JeoViewer can provide links to any object’s data and behaviors, and is optimized for spatial geometry representation. Unlike traditional ​“static” GIS systems, JeoViewer is dynamic and can be dynamically linked to objects, models and other live data streams.

JeoViewer’s object-oriented approach provides a more natural representation of spatial data. It can used as a stand-alone module or embedded in another framework. It is intended for web use and its Java programming makes it more practical, functional, and useful for Java programmers.

Applications

• Geographical information systems

Features

• Dynamic linking to objects
• More natural representation of spatial data
• Can be used alone or embedded in another framework
• Polygon to grid extensions
• Legend capabilities
• Thematic mapping (i.e., color coding)
• Practical for Java users

Technical Details/Requirements

• Runs on any platform supporting Java JDK 1.3 or higher with Windows 2000, XP, Solaris, or Linux operating systems
• Typical applications require 128 RAM and a Pentium III or higher CPU.
• C compiler required

Argonne’s Battery Life Estimator (BLE) software is a state-of-the-art tool kit for fitting battery aging data and for battery life estimation. It was designed to make life-cycle estimates using two years of aging data.

BLE helps answer key questions on how battery performance will change with calendar age, cycles, internal component aging, cell-to-cell manufacturing variations, summer and winter temperature extremes, differing anode and cathode materials, and electrolyte variations and additives.

The software employs a generalized statistical approach to fit data from accelerated aging experiments to a life equation. The BLE software is different from other curve-fitting routines as it employs robust fitting techniques and estimates battery life by using Monte Carlo techniques (which most generalized curve-fitting software does not consider).

Applications

• Fit battery aging data to life equations
• Estimate battery life

Features

• Easy to learn
• Fast run times
• Easy-to-use graphical user interface
• User guide includes examples and frequently asked questions

Technical Details/Requirements

• Requires PC computer with a Pentium 4 processor, 1 GB of memory and VGA graphics
• Operates on a Windows 2000 or later system and requires Microsoft .NET framework versions 1.1 through 3.5

The patent-pending system, called ARG-US Remote Area Modular Monitoring, or RAMM, uses hig-tech sensors paired with redundant, self-healing communications platforms that can work even in the most challenging conditions.

The work is supported by the U.S. Department of Energy, Office of Environmental Management, and Packaging Certification Program.

Reference: 
SF-08-046(multiple); SF-17-016

The platform, which consists of a set of statistical algorithms to generate wind power point and uncertainty forecasts, can be used for systematic testing and comparison of different computational learning algorithms.

For wind power point forecasting, ARGUS-PRIMA uses concepts from information theoretic learning (ITL) for training a neural network. In tests on real-world data from two large-scale wind farms in the Midwestern United States, results showed distinct advantages of using ITL training criteria as compared to the traditional minimum square error criterion.

For wind power uncertainty forecasting, ARGUS-PRIMA enlists two methods for estimating uncertainty based on kernel density forecasting (KDF). Both KDF algorithms are suitable for online learning. The new algorithms have been tested on datasets from the Eastern Wind Integration and Transmission Study, as well as on two wind farms in the Midwestern United States. Testing shows that the KDF algorithms result in a better match to observed wind power distribution than results obtained using traditional quantile regression.

Applications

• Wind power point forecasting
• Wind power uncertainty forecasting

Features

• Inputs: Can use numerical weather prediction variables, weather observations and power output from wind power farms
• Outputs: wind power predictions (deterministic point forecasts or probability density functions)
• Standard forecast evaluation scores can be calculated

Technical Details/Requirements

Four software environments are used: a PostgreSQL relational database, a C++ neural network library, a kernel density forecast library and supporting algorithm codes. The ARGUS-PRIMA platform consists of source code without an explicit user interface. Users will need to possess considerable programming skills to set up and run the code.