Kinematic Control of Human Postures for Task Stimulation

Kinematic control of human postures for task simulation is important in human factor analysis, simulation and training. It is a challenge to control the postures of a synthesized human figure in real-time on today’s graphics workstations because the human body is highly articulated. In addition, we need to consider many spatial restrictions imposed on the human body while performing a task. In this study, we simplify the human posture control problem by decoupling the degrees of freedom (dof) in the human body. Based on several decoupling schemes, we develop an analytical human posture control algorithm. This analytical algorithm has a number of advantages over existing methods. It eliminates the local minima problem, it is efficient enough to control whole human body postures in real-time, and it provides more effective and convenient control over redundant degrees of freedom. The limitation of this algorithm is that it cannot handle over-constrained problems or general constraint functions. To overcome this limitation, we transform the human posture control problem from a 40 variable joint space to a 4 to 9 redundancy parameter space. We then apply nonlinear optimization techniques on the transformed problem. Because the search space is reduced, this new numerical algorithm is more likely to find a solution than existing methods which apply optimization techniques directly in the joint space. The contributions of this thesis include a decoupling approach for simplifying the human posture control problem, an analytical human posture control algorithm based on this decoupling approach, and a numerical human posture control algorithm in redundancy parameter space. These two new algorithms are more efficient and effective than existing methods, and they also give the user control to select the desired solution. Moreover, the analytical algorithm can control postures of a few 92 dof human figures at 30 Hz

High-resolution temporal constraints on the dynamics of dark energy

We use the recent type Ia supernova, cosmic microwave background and large-scale structure data to shed light on the temporal evolution of the dark energy equation of state $w(z)$ out to redshift one. We constrain the most flexible parametrization of dark energy to date, and include the dark energy perturbations consistently throughout. Interpreting our results via the principal component analysis, we find no significant evidence for dynamical dark energy: the cosmological constant model is consistent with data everywhere between redshift zero and one at 95% C.L.Comment: 5 pages, 2 figures Version for PRD (Rapid Communications

Probing for the Cosmological Parameters with PLANCK Measurement

We investigate the constraints on cosmological parameters especially for EoS of dark energy, inflationary parameters, neutrino mass and curvature of universe using simulated Planck data. Firstly we determine cosmological parameters with current observations including ESSENCE, WMAP3, Boomerang-2K2, CBI, VSA, ACBAR, SDSS LRG and 2dFGRS, and take best-fit model as the fiducial model in simulations. In simulations we pay attention to the effects of dynamical dark energy in determination of cosmological parameters. We add simulated SNAP data to do all the simulations. Using present data, we find Quintom dark energy model is mildly favored while \LambdaCDM remains a good fit. In the framework of dynamical dark energy, the constraints on inflationary parameters, m_{\nu} and \Omega_{K} become weak compared with the constraints in \LambdaCDM. Intriguingly, we find that the inflationary models with a "blue" tilt, which are excluded about 2\sigma in \LambdaCDM model, are well within 2\sigma region with the presence of the dynamics of dark energy. The upper limits of neutrino mass are weakened by a factor of 2 (95% C.L.), say, m_{\nu}<1.59 eV and m_{\nu}<1.53 eV for two forms of parametrization of the equation of state of dark energy. The flat universe is a good fit to the current data, namely, |\Omega_{K}|<0.03 (95% C.L.). With the simulated Planck and SNAP data, dynamical dark energy and \LambdaCDM might be distinguished at 4\sigma. And uncertainties of inflationary parameters, m_{\nu} and \Omega_{K} can be reduced obviously. We also constrain the rotation angle \Delta\alpha, denoting possible cosmological CPT violation, with simulated Planck and CMBpol data and find that our results are much more stringent than current constraint and will verify cosmological CPT symmetry with a higher precision. (Abridged)Comment: 15 pages, 8 figures and 3 tables, Accepted for publication in Int.J.Mod.Phys.

Determining Cosmological Parameters with Latest Observational Data

In this paper, we combine the latest observational data, including the WMAP five-year data (WMAP5), BOOMERanG, CBI, VSA, ACBAR, as well as the Baryon Acoustic Oscillations (BAO) and Type Ia Supernoave (SN) "Union" compilation (307 sample) to determine the cosmological parameters. Our results show that the $\Lambda$CDM model remains a good fit to the current data. In a flat universe, we obtain the tight limit on the constant EoS of dark energy as, $w=-0.977\pm0.056$ ($1 \sigma$). For the dynamical dark energy models with time evolving EoS, we find that the best-fit values are $w_0=-1.08$ and $w_1=0.368$, implying the preference of Quintom model whose EoS gets across the cosmological constant boundary. For the curvature of universe, our results give $-0.012<\Omega_k<0.009$ (95% C.L.) when fixing w_{\DE}=-1. When considering the dynamics of dark energy, the flat universe is still a good fit to the current data. Regarding the neutrino mass limit, we obtain the upper limits, $\sum m_{\nu}<0.533$ eV (95% C.L.) within the framework of the flat $\Lambda$CDM model. When adding the SDSS Lyman-$\alpha$ forest power spectrum data, the constraint on $\sum m_{\nu}$ can be significantly improved, $\sum m_{\nu}<0.161$ eV (95% C.L.). Assuming that the primordial fluctuations are adiabatic with a power law spectrum, within the $\Lambda$CDM model, we find that the upper limit on the ratio of the tensor to scalar is $r<0.200$ (95% C.L.) and the inflationary models with the slope $n_s\geq1$ are excluded at more than $2 \sigma$ confidence level. However, in the framework of dynamical dark energy models, the allowed region in the parameter space of ($n_s$,$r$) is enlarged significantly. Finally, we find no evidence for the large running of the spectral index. (Abridged)Comment: 8 pages, 5 figures, 2 tables, More discussion on NE

Probing Dark Energy Dynamics from Current and Future Cosmological Observations

We report the constraints on the dark energy equation-of-state w(z) using the latest 'Constitution' SNe sample combined with the WMAP5 and SDSS data. Based on the localized principal component analysis and the model selection criteria, we find that the LCDM model is generally consistent with the current data, yet there exists weak hint of the possible dynamics of dark energy. In particular, a model predicting w(z)-1 at z\in[0.5,0.75), which means that w(z) crosses -1 in the range of z\in[0.25,0.75), is mildly favored at 95% confidence level. Given the best fit model for current data as a fiducial model, we make future forecast from the joint data sets of JDEM, Planck and LSST, and we find that the future surveys can reduce the error bars on the w bins by roughly a factor of 10 for a 5-w-bin model.Comment: Accepted by PRD; minor changes from v

Fables of reconstruction: controlling bias in the dark energy equation of state

We develop an efficient, non-parametric Bayesian method for reconstructing the time evolution of the dark energy equation of state w(z) from observational data. Of particular importance is the choice of prior, which must be chosen carefully to minimise variance and bias in the reconstruction. Using a principal component analysis, we show how a correlated prior can be used to create a smooth reconstruction and also avoid bias in the mean behaviour of w(z). We test our method using Wiener reconstructions based on Fisher matrix projections, and also against more realistic MCMC analyses of simulated data sets for Planck and a future space-based dark energy mission. While the accuracy of our reconstruction depends on the smoothness of the assumed w(z), the relative error for typical dark energy models is <10% out to redshift z=1.5.Comment: 13 pages, 11 figure

An efficient probe of the cosmological CPT violation

We develop an efficient method based on the linear regression algorithm to probe the cosmological CPT violation using the CMB polarisation data. We validate this method using simulated CMB data and apply it to recent CMB observations. We find that a combined data sample of BICEP1 and BOOMERanG 2003 favours a nonzero isotropic rotation angle at $2.3\sigma$ confidence level, ie, $\Delta\alpha=-3.3 \pm1.4$ deg (68% CL) with systematics included.Comment: 10 pages, 5 figures, 2 tables. The published versio