Abstract: A system and method for dispatching haul trucks includes a production planner configured to operate based on a production plan. The production planner computationally defines production arcs for transferring material from loading tools to dump sites, computationally develops one or more possible return arcs for each production arc, compiles a set of possible return arcs, and computationally selects a sub-set of the possible return arcs to command a real time dispatcher.

Abstract: A system and method for dispatching haul trucks includes a production planner configured to operate based on a production plan. The production planner computationally defines production arcs for transferring material from loading tools to dump sites, computationally develops one or more possible return arcs for each production arc, compiles a set of possible return arcs, and computationally selects a sub-set of the possible return arcs to command a real time dispatcher.

Abstract: A system for mining site production planning includes a control system configured to specify a problem-solving technique and associated optimization problem for a mining site by setting production goals and priorities for each of loading tools, processors, production arcs, and materials of the mining site, sorting the production arcs in an order based on travel distances, modifying the order based on the production goals for each of the loading tools, processors, production arcs, and/or materials, and further modifying the order based on set priorities for the loading tools, processors, production arcs, and/or materials. In addition, target values are set for each of the loading tools, processors, and production arcs according to their order of the sorted production arcs. The control system is further configured to solve the optimization problem to produce production values for each of the loading tools, processors, and production arcs based on the target values.

Abstract: A system for mining site production planning includes a control system configured to specify a problem-solving technique and associated optimization problem for a mining site by setting production goals and priorities for each of loading tools, processors, production arcs, and materials of the mining site, sorting the production arcs in an order based on travel distances, modifying the order based on the production goals for each of the loading tools, processors, production arcs, and/or materials, and further modifying the order based on set priorities for the loading tools, processors, production arcs, and/or materials. In addition, target values are set for each of the loading tools, processors, and production arcs according to their order of the sorted production arcs. The control system is further configured to solve the optimization problem to produce production values for each of the loading tools, processors, and production arcs based on the target values.

Abstract: A system for determining hang time for a machine operating at a worksite is provided. The system includes a sensing module associated with the machine. The sensing module is configured to track at least one parameter associated with the machine. The at least one parameter includes a machine heading, a machine speed, or a machine location. The system also includes a control module communicably coupled to the sensing module. The control module is configured to receive tracked information corresponding to the at least one parameter from the sensing module. The control module is also configured to analyze the tracked information corresponding to the at least one parameter, based on the received tracked information. The control module is further configured to determine the hang time for the machine, based on the analysis.

Abstract: A method for feature reduction in a training set for a learning machine such as a Support Vector Machine (SVM). In one embodiment the method includes a step (35) of receiving input training data vectors xi of a training set. The input training data vectors are typically derived from a set of features in a feature space. At step (37) the input data vectors are mapped into a multi-dimensional space. At step (39) a least squares problem, derived from a formulation of the SVM, is solved to determine which features comprising the training vectors are to be deemed significant. At step (41) decision parameters and vectors of the chosen decision machine, e.g. SVM, are determined using the features determined to be significant in step (39).

Abstract: A method for feature reduction in a training set for a learning machine such as a Support Vector Machine (SVM). In one embodiment the method includes a step (35) of receiving input training data vectors xi of a training set. The input training data vectors are typically derived from a set of features in a feature space. At step (37) the input data vectors are mapped into a multi-dimensional space. At step (39) a least squares problem, derived from a formulation of the SVM, is solved to determine which features comprising the training vectors are to be deemed significant. At step (41) decision parameters and vectors of the chosen decision machine, e.g. SVM, are determined using the features determined to be significant in step (39).

Abstract: A method for operating a computer as a support vector machine (SVM) in order to define a decision surface separating two opposing classes of a training set of vectors. The method involves associating a distance parameter with each vector of the SVM's training set. The distance parameter indicates a distance from its associated vector, being in a first class, to the opposite class. A number of approaches to calculating distance parameters are provided. For example, a distance parameter may be calculated as the average of the distances from its associated vector to each of the vectors in the opposite class. The method further involves determining a linearly independent set of support vectors from the training set such that the sum of the distances associated with the linearly independent support vectors is minimized.

Abstract: A method is provided of operating a computer to enhance extraction of information associated with a first training set of vectors for a decision machine, such as a classification Support Vector Machine (SVM). The method includes operating the computer to perform the steps of: (a) forming a plurality of mutually orthogonal training sets from said first training set; (b) training each of a plurality of classification support vector machines with a corresponding one of the plurality of mutually orthogonal training sets; and (c) classifying one or more test vectors with reference to the plurality of classification support vector machines. The invention is applicable where the feature space from which the first training set is derived exceeds the true dimensionality associated with the classification problem to be addressed.

Abstract: Homotopy principles are used in computer simulation of magnetic resonance spectra. Multidimensional homotopy provides an efficient method for accurately tracing energy levels, and hence transitions, in the presence of energy level anticrossings and looping transitions. The application describes the implementation of homotopy to the analysis of continuous wave electron paramagnetic resonance spectra. The method can also be applied to electron nuclear double resonance, electron spin echo envelope modulation, solid state nuclear magnetic resonance and nuclear quadrupole resonance spectra.