Laser-induced Breakdown spectroscopy quantitative analysis method via adaptive analytical line selection and relevance vector machine regression model

In this paper, we present a comparative evaluation of several appearance and shape descriptors in the context of 3D human pose estimation. Among the shape descriptors, we evaluate the Discrete Cosine Transform (DCT) and the Histogram of Shape Context (HoSC) descriptors. The five appearance descriptors that we evaluate are all variants of the Histogram of Oriented Gradients (HOG) descriptor. We evaluate these descriptors quantitatively using the HumanEva-I dataset. We report the performance of the descriptors using the Relevance Vector Machine (RVM) regression and K-nearest neighbor (KNN) regression methods. We found that the appearance descriptor computed at multiple spatial regions gave the best performance when RVM regression was used for pose estimation. The DCT descriptor performed the best when KNN regression was used for pose estimation.

Due to the influence of many factors, there is no reasonable evaluation method for the heat loss evaluation of the envelope, which leads to the deviation of the evaluation results of building energy consumption. By comparing different regression analysis models and considering the heat transfer characteristics of the envelope and heat loss evaluation needs, the dynamic reference value of heat loss evaluation “temperature baseline” which meets different energy saving standards is established. The evaluation method of instantaneous heat loss of the envelope is proposed and the heat loss is studied. The results showed that the performance indexes of relevance vector machine (RVM) regression model was superior to the other two regression models(response surface methodology (RSM), support vector machine (SVM). Based on the RVM regression model, the dynamic prediction model of “temperature baseline” was established by combining numerical simulation with regression analysis. Taking a residential building as an example, the envelope temperature baselines meeting the energy saving standard of 75% and 50% were obtained by using the prediction model, the temperature baselines of walls and windows that save 75% energy were −0.6°C and 1.9 °C respectively, and those that save 50% energy were 1.2°C and 4.5°C. The infrared image and heat loss evaluation method found that 87% of the points exceeded the temperature baseline by more than 2°C, and the total heat loss of the wall with an area of 13m2 was 471W. The heat loss evaluation method based on RVM proposed in this paper can objectively evaluate the heat loss of building.

Although Relevance Vector Machine (RVM) is the most popular algorithms in machine learning and computer vision, outliers in the training data make the estimation unreliable. In the paper, a robust RVM model under non-parametric Bayesian framework is proposed. We decompose the noise term in the RVM model into two components, a Gaussian noise term and a spiky noise term. Therefore the observed data is assumed represented as: y= Dw+s+e where Dw is the relevance vector component, of which D is the kernel function matrix and w is the weight matrix, s is the spiky term and e is the Gaussian noise term. A spike-slab sparse prior is imposed on the weight vector, w which gives a more intuitive constraint on the sparsity than the Student’s t-distribution described in the traditional RVM. For the spiky component, s a spike-slab sparse prior is also introduced to recognize outliers in the training data effectively. Several experiments demonstrate the better performance over the RVM regression.

Application of relevance vector machine and logistic regression for machine degradation assessment

In this paper we exploit Artificial Neural Networks (ANN) to model the functional relationship between LIBS spectra and the corresponding composition of bronze alloys, expressed in terms of concentrations of the four elements constituting the alloy. The typical approach to Laser-Induced Breakdown Spectroscopy (LIBS) quantitative analysis uses calibration curves, suitably built based on appropriate reference standards. More recently, statistical methods relying on the principles of ANNs are increasingly used. In particular, an ANN can be used for a preliminary exploration of the LIBS spectra in order to find out the most significant areas of the spectrum, which will be used by another ANN dedicated to the calibration. In this paper we will show that the use of ANNs to deal with LIBS spectra provides a viable, fast and robust method for LIBS quantitative analysis. Actually, this approach requires a relatively limited number of reference samples for the training of the network, with respect to the current approaches, and can automatically analyze a large number of samples.

Electrocardiogram (ECG) analysis is one of the most important approaches to cardiac arrhythmia detection. This paper proposed an ECG analysis approach with adaptive feature selection and support vector machines (SVMs). Many wavelet transform-based coefficients are used as candidates, but only a few coefficients are selected for classification problem of each class pair. In addition, the several variation classes are partitioned into two or more subclasses to improve the training efficiency of SVMs. The experimental results show that the proposed ECG analysis approach can obtain high recognition rate and reliable results

Bearings are classified as one of the most safety-critical components in industrial machinery, and their prognostics have proven to be an efficient way to reduce costly unplanned maintenance and guarantee reliability and safety. However, accurate data-driven remaining useful life (RUL) prediction remains a significant challenge because of nonlinear degradation and prediction uncertainty. In this study, an integrated prognostics method is designed for a rolling element bearing to identify its health state changes adaptively and further improve the accuracy of RUL prediction, particularly for its long-term prediction. First, a new health indicator (HI) is proposed based on fuzzy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$c$ </tex-math></inline-formula> -means clustering and a modified confidence value (CV). The former makes the HI sensitive to state changes, and the latter improves its monotonicity and smoothness. A relevance vector machine (RVM) regression model functions with multikernel widths with a modified degradation model to improve the accuracy of RUL prediction, even when fewer samples are available for long-term prediction. The experimental results of the two case studies indicate that the proposed HI provides a better representation for bearing monotonic and smooth degradation processes while automatically identifying its first predicting time and failure threshold. Moreover, the proposed RUL prediction method exhibits stable performance for various prediction ranges and higher accuracy for bearings with different degradation rates.

Bus headway regularity heavily affects transit riders’ attitude for choosing public transportation and also serves as an important indicator for transit performance evaluation. Therefore, an accurate estimate of bus headway can benefit both transit riders and transit operators. This paper proposed a relevance vector machine (RVM) algorithm to predict bus headway by incorporating the time series of bus headways, travel time, and passenger demand at previous stops. Different from traditional computational intelligence approaches, RVM can output the probabilistic prediction result, in which the upper and lower bounds of a predicted headway within a certain probability are yielded. An empirical experiment with two bus routes in Beijing, China, is utilized to confirm the high precision and strong robustness of the proposed model. Five algorithms [support vector machine (SVM), genetic algorithm SVM, Kalman filter, k-nearest neighbor, and artificial neural network] are used for comparison with the RVM model and the result indicates that RVM outperforms these algorithms in terms of accuracy and confidence intervals. When the confidence level is set to 95%, more than 95% of actual bus headways fall within the prediction bands. With the probabilistic bus headway prediction information, transit riders can better schedule their trips to avoid late and early arrivals at bus stops, while transit operators can adopt the targeted correction actions to maintain regular headway for bus bunching prevention.

This paper employs three different kernel-based models--support vector regression (SVR), relevance vector machine (RVM) and Gaussian process regression (GPR)--for the prediction of cement compressive strength. The input variables for the model are taken as C3S (%), SO3 (%), Alkali (%) and Blaine (cm2/g), while the output is 28-day cement compressive strength (N/mm2) of the cement. The hyperparameters of the SVR are obtained using two different metaheuristic optimization algorithms--particle swarm optimization (PSO) and symbiotic organism search (SOS). Trial-and-error-based approach is used for arriving at the hyperparameters of RVM and GPR. The compressive strength predicted using different kernel-based models is also compared with that obtained from ANN and fuzzy logic models reported in the literature. The performance of the different kernel-based models is benchmarked using six different error indices and residual analysis. The performance of the kernel-based models is found to be at par with ANN. The better generalization capability and excellent empirical performance of the kernel-based models overcome the disadvantages associated with ANN and provide a good tool for the prediction of the cement compressive strength.

Laser-induced breakdown spectroscopy (LIBS) has played a critical role in Mars exploration missions, substantially contributing to the geochemical analysis of Martian surface substances. However, the complex nonlinearity of LIBS processes can considerably limit the quantification accuracy of conventional LIBS chemometric methods. Hence chemometrics based on artificial neural network (ANN) algorithms have become increasingly popular in LIBS analysis due to their extraordinary ability in nonlinear feature modeling. The hidden layer activation functions are key to ANN model performance, yet common activation functions usually suffer from problems such as gradient vanishing (e.g., Sigmoid and Tanh) and dying neurons (e.g., ReLU). In this study, we propose a novel LIBS quantification method, named the Bayesian optimization-based tunable Softplus backpropagation neural network (BOTS-BPNN). Based on a dataset comprising 1800 LIBS spectra collected by a laboratory duplicate of the MarSCoDe instrument onboard the Zhurong Mars rover, we have revealed that a BPNN model adopting a tunable Softplus activation function can achieve higher prediction accuracy than BPNN models adopting other common activation functions if the tunable Softplus parameter β is properly selected. Moreover, the way to find the proper β value has also been investigated. We demonstrate that the Bayesian optimization method surpasses the traditional grid search method regarding both performance and efficiency. The BOTS-BPNN model also shows superior performance over other common machine learning models like random forest (RF). This work indicates the potential of BOTS-BPNN as an effective chemometric method for analyzing Mars in situ LIBS data and sheds light on the use of chemometrics for data analysis in future planetary explorations.

Application of elastic net in quantitative analysis of major elements using Martian laser-induced breakdown spectroscopy datasets

Hybrid ensemble soft computing approach for predicting penetration rate of tunnel boring machine in a rock environment

The application scenarios of rotating machinery are becoming increasingly complicated due to the rapid development of the manufacturing industry. The remaining useful life (RUL) prediction of rolling bearings has gradually been considered in many industry fields for ensuring the safety and reliability of whole systems. As an effective way to analyze data, the relevance vector machine (RVM) approach holds great potential for RUL prediction. However, the redundant features of rolling bearing vibration signals can easily lead to overfitting and low accuracy of the RVM model for RUL prediction. To conquer these issues, inspired by the idea of the boosting algorithm and ensemble learning, this paper proposes a new RVM model, called the weight-tracking relevance vector machine (WTRVM). Within the proposed WTRVM model, an adaptive sequential optimal feature selection method is designed to avoid overfitting by selecting the best features. The error between the prediction value of the RVM model and the true value is counted for the RVM model training and weight tracking. The most accurate model can be obtained when all selected features have been trained. Finally, the proposed WTRVM algorithm is experimentally demonstrated to be effective for the RUL prediction of rolling bearings.

Tracking the pose of human face has long been an important research topic which has many important applications, and it is particularly challenging with a monocular camera because the depth information is lost due to the perspective projection. This work adopts particle filter with partitioned sampling to decompose the state space of face pose tracking into two subspaces for increasing the sampling efficiency, thus achieving satisfactory performance with fewer particles. The parameters in the first subspace describe the target on image plane, and the parameter in the second subspace is used for the estimate of the face pose in yaw angle direction. For the evaluation of each hypothesis in the second subspace, a statistical learning algorithm called relevance vector machine (RVM) is used to map a face containing image to the pose of the face. The training of RVM is tailored to each detected frontal face, and it takes less than half second, which is suitable for a real-time application. The learning based regression model also presents the insensitive ability to expression variation and unmodeled degree of freedom. The experimental results verify that the combination of particle filter and RVM can efficiently reduce the processing time and add robustness to the performance of the system, thus making this algorithm applicable to human-machine interface with low-cost webcams.

The quantitative analysis accuracy of calibration-free laser-induced breakdown spectroscopy (CF-LIBS) is severely affected by the self-absorption effect and estimation of plasma temperature. Herein, a CF-LIBS quantitative analysis method based on the auto-selection of internal reference line and the optimized estimation of plasma temperature is proposed. The internal reference line of each species is automatically selected from analytical lines by a programmable procedure through easily accessible parameters. Furthermore, the self-absorption effect of the internal reference line is considered during the correction procedure. To improve the analysis accuracy of CF-LIBS, the particle swarm optimization (PSO) algorithm is introduced to estimate the plasma temperature based on the calculation results from the Boltzmann plot. Thereafter, the species concentrations of a sample can be calculated according to the classical CF-LIBS method. A total of 15 certified alloy steel standard samples of known compositions and elemental weight percentages were used in the experiment. Using the proposed method, the average relative errors of Cr, Ni, and Fe calculated concentrations were 4.40%, 6.81%, and 2.29%, respectively. The quantitative results demonstrated an improvement compared with the classical CF-LIBS method and the promising potential of in situ and real-time application.