Relevant bibliographies by topics / Identification and monitoring

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Identification and monitoring'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 February 2022

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Journal articles on the topic "Identification and monitoring"

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Cojocar, Grigoreta-Sofia, and Adriana-Mihaela Guran. "ON AUTOMATIC IDENTIFICATION OF MONITORING CONCERNS IMPLEMENTATION." Acta Electrotechnica et Informatica 18, no. 3 (September 27, 2018): 9–17. http://dx.doi.org/10.15546/aeei-2018-0020.

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Eradus, Wim J., and Mans B. Jansen. "Animal identification and monitoring." Computers and Electronics in Agriculture 24, no. 1-2 (November 1999): 91–98. http://dx.doi.org/10.1016/s0168-1699(99)00039-3.

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Bewley, Beulah R., and N. D. Noah. "Iatrogenic Diseases – Identification and Monitoring." Journal of the Royal Society of Medicine 80, no. 3 (March 1987): 187–88. http://dx.doi.org/10.1177/014107688708000321.

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&NA;. "Hospital monitoring improves ADR identification." Inpharma Weekly &NA;, no. 823 (February 1992): 19–20. http://dx.doi.org/10.2165/00128413-199208230-00041.

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Begovic, M. M., and R. Q. Mills. "Load identification and voltage stability monitoring." IEEE Transactions on Power Systems 10, no. 1 (1995): 109–16. http://dx.doi.org/10.1109/59.373933.

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White, J., J. McCowan, M. Whitaker, and M. Laughter. "Global identification and monitoring of UF6cylinders." Packaging, Transport, Storage & Security of Radioactive Material 22, no. 2 (June 2011): 78–82. http://dx.doi.org/10.1179/1746510911y.0000000003.

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Griffin, J. P. "Adverse reaction monitoring using cohort identification." BMJ 294, no. 6571 (February 28, 1987): 576. http://dx.doi.org/10.1136/bmj.294.6571.576-c.

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Inman, W. H., and N. S. Rawson. "Adverse reaction monitoring using cohort identification." BMJ 294, no. 6576 (April 4, 1987): 902. http://dx.doi.org/10.1136/bmj.294.6576.902.

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Wiley, Terry L., and Daniel T. Stoppenbach. "Audiologic identification and monitoring of ototoxicity." Current Opinion in Otolaryngology & Head and Neck Surgery 2 (October 1994): 420–25. http://dx.doi.org/10.1097/00020840-199410000-00011.

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Et. al., Ganesh Birajadar,. "Epilepsy Identification using EEG signal monitoring." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 10, 2021): 2366–71. http://dx.doi.org/10.17762/turcomat.v12i2.2022.

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Electroencephalogram (EEG) is nothing but measuring electric activity of brain. EEG is non-stationary signal. EEG characterizes human behavior. There are many brain abnormalities that can be identified and treated using EEG behavior analysis. As per researchers study Epilepsy is commonly happening disorder that is getting spread over the time. It is nothing but sudden stroke in brain where patient suffers from unusual activities seizures. Sometimes symptoms are such severe that ignorance leads to death. So it is important to identify its earlier symptoms and treat it in time so as to avoid risk. EEG signals are used for getting features in time as well as frequency domain. These features are further analyzed and classified to identify EEG abnormality.
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Dissertations / Theses on the topic "Identification and monitoring"

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Ayland, Nicholas D. "Automatic vehicle identification for road traffic monitoring." Thesis, University of Nottingham, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.254395.

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Fernandes, Winnie Cezario. "Thrips on roses: identification, monitoring and chemical control." Universidade Federal do CearÃ, 2015. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=14048.

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Conselho Nacional de Desenvolvimento CientÃfico e TecnolÃgico
The growth in the production of ornamental plants is increasingly significant in Brazil and in the Northeast region, but the occurrence of pests is shown as a limiting factor. To minimize losses, adequate control measures should be employed. Accordingly, the correct identification of pests, population monitoring and studies on managements should be performed. The objective of this study was to identify thrips species in rose, characterize and quantify the damage loss caused by arthropod pests in the production of roses in Serra da Ibiapaba; to assess the fluctuation of thrips species in ten cultivars of rose, at different stages of flower development and monitoring systems, and; evaluate the efficiency of pesticides on Frankliniella spp. The experiments were conducted at the Company âReijers ProduÃÃo de Rosasâ, SÃo Benedito, Cearà State, âLagoa Jussaraâ in planting roses in greenhouses. Three species of thrips have been identified: Frankliniella schultzei (Trybom, 1910), F. occidentalis (Pergande, 1895) and Caliothrips phaseoli (Pergande, 1825) (Thysanoptera: Thripidae) with the largest recorded infestations for F. occidentalis and F. schultzei in phenological phases of roses, especially in flowering. The injury caused by thrips in floral cut roses button affected the quality invalidating them for marketing. There was no difference between the sampling periods (morning and afternoon) and sampling (tray beat and direct view of the floral button) to the ten cultivars of roses, so the choice of the time and method must be reconciled with practicality and cost. The insecticides demonstrated ability to cause mortality of thrips in extreme conditions, within completely enclosed structures (flower buds).
O crescimento na produÃÃo de plantas ornamentais à cada vez mais significativo no Brasil e na regiÃo Nordeste do paÃs, porÃm a ocorrÃncia de pragas mostra-se como fator limitante. Para minimizar as perdas, medidas adequadas de controle devem ser empregadas. Nesse sentido, a identificaÃÃo correta das pragas, seu monitoramento populacional e estudos sobre manejos devem ser realizados. O objetivo deste estudo foi identificar espÃcies de tripes em roseira, caracterizar danos e quantificar as perdas ocasionadas pelo artrÃpode-praga na produÃÃo de rosas na Serra da Ibiapaba; avaliar a flutuaÃÃo populacional das espÃcies de tripes em dez cultivares de roseira, em diferentes fases do desenvolvimento floral e sistemas de monitoramento, e; avaliar a eficiÃncia de produtos fitossanitÃrios sobre Frankliniella spp. Os experimentos foram conduzidos na Empresa Reijers ProduÃÃo de Rosas, Unidade SÃo Benedito/CE, Fazenda Lagoa Jussara, em plantio de roseiras sob cultivo protegido. Foram identificadas trÃs espÃcies de tripes: Frankliniella schultzei (Trybom, 1910), F. occidentalis (Pergande, 1895) e Caliothrips phaseoli (Pergande, 1825) (Thysanoptera: Thripidae) sendo as maiores infestaÃÃes registradas para F. occidentalis e F. schultzei nas diferentes fases fenolÃgicas das roseiras, especialmente na floraÃÃo. As injÃrias causadas pelos tripes no botÃo floral de rosas de corte afetaram aqualidade inviabilizando-as para a comercializaÃÃo. NÃo houve diferenÃa estatÃstica entre os perÃodos de amostragem (manhà e tarde) e os mÃtodos de amostragem (batida de bandeja e visualizaÃÃo direta do botÃo floral) para as dez cultivares de roseiras, assim a escolha do horÃrio e do mÃtodo devem ser conciliadascom praticidade e custo.Os inseticidas demonstraram capacidade de causar mortalidade de tripes em condiÃÃes extremas, ou seja, dentro de estruturas completamente fechadas (botÃes florais).
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Eriksson, Martin. "Monitoring, Modelling and Identification of Data Center Servers." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-69342.

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Energy efficient control of server rooms in modern data centers can help reducing the energy usage of this fast growing industry. Efficient control, however, cannot be achieved without: i) continuously monitoring in real-time the behaviour of the basic thermal nodes within these infras- tructures, i.e., the servers; ii) analyzing the acquired data to model the thermal dynamics within the data center. Accurate data and accurate models are indeed instrumental for implementing efficient data centers cooling strategies. In this thesis we focus on Open Compute Servers, a class of servers designed in an open-source fashion and used by big players like Facebook. We thus propose a set of appropriate methods for collecting real-time data from these platforms and a dedicated thermal model describing the thermal dynamics of the CPUs and RAMs of these servers as a function of both controllable and non-controllable inputs (e.g., the CPU utilization levels and the air mass flow of the server’s fans). We also identify this model from real data and provide the results so to be reusable by other researchers.
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Zhang, Yi 1973. "Multi-channel blind system identification for central hemodynamic monitoring." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/29622.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2002.
Includes bibliographical references (leaves 89-91).
Multi-channel Blind System Identification (MBSI) is a technique for estimating both an unknown input and unknown channel dynamics from simultaneous output measurements at different channels through which the input signal propagates. It is a powerful tool particularly for the identification and estimation of dynamical systems in which a sensor, for measuring the input, is difficult to place. All of the existing MBSI algorithms, however, are not applicable to multi-channel systems sharing common dynamics among the channels, since these algorithms, by nature, exploit "differences" among the multiple channel dynamics. This requirement renders the MBSI algorithms useless in systems that have both a lumped-parameter nature and a distributed nature; all channels in a system of this type share poles dictated by the lumped-parameter dynamics. To overcome this difficulty, this thesis investigates a new approach, Intermediate Input Identification (IIID). This thesis proves that the distinct dynamics in each channel can be identified up to a scalar factor even when common dynamics are present. Based on this discovery, the MBSI problem is reformulated and an intermediate input is introduced, which integrates the original system input and the common dynamics shared by all the channels. The two-step IIID approach is developed to solve the problem: first, the distinct dynamics are identified from the outputs; second, the common dynamics are identified from the intermediate input by exploiting the zero-input response of the system. The identifiability conditions are thoroughly investigated. The sufficient and necessary conditions and the relationship between the linear-complexity condition of the original input and that of the intermediate input are derived in this thesis.
(cont.) This thesis also develops a central hemodynamic monitoring scheme based on IIID. The similarities between the structure of a digital wireless communication system and that of the cardiovascular system are explained. The input, the common dynamics and the distinct dynamics in the cardiovascular multi-channel system are derived based on the determinants of arterial blood pressure. Analysis of the data from a cardiovascular simulator and animal experiments verify the validity of this scheme. The positive results demonstrate that the IIID approach could open up the possibility for noninvasive central hemodynamic monitoring, which could significantly reduce the risks to which patients are exposed.
by Yi Zhang.
Ph.D.
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Bisht, Saurabh Singh. "Vibration Measurement Based Damage Identification for Structural Health Monitoring." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/77301.

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The focus of this research is on the development of vibration response-based damage detection in civil engineering structures. Modal parameter-based and model identification-based approaches have been considered. In the modal parameter-based approach, the flexibility and curvature flexibility matrices of the structure are used to identify the damage. It is shown that changes in these matrices can be related to changes in stiffness values of individual structural members. Using this relationship, a method is proposed to solve for the change in stiffness values. The application of this approach is demonstrated on the benchmark problem developed by the joint International Association of Structural Control and American Society of Civil Engineers Structural Health Monitoring task group. The proposed approach is found to be effective in identifying various damage scenarios of this benchmark problem. The effect of missing modes on the damage identification scheme is also studied. The second method for damage identification aims at identifying sudden changes in stiffness for real time applications. It is shown that the high-frequency content of the response acceleration can be used to identify the instant at which a structure suffers a sudden reduction in its stiffness value. Using the Gibb's phenomenon, it is shown why a high-pass filter can be used for identifying such damages. The application of high-pass filters is then shown in identifying sudden stiffness changes in a linear multi-degree-of-freedom system and a bilinear single degree of freedom system. The impact of measurement noise on the identification approach is also studied. The noise characteristics under which damage identification can or cannot be made are clearly identified. The issue of quantification of the stiffness reduction by this approach is also examined. It is noted that even if the time at which the reduction in stiffness happens can be identified, the quantification of damage requires the knowledge of system displacement values. In principle, such displacements can be calculated by numerical integration of the acceleration response, but the numerical integrations are known to suffer from the low frequency drift error problems. To avoid the errors introduced due to numerical integration of the acceleration response, an approach utilizing the unscented Kalman filter is developed to track the sudden changes in stiffness values. This approach is referred to as the adaptive unscented Kalman filter (AUKF) approach. The successful application of the proposed AUKF approach is shown on two multi-degree of freedom systems that experience sudden loss of stiffness values while subjected to earthquake induced base excitation.
Ph. D.
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Sakki, Kranthi Kumar. "A Radio Frequency Identification Multi-Sensor Health Monitoring System." Thesis, California State University, Long Beach, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10262351.

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Health Monitoring Systems (HMS) are used to monitor physiological signals such as the blood pressure, heart rate, and temperature of patients. The use of a HMS for continuous monitoring of the Vital Signs of patients requiring constant medical supervision, is particularly important. The current project presents the development and implementation of a multi-sensor HMS to track and record multiple parameters of a patient (Electrocardiogram, pulse, temperature, and body position). The project development uses biomedical sensor technology for monitoring the physiological signals, Radio Frequency Identification (RFID) technology for patient identification, and the Internet of Things (IoT) for information transmission. Sensors attached to a patient’s body collect data that alert users to abnormal values via smart devices, such as mobile phones or laptops. Experimental testing of the multi-sensor HMS developed and implemented for this project, demonstrates the system’s effectiveness in sensing, collecting, and transmitting accurate patient information for remote monitoring.

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Elbadawy, Mohamed Mohamed Zeinelabdin Mohamed. "Dynamic Strain Measurement Based Damage Identification for Structural Health Monitoring." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/86167.

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Structural Health Monitoring (SHM) is a non-destructive evaluation tool that assesses the functionality of structural systems that are used in the civil, mechanical and aerospace engineering practices. A much desirable objective of a SHM system is to provide a continuous monitoring service at a minimal cost with ability to identify problems even in inaccessible structural components. In this dissertation, several such approaches that utilize the measured dynamic response of structural systems are presented to detect, locate, and quantify the damages that are likely to occur in structures. In this study, the structural damage is identified as a reduction in the stiffness characteristics of the structural elements. The primary focus of this study is on the utilization of measured dynamic strains for damage identification in the framed structures which are composed of interconnected beam elements. Although linear accelerations, being more convenient to measure, are commonly used in most SHM practices, herein the strains being more sensitive to elemental damage are considered. Two different approaches are investigated and proposed to identify the structural element stiffness properties. Both approaches are mode-based, requiring first the identification of system modes from the measured strain responses followed by the identification of the element stiffness coefficients. The first approach utilizes the Eigen equation of the finite element model of the structure, while the second approach utilizes the changes caused by the damage in the structural curvature flexibilities. To reduce size of the system which is primarily determined by the number of sensors deployed for the dynamic data collection, measurement sensitivity-based sensor selection criterion is observed to be effective and thus used. The mean square values of the measurements with respect to the stiffness coefficients of the structural elements are used as the effective measures of the measurement sensitivities at different sensor locations. Numerical simulations are used to evaluate the proposed identification approaches as well as to validate the sensitivity-based optimal sensor deployment approach.
Ph. D.
All modern societies depend heavily on civil infrastructure systems such as transportation systems, power generation and transmission systems, and data communication systems for their day-to-day activities and survival. It has become extremely important that these systems are constantly watched and maintained to ensure their functionality. All these infrastructure systems utilize structural systems of different forms such as buildings, bridges, airplanes, data communication towers, etc. that carry the service and environmental loads that are imposed on them. These structural systems deteriorate over time because of natural material degradation. They can also get damaged due to excessive load demands and unknown construction deficiencies. It is necessary that condition of these structural systems is known at all times to maintain their functionality and to avoid sudden breakdowns and associated ensuing problems. This condition assessment of structural systems, now commonly known as structural health monitoring, is commonly done by visual onsite inspections manually performed at pre-decided time intervals such as on monthly and yearly basis. The length of this inspection time interval usually depends on the relative importance of the structure towards the functionality of the larger infrastructure system. This manual inspection can be highly time and resource consuming, and often ineffective in catching structural defects that are inaccessible and those that occur in between the scheduled inspection times and dates. However, the development of new sensors, new instrumentation techniques, and large data transfer and processing methods now make it possible to do this structural health monitoring on a continuous basis. The primary objective of this study is to utilize the measured dynamic or time varying strains on structural components such as beams, columns and other structural members to detect the location and level of a damage in one or more structural elements before they become serious. This detection can be done on a continuous basis by analyzing the available strain response data. This approach is expected to be especially helpful in alerting the owner of a structure by identifying the iv occurrence of a damage, if any, immediately after an unanticipated occurrence of a natural event such as a strong earthquake or a damaging wind storm.
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Appler, Jason A. Finney Sean M. McMellon Michael A. "Aerial remote radio frequency identification system for small vessel monitoring." Monterey, California : Naval Postgraduate School, 2009. http://edocs.nps.edu/npspubs/scholarly/MBAPR/2009/Dec/09Dec%5FAppler%5FMBA.pdf.

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"Submitted in partial fulfillment of the requirements for the degree of Master of Business Administration from the Naval Postgraduate School, December 2009."
Advisor(s): Dew, Nicholas ; Hudgens, Bryan. "December 2009." "MBA Professional report"--Cover. Description based on title screen as viewed on January 26, 2010. Author(s) subject terms: RFID, Radio Frequency Identification, airborne, vessel monitoring. Includes bibliographical references (p. 103-110). Also available in print.
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Jiang, Bing. "Ubiquitous monitoring of distributed infrastructures /." Thesis, Connect to this title online; UW restricted, 2006. http://hdl.handle.net/1773/6118.

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Bakhary, Norhisham. "Structural condition monitoring and damage identification with artificial neural network." University of Western Australia. School of Civil and Resource Engineering, 2009. http://theses.library.uwa.edu.au/adt-WU2009.0102.

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Many methods have been developed and studied to detect damage through the change of dynamic response of a structure. Due to its capability to recognize pattern and to correlate non-linear and non-unique problem, Artificial Neural Networks (ANN) have received increasing attention for use in detecting damage in structures based on vibration modal parameters. Most successful works reported in the application of ANN for damage detection are limited to numerical examples and small controlled experimental examples only. This is because of the two main constraints for its practical application in detecting damage in real structures. They are: 1) the inevitable existence of uncertainties in vibration measurement data and finite element modeling of the structure, which may lead to erroneous prediction of structural conditions; and 2) enormous computational effort required to reliably train an ANN model when it involves structures with many degrees of freedom. Therefore, most applications of ANN in damage detection are limited to structure systems with a small number of degrees of freedom and quite significant damage levels. In this thesis, a probabilistic ANN model is proposed to include into consideration the uncertainties in finite element model and measured data. Rossenblueth's point estimate method is used to reduce the calculations in training and testing the probabilistic ANN model. The accuracy of the probabilistic model is verified by Monte Carlo simulations. Using the probabilistic ANN model, the statistics of the stiffness parameters can be predicted which are used to calculate the probability of damage existence (PDE) in each structural member. The reliability and efficiency of this method is demonstrated using both numerical and experimental examples. In addition, a parametric study is carried out to investigate the sensitivity of the proposed method to different damage levels and to different uncertainty levels. As an ANN model requires enormous computational effort in training the ANN model when the number of degrees of freedom is relatively large, a substructuring approach employing multi-stage ANN is proposed to tackle the problem. Through this method, a structure is divided to several substructures and each substructure is assessed separately with independently trained ANN model for the substructure. Once the damaged substructures are identified, second-stage ANN models are trained for these substructures to identify the damage locations and severities of the structural ii element in the substructures. Both the numerical and experimental examples are used to demonstrate the probabilistic multi-stage ANN methods. It is found that this substructuring ANN approach greatly reduces the computational effort while increasing the damage detectability because fine element mesh can be used. It is also found that the probabilistic model gives better damage identification than the deterministic approach. A sensitivity analysis is also conducted to investigate the effect of substructure size, support condition and different uncertainty levels on the damage detectability of the proposed method. The results demonstrated that the detectibility level of the proposed method is independent of the structure type, but dependent on the boundary condition, substructure size and uncertainty level.
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Books on the topic "Identification and monitoring"

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Sargeant, Debby. Lilliwaup Bay bacterial source identification monitoring. [Olympia, Wash.]: Washington State Dept. of Ecology, 1999.

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Chatzi, Eleni, and Costas Papadimitriou, eds. Identification Methods for Structural Health Monitoring. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32077-9.

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Tarry, S. Development of a lorry monitoring and identification system. Crowthorne: Transport and Road Research Laboratory, 1989.

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Bellinger, E. G. Freshwater algae: Identification and use as bioindicators. Chichester, West Sussex, UK: Wiley-Blackwell, 2010.

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Bellinger, E. G. Freshwater algae: Identification and use as bioindicators. Chichester, West Sussex, UK: Wiley-Blackwell, 2010.

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Bellinger, E. G. Freshwater algae: Identification and use as bioindicators. Chichester, West Sussex, UK: Wiley-Blackwell, 2010.

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1937-, Schroeder John Speer, ed. Hemodynamic waveforms: Exercises in identification and analysis. 2nd ed. St. Louis: Mosby, 1990.

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Environmental risk: Identification and management. Chelsea, MI: Lewis Publishers, 1991.

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Adams, Jeffrey W. Stream bugs as biomonitors: Guide to Pacific Northwest macroinvertebrate monitoring and identification. [Portland, Or.]: Xerces Society, 2004.

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Seaber, Paul R. Identification and description of potential ground-water quality monitoring wells in Florida. Tallahassee, Fla: U.S. Dept. of the Interior, Geological Survey, 1986.

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Book chapters on the topic "Identification and monitoring"

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Møller, Aage R. "Identification of Specific Neural Tissue." In Intraoperative Neurophysiological Monitoring, 275–94. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7436-5_14.

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Rembovsky, Anatoly M., Alexander V. Ashikhmin, Vladimir A. Kozmin, and Sergey M. Smolskiy. "Detection and Identification of Digital Radio Sources." In Radio Monitoring, 381–453. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74277-9_9.

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Verde, Cristina, and Jorge Rojas. "Recursive Scheme for Sequential Leaks’ Identification." In Applied Condition Monitoring, 125–45. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55944-5_7.

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Mullen, Thomas J., S. M. Ramakrishna Mukkamala, and Richard J. Cohen. "Cardiovascular System Identification." In Advances in Noninvasive Electrocardiographic Monitoring Techniques, 453–61. Dordrecht: Springer Netherlands, 2000. http://dx.doi.org/10.1007/978-94-011-4090-4_44.

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Bateman, Richard M. "Fluid Identification." In Cased-Hole Log Analysis and Reservoir Performance Monitoring, 99–111. Dordrecht: Springer Netherlands, 1985. http://dx.doi.org/10.1007/978-94-017-0977-4_7.

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Bateman, Richard M. "Fluid Identification." In Cased-Hole Log Analysis and Reservoir Performance Monitoring, 89–104. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-2068-6_7.

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Liu, Hui. "Machine Learning Based Appliance Identification." In Non-intrusive Load Monitoring, 141–62. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1860-7_6.

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Liu, Hui. "Deep Learning Based Appliance Identification." In Non-intrusive Load Monitoring, 191–214. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1860-7_8.

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Ghahari, S. Farid, Fariba Abazarsa, and Ertugrul Taciroglu. "Identification of Soil-Structure Systems." In Seismic Structural Health Monitoring, 139–67. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13976-6_6.

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Wang, Danwei, Ming Yu, Chang Boon Low, and Shai Arogeti. "Fault Identification Techniques." In Model-based Health Monitoring of Hybrid Systems, 147–89. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7369-5_4.

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Conference papers on the topic "Identification and monitoring"

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"Damage Identification of High-speed Maglev Guideway Girder Based on Modal Identification." In Structural Health Monitoring. Materials Research Forum LLC, 2021. http://dx.doi.org/10.21741/9781644901311-34.

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Abstract. As a modern high-tech rail vehicle, the maglev train realizes the non-contact suspension and guidance between the train and the guideway, which greatly reduces the resistance of the system. Due to the high-speed operation characteristics of maglev trains, the structural health monitoring of guideway girders is particularly important for the safety and stability of maglev train operation. This paper takes the maglev train guideway girder as the monitoring target, and the finite element model of the maglev vehicle-guideway is established to simulate the running state of the train passing through the guideway girder. The dynamic response data of the guideway girder is obtained in the finite element model, considering healthy states and different damage states of the guideway girder. Then, a modal-based damage identification method is proposed, which obtains the guideway girder damage sensitive characteristics by decomposing the guideway girder acceleration response signal. Finally, based on the measured guideway girder acceleration data, this paper verifies the effectiveness of the damage identification method in guideway girder structure health monitoring, which provides reference and guidance for the future maintenance of the maglev guideway girder.
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MADDEN, RYAN, ALEXANDER PESCH, and JERZY SAWICKI. "A Combined Model-Based Identification and Model Validation Approach for Damage Identification." In Structural Health Monitoring 2015. Destech Publications, 2015. http://dx.doi.org/10.12783/shm2015/64.

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YAO, YAO, and BRANKO GLISIC. "Crack Identification Using Sensing Sheets." In Structural Health Monitoring 2015. Destech Publications, 2015. http://dx.doi.org/10.12783/shm2015/374.

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Aloul, Fadi, Assim Sagahyroon, Ali Nahle, Makram Abou Dehn, and Raneem Al Anani. "GuideME: An Effective RFID-based Traffic Monitoring System." In Modelling, Identification and Control. Calgary,AB,Canada: ACTAPRESS, 2012. http://dx.doi.org/10.2316/p.2012.770-036.

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Carter, Michael R., Charles L. Bennett, David J. Fields, and John A. M. Hernandez. "Gaseous effluent monitoring and identification using an imaging Fourier transform spectrometer." In Substance Identification Technologies, edited by Geoffrey L. Harding, Richard C. Lanza, Lawrence J. Myers, and Peter A. Young. SPIE, 1994. http://dx.doi.org/10.1117/12.171245.

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TAKEWAKI, IZURU, YUHEI FUJIMORI, and KOHEI FUJITA. "Stiffness Identification of Stiffness Identification of High-rise Buildings via Subspace and Inverse-mode Methods." In Structural Health Monitoring 2017. Lancaster, PA: DEStech Publications, Inc., 2017. http://dx.doi.org/10.12783/shm2017/13879.

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Andersen, Eric S., Todd J. Samuel, and Kevin L. Gervais. "Portable source identification device." In Nondestructive Evaulation for Health Monitoring and Diagnostics, edited by Aaron A. Diaz, A. Emin Aktan, H. Felix Wu, Steven R. Doctor, and Yoseph Bar-Cohen. SPIE, 2005. http://dx.doi.org/10.1117/12.606665.

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Koh, Chan G., S. L. Zhao, Y. F. Chen, and C. Y. Liaw. "Nondestructive parameter identification of structures." In NDE For Health Monitoring and Diagnostics, edited by Andrew L. Gyekenyesi, Steven M. Shepard, Dryver R. Huston, A. Emin Aktan, and Peter J. Shull. SPIE, 2002. http://dx.doi.org/10.1117/12.470730.

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Alampalli, Sreenivas, and Mohammed Ettouney. "Structural identification, damage identification and structural health monitoring." In The 14th International Symposium on: Smart Structures and Materials & Nondestructive Evaluation and Health Monitoring, edited by H. Felix Wu, Aaron A. Diaz, and Peter J. Shull. SPIE, 2007. http://dx.doi.org/10.1117/12.715000.

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MALIK, MUHAMMAD KHALID, SERGIO CANTERO CHINCHILLA, DIMITRIOS CHRONOPOULOS, JUAN CHIACHIAO, and YASSER ESSA. "Ultrasonic Guided-Wave Based System Identification for Beams." In Structural Health Monitoring 2019. Lancaster, PA: DEStech Publications, Inc., 2019. http://dx.doi.org/10.12783/shm2019/32371.

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Reports on the topic "Identification and monitoring"

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M.A. Ebadian, Ph D. IDENTIFICATION OF DOE'S POST-CLOSURE MONITORING NEEDS AND REQUIREMENTS. Office of Scientific and Technical Information (OSTI), January 1999. http://dx.doi.org/10.2172/772511.

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Vogelsberger, R. R., E. D. Smith, M. Broz, and J. C. Wright, Jr. Identification of technical guidance related to ground water monitoring. Office of Scientific and Technical Information (OSTI), May 1987. http://dx.doi.org/10.2172/6282131.

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White-Horton, Jessica L., J. Michael Whitaker, James B. Morgan, and Sean Branney. Global Cylinder Identification and Monitoring System: Nonproliferation Concerns and Baseline Definition. Office of Scientific and Technical Information (OSTI), May 2013. http://dx.doi.org/10.2172/1088139.

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Roch, Marie A. Passive Acoustic Monitoring for the Detection and Identification of Marine Mammals. Fort Belvoir, VA: Defense Technical Information Center, September 2010. http://dx.doi.org/10.21236/ada541770.

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Whitaker, J., J. White-Horton, and J. Morgan. Preliminary Concept of Operations for a Global Cylinder Identification and Monitoring System. Office of Scientific and Technical Information (OSTI), August 2013. http://dx.doi.org/10.2172/1146985.

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Young, Stanley, and Dennis So Ting Fong. Real-Time Monitoring Concepts for Arterials Using Re-Identification and High-Resolution Data. Purdue University, December 2017. http://dx.doi.org/10.5703/1288284316559.

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Farrar, C. R., S. W. Doebling, and M. B. Prime. A comprehensive monitoring system for damage identification and location in large structural and mechanical systems. Office of Scientific and Technical Information (OSTI), November 1998. http://dx.doi.org/10.2172/677155.

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Blanton, M. L., A. T. Cooper, and K. J. Castleton. Nonradiological chemical pathway analysis and identification of chemicals of concern for environmental monitoring at the Hanford Site. Office of Scientific and Technical Information (OSTI), November 1995. http://dx.doi.org/10.2172/137162.

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Johnson, E. L., T. S. Clabough, M. L. Keefer, C. C. Caudill, P. N. Johnson, W. T. Nagy, and M. A. Jepson. Evaluation of Dual Frequency Identification Sonar (DIDSON) for Monitoring Pacific Lamprey Passage Behavior at Fishways of Bonneville Dam, 2011. Fort Belvoir, VA: Defense Technical Information Center, January 2012. http://dx.doi.org/10.21236/ada581330.

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Doebling, S. W., C. R. Farrar, M. B. Prime, and D. W. Shevitz. Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: A literature review. Office of Scientific and Technical Information (OSTI), May 1996. http://dx.doi.org/10.2172/249299.

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