978 resultados para Rotating electrical machine


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The effect of plasmonoscillations, induced by pulsed laserirradiation, on the DC tunnel current between islands in a discontinuous thin goldfilm is studied. The tunnel current is found to be strongly enhanced by partial rectification of the plasmon-induced AC tunnel currents flowing between adjacent gold islands. The DC tunnel current enhancement is found to increase approximately linearly with the laser intensity and the applied DC bias voltage. The experimental data can be well described by an electron tunnelling model which takes the plasmon-induced AC voltage into account. Thermal heating seems not to contribute to the tunnel current enhancement.

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We have demonstrated the nonlinear absorption at 532 nm wavelength in an Au semi-continuous film (SF) resulting from smearing of the Fermi distribution and diffusion of conduction electrons into the substrate. The Au SF was irradiated by a pulsed laser with 8 ns pulse width at 532 nm in near resonance with the interband transition of the Au. We determined the temperature increase in the SF for different intensities by electrical measurement. We calculated the temperature increase by using a 1D heat transport equation; comparing the results of the calculation with measured values for the temperature increase, revealed the nonlinear absorption in the Au SF. We employed this deviation from linear behaviour to determine the nonlinear absorption coefficient.

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Controlling the electrical resistance of granular thin films is of great importance for many applications, yet a full understanding of electron transport in such films remains a major challenge. We have studied experimentally and by model calculations the temperature dependence of the electrical resistance of ultrathin gold films at temperatures between 2 K and 300 K. Using sputter deposition, the film morphology was varied from a discontinuous film of weakly coupled meandering islands to a continuous film of strongly coupled coalesced islands. In the weak-coupling regime, we compare the regular island array model, the cotunneling model, and the conduction percolation model with our experimental data. We show that the tunnel barriers and the Coulomb blockade energies are important at low temperatures and that the thermal expansion of the substrate and the island resistance affect the resistance at high temperatures. At low temperatures our experimental data show evidence for a transition from electron cotunneling to sequential tunneling but the data can also be interpreted in terms of conduction percolation. The resistivity and temperature coefficient of resistance of the meandering gold islands are found to resemble those of gold nanowires. We derive a simple expression for the temperature at which the resistance changes from non-metal-like behavior into metal-like behavior. In the case of strong island coupling, the total resistance is solely determined by the Ohmic island resistance.

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A simple, uniquely plasma-enabled and environment-friendly process to reduce the thickness of vertically standing graphenes to only 4–5 graphene layers and arranging them in dense, ultra-large surface area, ultra-open-edge-length, self-organized and interconnected networks is demonstrated. The approach for the ultimate thickness reduction to 1–2 graphene layers is also proposed. The vertical graphene networks are optically transparent and show tunable electric properties from semiconducting to semi-metallic and metallic at room and near-room temperature, thus recovering semi-metallic properties of a single-layer graphene.

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The possibility of effective control of morphology and electrical properties of self-organized graphene structures on plasma-exposed Si surfaces is demonstrated. The structures are vertically standing nanosheets and can be grown without any catalyst and any external heating upon direct contact with high-density inductively coupled plasmas at surface temperatures not exceeding 673–723 K. Study of nucleation and growth dynamics revealed the possibility to switch-over between the two most common (turnstile- and maze-like) morphologies on the same substrates by a simple change of the plasma parameters. This change leads to the continuous or discontinuous native oxide layer that supports self-organized patterns of small carbon nanoparticles on which the structures nucleate. It is shown that by tailoring the nanoparticle arrangement one can create various three-dimensional architectures and networks of graphene nanosheet structures. We also demonstrate effective control of the degree of graphitization of the graphene nanosheet structures from the initial through the final growth stages. This makes it possible to tune the electrical resistivity properties of the produced three-dimensional patterns/networks from strongly dielectric to semiconducting. Our results contribute to enabling direct integration of graphene structures into presently dominant Si-based nanofabrication platform for next-generation nanoelectronic, sensor, biomedical, and optoelectronic components and nanodevices.

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Al-doped zinc oxide (AZO) thin films are deposited onto glass substrates using radio-frequency reactive magnetron sputtering and the improvements in their physical properties by post-synthesis thermal treatment are reported. X-ray diffraction spectra show that the structure of films can be controlled by adjusting the annealing temperatures, with the best crystallinity obtained at 400°C under a nitrogen atmosphere. These films exhibit improved quality and better optical transmittance as indicated by the UV-Vis spectra. Furthermore, the sheet resistivity is found to decrease from 1.87 × 10-3 to 5.63 × 10-4Ω⋅cm and the carrier mobility increases from 6.47 to 13.43 cm2 ⋅ V-1 ⋅ s-1 at the optimal annealing temperature. Our results demonstrate a simple yet effective way in controlling the structural, optical and electrical properties of AZO thin films, which is important for solar cell applications.

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The results on the synthesis, mechanical and electrical properties of carbon microcoils and nanocoils (CMCs, CNCs) synthesized using catalytic CVD and Ni-P and Co-P catalyst alloys, respectively, are reported. SEM analysis reveals that the CMCs and CNCs have unique helical morphologies, and diameters of 5.0-9.0 μm and 450-550 nm, respectively. Moreover, CMCs with flat cross-section can be stretched to 3 times their original coil lengths. Current-voltage characteristics of a single microcoil have also been obtained. It is found that the CMCs have the electrical conductivity between 100 and 160 S/cm, whereas the electrical resistance increases by about 20% during the coil extension. Besides, the microcoils can produce light in vacuum when the test voltage reaches 10 V. The emission intensity increases as the voltage increases. The mechanical and electrical properties of CMCs and CNC make them potentially useful in many applications in micromagnetic sensors, mechanical microsprings and optoelectronics.

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The effects of an inductively rotating current were observed on low-frequency inductively coupled plasmas. The spatial distribution of electromagnetic fields was investigated in a cylindrical metallic chamber filled with dense plasma. The distribution of the magnetic field in plasma chamber was observed for rarefied and dense plasmas. The plasma was assumed as uniform in the electromagnetic fields. The results showed the plasma density increased with power and the electron density increased with pressure.

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Brain decoding of functional Magnetic Resonance Imaging data is a pattern analysis task that links brain activity patterns to the experimental conditions. Classifiers predict the neural states from the spatial and temporal pattern of brain activity extracted from multiple voxels in the functional images in a certain period of time. The prediction results offer insight into the nature of neural representations and cognitive mechanisms and the classification accuracy determines our confidence in understanding the relationship between brain activity and stimuli. In this paper, we compared the efficacy of three machine learning algorithms: neural network, support vector machines, and conditional random field to decode the visual stimuli or neural cognitive states from functional Magnetic Resonance data. Leave-one-out cross validation was performed to quantify the generalization accuracy of each algorithm on unseen data. The results indicated support vector machine and conditional random field have comparable performance and the potential of the latter is worthy of further investigation.

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Inductive fault current limiters (FCLs) have several advantages, such as significant current limitation, immediate triggering and relatively low losses. Despite these advantages, saturated core FCLs have not been commercialized due to its large size and associated high costs. A major remaining challenge is to reduce the footprint of the device. In this paper, a solution to reduce the overall footprint is proposed and discussed. In arrangements of windings on a core in reactors such as FCLs, the core is conventionally grounded. The electrical insulation distance between high voltage winding and core can be reduced if the core is left at floating potential. This paper shows the results of the investigation carried out on the insulation of such a coil-core assembly. Two experiments were conducted. In the first, the behavior of the apparatus under high voltage conditions was assessed by performing power frequency and lightning impulse tests. In the second experiment, a low voltage test was conducted during which voltages of different frequencies and pulses with varying rise times were applied. A finite element simulation was also carried out for comparison and further investigation

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Next Generation Sequencing (NGS) has revolutionised molecular biology, resulting in an explosion of data sets and an increasing role in clinical practice. Such applications necessarily require rapid identification of the organism as a prelude to annotation and further analysis. NGS data consist of a substantial number of short sequence reads, given context through downstream assembly and annotation, a process requiring reads consistent with the assumed species or species group. Highly accurate results have been obtained for restricted sets using SVM classifiers, but such methods are difficult to parallelise and success depends on careful attention to feature selection. This work examines the problem at very large scale, using a mix of synthetic and real data with a view to determining the overall structure of the problem and the effectiveness of parallel ensembles of simpler classifiers (principally random forests) in addressing the challenges of large scale genomics.

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(Figure Presented) Unusual conductivity effects: Suitably functionalized dendrimers (see picture) are capable of forming truly covalent three-dimensional networks with remarkably high conductivity on electrochemical doping. Depending on the charging level of the electroactive components used as building blocks for the dendrimer core and the perimeter, two separated regimes of electrical conductivity can be observed.

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We describe and discuss the unique electrical characteristics of an organic field-effect transistor in which the active layer consists of a type II lateral heterojunction located approximately midway between the source and drain. The two active semiconductors on either side of the junction transport only one carrier type each, with the other becoming trapped, which leads to devices that operate in only the steady state when there is balanced electron and hole injections from the drain and source. We describe the unique transfer characteristics of such devices in two material systems.

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Live migration of multiple Virtual Machines (VMs) has become an integral management activity in data centers for power saving, load balancing and system maintenance. While state-of-the-art live migration techniques focus on the improvement of migration performance of an independent single VM, only a little has been investigated to the case of live migration of multiple interacting VMs. Live migration is mostly influenced by the network bandwidth and arbitrarily migrating a VM which has data inter-dependencies with other VMs may increase the bandwidth consumption and adversely affect the performances of subsequent migrations. In this paper, we propose a Random Key Genetic Algorithm (RKGA) that efficiently schedules the migration of a given set of VMs accounting both inter-VM dependency and data center communication network. The experimental results show that the RKGA can schedule the migration of multiple VMs with significantly shorter total migration time and total downtime compared to a heuristic algorithm.

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Age-related Macular Degeneration (AMD) is one of the major causes of vision loss and blindness in ageing population. Currently, there is no cure for AMD, however early detection and subsequent treatment may prevent the severe vision loss or slow the progression of the disease. AMD can be classified into two types: dry and wet AMDs. The people with macular degeneration are mostly affected by dry AMD. Early symptoms of AMD are formation of drusen and yellow pigmentation. These lesions are identified by manual inspection of fundus images by the ophthalmologists. It is a time consuming, tiresome process, and hence an automated diagnosis of AMD screening tool can aid clinicians in their diagnosis significantly. This study proposes an automated dry AMD detection system using various entropies (Shannon, Kapur, Renyi and Yager), Higher Order Spectra (HOS) bispectra features, Fractional Dimension (FD), and Gabor wavelet features extracted from greyscale fundus images. The features are ranked using t-test, Kullback–Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance (CBBD), Receiver Operating Characteristics (ROC) curve-based and Wilcoxon ranking methods in order to select optimum features and classified into normal and AMD classes using Naive Bayes (NB), k-Nearest Neighbour (k-NN), Probabilistic Neural Network (PNN), Decision Tree (DT) and Support Vector Machine (SVM) classifiers. The performance of the proposed system is evaluated using private (Kasturba Medical Hospital, Manipal, India), Automated Retinal Image Analysis (ARIA) and STructured Analysis of the Retina (STARE) datasets. The proposed system yielded the highest average classification accuracies of 90.19%, 95.07% and 95% with 42, 54 and 38 optimal ranked features using SVM classifier for private, ARIA and STARE datasets respectively. This automated AMD detection system can be used for mass fundus image screening and aid clinicians by making better use of their expertise on selected images that require further examination.