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Journal articles on the topic 'Identification and monitoring'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

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1

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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2

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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3

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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4

&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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5

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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6

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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7

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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8

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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9

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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10

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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11

Gabassi, Pier Giorgio, and Lucia Zanuttini. "Self-Monitoring and Identification of Olfactory Dimensions." Perceptual and Motor Skills 75, no. 3 (December 1992): 787–95. http://dx.doi.org/10.2466/pms.1992.75.3.787.

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The basic categories of odors are not agreed on. Many classifications of odors have been proposed, but none has met widespread acceptance. The variability among qualitative judgments of odors which makes it difficult to construct reliable classifications may depend on cultural or personal idiosyncrasies. To check personality factors in odor evaluation, we asked 40 subjects, who had previously completed a personality questionnaire, to make qualitative judgments about 10 odors on 10 semantic differential scales. From comparison of two different self-monitoring groups, on the semantic differential, reliable differences did not emerge. While high self-monitoring subjects tended to maintain intermediate positions, for some odors, low self-monitors hazarded more polarized evaluations.

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12

van de Ven, Pepijn, Hugh O'Brien, John Nelson, and Amanda Clifford. "Unobtrusive monitoring and identification of fall accidents." Medical Engineering & Physics 37, no. 5 (May 2015): 499–504. http://dx.doi.org/10.1016/j.medengphy.2015.02.009.

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13

Rösch, Petra, Michaela Harz, Klaus-Dieter Peschke, Olaf Ronneberger, Hans Burkhardt, Andreas Schüle, Günther Schmauz, et al. "On-Line Monitoring and Identification of Bioaerosols." Analytical Chemistry 78, no. 7 (April 2006): 2163–70. http://dx.doi.org/10.1021/ac0514974.

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14

Tan, Shuai, Fuli Wang, Jun Peng, Yuqing Chang, and Shu Wang. "Multimode Process Monitoring Based on Mode Identification." Industrial & Engineering Chemistry Research 51, no. 1 (December 16, 2011): 374–88. http://dx.doi.org/10.1021/ie102048f.

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15

Troyk, Philip R. "Injectable Electronic Identification, Monitoring, and Stimulation Systems." Annual Review of Biomedical Engineering 1, no. 1 (August 1999): 177–209. http://dx.doi.org/10.1146/annurev.bioeng.1.1.177.

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16

Caicedo, Juan M., and Johannio Marulanda. "Fast mode identification technique for online monitoring." Structural Control and Health Monitoring 18, no. 4 (March 9, 2010): 416–29. http://dx.doi.org/10.1002/stc.381.

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17

Khalil, Abdelrahman, and Khaled F. Aljanaideh. "Aircraft Structural Health Monitoring Using Transmissibility Identification." IFAC-PapersOnLine 51, no. 15 (2018): 969–74. http://dx.doi.org/10.1016/j.ifacol.2018.09.068.

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18

Leyden, Kevin, and Bill Goodwine. "Fractional-order system identification for health monitoring." Nonlinear Dynamics 92, no. 3 (February 16, 2018): 1317–34. http://dx.doi.org/10.1007/s11071-018-4128-y.

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19

Bilal, Huda. "Automated Identification and Monitoring of Fruit Fly." Current Research in Agriculture and Farming 2, no. 3 (June 30, 2021): 41–44. http://dx.doi.org/10.18782/2582-7146.144.

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The fruit fly is an invasive pest that causes significant loss in horticultural crop production by damaging the fruit quality. For the integrated management of fruit fly, monitoring is a major component. Automated pest monitoring is most promising and advanced for monitoring and accurate assessment of the status of the fruit fly. The need of the hour is to automate and monitor pest populations using advanced technologies to save time and to examine the real-time situation of the field. The purpose of this paper is to discuss the approaches and sensors which detect and monitor fruit flies automatically.The review focuses on image-based sensors for identifying fruit flies, analyzing the wing beat biometric signature with optoelectronics sensors, direct counting with photo interruption sensors and E-Traps that can provide real-time field information, and presenting the various integrated systems available.With a real-time imaging system, all information about the fruit fly population and infection rate is recorded and maintained in a database. All of this has resulted in enhanced monitoring systems for fruit fly integrated pest management.

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20

Guzowski, Bartlomiej, Roman Gozdur, Mateusz Lakomski, and Lukasz Bernacki. "RFID monitoring system of fiber optic connectors." Circuit World 43, no. 1 (February 6, 2017): 32–37. http://dx.doi.org/10.1108/cw-10-2016-0041.

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Purpose The purpose of this paper is to develop identification system for fiber optic connectors in passive distribution cabinets. The system should have alternative power supply and wireless communication unlike the identification systems commercially available, which will make the system fully autonomous. Design/methodology/approach Detailed project and realization of real demonstrator of fully autonomous identification system. After the realization of the demonstrator, its optimization was performed. Findings It is possible to build fully autonomous identification system which requires 15.6 mJ energy to perform one cycle of identification process. Moreover, it is possible to use the alternative power method to supply this system. Originality/value Fully autonomous identification system of fiber optic connectors with alternative power supply and wireless communication.

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21

Włodarczyk, Bogdan, and Marek Szturo. "Identification and Monitoring of Instability in Financial Systems." Olsztyn Economic Journal 11, no. 2 (June 30, 2016): 177–88. http://dx.doi.org/10.31648/oej.2919.

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Financial instability is often the result of a positive feedback loop, which is an inseparable element of the functioning of the financial system. The task dealing with identification, modeling and analyzing the causes and effects of such feedback loops requires assuming a systems' engineering perspective, which is rarely taken into consideration when designing remedial solutions. The aim of the study was to evaluate how it ispossible to identify and monitor the susceptibility of a financial system to potential threats. The popular method of data modeling known as signed directed graphs (SDG) was used. The method is able to pick up information which is not taken into account by a traditional network model of the financial system. It provides essential information about the direction of impact and control between the nodes. Thanks to this technique, it is possible to analyze the potential instability spots of the system.

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22

Ramos, Luís F., Murat Alaboz, and Rafael Aguilar. "Dynamic Identification and Monitoring of St. Torcato Church." Advanced Materials Research 133-134 (October 2010): 275–80. http://dx.doi.org/10.4028/www.scientific.net/amr.133-134.275.

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The paper is related to the San Torcato Church, in Guimarães, Portugal. At the moment, the church has significant structural problems due to soil settlements. Cracks can be observed on the main and the lateral façades, the bell-towers are leaning, and the arches in the nave exhibit a failure mechanism with cracks and vertical deformations. Non-stabilized phenomena are present in the structure. To stabilize the damage, a structural intervention is planned to occur soon and the church is already monitored to follow the intervention. The paper clearly presents the problem with emphasis to the dynamic analysis carried out before the structural strengthening, namely: the experimental tests with output-only techniques for frequencies, damping and mode shapes estimation, FE model updating analysis and dynamic monitoring

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23

Comisu, Cristian-Claudiu, and Gheorghita Boaca. "Structural identification of bridges: Monitoring, Maintenance and Repair." IABSE Symposium Report 97, no. 24 (January 1, 2010): 46–51. http://dx.doi.org/10.2749/222137810796025032.

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24

Park, Jonghyun, Sungwon Ha, and Fu-Kuo Chang. "Monitoring Impact Events Using a System-Identification Method." AIAA Journal 47, no. 9 (September 2009): 2011–21. http://dx.doi.org/10.2514/1.34895.

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25

Aide, T. Mitchell, Carlos Corrada-Bravo, Marconi Campos-Cerqueira, Carlos Milan, Giovany Vega, and Rafael Alvarez. "Real-time bioacoustics monitoring and automated species identification." PeerJ 1 (July 16, 2013): e103. http://dx.doi.org/10.7717/peerj.103.

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26

Razaque, Abdul, Fathi Amsaad, Musbah Abdulgader, Bhanuteja Deekonda, Chandra Sekhara, Soumya Susan, and Naveen Tankala. "Intelligent Healthcare Monitoring using Radio Frequency Identification Detection." Communications on Applied Electronics 4, no. 5 (February 25, 2016): 6–13. http://dx.doi.org/10.5120/cae2016652058.

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27

Borichenko, Olena, and Anatolii Cherniavskyi. "Identification of objects priority for conducting energy monitoring." Technology audit and production reserves 3, no. 1(41) (January 23, 2018): 58–63. http://dx.doi.org/10.15587/2312-8372.2018.135497.

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28

G S, Mr Ashok Kumar. "Hazard Identification, Monitoring and Control in Blasting Operations." International Journal for Research in Applied Science and Engineering Technology V, no. VIII (August 30, 2017): 2224–30. http://dx.doi.org/10.22214/ijraset.2017.8319.

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29

Stork, Chris L., David J. Veltkamp, and Bruce R. Kowalski. "Identification of Multiple Sensor Disturbances during Process Monitoring." Analytical Chemistry 69, no. 24 (December 1997): 5031–36. http://dx.doi.org/10.1021/ac9704366.

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30

Giuliano, Karen K. "Continuous Physiologic Monitoring and the Identification of Sepsis." AACN Advanced Critical Care 17, no. 2 (April 2006): 215–23. http://dx.doi.org/10.1097/01256961-200604000-00017.

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31

Huang, Hsiao-Ping, and Jyh-Cheng Jeng. "Identification for Monitoring and Autotuning of PID Controllers." JOURNAL OF CHEMICAL ENGINEERING OF JAPAN 36, no. 3 (2003): 284–96. http://dx.doi.org/10.1252/jcej.36.284.

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32

Belloli, Marco, Ilmas Bayati, Stefano Giappino, Sara Muggiasca, and Lorenzo Rosa. "Modal parameters identification and monitoring of two arches." MATEC Web of Conferences 24 (2015): 04002. http://dx.doi.org/10.1051/matecconf/20152404002.

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33

Mitchell, Kenneth N., and Brandan Scully. "Waterway Performance Monitoring with Automatic Identification System Data." Transportation Research Record: Journal of the Transportation Research Board 2426, no. 1 (January 2014): 20–26. http://dx.doi.org/10.3141/2426-03.

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34

Tanaka, H. W., T. Kanbe, M. Kaise, Y. Yamada, and T. Semba. "Capillary electrophoretic monitoring for C-terminal fragment identification." Journal of High Resolution Chromatography 14, no. 7 (July 1991): 491–92. http://dx.doi.org/10.1002/jhrc.1240140716.

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35

Vincent, Jean-Louis. "The Clinical Challenge of Sepsis Identification and Monitoring." PLOS Medicine 13, no. 5 (May 17, 2016): e1002022. http://dx.doi.org/10.1371/journal.pmed.1002022.

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36

Huang, Hongwei, and Jann N. Yang. "Damage identification of substructure for local health monitoring." Smart Structures and Systems 4, no. 6 (November 25, 2008): 795–807. http://dx.doi.org/10.12989/sss.2008.4.6.795.

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37

Liu, Menghan, and Ming-Chun Huang. "Asthma Pattern Identification via Continuous Diaphragm Motion Monitoring." IEEE Transactions on Multi-Scale Computing Systems 1, no. 2 (April 1, 2015): 76–84. http://dx.doi.org/10.1109/tmscs.2015.2496214.

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38

Kumar, Arun, M. L. Kansal, and Geeta Arora. "Identification of Monitoring Stations in Water Distribution System." Journal of Environmental Engineering 123, no. 8 (August 1997): 746–52. http://dx.doi.org/10.1061/(asce)0733-9372(1997)123:8(746).

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39

Minocha, V. K., and B. M. Prasad. "Identification of Monitoring Stations in Water Distribution System." Journal of Environmental Engineering 125, no. 2 (February 1999): 202–4. http://dx.doi.org/10.1061/(asce)0733-9372(1999)125:2(202).

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40

Hartana, R. K., and G. G. Richards. "Harmonic source monitoring and identification using neural networks." IEEE Transactions on Power Systems 5, no. 4 (1990): 1098–104. http://dx.doi.org/10.1109/59.99358.

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41

Gutierrez, Hector M., and Jon P. Rust. "System identification for a photodiode-based monitoring system." ISA Transactions 34, no. 1 (March 1995): 93–99. http://dx.doi.org/10.1016/0019-0578(95)00006-l.

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42

Arisandi, D., M. Elveny, and R. Rahayu. "Human Detection and Identification for Home Monitoring System." Journal of Physics: Conference Series 1898, no. 1 (June 1, 2021): 012026. http://dx.doi.org/10.1088/1742-6596/1898/1/012026.

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43

D, Dr Girish Kumar. "Covid-19 Indoor Monitoring System with Human Identification." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (August 31, 2021): 1596–99. http://dx.doi.org/10.22214/ijraset.2021.37385.

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Abstract: Covid-19 has become the worst of all pandemics seen with over 63,549,184 active cases recorded globally as on January 16, 2021. India alone has 10,553,529 active cases till date. It has brought on a new era and new way of life-rather reality where in safety has become a must for survival. Form the statistics it is observed that the number of active cases and health rate have only increased abruptly post lockdown even after implementing the guidelines proposed by the government to tackle the problem of the hour. since fever is developed in most communicable diseases, body temperature monitoring is effective for such disease, body temperature monitoring is effective for the prevention of outbreak of such diseases. Covid-19 being one of such, temperature records of symptoms and it is checked prior to allowing a person in, In social stratum it is done by person one should hold the temperature gun at entrance, and the sensor records is often inaccurate. This method also involves two people standing close, which itself is a problem in social-distancing rule. In this paper to design and develop Covid-19 indoor safety Monitoring with human identification using iot based devices. social distancing- since we use hands-free temperature sensing method which records the accurate temperature as well as human identification. Keywords: IOT, Indoor-safety, Human Identification with high Temperature.

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44

Novis, David A., Karen A. Miller, Peter J. Howanitz, Stephen W. Renner, and Molly K. Walsh. "Audit of Transfusion Procedures in 660 Hospitals." Archives of Pathology & Laboratory Medicine 127, no. 5 (May 1, 2003): 541–48. http://dx.doi.org/10.5858/2003-127-0541-aotpih.

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Abstract Context.—Hemolytic transfusion reactions are often the result of failure to follow established identification and monitoring procedures. Objective.—To measure the frequencies with which health care workers completed specific transfusion procedures required for laboratory and blood bank accreditation. Design.—In 2 separate studies, participants in the College of American Pathologists Q-Probes laboratory quality improvement program audited nonemergent red blood cell transfusions prospectively and completed questionnaires profiling their institutions' transfusion policies. Setting and Participants.—A total of 660 institutions, predominantly in the United States, at which transfusion medicine services are provided. Main Outcome Measures.—The percentages of transfusions for which participants completed 4 specific components of patient and blood unit identifications, and for which participants monitored vital signs at 3 specific intervals during transfusions. Results.—In the first study, all components of patient identification procedures were performed in 62.3%, and all required patient vital sign monitoring was performed in 81.6% of 12 448 transfusions audited. The median frequencies with which institutions participating in the first study performed all patient identification and monitoring procedures were 69.0% and 90.2%, respectively. In the second study, all components of patient identification were performed in 25.4% and all patient vital sign monitoring was performed in 88.3% of 4046 transfusions audited. The median frequencies with which institutions participating in the second study performed all patient identification and monitoring procedures were 10.0% and 95.0%, respectively. Individual practices and/or institutional policies associated with greater frequencies of patient identification and/or vital sign monitoring included transporting units of blood directly to patient bedsides, having no more than 1 individual handle blood units in route, checking unit labels against physicians' orders, having patients wear identification tags (wristbands), reading identification information aloud when 2 or more transfusionists participated, using written checklists to guide the administration of blood, instructing health care personnel in transfusion practices, and routinely auditing the administration of transfusions. Conclusions.—In many hospitals, the functions of identification and vital sign monitoring of patients receiving blood transfusions do not meet laboratory and blood bank accreditation standards. Differences in hospital transfusion policies influence how well health care workers comply with standard practices. We would expect that efforts designed to perfect transfusion policies might also improve performance in those hospitals in which practice compliance is substandard.

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45

Harada, Kazuhiro, Tsutomu Watanabe, Masamichi Sogabe, Yukihiro Tanimura, Kiyoyuki Kaito, and Kouichi Sugisaki. "360369 A SIMPLE IDENTIFICATION METHOD FOR MEMBER VIBRATION PROPERTIES OF RAILWAY VIADUCTS(Condition Monitoring,Technical Session)." Proceedings of International Symposium on Seed-up and Service Technology for Railway and Maglev Systems : STECH 2009 (2009): _360369–1_—_360369–6_. http://dx.doi.org/10.1299/jsmestech.2009._360369-1_.

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46

Cheng, Xiao-Xiang, Yang Peng, Zargham Ahmad Khan, and Jun Dong. "Beam-end stiffness identification for a structural health monitoring-oriented finite-element transmission tower model using effective optimization techniques." Advances in Structural Engineering 22, no. 2 (August 3, 2018): 364–83. http://dx.doi.org/10.1177/1369433218788584.

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As an important issue for establishing structural health monitoring–oriented finite element models for large steel pylons, identification of beam-end stiffness draws the attention of the engineering circle. Since the methods adopted by other researchers for parameter identification are impracticable, a beam-end stiffness identification method which combines in situ measurements for the structure’s global dynamic properties with effective multi-variable optimization methods is utilized to improve the accuracy of established finite element models. A 131-m-high large transmission tower is employed as a case study to validate the method. In situ measurements for the tower’s global dynamic characteristics are performed, and identifications of Young’s modulus for 20 semi-rigid connections distributed along each of the tower’s four main chords are undertaken utilizing three multi-variable optimization methods, that is, the first-order method, the subproblem approximation method, and the response surface method. Static numerical simulations on two detailed connection models prove that multiple uncertain parameters can be correctly and simultaneously identified when appropriate optimization techniques are chosen. Finally, the influence of beam-end stiffness identification on the structural health monitoring–oriented structural safety assessment is revealed by calculating the wind-induced dynamic structural responses for the single tower and the transmission tower-line system, which indicates that identification of the correct beam-end stiffness and updating the structural health monitoring–oriented finite element model are indispensable procedures for reliable structural health monitoring.

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47

Oganesyants, Lev, Lev Oganesyants, Ramil Vafin, Ramil Vafin, Aram Galstyan, Aram Galstyan, Vladislav Semipyatniy, et al. "Prospects for DNA authentication in wine production monitoring." Foods and Raw Materials 6, no. 2 (December 20, 2018): 438–48. http://dx.doi.org/10.21603/2308-4057-2018-2-438-448.

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Wines DNA authentication is a technological process of their authenticity verification by genetic identification of the main plant ingredient by means of molecular genetic analysis of the residual amounts of Vitis vinifera L nucleic acids extracted from end product cellular debris. The main aim of the research was the analysis of scientific and methodological approaches to the extraction of residual amounts of nucleic acids in wine raw materials and DNA authentication of wines for their subsequent application in solving the problem of determining wine products authenticity and place of origin. The prior art includes various approaches to the extraction of Vitis vinifera L. nucleic acids among which the three methods by Savazzini & Martinelli, Pereira and Bigliazzi can be named basically. Analysis of the effectiveness of different methods of DNA extraction from wines indicates the superiority of the Pereira method over other traditional methods of extraction in terms of DNA yield and quality. Besides, the nucleic acid extracted from wines is characterized as residual since its concentration is significantly reduced in a multi-stage wine production process. The yield of extracted nucleic acid also decreases as the wine ages. The use of microsatellite DNA loci designed for grapes genetic identification is one of the approaches applicable for wine DNA authentication.

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48

Fomin, O. "MONITORING OF PRODUCTION PROCESSES OF RAILWAY TRANSPORT ENTERPRISES." Collection of scientific works of the State University of Infrastructure and Technologies series "Transport Systems and Technologies" 1, no. 37 (June 29, 2021): 41–49. http://dx.doi.org/10.32703/2617-9040-2021-37-5.

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The publication proposes a monitoring technology for production automation based on automatic identification technologies, which will allow real-time monitoring of the progress of technological processes, ensure interdepartmental logistics, as well as perform access and identification security functions in production. On the example of one of the carriage depots, typical shortcomings of the organization of the production process, accounting and identification of parts of a railway transport enterprise are considered. The ways and stages of implementation of automatic identification technologies are proposed, in particular, it is indicated that it is advisable to introduce a system for monitoring production processes of railway transport enterprises in stages, starting with bar-coding technologies. The process of monitoring railway transport enterprises, the interaction of subjects and objects of the production process is disclosed in detail.

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49

Kokot, M., and Jan Holnicki-Szulc. "Health Monitoring of Electric Circuits." Key Engineering Materials 293-294 (September 2005): 669–76. http://dx.doi.org/10.4028/www.scientific.net/kem.293-294.669.

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A methodology of defect identification in linear DC circuits based on so called Virtual Distortion Method (VDM) will be presented. The proposed approach takes adventage of the analogy linking mechanical models of truss structures and electric circuits. In this paper we cover the issue of modelling defects in electric circuits by compatible state of distortions, next we introduce the concept of influence matrix Dij, which describes global sensitivity of the circuit and enables fast recalculation of system response, and we formulate a gradient method of defect identification (inverse problem) based on local current and voltage measurements. In the following analysis we focus on so-called impotent states of distortions, which are the source of ambiguity of solution. We show that these difficulties can be overcome by a proper measuring strategy.

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50

Mosalam, Khalid, Sifat Muin, and Yuqing Gao. "NEW DIRECTIONS IN STRUCTURAL HEALTH MONITORING." NED University Journal of Research 2, Special Issue on First SACEE'19 (June 15, 2019): 77–112. http://dx.doi.org/10.35453/nedjr-stmech-2019-0006.

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This paper presents two on-going efforts of the Pacific Earthquake Engineering Research (PEER) center in the area of structural health monitoring. The first is data-driven damage assessment, which focuses on using data from instrumented buildings to compute the values of damage features. Using machine learning algorithms, these damage features are used for rapid identification of the level and location of damage after earthquakes. One of the damage features identified to be highly efficient is the cumulative absolute velocity. The second is vision-based automated damage identification and assessment from images. Deep learning techniques are used to conduct several identification tasks from images, examples of which are the structural component type, and level and type of damage. The objective is to use crowdsourcing, allowing the general public to take photographs of damage and upload them to a server where damage is automatically identified using deep learning algorithms. The paper also introduces PEER.s effort and preliminary results in engaging the engineering and computer science communities in such developments through the PEER Hub Image-Net (F-Net) challenge.

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