Academic literature on the topic 'Anomaly patterns'
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Journal articles on the topic "Anomaly patterns"
Ilahude, Delyuzar. "MAGNETIC ANOMALY PATTERNS USING TREND SURFACE ANALYSIS APPLICATION (TSA) ON MARINE GEOLOGY MAPPING IN THE BALIKPAPAN WATERS." BULLETIN OF THE MARINE GEOLOGY 27, no. 1 (February 15, 2016): 19. http://dx.doi.org/10.32693/bomg.27.1.2012.42.
Full textSubagio, Subagio, and Tatang Patmawidjaya. "POLA ANOMALI BOUGUER DAN ANOMALI MAGNET DAN KAITANNYA DENGAN PROSPEK SUMBER DAYA MINERAL DAN ENERGI DI PULAU LAUT, PULAU SEBUKU DAN SELAT SEBUKU, KALIMANTAN SELATAN." JURNAL GEOLOGI KELAUTAN 11, no. 3 (February 16, 2016): 115. http://dx.doi.org/10.32693/jgk.11.3.2013.236.
Full textPeck, Sheldon. "Dental Anomaly Patterns (DAP)." Angle Orthodontist 79, no. 5 (September 1, 2009): 1015–16. http://dx.doi.org/10.2319/0003-3219-079.005.1015.
Full textCzeizel, Andrew, John M. Optiz, and James F. Reynolds. "Additive congenital anomaly patterns." American Journal of Medical Genetics 29, no. 4 (April 1988): 727–38. http://dx.doi.org/10.1002/ajmg.1320290402.
Full textFyfe, John C., and David J. Lorenz. "Characterizing Midlatitude Jet Variability: Lessons from a Simple GCM." Journal of Climate 18, no. 16 (August 15, 2005): 3400–3404. http://dx.doi.org/10.1175/jcli3486.1.
Full textMarpaung, Sartono, and Wawan K. Harsanugraha. "ANALYSIS OF SEA SURFACE HEIGHT ANOMALY CHARACTERISTICS BASED ON SATELLITE ALTIMETRY DATA (CASE STUDY: SEAS SURROUNDING JAVA ISLAND)." International Journal of Remote Sensing and Earth Sciences (IJReSES) 11, no. 2 (April 12, 2017): 137. http://dx.doi.org/10.30536/j.ijreses.2014.v11.a2611.
Full textSabeti, Elyas, Sehong Oh, Peter Song, and Alfred Hero. "A Pattern Dictionary Method for Anomaly Detection." Entropy 24, no. 8 (August 9, 2022): 1095. http://dx.doi.org/10.3390/e24081095.
Full textPlastun, Alex, Inna Makarenko, Lyudmila Khomutenko, Svitlana Shcherbak, and Olha Tryfonova. "Exploring price gap anomaly in the Ukrainian stock market." Investment Management and Financial Innovations 16, no. 2 (June 5, 2019): 150–58. http://dx.doi.org/10.21511/imfi.16(2).2019.13.
Full textKang, Na Ra, and Wonsun Paek. "Accruals Anomaly and Earnings Announcement Patterns." korean management review 45, no. 2 (April 30, 2016): 503. http://dx.doi.org/10.17287/kmr.2016.45.2.503.
Full textFauzi, Arwin Happy Nur, and Masduki Masduki. "Student’s Anomaly Reasoning in Solving Number Pattern in terms of Gender." Jurnal Didaktik Matematika 9, no. 2 (October 31, 2022): 328–42. http://dx.doi.org/10.24815/jdm.v9i2.27146.
Full textDissertations / Theses on the topic "Anomaly patterns"
Chung, Chi-hang, and 鍾志恆. "Teleconnection of global precipitation anomaly with climate patterns." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hdl.handle.net/10722/195968.
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Vural, Gurkan. "Anomaly Detection From Personal Usage Patterns In Web Applications." Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/12607973/index.pdf.
Full textÅkerström, Paul Linus Martin. "RETURN PATTERNS PROXIMAL TO CENTRAL BANK RATE DECISION ANNOUNCEMENTS : OMX 30 excess return and monetary policy announcements." Thesis, Stockholms universitet, Finansiering, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-105824.
Full textPal, Aritra. "Improving Service Level of Free-Floating Bike Sharing Systems." Scholar Commons, 2017. https://scholarcommons.usf.edu/etd/7433.
Full textCosta, Gabriel de Barros Paranhos da. "Detecção de anomalias utilizando métodos paramétricos e múltiplos classificadores." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-20112014-105415/.
Full textAnomalies or outliers are examples or group of examples that have a behaviour different from the expected. These examples may represent diseases in individuals or populations,as well as other events such as fraud and failures in banking systems.Several existing techniques seek to identify these anomalies, including adaptations of classification methods, statistical methods and methods based on information theory. The main challenges are that the number of samples of each class is unbalanced, the cases when anomalies are disguised among normal samples and the definition of normal behaviour associated with the formalization of a model for this behaviour. In this dissertation,we propose the use of a new space to helpwith the detection task, this space is called parameter space. We also present a new framework to perform anomaly detection by using the fusion of convex hulls in multiple parameter spaces to perform the detection.The method is considered a framework because it is possible to choose which parameter spaces will be used by the method according to the behaviour of the target data set.For the experiments, two parameter spaces were used (mean and standard deviation; mean, variance, skewness and kurtosis) and the results were compared to some commonly used anomaly detection methods. The results achieved were comparable or better than those obtained by the other methods. Furthermore, we believe that a parameter space created great fexibility for the proposed method, since it allowed the user to choose a parameter space that best models the application. Both the flexibility and extensibility provided by the use of parameter spaces, together with the good performance achieved by the proposed method in the experiments, make parameter spaces and, more specifically, the proposed methods appealing when solving anomaly detection problems.
Losik, Len, Sheila Wahl, and Lewis Owen. "Predicting Failures and Estimating Duration of Remaining Service Life from Satellite Telemetry." International Foundation for Telemetering, 1996. http://hdl.handle.net/10150/611451.
Full textThis paper addresses research completed for predicting hardware failures and estimating remaining service life for satellite components using a Failure Prediction Process (FPP). It is a joint paper, presenting initial research completed at the University of California, Berkeley, Center for Extreme Ultraviolet (EUV) Astrophysics using telemetry from the EUV EXPLORER (EUVE) satellite and statistical computation analysis completed by Lockheed Martin. This work was used in identifying suspect "failure precursors." Lockheed Martin completed an exploration into the application of statistical pattern recognition methods to identify FPP events observed visually by the human expert. Both visual and statistical methods were successful in detecting suspect failure precursors. An estimate for remaining service life for each unit was made from the time the suspect failure precursor was identified. It was compared with the actual time the equipment remained operable. The long-term objective of this research is to develop a resident software module which can provide information on FPP events automatically, economically, and with high reliability for long-term management of spacecraft, aircraft, and ground equipment. Based on the detection of a Failure Prediction Process event, an estimate of remaining service life for the unit can be calculated and used as a basis to manage the failure.
Rahman, Anisur. "Rare sequential pattern mining of critical infrastructure control logs for anomaly detection." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/132077/1/__qut.edu.au_Documents_StaffHome_staffgroupW%24_wu75_Documents_ePrints_Anisur_Rahman_Thesis_Redacted.pdf.
Full textJakkula, Vikramaditya Reddy. "Enhancing smart home resident activity prediction and anomaly detection using temporal relations." Online access for everyone, 2007. http://www.dissertations.wsu.edu/Thesis/Fall2007/v_jakkula_102207.pdf.
Full textMcAbee, Ashley S. M. "Traffic pattern detection using the Hough transformation for anomaly detection to improve maritime domain awareness." Thesis, Monterey, California: Naval Postgraduate School, 2013. http://hdl.handle.net/10945/38977.
Full textTechniques for anomaly detection in the maritime domain by extracting traffic patterns from ship position data to generate atlases of expected ocean travel are developed in this thesis. An archive of historical data is used to develop a traffic density grid. The Hough transformation is used to extract linear patterns of elevated density from the traffic density grid, which can be considered the highways of the oceans. These highways collectively create an atlas that is used to define geographical regions of expected ship locations. Ship position reports are compared to the atlas of highways to flag as anomalous any ship that is not operating on an expected highway. The atlas generation techniques are demonstrated using automated information system (AIS) ship position data to detect highways in both open-ocean and coastal areas. Additionally, the atlas generation techniques are used to explore variability in ship traffic as a result of extreme weather and seasonal variation. Finally, anomaly detection is demonstrated by comparing AIS data from 2013 to the highways detected in the archive of data from 2012. The development of an automatic atlas generation technique that can be used to develop a definition of normal maritime behavior is the significant result of this thesis.
Spasic, Nemanja. "Anomaly Detection and Prediction of Human Actions in a Video Surveillance Environment." Thesis, University of Cape Town, 2007. http://pubs.cs.uct.ac.za/archive/00000449/.
Full textBooks on the topic "Anomaly patterns"
G, Hutchins D., and Petzel V, eds. The regional radiometric data set of Namibia: Comments on compilation of the data from west central and southern Namibia and on anomaly patterns thereof. Windhoek, Namibia: Ministry of Mines and Energy, Geological Survey of Namibia, 1997.
Find full textMillikan, Ruth Garrett. Perception, Especially Perception through Language. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198717195.003.0014.
Full textVostokov, Dmitry, and Software Diagnostics Institute. Trace, Log, Text, Narrative: An Analysis Pattern Reference for Data Mining, Diagnostics, Anomaly Detection, Fourth Edition. Opentask, 2021.
Find full textBehera, Swadhin, and Toshio Yamagata. Climate Dynamics of ENSO Modoki Phenomena. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.612.
Full textBook chapters on the topic "Anomaly patterns"
Dueholm, Jacob Velling, Kamal Nasrollahi, and Thomas Baltzer Moeslund. "Object-Centric Anomaly Detection Using Memory Augmentation." In Computer Analysis of Images and Patterns, 362–71. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89128-2_35.
Full textByrne, Charles J. "Bouguer Gravity Anomaly Patterns of Impact Features." In The Moon's Largest Craters and Basins, 29–34. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-22032-1_5.
Full textBoekhoudt, Kayleigh, Alina Matei, Maya Aghaei, and Estefanía Talavera. "HR-Crime: Human-Related Anomaly Detection in Surveillance Videos." In Computer Analysis of Images and Patterns, 164–74. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89131-2_15.
Full textCampbell, Scott, Mariam Kiran, and Fatema Bannat Wala. "Unsupervised Anomaly Detection in Daily WAN Traffic Patterns." In Communications in Computer and Information Science, 240–56. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63393-6_16.
Full textWacker, Esther-Sabrina, and Joachim Denzler. "Combining Structure and Appearance for Anomaly Detection in Wire Ropes." In Computer Analysis of Images and Patterns, 163–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23678-5_18.
Full textVy, Ngo Duy Khanh, and Duong Tuan Anh. "Detecting Variable Length Anomaly Patterns in Time Series Data." In Data Mining and Big Data, 279–87. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40973-3_28.
Full textNoh, Sang-Kyun, Yong-Min Kim, DongKook Kim, and Bong-Nam Noh. "Network Anomaly Detection Based on Clustering of Sequence Patterns." In Computational Science and Its Applications - ICCSA 2006, 349–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11751588_37.
Full textKozik, Rafał, Michał Choraś, Rafał Renk, and Witold Hołubowicz. "Patterns Extraction Method for Anomaly Detection in HTTP Traffic." In Advances in Intelligent Systems and Computing, 227–36. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19713-5_20.
Full textKim, Young Geun, Jeong-Han Yun, Siho Han, Hyoung Chun Kim, and Simon S. Woo. "Revitalizing Self-Organizing Map: Anomaly Detection Using Forecasting Error Patterns." In ICT Systems Security and Privacy Protection, 382–97. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78120-0_25.
Full textCavallaro, Claudia, and Elisabetta Ronchieri. "Identifying Anomaly Detection Patterns from Log Files: A Dynamic Approach." In Computational Science and Its Applications – ICCSA 2021, 517–32. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86960-1_36.
Full textConference papers on the topic "Anomaly patterns"
Sukhobok, Dina, Nikolay Nikolov, and Dumitru Roman. "Tabular Data Anomaly Patterns." In 2017 International Conference on Big Data Innovations and Applications (Innovate-Data). IEEE, 2017. http://dx.doi.org/10.1109/innovate-data.2017.10.
Full textRoh, Jong-hyuk, Sung-Hun Lee, and Soohyung Kim. "Anomaly detection of access patterns in database." In 2015 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2015. http://dx.doi.org/10.1109/ictc.2015.7354751.
Full textSeleznyov, Alexandr, and Oleksiy Mazhelis. "Learning temporal patterns for anomaly intrusion detection." In the 2002 ACM symposium. New York, New York, USA: ACM Press, 2002. http://dx.doi.org/10.1145/508791.508836.
Full textGutchess, Daniel, Neal Checka, and Magnús S. Snorrason. "Learning patterns of human activity for anomaly detection." In Defense and Security Symposium, edited by Kevin L. Priddy and Emre Ertin. SPIE, 2007. http://dx.doi.org/10.1117/12.741379.
Full textYu, Tsung-Chiao, Jyun-Yao Huang, I.-En Liao, and Kuo-Fong Kao. "Mining Anomaly Communication Patterns for Industrial Control Systems." In 2018 Australasian Universities Power Engineering Conference (AUPEC). IEEE, 2018. http://dx.doi.org/10.1109/aupec.2018.8757940.
Full textVespe, M., I. Visentini, K. Bryan, and P. Braca. "Unsupervised learning of maritime traffic patterns for anomaly detection." In 9th IET Data Fusion & Target Tracking Conference (DF&TT 2012): Algorithms & Applications. IET, 2012. http://dx.doi.org/10.1049/cp.2012.0414.
Full textParvathy, R., Soumya Thilakan, Meenu Joy, and K. M. Sameera. "Anomaly Detection Using Motion Patterns Computed from Optical Flow." In 2013 Third International Conference on Advances in Computing and Communications (ICACC). IEEE, 2013. http://dx.doi.org/10.1109/icacc.2013.18.
Full textParedes-Oliva, Ignasi, Ismael Castell-Uroz, Pere Barlet-Ros, Xenofontas Dimitropoulos, and Josep Sole-Pareta. "Practical anomaly detection based on classifying frequent traffic patterns." In IEEE INFOCOM 2012 - IEEE Conference on Computer Communications Workshops. IEEE, 2012. http://dx.doi.org/10.1109/infcomw.2012.6193518.
Full textKayode, J. S., M. N. M. Nawawi, Y. Yusup, A. E. Khalil, and M. H. Arifin. "Distributions of Subsurface Anomaly Patterns Using Machine Learning Tools." In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2022. http://dx.doi.org/10.1109/igarss46834.2022.9883869.
Full textPulfer, Brian, Yury Belousov, Joakim Tutt, Roman Chaban, Olga Taran, Taras Holotyak, and Slava Voloshynovskiy. "Anomaly localization for copy detection patterns through print estimations." In 2022 IEEE International Workshop on Information Forensics and Security (WIFS). IEEE, 2022. http://dx.doi.org/10.1109/wifs55849.2022.9975416.
Full textReports on the topic "Anomaly patterns"
Mueller, C., S. J. Piercey, M. G. Babechuk, and D. Copeland. Stratigraphy and lithogeochemistry of the Goldenville horizon and associated rocks, Baie Verte Peninsula, Newfoundland. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/328990.
Full textMaps showing anomaly patterns for silver, molybdenum, lead, and zinc in altered rocks and soils, Williams Fork and St. Louis Peak Roadless Areas, Clear Creek, Grand, and Summit counties, Colorado. US Geological Survey, 1985. http://dx.doi.org/10.3133/mf1588e.
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