Thematische Bibliographien / Single and Multiple Change Point Detection

Inhaltsverzeichnis

  1. Zeitschriftenartikel
  2. Dissertationen
  3. Bücher
  4. Buchteile
  5. Konferenzberichte
  6. Berichte der Organisationen

Auswahl der wissenschaftlichen Literatur zum Thema „Single and Multiple Change Point Detection“

Autor: Grafiati
Veröffentlicht am 2. November 2024

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Zeitschriftenartikel zum Thema "Single and Multiple Change Point Detection"

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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.

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Change-point detection (CPD) is to find abrupt changes in time-series data. Various computational algorithms have been developed for CPD applications. To compare the different CPD models, many performance metrics have been introduced to evaluate the algorithms. Each of the previous evaluation methods measures the different aspects of the methods. Based on the existing weighted error distance (WED) method on single change-point (CP) detection, a novel WED metrics (WEDM) was proposed to evaluate the overall performance of a CPD model across not only repetitive tests on single CP detection, but a
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Li, Zhaoyuan, and Maozai Tian. "Detecting Change-Point via Saddlepoint Approximations." Journal of Systems Science and Information 5, no. 1 (2017): 48–73. http://dx.doi.org/10.21078/jssi-2017-048-26.

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AbstractIt’s well-known that change-point problem is an important part of model statistical analysis. Most of the existing methods are not robust to criteria of the evaluation of change-point problem. In this article, we consider “mean-shift” problem in change-point studies. A quantile test of single quantile is proposed based on saddlepoint approximation method. In order to utilize the information at different quantile of the sequence, we further construct a “composite quantile test” to calculate the probability of every location of the sequence to be a change-point. The location of change-po
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Singh, 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 (2021): 3661–74. http://dx.doi.org/10.2166/wcc.2021.097.

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Abstract The reliability of using abrupt changes in the spatial Hurst exponent for identifying temporal points of abrupt change in climate dynamics is explored. If a spatio-temporal dynamical system undergoes an abrupt change at a particular time, the time series of spatial Hurst exponent obtained from the data of any variable of the system should also show an abrupt change at that time. As expected, spatial Hurst exponents for each of the two variables of a model spatio-temporal system – a globally coupled map lattice based on the Burgers' chaotic map – showed abrupt change at the same time t
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He, 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 (2021): 2969. http://dx.doi.org/10.3390/rs13152969.

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Due to differences in external imaging conditions, multispectral images taken at different periods are subject to radiation differences, which severely affect the detection accuracy. To solve this problem, a modified algorithm based on slow feature analysis is proposed for multispectral image change detection. First, single-band slow feature analysis is performed to process bitemporal multispectral images band by band. In this way, the differences between unchanged pixels in each pair of single-band images can be sufficiently suppressed to obtain multiple feature-difference images containing r
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Pillow, 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 (2011): 1–45. http://dx.doi.org/10.1162/neco_a_00058.

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One of the central problems in systems neuroscience is to understand how neural spike trains convey sensory information. Decoding methods, which provide an explicit means for reading out the information contained in neural spike responses, offer a powerful set of tools for studying the neural coding problem. Here we develop several decoding methods based on point-process neural encoding models, or forward models that predict spike responses to stimuli. These models have concave log-likelihood functions, which allow efficient maximum-likelihood model fitting and stimulus decoding. We present se
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Yang, Chong, Yu Fu, Jianmin Yuan, et al. "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.

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The vibration-based damage identification method extracts the damage location and severity information from the change of modal properties, such as natural frequency and mode shape. Its performance and accuracy depends on the measurement precision. Laser Doppler vibrometer (LDV) provides a noncontact vibration measurement of high quality, but usually it can only do sampling on a single point. Scanning LDV is normally used to obtain the mode shape with a longer scanning time. In this paper, a damage detection technique is proposed using a self-synchronizing multipoint LDV. Multiple laser beams
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R. 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 (2018): 6. http://dx.doi.org/10.33317/ssurj.v7i1.41.

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The traditional Harris detector are sensitive to noise and resolution because without the property of scale invariant. In this research, The Harris corner detector algorithm is improved, to work with multi resolution images, the technique has also been working with poor lighting condition by using histogram equalization technique. The work we have done addresses the issue of robustly detection of feature points, detected multiple of local features are characterized by the intensity changes in both horizontal and vertical direction which is called corner features. The goal of this work is to de
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R. 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 (2018): 6. http://dx.doi.org/10.33317/ssurj.41.

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The traditional Harris detector are sensitive to noise and resolution because without the property of scale invariant. In this research, The Harris corner detector algorithm is improved, to work with multi resolution images, the technique has also been working with poor lighting condition by using histogram equalization technique. The work we have done addresses the issue of robustly detection of feature points, detected multiple of local features are characterized by the intensity changes in both horizontal and vertical direction which is called corner features. The goal of this work is to de
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Tanaka, Kanji. "Fault-Diagnosing Deep-Visual-SLAM for 3D Change Object Detection." Journal of Advanced Computational Intelligence and Intelligent Informatics 25, no. 3 (2021): 356–64. http://dx.doi.org/10.20965/jaciii.2021.p0356.

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Although image change detection (ICD) methods provide good detection accuracy for many scenarios, most existing methods rely on place-specific background modeling. The time/space cost for such place-specific models is prohibitive for large-scale scenarios, such as long-term robotic visual simultaneous localization and mapping (SLAM). Therefore, we propose a novel ICD framework that is specifically customized for long-term SLAM. This study is inspired by the multi-map-based SLAM framework, where multiple maps can perform mutual diagnosis and hence do not require any explicit background modeling
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R. 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 (2018): 6. http://dx.doi.org/10.33317/ssurj.v1i1.41.

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The traditional Harris detector are sensitive to noise and resolution because without the property of scale invariant. In this research, The Harris corner detector algorithm is improved, to work with multi resolution images, the technique has also been working with poor lighting condition by using histogram equalization technique. The work we have done addresses the issue of robustly detection of feature points, detected multiple of local features are characterized by the intensity changes in both horizontal and vertical direction which is called corner features. The goal of this work is to de
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Dissertationen zum Thema "Single and Multiple Change Point Detection"

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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.

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La détection de ruptures est un problème courant d'apprentissage non supervisé dans de nombreux domaines d'application, notamment en biologie, en génomique, en surveillance de réseaux de capteurs et en cybersécurité. En général, soit une détection de ruptures a posteriori, c'est-à-dire hors ligne, soit une détection de ruptures séquentielle, c'est-à-dire en ligne, est envisagée.Des méthodes standard de programmation dynamique pour la détection de ruptures ont été proposées pour optimiser le logarithme du rapport de vraisemblance. Ces méthodes sont exactes et récupèrent des segmentations optima
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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.

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Very long and noisy sequence data arise from biological sciences to social science including high throughput data in genomics and stock prices in econometrics. Often such data are collected in order to identify and understand shifts in trends, for example, from a bull market to a bear market in finance or from a normal number of chromosome copies to an excessive number of chromosome copies in genetics. Thus, identifying multiple change points in a long, possibly very long, sequence is an important problem. In this article, we review both classical and new multiple change-point detection strate
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Hunter, Brandon. "Channel Probing for an Indoor Wireless Communications Channel." BYU ScholarsArchive, 2003. https://scholarsarchive.byu.edu/etd/64.

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The statistics of the amplitude, time and angle of arrival of multipaths in an indoor environment are all necessary components of multipath models used to simulate the performance of spatial diversity in receive antenna configurations. The model presented by Saleh and Valenzuela, was added to by Spencer et. al., and included all three of these parameters for a 7 GHz channel. A system was built to measure these multipath parameters at 2.4 GHz for multiple locations in an indoor environment. Another system was built to measure the angle of transmission for a 6 GHz channel. The addition of th
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"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.

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This thesis develops a composite likelihood-based approach for multiple change-points estimation in general multivariate time series models. Specifically, we derive a criterion function based on pairwise likelihood and minimum description length principle for estimating the number and locations of change-points and performing model selection in each segment. By the virtue of pairwise likelihood, the number and location of change-points can be consistently estimated under mild conditions. The computation can be conducted efficiently with a pruned dynamic programming algorithm. Simulation studie
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Bücher zum Thema "Single and Multiple Change Point Detection"

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Wright, A. G. The Photomultiplier Handbook. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199565092.001.0001.

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This handbook is aimed at helping users of PMTs who are faced with the challenge of designing sensitive light detectors for scientific and industrial purposes. The raison d’être for photomultipliers (PMTs) stems from four intrinsic attributes: large detection area, high, and noiseless gain, and wide bandwidth. Detection involves a conversion process from photons to photoelectrons at the photocathode. Photoelectrons are subsequently collected and increased in number by the action of an incorporated electron multiplier. Photon detection, charge multiplication, and many PMT applications are stati
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Oakes, Lisa M., and David H. Rakison. Developmental Cascades. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780195391893.001.0001.

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Children take their first steps, produce their first words, and become able to solve many new problems seemingly overnight. Yet, each change reflects many other previous developments that occurred in the whole child across a range of domains, and each change, in turn, will provide opportunities for future development. This book proposes that all change can be explained in terms of developmental cascades such that events that occur at one point in development set the stage, or cause a ripple effect, for the emergence or development of different abilities, functions, or behaviors at another poin
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Buchteile zum Thema "Single and Multiple Change Point Detection"

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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. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10984-8_3.

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Wu, Yanhong. "Sequential change point detection and estimation for multiple alternative hypothesis1." In Systems modelling and optimization. Routledge, 2022. http://dx.doi.org/10.1201/9780203737422-43.

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Akbari, 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. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5714-4_8.

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Oke, Masahiro, and Hideyuki Kawashima. "A Multiple Query Optimization Scheme for Change Point Detection on Stream Processing System." In Lecture Notes in Business Information Processing. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46839-5_10.

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Rigaill, 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. Physica-Verlag HD, 2010. http://dx.doi.org/10.1007/978-3-7908-2604-3_57.

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Qian, 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. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02651-0_18.

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Quoc 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. Firenze University Press, 2023. http://dx.doi.org/10.36253/10.36253/979-12-215-0289-3.61.

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The use of closed-circuit television (CCTV) for safety monitoring is crucial for reducing accidents in construction sites. However, the majority of currently proposed approaches utilize single detection models without considering the context of CCTV video inputs. In this study, a multimodal detection, and depth map estimation algorithm utilizing deep learning is proposed. In addition, the point cloud of the test site is acquired using a terrestrial laser scanning scanner, and the detected object's coordinates are projected into global coordinates using a homography matrix. Consequently, the effectiveness of the proposed monitoring system is enhanced by the visualization of the entire monitored scene. In addition, to validate our proposed method, a synthetic dataset of construction site accidents is simulated with Twinmotion. These scenarios are then evaluated with the proposed method to determine its precision and speed of inference. Lastly, the actual construction site, equipped with multiple CCTV cameras, is utilized for system deployment and visualization. As a result, the proposed method demonstrated its robustness in detecting potential hazards on a construction site, as well as its real-time detection speed
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Quoc 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. Firenze University Press, 2023. http://dx.doi.org/10.36253/979-12-215-0289-3.61.

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The use of closed-circuit television (CCTV) for safety monitoring is crucial for reducing accidents in construction sites. However, the majority of currently proposed approaches utilize single detection models without considering the context of CCTV video inputs. In this study, a multimodal detection, and depth map estimation algorithm utilizing deep learning is proposed. In addition, the point cloud of the test site is acquired using a terrestrial laser scanning scanner, and the detected object's coordinates are projected into global coordinates using a homography matrix. Consequently, the effectiveness of the proposed monitoring system is enhanced by the visualization of the entire monitored scene. In addition, to validate our proposed method, a synthetic dataset of construction site accidents is simulated with Twinmotion. These scenarios are then evaluated with the proposed method to determine its precision and speed of inference. Lastly, the actual construction site, equipped with multiple CCTV cameras, is utilized for system deployment and visualization. As a result, the proposed method demonstrated its robustness in detecting potential hazards on a construction site, as well as its real-time detection speed
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Goossens, 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. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27815-0_2.

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AbstractWhen multiple objects are involved in a process, there is an opportunity for processes to be discovered from different angles with new information that previously might not have been analyzed from a single object point of view. This does require that all the information of event/object attributes and their values are stored within logs including attributes that have a list of values or attributes with values that change over time. It also requires that attributes can unambiguously be linked to an object, an event or both. As such, object-centric event logs are an interesting development in process mining as they support the presence of multiple types of objects. First, this paper shows that the current object-centric event log formats do not support the aforementioned aspects to their full potential since the possibility to support dynamic object attributes (attributes with changing values) is not supported by existing formats. Next, this paper introduces a novel enriched object-centric event log format tackling the aforementioned issues alongside an algorithm that automatically translates XES logs to this Data-aware OCEL (DOCEL) format.
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Fernández, 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. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-33157-3_18.

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AbstractEssential biodiversity variables (EBVs) are designed to support the detection and quantification of biodiversity change and to define priorities in biodiversity monitoring. Unlike most primary observations of biodiversity phenomena, EBV products should provide information readily available to produce policy-relevant biodiversity indicators, ideally at multiple spatial scales, from global to subnational. This information is typically complex to produce from a single set of data or type of observation, thus requiring approaches that integrate multiple sources of in situ and remote sensing (RS) data. Here we present an up-to-date EBV concept for biodiversity data integration and discuss the critical components of workflows for EBV production. We argue that open and reproducible workflows for data integration are critical to ensure traceability and reproducibility so that each EBV endures and can be updated as novel biodiversity models are adopted, new observation systems become available, and new data sets are incorporated. Fulfilling the EBV vision requires strengthening efforts to mobilize massive amounts of in situ biodiversity data that are not yet publicly available and taking full advantage of emerging RS technologies, novel biodiversity models, and informatics infrastructures, in alignment with the development of a globally coordinated system for biodiversity monitoring.
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Konferenzberichte zum Thema "Single and Multiple Change Point Detection"

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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.

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This paper presents a fused metric for the assessment of physical workload that can improve fatigue detection using a statistical visualization approach. The goal for considering this combined metric is to concisely reduce the number of variables acquired from multiple sensors. The sensor system gathers data from a heart rate monitor and accelerometers placed at different locations on the body including trunk, wrist, hip and ankle. Two common manufacturing tasks of manual material handling and small parts assembly were tested. Statistical process control was used to monitor the metrics for the
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Roy, 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.

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Abstract Introduction With the advent of global climate change, it has become incumbent on governments and industries to monitor and limit greenhouse gas emissions to prevent a catastrophic rise in the average global temperature. The Paris agreement [Paris 2015] aims to lower global greenhouse gas emissions by 40% (in comparison to greenhouse gas levels observed in 1990) by 2030. Methane is a greenhouse gas whose 100- year global warming potential is 25 times that of carbon dioxide [GWP] and whose atmospheric concentration has been increasing since 2007 [Nisbet 2016, Theo Stein, et al. 2021].
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Mata, Jose, Zunerge Guevara, Luis Quintero, et al. "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.

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Abstract Although leakages in well tubulars have always existed, their occurrence has become very frequent as the number of active wells in mature fields increases. The catastrophic risk of these leaks is an increase in the number of environmental accidents in the oil and gas industry. One of the fundamental causes of leaks is corrosion, which plays a negative role in the productive life of the wells. Generally, these environmental events are associated with surface or near-surface sources. Since multiple casing strings exist within this depth range, the identification of the leak location bec
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Zhang, 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.

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Chalise, 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.

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Halme, 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.

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Halme, 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.

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Nitzan, 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.

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Barooah, Abinash, Muhammad Saad Khan, Hicham Ferroudji, et al. "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.

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Abstract Detecting chronic small leak sizes can be challenging because they may not produce significant or easily noticeable changes in flow rates or pressure differentials. Therefore, specialized techniques are often required to identify and locate chronic small leaks accurately in pipeline systems. The current study aims to address this gap by developing a method to identify multiphase flow leaks in pipelines using time series analysis techniques. An experimental flow loop apparatus, featuring a 2-inch (0.0508 m) diameter and extending 22.6 feet (6.9 m) in length, has been employed to carry
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Nath, 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.

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Berichte der Organisationen zum Thema "Single and Multiple Change Point Detection"

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Belkin, Shimshon, Sylvia Daunert, and Mona Wells. Whole-Cell Biosensor Panel for Agricultural Endocrine Disruptors. United States Department of Agriculture, 2010. http://dx.doi.org/10.32747/2010.7696542.bard.

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Objectives: The overall objective as defined in the approved proposal was the development of a whole-cell sensor panel for the detection of endocrine disruption activities of agriculturally relevant chemicals. To achieve this goal several specific objectives were outlined: (a) The development of new genetically engineered wholecell sensor strains; (b) the combination of multiple strains into a single sensor panel to effect multiple response modes; (c) development of a computerized algorithm to analyze the panel responses; (d) laboratory testing and calibration; (e) field testing. In the course
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Burns, Malcom, and Gavin Nixon. Literature review on analytical methods for the detection of precision bred products. Food Standards Agency, 2023. http://dx.doi.org/10.46756/sci.fsa.ney927.

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The Genetic Technology (Precision Breeding) Act (England) aims to develop a science-based process for the regulation and authorisation of precision bred organisms (PBOs). PBOs are created by genetic technologies but exhibit changes which could have occurred through traditional processes. This current review, commissioned by the Food Standards Agency (FSA), aims to clarify existing terminologies, explore viable methods for the detection, identification, and quantification of products of precision breeding techniques, address and identify potential solutions to the analytical challenges presente
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Botulinum Neurotoxin-Producing Clostridia, Working Group on. Report on Botulinum Neurotoxin-Producing Clostridia. Food Standards Agency, 2023. http://dx.doi.org/10.46756/sci.fsa.ozk974.

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In 1992 a working group of the UK Advisory Committee on the Microbiological Safety of Food presented a report on Vacuum Packaging and Associated Processes regarding the microbiological safety of chilled foods. The report supported subsequent guidance provided by the UK Food Standards Agency for the safe manufacture of vacuum packed and modified atmosphere packed chilled foods. In 2021 the ACMSF requested that a new subgroup should update and build on the 1992 report as well as considering, in addition to chilled foods, some foods that are intended to be stored at ambient temperatures. The new
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Ley, 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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The Big Thicket National Preserve (BITH) vegetation inventory project classified and mapped vegetation within the administrative boundary and estimated thematic map accuracy quantitatively. National Park Service (NPS) Vegetation Mapping Inventory Program provided technical guidance. The overall process included initial planning and scoping, imagery procurement, vegetation classification field data collection, data analysis, imagery interpretation/classification, accuracy assessment (AA), and report writing and database development. Initial planning and scoping meetings took place during May, 2
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