Torque-ripple minimization in modular permanent-magnet brushless machines

This paper discusses the suitability of four-phase, five-phase, and six-phase modular machines, for use in applications where servo characteristics and fault tolerance are key requirements. It is shown that an optimum slot number and pole number combination exists, for which excellent servo characteristics could be achieved, under healthy operating conditions, with minimum effects on the power density of the machine. To eliminate torque ripple due to residual cogging and various fault conditions, the paper describes a novel optimal torque control strategy for the modular permanent-magnet machines operating in both constant torque and constant power modes. The proposed control strategy enables ripple-free torque operation to be achieved, while minimizing the copper loss under voltage and current constraints. The utility of the proposed strategy is demonstrated by computer simulations on a four-phase fault-tolerant drive system

Optimal torque control of fault-tolerant permanent magnet brushloss machines

Describes a novel optimal torque control strategy for fault-tolerant permanent magnet brushless ac drives operating in both constant torque and constant power modes. The proposed control strategy enables ripple-free torque operation to be achieved while minimizing the copper loss under voltage and current constraints. The utility of the proposed strategy is demonstrated by computer simulations on a five-phase fault-tolerant drive system

Genetic algorithm and neural network hybrid approach for job-shop scheduling

Copyright @ 1998 ACTA PressThis paper proposes a genetic algorithm (GA) and constraint satisfaction adaptive neural network (CSANN) hybrid approach for job-shop scheduling problems. In the hybrid approach, GA is used to iterate for searching optimal solutions, CSANN is used to obtain feasible solutions during the iteration of genetic algorithm. Simulations have shown the valid performance of the proposed hybrid approach for job-shop scheduling with respect to the quality of solutions and the speed of calculation.This research is supported by the National Nature Science Foundation and National High -Tech Program of P. R. China

Figure of Merit for Dark Energy Constraints from Current Observational Data

Choosing the appropriate figure of merit (FoM) for dark energy (DE) constraints is key in comparing different DE experiments. Here we show that for a set of DE parameters {f_i}, it is most intuitive to define FoM = 1/\sqrt{Cov(f1,f2,f3,...)}, where Cov(f1,f2,f3,...) is the covariance matrix of {f_i}. The {f_i} should be minimally correlated. We demonstrate two useful choices of {f_i} using 182 SNe Ia (compiled by Riess et al. 2007), [R(z_*), l_a(z_*), \Omega_b h^2] from the five year Wilkinson Microwave Anisotropy Probe (WMAP) observations, and SDSS measurement of the baryon acoustic oscillation (BAO) scale, assuming the HST prior of H_0=72+/-8 km/s Mpc^{-1} and without assuming spatial flatness. We find that the correlation of (w_0,w_{0.5}) [w_0=w_X(z=0), w_{0.5}=w_X(z=0.5), w_X(a) = 3w_{0.5}-2w_0+3(w_0-w_{0.5})a] is significantly smaller than that of (w_0,w_a) [w_X(a)=w_0+(1-a)w_a]. In order to obtain model-independent constraints on DE, we parametrize the DE density function X(z)=\rho_X(z)/\rho_X(0) as a free function with X_{0.5}, X_{1.0}, and X_{1.5} [values of X(z) at z=0.5, 1.0, and 1.5] as free parameters estimated from data. If one assumes a linear DE equation of state, current data are consistent with a cosmological constant at 68% C.L. If one assumes X(z) to be a free function parametrized by (X_{0.5}, X_{1.0}, X_{1.5}), current data deviate from a cosmological constant at z=1 at 68% C.L., but are consistent with a cosmological constant at 95% C.L.. Future DE experiments will allow us to dramatically increase the FoM of constraints on (w_0,w_{0.5}) and of (X_{0.5}, X_{1.0}, X_{1.5}). This will significantly shrink the DE parameter space to enable the discovery of DE evolution, or the conclusive evidence for a cosmological constant.Comment: 7 pages, 3 color figures. Submitte

Cooling of a Micro-mechanical Resonator by the Back-action of Lorentz Force

Using a semi-classical approach, we describe an on-chip cooling protocol for a micro-mechanical resonator by employing a superconducting flux qubit. A Lorentz force, generated by the passive back-action of the resonator's displacement, can cool down the thermal motion of the mechanical resonator by applying an appropriate microwave drive to the qubit. We show that this onchip cooling protocol, with well-controlled cooling power and a tunable response time of passive back-action, can be highly efficient. With feasible experimental parameters, the effective mode temperature of a resonator could be cooled down by several orders of magnitude.Comment: 10 pages, 4 figure

Dephasing in (Ga,Mn)As nanowires and rings

To understand quantum mechanical transport in ferromagnetic semiconductor the knowledge of basic material properties like phase coherence length and corresponding dephasing mechanism are indispensable ingredients. The lack of observable quantum phenomena prevented experimental access to these quantities so far. Here we report about the observations of universal conductance fluctuations in ferromagnetic (Ga,Mn)As. The analysis of the length and temperature dependence of the fluctuations reveals a T^{-1} dependence of the dephasing time.Comment: 5 pages, 4 figure

Effect of optimal torque control on rotor loss of fault-tolerant permanent-magnet brushless machines

A faulted phase in a fault-tolerant permanent-magnet brushless machine can result in significant torque ripple. However, this can be minimized by using an appropriate optimal torque control strategy. Inevitably, however, this results in significant time harmonics in the phase current waveforms, which when combined with inherently large space harmonics, can result in a significant eddy-current loss in the permanent magnets on the rotor. This paper describes the optimal torque control strategy which has been adopted, and discusses its effect on the eddy-current loss in the permanent magnets of four-, five-, and six-phase fault-tolerant machines

ManiNetCluster: a novel manifold learning approach to reveal the functional links between gene networks.

BACKGROUND:The coordination of genomic functions is a critical and complex process across biological systems such as phenotypes or states (e.g., time, disease, organism, environmental perturbation). Understanding how the complexity of genomic function relates to these states remains a challenge. To address this, we have developed a novel computational method, ManiNetCluster, which simultaneously aligns and clusters gene networks (e.g., co-expression) to systematically reveal the links of genomic function between different conditions. Specifically, ManiNetCluster employs manifold learning to uncover and match local and non-linear structures among networks, and identifies cross-network functional links. RESULTS:We demonstrated that ManiNetCluster better aligns the orthologous genes from their developmental expression profiles across model organisms than state-of-the-art methods (p-value <2.2×10-16). This indicates the potential non-linear interactions of evolutionarily conserved genes across species in development. Furthermore, we applied ManiNetCluster to time series transcriptome data measured in the green alga Chlamydomonas reinhardtii to discover the genomic functions linking various metabolic processes between the light and dark periods of a diurnally cycling culture. We identified a number of genes putatively regulating processes across each lighting regime. CONCLUSIONS:ManiNetCluster provides a novel computational tool to uncover the genes linking various functions from different networks, providing new insight on how gene functions coordinate across different conditions. ManiNetCluster is publicly available as an R package at https://github.com/daifengwanglab/ManiNetCluster