Applied Mechanics and Materials Vols. 423-426

Abstract: In view of the problem of quartz flexible accelerometer output which is influenced by temperature, through the 12 gravity rolling experiment method, the temperature model of the partial value and the scaling factor of the accelerometer can be established in the designed experimental platform. Thus, the temperature compensation model of the accelerometer can be determined. It basically eliminates the output error of the accelerometer which varies with the temperature and improves the output precision of the accelerometer.

Abstract: This communication reports on a new electrochemical method to detect the hybridization specificity in homogenous solution by using host-guest recognition technique. A hairpin DNA with a dabcyl molecule which is typical guest molecule to b-CD at the 3’-terminus and a NH2 linked at the 5’-terminus as the probe DNA. The probe DNA was immobilized on the PdS nanoparticle to construct a double-labeled probe (DLP) and could selectively hybridize with its target DNA in homogenous solution. A b-CD modified Poly(N-acetylaniline) glassy carbon electrode was used for capturing dabcyl in DLP. Without binding with target DNA, the DLP keep stem-loop structure and block dabcyl enter into the cavity of b-CD on electrode. However, a target-binding DLP is incorporated into double stranded DNA, causing loop-stem structure opened and dabcyl could be easy captured by b-CD which brought DLP on electrode surface. With electrochemical measurement, the signal come from Pd2+ be used for target DNA quantitative analysis.

Abstract: A novel design of micro-hotplate is proposed for micro-structural gas sensor. The simulation results of ANSYS reveal that higher temperature and more uniform temperature distribution was achieved in the micro-hotplate when the thickness of SiO₂, thickness of Si substrate, electrode width and electrode space were designed to be 100, 200, 20 and 230 μm, respectively. The new micro-hotplate is benefit for the improvement of sensor sensitivity.

Abstract: Spiral macrobend losses of silica multimode fibers were investigated experimentally with LED light source having a central wavelength of 890 nm. It is indicated that the bend loss vary acutely with change of real curvature radius of fiber axis when the latter is less than about 3 cm. The bend loss oscillation phenomenon caused by the whispering-gallery mode has been observed when the real curvature radii are in the range of 1.77-1.87 cm. The real curvature radius and the number of spirals are the main factors affecting bend loss and there is an approximate linear relation between bend loss and the product of the square of real curvature and the number of spirals. The modified forms of the Marcuse formula or the Petermann formula of bend loss by a multiplication factor called as winding circle density, i.e. the number of spirals on unit length of curved part of optical fiber, can fit the experimental data better.

Abstract: We herein constructed a sensor that converts target DNA hybridization-induced conformational transformation of the probe DNA to electrochemical response based on host-guest recognition and nanoparticle label. In the sensor, the hairpin DNA terminal-labeled with 4-((4-(dimethylamino)phenyl)azo)benzoic acid (dabcyl) and NH2 group was immobilized on multi-walled carbon nanotube modified glassy carbon electrode (MCNTs/GCE) surface, and the Au nanoparticles surface-modified with b-cyclodextrins (CD-AuNPs) were employed as electrochemical signal provider and host-guest recognition element. Initially, the probe DNA immobilized on electrode kept the stem-loop configuration, which shielded dabcyl from docking with the CD-AuNPs in solution due to the steric effect. After target hybridization, the probe DNA underwent a significant conformational change, which forced dabcyl away from the electrode. As a result, formerly-shielded dabcyl became accessible to host-guest recognition between b-cyclodextrin (b-CD) and dabcyl, thus the target hybridization event could be sensitively transduced to electrochemical signal provided by CD-AuNPs. This host-guest recognition-based electrochemical sensor has been able to detect as low as picomolar DNA target with excellent differentiation ability for even single mismatch.

Abstract: In order to improve the detecting efficiency of the Hall vehicle speed sensor, an improved detection system was presented in this paper. This system was consists of five parts of system manager, A/D converter, D/A converter, voltage comparator, and a high-speed programmable counter array. The quality of Hall vehicle speed sensor online was evaluated, and the statistical result was graphically displayed by PC. Compared with the synchronous detection of digital storage oscilloscope, it is shown that the detecting accuracy of the system can then meet the requirements of industrial production, and will be suitable for mass production.

Abstract: To predict the aeroengine exhaust gas temperature (EGT) more precisely, a process neuron with time-varying threshold function is proposed in this paper, and then the time-varying threshold process neural network model comprised of the presented process neurons is used for EGT prediction. By introducing a group of appropriate orthogonal basis functions, the input functions, the weight functions and the threshold functions of the time-varying threshold process neural network can be expanded as linear combinations of the given orthogonal basis functions, thus to eliminate the integration operation, then to simplify the time aggregation operation. The corresponding learning algorithm is also presented, and the effectiveness of the time-varying threshold process neural network model is evaluated through the prediction of EGT series from practical aeroengine condition monitoring.

Abstract: Considering the problem of health condition prognostics of complex equipment, a discrete input process neural networks (DPNN) model based on process neural networks (PNN) is proposed in this paper. DPNN utilizes vector inputs together with convolution operator to gain the capability of time and spatial aggregation operation, which is implemented with continuous function inputs and integral operator by PNN. Different from PNN, DPNN can use discrete samples as inputs directly, thus can avoid precision loss during procedures of data fitting and function expanding required by PNN. The application of DPNN to health condition prognostics of complex equipment is described through the prediction of the future health state of the civil aircraft engines, where the short-term and long-term predictions of the health condition represented by the exhausted gas temperature time series are conducted. Moreover, the performance of DPNN is compared with common artificial neural networks (NN) and PNN. The results show that DPNN has satisfied performance for health condition prognostics of civil aircraft engines, and DPNN performs better than both NN and PNN, which prove that DPNN is suitable for health condition prognostics of complex equipment.

Abstract: To solve the aeroengine health condition prediction problem, a process extreme learning machine (P-ELM) is proposed based on the process neural networks (PNN) and the extreme learning machine (ELM). The proposed P-ELM has an ability of processing time accumulation effects widely existing in practical systems. The proposed P-ELM has only one unknown parameter which can be calculated directly rather than in the iteration way, thus the training time can be significantly reduced. After being validated via the prediction of Mackey-Glass time series, the proposed P-ELM is utilized to predict the aeroengine exhausted gas temperature, and the test results is satisfied. It has shown by the contrast tests that the proposed P-ELM can outperform the ELM, but has equal performance with the PNN. However, with just one unknown parameter which can be calculated directly, the proposed P-ELM is much easier to use and it needs much less training time. Thus, the proposed P-ELM is more adaptable to the practical situation of aeroengine health condition prediction compared with the PNN.

Abstract: The intelligent software testing system for axles based on Visual C++ is introduced. The method is simple, efficient and economical and can meet the needs for axle intelligent testing at present. The testing procedure of this system is described. The software structure and its function are given.