• Journal of Korean Elementary Science Education
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• v.43 no.1
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• pp.81-94
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• 2024

This study explores and conducts an in-depth comparison of the epistemic characteristics in different data types utilized in the science inquiries of preservice teachers regarding noise as a risk in everyday life. Focusing on primary and secondary data in the context of science inquiries about noise, we examined how these data types differ in science inquires in terms of inquiry design, data collection, and analyses. The findings reveal that sensor-based primary data enable direct measurement and observation of key phenomena. Conversely, secondary data rely on predetermined measurement methods within a public data system. These differences require different epistemic considerations during the inquiry process. Based on these findings, we discuss the educational implications concerning teaching approaches for science inquiries, teacher education for inquiry teaching, and the development of risk response competencies in preparation for the VUCA (Volatility, Uncertainty, Complexity, and Ambiguity) era.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.3
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• pp.397-402
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• 2013

Time variation is the essential component of the context awareness. It is a beneficial way not only including time lapse but also clustering time interval for the context inference using the information from sensor mote. In this study, we proposed a novel way of clustering based multi-sensor data fusion for the context inference. In the time interval, we fused the sensed signal of each time slot, and fused again with the results of th first fusion. We could reach the enhanced context inference with assessing the segmented signal according to the time interval at the Dempster-Shafer evidence theory based multi-sensor data fusion.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.41-47
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• 2023

Today, due to the increase in global population growth, the international community is discussing solving the food problem. The aquaculture industry is emerging as an alternative to solving the food problem. For the innovative growth of the aquaculture industry, smart fish farms that combine the fourth industrial technology are recently being distributed, and full-cycle digitalization is being promoted. Water quality sensors, which are important in the aquaculture industry, are electrochemical portable sensors that check water quality individually and intermittently, making it impossible to analyze and manage water quality in real time. Recently, optically-based monitoring sensors have been developed and applied, but the reliability of monitoring data cannot be guaranteed because the state information of the water quality sensor is unknown. Therefore, this paper proposes an algorithm representing self-diagnosis status such as Failure, Out of Specification, Maintenance Required, and Check Function based on monitoring data collected by water quality sensors to ensure data reliability.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10c
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• pp.140-145
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• 2007

최근에 에너지의 효율성이 좋고 대용량화가 쉬운 낸드 플래시가 센서 노드를 위한 차세대 저장소로 각광을 받고 있다. 현재 대부분의 센서 노드용 파일 시스템은 노어 플래시 기반으로 개발되어 있으며 낸드 플래시에 적용할 수 있는 파일 시스템은 거의 존재하지 않는다. 대용량 낸드 플래시 메모리의 특성을 고려한 새로운 파월 시스템의 구축이 요구되지만, 센서 노드는 오직 4-10 KByte의 매우 작은 크기의 메모리를 지원하므로 효율성이 뛰어난 파일 시스템을 구축하는 것은 매우 어렵다. 본 논문은 1 Kbit의 매우 작은 크기의 EEPROM을 부착하여 이러한 메모리 한계를 극복하였으며 자원의 효율성, 대용량의 지원 및 신뢰성을 고려한 새로운 파일 시스템의 설계에 대하여 논한다. 위치를 유지해야 하는 데이터의 위치저장을 위하여 EEPROM을 사용하며 장기간 데이터를 수집할 때 페이지의 갱신을 최소화 할 수 있는 로그 리스트 기반의 페이지 처리 방법에 대해 제안한다. 이는 획기적으로 페이지 갱신 횟수를 줄임으로써 에너지를 절약하고 보다 긴 시간동안 데이터의 수집을 용이하게 만들며 센서 노드의 수명을 증가시킨다.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.5
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• pp.1-7
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• 2021

This study attempted to detect possible collapse and fire accidents in facilities for disaster monitoring of large facilities, and to develop a customized beacon to recognize the internal situation of an IoT-based facility when a disaster occurs. In the case of data measurement using the existing strain gauge sensor, the strain gauge sensor was connected by wire to measure it, but this study changed it to wireless so that the presence and absence of structural deformation can be monitored in real time. In this process, in order to use the Wheatstone bridge, a strain sensor module that can be connected to a customized beacon was manufactured, and a system configuration was conducted to remotely check the measurement data. To verify measurement data, 10 customized beacons and 2 gateways were installed on the 15th floor of the Advanced Institue of Convergence Technology, and as a result of analysis of measurement data, it was confirmed that the strain data values were distributed between 7 and 8.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.161-169
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• 2023

Due to the Fourth Industrial Revolution, innovation in various technologies such as artificial intelligence (AI), big data (Big Data), and cloud (Cloud) is accelerating, and data is considered an important asset. With the innovation of these technologies, various efforts are being made to lead technological innovation in the field of defense science and technology. In Korea, the government also announced the "Defense Innovation 4.0 Plan," which consists of five key points and 16 tasks to foster advanced science and technology forces in March 2023. The plan also includes the establishment of a Condition-Based Maintenance system (CBM+) to improve the operability and availability of weapons systems and reduce defense costs. Condition Based Maintenance (CBM) aims to secure the reliability and availability of the weapon system and analyze changes in equipment's state information to identify them as signs of failure and defects, and CBM+ is a concept that adds Remaining Useful Life prediction technology to the existing CBM concept [1]. In order to establish a CBM+ system for the weapon system, sensors are installed and sensor data are required to obtain condition information of the weapon system. In this paper, we propose a sensor data metadata schema to efficiently and effectively manage sensor data collected from sensors installed in various weapons systems.

• Journal of Satellite, Information and Communications
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• v.7 no.1
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• pp.27-32
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• 2012

In this paper, the wastewater treatment plant sludge proposed TN/TP sensor data collected an efficient monitoring system in order to implement status monitoring to build WiFi networks. Also we sludge concentration and TN/TP sensor data were collected from wastewater treatment plant. It is able to be monitored sensor data through smart devices(Smart phones, smart pad, tablet PC, etc.) and pc. In addition, when certain events occur immediately be able to cope by adding features to enable efficient and rapid processing, real-time status can be checked by ensuring improved user access and convenience. We has built a WiFi network for to transfer data efficiently. It proved its effectiveness by analysis of sensor communication network. Therefore, we have verified the usefulness of the proposed technology.

• Journal of the Korean GEO-environmental Society
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• v.21 no.5
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• pp.5-11
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• 2020

Outliers detection and elimination included in field monitoring datum are essential for effective foundation of unusual movement, long and short range forecast of stability and future behavior to various structures. Integrated outlier analysis system for assessing long term time series data was developed in this study. Outlier analysis could be conducted in two step of primary analysis targeted at single dataset and second multi datasets analysis using synthesis value. Integrated outlier analysis system presents basic information for evaluating stability and predicting movement of structure combined with real-time safety management platform. Field application results showed increased correlation between synthesis value including similar sort of sensor showing constant trend and each single dataset. Various monitoring data in case of showing different trend can be used to analyse outlier through correlation-weighted value.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.163-169
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• 2020

Recently, research on automation and unmanned operation of machines in the industrial field has been conducted with the advent of AI, Big data, and the IoT, which are the core technologies of the Fourth Industrial Revolution. The machines for these automation processes are controlled based on the data collected from the sensors attached to them, and further, the processes are managed. Conventionally, the abnormalities of sensors are periodically checked and managed. However, due to various environmental factors and situations in the industrial field, there are cases where the inspection due to the failure is not missed or failures are not detected to prevent damage due to sensor failure. In addition, even if a failure occurs, it is not immediately detected, which worsens the process loss. Therefore, in order to prevent damage caused by such a sudden sensor failure, it is necessary to identify the failure of the sensor in an embedded system in real-time and to diagnose the failure and determine the type for a quick response. In this paper, a deep neural network-based fault diagnosis system is designed and implemented using Raspberry Pi to classify typical sensor fault types such as erratic fault, hard-over fault, spike fault, and stuck fault. In order to diagnose sensor failure, the network is constructed using Google's proposed Inverted residual block structure of MobilieNetV2. The proposed scheme reduces memory usage and improves the performance of the conventional CNN technique to classify sensor faults.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.14-14
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• 2023

산업발전에 따른 인구증가, 기후위기에 따른 가뭄 및 물 부족심화, 그리고 수질오염 등은 2015년 제79차 UN총회의 물 안보측면에서 국제사회의 물 분야 위기관리를 위해 2030년을 지속가능한 발전 목표(Sustainable Development Goals)로 하였다. 또한, 현재 물 산업은 빠르게 성장하고 있으며, 2016년 세계경제포럼(World Economic Forum) 의장 클라우스 슈밥(Klaus Schwab)부터 주창된 제4차 산업혁명로 인해 현재 물 산업의 패러다임 또한 급속히 변화하고 있다. 이는 컴퓨터를 기반으로 하는 CPS(Cyber Physical System) 및 DT(Digital Twin) 연계 분석방식의 혁신을 일컫는다. 2002년경에 DT의 기본개념이 제시되었고, 2006년경에는 Embedded System에서의 DT와 같은 개념으로 CPS의 용어가 등장했다. DT는 현실세계에 존재하는 사물, 시스템, 환경 등을 S/W시스템의 가상공간에 동일하게 모사(Virtualization) 및 모의(Simulation)할 수 있도록 하고, 모의결과를 가상시스템으로 현실세계를 최적화 체계 구현 기술을 말한다. DT의 6가지 기능은 ① 실제 데이터(Live Data), ② 모사, ③ 분석정보(Analytics), ④ 모의, ⑤ 예측(Predictions), ⑥ 자동화(Automation) 이다. 또한, CPS는 대규모 센서 및 액추에이터(Actuator)를 가지는 물리적 요소와 이를 실시간으로 제어하는 컴퓨팅 요소가 결합된 복합시스템을 말한다. CPS는 물리세계에서 발생하는 변화를 감지할 수 있는 다양한 센서를 통해 환경인지 기능을 수행한다. 센서로부터 수집된 정보와 물리세계를 재현 및 투영하는 고도화된 시스템 모델들을 기반으로 사이버 물리공간을 인지·분석·예측할 수 있다. CPS의 6가지 구성요소는 ① 상호 운용성(Interoperability), ② 가상화(Virtualization), ③ 분산화(Decentralization), ④ 실시간(Real-time Capability), ⑤ 서비스 오리엔테이션(Service Orientation), ⑥ 모듈화(Modularity)이다. DT와 CPS는 본질적으로 같은 목적, 내용, 그리고 결과를 만들어내고자 하는 같은 종류의 기술이라고 할 수 있다. CPS 및 DT는 물리세계에서 발생하는 변화를 감지할 수 있으며, 토양-지하수 센서를 포함한 관측기술을 통해 환경인지 기능을 수행한다. 지하수 관측기술로부터 수집된 정보와 물리세계를 재현 및 투영하는 고도화된 시스템 모델들을 기반으로 사이버 물리공간 및 디지털 트윈 공간을 인지·분석·예측할 수 있다. CPS 및 DT의 기본 요소들을 실현시키는 것은 양질의 데이터를 모니터링할 수 있는 정확하고 정밀한 1차원 연직 프로파일링 관측기술이며, 이를 토대로 한 수자원 관련 빅데이터의 증가, 빅데이터의 저장과 분석을 가능하게 하는 플랫폼의 개발이다. 본 연구는 CPS 및 DT 기반 토양수분-지하수 관측기술을 이용한 지표수-지하수 연계, 지하수 순환 및 관리, 정수 운영 및 진단프로그램 개발을 위한 토양수분-지하수 관측장치를 지하수 플랫폼 동시성과 디지털 트윈 시뮬레이터 시스템 개발 방향으로 제시하고자 한다.