Готові списки джерел за темами / Cycle detection

Добірка наукової літератури з теми "Cycle detection"

Автор: Grafiati
Опубліковано: 10 грудня 2022
Оновлено: 28 січня 2023

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Статті в журналах з теми "Cycle detection"

1

Cherkassky, Boris V., and Andrew V. Goldberg. "Negative-cycle detection algorithms." Mathematical Programming 85, no. 2 (June 1, 1999): 277–311. http://dx.doi.org/10.1007/s101070050058.

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2

Amir, Amihood, Estrella Eisenberg, Avivit Levy, Ely Porat, and Natalie Shapira. "Cycle detection and correction." ACM Transactions on Algorithms 9, no. 1 (December 2012): 1–20. http://dx.doi.org/10.1145/2390176.2390189.

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3

Moinuddin, Rizwan, Bulent Mamikoglu, Sameer Barkatullah, and Jacquelynne P. Corey. "Detection of the Nasal Cycle." American Journal of Rhinology 15, no. 1 (January 1, 2001): 35–39. http://dx.doi.org/10.2500/105065801781329473.

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4

Oliva, Gabriele, Roberto Setola, Luigi Glielmo, and Christoforos N. Hadjicostis. "Distributed Cycle Detection and Removal." IEEE Transactions on Control of Network Systems 5, no. 1 (March 2018): 194–204. http://dx.doi.org/10.1109/tcns.2016.2593264.

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5

Nivasch, Gabriel. "Cycle detection using a stack." Information Processing Letters 90, no. 3 (May 2004): 135–40. http://dx.doi.org/10.1016/j.ipl.2004.01.016.

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6

Cidon, I., J. M. Jaffe, and M. Sidi. "Local Distributed Deadlock Detection by Cycle Detection and Clusterng." IEEE Transactions on Software Engineering SE-13, no. 1 (January 1987): 3–14. http://dx.doi.org/10.1109/tse.1987.232560.

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7

Sumathi, S., P. Bhuvaneshwari, V. Harikrishnaveni, S. A. Sangami, and N. S. Shivanee Bhuvaneshwari. "A Deep Learning based Advanced Gait Detection." Journal of Physics: Conference Series 2325, no. 1 (August 1, 2022): 012056. http://dx.doi.org/10.1088/1742-6596/2325/1/012056.

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Abstract A gait is a biometric sign that people use to provide visual verification of viewing. Distinctive highlights need more than one movement cycle. In Gait Energy Image (GEI), for example, typical images of a frame with more than one cycle motion are analyzed. The unique stride highlights the required movement of an individual strolling more than one complete walking cycle. For instance, in Gait Energy Image (GEI), normal outline pictures of more than one entire walk cycle are processed. Be that as it may, there may be an incomplete step cycle in which information is accessible due to impediments. This research work has designed and developed a model for implementing stride arrangement and profound learning strategies. A separate walk dataset is used in our trial. The walk marks are removed from the Gait Energy Image (GEI) using the Convolution Neural Network (CNN). The classifier proposed here for grouping human gait postures is Mobile Net. Finally, the proposed methodology is evaluated on the OULP step dataset, affirming that the proposed design effectively reproduces total GEI even from the outrageously deficient walk cycles.
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8

Chen, Qusen, Hua Chen, Weiping Jiang, Xiaohui Zhou, and Peng Yuan. "A New Cycle Slip Detection and Repair Method for Single-Frequency GNSS Data." Journal of Navigation 71, no. 6 (May 9, 2018): 1492–510. http://dx.doi.org/10.1017/s0373463318000243.

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Cycle slip detection for single frequency Global Navigation Satellite System (GNSS) data is currently mainly based on measurement modelling or prediction, which cannot be effectively performed for kinematic applications and it is difficult to detect or repair small cycle slips such as half-cycle slips. In this paper, a new method that is based on the total differential of ambiguity and Least-Squares Adjustment (LSA) for cycle slip detection and repair is introduced and validated. This method utilises only carrier-phase observations to build an ambiguity function. LSA is then conducted for detecting and repairing cycle slips, where the coordinate and cycle slips are obtained successively. The performance of this method is assessed through processing short and long baselines in static and kinematic modes and the impact of linearization and atmospheric errors are analysed at the same time under a controlled variable method. The results indicate this method is very effective and reliable in detecting and repairing multiple cycle slips, especially small cycle slips.
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9

Misiaszek, M. "Experimental Detection of the CNO Cycle." Acta Physica Polonica B Proceedings Supplement 15, no. 3 (2022): 1. http://dx.doi.org/10.5506/aphyspolbsupp.15.3-a24.

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10

Bernal, D., E. Memarzadeh, and M. Ulriksen. "Limit cycle periods in damage detection." Mechanical Systems and Signal Processing 162 (January 2022): 108037. http://dx.doi.org/10.1016/j.ymssp.2021.108037.

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Дисертації з теми "Cycle detection"

1

Yin, Lan 1969. "GPS based positioning with cycle slip detection." Thesis, McGill University, 2003. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=79206.

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This thesis is concerned with development and implementation of an efficient and numerically reliable positioning algorithm based on the combination of code pseudorange (C/A) and carrier phase (L1) measurements with cycle slip detection.
In GPS a typical technique for kinematic position estimation is relative positioning where two receivers are used, one receiver is stationary and its exact position is known, the other is roving and its position is to be estimated. We describe the physical situation and give the mathematical model based on the difference of the measurements at the stationary and roving receivers. The model we consider combines both code pseudorange and carrier phase measurements. We then present: a recursive least squares approach for position estimation. We take full account of the structure of the problem to make our algorithm efficient, and use orthogonal transformations to ensure numerical reliability of the algorithm.
At each epoch, possible cycle slips must be detected, otherwise it may significant deteriorate the positioning accuracy. A cycle slip detection method based on the higher-order difference technique, one of typical techniques for cycle slip detection, is developed and incorporated into the preprocess of our positioning algorithm.
Finally, real data testing for our positioning algorithm and cycle slip detection algorithm are performed. The results suggest our algorithms are very effective.
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2

DISPA, LIMOUZIN CHRISTIANE. "Reperage de la periode fertile et de l'ovulation : les moyens actuels et leur utilisation." Lille 2, 1989. http://www.theses.fr/1989LIL2M360.

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3

Gregory, Connor. "Non-intrusive fault detection of reverse cycle air conditioning systems – Dissertation." Thesis, Gregory, Connor (2018) Non-intrusive fault detection of reverse cycle air conditioning systems – Dissertation. Honours thesis, Murdoch University, 2018. https://researchrepository.murdoch.edu.au/id/eprint/44815/.

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The influence from buildings on global energy consumption, both residential and commercial, steadily increases each year. Growth in population, demand for building comfort and climate control ensure buildings will continue as a prominent energy consumer far into the future. Equating to over half a buildings total energy consumption, heating ventilation and cooling systems (HVAC) often operate in substandard conditions, decreasing efficiency by 15% to 30%. Much of this efficiency could improve with the widespread adoption of automated condition-based maintenance. Automated fault detection and diagnostic systems (FDD) provide a cornerstone for condition based-monitoring within HVAC systems. However, the current available FDD approaches employ invasive installation techniques, requiring fixing of various mechanical sensors to the pre-existing unit. With new research in the field of system monitoring via electrical measurements, this paper will explore a less invasive approach to HAVC fault detection. Using only electrical measurements, air-conditioning systems will undergo testing with simulated fault conditions. Machine learning algorithms are studied to predict the fault status, ultimately providing the client with appropriate diagnostic information. Providing fault detection capabilities via electrical measurements ensure a much less intrusive approach and does not restrict the measurement equipment to be local to the system. Ultimately the system would become far cheaper and convenient than traditional systems promoting installation and increasing the HVAC system’s efficiency.
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4

Leal, Soraya Cristina de Macedo. "Detection and characterization of Metarhizium anisopliae using molecular markers." Thesis, University of Nottingham, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307762.

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5

Meless, Dejen. "Test Cycle Optimization using Regression Analysis." Thesis, Linköping University, Automatic Control, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-54809.

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Industrial robots make up an important part in today’s industry and are assigned to a range of different tasks. Needless to say, businesses need to rely on their machine park to function as planned, avoiding stops in production due to machine failures. This is where fault detection methods play a very important part. In this thesis a specific fault detection method based on signal analysis will be considered. When testing a robot for fault(s), a specific test cycle (trajectory) is executed in order to be able to compare test data from different test occasions. Furthermore, different test cycles yield different measurements to analyse, which may affect the performance of the analysis. The question posed is: Can we find an optimal test cycle so that the fault is best revealed in the test data? The goal of this thesis is to, using regression analysis, investigate how the presently executed test cycle in a specific diagnosis method relates to the faults that are monitored (in this case a so called friction fault) and decide if a different one should be recommended. The data also includes representations of two disturbances.

The results from the regression show that the variation in the test quantities utilised in the diagnosis method are not explained by neither the friction fault or the test cycle. It showed that the disturbances had too large effect on the test quantities. This made it impossible to recommend a different (optimal) test cycle based on the analysis.

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6

Lee, Seungyup. "A rapid cycle length variability detection technique of atrial electrographs in atrial fibrillation." online version, 2008. http://rave.ohiolink.edu/etdc/view.cgi?acc%5Fnum=case1207255208.

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7

Lee, Seungyup. "A RAPID CYCLE LENGTH VARIABILITY DETECTION TECHNIQUE OF ATRIAL ELECTROGRAMS IN ATRIAL FIBRILLATION." Case Western Reserve University School of Graduate Studies / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=case1207255208.

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8

Harshman, D. K., B. M. Rao, J. E. McLain, G. S. Watts, and J. Y. Yoon. "Innovative qPCR using interfacial effects to enable low threshold cycle detection and inhibition relief." AAAS, 2015. http://hdl.handle.net/10150/621255.

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UA Open Access Publishing Fund
Molecular diagnostics offers quick access to information but fails to operate at a speed required for clinical decision-making. Our novel methodology, droplet-on-thermocouple silhouette real-time polymerase chain reaction (DOTS qPCR), uses interfacial effects for droplet actuation, inhibition relief, and amplification sensing. DOTS qPCR has sample-to-answer times as short as 3 min 30 s. In infective endocarditis diagnosis, DOTS qPCR demonstrates reproducibility, differentiation of antibiotic susceptibility, subpicogram limit of detection, and thermocycling speeds of up to 28 s/cycle in the presence of tissue contaminants. Langmuir and Gibbs adsorption isotherms are used to describe the decreasing interfacial tension upon amplification. Moreover, a log-linear relationship with low threshold cycles is presented for real-time quantification by imaging the droplet-on-thermocouple silhouette with a smartphone. DOTS qPCR resolves several limitations of commercially available real-time PCR systems, which rely on fluorescence detection, have substantially higher threshold cycles, and require expensive optical components and extensive sample preparation. Due to the advantages of low threshold cycle detection, we anticipate extending this technology to biological research applications such as single cell, single nucleus, and single DNA molecule analyses. Our work is the first demonstrated use of interfacial effects for sensing reaction progress, and it will enable point-of-care molecular diagnosis of infections.
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9

Cockerton, Helen Elizabeth. "Late-glacial and Holocene variations in the Si cycle in the Nile Basin : multi-isotope evidence from modern waters and lake sediments." Thesis, Swansea University, 2012. https://cronfa.swan.ac.uk/Record/cronfa42906.

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10

He, Han M. Eng Massachusetts Institute of Technology. "Applications of reference cycle building and K-shape clustering for anomaly detection in the semiconductor manufacturing process." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120246.

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Анотація:
Thesis: M. Eng. in Advanced Manufacturing and Design, Massachusetts Institute of Technology, Department of Mechanical Engineering, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 68-69).
Early and accurate anomaly detection plays a key role in reducing costs and improving benefits, especially for complicated and time-consuming manufacturing such as semiconductor production. A case study of detecting anomalies from several monitored parameters during one plasma etching process is presented in this thesis. The thesis focuses on optimized ways to build reference cycles, or centroids of univariate parameters, a critical component to determine clustering accuracy and to facilitate process engineers' offline anomaly detections and diagnoses. Three time series centroid building methods are discussed and evaluated in the thesis, arithmetic, the Dynamic Time Warping Barycenter Averaging (DBA), and the soft-DTW-based centroid. As a result, DBA is chosen considering its comprehensive performance of accuracy and calculation time. Optimizations on DBA is further discussed to reduce calculation time. The window constraint, as well as the recalculation method of combining the previous centroid and new datasets, substantially reduce calculation time with slight accuracy loss. Based upon one centroid building method, shape extraction, a novel clustering method, k-shape, is implemented and applied to the plasma etching process. It is found that it achieves great accuracy with substantially shorter calculation time than one mainstream clustering method, k-means.
by Han He.
M. Eng. in Advanced Manufacturing and Design
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Книги з теми "Cycle detection"

1

Bateman, Colin. Cycle of violence. New York: Arcade Pub, 1996.

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2

Johansen, Iris. Storm cycle. New York: St. Martin's Press, 2009.

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3

Johansen, Iris. Storm cycle. Thorndike, Me: Center Point Pub., 2009.

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4

Pepper, Mark. Man on a murder cycle. London: Hodder & Stoughton, 1997.

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5

Bob, Robbins. Signal investing: Detecting powerful trends in risk and market cycles. New York: McGraw-Hill, 2012.

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6

Barney Buck and the flying solar-cycle. Wheaton, Ill: WindRider Books, 1985.

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7

King, Stephen. Cycle of the Werewolf. New York, USA: Signet, 1985.

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8

King, Stephen. Cycle of the Werewolf. New York, USA: New American Library, 1985.

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9

Johannes, Mark Robert Stephen. Detecting and understanding marine-terrestrial linkages in a developing watershed: Nutrient cycling in the Kenai River watershed. Anchorage, Alaska: EVOS Trustee Council, 2003.

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10

Kölker, Stefan, Johannes Häberle, and Valerie Walker. Urea Cycle Disorders. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199972135.003.0017.

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The urea cycle is the final pathway for removal of surplus nitrogen from the body, the major route in humans for irreversible detoxification of ammonia and a source of arginine.2,3 Patients with adult-onset urea cycle disorders present with clinical symptoms that have a broad differential diagnosis (e.g., protein aversion, inappetence, cyclic vomiting, epilepsy, psychiatric disease, migraine, liver dysfunction). Unlike infants and children some adult-onset patients may never develop acute hyperammonemic crises. Since symptoms are often fluctuating and the diagnosis can only be made if specific tests are made, the diagnosis is often delayed or missed. Earlier detection and intervention would improve the patients’ outcome, which currently is poor.
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Частини книг з теми "Cycle detection"

1

Wong, Chi-Him, and Yiu-Cheong Tam. "Negative Cycle Detection Problem." In Algorithms – ESA 2005, 652–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11561071_58.

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2

Amir, Amihood, Estrella Eisenberg, Avivit Levy, Ely Porat, and Natalie Shapira. "Cycle Detection and Correction." In Automata, Languages and Programming, 43–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14165-2_5.

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3

Cherkassky, Boris V., and Andrew V. Goldberg. "Negative-cycle detection algorithms." In Algorithms — ESA '96, 349–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61680-2_67.

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4

Frangopol, Dan M., and Sunyong Kim. "Probabilistic Damage Detection." In Life-Cycle of Structures Under Uncertainty, 51–71. Boca Raton, FL : CRC Press, 2019. | “A science publishers book.”: CRC Press, 2019. http://dx.doi.org/10.1201/9780429053283-3.

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5

Barber, Anna Estevan, and David W. Meek. "Detection of Post-translationally Modified p53 by Western Blotting." In Cell Cycle Checkpoints, 7–18. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1217-0_2.

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6

Xu, Guochang, and Yan Xu. "Cycle Slip Detection and Ambiguity Resolution." In GPS, 229–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-50367-6_8.

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7

Chatterjee, Soumya, Roxana Moogoui, and Dharmendra K. Gupta. "Arsenic: Source, Occurrence, Cycle, and Detection." In Arsenic Contamination in the Environment, 13–35. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54356-7_2.

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8

Bernard, Philip S., Astrid Reiser, and Gregory H. Pritham. "Mutation Detection by Fluorescent Hybridization Probe Melting Curves." In Rapid Cycle Real-Time PCR, 11–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-59524-0_2.

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9

Levin, Vladimir, Robert Palmer, Shaz Qadeer, and Sriram K. Rajamani. "Sound Transaction-Based Reduction Without Cycle Detection." In Model Checking Software, 106–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11537328_11.

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10

Lichtenegger, H., and B. Hofmann-Wellenhof. "GPS-Data Preprocessing for Cycle-Slip Detection." In Global Positioning System: An Overview, 57–68. New York, NY: Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4615-7111-7_4.

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Тези доповідей конференцій з теми "Cycle detection"

1

Kay, Bill, Catherine Schuman, Jade O'Connor, Prasanna Date, and Thomas Potok. "Neuromorphic Graph Algorithms: Cycle Detection, Odd Cycle Detection, and Max Flow." In ICONS 2021: International Conference on Neuromorphic Systems 2021. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3477145.3477172.

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Razavi Haeri, Ali Asghar, and Aminghasem Safarian. "Cycle by Cycle Envelope Detection and ASK Demodulation." In 2019 27th Iranian Conference on Electrical Engineering (ICEE). IEEE, 2019. http://dx.doi.org/10.1109/iraniancee.2019.8786394.

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Shen, Qing, Chang Tian, and Lin Du. "Pose-based Gait Cycle Detection." In 2019 IEEE 2nd International Conference on Electronic Information and Communication Technology (ICEICT). IEEE, 2019. http://dx.doi.org/10.1109/iceict.2019.8846361.

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4

Li, Cheng, Douglas F. Parham, and Yanwu Ding. "Cycle detection in speech breathing signals." In 2011 Biomedical Sciences and Engineering Conference (BSEC). IEEE, 2011. http://dx.doi.org/10.1109/bsec.2011.5872314.

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Dabrowski, Adrian, Heidelinde Hobel, Johanna Ullrich, Katharina Krombholz, and Edgar Weippl. "Towards a Hardware Trojan Detection Cycle." In 2014 Ninth International Conference on Availability, Reliability and Security (ARES). IEEE, 2014. http://dx.doi.org/10.1109/ares.2014.45.

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Tang, Hong, Ting Li, and Tianshuang Qiu. "Cardiac cycle detection for heart sound signal based on instantaneous cycle frequency." In 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI). IEEE, 2011. http://dx.doi.org/10.1109/bmei.2011.6098371.

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Wang, Long, and Zheng Liu. "An infrared data cycle extraction method based on CEEMDAN." In Seventh Symposium on Novel Photoelectronic Detection Technology and Application 2020, edited by Junhao Chu, Qifeng Yu, Huilin Jiang, and Junhong Su. SPIE, 2021. http://dx.doi.org/10.1117/12.2587230.

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8

Zhang, Peiyun, Sheng Shu, and Mengchu Zhou. "Adaptively Adjusting Dynamic Detection Cycle for Fault Detection in Clouds." In 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2018. http://dx.doi.org/10.1109/smc.2018.00686.

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Chandrachoodan, Nitin, Shuvra S. Bhattacharyya, and K. J. Ray Liu. "Adaptive negative cycle detection in dynamic graphs." In ISCAS 2001. The 2001 IEEE International Symposium on Circuits and Systems. IEEE, 2001. http://dx.doi.org/10.1109/iscas.2001.922010.

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D'Silva, V., and D. Kroening. "Fixed points for multi-cycle path detection." In 2009 Design, Automation & Test in Europe Conference & Exhibition (DATE'09). IEEE, 2009. http://dx.doi.org/10.1109/date.2009.5090938.

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Звіти організацій з теми "Cycle detection"

1

Villa-Aleman, E., A. Houk, and W. Spencer. Advanced Ultrafast Spectroscopy for Chemical Detection of Nuclear Fuel Cycle Materials. Office of Scientific and Technical Information (OSTI), September 2017. http://dx.doi.org/10.2172/1395974.

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2

Ernest A. Mancini. T-R Cycle Characterization and Imaging: Advanced Diagnostic Methodology for Petroleum Reservoir and Trap Detection and Delineation. Office of Scientific and Technical Information (OSTI), August 2006. http://dx.doi.org/10.2172/907881.

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Ernest A. Mancini. T-R Cycle Characterization and Imaging: Advanced Diagnostic Methodology for Petroleum Reservoir and Trap Detection and Delineation. US: University Of Alabama, March 2004. http://dx.doi.org/10.2172/898354.

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Ernest A. Mancini. T-R Cycle Characterization and Imaging: Advanced Diagnostic Methodology for Petroleum Reservoir and Trap Detection and Delineation. US: University Of Alabama, December 2004. http://dx.doi.org/10.2172/898355.

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Ernest A. Mancini, William C. Parcell, and Bruce S. Hart. T-R Cycle Characterization and Imaging: Advanced Diagnostic Methodology for Petroleum Reservoir and Trap Detection and Delineation. Office of Scientific and Technical Information (OSTI), March 2006. http://dx.doi.org/10.2172/877656.

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Ernest A. Mancini. T-R CYCLE CHARACTERIZATION AND IMAGING: ADVANCED DIAGNOSTIC METHODOLOGY FOR PETROLEUM RESERVOIR AND TRAP DETECTION AND DELINEATION. Office of Scientific and Technical Information (OSTI), September 2004. http://dx.doi.org/10.2172/834185.

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Ernest A. Mancini, William C. Parcell, and Bruce S. Hart. T-R CYCLE CHARACTERIZATION AND IMAGING: ADVANCED DIAGNOSTIC METHODOLOGY FOR PETROLEUM RESERVOIR AND TRAP DETECTION AND DELINEATION. Office of Scientific and Technical Information (OSTI), March 2004. http://dx.doi.org/10.2172/840256.

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Ernest A. Mancini. T-R CYCLE CHARACTERIZATION AND IMAGING: ADVANCED DIAGNOSTIC METHODOLOGY FOR PETROLEUM RESERVOIR AND TRAP DETECTION AND DELINEATION. Office of Scientific and Technical Information (OSTI), June 2004. http://dx.doi.org/10.2172/829959.

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Ernest A. Mancini. T-R CYCLE CHARACTERIZATION AND IMAGING: ADVANCED DIAGNOSTIC METHODOLOGY FOR PETROLEUM RESERVOIR AND TRAP DETECTION AND DELINEATION. Office of Scientific and Technical Information (OSTI), June 2005. http://dx.doi.org/10.2172/840803.

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Ernest A. Mancini, William C. Parcell, and Bruce S. Hart. T-R Cycle Characterization and Imaging: Advanced Diagnostic Methodology for Petroleum Reservoir and Trap Detection and Delineation. Office of Scientific and Technical Information (OSTI), September 2005. http://dx.doi.org/10.2172/859241.

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