基于Duffing方程参数敏感性提取谐振型传感器频率的仿真研究

由于谐振型传感器在感测被测之量变化时 ,表现为谐振频率的偏移 ,因此 ,为提高传感器的分辨率和灵敏度 ,对探索频偏的精确测定很为重要 .本文首先对 Duffing方程进行仿真 ,指出 Duffing方程在从混沌状态到大周期转变时 ,同时具有幅值敏感性和频率敏感性 ,进一步提出频率敏感性可应用于传感器微弱频率变化的提取 .仿真表明该方法还能实现对不同频率变化精度的预性测量 ,文中还进一步分析了其理论基础 。

Rock critical porosity inversion and S-wave velocity prediction

A critical porosity model is often used to calculate the dry frame elastic modulus by the rock critical porosity value which is affected by many factors. In practice it is hard for us to obtain an accurate critical porosity value and we can generally take only an empirical critical porosity value which often causes errors. In this paper, we propose a method to obtain the rock critical porosity value by inverting P-wave velocity and applying it to predict S-wave velocity. The applications of experiment and log data both show that the critical porosity inversion method can reduce the uncertainty resulting from using an empirical value in the past and provide the accurate critical porosity value for predicting S-wave velocity which significantly improves the prediction accuracy.

Application of support vector machine in trip chaining pattern recognition and analysis of explanatory variable effects

In order to improve the accuracy of travel demand forecast and considering the distribution of travel behaviors within time dimension, a trip chaining pattern recognition model was established based on activity purposes by applying three methods： the support vector machine （SVM） model, the radial basis function neural network （RBFNN） model and the multinomial logit （MNL） model. The effect of explanatory factors on trip chaining behaviors and their contribution to model performace were investigated by sensitivity analysis. Results show that the SVM model has a better performance than the RBFNN model and the MNL model due to its higher overall and partial accuracy, indicating its recognition advantage under a smai sample size scenario. It is also proved that the SVM model is capable of estimating the effect of multi-category factors on trip chaining behaviors more accurately. The different contribution of explanatory, factors to trip chaining pattern recognition reflects the importance of refining trip chaining patterns ad exploring factors that are specific to each pattern. It is shown that the SVM technology in travel demand forecast modeling and analysis of explanatory variable effects is practical.

Application of seismic multi-attribute fusion method based on D-S evidence theory in prediction of CBM-enriched area

D-S evidence theory provides a good approach to fuse uncertain inlbrmation. In this article, we introduce seismic multi-attribute fusion based on D-S evidence theory to predict the coalbed methane （CBM） concentrated areas. First, we choose seismic attributes that are most sensitive to CBM content changes with the guidance of CBM content measured at well sites. Then the selected seismic attributes are fused using D-S evidence theory and the fusion results are used to predict CBM-enriched area. The application shows that the predicted CBM content and the measured values are basically consistent. The results indicate that using D-S evidence theory in seismic multi-attribute fusion to predict CBM-enriched areas is feasible.

Nonlinear Model Predictive Control Based on Support Vector Machine with Multi-kernel

Multi-kernel-based support vector machine （SVM） model structure of nonlinear systems and its specific identification method is proposed, which is composed of a SVM with linear kernel function followed in series by a SVM with spline kernel function. With the help of this model, nonlinear model predictive control can be transformed to linear model predictive control, and consequently a unified analytical solution of optimal input of multi-step-ahead predictive control is possible to derive. This algorithm does not require online iterative optimization in order to be suitable for real-time control with less calculation. The simulation results of pH neutralization process and CSTR reactor show the effectiveness and advantages of the presented algorithm.

Performance Monitoring and Diagnosis of Multivariable Model Predictive Control Using Statistical Analysis

A statistic-based benchmark was proposed for performance assessment and monitoring of model predic- tive control; the benchmark was straightforward and achievable by recording a set of output data only when the control performance was good according to the user’s selection. Principal component model was built and an auto- regressive moving average filter was identified to monitor the performance; an improved T2 statistic was selected as the performance monitor index. When performance changes were detected, diagnosis was done by model validation using recursive analysis and generalized likelihood ratio (GLR) method. This distinguished the fact that the per- formance change was due to plant model mismatch or due to disturbance term. Simulation was done about a heavy oil fractionator system and good results were obtained. The diagnosis result was helpful for the operator to improve the system performance.

Nonlinear Time Series Prediction Using LS-SVM with Chaotic Mutation Evolutionary Programming for Parameter Optimization

Nonlinear time series prediction is studied by using an improved least squares support vector machine （LSSVM） regression based on chaotic mutation evolutionary programming （CMEP） approach for parameter optimization. We analyze how the prediction error varies with different parameters （σ, γ） in LS-SVM. In order to select appropriate parameters for the prediction model, we employ CMEP algorithm. Finally, Nasdaq stock data are predicted by using this LS-SVM regression based on CMEP, and satisfactory results are obtained.

Prediction of multi-borehole undermine coalbed gas drainage

By analyzing the flow character of a single drainage borehole in its effectingtime and the correlative theory introduced,the reason for 'inflexion' appearance in theflow character curve of the single draining borehole in a multi-borehole was studied.Takingthe theory of permeation fluid mechanics and so on as basis,the coalbed gas flowmodel was set up,and the numerical simulation analyzer was built for undermine gasproducts.With the results from the analyzer,the gas capacity could be calculated underdifferent conditions and comparisons made with the site measurement data.

Investigation of effective dimensionless numbers on initiation of instability in combustion of moisty organic dust

In this work, the effect of various effective dimensionless numbers and moisture contents on initiation of instability in combustion of moisty organic dust is calculated. To have reliable model, effect of thermal radiation is taken into account. One- dimensional flame structure is divided into three zones： preheat zone, reaction zone and post-flame zone. To investigate pulsating characteristics of flame, governing equations are rewritten in dimensionless space-time （（, r/, ~） coordinates. By solving these newly achieved governing equations and combining them, which is completely discussed in body of article, a new expression is obtained. By solving this equation, it is possible to predict initiation of instability in organic dust flame. According to the obtained results by increasing Lewis number, threshold of instability happens sooner. On the other hand, pulsating is postponed by increasing Damk6hler number, pyrolysis temperature or moisture content. Also, by considering thermal radiation effect, burning velocity predicted by our model is closer to experimental results.

Discussion About Nonlinear Time Series Prediction Using Least Squares Support Vector Machine

The least squares support vector machine (LS-SVM) is used to study the nonlinear time series prediction. First, the parameter gamma and multi-step prediction capabilities of the LS-SVM network are discussed. Then we employ clustering method in the model to prune the number of the support values.. The learning rate and the capabilities of filtering noise for LS-SVM are all greatly improved.

Effects of automatic variable preload device on performance of spindle

To increase the machine accuracy by improving the stiffness of bearings,a preload was applied to bearings.A variable preload technology was necessary to perform machining processes in both low and high speed regions.An automatic variable preload device was fabricated using an eccentric mass.By installing the fabricated device on a spindle,the effect of the automatic variable preload device on the performance of the spindle was analyzed.In the results of the vibration measurement of the spindle,the vibration is increased by 20%-37% according to measurement points at the maximum rotation speed of 5 000 r/min.And,in the results of the noise measurement of the spindle,the spindle rotation speed is increased by about 1.9% and 1.5% at the front and side of the spindle,respectively.Based on the results of this analysis,an improved method that reduces such effects on the performance of the spindle is proposed.

Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks

Uniaxial Compressive Strength (UCS) and modulus of elasticity (E) are the most important rock parameters required and determined for rock mechanical studies in most civil and mining projects. In this study, two mathematical methods, regression analysis and Artificial Neural Networks (ANNs), were used to predict the uniaxial compressive strength and modulus of elasticity. The P-wave velocity, the point load index, the Schmidt hammer rebound number and porosity were used as inputs for both meth-ods. The regression equations show that the relationship between P-wave velocity, point load index, Schmidt hammer rebound number and the porosity input sets with uniaxial compressive strength and modulus of elasticity under conditions of linear rela-tions obtained coefficients of determination of (R2) of 0.64 and 0.56, respectively. ANNs were used to improve the regression re-sults. The generalized regression and feed forward neural networks with two outputs (UCS and E) improved the coefficients of determination to more acceptable levels of 0.86 and 0.92 for UCS and to 0.77 and 0.82 for E. The results show that the proposed ANN methods could be applied as a new acceptable method for the prediction of uniaxial compressive strength and modulus of elasticity of intact rocks.

An investigation of surface deformation after fully mechanized,solid back fill mining

The surface deformation after fully mechanized back filling mining was analyzed.The surface deformation for different backfill materials was predicted by an equivalent mining height model and numerical simulations.The results suggest that:(1) As the elastic modulus,E,of the backfill material increases the surface subsidence decreases.The rate of subsidence decrease drops after E is larger than 5 GPa;(2) Fully mechanized back fill mining technology can effectively control surface deformation.The resulting surface deformation is within the specification grade I,which means surface maintenance is not needed.A site survey showed that the equivalent mining height model is capable of predicting and analyzing surface deformation and that the model is conservative enough for engineering safety.Finally,the significance of establishing a complete error correction system based on error analysis and correction is discussed.

Improvement of System Dynamic Precision by Dynamic Prediction

According to the characteristic of the sensor inertia, the dynamic prediction to improve the system dynamic precision is presented in this paper. With the recurrence calculation of time constant of the sensor, the system dynamic precision is greatly improved. The example using this method is given.

Energy-absorption forecast of thin-walled structure by GA-BP hybrid algorithm

In order to analyze the influence rule of experimental parameters on the energy-absorption characteristics and effectively forecast energy-absorption characteristic of thin-walled structure, the forecast model of GA-BP hybrid algorithm was presented by uniting respective applicability of back-propagation artificial neural network （BP-ANN） and genetic algorithm （GA）. The detailed process was as follows. Firstly, the GA trained the best weights and thresholds as the initial values of BP-ANN to initialize the neural network. Then, the BP-ANN after initialization was trained until the errors converged to the required precision. Finally, the network model, which met the requirements after being examined by the test samples, was applied to energy-absorption forecast of thin-walled cylindrical structure impacting. After example analysis, the GA-BP network model was trained until getting the desired network error only by 46 steps, while the single BP-ANN model achieved the same network error by 992 steps, which obviously shows that the GA-BP hybrid algorithm has faster convergence rate. The average relative forecast error （ARE） of the SEA predictive results obtained by GA-BP hybrid algorithm is 1.543%, while the ARE of the SEA predictive results obtained by BP-ANN is 2.950%, which clearly indicates that the forecast precision of the GA-BP hybrid algorithm is higher than that of the BP-ANN.

Grey correlational and SSR analyses of cotton hybrids

Ten upland cotton strains exhibiting 3 fiber quality traits and 8 yield traits, were grown for two years in an investigation of the correlation between grey relational analysis （GRA） and genetic identity in heterosis of cot- ton hybrid. The aim was to establish the optimal approach for heterosis prediction and parent selection. Plant traits data were collected and analyzed for GRA. In addition, 72 simple sequence repeat （SSR） markers were examined and 148 polymorphisms were detected. Correlation analysis of GRA, genetic identity, Ft fiber quality and yield heterosis was conducted. Significant differences were observed between the two analytic methods, whereas compa- rable predictions were given for yield heterosis. GRA for yield exhibited slightly higher correlation than genetic identity analysis, with a correlation coefficient of 0.49. GRA and genetic analysis exhibited overlapping yet dis- tinct advantages in heterosis prediction. Therefore, these analytical methods should be integrated to achieve the op- timal heterosis prediction and parent selection.

Measured and Predicted Aging Properties of Arizona Asphalt Binders

The Arizona Department of Transportation （ADOT） in the USA conducted a series of asphalt aging related research and special studies between the 1970s and 1990s. The studies covered over 157 test sections representing both neat （virgin） asphalt and crumb rubber modified （asphalt-rubber） binders. The data comprised of a wide range of penetration, viscosity, and Performance Grade （PG） parameters, at original and aged conditions. In the late 1990s, asphalt PG complex shear modulus （G＊）, and phase angle （5） data were collected. The main purpose of this paper was to use the assembled database of the field core-aged asphalt test data and compare the test results to the American Association of State Highway and Transportation Officials approved Mechanistic-Empirical Pavement Design Guide （MEPDG） predictive modeled asphalt properties such as penetration and viscosity, G＊, and 5. Furthermore, G＊ and laboratory measurements on neat and asphalt-rubber binders extracted from the field cores of the pavement sections aged ten or more years were compared to the pressure aging vessel PG G＊ and ~. values. It was observed that the MEPDG predicted asphalt binder properties were rational for originally （tank） sampled binders, but fairly correlated for the aged binders. Additionally, penetration and viscosity aging indices representing over 20 years of field aged sections were established for a wide variety of asphalt binder grades. Overall, the relationships for aging indices were meaningful and rational. Results of this research indicated the degree of difficulty in predicting asphalt binder properties for pavements with ten or more years of field aging. The findings from this research study are envisioned to be of substantial value in future asphalt binder aging studies.

The Effect of Error Growth and Propagation on the Predictability of Quantitative Precipitation in a Cloud-Resolving Model

Limitations in the predictability of quantitative precipitation forecasting (QPF) that arise from initial errors of small amplitude and scale are investigated by means of real-case high-resolution (cloud-resolving) numerical weather prediction (NWP) integrations. The case considered is the hail and wind disaster that occurred in Sichuan on 8 April 2005. A total of three distinct perturbation methods are used. The results suggest that a tiny initial error in the temperature field can amplify and influence the weather in a large domain, changing the 12-h forecasted rainfall by as much as one-third of the original magnitude. Furthermore, the comparison of the perturbation methods indicates that all of the methods pinpoint the same region (the heavy rainfall areas in the control experiment) as suffering from limitations in predictability. This result reveals the important role of nonlinearity in severe convective events.

Using nutritional assessment to predict gender-specific risks of sarcopenia among the elderly receiving health check-up in Taipei

Quality of life for the elderly in an ageing society is receiving more attention than ever. After age 40, muscle mass loses at the rate of 3% to 8% every 10 years. More- over, the decline intensifies after 60 years old. Although many people do not experience changes in total body weight, their muscle mass is slowly replaced by body fat. Decreased muscle mass means lower muscle strength, which affects physical functioning. As a result, daily activities become con- strained, risks of fall and bone fracture elevated,

Control-oriented dynamic fuzzy model and predictive control for proton exchange membrane fuel cell stack

Proton exchange membrane fuel cell (PEMFC) stack temperature and cathode stoichiometric oxygen are very important control parameters. The performance and lifespan of PEMFC stack are greatly dependent on the parameters. So, in order to improve the performance index, tight control of two parameters within a given range and reducing their fluctuation are indispensable. However, control-oriented models and control strategies are very weak junctures in the PEMFC development. A predictive control algorithm was presented based on their model established by input-output data and operating experiences. It adjusts the operating temperature to 80 ℃. At the same time, the optimized region of stoichiometric oxygen is kept between 1.8?2.2. Furthermore, the control algorithm adjusts the variants quickly to the destination value and makes the fluctuation of the variants the least. According to the test results, compared with traditional fuzzy and PID controllers, the designed controller shows much better performance.