• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.216-224
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• 2017

Abnormal situation caused by aggressive behavior of pigs adversely affects the growth of pigs, and comes with an economic loss in intensive pigsties. Therefore, IT-based video surveillance system is needed to monitor the abnormal situations in pigsty continuously in order to minimize the economic demage. Recently, some advances have been made in pig monitoring; however, detecting each pig is still challenging problem. In this paper, we propose a new color image-based monitoring system for the detection of the individual pig using a fast region-based convolution neural network with consideration of detecting touching pigs in a crowed pigsty. The experimental results with the color images obtained from a pig farm located in Sejong city illustrate the efficiency of the proposed method.

• Journal of Advanced Navigation Technology
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• v.17 no.6
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• pp.664-671
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• 2013

In this paper, in an aircraft power can be measured by wireless AEMS (aircraft electric power measurement monitoring system) system is proposed. AEMS has been design based on current commercialized power measuring systems analysis with improvement and connects it with most talked about item, smart phone and monitoring system. And also adopting real time power measuring system, constitute more practical power measuring system by controlling electricity usage in real time.

• Journal of Power System Engineering
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• v.12 no.6
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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.

• IE interfaces
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• v.23 no.3
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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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.8
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• pp.1613-1618
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• 2012

For network is growing at a rapid rate, network environment is more complex. The technology of using network traffic to monitor our network in real-time is developed. Cacti is a representative monitoring tool which based on RRDTool(Round Robin Database tool), SNMP(Simple Network Management Protocol). In this paper, it show you how to develop a system which based on Cacti and Libpcap to monitor our monitored objects. At this system, using Libpcap to capture network traffic packets, analyze these packets and then turn out in Cacti in graphical form. So as to achieve monitoring system. This system's execution is efficient and the management is easy and the results are accurate, so it can be widely utilized in the future.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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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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• v.7 no.2
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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.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.6
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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.

• Journal of the Korean Society of Hazard Mitigation
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• v.8 no.5
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• pp.35-44
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• 2008

In this study, to establish a web-based bridge monitoring system with wireless sensor network environment, we constructed microminiaturized sensor based wireless communication techniques and micro processing, databases for data combination and administration, variable control programs and processors for transferring data by internet. Then those data are measured and analyzed by the constructed bridge monitoring system with wireless sensors. To evaluate the practicability of the bridge monitoring system with wireless sensor, we compared the values measured in the tests with wire sensor under same conditions. The results show that the trend of the data obtained from the monitoring systems with wire sensors and wireless sensors was very similar but the some lost data in the communication process with wireless sensor network environment. And through laboratory and field tests, the effectiveness and the applicability of the proposed methods were verified.

• Smart Structures and Systems
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• v.28 no.6
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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.