the control center to monitor the state of the worker. In addition, when an abnormal biological signal or fall occurs to the worker, the image is transmitted to the control center. By using the proposed system, it is possible to check the status of the worker in real time, so that it has an advantage that it can cope with the accident quickly.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.1
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• pp.221-228
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• 2018

Big data is an important keyword in World's Fourth Industrial Revolution in public and private division including IoT(Internet of Things), AI(Artificial Intelligence) and Cloud system in the fields of science, technology, industry and society. Big data based on services are available in various fields such as transportation, weather, medical care, and marketing. In particular, in the field of sports, various types of bio-signals can be collected and managed by the appearance of a wearable device that can measure vital signs in training or rehabilitation for daily life rather than a hospital or a rehabilitation center. However, research on big data with vital signs from wearable devices for training and rehabilitation for baseball players have not yet been stimulated. Therefore, in this paper, we propose a system for baseball infield and outfield players, especially which can store and analyze the momentum measurement vital signals based on big data.

• Journal of Digital Contents Society
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• v.18 no.5
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• pp.895-904
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• 2017

In this paper, we propose an IoT based smart outlet system for one-person household with electric safety, electric energy consumption management and transmission of messages to the reserved contacts by connecting the emergency network. The proposed smart outlet system is implemented by parsing packet data and protocol between server, module, application and WiFi AP receiver. The WiFi AP built for communication can be used not only for the outlet but also for the gas barrier, the door lock and the like. In addition, the proposed method can provide the remote monitoring function by receiving the amount of power of the receptacle received through the AC current sensor (SCT-013) in real time. The smart outlet system is expected to be capable of automatically shutting off the power in case of emergency through automatic power use analysis in addition to the standby power cutoff function.

• Smart Media Journal
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• v.8 no.4
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• pp.9-16
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• 2019

In order to realize IoT(Internet of Things) service, it is necessary to manage sensing data and build a service with respect to its scalability. However, existing internet services use unique protocols and non-standardized functions for each service provider, and it is difficult to provide data management and service because they use short-range communication technology such as Bluetooth. In addition, plurality of APs and gateways must be taken into consideration in establishing a wide area network. In this paper, we propose a sensing data management system using LoRa(Long Range) communication based on Mobius platform. The end device that drives Tas is configured to collect sensing data, configure an application gateway that drives &Cube, and transmit sensing data to the server. In addition, a server that manages the Mobius is configured to handle the sensing data transmitted from the application gateway to provide a monitoring service. We establish a wide area network through LoRa communication between the end device and the gateway and provide data management and service corresponding to the internet through the Mobius platform.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.12
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• pp.1675-1683
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• 2018

This paper is a study on modeling of an NFC antenna to be inserted into a steel pallet for conveying selected good products through a vision system to a threaded screw from CNC equipment. The CNC equipment used here incorporates the information communication technology (ICT) corresponding to the Internet of Things (IoT), and the smart factory system technology that produces information by exchanging information freely in two directions by connecting the POP corresponding to the service Internet is evolved Equipment. Therefore, it is possible to collect position information on the threaded workpiece by applying NFC antenna designed considering iron pallet used for material management so as to grasp estimated completion time and actual production amount according to production instruction from existing analog type equipment to POP monitoring system And investigated its characteristics.

• Journal of IKEEE
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• v.22 no.4
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• pp.953-958
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• 2018

In this study, a portable detection system was developed that can detect harmful gas and signals simultaneously in an enclosed space of industrial sites and underground facilities. The developed system is a sensor module for gas detection, a patch type 1 channel small ECG sensor, a module for three-axial acceleration detection sensor, and a system for statistics. In order to verify the performance of the system modules, the digital resolution, signal frequency, output voltage, and ultra-small modules were evaluated. As a result of the performance of the developed system, the digital resolution was 300 (rps) and the signal amplification gain was 500 dB or more, and the ECG module was manufactured with $50mm{\times}10mm{\times}10mm$ to increase patch utilization. It is believed that the product of this research will be valuable if it is used as an IoT-based management system for real-time monitoring of industrial workers.

• Journal of Platform Technology
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• v.9 no.4
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• pp.79-89
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• 2021

In this paper, the fine dust (PM10) and ultrafine dust (PM2.5) measurement platforms are designed to be mobile and fixed using oneM2M, the international standard for IoT. The fine dust measurement platform is composed and designed with a fine dust measurement device, agent, oneM2M platform, oneM2M IPE, and monitoring system. The main difference between mobile and fixed is that the mobile uses the MQTT protocol for interconnection between devices and services without blind spots based on LTE connection, and the fixed uses the LoRaWAN protocol with low power and wide communication range. Not only fine dust, but also temperature, humidity, atmospheric pressure, volatile organic compounds (VOC), carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2), and noise data related to daily life were collected. The collected sensor values were managed using the common API provided by oneM2M through the agent and oneM2M IPE, and it was designed into four resource types: AE and container. Six functions of operability, flexibility, convenience, safety, reusability, and scalability were analyzed through the fine dust measurement platform design.

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.19-27
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• 2022

In this study, we propose a language and platform to describe and manage the MLOps(Machine Learning Operations) workflow for time series data anomaly detection. Time series data is collected in many fields, such as IoT sensors, system performance indicators, and user access. In addition, it is used in many applications such as system monitoring and anomaly detection. In order to perform prediction and anomaly detection of time series data, the MLOps platform that can quickly and flexibly apply the analyzed model to the production environment is required. Thus, we developed Python-based AI/ML Modeling Language (AMML) to easily configure and execute MLOps workflows. Python is widely used in data analysis. The proposed MLOps platform can extract and preprocess time series data from various data sources (R-DB, NoSql DB, Log File, etc.) using AMML and predict it through a deep learning model. To verify the applicability of AMML, the workflow for generating a transformer oil temperature prediction deep learning model was configured with AMML and it was confirmed that the training was performed normally.