A characterization of the power of vector machines

A new formal model of register machines is described. Registers contain bit vectorswhich are manipulated using bitwise Boolean operations and shifts. Our main results relate the language recognition power of such vector machines to that of Turing machines. A class of vector machines is exhibited for which time on a vector machine supplies, to within a polynomial, just as much power as space on a Turing machine. Moreover, this is true regardless of whether the machines are deterministic or non-deterministic

Electroencephalography-based machine learning for cognitive profiling in Parkinson's disease:Preliminary results

Background Cognitive symptoms are common in patients with Parkinson's disease. Characterization of a patient's cognitive profile is an essential step toward the identification of predictors of cognitive worsening. Objective The aim of this study was to investigate the use of the combination of resting-state EEG and data-mining techniques to build characterization models. Methods Dense EEG data from 118 patients with Parkinson's disease, classified into 5 different groups according to the severity of their cognitive impairments, were considered. Spectral power analysis within 7 frequency bands was performed on the EEG signals. The obtained quantitative EEG features of 100 patients were mined using 2 machine-learning algorithms to build and train characterization models, namely, support vector machines and k-nearest neighbors models. The models were then blindly tested on data from 18 patients. Results The overall classification accuracies were 84% and 88% for the support vector machines and k-nearest algorithms, respectively. The worst classifications were observed for patients from groups with small sample sizes, corresponding to patients with the severe cognitive deficits. Whereas for the remaining groups for whom an accurate diagnosis was required to plan the future healthcare, the classification was very accurate. Conclusion These results suggest that EEG features computed from a daily clinical practice exploration modality in-that it is nonexpensive, available anywhere, and requires minimal cooperation from the patient-can be used as a screening method to identify the severity of cognitive impairment in patients with Parkinson's disease. (c) 2018 International Parkinson and Movement Disorder Society</p

Right Harmonic Spectrum for the back-electromotive force of a n-phase synchronous motor

This paper deals with a vector control of n-phase permanent magnet synchronous machine. To use control algorithms already developed for sine-wave 3-phase machines, the spectrum of back electromotive force (EMF) must contain only odd 2k+1 harmonics which verify the following inequality, 1≤ 2k +1< n . In an experimental vector control of a 5-phase drive, two usual algorithms of sine-wave 3-phase machine are thus used to supply a machine with trapezoidal waveform back EMF. In this case, the first and third harmonics are used to produce torque: the other harmonics, and particularly the 7th one, induce effects as torque ripples and parasitic currents

Comparizon of Conventional and Unconventional 5-phase PM Motor Structures for Naval Applications

Multi-phase motors are widely used in marine propulsion. In this paper, a Multi-machine modeling of Surface Mounted PM motors is presented and applied to a 5-phase one. The latter is proved to be equivalent to a set of two-phase fictitious machines each ones being characterized by a set of specific harmonic rank. A simple control consists in supplying each fictitious machine by a current which contains only one harmonic. A five phase machine is then supplied by currents with only both first and third harmonics. Considering this kind of control, it is proved that for given stator resistance and average torque the Joule losses and the torque ripple are minimized if a simple criterion on the harmonics of electromotive force at constant speed is fullfilled. Different structures of rotor are then compared to examine numerically which improvements can be practically obtaine

Unbounded-error quantum computation with small space bounds

We prove the following facts about the language recognition power of quantum Turing machines (QTMs) in the unbounded error setting: QTMs are strictly more powerful than probabilistic Turing machines for any common space bound $s$ satisfying $s(n)=o(\log \log n)$. For "one-way" Turing machines, where the input tape head is not allowed to move left, the above result holds for $s(n)=o(\log n)$. We also give a characterization for the class of languages recognized with unbounded error by real-time quantum finite automata (QFAs) with restricted measurements. It turns out that these automata are equal in power to their probabilistic counterparts, and this fact does not change when the QFA model is augmented to allow general measurements and mixed states. Unlike the case with classical finite automata, when the QFA tape head is allowed to remain stationary in some steps, more languages become recognizable. We define and use a QTM model that generalizes the other variants introduced earlier in the study of quantum space complexity.Comment: A preliminary version of this paper appeared in the Proceedings of the Fourth International Computer Science Symposium in Russia, pages 356--367, 200

Vectorial formalism for analysis and design of polyphase synchronous machines

A vectorial formalism for analysis and design of polyphase synchronous machines without reluctance and saturation effects is described. We prove the equivalence of such a machine with a set of magnetically independent machines, which are electrically and mechanically coupled. Specific problems of polyphase machines can thus be favorably analyzed with this concept. Rules of conception and constraints on electric supply can be deduced. Moreover the vectorial approach, which generalizes the complex phasor method, can also be used to control n-leg Voltage Source Inverters. This methodology is applied to 3-phase and 6- phase synchronous machines