Academic literature on the topic 'Guided sampling'
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Journal articles on the topic "Guided sampling"
Koch, Thomas, and Michael Wimmer. "Guided Visibility Sampling++." Proceedings of the ACM on Computer Graphics and Interactive Techniques 4, no. 1 (April 26, 2021): 1–16. http://dx.doi.org/10.1145/3451266.
Full textWonka, Peter, Michael Wimmer, Kaichi Zhou, Stefan Maierhofer, Gerd Hesina, and Alexander Reshetov. "Guided visibility sampling." ACM Transactions on Graphics 25, no. 3 (July 2006): 494–502. http://dx.doi.org/10.1145/1141911.1141914.
Full textZhou, Ting, and Amedeo Caflisch. "Free Energy Guided Sampling." Journal of Chemical Theory and Computation 8, no. 6 (May 4, 2012): 2134–40. http://dx.doi.org/10.1021/ct300147t.
Full textZhou, Ting, and Amedeo Caflisch. "Free Energy Guided Sampling." Journal of Chemical Theory and Computation 8, no. 9 (August 13, 2012): 3423. http://dx.doi.org/10.1021/ct300670n.
Full textKumar, Suhansanu, and Hari Sundaram. "Attribute-Guided Network Sampling Mechanisms." ACM Transactions on Knowledge Discovery from Data 15, no. 4 (June 2021): 1–24. http://dx.doi.org/10.1145/3441445.
Full textWaxman, Irving, and ChristopherG Chapman. "EUS-guided portal vein sampling." Endoscopic Ultrasound 7, no. 4 (2018): 240. http://dx.doi.org/10.4103/eus.eus_28_18.
Full textMenton, M., and E. Wiest. "Probe-Guided Chorionic Villus Sampling." Gynecologic and Obstetric Investigation 35, no. 3 (1993): 143–45. http://dx.doi.org/10.1159/000292685.
Full textYousuf, Muhammad Irfan, and Suhyun Kim. "Guided sampling for large graphs." Data Mining and Knowledge Discovery 34, no. 4 (March 18, 2020): 905–48. http://dx.doi.org/10.1007/s10618-020-00683-y.
Full textMorrison, Kenny. "Guided Sampling Using Mobile Electronic Diaries." International Journal of Mobile Human Computer Interaction 4, no. 1 (January 2012): 1–24. http://dx.doi.org/10.4018/jmhci.2012010101.
Full textHuang, Guoquan. "Particle filtering with analytically guided sampling." Advanced Robotics 31, no. 17 (September 2, 2017): 932–45. http://dx.doi.org/10.1080/01691864.2017.1378592.
Full textDissertations / Theses on the topic "Guided sampling"
Jun, Jaeyoon James. "Memory-guided Sensory Sampling During Self-guided Exploration in Pulse-type Electric Fish." Thesis, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/31496.
Full textMorrison, Kenneth. "Guided real time sampling using mobile electronic diaries." Thesis, University of Dundee, 2010. https://discovery.dundee.ac.uk/en/studentTheses/fdd4d015-d351-45db-9e9a-e193dcf02a7e.
Full textLe, Floch Brian (Brian Henri). "Sampling-based path planner for guided airdrop in urban environments." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112467.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (pages 79-81).
Aerial resupply can deliver cargo to locations across the globe. A challenge for modern guided parafoil systems is to land accurately in complex terrain, including canyons and cities. This thesis presents the Rewire-RRT algorithm for parafoil terminal guidance. The algorithm uses Rapidly-Exploring Random Trees (RRT) to efficiently search for feasible paths through complex environments. Most importantly, Rewire-RRT provides a mechanism to build and rewire the tree to explicitly minimize the risk of collision with obstacles along each path and to minimize the expected final miss distance from the target. This key adaptation allows for parafoil guidance in urban drop zones not previously considered for airdrop operations. The Rewire-RRT algorithm is first developed and tested in two dimensions and demonstrated to have greater performance than RRT for simple dynamical systems, finding paths that are shorter and safer than those found by RRT. Then, Rewire-RRT is shown to be an effective path planner for a guided parafoil with complex dynamics. Paths planned by Rewire-RRT better meet the performance objectives of guided parafoils than those planned by RRT. Finally, simulation results show that Rewire-RRT performs better than state-of- the-art terminal guidance strategies for guided parafoils when the target location is cluttered with multiple three-dimensional obstacles.
by Brian Le Floch.
S.M.
Walworth, James, Andrew Pond, and Michael W. Kilby. "Leaf Sampling Guide with Interpretation and Evaluation for Arizona Pecan Orchards." College of Agriculture and Life Sciences, University of Arizona (Tucson, AZ), 2006. http://hdl.handle.net/10150/146970.
Full textWalworth, James L., Andrew P. Pond, and Michael W. Kilby. "Leaf Sampling Guide with Interpretation and Evaluation for Arizona Pecan Orchards." College of Agriculture and Life Sciences, University of Arizona (Tucson, AZ), 2011. http://hdl.handle.net/10150/239608.
Full textRecoquillay, Arnaud. "Méthodes d'échantillonnage appliquées à l'imagerie de défauts dans un guide d'ondes élastiques." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLY001/document.
Full textWidely used structures in an industrial context, such as plates, pipes or rails, can be considered as waveguides. Hence efficient Non Destructive Testing techniques are needed in order to detect defects in these structure during their maintenance. This work is about adapting a sampling method, the Linear Sampling Method, to the context of NDT for elastic waveguides. This context implies that the sollicitations and measurements must be on the surface of the waveguide in a time-dependent regime. A modal and multi-frequency formulation of the LSM, specific to waveguides, has been chosen to solve the problem. This formulation allows an efficient and physical regularization of the inverse problem, which is naturally ill-posed. An optimization of the number of sources and measurements and of their positioning is possible thanks to the methodology used to solve the problem. The scalar case of an acoustic waveguide is considered as a first step, while the vectorial case of an elastic waveguide, more complex by nature, is addressed in a second time.The efficiency of the method is at first tested on artificial data (numerically made), and then on real data obtained from experiments on metallic plates. These experiments show the feasibility of using sampling methods for Non Destructive Testing in an industrial context. In the case when only one sollicitation is available, the LSM can not be applied. A completely different approach is then used, which is called the ``exterior'' approach, coupling a mixed formulation of quasi-reversibility and a level-set method in order to recover the shape of the defect
Siegmund, Florian. "Dynamic Resampling for Preference-based Evolutionary Multi-objective Optimization of Stochastic Systems : Improving the efficiency of time-constrained optimization." Doctoral thesis, Högskolan i Skövde, Institutionen för ingenjörsvetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-13088.
Full textVid preferensbaserad evolutionär flermålsoptimering försöker beslutsfattaren hitta lösningar som är fokuserade kring ett valt preferensområde i målrymden och som ligger så nära den optimala Pareto-fronten som möjligt. Eftersom lösningar utanför preferensområdet anses som mindre intressanta, eller till och med oviktiga, kan optimeringen fokusera på den intressanta delen av målrymden och hitta relevanta lösningar snabbare, vilket betyder att färre lösningar behöver utvärderas. Detta är en stor fördel vid simuleringsbaserad flermålsoptimering med långa simuleringstider eftersom antalet olika konfigurationer som kan simuleras och utvärderas är mycket begränsat. Även tidigare studier som använt fokuserad flermålsoptimering styrd av användarpreferenser, t.ex. med algoritmen R-NSGA-II, har visat positiva resultat men enbart få av dessa har tagit hänsyn till det stokastiska beteendet hos de simulerade systemen. I litteraturen kallas optimering med stokastiska utvärderingsfunktioner ibland "noisy optimization". Om en optimeringsalgoritm inte tar hänsyn till att de utvärderade målvärdena är stokastiska kommer prestandan vara lägre jämfört med om optimeringsalgoritmen har tillgång till de verkliga målvärdena. Statisk upprepad utvärdering av lösningar med syftet att reducera osäkerheten hos alla evaluerade lösningar hjälper optimeringsalgoritmer att undvika problemet, men leder samtidigt till en betydande ökning av antalet nödvändiga simuleringar och därigenom en ökning av optimeringstiden. Detta är problematiskt eftersom det innebär att många simuleringar utförs i onödan på undermåliga lösningar, där exakta målvärden inte bidrar till att förbättra optimeringens resultat. Upprepad utvärdering reducerar ovissheten och hjälper till att förbättra optimeringen, men har också ett pris. Om flera simuleringar används för varje lösning så minskar antalet olika lösningar som kan simuleras och sökrymden kan inte utforskas lika mycket, givet att det totala antalet simuleringar är begränsat. Dynamisk upprepad utvärdering kan däremot effektivisera flermålsoptimeringens avvägning mellan utforskning och exploatering av sökrymden baserat på det faktum att den nödvändiga precisionen i målvärdena varierar mellan de olika lösningarna i målrymden. I en tät och konvergerad population av lösningar är det viktigt att känna till de exakta målvärdena, medan osäkra målvärden är mindre skadliga i ett tidigt stadium i optimeringsprocessen när algoritmen utforskar målrymden. En dynamisk strategi för upprepad utvärdering med en noggrann allokering av utvärderingarna kan därför uppnå bättre resultat än en allokering som är statisk. Trots att finns ett rikligt antal studier inom simuleringsbaserad optimering som använder sig av dynamisk upprepad utvärdering så har inga relaterade studier hittats som undersöker hur kombinationer av dynamisk upprepad utvärdering och preferensbaserad styrning kan förbättra prestandan hos algoritmer för flermålsoptimering ytterligare. Speciell avsaknad finns det av studier om optimering av problem med långa simuleringstider, som t.ex. simulering av produktionssystem. Avhandlingens mål är därför att studera, konstruera och jämföra nya kombinationer av preferensbaserade optimeringsalgoritmer och dynamiska strategier för upprepad utvärdering. Syftet är att förbättra resultatet av simuleringsbaserad flermålsoptimering som har stokastiska målvärden när antalet utvärderingar eller optimeringstiden är begränsade. Avhandlingen har speciellt fokuserat på att undersöka prestandahöjande åtgärder hos algoritmen R-NSGA-II i kombination med dynamisk upprepad utvärdering, baserad på fördelarna och flexibiliteten som interaktiva referenspunktbaserade algoritmer erbjuder. Exempel på förbättringsåtgärder är dynamiska algoritmer för upprepad utvärdering med förbättrad statistisk osäkerhetshantering och adaptiva optimeringsparametrar. Resultaten från avhandlingen visar tydligt att optimeringsresultaten kan förbättras om hybrida dynamiska algoritmer för upprepad utvärdering används och adaptiva optimeringsparametrar väljs beroende på osäkerhetsnivån och komplexiteten i optimeringsproblemet. För de fall där simuleringstiden är begränsad är slutsatsen från avhandlingen att både användarpreferenser och dynamisk upprepad utvärdering bör användas samtidigt för att uppnå de bästa resultaten i simuleringsbaserad flermålsoptimering.
Wu, Yu-Ting, and 吳昱霆. "Visibility-Guided Importance Sampling." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/66706952481930533425.
Full text國立交通大學
多媒體工程研究所
97
We propose a novel sampling algorithm by considering the importance of visibility in the sampling process. This algorithm extends the bidirectional importance sampling techniques based on SRBF representation by adjusting the weight of each SRBF basis according to the previous history in visibility tests, thus combing the visibility term into importance function. Unlike previous visibility-related researches in importance sampling exploit image-space visibility coherence, we consider visibility in object space by avoiding redrawing samples in invisible directions. Consequently more samples pass the visibility test and contribute to the final rendered result. Considering visibility in object space would make our algorithm more flexible, even for scenes which have heavy occlusion. Our approach successfully reduces the variance over the entire image, not only along the shadow boundaries. Under the same computing performance, we can obtain higher quality than previous bidirectional importance approaches. Although our proposed algorithm is based on the SRBF representation, it can also be applied to other basis such as wavelet or spherical harmonics.
Chang, Shu-Yu, and 張書瑜. "RD Guided Adaptive Sampling for Transmission Reduction on WSNs." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/33461813136754887354.
Full text國立清華大學
資訊工程學系
100
Wireless Sensor Networks (WSNs) have been widely applied to many different areas such as surveillance, healthcare, environmental and utility monitoring, etc. In WSNs, each sensor node has the characteristics of small size, limited power, and connected wirelessly. It is responsible for gathering and delivering sensing data over the network periodically. Thus, the energy consumption problem becomes a challenging issue to prolong the lifetime of WSNs. Several research works utilize data aggregation and/or data compression concept to reduce the quantity of necessary transmission, since it is the primary issue that consumes sensors’ power particularly. However, the implementation of these operations requires high computational power. In this thesis, two approaches adapting to sensing data distribution to largely reduce the amount of required data transmission with limited computation are proposed. They are: Adaptive Sampling with RD Model and Adaptive Sampling in Dynamic Mode. In the first approach, the target distortion is near-optimally distributed (in the rate-distortion sense) to every sensor node corresponding to their relative fluctuation. In the latter one, the possible occurrence of rapid data change in the sensing period is concerned and deliberately manipulated. To combine these two methods, we verify the data trend of each sensor when the prediction function needs to be updated. Then according to the data trend we can decide whether to use Adaptive Sampling with RD Model or Adaptive Sampling in Dynamic Mode. Finally, several real sensed data were gathered and employed to demonstrate the performance of the proposed methods.
Burns, Brendan. "Exploiting structure: A guided approach to sampling-based robot motion planning." 2007. https://scholarworks.umass.edu/dissertations/AAI3275736.
Full textBooks on the topic "Guided sampling"
Baker, Tomas W. What's next?: A guide to veterinary ultrasound of the eye, neck, and shoulder and guided sampling techniques. Lakewood, Colo: American Animal Hospital Association Press, 2012.
Find full textMcGuire, Sam. Audio sampling: A practical guide. Amsterdam: Focal Press, 2008.
Find full text1941-, Carmichael D. R., and Whittington Ray 1948-, eds. Practitioner's guide to audit sampling. New York: J. Wiley, 1998.
Find full textCanada. Statistics Canada. Social Survey Methods Division. Survey sampling: a non-mathematical guide. Ottawa: Statistics Canada, 1993.
Find full textSatin, A. Survey sampling: A non-mathematical guide. 2nd ed. [Ottawa]: Minister of Industry Science and Technology, 1993.
Find full textMassachusetts. Dept. of Environmental Protection. Office of Research and Standards. Indoor air sampling and evaluation guide. Boston, MA: Office of Research and Standards, Dept. of Environmental Protection, 2002.
Find full textBarth, Delbert S. Soil sampling quality assurance user's guide. 2nd ed. Las Vegas, Nev: Environmental Monitoring Systems Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, 1989.
Find full textW, Shastry, ed. Survey sampling: A non-mathematical guide. Ottawa: Statistics Canada, 1985.
Find full textLightfoot, Peter C. An introductory guide to sampling for geoanalysis. Toronto, Ont: Ministry of Northern Development and Mines, 1991.
Find full textHann, David. Sampling Kansas: A guide to the curious. [Lawrence, Kansas] (1640 New Hampshire, Lawrence 66044): D. Hann, 1990.
Find full textBook chapters on the topic "Guided sampling"
Iglesias-Garcia, Julio, and Jose Lariño-Noia. "EUS-Guided Pancreatic Sampling." In Gastrointestinal and Pancreatico-Biliary Diseases: Advanced Diagnostic and Therapeutic Endoscopy, 1–21. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-29964-4_105-1.
Full textIglesias-Garcia, Julio, and Jose Lariño-Noia. "EUS-Guided Pancreatic Sampling." In Gastrointestinal and Pancreatico-Biliary Diseases: Advanced Diagnostic and Therapeutic Endoscopy, 1799–819. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-56993-8_105.
Full textAbi Fadel, Sandra. "Adrenal Venous Sampling." In Procedural Dictations in Image-Guided Intervention, 595–97. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40845-3_129.
Full textAbi Fadel, Sandra. "Parathyroid Venous Sampling." In Procedural Dictations in Image-Guided Intervention, 603–5. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40845-3_131.
Full textLee, Tae Hee. "EUS-Guided Sampling for Subepithelial Tumors." In Therapeutic Gastrointestinal Endoscopy, 379–93. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-1184-0_23.
Full textLee, Tae Hee. "EUS-Guided Sampling for Subepithelial Tumors." In Therapeutic Gastrointestinal Endoscopy, 463–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-55071-3_21.
Full textAbi Fadel, Sandra. "Inferior Petrosal Vein Sampling." In Procedural Dictations in Image-Guided Intervention, 599–602. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40845-3_130.
Full textAbi Fadel, Sandra. "Renal Vein Renin Sampling." In Procedural Dictations in Image-Guided Intervention, 607–9. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40845-3_132.
Full textTordoff, Ben, and David W. Murray. "Guided Sampling and Consensus for Motion Estimation." In Computer Vision — ECCV 2002, 82–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-47969-4_6.
Full textPenedo, Francisco, Cristian-Ioan Vasile, and Calin Belta. "Language-Guided Sampling-based Planning using Temporal Relaxation." In Springer Proceedings in Advanced Robotics, 128–43. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43089-4_9.
Full textConference papers on the topic "Guided sampling"
Wonka, Peter, Michael Wimmer, Kaichi Zhou, Stefan Maierhofer, Gerd Hesina, and Alexander Reshetov. "Guided visibility sampling." In ACM SIGGRAPH 2006 Papers. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1179352.1141914.
Full textFan, Renjie, Zhiwen Yu, Bin Guo, Liang Wang, and Dingqi Yang. "Target Distribution Guided Network Sampling." In 2017 Fifth International Conference on Advanced Cloud and Big Data (CBD). IEEE, 2017. http://dx.doi.org/10.1109/cbd.2017.71.
Full textKabierski, Martin, Hoang Lam Nguyen, Lars Grunske, and Matthias Weidlich. "Sampling What Matters: Relevance-guided Sampling of Event Logs." In 2021 3rd International Conference on Process Mining (ICPM). IEEE, 2021. http://dx.doi.org/10.1109/icpm53251.2021.9576875.
Full textFragoso, Victor, and Matthew Turk. "SWIGS: A Swift Guided Sampling Method." In 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2013. http://dx.doi.org/10.1109/cvpr.2013.357.
Full textTiwari, Lokender, and Saket Anand. "DGSAC: Density Guided Sampling and Consensus." In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2018. http://dx.doi.org/10.1109/wacv.2018.00112.
Full textKnyazev, Andrew, Hassan Mansour, Dong Tian, and Akshay Gadde. "A brief theory of guided signal reconstruction." In 2017 International Conference on Sampling Theory and Applications (SampTA). IEEE, 2017. http://dx.doi.org/10.1109/sampta.2017.8024371.
Full textBacklund, Peter B., and John P. Eddy. "Autonomous Microgrid Design Using Classifier-Guided Sampling." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-46107.
Full textDutra, Rafael, Jonathan Bachrach, and Koushik Sen. "GUIDEDSAMPLER: Coverage-guided Sampling of SMT Solutions." In 2019 Formal Methods in Computer Aided Design (FMCAD). IEEE, 2019. http://dx.doi.org/10.23919/fmcad.2019.8894251.
Full textMoore, R. O., G. Biondini, and W. L. Kath. "Importance sampling for noise-induced amplitude and timing jitter in soliton transmission systems." In Nonlinear Guided Waves and Their Applications. Washington, D.C.: OSA, 2002. http://dx.doi.org/10.1364/nlgw.2002.nlma6.
Full textBajer, Lukáš, and Martin Holeňa. "Model Guided Sampling Optimization for Low-dimensional Problems." In International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005222404510456.
Full textReports on the topic "Guided sampling"
Backlund, Peter B., and John P. Eddy. Classifier-Guided Sampling for Complex Energy System Optimization. Office of Scientific and Technical Information (OSTI), September 2015. http://dx.doi.org/10.2172/1221709.
Full textHeline, Tiffany R. Laboratory Sampling Guide. Fort Belvoir, VA: Defense Technical Information Center, May 2012. http://dx.doi.org/10.21236/ada563615.
Full textChu, Xuehao. Customized Sampling Plans: A Guide to Alternative Sampling Techniques for National Transit Database (NTD) Reporting. Tampa, FL: University of South Florida, May 2004. http://dx.doi.org/10.5038/cutr-nctr-rr-2003-03.
Full textLewis, Jack, and Rand Eads. Implementation guide for turbidity threshold sampling: principles, procedures, and analysis. Albany, CA: U.S. Department of Agriculture, Forest Service, Pacific Southwest Research Station, 2009. http://dx.doi.org/10.2737/psw-gtr-212.
Full textPrichard, Susan J., Anne G. Andreu, Roger D. Ottmar, and Ellen Eberhardt. Fuel Characteristic Classification System (FCCS) field sampling and fuelbed development guide. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, 2019. http://dx.doi.org/10.2737/pnw-gtr-972.
Full textPrichard, Susan J., Anne G. Andreu, Roger D. Ottmar, and Ellen Eberhardt. Fuel Characteristic Classification System (FCCS) field sampling and fuelbed development guide. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, 2019. http://dx.doi.org/10.2737/pnw-gtr-972.
Full textHogue, M., D. Hadlock, M. Thompson, and E. Farfan. GUIDE TO CALCULATING TRANSPORT EFFICIENCY OF AEROSOLS IN OCCUPATIONAL AIR SAMPLING SYSTEMS. Office of Scientific and Technical Information (OSTI), November 2013. http://dx.doi.org/10.2172/1107896.
Full textWyss, G. D., and K. H. Jorgensen. A user`s guide to LHS: Sandia`s Latin Hypercube Sampling Software. Office of Scientific and Technical Information (OSTI), February 1998. http://dx.doi.org/10.2172/573301.
Full textQuigley, J. T. Tank farms solid waste characterization guide with sampling and analysis plan attachment. Office of Scientific and Technical Information (OSTI), April 1997. http://dx.doi.org/10.2172/16864.
Full textShort, Mary, and Sherry Leis. Vegetation monitoring in the Manley Woods unit at Wilson’s Creek National Battlefield: 1998–2020. Edited by Tani Hubbard. National Park Service, June 2022. http://dx.doi.org/10.36967/nrr-2293615.
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