Journal articles on the topic 'Dynamic monitoring'

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1

Drummond, John. "Dynamic Task Monitoring." Electronic Notes in Theoretical Computer Science 25 (1999): 35–37. http://dx.doi.org/10.1016/s1571-0661(04)00129-x.

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2

Ge, Yongquan, Chengxin Yu, Xiaodong Liu, and Ronghui Wang. "Monitoring dynamic deformation of communication tower using photography dynamic monitoring system." IOP Conference Series: Earth and Environmental Science 558 (September 5, 2020): 022061. http://dx.doi.org/10.1088/1755-1315/558/2/022061.

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3

Natalia N., Popova. "Factor Dynamic Model of Pedagogical Monitoring." Scholarly Notes of Transbaikal State University 16, no. 1 (March 2021): 13–19. http://dx.doi.org/10.21209/2658-7114-2021-16-1-13-19.

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The article examines the topical problems of scientific validity and practice-oriented monitoring in education in the context of increasing requirements for individualization, diversification and differentiation of education. Based on the data of psychological and pedagogical diagnostics, a dynamic factor model of pedagogical monitoring has been developed. For this, the essential characteristics of pedagogical monitoring as a continuous, dynamic and multifactorial technology for supporting students have been determined. Representative representation of the monitoring results was carried out on a sample of students from a number of educational institutions of the Transbaikal Territory, numbering 250 people. The focus of the article is on the creativity of students as a manifestation of their non-standard and individuality, capable of ensuring their success in professional activities. The results of dynamic monitoring of students creativity indicators collected using a battery of tests of verbal and non-verbal creativity, as well as a number of personal techniques are presented. It has been empirically proven that the factorial dynamic model, built on measuring the level of development of the main indicators of creativity, ensures the comparability of results and the identification of latent factors (activity, personality creativity and values). These explain the presence or absence of dynamics in the studied indicators (creative attitude to the profession, originality, uniqueness and personality traits). The data obtained demonstrate the presence of statistically significant changes at the level of originality, the uniqueness of the developed fluency and flexibility in the process of the correlation conditioning of the dynamics of the integral characteristics of the personality (values: traditions, hedonism; self-esteem; emotionality, sociability, high normality of behavior and sensitivity). Keywords: pedagogical monitoring, creativity, dynamic model, factor analysis, students
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4

Qiu, Peihua, Xuemin Zi, and Changliang Zou. "Nonparametric Dynamic Curve Monitoring." Technometrics 60, no. 3 (May 7, 2018): 386–97. http://dx.doi.org/10.1080/00401706.2017.1361340.

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5

Fareed, Abid, and Abita Devi. "IOT a Dynamic Approach for Smart System Monitoring on Soil." International Journal of Trend in Scientific Research and Development Volume-2, Issue-5 (August 31, 2018): 2373–76. http://dx.doi.org/10.31142/ijtsrd18359.

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6

Ullah, Najeeb, Faizullah Khan, Abdul Ali Khan, Surat Khan, Abdul Wahid Tareen, Muhammad Saeed, and Akbar Khan. "Optimal Real-time Static and Dynamic Air Quality Monitoring System." Indian Journal of Science and Technology 13, no. 1 (January 20, 2020): 1–12. http://dx.doi.org/10.17485/ijst/2020/v13i01/148375.

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7

Solan, Eilon, and Chang Zhao. "Dynamic monitoring under resource constraints." Games and Economic Behavior 129 (September 2021): 476–91. http://dx.doi.org/10.1016/j.geb.2021.06.009.

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8

Melcer, Jozef, Daniela Kuchárová, and Gabriela Lajčáková. "The bridge dynamic properties monitoring." MATEC Web of Conferences 265 (2019): 03009. http://dx.doi.org/10.1051/matecconf/201926503009.

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The SNP Bridge over the Danube in Bratislava represents an attractive steel cable-stayed bridge. Its length is 431.8 m. The submitted paper describes the methodology of experimental testing and presents some results of the loading test. During this test it was observed that the dilatation unit on the right water side shows some failure. Due to this failure the dilatation unit acts as a generator of vibration of the end bridge span. On the basis of experimental measurements this failure was detected and the dilatation unit was renovated.
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9

Barbos, Andrei. "Dynamic contracts with random monitoring." Journal of Mathematical Economics 85 (December 2019): 1–16. http://dx.doi.org/10.1016/j.jmateco.2019.07.008.

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10

Ateniese, G., C. Riley, and C. Scheideler. "Survivable Monitoring in Dynamic Networks." IEEE Transactions on Mobile Computing 5, no. 9 (September 2006): 1242–54. http://dx.doi.org/10.1109/tmc.2006.138.

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11

Lee, Philip, Shin-Yi Cindy Chen, Fen Xu, Terry Gaige, and Paul J. Hung. "Monitoring Dynamic Cancer Cell Behavior." Genetic Engineering & Biotechnology News 35, no. 21 (December 2015): 18–19. http://dx.doi.org/10.1089/gen.35.21.10.

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12

Wu, Dehao, Li Sheng, Donghua Zhou, and Maoyin Chen. "Dynamic Stationary Subspace Analysis for Monitoring Nonstationary Dynamic Processes." Industrial & Engineering Chemistry Research 59, no. 47 (November 16, 2020): 20787–97. http://dx.doi.org/10.1021/acs.iecr.0c04059.

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13

Choi, Sang Wook, and In-Beum Lee. "Nonlinear dynamic process monitoring based on dynamic kernel PCA." Chemical Engineering Science 59, no. 24 (December 2004): 5897–908. http://dx.doi.org/10.1016/j.ces.2004.07.019.

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14

Wang, Xiao Yong, Xu Dong Cui, Hong Yun Ma, and Zheng Ping Tao. "The Optimization of Groundwater Dynamic Monitoring Network – An Example of the North Ordos Basin." Advanced Materials Research 518-523 (May 2012): 4057–61. http://dx.doi.org/10.4028/www.scientific.net/amr.518-523.4057.

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Groundwater management is base on an sufficient dynamic monitoring network of groundwater depth and quality. Whil the monitoring network in Ordos Bain is insufficient due to the network is not been finished yet. Thus, in this paper, the dynamic type mapping method of groundwater based on GIS developing is used to optimize the distribution of dynamic monitoring positions. The purpose of the optimization is to observe reginal dynamics of groundwater with less monitoring wells. The reginal groundwater depth has a impact of hydroecology. Thus the optimization also consider the distribution of vegetations which are closely related with groundwater. The optimization shows that at least 28 new monitoring wells are needed depend on the existing groundwater monitoring network. The monitoring positions of 28 new wells are also estimated by the method.
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15

Liu, Ru, Yan Jun Liu, and Lu Ji Zhang. "Dynamic Intelligence Scouting and Monitoring System." Applied Mechanics and Materials 525 (February 2014): 727–30. http://dx.doi.org/10.4028/www.scientific.net/amm.525.727.

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In the present under the uncertain and change prevailing environment atmospheres influence, competitive intelligence without well-grounded methodologies and system for scouting and monitoring dynamic intelligence has become impossible. Dynamic intelligence process refers to continuous process of gathering, analyzing and reporting information about specified topics to users. The dynamic intelligence process should always be anchored to four key factors (IndustryTechnologyKey Person and Customer) within which information will be used. Whats more, we also need to know how to set up systems and models to identify and analyze competitors first of all.
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16

Shohreh, Honarbakhsh, Zamani Mazdak, and Honarbakhsh Roza. "Dynamic Monitoring in Ad Hoc Network." Applied Mechanics and Materials 229-231 (November 2012): 1481–86. http://dx.doi.org/10.4028/www.scientific.net/amm.229-231.1481.

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This paper, present a novel model to resolve the problems of evaluating trust value of nodes, and prepare a dynamic supervising in ad hoc network as well. Trust evaluation is not only based on direct interactions among nodes, but also observing previous behaviors of nodes, which come from all interactions through the network. This model specially decreases the delay time of selecting a new head for each cluster, which leads to prepare a dynamic monitoring in intra cluster and inter cluster in ad hoc network.
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17

Lipták, Imrich, Alojz Kopáčik, Ján Erdélyi, and Peter Kyrinovič. "Dynamic Deformation Monitoring of Bridge Structure." Selected Scientific Papers - Journal of Civil Engineering 8, no. 2 (November 1, 2013): 13–20. http://dx.doi.org/10.2478/sspjce-2013-0014.

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Abstract Building structures are extremely sensitive at influence of outdoor conditions. Most often these are the influence of wind, sunshine, temperature changes of the surrounding and at least the influence of the own or other loading. According to resonance of the structure with the surrounding is coming to vibration and oscillation in relative high frequency interval (0.1 Hz - 100.0 Hz). These phenomena significantly affect the static and dynamic characteristics of structures, their safety and functionality. The paper brings example of monitoring these phenomena. The object of monitoring is the Danube Bridge Apollo in Bratislava, which main steel structure was measured by acceleration sensors with frequency up to10 Hz. The main topic of the paper is the analysis of dynamic behavior of structure using spectral analysis method. The usage of Fourier Transform is described, own frequencies and amplitudes of structure oscillation are calculated.
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18

Caetano, Elsa Sá, Álvaro Cunha, Carlos Moutinho, and Hu Weihua. "Continuous Dynamic Monitoring of Lively Footbridges." International Journal of Online Engineering (iJOE) 12, no. 04 (April 28, 2016): 49. http://dx.doi.org/10.3991/ijoe.v12i04.5136.

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The paper describes two dynamic monitoring systems installed in lively footbridges with the purpose of characterizing the dynamic behavior and response of these structures and possibly detecting damage at early stages. Some monitoring results illustrate the capabilities of the presently existing technology.
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19

Cunha, Álvaro, Elsa Caetano, Carlos Moutinho, Wei-Hua Hu, and António Cardoso. "Continuous dynamic monitoring of lively footbridges." IABSE Symposium Report 105, no. 21 (September 23, 2015): 1–8. http://dx.doi.org/10.2749/222137815818358619.

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20

Ciobanu, Gabriel, and Dragoş Sburlan. "Monitoring Changes in Dynamic Multiset Systems." Fundamenta Informaticae 134, no. 1-2 (2014): 67–82. http://dx.doi.org/10.3233/fi-2014-1091.

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21

Lund-Jensen, Kasper. "Monitoring Systemic Risk Basedon Dynamic Thresholds." IMF Working Papers 12, no. 159 (2012): i. http://dx.doi.org/10.5089/9781475504576.001.

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22

Doerflinger, C. W., and V. R. Basili. "Monitoring Software Development Through Dynamic Variables." IEEE Transactions on Software Engineering SE-11, no. 9 (September 1985): 978–85. http://dx.doi.org/10.1109/tse.1985.232833.

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23

Xie, Lei, Shu-qing Wang, and Jian-ming Zhang. "INVESTIGATION OF DYNAMIC MULTIVARIATE PROCESS MONITORING." IFAC Proceedings Volumes 38, no. 1 (2005): 279–84. http://dx.doi.org/10.3182/20050703-6-cz-1902.01622.

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24

Nicholson, A. E., and J. M. Brady. "Dynamic belief networks for discrete monitoring." IEEE Transactions on Systems, Man, and Cybernetics 24, no. 11 (1994): 1593–610. http://dx.doi.org/10.1109/21.328910.

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25

Dellinger, R. Phillip. "Dynamic Systemic Inflammatory Response Syndrome Monitoring." Critical Care Medicine 44, no. 12 (December 2016): 2285–86. http://dx.doi.org/10.1097/ccm.0000000000002112.

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26

WANG, Lei. "Dynamic Inlining Based on Continuous Monitoring." Journal of Software 18, no. 10 (2007): 2393. http://dx.doi.org/10.1360/jos182393.

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27

Jeng, Jyh‐Cheng, Cheng‐Chih Li, and Hsiao‐Ping Huang. "Dynamic processes monitoring using predictive PCA." Journal of the Chinese Institute of Engineers 29, no. 2 (March 2006): 311–18. http://dx.doi.org/10.1080/02533839.2006.9671127.

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28

Chen, Mingliu, Peng Sun, and Yongbo Xiao. "Optimal Monitoring Schedule in Dynamic Contracts." Operations Research 68, no. 5 (September 2020): 1285–314. http://dx.doi.org/10.1287/opre.2019.1968.

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29

Molina, Francisco J., Georges Magonette, Pierre Pegon, and Beatriz Zapico. "Monitoring Damping in Pseudo-Dynamic Tests." Journal of Earthquake Engineering 15, no. 6 (June 29, 2011): 877–900. http://dx.doi.org/10.1080/13632469.2010.544373.

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30

Reyes-Romero, D. F., O. Behrmann, G. Dame, and G. A. Urban. "Dynamic thermal sensor for biofilm monitoring." Sensors and Actuators A: Physical 213 (July 2014): 43–51. http://dx.doi.org/10.1016/j.sna.2014.03.032.

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31

Zhang, Qinghua, Michèle Basseville, and Albert Benveniste. "Monitoring Dynamic Systems with Polynomial Nonlinearities." IFAC Proceedings Volumes 30, no. 18 (August 1997): 1011–16. http://dx.doi.org/10.1016/s1474-6670(17)42533-x.

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32

McCreery, Richard L., and Richard T. Packard. "Raman monitoring of dynamic electrochemical events." Analytical Chemistry 61, no. 13 (July 1989): 775A—789A. http://dx.doi.org/10.1021/ac00188a001.

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33

Asseyer, Andreas. "Optimal monitoring in dynamic procurement contracts." International Journal of Industrial Organization 59 (July 2018): 222–52. http://dx.doi.org/10.1016/j.ijindorg.2018.03.004.

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34

Wang, Kaibo, and Fugee Tsung. "An AdaptiveT2Chart for Monitoring Dynamic Systems." Journal of Quality Technology 40, no. 1 (January 2008): 109–23. http://dx.doi.org/10.1080/00224065.2008.11917716.

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35

Yingwei Zhang, Tianyou Chai, Zhiming Li, and Chunyu Yang. "Modeling and Monitoring of Dynamic Processes." IEEE Transactions on Neural Networks and Learning Systems 23, no. 2 (February 2012): 277–84. http://dx.doi.org/10.1109/tnnls.2011.2179669.

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36

AMANT, ROBERT ST, PAUL R. COHEN, and YOSHITAKA KUWATA. "MONITORING PROGRESS WITH DYNAMIC PROGRAMMING ENVELOPES." International Journal on Artificial Intelligence Tools 05, no. 01n02 (June 1996): 143–53. http://dx.doi.org/10.1142/s0218213096000109.

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Envelopes are a form of decision rule for monitoring plan execution. We describe one type, the DP envelope, that draws its decisions from a look-up table computed off-line by dynamic programming. Based on an abstract model of agent progress, DP envelopes let a developer approach execution monitoring as a problem independent of issues in agent design. We discuss the application of DP envelopes to a small transportation planning simulation, and discuss the issues that arise in an empirical analysis of the results.
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37

Wilkins, David E., Stephen F. Smith, Laurence A. Kramer, Thomas J. Lee, and Timothy W. Rauenbusch. "Airlift mission monitoring and dynamic rescheduling." Engineering Applications of Artificial Intelligence 21, no. 2 (March 2008): 141–55. http://dx.doi.org/10.1016/j.engappai.2007.04.001.

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38

Antinolfi, Gaetano, and Francesco Carli. "Costly monitoring, dynamic incentives, and default." Journal of Economic Theory 159 (September 2015): 105–19. http://dx.doi.org/10.1016/j.jet.2015.05.011.

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39

Choi, Sang Wook, Julian Morris, and In-Beum Lee. "Dynamic model-based batch process monitoring." Chemical Engineering Science 63, no. 3 (February 2008): 622–36. http://dx.doi.org/10.1016/j.ces.2007.09.046.

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40

Ommen, J. Ruud van, Jaap C. Schouten, Marc-Olivier Coppens, and Cor M. van den Bleek. "Monitoring Fluidization by Dynamic Pressure Analysis." Chemical Engineering & Technology 22, no. 9 (September 1999): 773. http://dx.doi.org/10.1002/(sici)1521-4125(199909)22:9<773::aid-ceat773>3.0.co;2-i.

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41

Li, Simin, Shuanghua Yang, Yi Cao, and Zuzhen Ji. "Nonlinear dynamic process monitoring using deep dynamic principal component analysis." Systems Science & Control Engineering 10, no. 1 (January 8, 2022): 55–64. http://dx.doi.org/10.1080/21642583.2021.2024915.

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42

Zhu, Jinlin, Zhiqiang Ge, and Zhihuan Song. "Bayesian robust linear dynamic system approach for dynamic process monitoring." Journal of Process Control 40 (April 2016): 62–77. http://dx.doi.org/10.1016/j.jprocont.2016.01.010.

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43

Noskov, M. V., I. P. Peregudova, P. P. Dyachuk, and O. I. Denisenko. "Dynamic adaptive audio training tests as a means of monitoring bilingual education." Informatics and education, no. 10 (December 22, 2019): 46–54. http://dx.doi.org/10.32517/0234-0453-2019-34-10-46-54.

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The article discusses the conditions for the use of computerized dynamic adaptive tests to provide developing training and monitoring in the field of foreign language education. Dynamic adaptive test simulators used as a tool for personalizing and monitoring foreign language education are still insufficiently represented in the educational practice of the Russian Federation. The authors of the article offer recommendations on the use of dynamic adaptive audio test simulators for monitoring foreign language education. As indicators of monitoring, the parameters characterizing the learning ability of students are considered: feedback coefficient; the complexity and time pace of learning activities of students of a foreign language.The purpose of the article is to present authors’ recommendations on the implementation of dynamic adaptive audio test simulators based on evaluative feedback in the field of foreign language education, providing for monitoring the quality of bilingual education based on the integration of individualized learning and dynamic adaptive testing of educational activities.The research methodology is an analysis of the use of existing dynamic adaptive test simulators in the field of foreign language education; studying the results of interdisciplinary studies of domestic and foreign scientists on the creation and use of dynamic adaptive tests for the learning process as a whole and the creation of dynamic adaptive tests for foreign education in particular; analysis and generalization of the author’s experience of using dynamic adaptive audio tests simulators as a tool for monitoring educational activities in the field of foreign language education.Results of the reseach are: authors developed computerized dynamic adaptive audio test simulators based on evaluative feedback in the field of foreign language education; indicators of monitoring the learning process in the field of foreign language education are identified, including the total feedback coefficient, actiograms, time pace and laboriousness; testing was carried out and recommendations were given for the use of dynamic adaptive audio test simulators for monitoring educational activities in the field of foreign language education.Analyzing the results of testing dynamic adaptive audio test simulators in a foreign language, the authors conclude that their use in practice implements personalized dynamic monitoring of the process of teaching foreign languages, thereby improving the quality of teaching foreign languages. Dynamic adaptive audio test simulators can provide monitoring of the dynamics of the process of teaching native and foreign languages.
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44

Zou, M., J. Dayan, and I. Green. "Dynamic simulation and monitoring of a non-contacting flexibly mounted rotor mechanical face seal." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 214, no. 9 (September 1, 2000): 1195–206. http://dx.doi.org/10.1243/0954406001523632.

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Mechanical face seal rotor dynamics is investigated through both simulation and real-time monitoring of a non-contacting flexibly mounted rotor (FMR) mechanical face seal in a seal test rig. Dynamic simulation is performed to investigate the seal rotor angular response to the stator misalignment, the stator angle, the initial rotor misalignment and clearance. Rotor angular response orbit is introduced and is able to characterize the rotor dynamic response. A real-time monitoring system is constructed in the test rig to monitor the instantaneous dynamic behaviour of the seal rotor, including its angularresponse, precession angle and angular response orbit. Experimental results agree qualitatively well with those of the dynamic simulation. Potential applications of the monitoring system for detecting seal face contact and for seal control are stated.
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45

Yang, Shuai, Yuzhuo ZHANG, Yuan CAO, and Yujue WANG. "1C12 Dynamic Time Warping based State Monitoring of Train Axle Temperature(Safety-Infrastructure)." Proceedings of International Symposium on Seed-up and Service Technology for Railway and Maglev Systems : STECH 2015 (2015): _1C12–1_—_1C12–7_. http://dx.doi.org/10.1299/jsmestech.2015._1c12-1_.

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46

Li, Shanzhi, Chudong Tong, Yang Chen, and Ting Lan. "Dynamic statistical process monitoring based on online dynamic discriminative feature analysis." Journal of Process Control 103 (July 2021): 67–75. http://dx.doi.org/10.1016/j.jprocont.2021.05.002.

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47

Dong, Yining, and S. Joe Qin. "New Dynamic Predictive Monitoring Schemes Based on Dynamic Latent Variable Models." Industrial & Engineering Chemistry Research 59, no. 6 (January 10, 2020): 2353–65. http://dx.doi.org/10.1021/acs.iecr.9b04741.

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48

Lee, Jong-Min, ChangKyoo Yoo, and In-Beum Lee. "Statistical monitoring of dynamic processes based on dynamic independent component analysis." Chemical Engineering Science 59, no. 14 (July 2004): 2995–3006. http://dx.doi.org/10.1016/j.ces.2004.04.031.

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49

Kong, Yulei, Zhengshan Luo, Xiaomin Wang, and Yuchen Wang. "Spatial-Temporal Data Integration Modeling and Dynamic Simulation of Coal-Rock Dynamic Disasters." Mathematical Problems in Engineering 2022 (June 30, 2022): 1–10. http://dx.doi.org/10.1155/2022/5365536.

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To solve the problem of accurate identification of coal-rock dynamic disaster precursors, a spatiotemporal data integrated monitoring, and early warning system was proposed. The system consists of a spatiotemporal data integration model, a time series and visual monitoring, and an early warning platform. It takes the comprehensive mining face of a deep coal mine as the monitoring object. It uses structured light 3D scanning and Brillouin optical time domain reflectometry to collect physical entity data in the monitoring area, reconstructs data and processes data redundancy through edge microprocessors, and decomposes spatiotemporal objects into elements to construct a data integration model for data integration. Inner relationship and space-time unity. Using the time series database as the data integration model carrier, the processed physical entity data is mapped to the visual monitoring and early warning platform for dynamic simulation display, which provides data support for accurate early warning of coal-rock dynamic disasters. Finally, a prototype system is developed to verify the generality and feasibility of the system.
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50

Bersimis, Sotiris, and Kostas Triantafyllopoulos. "Dynamic Non-parametric Monitoring of Air-Pollution." Methodology and Computing in Applied Probability 22, no. 4 (September 6, 2018): 1457–79. http://dx.doi.org/10.1007/s11009-018-9661-0.

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AbstractAir pollution poses a major problem in modern cities, as it has a significant effect in poor quality of life of the general population. Many recent studies link excess levels of major air pollutants with health-related incidents, in particular respiratory-related diseases. This introduces the need for city pollution on-line monitoring to enable quick identification of deviations from “normal” pollution levels, and providing useful information to public authorities for public protection. This article considers dynamic monitoring of pollution data (output of multivariate processes) using Kalman filters and multivariate statistical process control techniques. A state space model is used to define the in-control process dynamics, involving trend and seasonality. Distribution-free monitoring of the residuals of that model is proposed, based on binomial-type and generalised binomial-type statistics as well as on rank statistics. We discuss the general problem of detecting a change in pollutant levels that affects either the entire city (globally) or specific sub-areas (locally). The proposed methodology is illustrated using data, consisting of ozone, nitrogen oxides and sulfur dioxide collected over the air-quality monitoring network of Athens.
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