Dr. John Abbot has been a scientific researcher for over 30-years both in academia and industrial settings. Currently, Dr. Abbot is a Professorial Research Fellow at Central Queensland University in Noosa, Australia. The majority of his projects are related to environmental issues including his current project for forecasting medium and long-term monthly rainfall in Australia. Dr.
In this quantitative correlational study, simulated data were employed to examine artificial-intelligence techniques or, more specifically, artificial neural networks, as they relate to the location prediction of improvised explosive devices (IEDs). An ANN model was developed using NeuroSolutions to predict IED placement, based upon terrain features and objects related to historical IED detonation events, the associated visual and radio-frequency lines of sight of these features and objects, and the volume of target-vehicle traffic during a 24-hour period.
Gary Howorth has been working in the oil and gas industry for 25-years and has a Degree in Electronic Engineering specializing in digital control theory and a MBA. Mr. Howorth has worked for several key corporations in the industry including BP, Arthur Andersen (Petroleum Services Group), PA Consulting and currently PFC Energy performing quantitative analysis on new and obscure modeling.
Fraser Cook is the Principal Engineer for Artificial Intelligence (AI) at Qualitrol-DMS in the United Kingdom. Qualitrol-DMS supply condition monitoring systems and services for the electrical power transmission and distribution industry. Qualitrol-DMS is the world leader in Partial Discharge (PD) monitoring for Gas Insulated Switchgear (GIS). Mr. Cook studied Artificial Intelligence while receiving his Masters degree in Cognitive Science from Division of Informatics, University of Edinburgh.
This paper reports on oil classification with fluorescence spectroscopy. Using NeuroSolutions, the objective is to classify the oil fluorescence spectra based on a laboratory dataset of fluorescence spectra of several oil classes (sludge, crude and heavy oil). The classification was carried out using the following three methods: channel relationship method (CRM), artificial neural networks (ANNs), and support vector machines (SVMs).
This paper deals with a path planning and intelligent control of an autonomous robot which should move safely in partially structured environment. This environment may involve any number of obstacles of arbitrary shape and size; some of them are allowed to move. We describe our approach to solving the motion-planning problem in mobile robot control using neural networks-based technique. Our method of the construction of a collision-free path for moving robot among obstacles is based on two neural networks. The first neural network is used to determine the “free” space using ultrasound range finder data. The second neural network “finds” a safe direction for the next robot section of the path in the workspace while avoiding the nearest obstacles. Simulation examples of generated path with proposed techniques will be presented
Using remote sensing satellite imagery, multi-spectral sensor data is used for applications to map forest types or changes in a forest or to extract statistical information from the images together with available maps. In the case of mapping of forest types and changes, the information is essential for forestry companies in order to keep their databases up-to-date for planning and forest management. In addition, the imagery has been used to do wall-to-wall mapping of land use and vegetation from space as well as measuring the depth in the sea water, mapping along the coastlines and measuring the vegetation at the bottom of the sea. The primary benefit of using NeuroSolutions over other methods is not having to set up a mathematical model for the classification and regression applications. Common classification methods used in remote sensing is based on the assumption of normal distributed classes. However, in Mr. Rosengren's applications, the classes often have multimodal distributions. Using NeuroSolutions rather than other classification and regression methods, often save as much as 25% of the time and cost of the project!
Side Scan Sonar (SSS) imaging is one of the advanced methods for data acquisition about the sea floor. The possibilities of intelligence-based approaches in the analysis of sonar images and classification of seabed material have been explored in this study. The only available type of measurement for classification SSS images is the grey level of the pixels corresponding to the acoustic reflectance. It is difficult to recognize and classify objects based on a single feature. However, the spatial order of the grey level transitions gives ‘texture’ characteristics to the image and it is these that act as an important aid in human interpretation. Image texture can be characterized by the Spatial Grey Level Dependence Method (SGLDM) based on the cooccurrence matrix of pairs of grey levels.
Using NeuroSolutions, Mr. Roebber developed a snow density system that is currently employed by the National Weather Service to assist in the prediction of snow fall depths. Mr Roebber's system uses several different types of neural network architectures including the Principal Component Analysis (PCA) and Multilayer Perceptron (One and Two Hidden Layer) to complete his ensemble of 10 artificial neural networks. The ensemble correctly diagnoses 60.4% of the snow event cases examined, which is a substantial improvement over the 41.7% correct using the sample climatology, 45% correct using the 10-to-1 ratio (see table), and 51.7% correct using the National Weather Service "new snowfall to estimated meltwater conversion" table. The Heidke skill score measures the fraction of correct forecasts after eliminating those forecasts which would be correct due purely to random chance. The ensemble technique attains Heidke skill scores of 0.34 - 0.42, which is an increase of 75% - 183% over the next most skillful approach!
A non-invasive means to detect and characterize concealed lethal agents employs spatial imaging of their characteristic transmission or reflection wavelength spectrum in the Terahertz (THz) electro-magnetic range. Artificial neural network (NN) analyses of these THz spectral images provide specificity of agent detection at reduced false alarm rates. Published THz spectra are utilized to generate simulated interferometric images of bioagent contained within an envelope, and a suicide bomber. Both multilayer perceptron and radial basis function NN architectures are used to analyze these spectral images. Positive identifications are generally made, with radial basis function NNs generally yielding superior results.