• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.533-535
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• 2021

The differences between this study and previous studies are as follows. First, by building a cloud-based system using IoT technology, the system was built to monitor the status of buildings in real time from anywhere with an internet connection. Second, a model for predicting the future was developed using artificial intelligence (LSTM) and statistical (ARIMA) methods for the measured time series sensor data, and the effectiveness of the proposed prediction model was experimentally verified using a scaled-down building model. Third, a method to analyze the condition of a building more three-dimensionally by visualizing the structural deformation of a building by convergence of multiple sensor data was proposed, and the effectiveness of the proposed method was demonstrated through the case of an actual earthquake-damaged building.

• Journal of the Society of Disaster Information
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• v.20 no.1
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• pp.117-127
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• 2024

Purpose: The purpose is to conduct basic research to save energy and improve the safety of manufacturing plant infrastructure by comprehensively monitoring energy management, temperature, humidity, dust and gas, air quality, and machine operation status in small and medium-sized manufacturing plants. Method: To this end, energy-related data and environmental information were collected in real time through digital power meters and IoT sensors, and research was conducted to disseminate and respond to situations for energy saving through monitoring and analysis based on the collected information. Result: We presented an application plan that takes into account energy management, cost reduction, and safety improvement, which are key indicators of ESG management activities. Conclusion: This study utilized various sensor devices and related devices in a smart factory as a practical case study in a company. Based on the information collected through research, a basic system for energy saving and safety improvement was presented.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.175-185
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• 2023

Recently, novel viral infections such as COVID-19 have spread and pose a serious public health problem. In particular, these diseases have a fatal effect on the elderly, threatening life and causing serious social and economic losses. Accordingly, applications such as telemedicine, healthcare, and disease prevention using the Internet of Things (IoT) and artificial intelligence (AI) have been introduced in many industries to improve disease detection, monitoring, and quarantine performance. However, since existing technologies are not applied quickly and comprehensively to the sudden emergence of infectious diseases, they have not been able to prevent large-scale infection and the nationwide spread of infectious diseases in society. Therefore, in this paper, we try to predict the spread of infection by collecting various infection information with regional limitations through a virus disease information collector and performing AI analysis and severity matching through an AI broker. Finally, through the Korea Centers for Disease Control and Prevention, danger alerts are issued to the elderly, messages are sent to block the spread, and information on evacuation from infected areas is quickly provided. A realistic elderly support system compares the location information of the elderly with the information of the infected area and provides an intuitive danger area (infected area) avoidance function with an augmented reality-based smartphone application. When the elderly visit an infected area is confirmed, quarantine management services are provided automatically. In the future, the proposed system can be used as a method of preventing a crushing accident due to sudden crowd concentration in advance by identifying the location-based user density.

• 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.

• 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 Korea Water Resources Association Conference
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• 2023.05a
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• pp.511-511
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• 2023

기후변화로 동절기 기온 저하에 따른 수도계량기의 동파는 지속적으로 심화되고 있으며, 이는 계량기 교체 비용, 누수, 누수량 동결에 의한 2차 피해, 단수 등 사회적 문제를 야기한다. 이와같은 문제를 해결하고자 구조적 대책으로 개별 가정에서 동파 방지형 계량기를 설치할 수 있으나 이를 위한 비용발생이 상당하고, 비구조적 대책으로는 기상청의 동파 지도 알림 서비스를 활용하여 사전적으로 대응하고자 하나, 기상청자료는 대기 온도를 중심으로 제공하고 있기 때문에 해당서비스만으로는 계량기의 동파를 예측하는데 필요한 추가적인 다양한 변수를 활용하는데 한계가 있다. 최근 정부와 공공부문에서 22개 지역, 110개소 이상의 수도계량기함내 IoT 온도센서를 시범 설치하여 계량기 함내의 상태 등을 확인할 수 있는 사업을 수행했다. 전국적인 계량기 상태의 예측과 진단을 위해서는 추가적인 센서 설치가 필요할 것이나, IoT센서 설치 비용 등의 문제로 추가 설치가 더딘 실정이다. 본 연구에서는 겨울 동파 예방을 위해 실제 온도센서를 기반으로 가상센서를 구축하고, 이를 혼합한 하이브리드 방식으로 동파위험 기준에 따라 전국 동파위험 지도를 구축하였다. 가상센서 개발을 위해 독립변수로 위경도, 고도, 음·양지, 보온재 여부 및 기상정보(기온, 강수량, 풍속, 습도)를 활용하고, 종속변수로 실제 센서의 온도를 사용하여 기계학습 모델을 개발하였다. 지역 특성에 따라 정확한 모델을 구축하기 위해 위치정보 및 보온재여부 등의 변수를 활용하여 K-means 방법으로 군집화 하였으며, 각 군집별로 3가지의 기계학습 회귀모델을 적용하였다. 최적의 군집 수를 검토한 결과 4개가 적정한 것으로 판단되었다. 군집의 특성은 지역별 구분과 유사한 패턴을 보이며, 모든 군집에서 Gradient Boosting 회귀모델을 적용하는 것이 적합한 것으로 나타났다. 본 연구에서 개발한 모델을 바탕으로 조건에 따라 동파 예측 알람서비스에 실무적으로 활용할 수 있도록 양호·주의·위험·매우위험 총 4개의 기준을 설정하였다. 실제 본 연구에서 개발된 알고리즘을 국가상수도정보 시스템에 반영하여 테스트 수행중에 있으며, 향후 지속 검증을 할 예정에 있다. 이를 통해 동파 예방 및 피해 최소화, 물절약 등 직간접적 편익이 기대된다.

• Journal of the Korea Society of Computer and Information
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• v.29 no.4
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• pp.23-30
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• 2024

In this paper, we propose a deep learning-based personalized senior care service application. The proposed application uses Speech to Text technology to convert the user's speech into text and uses it as input to Autogen, an interactive multi-agent large-scale language model developed by Microsoft, for user convenience. Autogen uses data from previous conversations between the senior and ChatBot to understand the other user's intent and respond to the response, and then uses a back-end agent to create a wish list, a shared calendar, and a greeting message with the other user's voice through a deep learning model for voice cloning. Additionally, the application can perform home IoT services with SKT's AI speaker (NUGU). The proposed application is expected to contribute to future AI-based senior care technology.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.4
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• pp.257-270
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• 2022

In this paper, we design a smart factory risk improvement system and risk improvement method using AI edge devices. The smart factory risk improvement system collects, analyzes, prevents, and promptly responds to the worker's work performance process in the smart factory using AI edge devices, and can reduce the risk that may occur during work with improving the defect rate when workers perfom jobs. In particular, based on worker image information, worker biometric information, equipment operation information, and quality information of manufactured products, it is possible to set an abnormal risk condition, and it is possible to improve the risk so that the work is efficient and for the accurate performance. In addition, all data collected from cameras and IoT sensors inside the smart factory are processed by the AI edge device instead of all data being sent to the cloud, and only necessary data can be transmitted to the cloud, so the processing speed is fast and it has the advantage that security problems are low. Additionally, the use of AI edge devices has the advantage of reducing of data communication costs and the costs of data transmission bandwidth acquisition due to decrease of the amount of data transmission to the cloud.

• Journal of Internet of Things and Convergence
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• v.10 no.1
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• pp.53-58
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• 2024

IoT, Big-data, AI, and Cloud technologies, which are core technologies of the 4th Industrial Revolution, have recently been applied to various fields and are being used as core technologies for new growth engines. Accordingly, these core technologies are applied to the agricultural field without exception, contributing to solving the problem of labor shortage, reducing production costs, and reducing environmental burden through remote and automated production without time and space constraints. However, as these core technologies are utilized, security incidents are occurring in the agricultural field as well. Accordingly, this study divides smart farms into three stages(Basic, Middle, and High) and presents the characteristics and security threats of each stage. In particular, as the number of container-based services and research increases under cloud platforms, we would like to suggest countermeasures focusing on security threats.

• Convergence Security Journal
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• v.20 no.2
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• pp.91-99
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• 2020

The purpose of this study is to present the application of Information and Communication Technology(ICT) during the 4th Industrial Revolution for the efficient implementation of government emergency preparedness policies. Brainstorming by experts categorized the government's emergency preparedness policies into 4 types and 12 detailed tasks. Classification results were used by AHP(Analytic Hierarchy Process) to analyze relative importance and priorities. The AHP survey found that strengthening crisis management responsiveness was the most important detailed task. Artificial Intelligence(AI), Internet of Things(IoT), Unmanned Autonomy System, Virtual Reality(VR), and Augmented Reality(AR) were presented as major information and communication technology(ICT) for the efficient execution of detailed tasks.