• Journal of Institute of Control, Robotics and Systems
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• v.16 no.8
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• pp.780-785
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• 2010

The OPRoS (Open Platform for Robotic Services) framework is proposed as an application runtime environment for service robot systems. For the successful deployment of the OPRoS framework, fault tolerance support is crucial on top of its basic functionalities of lifecycle, thread and connection management. In the previous work [1] on OPRoS fault tolerance supports, we presented a framework-based fault tolerance architecture. In this paper, we extend the architecture with component-based fault tolerance techniques, which can provide more simplicity and efficiency than the pure framework-based approach. This argument is especially true for fault detection, since most faults and failure can be defined when the system cannot meet the requirement of the application functions. Specifically, the paper applies two widely-used fault detection techniques to the OPRoS framework: 'bridge component' and 'process model' component techniques for fault detection. The application details and performance of the proposed techniques are demonstrated by the same application scenario in [1]. The combination of component-based techniques with the framework-based architecture would improve the reliability of robot systems using the OPRoS framework.

• International Journal of Naval Architecture and Ocean Engineering
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• v.10 no.5
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• pp.617-628
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• 2018

Several techniques have been developed in the last years for energy conversion and aeronautic propulsion plants monitoring and diagnostics, to ensure non-stop availability and safety, mainly based on machine learning and pattern recognition methods, which need large databases of measures. This paper aims to describe a simulation based monitoring and diagnostic method to overcome the lack of data. An application on a gas turbine powered frigate is shown. A MATLAB-SIMULINK(R) model of the frigate propulsion system has been used to generate a database of different faulty conditions of the plant. A monitoring and diagnostic system, based on Mahalanobis distance and artificial neural networks have been developed. Experimental data measured during the sea trials have been used for model calibration and validation. Test runs of the procedure have been carried out in a number of simulated degradation cases: in all the considered cases, malfunctions have been successfully detected by the developed model.

• Environmental Analysis Health and Toxicology
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• v.19 no.2
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• pp.161-167
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• 2004

Air quality monitoring is a primary activity for industrial and social environment. The government identifies the pollutants that each industry must monitor. Especially, the VOCs (Volatile Organic Compounds), which are very harmful to human body and environment atmosphere, should be controlled under the government policy. However, the VOCs, which have not been confirmed in emission sources are very difficult to monitor. It is needed to develop the monitoring system that allow the continuous and in situ measurement of VOCs mixture in different environmental matrices. Gas chromatography and mass spectrometry are the most prevalent current techniques among those available for the analysis of VOCs. But, they need a large size analytical instrument, which costs a great deal for purchase and operation. In addition, it has some limitations for realtime environmental monitoring such as location problems and slow processing time. Recently, several companies have commercialized a portable VOCs measurement systems, which cannot classify various kinds of VOCs but total quantities. We have developed a VOCs measurement system, which recognizes various kinds and quantities of VOCs, such as benzene, toluene, and xylene (BTX). Also, it can be used as a stand- alone type and/or fixed type in the vehicle with rack for real -time environmental monitoring.

• Korean Journal of Clinical Laboratory Science
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• v.45 no.2
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• pp.77-85
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• 2013

Intraoperative Neurophysiological Monitoring (INM) is very useful in monitoring the motorsensory pathway and vascular circulation system during intraspinal, or intracranial neurosurgery. Brainstem Auditory Evoked Potentials (BAEPs) are for detecting the problems along the auditory pathways including, the eighth cranial nerve and brainstem. Motor Evoked Potentials (MEPs) is a useful adjunct to conventional monitoring of Somato-sensory Evoked Potentials (SEPs) during surgery. Visual Evoked Potentials (VEPs) has been regarded as having limited significance for the preservation of visual function during neurosurgical procedures. In this paper, we propose that the most appropriate averaging of the number of inspections in the inspection of each used in the operative field, is good and efficient, functionally.

• Smart Structures and Systems
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• v.5 no.4
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• pp.357-367
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• 2009

Structural Health Monitoring (SHM), particularly remote monitoring, is an emerging field with great potential to help infrastructure owners obtain more and up-to-date knowledge of their structures. The methodology could provide supplemental information to guide the frequency and extent of visual inspections, and the possible need for maintenance. The instrumentation for a SHM system needs to be developed with longevity and the objectives for the system in mind. Sensors need to be selected for reliability and durability, sited where they provide the maximum information for the objectives, and where they can be accessed and replaced should the need arise over the monitoring period. With the rapid changes now occurring with sensors and software, flexibility needs to be in place to allow the system to be upgraded over time. Damage detection needs to be considered in terms of the type of damage that needs to be detected, informing maintenance requirements, and how detection can be achieved. Current vibration analysis techniques appear not yet to have achieved the necessary sensitivity for that purpose. Societal factors will influence the design of a SHM system in terms of the sophistication of the instrumentation and methodology employed.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.3
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• pp.318-322
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• 1997

A prototype of combustion zone monitoring system as been developed and installed into tuyeres of the blast furnace. The system consists of CCD(charge coupled device) cameras, sonic flow meters, an image processor and a personal computer. The personal computer collects raceway luminance data and operational data from the image processor that is connected to the color CCD camera from the blast furnace process computer, respectively. In addition, the sonic flow meters supply coal injection rate data to the personal computer. Then, the personal computer evaluates the combustion conditions with the raceway inspection algorithm. This integrated monitoring system allows us to detect abnormal raceway conditions and the clogging status of coal injection pipe. The image processing techniques of the system enable us to effectively monitor unburnt coal sticking to tuyere tip and injection lance wear conditions. Such a developed system ensures rapid and precise raceway inspection. The image processing capability of the system has helped operator to early detect both the unburnt coal sticking problem and the errosion problem of injection lance. Furthermore, the system could control the abnormal raceway condition based the the analysis results obtained from combustion monitoring.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2007.06a
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• pp.527-528
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• 2007

A field-oriented UHF system for on-line PD monitoring of transformers is designed, which has been installed inside the oil tank of a 220kV transformer in a substation. This system has successfully captured long intermittent discharge signals that hadn't been detected through conventional techniques, and solved the problem successfully. The results demonstrate that UHF technique has great advantages for on-line PD monitoring of transformers. By adopting the peak detection technique, it becomes easy and effective for the transplantation of the phase-resolved pattern recognition technique from conventional method to UHF method, and then to realize continuous on-line monitoring, source characterization and trending analysis.

• Smart Structures and Systems
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• v.6 no.5_6
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• pp.545-559
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• 2010

This paper presents the results of a pilot study and verification of a concept of a novel methodology for damage detection and assessment of water distribution system. The unique feature of the proposed noninvasive methodology is the use of accelerometers installed on the pipe surface, instead of pressure sensors that are traditionally installed invasively. Experimental observations show that a sharp change in pressure is always accompanied by a sharp change of pipe surface acceleration at the corresponding locations along the pipe length. Therefore, water pressure-monitoring can be transformed into acceleration-monitoring of the pipe surface. The latter is a significantly more economical alternative due to the use of less expensive sensors such as MEMS (Micro-Electro-Mechanical Systems) or other acceleration sensors. In this scenario, monitoring is made for Maximum Pipe Acceleration Gradient (MPAG) rather than Maximum Water Head Gradient (MWHG). This paper presents the results of a small-scale laboratory experiment that serves as the proof of concept of the proposed technology. The ultimate goal of this study is to improve upon the existing SCADA (Supervisory Control And Data Acquisition) by integrating the proposed non-invasive monitoring techniques to ultimately develop the next generation SCADA system for water distribution systems.

• Smart Structures and Systems
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• v.31 no.6
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• pp.585-599
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• 2023

Field monitoring techniques offer an attractive approach for understanding bridge behavior under in-service loads. However, the investigations on bridge behavior under high-speed train load using field monitoring data are limited. The focus of this study is to explore the structural behavior of an in-service long-span steel truss arch bridge based on field monitoring data. First, the natural frequencies of the structure, as well as the train driving frequencies, are extracted. Then, the train-induced bearing displacement and structural strain are explored to identify the effects of train loads and bearings. Subsequently, a sensitivity analysis is performed for the impact factor of strain responses with respect to the train speed, train weight, and temperature to identify the fundamental issues affecting these responses. Additionally, a similar sensitivity analysis is conducted for the peak acceleration. The results indicate that the friction force in bearings provides residual deformations when two consecutive trains are in opposite directions. In addition, the impact factor and peak acceleration are primarily affected by train speed, particularly near train speeds that result in the resonance of the bridge response. The results can provide additional insight into the behavior of the long-span steel truss bridges under in-service high-speed train loads.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1073-1080
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• 2024

The imperative for real-time automatic construction progress monitoring (ACPM) to avert project delays is widely acknowledged in construction project management. Current ACPM methodologies, however, face a challenge as they rely on collecting data from construction sites and processing it offline for progress analysis. This delayed approach poses a risk of late identification of critical construction issues, potentially leading to rework and subsequent project delays. This research introduces a real-time construction progress monitoring framework that integrates cutting-edge semantic Simultaneous Localization and Mapping (SLAM) techniques. The innovation lies in the framework's ability to promptly identify structural components during site inspections conducted through a robotic system. Incorporating deep learning models, specifically those employing semantic segmentation, enables the system to swiftly acquire and process real-time data, identifying specific structural components and their respective locations. Furthermore, by seamlessly integrating with Building Information Modeling (BIM), the system can effectively evaluate and compare the progress status of each structural component. This holistic approach offers an efficient and practical real-time progress monitoring solution for construction projects, ensuring timely issue identification and mitigating the risk of project delays.