Sensorless control of synchronous reluctance motors using on‐line parameter identification method not affected by position estimation accuracy

This paper presents a novel on‐line parameter identification method for sensorless control of Synchronous Reluctance Motors (SynRMs). Although conventional sensorless control methods based on mathematical models usually need some complex measurements of motor parameters in advance, the proposed identification method does not require them and can be realized on‐line. The proposed method identifies motor parameters under sensorless control, so rotor position and velocity cannot be used to identify these parameters. However, the proposed method does not need rotor position and velocity, and identified parameters are not affected by these estimation errors. The sensorless control using identified motor parameters is realized, and the effectiveness of the proposed method is verified by experimental results. © 2006 Wiley Periodicals, Inc. Electr Eng Jpn, 155(3): 62–69, 2006; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20258     

A load dispatch method using fractional programming and semidefinite programming

This paper describes a load dispatch method which minimizes power cost—[fuel cost]/[electric output]—for a power system with thermal plants and energy storage facilities. The proposed method employs fractional programming to convert a minimization problem with fractional objective function to a series of quadratic minimization problems, and semidefinite programming to solve converted problems. The method provides the optimum time‐dependent power output/input and storage level of energy storage facilities as well as time‐dependent power output of thermal plants. The method has been applied to a power system with five thermal plants, two energy storage facilities of various performances, and five load demands. The optimum load scheme of four time mesh points is obtained for the thermal plants and energy storage facilities. The fractional programming successfully converges the optimal scheme through a few iterations. The semidefinite programming deals with a variable matrix of 164 dimensions, and 185 inequality constraints. © 2001 Scripta Technica, Electr Eng Jpn, 138(2): 49–58, 2002     

Influence of third monomer on the crystal phase transition behavior of ethylene-tetrafluoroethylene copolymer

Crystal phase transition between the low- and high-temperature phases has been investigated for ethylene (E)-tetrafluoroethylene (TFE) alternating copolymer (ETFE) containing the third monomeric species by the temperature dependent measurements of wide-angle X-ray diffraction (WAXD) and small-angle X-ray scattering (SAXS) and differential scanning calorimetry. Nonafluoro-1-hexene (NFH) and hexafluoropropylene (HFP) were chosen as the third monomers, where they are different in the side-branch length, -(CF₂)₃CF₃ and -CF₃, respectively. In the case of E/TFE/NFH copolymer (ET-C4F9), the crystal phase transition temperature of the original ETFE two-components copolymer was not very much affected by the existence of NFH in the range of NFH content from 0.7 to 3 mol%. Contrarily, the crystal phase transition temperature of E/TFE/HFP copolymer (ET-CF3) was found to decrease drastically with increasing HFP content. The melting temperature and the higher-order structure were also affected sensitively depending on the HFP content. This difference in phase transition behavior between ET-C4F9 and ET-CF3 copolymers is reasonably interpreted as follows: the short side groups (-CF₃) of HFP monomeric unit are included in the crystal lattice of E/TFE chains and the unit cell is expanded gradually with an increment of the HFP content, resulting in the decrease in phase transition point because of easier thermal motion of the chains. On the other hand, the long side groups [-(CF₂)₃CF₃] of NFH monomeric units are excluded out of the crystal lattice and located on the lamellar surfaces or in the amorphous region and do not affect very much the phase transition temperature even when the NFH content is increased. In association with such a change in crystal structure, the long period of stacked lamellar structure was found to decrease remarkably in the case of NFH, whereas it does not change very much for HFP, consistent with the interpretation of the above-mentioned WAXD data.     

Designing elastomer network for desired tire performance characteristics

Rubber elasticity is associated with changes in configurational entropy of a long chain. Because the chain cannot change its configuration instantaneously, there is a time delay in deformation to an applied force. This delayed response is the source of viscoelasticity and hysteresis energy loss of elastomer networks. Many tire performance properties are related to the viscoelasticity of tire components. Wet and dry traction of tire is related to the energy loss of the tread material at very high frequencies. On the other hand, rolling resistance of tire is characterized by the energy loss of tread material at relatively low frequencies. The dynamic viscoelastic properties of elastomer network shows characteristic zones on a frequency scale. At very high frequencies the energy loss is controlled by the segmental motions of the polymer chain. At lower frequencies the energy loss is related to the longer range motions of the chain. A series of polymers was synthesized to study the effect of micro- and macro-structure of the polymer on the viscoelastic properties of tread compounds and their tire performance properties. As expected from the theory, the wet traction of the tire was highly correlated to the segmental motions of the chains; namely, the glass transition temperature of the polymer. The energy loss of the compounds at a higher temperature, however, was related to the macrostructure of the polymer chain. Those examples illustrate that the fundamental understanding of the theory of elastomer network allows a tire engineer to obtain the best balance of tire performance characteristics.     

An apparent ensemble effect in the oxidative coupling of methane on hydroxyapatites with incorporated lead

The introduction of small quantities of lead into calcium hydroxyapatite catalysts produces marked increases in the selectivity to C₂₊ hydrocarbons, while the conversion of methane remains relatively constant. Small surface concentrations of lead are sufficient to achieve C₂₊ selectivities of 80 and 90%, with oxygen and nitrous oxide, respectively, in contrast with 18 and 46%, respectively, obtained in the absence of lead. Since surface concentration of lead species sufficient to stabilize pairs of methyl radicals in close proximity to each other would be expected to facilitate the formation of C₂ hydrocarbons, an ensemble effect appears to be extant.     

Effect of center block structure on the physical and rheological properties of ABA block copolymers. Part II. Rheological properties

The effect of the chemical structure of the center block on the rheological properties of ABA block copolymers with polystyrene end blocks has been investigated. The chemical structure of the center blocks investigated in the present paper are polybutadiene, polyisoprene, ethylene/butene copolymer, ethylene/propylene copolymer and polydimethyl-siloxane. The chemical structure of the center block was found to have a pronounced effect on the rheological properties of the ABA block copolymer melts. The long range relaxation times of these block copolymer melts increases with increasing incompatibility between the styrene block and the center block. However, the rheological properties of the block copolymers are not influenced significantly by the glass transition or the entanglement molecular weight of the center block.     

Microstructure development of steel during severe plastic deformation

The microstructure development during plastic deformation was reviewed for iron and steel which were subjected to cold rolling or mechanical milling (MM) treatment, and the change in strengthening mechanism caused by the severe plastic deformation (SPD) was also discussed in terms of ultra grain refinement behavior. The microstructure of cold-rolled iron is characterized by a typical dislocation cell structure, where the strength can be explained by dislocation strengthening. It was confirmed that the increase in dislocation density by cold working is limited at 10¹⁶m⁻², which means the maximum hardness obtained by dislocation strengthening is HV3.7 GPa. However, the iron is abnormally work-hardened over the maximum dislocation strengthening by SPD of MM because of the ultra grain refinement caused by the SPD. In addition, impurity of carbon plays an important role in such grain refinement: the carbon addition leads to the formation of nano-crystallized structure in iron.     

Estimation of time-dependent input from neuronal membrane potential

The set of firing rates of the presynaptic excitatory and inhibitory neurons constitutes the input signal to the postsynaptic neuron. Estimation of the time-varying input rates from intracellularly recorded membrane potential is investigated here. For that purpose, the membrane potential dynamics must be specified. We consider the Ornstein-Uhlenbeck stochastic process, one of the most common single-neuron models, with time-dependent mean and variance. Assuming the slow variation of these two moments, it is possible to formulate the estimation problem by using a state-space model. We develop an algorithm that estimates the paths of the mean and variance of the input current by using the empirical Bayes approach. Then the input firing rates are directly available from the moments. The proposed method is applied to three simulated data examples: constant signal, sinusoidally modulated signal, and constant signal with a jump. For the constant signal, the estimation performance of the method is comparable to that of the traditionally applied maximum likelihood method. Further, the proposed method accurately estimates both continuous and discontinuous time-variable signals. In the case of the signal with a jump, which does not satisfy the assumption of slow variability, the robustness of the method is verified. It can be concluded that the method provides reliable estimates of the total input firing rates, which are not experimentally measurable.     

Optimizing time histograms for non-Poissonian spike trains

The time histogram is a fundamental tool for representing the inhomogeneous density of event occurrences such as neuronal firings. The shape of a histogram critically depends on the size of the bins that partition the time axis. In most neurophysiological studies, however, researchers have arbitrarily selected the bin size when analyzing fluctuations in neuronal activity. A rigorous method for selecting the appropriate bin size was recently derived so that the mean integrated squared error between the time histogram and the unknown underlying rate is minimized (Shimazaki & Shinomoto, 2007 ). This derivation assumes that spikes are independently drawn from a given rate. However, in practice, biological neurons express non-Poissonian features in their firing patterns, such that the spike occurrence depends on the preceding spikes, which inevitably deteriorate the optimization. In this letter, we revise the method for selecting the bin size by considering the possible non-Poissonian features. Improvement in the goodness of fit of the time histogram is assessed and confirmed by numerically simulated non-Poissonian spike trains derived from the given fluctuating rate. For some experimental data, the revised algorithm transforms the shape of the time histogram from the Poissonian optimization method.