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0 −5 −10 −15 goal: build a map of the environment using the robot’s sensors mapping with limited sensing 1mapping with limited sensing • cheap: ir array ≤ us $50 vs. laser rangefinder ≥ us $5000... Computational cost – simplify for implementation on real hardware • Many ideas also applicable for robots with powerful sensing Mapping with limited sensing 3Mapping: basic approach 1: loop 2: Move; update pose estimate based on odometry 3: Sense the environment 4: Extract features from the raw sensing data 5: Match features with the current map 6: Based on matches, update pose and map estimates 7: Add unmatched features to the map 8: end loop Mapping with limited sensing 4Mapping: formulation and assumptions x m 1 x m 2 x m n n1 n2 n3 nt z1 z2 z3 zt x r 0 x r 1 x r 2 x r 3 x r t u1 u2 u3 u
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Modulation of intramolecular interactions in superhelical dna by curved sequences: a monte carlo simulation study ... The computed structures were then analyzed with respect to the number and positions of the end loops of the interwound superhelix, and the intramolecular interaction probability of different segments of the DNA....
Bayesian inference for efficient learning in control...3.2 Inverted Pendulum How can we learn motor tasks efficiently without expert knowledge? • experience-based autonomous learning from scratch • no task-specific prior assumptions • artificial learning often requires many (millions) trials, humans don’t use key features from human/animal learning to make artificial learning more efficient probabilistic model for predictions is of central importance 1 Key Ingredients and Algorithm • some important features of human experience-based learning: – generalization and predictions using a forward model – representation and incorporation of uncertainty into the decisionmaking process describe features by a probabilistic model of the world • properties of a probabilistic model – extract more useful information from data – represent and quantify uncertainty – simultaneous consideration of all “plausible ” transitions Algorithm 1 Learning algorithm init: try some policy (real world) loop record collected experience update probabilistic dynamics model loop simulate model with current policy compute corresponding expected long-term cost improve policy end loop try new policy (real world) end loop ⊲ interaction ⊲ internal ⊲ policy optimization ⊲ internal ⊲ internal ⊲ internal ⊲ interaction policy time t state x action u state...
Brownian dynamics simulations of supercoiled dna with bent sequences.... The structural fluctuations in an interwound superhelix, as reflected in the change in end loop positions, seem to occur by destruction/creation of loops rather than by a sliding motion of the DNA around its contour....
Brownian dynamics simulations of supercoiled dna with bent sequences ... The structural fluctuations in an interwound superhelix, as reflected in the change in end loop positions, seem to occur by destruction/creation of loops rather than by a sliding motion of the DNA around its contour....

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