• 한국항행학회논문지
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• 제17권6호
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• pp.664-671
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• 2013

본 논문에서는 항공기내에서 전력을 무선으로 측정할 수 있는 AEMS(Zigbee electric power measurement monitoring system)시스템을 제안한다. AEMS시스템은 현재 상용화되고 있는 전력 측정 시스템을 분석하여 이를 보완하고 항공기내에서도 전력 변화를 쉽게 알 수 있도록 최근 가장 이슈가 되는 스마트폰과 모니터링 시스템을 연결하여 설계하였다. 또한, 실시간 전력 측정 시스템을 도입하여 전기 사용량을 실시간으로 제어함으로써 보다 실용성 있는 전력계측 시스템을 구축하였다.

• 동력기계공학회지
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• 제12권6호
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• pp.18-23
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• 2008

This paper describes development of real-time condition monitoring system to observe state of a container crane in a port. To analyze the state of a crane, the strength and the direction of wind are measured with sensors along with the load resulted a crane at the moment. The measured signals are processed by especially developed conditioning board and converted into digital data. Measured data are analyzed to define the state of the crane at an indicator. For transmission of these data to the indicator, we implemented wireless sensor network based on IEEE 802.15.4 MAC(Media Access Control) protocol and Bluetooth network protocol. To extend the networking distance between the indicator and sensor nodes, the shortest path routing algorithm was applied for WSN(Wireless Sensor Network) networks. The indicator sends the state information of the crane to monitoring server through IEEE 802.11 b wireless LAN(Local Area Network). Monitoring server decides whether alarm should be issued or not. The performance of developed WSN and Bluetooth network were evaluated and analyzed in terms of communication delay and throughput.

• 산업공학
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• 제23권3호
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• pp.246-256
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• 2010

As wireless and mobile technologies have advanced significantly, lots of large sized healthcare organizations have implemented so called mobile hospital (m-Hospital) which provides a location independent and point of care (POC) clinical environment. Implementation of m-Hospital enhances quality of care because health professionals such as physicians and nurses can use hospital information systems at the very place where patients are located without any delay. This paper presents a real-time patient monitoring system based on wireless network technologies. A general framework for the patient monitoring process is introduced and the architecture and components of the proposed monitoring system is described. The system collects and analyzes biometric signals of in-patients who suffer from cancer. Specifically, it continuously monitors oxygen saturation of patients in bed and alarms health professionals instantly when an abnormal status of the patient is detected. The monitoring system has been used and clinically verified in a university hospital.

• 한국정보통신학회논문지
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• 제16권8호
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• pp.1613-1618
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• 2012

네트워크 기술이 빠르게 성장하고 있어서 네트워크 환경이 복잡해지고 있다. 이에 따라, 네트워크 트래픽이나 정보를 이용하여 실시간으로 자원을 모니터링 하는 기술들이 발전하고 있다. 대표적인 모니터링 툴은 Cacti이다. Cacti는 RRDTool(Round Robin Database tool), SNMP(Simple Network Management Protocol)를 기반으로 한 모니터링툴 이고, Libpcap는 네트워크 카드에서 패킷 캡쳐를 용이하게 해주는 라이브러리이다. 본 논문에서는 Cacti와 Libpcap 기반으로 시스템을 개발하여 실시간으로 대상을 모니터링 할 수 있고 스마트폰으로 실시간 알림 이메일을 받을 수 있다. 본 시스템은 Libpcap으로 포착한 네트워크 트래픽 패킷을 분석하고 그래프 형식으로 Cacti에서 표현되어 모니터링 할 수 있다. 이는 높은 효율성을 가지며 관리가 간편하고 정확성을 가지므로, 향후 널리 활용될 것으로 보인다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권3호
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• pp.1229-1248
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• 2016

With the feature of convenience and low cost, remote healthcare monitoring (RHM) has been extensively used in modern disease management to improve the quality of life. Due to the privacy of health data, it is of great importance to implement RHM based on a secure and dependable network. However, the network connectivity of existing RHM systems is unreliable in disaster area because of the unforeseeable damage to the communication infrastructure. To design a secure RHM system in disaster area, this paper presents a Secure VANET-Assisted Remote Healthcare Monitoring System (SVC) by utilizing the unique "store-carry-forward" transmission mode of vehicular ad hoc network (VANET). To improve the network performance, the VANET in SVC is designed to be a two-level network consisting of two kinds of vehicles. Specially, an innovative two-level key management model by mixing certificate-based cryptography and ID-based cryptography is customized to manage the trust of vehicles. In addition, the strong privacy of the health information including context privacy is taken into account in our scheme by combining searchable public-key encryption and broadcast techniques. Finally, comprehensive security and performance analysis demonstrate the scheme is secure and efficient.

• Structural Monitoring and Maintenance
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• 제7권2호
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• pp.109-124
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• 2020

Benefiting from the massive monitoring data collected by the Structural health monitoring (SHM) system, scholars can grasp the complex environmental effects and structural state during structure operation. However, the monitoring data is often missing due to sensor faults and other reasons. It is necessary to study the recovery method of missing monitoring data. Taking the structural temperature monitoring data of Nanjing Dashengguan Yangtze River Bridge as an example, the long short-term memory (LSTM) network-based recovery method for missing structural temperature data is proposed in this paper. Firstly, the prediction results of temperature data using LSTM network, support vector machine (SVM), and wavelet neural network (WNN) are compared to verify the accuracy advantage of LSTM network in predicting time series data (such as structural temperature). Secondly, the application of LSTM network in the recovery of missing structural temperature data is discussed in detail. The results show that: the LSTM network can effectively recover the missing structural temperature data; incorporating more intact sensor data as input will further improve the recovery effect of missing data; selecting the sensor data which has a higher correlation coefficient with the data we want to recover as the input can achieve higher accuracy.

• 한국정밀공학회지
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• 제16권6호
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• pp.133-140
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• 1999

In this study, a monitoring method to detect chip disposal state in drilling system based on neural network was proposed and its performance was evaluated. If chip flow is bad during drilling, not only the static component but also the fluctuation of dynamic component of drilling. Drilling torque is indirectly measured by sensing spindle motor power through a AC spindle motor drive system. Spindle motor power being measured drilling, four quantities such as variance/mean, mean absolute deviation, gradient, event count were calculated as feature vectors and then presented to the neural network to make a decision on chip disposal state. The selected features are sensitive to the change of chip disposal state but comparatively insensitive to the change of drilling condition. The 3 layerd neural network with error back propagation algorithm has been used. Experimental results show that the proposed monitoring system can successfully recognize the chip disposal state over a wide range of drilling condition even though it is trained under a certain drilling condition.

• 한국방재학회 논문집
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• 제8권5호
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• pp.35-44
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• 2008

무선센서네트워크 환경의 웹기반(web-based) 교량모니터링시스템을 갖추기 위하여 무선통신을 기반으로 디지털 초소형센서와 마이크로 프로세싱, 데이터 취합 및 관리를 위한 데이터베이스, 각종 제어 프로그램, 인터넷 데이터 전송 프로세서를 기본적으로 구축하여 무선으로 수신된 데이터를 수집하고 분석하였다. 그리고 이러한 교량모니터링 시스템의 적용성을 검증을 위하여 동일조건에서 유선방식과 무선방식으로 실험을 병행 수행한 후 각각의 계측결과들을 비교하였다. 비교한 결과 유선으로 계측한 결과와 무선으로 계측한 값은 유사하지만 무선센서의 통신과정에서 데이터의 손실이 발생하는 것으로 나타났다. 또한 실내실험과 현장실험을 통하여 본 연구의 효율성과 적용성을 검증하였다.

• Smart Structures and Systems
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• 제28권6호
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• pp.799-810
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• 2021

When monitoring the structural integrity of a bridge using data collected through accelerometers, identifying the profile of the load exerted on the bridge from the vehicles passing over it becomes a crucial task. In this study, the speed and location of vehicles on the deck of a bridge is reconfigured using real-time video to implicitly associate the load applied to the bridge with the response from the bridge sensors to develop an image-based deep learning network model. Instead of directly measuring the load that a moving vehicle exerts on the bridge, the intention in the proposed method is to replace the correlation between the movement of vehicles from CCTV images and the corresponding response by the bridge with a neural network model. Given the framework of an input-output-based system identification, CCTV images secured from the bridge and the acceleration measurements from a cantilevered beam are combined during the process of training the neural network model. Since in reality, structural damage cannot be induced in a bridge, the focus of the study is on identifying local changes in parameters by adding mass to a cantilevered beam in the laboratory. The study successfully identified the change in the material parameters in the beam by using the deep-learning neural network model. Also, the method correctly predicted the acceleration response of the beam. The proposed approach can be extended to the structural health monitoring of actual bridges, and its sensitivity to damage can also be improved through optimization of the network training.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1727-1731
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• 2004

We propose an ART2 neural network-based fault diagnosis method to diagnose of sensor in the gas monitoring system. In the proposed method, using thermal modulation of operating temperature of sensor, the signal patterns are extracted from the voltage of load resistance. Also, fault classifier by ART2 NN (adaptive resonance theory 2 neural network) with uneven vigilance parameters is used for fault isolation. The performances of the proposed fault diagnosis method are shown by simulation results using real data obtained from the gas monitoring system.