Готові списки джерел за темами / Guided sampling

Добірка наукової літератури з теми "Guided sampling"

Автор: Grafiati
Опубліковано: 10 грудня 2022
Оновлено: 28 січня 2023

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Статті в журналах з теми "Guided sampling"

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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.

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Анотація:
Visibility computation is a common problem in the field of computer graphics. Examples include occlusion culling, where parts of the scene are culled away, or global illumination simulations, which are based on the mutual visibility of pairs of points to calculate lighting. In this paper, an aggressive from-region visibility technique called Guided Visibility Sampling++ (GVS++) is presented. The proposed technique improves the Guided Visibility Sampling algorithm through improved sampling strategies, thus achieving low error rates on various scenes, and being over four orders of magnitude faster than the original CPU-based Guided Visibility Sampling implementation. We present sampling strategies that adaptively compute sample locations and use ray casting to determine a set of triangles visible from a flat or volumetric rectangular region in space. This set is called a potentially visible set (PVS). Based on initial random sampling, subsequent exploration phases progressively grow an intermediate solution. A termination criterion is used to terminate the PVS search. A modern implementation using the Vulkan graphics API and RTX ray tracing is discussed. Furthermore, we show optimizations that allow for an implementation that is over 20 times faster than a naive implementation.
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Wonka, 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.

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Zhou, 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.

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Zhou, 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.

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Kumar, 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.

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This article introduces a novel task-independent sampler for attributed networks. The problem is important because while data mining tasks on network content are common, sampling on internet-scale networks is costly. Link-trace samplers such as Snowball sampling, Forest Fire, Random Walk, and Metropolis–Hastings Random Walk are widely used for sampling from networks. The design of these attribute-agnostic samplers focuses on preserving salient properties of network structure, and are not optimized for tasks on node content. This article has three contributions. First, we propose a task-independent, attribute aware link-trace sampler grounded in Information Theory. Our sampler greedily adds to the sample the node with the most informative (i.e., surprising) neighborhood. The sampler tends to rapidly explore the attribute space, maximally reducing the surprise of unseen nodes. Second, we prove that content sampling is an NP-hard problem. A well-known algorithm best approximates the optimization solution within 1 − 1/ e , but requires full access to the entire graph. Third, we show through empirical counterfactual analysis that in many real-world datasets, network structure does not hinder the performance of surprise based link-trace samplers. Experimental results over 18 real-world datasets reveal: surprise-based samplers are sample efficient and outperform the state-of-the-art attribute-agnostic samplers by a wide margin (e.g., 45% performance improvement in clustering tasks).
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Waxman, 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.

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Menton, 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.

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Yousuf, 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.

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Morrison, 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.

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Анотація:
Pocket Interview is an easily configurable handheld electronic data collection and diary tool. The Pocket Interview system can be used to apply experience sampling methods that allow the collection of data in real-time and in the user’s natural environment. The system client is usually run on a personal digital assistant or smartphone. It can prompt the user to make diary entries at fixed and/or random intervals and includes an option that allows this sampling to be ‘guided’ whereby inconvenient prompts are temporarily deferred until a more convenient time through the use of contextual audio information. Subjects participating in real-time studies require high levels of commitment and exhibit difficulties maintaining their motivation. This paper describes a series of studies using Pocket Interview that explore how Guiding offers to reduce the perceived burden on study participants, improve response rates and increase the quantity and quality of replies.
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Huang, 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.

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Більше джерел

Дисертації з теми "Guided sampling"

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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.

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Animals must sense their surroundings to update their internal representations of the external environment, and exploratory behaviours such as sensory sampling are influenced by past experiences. This thesis investigates how voluntary sensory sampling activities undergo learning-dependent changes. Studies of freely behaving animals impose two major challenges: 1) the accuracy of biological measurements is compromised by movement-induced artifacts, and 2) large degrees of freedom in unrestrained behaviours confound well-controlled studies. Pulse-type weakly electric fish (WEF) are an ideal choice to study adaptive sensory sampling from unrestrained animals, since they generate readily observable and quantifiable sensory capture events expressed by discrete pulses of electric organ discharges (EODs). To study the voluntarily movements and sensory sampling while animals navigated in darkness, we developed three novel experimental techniques to track movements and detect sensory sampling from a freely behaving WEF: 1) an EOD detector to remotely and accurately measure the sensory sampling rate, 2) an electrical tracking method to track multiple WEF using their own EODs, and 3) visual tracking algorithm for robust body tracking through water under infrared illumination. These techniques were successfully applied to reveal novel sensory sampling behaviours in freely exploring Gymnotus sp. Cortical activity precedes self-initiated movements by several seconds in mammals; this observation has led into inquiries on the nature of volition. Here we demonstrate the sensory sampling enhancement also precedes self-initiated movement by a few seconds in Gymnotus sp. Next, we tested whether these animals can be trained to learn a location of food using electrically detectable landmarks and, if so, whether they can use their past experiences to optimize their sensory sampling. We found that animals revisited the missing food location with high spatial accuracy, and they intensified their sensory sampling near the expected food location by increasing the number of EOD pulses per unit distance travelled.
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Morrison, 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.

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This thesis describes the motivation and development of Pocket Interview, an easily configurable handheld electronic data collection and diary tool. The system can be used to apply ‘experience sampling’ methods that allow the collection of data in real-time and in the user’s natural environment. Pocket Interview can prompt the user to make diary entries at fixed and/or random intervals. The system is configured via graphical user interfaces that are shown to be easily usable by non-computing users. Pocket Interview includes an option that allows this sampling to be ‘Guided’ whereby inconvenient prompts are temporarily deferred until a more convenient time through the use of contextual audio information. Subjects participating in real-time studies require high levels of commitment and exhibit difficulties maintaining their motivation. Guiding can offer to reduce the perceived burden on the user, improve response rates, increase the quantity of replies and the quality of those replies. This thesis describes a series of studies that investigate the following: • What are the more convenient times for sampling and how can they be detected? • Does Guided real-time sampling improve the data quality and participant compliance rates? • Participants attitudes towards mobile devices automatically gathering their context information. Guiding is a strategy that could be applied to all context-aware computing, phone call or message delivery and indeed all other prompting. As computing power continues to expand and more powerful mobile devices become available we will see an increase in the quantity and sophistication of applications that interrupt their users. This will add to user’s feelings of overload. To maximise user acceptability designers of computing systems require strategies, such as Guiding, to minimise the interruptions caused by proactive prompting.
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Le, 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.

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Анотація:
Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2017.
Cataloged 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.
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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.

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Walworth, 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.

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6

Recoquillay, 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.

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Анотація:
De nombreuses structures utilisées industriellement peuvent être considérées comme des guides d'ondes, comme les plaques, les tuyaux ou encore le rails. La maintenance de ces structures nécessite de pouvoir détecter efficacement des défauts internes par le Contrôle Non Destructif. Nous nous intéressons dans ce manuscrit à l'application d'une méthode d'échantillonnage, la Linear Sampling Method, au CND des guides d'ondes élastiques, qui en particulier impose des sollicitations et des mesures à la surface du guide en régime temporel. La stratégie choisie repose sur une formulation modale et multi-fréquentielle de la LSM, spécifique aux guides d'ondes, qui permet une régularisation efficace et de nature physique du problème inverse, qui est par nature mal posé. Cette stratégie permet par ailleurs une optimisation du nombre et de la position des émetteurs et des récepteurs. Nous nous limitons dans un premier temps au cas scalaire du guide d'ondes acoustiques, pour ensuite s'attaquer au cas vectoriel, et par conséquent plus complexe, du guide d'ondes élastiques.L'efficacité de la méthode inverse est dans un premier temps démontrée sur des données artificielles (obtenues numériquement), puis sur des données réelles obtenues à l'aide d'expériences réalisées sur des plaques métalliques. Ces expériences confirment la faisabilité du CND par méthode d'échantillonnage dans un cadre industriel. Dans le cas où une seule sollicitation est réalisée, l'utilisation de la LSM est exclu. Nous utilisons une approche tout à fait différente et dite "extérieure", couplant une formulation mixte de quasi-réversibilité et une méthode de lignes de niveau, pour reconstruire le défaut
Widely 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
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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.

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Анотація:
In preference-based Evolutionary Multi-objective Optimization (EMO), the decision maker is looking for a diverse, but locally focused non-dominated front in a preferred area of the objective space, as close as possible to the true Pareto-front. Since solutions found outside the area of interest are considered less important or even irrelevant, the optimization can focus its efforts on the preferred area and find the solutions that the decision maker is looking for more quickly, i.e., with fewer simulation runs. This is particularly important if the available time for optimization is limited, as is the case in many real-world applications. Although previous studies in using this kind of guided-search with preference information, for example, withthe R-NSGA-II algorithm, have shown positive results, only very few of them considered the stochastic outputs of simulated systems. In the literature, this phenomenon of stochastic evaluation functions is sometimes called noisy optimization. If an EMO algorithm is run without any countermeasure to noisy evaluation functions, the performance will deteriorate, compared to the case if the true mean objective values are known. While, in general, static resampling of solutions to reduce the uncertainty of all evaluated design solutions can allow EMO algorithms to avoid this problem, it will significantly increase the required simulation time/budget, as many samples will be wasted on candidate solutions which are inferior. In comparison, a Dynamic Resampling (DR) strategy can allow the exploration and exploitation trade-off to be optimized, since the required accuracy about objective values varies between solutions. In a dense, converged population, itis important to know the accurate objective values, whereas noisy objective values are less harmful when an algorithm is exploring the objective space, especially early in the optimization process. Therefore, a well-designed Dynamic Resampling strategy which resamples the solution carefully, according to the resampling need, can help an EMO algorithm achieve better results than a static resampling allocation. While there are abundant studies in Simulation-based Optimization that considered Dynamic Resampling, the survey done in this study has found that there is no related work that considered how combinations of Dynamic Resampling and preference-based guided search can further enhance the performance of EMO algorithms, especially if the problems under study involve computationally expensive evaluations, like production systems simulation. The aim of this thesis is therefore to study, design and then to compare new combinations of preference-based EMO algorithms with various DR strategies, in order to improve the solution quality found by simulation-based multi-objective optimization with stochastic outputs, under a limited function evaluation or simulation budget. Specifically, based on the advantages and flexibility offered by interactive, reference point-based approaches, studies of the performance enhancements of R-NSGA-II when augmented with various DR strategies, with increasing degrees of statistical sophistication, as well as several adaptive features in terms of optimization parameters, have been made. The research results have clearly shown that optimization results can be improved, if a hybrid DR strategy is used and adaptive algorithm parameters are chosen according to the noise level and problem complexity. In the case of a limited simulation budget, the results allow the conclusions that both decision maker preferences and DR should be used at the same time to achieve the best results in simulation-based multi-objective optimization.
Vid 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.
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Wu, Yu-Ting, and 吳昱霆. "Visibility-Guided Importance Sampling." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/66706952481930533425.

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Анотація:
碩士
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.
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9

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.

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Анотація:
碩士
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.
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Burns, Brendan. "Exploiting structure: A guided approach to sampling-based robot motion planning." 2007. https://scholarworks.umass.edu/dissertations/AAI3275736.

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Robots already impact the way we understand our world and live our lives. However, their impact and use is limited by the skills they possess. Currently deployed autonomous robots lack the manipulation skills possessed by humans. To achieve general autonomy and applicability in the real world, robots must possess such skills. Autonomous manipulation requires algorithms that rapidly and reliably compute collision-free motion for robotic limbs with many degrees of freedom. Unfortunately, adequate algorithms for this task do not currently exist. Though there are many dimensions of the real-world planning task that require further research. A central problem of reliable real-world planning is that planners must rely on incomplete and inaccurate information about the world in which they are planning. The motion planning problem has exponential complexity in the robot's degrees of freedom. Consequently, the most successful planning algorithms use incomplete information obtained via sampling a subset of all possible movements. Additionally, real-world robots generally obtain information about the state of their environment through lasers, cameras and other sensors. The information obtained from these sensors contains noise and error. Thus the planner's incomplete information about the world is possibly inaccurate as well. Despite such limited information, a planner must be capable of quickly generating collision free motions to facilitate general purpose autonomous robots. This thesis proposes a new utility-guided framework for motion planning that can reliably compute collision-free motions with the efficiency required for real-world planning. The utility-guided approach begins with the observation there is regularity in space of possible motions available to a robot. Further, certain motions are more crucial than others for computing collision free paths. Together these observations form structure in the robot's space of possible movements. This structure provides a guide for the planner's exploration of possible motions. Because a complete understanding of this structure is computationally intractable, the utility-guided framework incrementally develops an approximate model discovered by past exploration. This model of the structure is used to select explorations that maximally benefit the planner. Information provided by each exploration improves the planner's approximation. The process of incremental improvement and further guided exploration iterates until an adequate model of configuration space is constructed. Discovering and exploiting structure in a robot's configuration space enables a utility-guided planner to achieve the performance and reliability required by real-world motion planning. This thesis describes applications of the utility-guided motion-planning framework to multi-query sampling-based roadmap and random-tree motion planning. Additionally, the utility-guided framework is extended to develop a planner that can successfully plan despite inaccuracies in its perception of the environment and to guide further sensing to reduce uncertainty and maximally improve the utility of the path.
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Книги з теми "Guided sampling"

1

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.

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2

McGuire, Sam. Audio sampling: A practical guide. Amsterdam: Focal Press, 2008.

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3

1941-, Carmichael D. R., and Whittington Ray 1948-, eds. Practitioner's guide to audit sampling. New York: J. Wiley, 1998.

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4

Canada. Statistics Canada. Social Survey Methods Division. Survey sampling: a non-mathematical guide. Ottawa: Statistics Canada, 1993.

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5

Satin, A. Survey sampling: A non-mathematical guide. 2nd ed. [Ottawa]: Minister of Industry Science and Technology, 1993.

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6

Massachusetts. 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.

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7

Barth, 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.

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8

W, Shastry, ed. Survey sampling: A non-mathematical guide. Ottawa: Statistics Canada, 1985.

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9

Lightfoot, Peter C. An introductory guide to sampling for geoanalysis. Toronto, Ont: Ministry of Northern Development and Mines, 1991.

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10

Hann, David. Sampling Kansas: A guide to the curious. [Lawrence, Kansas] (1640 New Hampshire, Lawrence 66044): D. Hann, 1990.

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Частини книг з теми "Guided sampling"

1

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.

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Iglesias-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.

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Abi 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.

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Abi 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.

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Lee, 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.

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Lee, 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.

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Abi 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.

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Abi 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.

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9

Tordoff, 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.

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10

Penedo, 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.

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Тези доповідей конференцій з теми "Guided sampling"

1

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.

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2

Fan, 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.

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Kabierski, 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.

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4

Fragoso, 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.

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5

Tiwari, 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.

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6

Knyazev, 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.

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7

Backlund, 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.

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Identifying high-performance, system-level microgrid designs is a significant challenge due to the overwhelming array of possible configurations. Uncertainty relating to loads, utility outages, renewable generation, and fossil generator reliability further complicates this design problem. In this paper, the performance of a candidate microgrid design is assessed by running a discrete event simulation that includes extended, unplanned utility outages during which microgrid performance statistics are computed. Uncertainty is addressed by simulating long operating times and computing average performance over many stochastic outage scenarios. Classifier-guided sampling, a Bayesian classifier-based optimization algorithm for computationally expensive design problems, is used to search and identify configurations that result in reduced average load not served while not exceeding a predetermined microgrid construction cost. The city of Hoboken, NJ, which sustained a severe outage following Hurricane Sandy in October, 2012, is used as an example of a location in which a well-designed microgrid could be of great benefit during an extended, unplanned utility outage. The optimization results illuminate design trends and provide insights into the traits of high-performance configurations.
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8

Dutra, 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.

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9

Moore, 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.

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10

Bajer, 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.

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Звіти організацій з теми "Guided sampling"

1

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.

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Heline, Tiffany R. Laboratory Sampling Guide. Fort Belvoir, VA: Defense Technical Information Center, May 2012. http://dx.doi.org/10.21236/ada563615.

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3

Chu, 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.

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4

Lewis, 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.

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5

Prichard, 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.

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6

Prichard, 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.

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7

Hogue, 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.

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8

Wyss, 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.

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9

Quigley, 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.

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10

Short, Mary, та Sherry Leis. Vegetation monitoring in the Manley Woods unit at Wilson’s Creek National Battlefield: 1998–2020. Редактор Tani Hubbard. National Park Service, червень 2022. http://dx.doi.org/10.36967/nrr-2293615.

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Natural resource management at Wilson’s Creek National Battlefield (NB) is guided by our understanding of the woodlands and prairies at the time of the Civil War battle in 1861. This report is focused on the Manley Woods unit of the park. This unit is an oak-hickory woodland in the Springfield Plain subsection of the Ozarks. Canopy closure for Missouri oak woodlands can be highly variable and ranges from 30–100% across the spectrum of savanna, open woodland, and closed woodland types. In 1861, the woodland was likely a savanna community. Changes in land use (e.g., fire exclusion) caused an increase in tree density in woodlands at Wilson’s Creek NB and across the Ozarks. Savannas and open woodlands transitioned to closed canopy woodlands over time. Park management plans include restoring the area to a savanna/open woodland structure. Prescribed fire was reintroduced to Wilson’s Creek NB in 1988 and continues as the primary mechanism for reducing the tree canopy. The Manley Woods unit of Wilson’s Creek NB has been subject to intense natural and anthropogenic disturbance events such as a tornado in 2003, timber removal in 2005, prescribed fires in 2006, 2009, and 2019, an ice storm in 2007, and periodic drought. The Heartland Inventory and Monitoring Network (hereafter, Heartland Network) installed four permanent monitoring sites within the Manley Woods area of the park in 1997. Initially, we assessed ground flora and regeneration within the sites (1998–1999). We added fuel sampling after the 2003 tornado. Although overstory sampling occurred prior to the tornado, the protocol was not yet stabilized and pre-2003 overstory data were not included in these analyses. In this report, we focus on the overstory, tree regeneration, and ground cover metrics; ground flora data will be assessed in future analyses. Heartland Network monitoring data reveal that Manley Woods has undergone substantial change in canopy cover and midstory trees since 1998. While basal area and density metrics classify Manley Woods as an open woodland, the closed canopy of the midstory and overstory reveal a plant community that is moving toward closed woodland or forest structure. The most recent fire in 2019 was patchy and mild, resulting in continued increases in fuels. Ground cover metrics indicate infrequent disturbance since leaf litter continued to increase. Management objectives to restore savanna or woodland composition and structure to the Manley Woods overstory, regeneration layer, and ground cover will require implementation of prescribed fire in the future. Repeated fires can thin midstory trees and limit less fire tolerant early seral species. Additionally, mechanical or chemical treatments to reduce undesirable tree species should be considered for woodland restoration. Decreasing canopy closure is an important and essential step toward the restoration of a functioning savanna/open woodland plant community in Manley Woods. Treatments that thin the midstory and reduce fuel loading will also benefit these plant communities. With the anticipated changing climate, maintaining an open woodland community type may also provide resilience through management for native species tolerant of increasingly warmer temperatures.
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