Gauss process regression for real-time ionospheric delay estimation from GNSS observations

The number of devices equipped with global satellite positioning has exceeded seven billion recently. There are a wide variety of receivers regarding their accuracy and reliability. Low cost, multi-frequency units have been released on the market latterly; however, the number of single-frequency receivers is still significant. Since their measurements are influenced by ionospheric delay, accurate ionosphere models are of utmost importance to reduce the effect. This paper summarizes how Gauss process regression (GPR) can be applied to derive near real-time regional ionosphere models using raw Global Navigation Satellite System (GNSS) observations of permanent stations. While Gauss process is widely used in machine learning, GPR is a nonparametric, Bayesian approach to regression. GPR has several benefits for ionosphere monitoring since it is quite robust and efficient to derive a grid model from data available in irregular set of ionospheric pierce points. The corresponding instrumental delays are estimated by a parallel Kalman filter. The presented algorithm can be applied near real-time, however the results are offline calculated and are compared to two high quality TEC map products. Based on the analysis, the accuracy of the GPR modell is in 2 TECu range. The developed methods could be efficiently applied in the field of autonomous vehicle navigation with meeting both accuracy and integrity requirements.

Decision-Making Optimization of Mine Gas Monitoring Based on Gauss Process Regression and Interval Number Correlation Analysis

For the subjective limitation of gas sensor calibration in coal mines, a decision-making method for gas sensor calibration under monitoring failure was studied based on the Gauss process regression (GPR) and the correlation analysis of interval numbers. Based on the correlation characteristics of gas monitoring data of each monitoring point in the work face area in coal mine, the initial confidence interval of gas concentration in monitoring failure period was obtained by GPR, and then the confidence interval was further optimized by the correlation analysis of interval numbers. According to the correlation characteristics of monitoring data of each monitoring point, its similarity of dynamic variation tendency was measured by using Euclidean distance of interval numbers, and the optimal confidence interval was determined by calculating the correlation degree of interval numbers. The case study shows that making full use of the effective monitoring information of multiple monitoring points ensures the reliability of the initial confidence interval; the dynamic adjustment of model parameters in correlation analysis of interval number avoids the subjectivity defect of similar methods and further obtains the consistency between interval numbers’ reliability and correlation degree, which can ensure the effectiveness of the application of this method.

Creep Parameter Inversion and Long-Term Stability Analysis of Tunnel Based on GP-DE Intelligent Algorithm

Aiming at the creep problem of Banshi Tunnel in Jilin province, the creep laws of rock are analyzed by the creep test, and the Cvsic model describing the creep characteristics of the tunnel is established. To obtain the creep parameters accurately, considering the advantages of Gauss process and differential evolution algorithm, coupling the two methods, a Gauss process-differential evolution intelligent inversion method is proposed. According to on-site monitoring data, the the creep parameters of the tunnel are accurately inverted. On this basis, the stability analysis of the tunnel and the selection of a reasonable construction plan are carried out. The research results show that to ensure the stability of the tunnel, the construction scheme of initial lining + pipe shed + advanced grouting anchor rod should be adopted. The research results have guiding significance for the long-term stability evaluation of the tunnel.

Application of Soft Compiting Techniques in River Flow Modeling in The Case of Euphrates-Tigres Basin

River stream estimation is a subject matter that needs constant research and development since it is all-important in the management of water resources, meeting the water demand, irrigation and agricultural activities, and providing distant signal in unwanted situations such as floods. Unfortunately, a universal technique has not been found yet although many techniques have been used for estimation and modelling. This has made it necessary to develop different techniques and/ or to make comparisons between techniques and to determine the most accurate method for the parameters used. In this study, using the 1981-2010 flow data of 14 stations located across Euphrates-Tigris basin, evaluations have been made through Adaptive-Network Based Fuzzy Inference Systems (ANFIS), Support Vector Regression (SVR-SVMR) techniques, and the newly used Gauss Process Regression (GPR), Extreme Learning Machine (ELM) and Emotional Neural Network (ENN) artificial intelligence techniques, and through rank analysis, it is aimed to find out which technique gives better results and to overcome some problems in traditional methods. Although all models work well, the sequence with regards to the comparison outcomes of the techniques obtained from rank analysis was observed to be ELM, GPR, ENN, SVM, ANFIS respectively. In addition, stream values were used in the whole study, these values were examined within 3 different combinations and it was observed that the best result was found in the combination of [input]Q(t-3),Q(t-2),Q(t-1)/[output]Q(t). Keywords: River Flow Modelling; ANFIS; SVM; GPR; ELM; ENN

Design and application of PneuNets bending actuator

Purpose The purpose of this study is to propose an improved design of PneuNets bending actuator which aims at obtaining larger deflection with the same magnitude of pressure. The PneuNets bending actuator shows potential application in the morphing trailing edge concept. Design/methodology/approach Finite element method is used to investigate the characteristics of the improved design bending actuator. Multiobjective optimal design of the PneuNets bending actuator is proposed based on the Gauss process regression models. Findings The maximum deflection is obtained when the height of the beams is smaller than half the height of the chambers. The spacing between chambers (beam length) has little effect on the deflection. Larger spacing could be used to reduce the actuator weight. Originality/value With the same pressure magnitude, the deflection of the improved design bending actuator is much larger than that of the baseline configuration. PneuNets bending actuator could increase the continuity of the aerodynamic surface compared to other actuators.

Gauss process state-space model optimization algorithm with expectation maximization

A Gauss process state-space model trained in a laboratory cannot accurately simulate a nonlinear system in a non-laboratory environment. To solve this problem, a novel Gauss process state-space model optimization algorithm is proposed by combining the expectation–maximization algorithm with the Gauss process Rauch–Tung–Striebel smoother algorithm, that is, the EM-GP-RTSS algorithm. First, a theoretical formulation of the Gauss process state-space model is proposed, which is not found in previous references. Second, a Gauss process state-space model optimization framework with the expectation–maximization algorithm is proposed. In the expectation–maximization algorithm, the unknown system state is considered as the lost data, and the maximization of measurement likelihood function is transformed into that of a conditional expectation function. Then, the Gauss process–assumed density filter algorithm and the Gauss process Rauch–Tung–Striebel smoother algorithm are proposed with the Gauss process state-space model defined in this article, in order to calculate the smoothed distribution in the conditional expectation function. Finally, the Monte Carlo numerical integral method is used to obtain the approximate expression of the conditional expectation function. The simulation results demonstrate that the Gauss process state-space model optimized by the EM-GP-RTSS can simulate the system in the non-laboratory environment better than the Gauss process state-space model trained in the laboratory, and can reach or exceed the estimation accuracy of the traditional state-space model.

A Modeling Method of Stochastic Parameters’ Inverse Gauss Process Considering Measurement Error under Accelerated Degradation Test

To solve the problem that the individual differences and the measurement errors affect the accuracy of life estimation in accelerated degradation test, the inverse Gauss process with stochastic parameters is applied in the accelerated degradation test with the consideration of the influence of individual differences, and the analysis of measurement uncertainty is carried out. An inverse Gauss accelerated degradation model considering both individual differences and measurement errors is established. In the maximum likelihood estimation of parameters, Genetic Algorithm and Monte Carlo integral are used to solve the problems caused by complex integral and the unobservable measurement errors in the calculation process. Finally, the proposed method is verified by the Monte Carlo simulation under the constant accelerated stress and step accelerated stress and the illustrative example of electrical connectors under the constant acceleration stress, respectively. The results show that the modeling tool is useful for improving the accuracy of the life prediction and the reliability evaluation.