Literatura científica selecionada sobre o tema "Data quality and noise"
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Artigos de revistas sobre o assunto "Data quality and noise"
Van Hulse, Jason, Taghi M. Khoshgoftaar e Amri Napolitano. "Evaluating the Impact of Data Quality on Sampling". Journal of Information & Knowledge Management 10, n.º 03 (setembro de 2011): 225–45. http://dx.doi.org/10.1142/s021964921100295x.
Texto completo da fonteLi, Benchong, e Qiong Gao. "Improving data quality with label noise correction". Intelligent Data Analysis 23, n.º 4 (26 de setembro de 2019): 737–57. http://dx.doi.org/10.3233/ida-184024.
Texto completo da fonteNing, Ai Min, Cheng Li e Zhao Liu. "Acoustic Transceiver Optimization Analysis for Downhole Sensor Data Telemetry via Drillstring". Applied Mechanics and Materials 302 (fevereiro de 2013): 389–94. http://dx.doi.org/10.4028/www.scientific.net/amm.302.389.
Texto completo da fonteTerbe, Dániel, László Orzó, Barbara Bicsák e Ákos Zarándy. "Hologram Noise Model for Data Augmentation and Deep Learning". Sensors 24, n.º 3 (1 de fevereiro de 2024): 948. http://dx.doi.org/10.3390/s24030948.
Texto completo da fonteV, Malathi, e Gopinath MP. "Noise Deduction in Novel Paddy Data Repository using Filtering Techniques". Scalable Computing: Practice and Experience 21, n.º 4 (20 de dezembro de 2020): 601–10. http://dx.doi.org/10.12694/scpe.v21i4.1718.
Texto completo da fonteHedderich, Michael A., Dawei Zhu e Dietrich Klakow. "Analysing the Noise Model Error for Realistic Noisy Label Data". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 9 (18 de maio de 2021): 7675–84. http://dx.doi.org/10.1609/aaai.v35i9.16938.
Texto completo da fonteAtaeyan, Mahdieh, e Negin Daneshpour. "Automated Noise Detection in a Database Based on a Combined Method". Statistics, Optimization & Information Computing 9, n.º 3 (9 de junho de 2021): 665–80. http://dx.doi.org/10.19139/soic-2310-5070-879.
Texto completo da fonteShin, Jaegwang, e Suan Lee. "Robust and Lightweight Deep Learning Model for Industrial Fault Diagnosis in Low-Quality and Noisy Data". Electronics 12, n.º 2 (13 de janeiro de 2023): 409. http://dx.doi.org/10.3390/electronics12020409.
Texto completo da fonteLiu, Xiaoqiong, Guang Li, Jin Li, Xiaohui Zhou, Xianjie Gu, Cong Zhou e Meng Gong. "Self-organizing Competitive Neural Network Based Adaptive Sparse Representation for Magnetotelluric Data Denoising". Journal of Physics: Conference Series 2651, n.º 1 (1 de dezembro de 2023): 012129. http://dx.doi.org/10.1088/1742-6596/2651/1/012129.
Texto completo da fonteKaspirzhny, Anton V., Paul Gogan, Ginette Horcholle-Bossavit e Suzanne Tyč-Dumont. "Neuronal morphology data bases: morphological noise and assesment of data quality". Network: Computation in Neural Systems 13, n.º 3 (janeiro de 2002): 357–80. http://dx.doi.org/10.1088/0954-898x_13_3_307.
Texto completo da fonteTeses / dissertações sobre o assunto "Data quality and noise"
Alkharboush, Nawaf Abdullah H. "A data mining approach to improve the automated quality of data". Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/65641/1/Nawaf%20Abdullah%20H_Alkharboush_Thesis.pdf.
Texto completo da fonteLie, Chin Cheong Patrick. "Iterative algorithms for fast, signal-to-noise ratio insensitive image restoration". Thesis, McGill University, 1987. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=63767.
Texto completo da fonteAl, Jurdi Wissam. "Towards next generation recommender systems through generic data quality". Electronic Thesis or Diss., Bourgogne Franche-Comté, 2024. http://www.theses.fr/2024UBFCD005.
Texto completo da fonteRecommender systems are essential for filtering online information and delivering personalized content, thereby reducing the effort users need to find relevant information. They can be content-based, collaborative, or hybrid, each with a unique recommendation approach. These systems are crucial in various fields, including e-commerce, where they help customers find pertinent products, enhancing user experience and increasing sales. A significant aspect of these systems is the concept of unexpectedness, which involves discovering new and surprising items. This feature, while improving user engagement and experience, is complex and subjective, requiring a deep understanding of serendipitous recommendations for its measurement and optimization. Natural noise, an unpredictable data variation, can influence serendipity in recommender systems. It can introduce diversity and unexpectedness in recommendations, leading to pleasant surprises. However, it can also reduce recommendation relevance, causing user frustration. Therefore, it is crucial to design systems that balance natural noise and serendipity. Inconsistent user information due to natural noise can negatively impact recommender systems, leading to lower-quality recommendations. Current evaluation methods often overlook critical user-oriented factors, making noise detection a challenge. To provide powerful recommendations, it’s important to consider diverse user profiles, eliminate noise in datasets, and effectively present users with relevant content from vast data catalogs. This thesis emphasizes the role of serendipity in enhancing recommender systems and preventing filter bubbles. It proposes serendipity-aware techniques to manage noise, identifies algorithm flaws, suggests a user-centric evaluation method, and proposes a community-based architecture for improved performance. It highlights the need for a system that balances serendipity and considers natural noise and other performance factors. The objectives, experiments, and tests aim to refine recommender systems and offer a versatile assessment approach
Sorensen, Thomas J. "Inverse Scattering Image Quality with Noisy Forward Data". Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2541.pdf.
Texto completo da fonteDemiroglu, Cenk. "Multisensor Segmentation-based Noise Suppression for Intelligibility Improvement in MELP Coders". Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/10455.
Texto completo da fonteCorreia, Fábio Gonçalves. "Quality control of ultra high resolution seismic data acquisition in real-time". Master's thesis, Universidade de Aveiro, 2017. http://hdl.handle.net/10773/22007.
Texto completo da fonteA aquisicção de grandes volumes de dados durante uma campanha sísmica exige, necessariamente, mais tempo para o controlo de qualidade (QC). No entanto, o tempo de QC não pode ser extendido devido a limitações do tempo de operação, tendo de ser feito mais rápido, o que pode comprometer a qualidade. A alternativa, alocar mais pessoas e recursos para QC e melhorar a eficiência, leva a aumentos de custo e à necessidade de maiores embarcações. Além disso, o QC tradicional requer tempo de análise após a aquisição, atrasando a desmobilização da embarcação, aumentando assim os custos da aquisição. A solução proposta passou pelo desenvolvimento de um QC automático em tempo real eficiente, testando a Comparação Espetral e o Atributo Razão Sinal-Ruído - ferramentas desenvolvidas no software SPW, usado para processamento de dados sísmicos. Usando este software foi testada a deteção e identificação de dados de fraca qualidade através das ferramentas de QC automáticas e os seus parâmetros ajustados para incluir pelo menos todos os maus registos encontrados manualmente. Foi também feita a deteção e identificação de vários problemas encontrados durante uma campanha de aquisição, tais como fortes ondulações e respetiva direção, o ruído de esteira provocado pelas hélices da embarcação e consequente Trouser’s Effect e mau funcionamento das fontes ou dos recetores. A deteção antecipada destes problemas pode permitir a sua resolução atempada, não comprometendo a aquisição dos dados. Foram feitos vários relatórios para descrever problemas encontrados durante os testes de versões beta do software SPW e os mesmos reportados à equipa da Parallel Geoscience, que atualizou o software de forma a preencher os requisitos necessários ao bom funcionamento do QC em tempo real. Estas atualizações permitiram o correto mapeamento dos headers dos ficheiros, otimização da velocidade de análise das ferramentas automáticas e correção de erros em processamento dos dados em multi-thread, para evitar atrasos entre o QC em tempo real e a aquisição dos dados, adaptação das ferramentas à leitura de um número variável de assinaturas das fontes, otimização dos limites de memória gráfica e correção de valores anómalos de semelhança espetral. Algumas atualizações foram feitas através da simulação da aquisição de dados na empresa, de forma a efetuar alguns ajustes e posteriormente serem feitos testes numa campanha futura. A parametrização destas ferramentas foi alcançada, assegurando-se assim a correta deteção automática dos vários problemas encontrados durante a campanha de aquisição usada para os testes, o que levará à redução do tempo gasto na fase de QC a bordo e ao aumento da sua eficácia.
The acquisition of larger volumes of seismic data during a survey requires, necessarily, more time for quality control (QC). Despite this, QC cannot be extended due operational time constraints and must be done faster, compromising its efficiency and consequently the data quality. The alternative, to allocate more people and resources for QC to improve efficiency, leads to prohibitive higher costs and larger vessel requirements. Therefore, traditional QC methods for large data require extended standby times after data acquisition, before the vessel can be demobilized, increasing the cost of survey. The solution tested here consisted on the development of an efficient Real- Time QC by testing Spectral Comparison and Signal to Noise Ratio Attribute (tools developed for the SPW seismic processing software). The detection and identification of bad data by the automatic QC tools was made and the parameters adapted to include at least all manual QC flags. Also, the detection and identification of common problems during acquisition, such strong wave motion and its direction, strong propeller’s wash, trouser’s effect and malfunction in sources or receivers were carried out. The premature detection of these problems will allow to solve them soon enough to not compromise the data acquisition. Several problem reports from beta tests of SPW were transmitted to the Parallel Geoscience team, to be used as a reference to update the software and fulfil Real-Time QC requirements. These updates brought the correct mapping of data headers in files, optimization of data analysis speed along with multi-thread processing debug, to assure it will be running fast enough to avoid delays between acquisition and Real-Time QC, software design to read a variable number of source signatures, optimization of graphic memory limits and debugging of anomalous spectral semblance values. Some updates resulted from a data acquisition simulation that was set up in the office, to make some adjustments to be later tested on an upcoming survey. The parameterization of these tools was finally achieved, assuring the correct detection of all major issues found during the survey, what will eventually lead to the reduction of time needed for QC stage on board, as also to the improvement of its efficiency.
Hardwick, Jonathan Robert. "Synthesis of Noise from Flyover Data". Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/50531.
Texto completo da fonteMaster of Science
Durand, Philippe. "Traitement des donnees radar varan et estimation de qualites en geologie, geomorphologie et occupation des sols". Paris 7, 1988. http://www.theses.fr/1988PA077183.
Texto completo da fonteGrillo, Aderibigbe. "Developing a data quality scorecard that measures data quality in a data warehouse". Thesis, Brunel University, 2018. http://bura.brunel.ac.uk/handle/2438/17137.
Texto completo da fonteStone, Ian. "The effect of noise on image quality". Thesis, University of Westminster, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.283456.
Texto completo da fonteLivros sobre o assunto "Data quality and noise"
Laboratories, Wyle, e Langley Research Center, eds. Data quality analysis at the National Transonic Facility. Washington, D.C: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1990.
Encontre o texto completo da fonteUnited States. National Aeronautics and Space Administration. Scientific and Technical Information Division., ed. Electrical noise reduction techniques contributing to improved data quality at the National Transonic Facility. [Washington, DC]: National Aeronautics and Space Administration, Scientific and Technical Information Division, 1988.
Encontre o texto completo da fonteTarpey, Simon. Data quality. [U.K]: NHS Executive, 1996.
Encontre o texto completo da fonteWang, Richard Y. Data quality. Boston: Kluwer Academic Publishers, 2001.
Encontre o texto completo da fonteWang, Richard Y. Data quality. New York: Kluwer Academic Publishers, 2002.
Encontre o texto completo da fonteWillett, Terrence, e Aeron Zentner. Assessing Data Quality. 2455 Teller Road, Thousand Oaks California 91320: SAGE Publications, Inc., 2021. http://dx.doi.org/10.4135/9781071858769.
Texto completo da fonteOtto, Boris, e Hubert Österle. Corporate Data Quality. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-46806-7.
Texto completo da fonteFisher, Peter F., e Michael F. Goodchild. Spatial Data Quality. Editado por Wenzhong Shi. Abingdon, UK: Taylor & Francis, 2002. http://dx.doi.org/10.4324/9780203303245.
Texto completo da fonteWang, Y. Richard. Quality data objects. Cambridge, Mass: Alfred P. Sloan School of Management, Massachusetts Institute of Technology, 1992.
Encontre o texto completo da fonteO'Day, James. Accident data quality. Washington, D.C: National Academy Press, 1993.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Data quality and noise"
Kąkol, Krzysztof, Gražina Korvel e Bożena Kostek. "Improving Objective Speech Quality Indicators in Noise Conditions". In Data Science: New Issues, Challenges and Applications, 199–218. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39250-5_11.
Texto completo da fonteBertrand, Yannis, Rafaël Van Belle, Jochen De Weerdt e Estefanía Serral. "Defining Data Quality Issues in Process Mining with IoT Data". In Lecture Notes in Business Information Processing, 422–34. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27815-0_31.
Texto completo da fonteSchott, Moritz, Adina Zell, Sven Lautenbach, Gencer Sumbul, Michael Schultz, Alexander Zipf e Begüm Demir. "Analyzing and Improving the Quality and Fitness for Purpose of OpenStreetMap as Labels in Remote Sensing Applications". In Volunteered Geographic Information, 21–42. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35374-1_2.
Texto completo da fonteLavandier, Catherine, Roalt Aalmoes, Romain Dedieu, Ferenc Marki, Stephan Großarth, Dirk Schreckenberg, Asma Gharbi e Dimitris Kotzinos. "Towards Innovative Ways to Assess Annoyance". In Aviation Noise Impact Management, 241–64. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-91194-2_10.
Texto completo da fonteZatuchny, Dmitry Alexandrovich, Ruslan Nikolaevich Akinshin, Nina Ivanovna Romancheva, Igor Viktorovich Avtin e Yury Grigorievich Shatrakov. "Quality Enhancement of Data Transmission via Civil Aircraft Communication Systems by Proper Use of Communication Resources". In Noise Resistance Enhancement in Aircraft Navigation and Connected Systems, 109–22. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0630-4_4.
Texto completo da fonteScherer, Andreas, Manhong Dai e Fan Meng. "Impact of Experimental Noise and Annotation Imprecision on Data Quality in Microarray Experiments". In Methods in Molecular Biology, 155–76. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-60327-337-4_10.
Texto completo da fonteJiang, Hongliang, Chaobo Lu, Chunfa Xiong e Mengkun Ran. "Seismic Data Denoising Analysis Based on Monte Carlo Block Theory". In Lecture Notes in Civil Engineering, 339–49. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2532-2_28.
Texto completo da fonteNikonorov, A., A. Kolsanov, M. Petrov, Y. Yuzifovich, E. Prilepin, S. Chaplygin, P. Zelter e K. Bychenkov. "Vessel Segmentation for Noisy CT Data with Quality Measure Based on Single-Point Contrast-to-Noise Ratio". In E-Business and Telecommunications, 490–507. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30222-5_23.
Texto completo da fonteVanlaer, Jef, Pieter Van den Kerkhof, Geert Gins e Jan F. M. Van Impe. "The Influence of Input and Output Measurement Noise on Batch-End Quality Prediction with Partial Least Squares". In Advances in Data Mining. Applications and Theoretical Aspects, 121–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31488-9_11.
Texto completo da fonteVerdonck, Lieven, e Michel Dabas. "Test with ImpulseRadar Raptor GPR array at Gisacum (Vieil-Évreux, France), and comparison with MALÅ MIRA". In Advances in On- and Offshore Archaeological Prospection, 561–70. Kiel: Universitätsverlag Kiel | Kiel University Publishing, 2023. http://dx.doi.org/10.38072/978-3-928794-83-1/p57.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Data quality and noise"
Brown, Clifford, Brenda Henderson e James Bridges. "Data Quality Assurance for Supersonic Jet Noise Measurements". In ASME Turbo Expo 2010: Power for Land, Sea, and Air. ASMEDC, 2010. http://dx.doi.org/10.1115/gt2010-22545.
Texto completo da fonteShah, Sayed Khushal, Zeenat Tariq, Jeehwan Lee e Yugyung Lee. "Real-Time Machine Learning for Air Quality and Environmental Noise Detection". In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9377939.
Texto completo da fonteFrank, Eric C., D. J. Pickering e Chris Raglin. "In-Vehicle Tire Sound Quality Prediction from Tire Noise Data". In SAE 2007 Noise and Vibration Conference and Exhibition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2007. http://dx.doi.org/10.4271/2007-01-2253.
Texto completo da fonteViswanathan, K. "Quality of jet noise data - Issues, implications and needs". In 40th AIAA Aerospace Sciences Meeting & Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2002. http://dx.doi.org/10.2514/6.2002-365.
Texto completo da fonteHerr, Michaela, Roland Ewert e J. Dierke. "Trailing-Edge Noise Data Quality Assessment for CAA Validation". In 16th AIAA/CEAS Aeroacoustics Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2010. http://dx.doi.org/10.2514/6.2010-3877.
Texto completo da fonteThom, Brian, Gabriella Cerrato e Mark Sturgill. "Augmenting Vehicle Production Audit with Objective Data and Sound Quality Metrics to Improve Customer Experience in a Changing Automotive Landscape". In Noise and Vibration Conference & Exhibition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2019. http://dx.doi.org/10.4271/2019-01-1531.
Texto completo da fontePurekar, Dhanesh. "Drive by Noise System and Corresponding Facility Upgrades for Test Efficiency, Data Quality and Customer Satisfaction". In SAE 2011 Noise and Vibration Conference and Exhibition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2011. http://dx.doi.org/10.4271/2011-01-1611.
Texto completo da fonteSimonich, John, Satish Narayanan e Robert Schlinker. "Data Quality and Facility Issues for Model-scale Jet Noise Testing". In 41st Aerospace Sciences Meeting and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2003. http://dx.doi.org/10.2514/6.2003-1057.
Texto completo da fonteAl-Sabbagh, Khaled Walid, Miroslaw Staron, Regina Hebig e Wilhelm Meding. "Improving Data Quality for Regression Test Selection by Reducing Annotation Noise". In 2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). IEEE, 2020. http://dx.doi.org/10.1109/seaa51224.2020.00042.
Texto completo da fonteGhosh, Arindam, Prithviraj Pramanik, Kartick Das Banerjee, Ashutosh Roy, Subrata Nandi e Sujoy Saha. "Analyzing Correlation Between Air and Noise Pollution with Influence on Air Quality Prediction". In 2018 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2018. http://dx.doi.org/10.1109/icdmw.2018.00133.
Texto completo da fonteRelatórios de organizações sobre o assunto "Data quality and noise"
Ichinose, G. A. Source Physics Experiment Data Quality Using Background Noise. Office of Scientific and Technical Information (OSTI), dezembro de 2018. http://dx.doi.org/10.2172/1490946.
Texto completo da fonteChandath, Him, Ing Chhay Por, Yim Raksmey e Diane Archer. Air Pollution and Workers’ Health in Cambodia’s Garment Sector. Stockholm Environment Institute, março de 2023. http://dx.doi.org/10.51414/sei2023.017.
Texto completo da fonteJob, Jacob. Mesa Verde National Park: Acoustic monitoring report. National Park Service, julho de 2021. http://dx.doi.org/10.36967/nrr-2286703.
Texto completo da fonteXiong, Hui, Gaurav Pandey, Michael Steinbach e Vipin Kumar. Enhancing Data Analysis with Noise Removal. Fort Belvoir, VA: Defense Technical Information Center, maio de 2005. http://dx.doi.org/10.21236/ada439494.
Texto completo da fonteSlagley, Jeremy M., e Steven E. Guffey. A Better Noise Compliance Method and Validation of Mine Noise Dosimetry Data. Fort Belvoir, VA: Defense Technical Information Center, junho de 2005. http://dx.doi.org/10.21236/ada434225.
Texto completo da fonteMellors, R. Preliminary noise survey and data report of Saudi Arabian data. Office of Scientific and Technical Information (OSTI), agosto de 1997. http://dx.doi.org/10.2172/641096.
Texto completo da fonteDEFENSE LOGISTICS AGENCY ALEXANDRIA VA. Data Quality Engineering Handbook. Fort Belvoir, VA: Defense Technical Information Center, junho de 1994. http://dx.doi.org/10.21236/ada315573.
Texto completo da fonteIchinose, G. Waveform Data Quality Assessment. Office of Scientific and Technical Information (OSTI), abril de 2022. http://dx.doi.org/10.2172/1863669.
Texto completo da fonteSasaki, Masaru, e Kazuhiro Nakashima. Sound Quality Evaluation Method in Time Domain for Diesel Engine Noise. Warrendale, PA: SAE International, maio de 2005. http://dx.doi.org/10.4271/2005-08-0026.
Texto completo da fonteCanavan, G. H. Example of scattering noise in radar data interpretation. Office of Scientific and Technical Information (OSTI), outubro de 1996. http://dx.doi.org/10.2172/434321.
Texto completo da fonte