Multiple solutions for a class of semilinear elliptic problems with Robin boundary condition

AbstractIn this paper, we show the existence of at least four nontrivial solutions for a class of semilinear elliptic problems with Robin boundary condition and jumping nonlinearities. Solvability of oscillating equations with Robin boundary condition is also investigated. We prove the conclusions by using sub-super-solution method, Fučík spectrum theory, mountain pass theorem in order intervals and Morse theory

CDMBE: A Case Description Model Based on Evidence

By combining the advantages of argument map and Bayesian network, a case description model based on evidence (CDMBE), which is suitable to continental law system, is proposed to describe the criminal cases. The logic of the model adopts the credibility logical reason and gets evidence-based reasoning quantitatively based on evidences. In order to consist with practical inference rules, five types of relationship and a set of rules are defined to calculate the credibility of assumptions based on the credibility and supportability of the related evidences. Experiments show that the model can get users’ ideas into a figure and the results calculated from CDMBE are in line with those from Bayesian model

Analysis of significant factors on cable failure using the Cox proportional hazard model

This paper proposes the use of the Cox proportional hazard model (Cox PHM), a statistical model, for the analysis of early-failure data associated with power cables. The Cox PHM analyses simultaneously a set of covariates and identifies those which have significant effects on the cable failures. In order to demonstrate the appropriateness of the model, relevant historical failure data related to medium voltage (MV, rated at 10 kV) distribution cables and High Voltage (HV, 110 kV and 220 kV) transmission cables have been collected from a regional electricity company in China. Results prove that the model is more robust than the Weibull distribution, in that failure data does not have to be homogeneous. Results also demonstrate that the method can single out a case of poor manufacturing quality with a particular cable joint provider by using a statistical hypothesis test. The proposed approach can potentially help to resolve any legal dispute that may arise between a manufacturer and a network operator, in addition to providing guidance for improving future practice in cable procurement, design, installations and maintenance

Self-learning PID Control for X-Y NC Position Table with Uncertainty Base on Neural Network

An adaptive radical basis function (RBF) neural network PID control scheme for X-Y position table is proposed by the paper. Firstly, X-Y position table model is established, controller based on neutral network is used to learn adaptive and compensate uncertainty model of X-Y position table, neutral network is used to study model. PID neural network controller base on augmented variable method is designed. PID controller is used as assistant direction error controller, neural network parameters base on stochastic gradient algorithm can be adjust adaptive on line. The simulation results show that the presented controller has important engineering value

Hyperspectral Remote Sensing Estimation Models of Aboveground Biomass in Gannan Rangelands

AbstractAccurate estimates of grass biomass can provide valuable information about the productivity and functioning of rangelands and grassland ecological resource utilizing more reasonable. In order to improve the biomass quantitative analysis with hyperspectral remote sensing data, a field experiment was carried out in Gannan rangelands, Gansu province. To achieve this objective, fresh grass aboveground biomass and hyperspectral canopy reflectance were collected at four types pasture in august 2007. On the base of the analysis of spectral characteristic of four grasslands and correlation between original spectral, hyperspectral feature variables and aboveground biomass of four rest grazing grasslands, the experiment data were classified two groups. One group was used as the training sample to build the regression of models with the one-sample linear method, the nonlinear method and stepwise analysis method, another group was used to the testing sample to predict the precision of regression models. Results show that the regression of quadratic model using RVI provide a better univariate regression involving hyperspectral indices for grass aboveground fresh biomass estimation compared other models in Gannan rangelands, the estimation standard deviation was 0.178 (kg/m2), In conclusion, the results of this paper indicate that the grassland biomass can be estimated at the canopy level using the hyperspectral reflectance