• Air Cleaning Technology
• /
• v.34 no.3
• /
• pp.1-9
• /
• 2021

• Air Cleaning Technology
• /
• v.34 no.3
• /
• pp.17-27
• /
• 2021

• Air Cleaning Technology
• /
• v.34 no.3
• /
• pp.28-37
• /
• 2021

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.10a
• /
• pp.2-5
• /
• 2021

With the recent development of the Internet of Things (IoT) technology, various terminals are being connected to the Internet. As a result, the amount of IoT data is also increasing, and an index key that can efficient analyze the large-scale IoT data is proposed. Existing index keys have only time and space information, so if data stored in index tables and instance tables were queried using repetition or join operation, IoT data was embedded in the index key of the proposal to create OLAP-based big tables to minimize the number of repetitions or join times.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2017.04a
• /
• pp.553-555
• /
• 2017

수전 설비 시스템은 전력 회사에서 3 상 전원을 받는 설비로, 전기를 공급받기 위한 설비이다. 정전이나 제품생산설비의 중단은 기업에 있어서는 경제적 손실이 매우 큰 사고일 수 밖에 없다. 요즘은 IoT 센서를 이용한 수전설비 관리 시스템의 활용이 늘어나고 있는 추세이다. IoT 센서를 이용한 수전 설비의 구축에서 정확한 상태 값의 센싱과 수집된 값의 전송, 그리고 정확성 판단에 대한 이슈들이 고려되어야 하며, 또한 기기간 통신을 통해 실시간 상호작용으로 수전설비의 고장을 어떻게 예방할 것인가에 대한 것이 중요하다. 본 연구에서는 수전 설비의 실시간 감지와 모니터링을 위한 목적으로 기존의 고장 및 오류 정보를 기반으로 하는 빅데이터 분석을 통해 발생 가능한 고장 및 오류를 사전 예측할 수 있도록 정보를 제공하는 것에 주안점을 두었다.

• Journal of Advanced Navigation Technology
• /
• v.19 no.5
• /
• pp.417-422
• /
• 2015

A service size of the IoT environment is determined by the number of sensors. The number of sensors increase means increases the amount of data generated by the IoT environment. There are studies to reliably operate a network for research and operational dynamic buffer for data when network congestion control congestion in the network environment. There are also studies of the stream data that has been processed in the connectionless network environment. In this study, we propose a sensor gateway for processing big data of the IoT environment. For this, review the RESTful for designing a sensor middleware, and apply the double-buffer algorithm to process the stream data efficiently. Finally, it generates a big data traffic using the MJpeg stream that is based on the HTTP protocol over TCP to evaluate the proposed system, with open source media player VLC using the image received and compare the throughput performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2016.10a
• /
• pp.893-895
• /
• 2016

Data center has a lot of management efforts for the facility, energy, and efficient usage monitoring. Data center power management is important to make the data center have reliable service and cost-effective business. In this paper, IoT based energy measurements monitoring which gives support to energy consumption analysis including indoor, outdoor temperature condition. This converged information for energy analysis gives various aspects of energy consumption effects. With IoT big data, energy machine learning system can give the relation of energy components and measurements, it is the key information of the quick energy analysis in the just one month data trend for the prediction and estimation.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2017.04a
• /
• pp.350-353
• /
• 2017

사물 인터넷(Internet of Things, IoT)이란 사물 인터넷으로서 사물을 서로 연결 및 통신하여 정보를 주고 받을 수 있게 하는 기술이다. 사물 인터넷의 급속한 성장으로 인해 수많은 데이터가 발생하게 되었고, 이러한 이유로 인해 빅데이터(big-data) 기술이 대두되었다. 빅데이터는 정형 데이터 뿐만 아니라 사진, 동영상 등의 비정형 데이터 또한 분석하고 활용하는 기술이기 때문에 사물 인터넷과 빅데이터 기술은 서로 보완적인 관계에 있다. 이러한 두 가지 기술의 특성에 기초하여, 본 논문에서는 빅데이터와 사물 인터넷에 대한 정의와 동향에 대하여 알아보고 이러한 두 가지 기술을 연계해 활용한 실제 플랫폼과 스마트 시티 등에 대한 실생활에 쓰이는 실제 사례 및 기술들에 대해 연구하였다.

• Journal of Korea Foundry Society
• /
• v.41 no.6
• /
• pp.550-557
• /
• 2021

With the advent of the fourth industrial revolution, the interest in the internet of things (IoT) in manufacturing is growing, even at foundries. There are several types of process data that can be automatically collected at a foundry, but considerable amounts of process data are still managed based on handwriting for reasons such as the limited functions of outdated production facilities and process design based on operator know-how. In particular, despite recognizing the importance of converting process data into big data, many companies have difficulty adopting these steps willingly due to the burden of system construction costs. In this study, the field applicability of IoT-based devices was examined by manufacturing devices and applying them directly to the site of a centrifugal foundry. For the centrifugal casting process, the temperature and humidity of the working site, the molten metal temperature, and mold rotation speed were selected as process parameters to be collected. The sensors were selected in consideration of the detailed product specifications and cost required for each process parameter, and the circuit was configured using a NodeMCU board capable of wireless communication for IoT-based devices. After designing the circuit, PCB boards were prepared for each parameter, and each device was installed on site considering the working environment. After the on-site installation process, it was confirmed that the level of satisfaction with the safety of the workers and the efficiency of process management increased. Also, it is expected that it will be possible to link process data and quality data in the future, if process parameters are continuously collected. The IoT-based device designed in this study has adequate reliability at a low cast, meaning that the application of this technique can be considered as a cornerstone of data collecting at foundries.

• Journal of Digital Contents Society
• /
• v.19 no.2
• /
• pp.377-383
• /
• 2018

Internet of Things (IoT) is producing various data as the smart environment comes. The IoT data collection is used as important data to judge systems's status. Therefore, it is important to monitor the anomaly state of the sensor in real-time and to detect anomaly data. However, it is necessary to convert the IoT data into a normalized data structure for anomaly detection because of the variety of data structures and protocols. Thus, we can expect a good quality effect such as accurate analysis data quality and service quality. In this paper, we propose an anomaly detection system based on big data from collected sensor data. The proposed system is applied to ensure anomaly detection and keep data quality. In addition, we applied the machine learning model of support vector machine using anomaly detection based on time-series data. As a result, machine learning using preprocessed data was able to accurately detect and predict anomaly.