Robust signal recognition algorithm based on machine learning in heterogeneous networks

There are various heterogeneous networks for terminals to deliver a better quality of service. Signal system recognition and classification contribute a lot to the process. However, in low signal to noise ratio（SNR） circumstances or under time-varying multipath channels, the majority of the existing algorithms for signal recognition are already facing limitations. In this series, we present a robust signal recognition method based upon the original and latest updated version of the extreme learning machine（ELM） to help users to switch between networks. The ELM utilizes signal characteristics to distinguish systems. The superiority of this algorithm lies in the random choices of hidden nodes and in the fact that it determines the output weights analytically, which result in lower complexity. Theoretically, the algorithm tends to offer a good generalization performance at an extremely fast speed of learning. Moreover, we implement the GSM/WCDMA/LTE models in the Matlab environment by using the Simulink tools. The simulations reveal that the signals can be recognized successfully to achieve a 95% accuracy in a low SNR（0 dB） environment in the time-varying multipath Rayleigh fading channel.

SCADA Data-Based Support Vector Machine for False Alarm Identification for Wind Turbine Management

Maintenance operations have a critical influence on power gen-eration by wind turbines(WT).Advanced algorithms must analyze large volume of data from condition monitoring systems(CMS)to determine the actual working conditions and avoid false alarms.This paper proposes different support vector machine(SVM)algorithms for the prediction and detection of false alarms.K-Fold cross-validation(CV)is applied to evaluate the classification reliability of these algorithms.Supervisory Control and Data Acquisition(SCADA)data from an operating WT are applied to test the proposed approach.The results from the quadratic SVM showed an accuracy rate of 98.6%.Misclassifications from the confusion matrix,alarm log and maintenance records are analyzed to obtain quantitative information and determine if it is a false alarm.The classifier reduces the number of false alarms called misclassifications by 25%.These results demonstrate that the proposed approach presents high reliability and accuracy in false alarm identification.

Machine learning classification approach for formation delineation at the basin-scale

Machine learning and artificial intelligence approaches have rapidly gained popularity for use in many subsurface energy applications.They are seen as novel methods that may enhance existing capabilities,providing for improved efficiency in exploration and production operations.Furthermore,their inte-gration into reservoir management workflows may shape the future landscape of the energy industry.This study implements a framework that generates predictive models using multiple machine learning classification-based algorithms which can identify specific stratigraphic units(i.e.,formations)as a function of total vertical depth and spatial positioning.The framework is applied in a case study to 13 specific formations of interest(Upper Spraberry through Atoka/Morrow reservoirs)in the Midland Basin,West Texas,United States;a prominent hydrocarbon producing sub-basin of the larger Permian Basin.The study dataset consists of over 275,000 records and includes data fields like formation iden-tifier,true vertical depth(in feet)of formations observed,and latitude and longitude coordinates(in decimal degrees).A subset of 134,374 data records were relevant to the 13 distinct formations of interest and were extracted and used for machine learning model training,validation,and testing.Four super-vised learning approaches including random forest(RF),gradient boosting(GB),support vector machine(SVM),and multilayer perceptron neural network(MLP)were evaluated and their prediction accuracy compared.The best performing model was ultimately built on the RF algorithm and is capable of an overall prediction accuracy of 93 percent on holdout data.The RF-based model demonstrated high prediction accuracy for major oil and gas producing zones including the San Andres,Upper Spraberry,Lower Spraberry,Clearfork,and Wolfcamp at 98,94,89,94,and 94 percent respectively.Overall,the resulting data-driven model provides a robust,cost-effective approach which can complement contemporary reservoir management approaches for multiple subsurface energy applications.

A Heterogeneous Ensemble of Extreme Learning Machines with Correntropy and Negative Correlation

The Extreme Learning Machine（ELM） is an effective learning algorithm for a Single-Layer Feedforward Network（SLFN）. It performs well in managing some problems due to its fast learning speed. However, in practical applications, its performance might be affected by the noise in the training data. To tackle the noise issue, we propose a novel heterogeneous ensemble of ELMs in this article. Specifically, the correntropy is used to achieve insensitive performance to outliers, while implementing Negative Correlation Learning（NCL） to enhance diversity among the ensemble. The proposed Heterogeneous Ensemble of ELMs（HE2 LM） for classification has different ELM algorithms including the Regularized ELM（RELM）, the Kernel ELM（KELM）, and the L2-norm-optimized ELM（ELML2）. The ensemble is constructed by training a randomly selected ELM classifier on a subset of the training data selected through random resampling. Then, the class label of unseen data is predicted using a maximum weighted sum approach. After splitting the training data into subsets, the proposed HE2 LM is tested through classification and regression tasks on real-world benchmark datasets and synthetic datasets. Hence, the simulation results show that compared with other algorithms, our proposed method can achieve higher prediction accuracy, better generalization, and less sensitivity to outliers.

Learning from the crowd:Road infrastructure monitoring system

The condition of the road infrastructure has severe impacts on the road safety, driving comfort, and on the rolling resistance. Therefore, the road infrastructure must be moni- tored comprehensively and in regular intervals to identify damaged road segments and road hazards. Methods have been developed to comprehensively and automatically digitize the road infrastructure and estimate the road quality, which are based on vehicle sensors and a supervised machine learning classification. Since different types of vehicles have various suspension systems with different response functions, one classifier cannot be taken over to other vehicles. Usually, a high amount of time is needed to acquire training data for each individual vehicle and classifier. To address this problem, the methods to collect training data automatically for new vehicles based on the comparison of trajectories of untrained and trained vehicles have been developed. The results show that the method based on a k-dimensional tree and Euclidean distance performs best and is robust in transferring the information of the road surface from one vehicle to another. Furthermore, this method offers the possibility to merge the output and road infrastructure information from multiple vehicles to enable a more robust and precise prediction of the ground truth.