• International Journal of Advanced Culture Technology
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• v.7 no.3
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• pp.143-148
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• 2019

In order to solve the urban problems caused by the increase of the urban population, the construction of smart city applying the latest technology is being carried out all over the world. In particular, we will create a smart city platform that utilizes data generated in the city to collect and store and analyze, thereby enhancing the city's continuous competitiveness and resilience and enhancing the quality of life of citizens. However, existing smart city platforms are not enough to construct a platform for smart city as a platform for solution elements such as IoT platform, big data platform, and AI platform. To complement this, we will reanalyze the existing overseas smart city platform and IoT platform in a comprehensive manner, combine the technical elements applied to it, and apply it to the future Korean smart city platform. This paper aims to investigate the trends of smart city platforms used in domestic and foreign countries and analyze the technology applied to smart city to study smart city platforms that solve various problems of the city such as environment, energy, safety, traffic, environment.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.2
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• pp.97-101
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• 2021

With the development of the Internet of Things technology, new devices are being developed and used to provide various applications and services. Most IoT devices have a high probability of error because they operate in harsh environments with limited resources. In addition, it is necessary to manage the Internet of Things devices dynamically because new devices are constantly deployed. In this paper, we design a system that allows users to monitor the mounting of new devices to perform the necessary tasks and implement prototypes to validate their operability. Our system also provides a web-based programming interface to direct work on new modules and share work content with each other.

• Annual Conference of KIPS
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• 2020.11a
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• pp.243-246
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• 2020

스마트 홈 시스템은 앞으로도 계속 기술의 발전과 수요가 증가하는 블루오션 시장이다. IT 시장의 주목을 받는 아이템을 다룬 만큼 이 작품이 높은 발전 가치와 시장성을 보유하고 있다고 볼 수 있다. 스마트 홈 시스템 구축을 통해 개인에게 최적화 된 라이프 스타일을 구축하고, 더 나아가 개인에게 맞는 환경을 설정하여 맞춤 라이프 연계 서비스를 제공한다. 더 나아가 주목받는 이슈인 인공지능 기술을 사용하여 스마트 기기들에 대한 지능형 제어 및 효율적인 관리가 가능하도록 한다. 게이트웨이 서버에 에어컨, 공기청정기 등 우리 실생활과 밀접한 기기들에 연결함으로써 기존의 기기들에 비해 중요한 기기들을 더 높은 빈도로 관리할 수 있다. 이 프로젝트는 스마트 홈의 기본이 되는 통합 제어시스템과 이를 위한 IoT 허브 시스템의 하드웨어를 모두 개발한 프로젝트로써 게이트웨이 서버로 대표 되는 하드웨어를 통해 스마트 기기의 상태를 모니터링 하다가, 특정 센서값을 받으면 액션을 취해줌으로써 스마트기기를 제어할 수 있다. 그리고 이들과 관련하여 IoT 기반의 다양한 기기들을 표준화 제어하기 위한 제어 시스템을 구축하고 이를 위한 소프트웨어도 함께 개발했다.

• Journal of Practical Engineering Education
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• v.16 no.4
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• pp.449-457
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• 2024

AIoT, the intelligent Internet of Things, refers to a technology that collects data measured by IoT devices and applies machine learning technology to create and utilize predictive models. Existing research on AIoT technology education focused on building an educational AIoT platform and teaching how to use it. However, there was a lack of case studies that taught the process of automatically creating and utilizing machine learning models from data measured by IoT devices. In this paper, we developed a case study using a machine learning model for AIoT technology education. The case developed in this paper consists of the following steps: data collection from AIoT devices, data preprocessing, automatic creation of machine learning models, calculation of accuracy for each model, determination of valid models, and data prediction using the valid models. In this paper, we considered that sensors in AIoT devices measure different ranges of values, and presented an example of data preprocessing accordingly. In addition, we developed a case where AIoT devices automatically determine what information they can predict by automatically generating several machine learning models and determining effective models with high accuracy among these models. By applying the developed cases, a variety of educational contents using AIoT, such as prediction-based object control using AIoT, can be developed.

• Knowledge Management Research
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• v.19 no.1
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• pp.119-132
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• 2018

Many advanced products and services are emerging in the market thanks to data-based technologies such as Internet (IoT), Big Data, and AI. The construction of a system for data processing under the IoT network environment is not simple in configuration, and has a lot of restrictions due to a high cost for constructing a high performance server environment. Therefore, in this paper, we will design a development environment for large data analysis computing platform using open source with low cost and practicality. Therefore, this study intends to implement a big data processing system using Raspberry Pi, an ultra-small PC environment, and open source API. This big data processing system includes building a portable server system, building a web server for web mining, developing Python IDE classes for crawling, and developing R Libraries for NLP and visualization. Through this research, we will develop a web environment that can control real-time data collection and analysis of web media in a mobile environment and present it as a curriculum for non-IT specialists.

• Journal of Information Technology Applications and Management
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• v.29 no.5
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• pp.27-37
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• 2022

This study intends to link agricultural machine history data with related organizations or collect them through IoT sensors, receive input from agricultural machine users and managers, and analyze them through AI algorithms. Through this, the goal is to track and manage the history data throughout all stages of production, purchase, operation, and disposal of agricultural machinery. First, LSTM (Long Short-Term Memory) is used to estimate oil consumption and recommend maintenance from historical data of agricultural machines such as tractors and combines, and C-LSTM (Convolution Long Short-Term Memory) is used to diagnose and determine failures. Memory) to build a deep learning algorithm. Second, in order to collect historical data of agricultural machinery, IoT sensors including GPS module, gyro sensor, acceleration sensor, and temperature and humidity sensor are attached to agricultural machinery to automatically collect data. Third, event-type data such as agricultural machine production, purchase, and disposal are automatically collected from related organizations to design an interface that can integrate the entire life cycle history data and collect data through this.

• Journal of Convergence for Information Technology
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• v.11 no.4
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• pp.83-101
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• 2021

This study is to investigate, using AHP, what technologies should be applied first to Korean SMEs in order to respond to the 4th industrial revolution and change to a smart enterprise. To this end, technologies related to the 4th industrial revolution and smart factory are synthesized, and the classification criteria of Dae-Hoon Kim et al. (2019) are applied, but additional opinions of experts are collected and related technologies are converted to artificial intelligence (AI), Big Data, and Cloud Computing. As a base technology, mobile, Internet of Things (IoT), block chain as hyper-connected technology, unmanned transportation (autonomous driving), robot, 3D printing, drone as a convergence technology, smart manufacturing and logistics, smart healthcare, smart transportation and smart finance were classified as smart industrial technologies. As a result of confirming the priorities for technical use by AHP analysis and calculating the total weight, manufacturing companies have a high ranking in mobile, artificial intelligence (AI), big data, and robots, while service companies are in big data and robots, artificial intelligence (AI), and smart healthcare are ranked high, and in all companies, it is in the order of big data, artificial intelligence (AI), robot, and mobile. Through this study, it was clearly identified which technologies should be applied first in order to respond to the 4th industrial revolution and change to a smart company.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.117-123
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• 2023

While applying new digital technologies, interest in software and artificial intelligence is quite high. In particular, many changes are being made for the development of software and artificial intelligence in the field of education. From 2025, software and artificial intelligence-related curricula will be applied to public education in elementary, middle and high schools. The Ministry of Education is also conducting various camps to experience software and artificial intelligence in various ways in elementary, middle and high schools before they are applied to public education. Several platforms for experience camps related to software and artificial intelligence are also being used. In this study, we intend to increase the educational efficiency of the learning method for software and artificial intelligence to be developed in the future by designing a model for software and artificial intelligence experiential learning platforms.

• 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.

• Journal of the FoodService Safety
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• v.4 no.2
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• pp.42-47
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• 2023

The FoodTech industry has been developed with the rise of start-up by using AI, big data, robotics, biotechnology. In addition, sustainable development is more important with the trend of population growth, aging, and climate change. We investigated the impact of FoodTech on the foodservice industry with the cases of the global and domestic companies. The technology of AI, IoT, blockchain, robotics, automation systems are widely used to improve food safety and hygiene while as the use of diagnostic biomarkers such as blood or DNA, digital platform and app, and AI-based solutions are used in the field of personalized nutrition. With the expand of FoodTech in foodservice industry, the competencies that the managers need to develop include understanding technology, resource management, self-development, work ethics, problem-solving, and communication, therefore the support of the related education and training is required.