972 resultados para dynamic loading device


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Luminescent ZnO nanoparticles have been synthesized on silicon and quartz substrates under extremely non-equilibrium conditions of energetic ion condensation during the post-focus phase in a dense plasma focus (DPF) device. Ar+, O+, Zn+ and ZnO+ ions are generated as a result of interaction of hot and dense argon plasma focus with the surfaces of ZnO pellets placed at the anode. It is found that the sizes, structural and photoluminescence (PL) properties of the ZnO nanoparticles appear to be quite different on Si(1 0 0) and quartz substrates. The results of x-ray diffractometry and atomic force microscopy show that the ZnO nanoparticles are crystalline and range in size from 5-7 nm on Si(1 0 0) substrates to 10-38 nm on quartz substrates. Room-temperature PL studies reveal strong peaks related to excitonic bands and defects for the ZnO nanoparticles deposited on Si (1 0 0), whereas the excitonic bands are not excited in the quartz substrate case. Raman studies indicate the presence of E2 (high) mode for ZnO nanoparticles deposited on Si(1 0 0).

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An overview of dynamic self-organization phenomena in complex ionized gas systems, associated physical phenomena, and industrial applications is presented. The most recent experimental, theoretical, and modeling efforts to understand the growth mechanisms and dynamics of nano- and micron-sized particles, as well as the unique properties of the plasma-particle systems (colloidal, or complex plasmas) and the associated physical phenomena are reviewed and the major technological applications of micro- and nanoparticles are discussed. Until recently, such particles were considered mostly as a potential hazard for the microelectronic manufacturing and significant efforts were applied to remove them from the processing volume or suppress the gas-phase coagulation. Nowadays, fine clusters and particulates find numerous challenging applications in fundamental science as well as in nanotechnology and other leading high-tech industries.

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The continuous steady-state current drive in a spherical argon plasma by transverse oscillating magnetic field (OMF) is investigated. The experimental results reveal that a rotating magnetic field is generated, and its amplitude depends linearly on the external steady vertical magnetic field. It has been shown that steady toroidal currents of up to about 400 A can be driven by a 490 kHz OMF with an input power of 1.4 kW. The generation of steady toroidal magnetic fields directed oppositely in the upper and lower hemispheres have been recorded. The measurements of time-varying magnetic fields unveil a strong nonlinear effect of the frequency-doubled field harmonics generation. The electron number density and temperature of up to 6.2×1018 m-3 and 12 eV have been obtained. The observed effects validate the existing theory of the OMF current drive in spherical plasmas.

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We consider the following problem: a user stores encrypted documents on an untrusted server, and wishes to retrieve all documents containing some keywords without any loss of data confidentiality. Conjunctive keyword searches on encrypted data have been studied by numerous researchers over the past few years, and all existing schemes use keyword fields as compulsory information. This however is impractical for many applications. In this paper, we propose a scheme of keyword field-free conjunctive keyword searches on encrypted data, which affirmatively answers an open problem asked by Golle et al. at ACNS 2004. Furthermore, the proposed scheme is extended to the dynamic group setting. Security analysis of our constructions is given in the paper.

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In the electricity market environment, load-serving entities (LSEs) will inevitably face risks in purchasing electricity because there are a plethora of uncertainties involved. To maximize profits and minimize risks, LSEs need to develop an optimal strategy to reasonably allocate the purchased electricity amount in different electricity markets such as the spot market, bilateral contract market, and options market. Because risks originate from uncertainties, an approach is presented to address the risk evaluation problem by the combined use of the lower partial moment and information entropy (LPME). The lower partial moment is used to measure the amount and probability of the loss, whereas the information entropy is used to represent the uncertainty of the loss. Electricity purchasing is a repeated procedure; therefore, the model presented represents a dynamic strategy. Under the chance-constrained programming framework, the developed optimization model minimizes the risk of the electricity purchasing portfolio in different markets because the actual profit of the LSE concerned is not less than the specified target under a required confidence level. Then, the particle swarm optimization (PSO) algorithm is employed to solve the optimization model. Finally, a sample example is used to illustrate the basic features of the developed model and method.

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In this paper we demonstrate that existing cooperative spectrum sensing formulated for static primary users cannot accurately detect dynamic primary users regardless of the information fusion method. Performance error occurs as the sensing parameters calculated by the conventional detector result in sensing performance that violates the sensing requirements. Furthermore, the error is accumulated and compounded by the number of cooperating nodes. To address this limitation, we design and implement the duty cycle detection model for the context of cooperative spectrum sensing to accurately calculate the sensing parameters that satisfy the sensing requirements. We show that longer sensing duration is required to compensate for dynamic primary user traffic.

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A novel gray-box neural network model (GBNNM), including multi-layer perception (MLP) neural network (NN) and integrators, is proposed for a model identification and fault estimation (MIFE) scheme. With the GBNNM, both the nonlinearity and dynamics of a class of nonlinear dynamic systems can be approximated. Unlike previous NN-based model identification methods, the GBNNM directly inherits system dynamics and separately models system nonlinearities. This model corresponds well with the object system and is easy to build. The GBNNM is embedded online as a normal model reference to obtain the quantitative residual between the object system output and the GBNNM output. This residual can accurately indicate the fault offset value, so it is suitable for differing fault severities. To further estimate the fault parameters (FPs), an improved extended state observer (ESO) using the same NNs (IESONN) from the GBNNM is proposed to avoid requiring the knowledge of ESO nonlinearity. Then, the proposed MIFE scheme is applied for reaction wheels (RW) in a satellite attitude control system (SACS). The scheme using the GBNNM is compared with other NNs in the same fault scenario, and several partial loss of effect (LOE) faults with different severities are considered to validate the effectiveness of the FP estimation and its superiority.

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The amount of metal residues from organometallic reagents used in preparation of poly(9,9-dioctylfluorene) by palladium catalysed Suzuki and nickel-induced Yamamoto polycondensations have been determined, and their effect upon the behaviour of the polymer in field-effect transistors (FETs) has been measured. The metal levels from material polymerised by Suzuki method were found to be much higher than from that made by the Yamamoto procedure. Simple treatment of the polymers with suitable metal trapping reagents lowered the metal levels significantly, with EDTA giving best results for nickel and triphenylphosphine for palladium. Comparison of the behaviour of FETs using polyfluorenes with varying levels of metal contamination, showed that the metal residues have little effect upon the mobility values, but often affect the degree of hysteresis, possibly acting as charge traps. Satisfactory device performances were obtained from polymer with palladium levels of 2000 μg/g suggesting that complete removal of metal residues may not be necessary for satisfactory device performance.

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We investigate the blend morphology and performance of bulk heterojunction organic photovoltaic devices comprising the donor polymer, pDPP-TNT (poly{3,6-dithiophene-2-yl-2,5-di(2-octyldodecyl)-pyrrolo[3,4-c]pyrrole-1, 4-dione-alt-naphthalene}) and the fullerene acceptor, [70]PCBM ([6,6]-phenyl C71-butyric acid methyl ester). The blend morphology is heavily dependent upon the solvent system used in the fabrication of thin films. Thin films spin-coated from chloroform possess a cobblestone-like morphology, consisting of thick, round-shaped [70]PCBM-rich mounds separated by thin polymer-rich valleys. The size of the [70]PCBM domains is found to depend on the overall film thickness. Thin films spin-coated from a chloroform:dichlorobenzene mixed solvent system are smooth and consist of a network of pDPP-TNT nanofibers embedded in a [70]PCBM-rich matrix. Rinsing the films in hexane selectively removes [70]PCBM and allows for analysis of domain size and purity. It also provides a means for investigating exciton dissociation efficiency through relative photoluminescence yield measurements. Devices fabricated from chloroform solutions show much poorer performance than the devices fabricated from the mixed solvent system; this disparity in performance is seen to be more pronounced with increasing film thickness. The primary cause for the improved performance of devices fabricated from mixed solvents is attributed to the greater donor-acceptor interfacial area and resulting greater capacity for charge carrier generation.

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The capability of storing multi-bit information is one of the most important challenges in memory technologies. An ambipolar polymer which intrinsically has the ability to transport electrons and holes as a semiconducting layer provides an opportunity for the charge trapping layer to trap both electrons and holes efficiently. Here, we achieved large memory window and distinct multilevel data storage by utilizing the phenomena of ambipolar charge trapping mechanism. As fabricated flexible memory devices display five well-defined data levels with good endurance and retention properties showing potential application in printed electronics.

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There is substantial evidence for facial emotion recognition (FER) deficits in autism spectrum disorder (ASD). The extent of this impairment, however, remains unclear, and there is some suggestion that clinical groups might benefit from the use of dynamic rather than static images. High-functioning individuals with ASD (n = 36) and typically developing controls (n = 36) completed a computerised FER task involving static and dynamic expressions of the six basic emotions. The ASD group showed poorer overall performance in identifying anger and disgust and were disadvantaged by dynamic (relative to static) stimuli when presented with sad expressions. Among both groups, however, dynamic stimuli appeared to improve recognition of anger. This research provides further evidence of specific impairment in the recognition of negative emotions in ASD, but argues against any broad advantages associated with the use of dynamic displays.