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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.

Trader's account has increased by over 90% in three months!

Mr. Honorowski is trading the New Zealand Dollar/U.S. Dollar with a 5x leverage and his four most recent trades alone have yielded 4.46%, 5.31%, 13.28% and 20.60% returns with an overall 77% wins since April '06! *
Mr. Honorowski said he "Absolutely loves the software (TradingSolutions)" and "Would recommend it to anyone and everyone". His favorite features are the Optimal Signal technology along with the neural network modeling. He likes that TradingSolutions was "non-blackbox" with all of the settings and parameters he had control over and said that it was "hard to beat TradingSolutions optimization results".

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 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:

Using Satellite Imagery for Mapping Forest Types or Changes

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!

Seabed Recognition Using Neural Networks

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.

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!


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