• Journal of Institute of Control, Robotics and Systems
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• v.17 no.11
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• pp.1074-1081
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• 2011

As robotics systems are becoming more complex there is the need to promote component based robot development, where systems can be constructed as the composition and integration of reusable building block. One of the most important challenges facing component based robot development is safeguarding against software component failures and malfunctions. The health monitoring of the robot software is most fundamental factors not only to manage system at runtime but also to analysis information of software component in design phase of the robot application. And also as a lot of monitoring events are occurred during the execution of the robot software components, a simple data treatment and efficient memory management method is required. In this paper, we propose an efficient events monitoring and data management method by modeling robot software component and monitoring factors based on robot software framework. The monitoring factors, such as component execution runtime exception, Input/Output data, execution time, checkpoint-rollback are deduced and the detail monitoring events are defined. Furthermore, we define event record and monitor record pool suitable for robot software components and propose a efficient data management method. To verify the effectiveness and usefulness of the proposed approach, a monitoring module and user interface has been implemented using OPRoS robot software framework. The proposed monitoring module can be used as monitoring tool to analysis the software components in robot design phase and plugged into self-healing system to monitor the system health status at runtime in robot systems.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.12
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• pp.1227-1236
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• 2012

The low speed machinery faults are usually caused by the bearing failure of the rolling elements. As the life time of the bearing is limited, the condition monitoring of bearing is very important to maintain the continuous operation without failures. A few monitoring techniques using time domain, frequency domain and fuzzy neural network vibration analysis are introduced to detect and diagnose the faults of the low speed machinery. This paper presents a method of fault detection for the rolling element bearing in the low speed machinery using the Wiener filtering and shock pulse counting techniques. Wiener filter is used for noise cancellation and it clearly makes the shock pulse emerge from the time signal with the high level of noise. The shock pulse counting is used to determine the various faults obviously from the shock signal with transient pulses not related with the bearing fault. Machine fault simulator is used for the experimental measurement in order to verify this technique is the powerful tool for the low speed machine compared with the frequency analysis. The test results show that the method proposed is very effective parameter even for the signal with high contaminated noise, speed variation and very low energy. The presented method shows the optimal tool for the condition monitoring purpose to detect the various bearing fault with high accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.1
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• pp.99-118
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• 2013

Electrocardiograms (ECGs) are widely used by clinicians to identify the functional status of the heart. Thus, there is considerable interest in automated systems for real-time monitoring of arrhythmia. However, intra- and inter-patient variability as well as the computational limits of real-time monitoring poses significant challenges for practical implementations. The former requires that the classification model be adjusted continuously, and the latter requires a reduction in the number and types of ECG features, and thus, the computational burden, necessary to classify different arrhythmias. We propose the use of adaptive learning to automatically train the classifier on up-to-date ECG data, and employ adaptive feature selection to define unique feature subsets pertinent to different types of arrhythmia. Experimental results show that this hybrid technique outperforms conventional approaches and is therefore a promising new intelligent diagnostic tool.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.761-765
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• 2008

This study presents an effective methodology on integrated monitoring system for a maglev guideway using WDM-based FBG sensors. The measuring quantities include both local and global quantities of the guideway response, such as stains, curvatures, and vertical deflections. The strains are directly measured from multiplexed FBG sensors at various locations of the test bridge followed by curvature calculations based on the plane section assumption. Vertical deflections are then estimated using the Bernoulli beam theory and regression analysis. Frequency contents obtained from the proposed method are compared with those from a conventional accelerometer. Verification tests were conducted on the newly-developed Korean Maglev test track. It has been shown that good agreement between the measured deflection and the estimated deflection is achieved. The difference between the two peak displacements was only 3.5% in maximum and the correlations between data from two sensing systems are overall very good. This confirms that the proposed technique is capable of tracing the dynamic behavior of the maglev guideway with an acceptable accuracy. Furthermore, it is expected that the proposed scheme provides an effective tool for monitoring the behavior of the maglev guideway structures without electro magnetic interference.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.15 no.1 s.94
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• pp.53-62
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• 2005

This study presents the NDE (non-destructive evaluation) technique for detecting the loosened bolts on joint steel structures on the basis of TOF (time of flight) and amplitudes of Lamb waves. Probabilistic neural network (PNN) technique which is an effective tool for pattern classification problem was applied to the damage estimation using PZT induced Lamb waves. Two kinds of damages were introduced by dominant damages (DD) which mean loosened bolts within the Lamb waves beam width and minor damages (MD) which mean loosened bolts out of the Lamb waves beam width. They were investigated for the establishment of the optimal decision boundaries which divide each damage class's region including the intact class. In this study, the applicability of the probabilistic neural networks was identified through the test results for the damage cases within and out of wave beam path. It has been found that the present methods are very efficient and reasonable in predicting the loosened bolts on the joint steel structures probabilistically.

• Structural Monitoring and Maintenance
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• v.4 no.1
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• pp.69-84
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• 2017

Delamination is the most common failure mode in layered composite materials. The author have found that the electrical potential change (EPC) technique using response surfaces method is very effective in assessment delamination in basalt fiber reinforced polymer (FRP) laminate composite pipe by using electrical capacitance sensor (ECS). In the present study, the effect of the electrodes number on the method is investigated using FEM analyses for delamination location/size detection by ANSYS and MATLAB, which are combined to simulate sensor characteristic. Three cases of electrodes number are analyzed here are eight, twelve and sixteen electrodes, afterwards, the delamination is introduced into between the three layers [$0^{\circ}/90^{\circ}/0^{\circ}$]s laminates pipe, split into eight, twelve and sixteen scenarios for cases of eight, twelve and sixteen electrodes respectively. Response surfaces are adopted as a tool for solving inverse problems to estimate delamination location/size from the measured EPC of all segments between electrodes. As a result, it was revealed that the estimation performances of delamination location/size depends on the electrodes number. For ECS, the high number of electrodes is required to obtain high estimation performances of delamination location/size. The illustrated results are in excellent agreement with solutions available in the literature, thus validating the accuracy and reliability of the proposed technique.

• Journal of Korean Society on Water Environment
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• v.29 no.5
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• pp.691-702
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• 2013

This review paper summarizes the theory, application, and potential drawbacks of diffusive gradient in thin film (DGT) probe which is a widely used in-situ passive sampling technique for monitoring inorganic contaminants in aquatic environments. The DGT probe employs a series of layers including a filter membrane, a diffusive hydrogel, and an ionic exchange resin gel in a plastic unit. The filter side is exposed to an aquatic environment after which dissolved inorganic contaminants, such as heavy metals and nuclides, diffuse through the hydrogel and are accumulated in the resin gel. After retrieval, the contaminants in the resin gel are extracted by strong acid or base and the concentrations are determined by analytical instruments. Then aqueous concentrations of the inorganic contaminants can be estimated from a mathematical equation. The DGT has also been used to monitor nutrients, such as ${PO_4}^{3-}$, in lakes, streams, and estuaries, which might be helpful in assessing eutrophic potential in aquatic environments. DGT is a robust in-situ passive sampling techniques for investigating bioavailability, toxicity, and speciation of inorganic contaminants in aquatic environments, and can be an effective monitoring tool for risk assessment.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.11a
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• pp.625-632
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• 2004

This study presents the NDE (non-destructive evaluation) technique for detecting the loosened bolts on joint steel structures on the basis of TOF (time of flight) and amplitudes of Lamb waves. Probabilistic neural network (PNN) technique which is an effective tool for pattern classification problem was applied to the damage estimation using PZT induced Lamb waves. Two kinds of damages were introduced by dominant damages (DD) which mean loosened bolts within the Lamb waves beam width and minor damages (MD) which mean loosened bolts out of the Lamb waves beam width. They were investigated for the establishment of the optimal decision boundaries which divide each damage class's region including the intact class. In this study, the applicability of the probabilistic neural networks was identified through the test results for the damage cases within and out of wave beam path. It has been found that the present methods are very efficient and reasonable in predicting the loosened bolts on the joint steel structures probabilistically.

• KIEAE Journal
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• v.10 no.4
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• pp.3-8
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• 2010

Recently, various daylight evaluation methods for visual environment have been developed; simulation analysis methods, numerical calculation, and data monitoring methods. However, it is impossible for simulation analysis to make real scenes and visualize real images exactly. Also, a numerical calculation is considered as an out of date and time-consuming mean. Therefore, for acquisition of accurate results, many studies often use the monitoring data methods. Especially, most studies regarding discomfort glare are evaluated by measuring the physical quantity of luminance through traditional measuring Minolta Luminance meters as an instrument. But, this method has a difficulty in measuring several points at the same time because of the limitation of spaces and time when mapping. So, this study focused on the potential usefulness of High Dynamic Range photography technique as a luminance mapping tool. In order to evaluate the accuracy of proposed programs such as webHDR, Photomatix and PHOTOLUX, this paper has conducted an experiment by using Canon EOS 5D and NICON Coolpix8400 digital camera.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 1998.04a
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• pp.314-320
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• 1998

Wear debris morphology is closely related to the wear mode and mechanism occured. Image recognition of wear debris is, therefore, a powerful tool in wear monitoring. But it has usually required expert's experience and the results could be too subjective. Development of automatic tools for wear debris recognition is needed to solve this problem. In this work, an algorithm for automatic wear debris recognition was suggested and implemented by PC base software. The presented method defined a characteristic 3-dimensional feature space where typical types of wear debris were separately located by the knowledge-based system and compared the similarity of object wear debris concerned. The 3-dimensional feature space was obtained from multiple feature vectors by using a multi-dimensional scaling technique. The results showed that the presented automatic wear debris recognition was satisfactory in many cases application.