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A PhysicallyBased Motion Retargeting Filter
, 2003
"... This paper presents a novel constraintbased motion editing technique. On the basis of animatorspecified kinematic and dynamic constraints, the method converts a given captured or animated motion to a physically plausible motion. In contrast to previous methods using spacetime optimization, we cast ..."
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This paper presents a novel constraintbased motion editing technique. On the basis of animatorspecified kinematic and dynamic constraints, the method converts a given captured or animated motion to a physically plausible motion. In contrast to previous methods using spacetime optimization, we cast the motion editing problem as a constrained state estimation problem based on the perframe Kalman filter framework. The method works as a filter that sequentially scans the input motion to produce a stream of output motion frames at a stable interactive rate. Animators can tune several filter parameters to adjust to different motions, or can turn the constraints on or off based on their contributions to the final re sult. One particularly appealing feature of the proposed technique is that animators find it very scalable and intuitive. Experiments on various systems show that the technique processes the motions of a human with 54 degrees of freedom at about 150 fps when only kinematic constraints are applied, and at about 10 fps when both kinematic and dynamic constraints are applied. Experiments on various types of motion show that the proposed method produces remarkably realistic animations.
Hand Motion Prediction for Distributed Virtual Environments
"... Abstract—We use our hands to manipulate objects in our daily life. The hand is capable of accomplishing diverse tasks such as pointing, gripping, twisting, and tearing. However, there is not much work that considers using the hand as input in distributed virtual environments (DVEs), in particular, o ..."
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Abstract—We use our hands to manipulate objects in our daily life. The hand is capable of accomplishing diverse tasks such as pointing, gripping, twisting, and tearing. However, there is not much work that considers using the hand as input in distributed virtual environments (DVEs), in particular, over the Internet. The main reasons are that the Internet suffers from high network latency, which affects interaction, and the hand has many degrees of freedom, which adds additional challenges to synchronizing the collaboration. In this paper, we propose a prediction method specifically designed for human hand motion to address the network latency problem in DVEs. Through a thorough analysis of finger motion, we have identified various finger motion constraints, and we propose a constraintbased motion prediction method for hand motion. To reduce the average prediction error under high network latency, for example, over the Internet, we further propose a revised deadreckoning scheme here. Our performance results show that the proposed prediction method produces a lower prediction error than some popular methods, and the revised deadreckoning scheme produces a lower average prediction error than the traditional deadreckoning scheme, particularly at high network latency. Index Terms—Motion prediction, hand motion prediction, hand interaction, network latency. 1
SetMembership Filtering for DiscreteTime Systems With Nonlinear Equality Constraints
"... have close zero frequency response. The model is truncated to 20 states by means of the described quasiconvex optimization technique (QCO method), Hankel model reduction. We implement QCO method on the frequency grid with 84 samples with tolerance in bisection procedure 10 06. The optimization toge ..."
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have close zero frequency response. The model is truncated to 20 states by means of the described quasiconvex optimization technique (QCO method), Hankel model reduction. We implement QCO method on the frequency grid with 84 samples with tolerance in bisection procedure 10 06. The optimization together with calculating frequency samples took 74 seconds and the resulting approximation error is 2:9 1 10 05. Hankel model reduction took around 20 minutes providing the error 7:98 1 10 05. Results, see in the Fig. 1. For the given frequency interval QCO provided a better model than Hankel reduction. However, in general we do not expect QCO approximations to be better than Hankel reduction approximations. This example shows, that for large/medium scale systems we win sufficiently in time and do not really lose in approximation quality. VII. CONCLUSION In this technical note we have discussed multiinputmultioutput extension
SetMembership Fuzzy Filtering for Nonlinear DiscreteTime Systems
"... Abstract—This paper is concerned with the setmembership filtering (SMF) problem for discretetime nonlinear systems. We employ the Takagi–Sugeno (TS) fuzzy model to approximate the nonlinear systems over the true value of state and to overcome the difficulty with the linearization over a state est ..."
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Abstract—This paper is concerned with the setmembership filtering (SMF) problem for discretetime nonlinear systems. We employ the Takagi–Sugeno (TS) fuzzy model to approximate the nonlinear systems over the true value of state and to overcome the difficulty with the linearization over a state estimate set rather than a state estimate point in the setmembership framework. Based on the TS fuzzy model, we develop a new nonlinear SMF estimation method by using the fuzzy modeling approach and the Sprocedure technique to determine a state estimation ellipsoid that is a set of states compatible with the measurements, the unknownbutbounded process and measurement noises, and the modeling approximation errors. A recursive algorithm is derived for computing the ellipsoid that guarantees to contain the true state. A smallest possible estimate set is recursively computed by solving the semidefinite programming problem. An illustrative example shows the effectiveness of the proposed method for a class of discretetime nonlinear systems via fuzzy switch. Index Terms—Convex optimization, linear setmembership filtering (SMF), nonlinear SMF, unknownbutbounded noise, Takagi–Sugeno (TS) fuzzy model. I.
SetMembership Filtering with State Constraints
"... In this paper, the problem of setmembership filtering is considered for discretetime systems with equality and inequality constraints between their state variables. We formulate the problem of setmembership filtering as finding the set of estimates that belong to an ellipsoid. A centre and a shap ..."
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In this paper, the problem of setmembership filtering is considered for discretetime systems with equality and inequality constraints between their state variables. We formulate the problem of setmembership filtering as finding the set of estimates that belong to an ellipsoid. A centre and a shape matrix of the ellipsoid are used to describe the set of estimates and the solution to the set of estimates is obtained in terms of matrix inequality. Unknown but bounded process and measurement noises are handled under the inequality constraints by using Sprocedure. We apply Finsler’s Lemma to project the set of estimates onto the constrained surface. A recursive algorithm is developed for computing the ellipsoid that guarantees to contain the true state under the state constraints, which is easily implemented by semidefinite programming via interiorpoint approach. A vehicle tracking example is provided to demonstrate the effectiveness of the proposed setmembership filtering with state equality constraints. I.
1 Mathematically Equivalent Approaches for Equality Constrained Kalman Filtering
, 902
"... Abstract — Kalman Filtering problems often have inherent and known constraints in the physical dynamics that are not exploited despite potentially significant gains (e.g., fixed speed of a motor). In this paper, we review existing methods and propose some new ideas for filtering in the presence of e ..."
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Abstract — Kalman Filtering problems often have inherent and known constraints in the physical dynamics that are not exploited despite potentially significant gains (e.g., fixed speed of a motor). In this paper, we review existing methods and propose some new ideas for filtering in the presence of equality constraints. We then show that three methods for incorporating state space equality constraints are mathematically equivalent to the more general “Projection ” method, which allows different weighting matrices when projecting the estimate. Still, the different approaches have advantages in implementations that may make one better suited than another for a given application. Index Terms — Kalman Filter, Equality Constrained Optimization I.
(revised manuscript submitted to Journal of Hydrometeorology for the special CAHMDA issue) Corresponding Author:
, 2005
"... A procedure is developed to incorporate equality constraints in Kalman filters, including the Ensemble Kalman filter (EnKF) and is referred to as the Constrained Ensemble Kalman Filter (CEnKF). The constraint is carried out as a twostep filtering approach, with the first step being the standard (En ..."
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A procedure is developed to incorporate equality constraints in Kalman filters, including the Ensemble Kalman filter (EnKF) and is referred to as the Constrained Ensemble Kalman Filter (CEnKF). The constraint is carried out as a twostep filtering approach, with the first step being the standard (Ensemble) Kalman filter. The second step is the constraint step carried out by another Kalman filter that optimally redistributes any imbalance from the first step. The CEnKF is implemented over a 75,000 sq. km. domain in the Southern Great Plains region of the United States, using the terrestrial water balance as the constraint. The observations, consisting of gridded fields of the upper two soil moisture layers from the Oklahoma Mesonet system, ARM/CART Energy Balance Bowen Ratio (EBBR) latent heat estimates and USGS streamflow from unregulated basins, are assimilated into the Variable Infiltration Capacity (VIC) land surface model. The water balance was applied at the domain scale, and estimates of the water balance components for the domain are updated from the data assimilation step so as to assure closure. 2