Academic literature on the topic 'Single and Multiple Change Point Detection'
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Journal articles on the topic "Single and Multiple Change Point Detection"
Qi, Jin Peng, Fang Pu, Ying Zhu, and Ping Zhang. "A Weighted Error Distance Metrics (WEDM) for Performance Evaluation on Multiple Change-Point (MCP) Detection in Synthetic Time Series." Computational Intelligence and Neuroscience 2022 (March 24, 2022): 1–17. http://dx.doi.org/10.1155/2022/6187110.
Full textLi, Zhaoyuan, and Maozai Tian. "Detecting Change-Point via Saddlepoint Approximations." Journal of Systems Science and Information 5, no. 1 (June 8, 2017): 48–73. http://dx.doi.org/10.21078/jssi-2017-048-26.
Full textSingh, Uday Pratap, and Ashok Kumar Mittal. "Testing reliability of the spatial Hurst exponent method for detecting a change point." Journal of Water and Climate Change 12, no. 8 (October 1, 2021): 3661–74. http://dx.doi.org/10.2166/wcc.2021.097.
Full textHe, Youxi, Zhenhong Jia, Jie Yang, and Nikola K. Kasabov. "Multispectral Image Change Detection Based on Single-Band Slow Feature Analysis." Remote Sensing 13, no. 15 (July 28, 2021): 2969. http://dx.doi.org/10.3390/rs13152969.
Full textPillow, Jonathan W., Yashar Ahmadian, and Liam Paninski. "Model-Based Decoding, Information Estimation, and Change-Point Detection Techniques for Multineuron Spike Trains." Neural Computation 23, no. 1 (January 2011): 1–45. http://dx.doi.org/10.1162/neco_a_00058.
Full textYang, Chong, Yu Fu, Jianmin Yuan, Min Guo, Keyu Yan, Huan Liu, Hong Miao, and Changchun Zhu. "Damage Identification by Using a Self-Synchronizing Multipoint Laser Doppler Vibrometer." Shock and Vibration 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/476054.
Full textR. Almaddah, Amr Reda, Tauseef Ahmad, and Abdullah Dubai. "Detection and Measurement of Displacement and Velocity of Single Moving Object in a Stationary Background." Sir Syed University Research Journal of Engineering & Technology 7, no. 1 (December 19, 2018): 6. http://dx.doi.org/10.33317/ssurj.v7i1.41.
Full textR. Almaddah, Amr Reda, Tauseef Ahmad, and Abdullah Dubai. "Detection and Measurement of Displacement and Velocity of Single Moving Object in a Stationary Background." Sir Syed University Research Journal of Engineering & Technology 7, no. 1 (December 19, 2018): 6. http://dx.doi.org/10.33317/ssurj.41.
Full textTanaka, Kanji. "Fault-Diagnosing Deep-Visual-SLAM for 3D Change Object Detection." Journal of Advanced Computational Intelligence and Intelligent Informatics 25, no. 3 (May 20, 2021): 356–64. http://dx.doi.org/10.20965/jaciii.2021.p0356.
Full textR. Almaddah, Amr Reda, Tauseef Ahmad, and Abdullah Dubai. "5 Detection and Measurement of Displacement and Velocity of Single Moving Object in a Stationary Background." Sir Syed Research Journal of Engineering & Technology 1, no. 1 (December 19, 2018): 6. http://dx.doi.org/10.33317/ssurj.v1i1.41.
Full textDissertations / Theses on the topic "Single and Multiple Change Point Detection"
Shabarshova, Liudmila. "Geometric functional pruning for change point detection in low-dimensional exponential family models." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASM026.
Full textChange point detection is a common unsupervised learning problem in many application areas, especially in biology, genomics, sensor network monitoring, and cyber-security. Typically, either a posteriori change detection, i.e. offline, or sequential change detection, i.e. online, is considered.Standard dynamic programming methods for change point detection have been proposed to optimise either the likelihood or the log-likelihood ratio of a change point model. These methods are exact and recover optimal segmentations. However, they have quadratic complexity. Continuously reducing the set of potential change point candidates, called pruning, is a way to reduce the computational complexity of standard dynamic programming methods. Over the last decade, a new class of dynamic programming methods, called functional pruning, has been proposed. The functional pruning techniques used in these methods have already proved to be computationally efficient for univariate parametric change point models. Extending univariate functional pruning rules to multivariate settings is difficult if we aim for the most efficient pruning. It leads to non-convex optimisation problems.This thesis introduces two novel, computationally efficient, functional pruning dynamic programming methods for the detection of change points in low-dimensional exponential family models: the offline multiple change point detection method, GeomFPOP (Kmax = ∞), and the online single change point detection method, MdFOCuS.Computational geometry is the basis of the functional pruning rules for these methods. The pruning rule of GeomFPOP (Kmax = ∞) uses a geometric heuristic to update and prune potential change point candidates over time. The pruning rule of MdFOCuS uses a connection to a convex hull problem that simplifies the search for change point location to be pruned. Further we mathematically demonstrate that this pruning technique leads to a quasi-linear runtime complexity.These two pruning rules show significant improvements in computational complexity for low-dimensional exponential family models in simulation studies. In one minute, the Rcpp implementations of these methods can process more than 2 × 106 observations in a bivariate signal without change with i.i.d. Gaussian noise
Niu, Yue S., Ning Hao, and Heping Zhang. "Multiple Change-Point Detection: A Selective Overview." INST MATHEMATICAL STATISTICS, 2016. http://hdl.handle.net/10150/622820.
Full textHunter, Brandon. "Channel Probing for an Indoor Wireless Communications Channel." BYU ScholarsArchive, 2003. https://scholarsarchive.byu.edu/etd/64.
Full text"A composite likelihood-based approach for multiple change-point detection in multivariate time series models." 2014. http://repository.lib.cuhk.edu.hk/en/item/cuhk-1291459.
Full text本論文目的為開發一套以複合似然為基礎的多變點估計方法,該方法可應用於一般多變量時間序列模型。具體而言,我們在最小描述長度原理及成對似然的基礎上推導出一個準則函數,用於估計變化點的數量及位置,並在各段進行模型選擇。憑藉成對似然,在適當條件下變點的數量和位置可以一致地估計。透過使用修剪動態規劃算法,相關的運算能有效地進行。模擬研究及真實數據實例都演示出該方法在統計及運算效率。
Ma, Ting Fung.
Thesis M.Phil. Chinese University of Hong Kong 2014.
Includes bibliographical references (leaves 51-54).
Abstracts also in Chinese.
Title from PDF title page (viewed on 05, October, 2016).
Detailed summary in vernacular field only.
Books on the topic "Single and Multiple Change Point Detection"
Wright, A. G. The Photomultiplier Handbook. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199565092.001.0001.
Full textOakes, Lisa M., and David H. Rakison. Developmental Cascades. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780195391893.001.0001.
Full textBook chapters on the topic "Single and Multiple Change Point Detection"
Priyadarshana, Madawa, Tatiana Polushina, and Georgy Sofronov. "Hybrid Algorithms for Multiple Change-Point Detection in Biological Sequences." In Signal and Image Analysis for Biomedical and Life Sciences, 41–61. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10984-8_3.
Full textWu, Yanhong. "Sequential change point detection and estimation for multiple alternative hypothesis1." In Systems modelling and optimization, 345–53. Boca Raton: Routledge, 2022. http://dx.doi.org/10.1201/9780203737422-43.
Full textAkbari, Shagufta, and M. Janga Reddy. "Detecting Changes in Regional Rainfall Series in India Using Binary Segmentation-Based Multiple Change-Point Detection Techniques." In Climate Change Impacts, 103–16. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5714-4_8.
Full textOke, Masahiro, and Hideyuki Kawashima. "A Multiple Query Optimization Scheme for Change Point Detection on Stream Processing System." In Lecture Notes in Business Information Processing, 150–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46839-5_10.
Full textRigaill, G., E. Lebarbier, and S. Robin. "Exact Posterior Distributions over the Segmentation Space and Model Selection for Multiple Change-Point Detection Problems." In Proceedings of COMPSTAT'2010, 557–64. Heidelberg: Physica-Verlag HD, 2010. http://dx.doi.org/10.1007/978-3-7908-2604-3_57.
Full textQian, Lianfen, and Wei Zhang. "Multiple Change-Point Detection in Piecewise Exponential Hazard Regression Models with Long-Term Survivors and Right Censoring." In Contemporary Developments in Statistical Theory, 289–304. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02651-0_18.
Full textQuoc Tran, Dai, Yuntae Jeon, Seongwoo Son, Minsoo Park, and Seunghee Park. "Identifying Hazards in Construction Sites Using Deep Learning-Based Multimodal with CCTV Data." In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality, 625–33. Florence: Firenze University Press, 2023. http://dx.doi.org/10.36253/10.36253/979-12-215-0289-3.61.
Full textQuoc Tran, Dai, Yuntae Jeon, Seongwoo Son, Minsoo Park, and Seunghee Park. "Identifying Hazards in Construction Sites Using Deep Learning-Based Multimodal with CCTV Data." In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality, 625–33. Florence: Firenze University Press, 2023. http://dx.doi.org/10.36253/979-12-215-0289-3.61.
Full textGoossens, Alexandre, Johannes De Smedt, Jan Vanthienen, and Wil M. P. van der Aalst. "Enhancing Data-Awareness of Object-Centric Event Logs." In Lecture Notes in Business Information Processing, 18–30. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27815-0_2.
Full textFernández, Néstor, Simon Ferrier, Laetitia M. Navarro, and Henrique M. Pereira. "Essential Biodiversity Variables: Integrating In-Situ Observations and Remote Sensing Through Modeling." In Remote Sensing of Plant Biodiversity, 485–501. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-33157-3_18.
Full textConference papers on the topic "Single and Multiple Change Point Detection"
Sedighi Maman, Zahra, Amir Baghdadi, Fadel Megahed, and Lora Cavuoto. "Monitoring and Change Point Estimation of Normal (In-Control) and Fatigued (Out-of-Control) State in Workers." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-60487.
Full textRoy, Arjun, Sangeeta Nundy, Okja Kim, and Godine Chan. "Emission Source Detection and Leak Rate Estimation Using Point Measurements of Concentration." In International Petroleum Technology Conference. IPTC, 2022. http://dx.doi.org/10.2523/iptc-22377-ea.
Full textMata, Jose, Zunerge Guevara, Luis Quintero, Carlos Vasquez, Hernando Trujillo, Alberto Muñoz, and Jorge Falla. "Combination of New Acoustic and Electromagnetic Frequency Technologies Detects Leaks Behind Multiple Casings. Case History." In SPE Annual Technical Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/206383-ms.
Full textZhang, Wenyu, Nicholas A. James, and David S. Matteson. "Pruning and Nonparametric Multiple Change Point Detection." In 2017 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2017. http://dx.doi.org/10.1109/icdmw.2017.44.
Full textChalise, Batu K., Jahi Douglas, and Kevin T. Wagner. "Multiple Change Point Detection-based Target Detection in Clutter." In 2023 IEEE Radar Conference (RadarConf23). IEEE, 2023. http://dx.doi.org/10.1109/radarconf2351548.2023.10149616.
Full textHalme, Topi, Eyal Nitzan, H. Vincent Poor, and Visa Koivunen. "Bayesian Multiple Change-Point Detection with Limited Communication." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9053654.
Full textHalme, Topi, Eyal Nitzan, and Visa Koivunen. "Bayesian Multiple Change-Point Detection of Propagating Events." In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021. http://dx.doi.org/10.1109/icassp39728.2021.9413434.
Full textNitzan, Eyal, Topi Halme, H. Vincent Poor, and Visa Koivunen. "Deterministic Multiple Change-Point Detection with Limited Communication." In 2020 54th Annual Conference on Information Sciences and Systems (CISS). IEEE, 2020. http://dx.doi.org/10.1109/ciss48834.2020.1570627514.
Full textBarooah, Abinash, Muhammad Saad Khan, Hicham Ferroudji, Mohammad Azizur Rahman, Rashid Hassan, Ibrahim Hassan, Ahmad K. Sleiti, Sina Rezaei Gomari, and Matthew Hamilton. "Investigation of Multiphase Flow Leak Detection in Pipeline Using Time Series Analysis Technique." In ASME 2024 43rd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/omae2024-127882.
Full textNath, Samrat, and Jingxian Wu. "BAYESIAN QUICKEST CHANGE POINT DETECTION WITH MULTIPLE CANDIDATES OF POST-CHANGE MODELS." In 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2018. http://dx.doi.org/10.1109/globalsip.2018.8646596.
Full textReports on the topic "Single and Multiple Change Point Detection"
Belkin, Shimshon, Sylvia Daunert, and Mona Wells. Whole-Cell Biosensor Panel for Agricultural Endocrine Disruptors. United States Department of Agriculture, December 2010. http://dx.doi.org/10.32747/2010.7696542.bard.
Full textBurns, Malcom, and Gavin Nixon. Literature review on analytical methods for the detection of precision bred products. Food Standards Agency, September 2023. http://dx.doi.org/10.46756/sci.fsa.ney927.
Full textBotulinum Neurotoxin-Producing Clostridia, Working Group on. Report on Botulinum Neurotoxin-Producing Clostridia. Food Standards Agency, August 2023. http://dx.doi.org/10.46756/sci.fsa.ozk974.
Full textLey, Matt, Tom Baldvins, Hannah Pilkington, David Jones, and Kelly Anderson. Vegetation classification and mapping project: Big Thicket National Preserve. National Park Service, 2024. http://dx.doi.org/10.36967/2299254.
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