NeuroDimension Neural Network Applications

A neural network is a powerful data modeling tool that is able to capture and represent complex input/output relationships. Neural network technology performs "intelligent" tasks similar to those performed by the human brain. It acquires knowledge through learning and then stores that knowledge within inter-neuron connection strengths known as synaptic weights.

Neural Networks can be applied to a wide variety of problems, from breast cancer detection to classification of satellite imagery. Below, we've included numerous examples of how NeuroSolutions and other NeuroDimension products can be used to apply neural network technology to real-world applications.

Trader uses TradingSolutions to achieve a 31% return in 6 months

Mr. Cuni began using TradingSolutions in June '06. He actively traded the Solution Service and models from the Sample Performance section of www.tradingsolutions.com while learning how to create his own profitable models in TradingSolutions. In March '07 he began trading his own models and has earned a 31% annualized return in the past 6 months alone! Mr. Cuni wanted to emphasize four aspects of his model development process that he attributes to his success with TradingSolutions:

Predicting the emplacement of improvised explosive devices: An innovative solution

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.

Helping to Save Lives in the Mining Industry

In the mining industry, most of the underground injuries and fatalities are due to rock falls (i.e. fall of hanging wall/roof). CSIR has developed a device that assists any miner in making an objective decision when determining the integrity of the hanging wall. A trained neural network model is embedded into the device. The device then records the sound emitted when a hanging wall is tapped. The sound is then preprocessed before being input into a trained neural network model and the trained model classifies the hanging wall as either intact or detached.

Shaping the Oil and Gas Industry with NeuroSolutions

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.

Enhancing Education for Future Generations

Cameron Cooper has been a instructor at Fort Lewis College in Southwestern Colorado for 4-years teaching developmental mathematics and computer science and also serves as an enrollment analyst. Mr. Cooper has an extensive educational background with a Bachelors in Mathematics from Occidental College, a Masters in Information Networking from Carnegie Mellon University, a Masters in Communications from Northwestern University and a Masters in Education from Harvard University. Mr. Cooper is a doctoral candidate in Applied Management & Decision Sciences at Walden University.

Electrical Power Transmission and Distribution

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.

Snow Fall Prediction for the National Weather Service

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!

Neural Network Analysis of Interferometric Terahertz Images for Detection of Lethal Agents

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.

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