Air Combat Effectiveness Evaluation for Fighter Based on Relevance Vector Machine

Three kernel-based modeling approaches are proposed to predict the local scour around bridge piers using field data. Modeling approaches include Gaussian processes regression (GPR), relevance vector machines (RVM) and a kernlised extreme learning machine (KELM). A dataset consisting of 232 upstream pier scour measurements derived from the Bridge Scour Data Management System (BSDMS) was used. The radial basis kernel function was used with all three kernel-based approaches and results were compared with support vector regression and four empirical relations. Coefficient of determination value of 0.922, 0.922 and 0.900 (root mean square error, RMSE = 0.297, 0.310 and 0.343 m) was achieved by GPR, RVM and KELM algorithm respectively. Comparisons of results with support vector regression and Froehlich equation, Froehlich design, HEC-18 and HEC-18/Mueller predictive equations suggest an improved performance by the proposed approaches. Results with dimensionless data using all three algorithms suggest a better performance by dimensional data.

Accurate and reliable remaining useful life (RUL) prediction is crucial for improving equipment reliability and safety, realizing predictive maintenance. The relevance vector machine (RVM) method is commonly utilized for RUL prediction, profiting from its sparse property under a Bayesian framework. However, the RVM faces the issue of poor robustness, which is mainly manifested as poor prediction accuracy and difficulty in fitting when the predicted data fluctuate greatly. This is due to weights and random errors following Gaussian distributions, which are highly sensitive to outliers. Also, the traditional model training process heavily relies on an additional feature extraction process, which suffers from the problem of effective data loss as well as the risk of overfitting. Thus, a robust regression framework against outliers is developed by incorporating t-distribution into the RVM. And a Q-learning (QL) algorithm is embedded into the constructed robust RVM model to replace the feature extraction process. In addition, this paper firstly predicts the degradation trend of RUL to enhance the accuracy and interpretability of RUL prediction. Finally, a comparative experiment on the performance degradation of capacitors in the traction system is designed, and the root mean square errors for the QL-RRVM, QL-RVM, RRVM, and RVM models are obtained as 0.751, 8.599, 38.316, and 41.892, respectively. The experimental results confirm the superiority of the proposed method.

Smart prediction of liquefaction-induced lateral spreading

This paper presents a new fuzzy inference system for modeling of nonlinear dynamic systems based on input and output data with measurement noise. The proposed fuzzy system has a number of fuzzy rules and parameter values of membership functions which are automatically generated using the extended relevance vector machine (RVM). The RVM has a probabilistic Bayesian learning framework and has good generalization capability. The RVM consists of the sum of product of weight and kernel function which projects input space into high dimensional feature space. The structure of proposed fuzzy system is same as that of the Takagi-Sugeno fuzzy model. However, in the proposed method, the number of fuzzy rules can be reduced under the process of optimizing a marginal likelihood by adjusting parameter values of kernel functions using the gradient ascent method. After a fuzzy system is determined, coefficients in consequent part are found by the least square method. Examples illustrate effectiveness of the proposed new fuzzy inference system.

Robotic radiotherapy can precisely ablate moving tumors when time latencies have been compensated. Recently, relevance vector machines (RVM), a probabilistic regression technique, outperformed six other prediction algorithms for respiratory compensation. The method has the distinct advantage that each predicted point is assumed to be drawn from a normal distribution. Second-order statistics, the predicted variance, were used to control RVM prediction error during a treatment and to construct hybrid prediction algorithms. First, the duty cycle and the precision were correlated to the variance by interrupting the treatment if the variance exceeds a threshold. Second, two hybrid algorithms based on the variance were developed, one consisting of multiple RVMs (HYB(RVM)) and the other of a combination between a wavelet-based least mean square algorithm (wLMS) and a RVM (HYB(wLMS-RVM)). The variance for different motion traces was analyzed to reveal a characteristic variance pattern which gives insight in what kind of prediction errors can be controlled by the variance. Limiting the variance by a threshold resulted in an increased precision with a decreased duty cycle. All hybrid algorithms showed an increased prediction accuracy compared to using only their individual algorithms. The best hybrid algorithm, HYB(RVM), can decrease the mean RMSE over all 304 motion traces from 0.18 mm for a linear RVM to 0.17 mm. The predicted variance was shown to be an efficient metric to control prediction errors, resulting in a more robust radiotherapy treatment. The hybrid algorithm HYB(RVM) could be translated to clinical practice. It does not require further parameters, can be completely parallelised and easily further extended.

Relevance vector machines (RVM) is a machine learning approach with good nonlinear approximation capacity and generalization performance. In order to solve the inverse model for nonlinear systems, a multiple relevance vector machines (MRVM) based inverse dynamics model identification approach was presented. The input and output variables were allocated into multiple calculational subspaces according to their differential orders for the system. The RVM was put forward to identify the influence of the outputs to the inputs with a certain differential order in each subspace. Moreover, another RVM was delivered to connect all subspaces, such that the MRVM based inverse dynamics identification model for the nonlinear systems was constructed. At last it was applied to identify the inverse dynamics of a high temperature exchanger for the generator. And the result validates the effectiveness of the proposed approach.KeywordsInverse dynamicsRelevance vector machinesNonlinear systemSparse Bayesian learningIdentification

Satellite lithium-ion battery remaining useful life estimation with an iterative updated RVM fused with the KF algorithm

In this paper, the structure, function, and technology index system of on-orbit servicing system are presented. The effectiveness index system of on-orbit servicing is established. We analyze the effect which single technology index has on the whole on-orbit servicing system efficiency, by using the idea of sensitivity analysis. Finally, we establish a reasonable and integrated index system of effectiveness evaluation. The new method of effectiveness evaluation presented in this paper could be applied for on-orbit servicing system in the future.

Force and vibration generated in apical direction by three endodontic files of different kinematics during simulated canal preparation: An in vitro analytical study.

Three-Dimensional Quantitative Comparative Analysis of Trapezial-Metacarpal Joint Surface Curvatures in Human Populations

The aim of the present research is to calculate the Relative Accuracy (R.A) and Root Mean Square Error (RMSE) for the location of Technical Institute, Mosul in Nineveh Governorate, Iraq. at the northern part of Mosul city by using surveying Instruments (GPS 1230 , Total Station 06 & Level NAK2), to calculate the coordinates and elevations of (16) Ground Control Points which covered the study area. The field data, remote sensing techniques and GIS software (Arc Map GIS10.3) are used to check the digital map of study area that helped the administrator to take the correct decision, determining positions of the future expansionist for buildings, roads, measuring lengths, areas and there coordinates, directions as well as the total costs estimation needed for any development from the final map. The gradient percentage between the highest and lowest points is calculated to be (9.954%). Relative accuracy for the based measured points (R.A) = 1:60000), and Root Mean Square error in Easting coordinates (RMSE) = ± 0.102m, in Northing coordinates (RMSN) = ± 0.096m, the Root Mean Square for the resultant (RMSR) = ± 0.099m, and Root Mean Square for Elevations ( RMSRL)= ± 0.005m. We Matched and correlated (10) measured points from study area with their graphic data positions on the aerial photo, determining the offsets for position and elevations as follows: (RMSE = ± 0.076m), (RMSN = ± 0.125m), (RMSR = ± 0.088m), and (Root Mean Square for Elevations = RMSRL= ± 0.045m). This research enable us after checking the results to produce new maps from Aerial photograph or Digital Image to use it for land uses and future extensions in whole study area.

Radar antennas in the open air are affected by dynamic wind loading during azimuthal scanning. In this state, the acquisition and prediction of the aerodynamic load is the focus of the structure design for the antenna. We performed wind tunnel force tests on flat plate antennas with different aspect ratios. The changes in mean, maximum, and root mean square values of the aerodynamic coefficients relative to the antenna aspect ratio and reduced frequency are given for the first time. The effects of the antenna aspect ratio and reduced frequency on aerodynamic characteristics are analyzed. The experimental results indicate that the magnitude of the azimuthal moment is related to the antenna aspect ratio and the reduced frequency. It is worth noting that the antenna aspect ratio has a significant impact on the mean, maximum, and root mean square values of dynamic aerodynamic coefficients. The mean, maximum, and root mean square values of the rolling moment coefficient, azimuth moment coefficient, and pitching moment coefficient monotonously decrease with the increase in aspect ratio. In addition, the influence of reduced frequency on the dynamic aerodynamic coefficient is closely related to the antenna aspect ratio. Under the same antenna aspect ratio, increasing the reduced frequency will increase the mean, maximum, and root mean square values of the aerodynamic coefficient. For flat plate antennas with different aspect ratios, at the condition of increasing the aspect ratios, the mean, maximum, and root mean square values of aerodynamic coefficients do not change significantly with the change of reduced frequency.

Online extraction of physical parameters of photovoltaic modules in a building-integrated photovoltaic system

Color, phenolic content, and chemical age values of red wines made from Cretan grape varieties (Kotsifali, Mandilari) were evaluated over nine months of maturation in different containers for two vintages. The wines differed greatly on their anthocyanin profiles. Mid-IR spectra were also recorded with the use of a Fourier Transform Infrared Spectrophotometer in ZnSe disk mode. Analysis of Variance was used to explore the parameter's dependency on time. Determination models were developed for the chemical age indexes using Partial Least Squares (PLS) (TQ Analyst software) considering the spectral region 1830–1500 cm−1. The correlation coefficients (r) for chemical age index i were 0.86 for Kotsifali (Root Mean Square Error of Calibration (RMSEC) = 0.067, Root Mean Square Error of Prediction (RMSEP) = 0,115, and Root Mean Square Error of Validation (RMSECV) = 0.164) and 0.90 for Mandilari (RMSEC = 0.050, RMSEP = 0.040, and RMSECV = 0.089). For chemical age index ii the correlation coefficients (r) were 0.86 and 0.97 for Kotsifali (RMSEC 0.044, RMSEP = 0.087, and RMSECV = 0.214) and Mandilari (RMSEC = 0.024, RMSEP = 0.033, and RMSECV = 0.078), respectively. The proposed method is simpler, less time consuming, and more economical and does not require chemical reagents.

BackgroundMost of the objective and quantitative methods proposed for spasticity measurement are not suitable for clinical application, and methods for surface electromyography (sEMG) signal processing are mainly limited to the time-domain. This study aims to quantify muscle activity in the time–frequency domain, and develop a practical clinical method for the objective and reliable evaluation of the spasticity based on the Hilbert–Huang transform marginal spectrum entropy (HMSEN) and the root mean square (RMS) of sEMG signals.MethodsTwenty-six stroke patients with elbow flexor spasticity participated in the study. The subjects were tested at sitting position with the upper limb stretched towards the ground. The HMSEN of the sEMG signals obtained from the biceps brachii was employed to facilitate the stretch reflex onset (SRO) detection. Then, the difference between the RMS of a fixed-length sEMG signal obtained after the SRO and the RMS of a baseline sEMG signal, denoted as the RMS difference (RMSD), was employed to evaluate the spasticity level. The relations between Modified Ashworth Scale (MAS) scores and RMSD were investigated by Ordinal Logistic Regression (OLR). Goodness-of-fit of the OLR was obtained with Hosmer–Lemeshow test.ResultsThe HMSEN based method can precisely detect the SRO, and the RMSD scores and the MAS scores were fairly well related (test: χ2 = 8.8060, p = 0.2669; retest: χ2 = 1.9094, p = 0.9647). The prediction accuracies were 85% (test) and 77% (retest) when using RMSD for predicting MAS scores. In addition, the test–retest reliability was high, with an interclass correlation coefficient of 0.914 and a standard error of measurement of 1.137. Bland–Altman plots also indicated a small bias.ConclusionsThe proposed method is manually operated and easy to use, and the HMSEN based method is robust in detecting SRO in clinical settings. Hence, the method is applicable to clinical practice. The RMSD can assess spasticity in a quantitative way and provide greater resolution of spasticity levels compared to the MAS in clinical settings. These results demonstrate that the proposed method could be clinically more useful for the accurate and reliable assessment of spasticity and may be an alternative clinical measure to the MAS.