Predicting the drilling rate of penetration (ROP) is one approach to optimizing drilling performance. However, as ROP behavior is unique to specific geological conditions its application is not straightforward. Moreover, ROP is typically affected by various operational factors (e.g. bit type, weight-on-bit, rotation rate, etc.) as well as the geological characteristics of the rocks being penetrated. This makes ROP prediction an intricate and multi-faceted problem. Here we compare data mining methods with several machine learning algorithms to evaluate their accuracy and effectiveness in predicting ROP. The algorithms considered are: artificial neural networks (ANN) applying a multi-layer perceptron (MLP); ANN applying a radial basis function (RBF); support vector regression (SVR), and an hybrid MLP trained using a particle swarm optimization algorithm (MLP-PSO). Data preparation prior to executing the algorithms involves applying a Savitzky–Golay (SG) smoothing filter to remove noise from petrophysical well-logs and drilling data from the mud-logs. A genetic algorithm is applied to tune the machine learning algorithms by identifying and ranking the most influential input variables on ROP. This tuning routine identified and selected eight input variables which have the greatest impact on ROP. These are: weight on bit, bit rotational speed, pump flow rate, pump pressure, pore pressure, gamma ray, density log and sonic wave velocity. Results showed that the machine learning algorithms evaluated all predicted ROP accurately. Their performance was improved when applied to filtered data rather than raw well-log data. The MLP-PSO model as a hybrid ANN demonstrated superior accuracy and effectiveness compared to the other ROP-prediction algorithms evaluated, but its performance is rivalled by the SVR model.

A machine learning approach to predict drilling rate using petrophysical and mud logging data

Application of decision tree, artificial neural networks, and adaptive neuro-fuzzy inference system on predicting lost circulation: A case study from Marun oil field

pdf/radial-basis-function-neural-network-aided-adaptive-extended-20rzk2i1tw.pdf

Radial basis function neural network aided adaptive extended Kalman filter for spacecraft relative navigation

This application describes an automated driving system and methods. The automated driving system includes a perception system disposed on an autonomous vehicle. The automated driving system can detect, using the perception system, information for an environment proximate to the autonomous vehicle. The information for the environment includes current behavior of an object of interest and traffic density information. The automated driving system can also determine a classifier for the environment based on a prediction of future behavior of the object of interest. Based on the classifier, the automated driving system can identify a transition between vehicle states, the vehicle states being associated with a planned action for the autonomous vehicle. The automated driving system can also send a command to one or more vehicle systems to control the autonomous vehicle to execute the planned action according to the transition.

Autonomous vehicle action planning using behavior prediction

This paper addresses the endemic problem of the gap between predicted and actual energy performance in public buildings. A system engineering approach is used to characterize energy performance factoring in building intrinsic properties, occupancy patterns, environmental conditions, as well as available control variables and their respective ranges. Due to the lack of historical data, a theoretical simulation model is considered. A semantic mapping process is proposed using principle component analysis (PCA) and multi regression analysis (MRA) to determine the governing (i.e., most sensitive) variables to reduce the energy gap with a (near) real-time capability. Further, an artificial neural network (ANN) is developed to learn the patterns of this semantic mapping, and is used as the cost function of a genetic algorithm (GA)-based optimization tool to generate optimized energy saving rules factoring in multiple objectives and constraints. Finally, a novel rule evaluation process is developed to evaluate the generated energy saving rules, their boundaries, and underpinning variables. The proposed solution has been tested on both a simulation platform and a pilot building – a care home in the Netherlands. Validation results suggest an average 25% energy reduction while meeting occupants’ comfort conditions.

/pdf/an-ann-ga-semantic-rule-based-system-to-reduce-the-gap-1hp8jqx9wa.pdf

An ANN-GA Semantic Rule-Based System to Reduce the Gap Between Predicted and Actual Energy Consumption in Buildings

The radial basis function (RBF) network has its foundation in the conventional approximation theory. It has the capability of universal approximation. The RBF network is a popular alternative to the well-known multilayer perceptron (MLP), since it has a simpler structure and a much faster training process. In this paper, we give a comprehensive survey on the RBF network and its learning. Many aspects associated with the RBF network, such as network structure, universal approimation capability, radial basis functions, RBF network learning, structure optimization, normalized RBF networks, application to dynamic system modeling, and nonlinear complex-valued signal processing, are described. We also compare the features and capability of the two models.

/pdf/using-radial-basis-function-networks-for-function-1bb48uzio8.pdf

Using Radial Basis Function Networks for Function Approximation and Classification

Many information processing problems can be transformed into some form of eigenvalue or singular value problems. Eigenvalue decomposition (EVD) and singular value decomposition (SVD) are usually used for solving these problems. In this paper, we give an introduction to various neural network implementations and algorithms for principal component analysis (PCA) and its various extensions. PCA is a statistical method that is directly related to EVD and SVD. Minor component analysis (MCA) is a variant of PCA, which is useful for solving total least squares (TLSs) problems. The algorithms are typical unsupervised learning methods. Some other neural network models for feature extraction, such as localized methods, complex-domain methods, generalized EVD, and SVD, are also described. Topics associated with PCA, such as independent component analysis (ICA) and linear discriminant analysis (LDA), are mentioned in passing in the conclusion. These methods are useful in adaptive signal processing, blind signal separation (BSS), pattern recognition, and information compression.

/pdf/neural-network-implementations-for-pca-and-its-extensions-g2djaumudt.pdf

Neural Network Implementations for PCA and Its Extensions

A system and method that facilitates urban traffic state detection based on support vector machine (SVM) and multilayer perceptron (MLP) classifiers is provided. Moreover, the SVM and MLP classifiers are fused into a cascaded two-tier classifier that improves the accuracy of the traffic state classification. To further improve the accuracy, the cascaded two-tier classifier (e.g., MLP-SVM), a single SVM classifier and a single MLP classifier are fused to determine a final decision for a traffic state. In addition, fusion strategies are employed during training and implementation phases to compensate for data acquisition and classification errors caused by noise and/or outliers.

Urban traffic state detection based on support vector machine and multilayer perceptron

The invention discloses an urban road traffic state detection method combined with a support vector machine (SVM) and a back propagation (BP) neural network. The method comprises the following steps of: 1) monitoring traffic characteristic parameters of a road section in real time, and extracting the traffic characteristic parameters to obtain a test sample set, wherein the traffic characteristic parameters comprise a vehicle average speed v (m/s), a vehicle flow size f (veh/s), time occupancy s and travel time t (s); and 2) inputting the test sample set into two layers of cascade classifiers of SVM1-SVM2/BP, wherein the step 2) comprises the following substeps of: 2.1) training the two layers of cascade classifiers by applying an SVM1 training function, and inputting into an SVM1 classification function together with test sample data, judging whether the SVM1 classification function is in a smooth state, if so, determining that the current state is the smooth state, otherwise, entering the substep 2.2); and 2.2) performing vote combination classification on the test sample set by the second layer of SVM2 and BP network classifier, and judging whether the test sample set is in a busy state or a congestion state. By the method, the accuracy can be effectively improved.

Urban road traffic state detection method combined with support vector machine (SVM) and back propagation (BP) neural network

Pedestrian detection in a single image takes more time than that in a video due to the requirement of scanning the whole image. In this paper, an improved pedestrian detection system based on region of interest (ROI) is proposed for single images. In the improved pedestrian detection system, principal component analysis (PCA) is introduced to improve the detection rate and accuracy of histogram of oriented gradients (HOG) based support vector machine (SVM) classifier for pedestrian detection. PCA eliminates the redundant HOG feature dimensions that have no contribution for the pedestrian classification. A novel ROI extraction method based on fuzzy C-means (FCM) clustering algorithm is used to select the regions that possibly contain pedestrians in a single image. ROI extraction reduces the number of detection windows, resulting in a significant reduction in detection time of a single image. Computer experiments show that the proposed pedestrian detection system can correctly detect the positions of pedestrians in single images in real time.

An ROIs based pedestrian detection system for single images

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