Machine Learning for the New York City Power Grid

Power companies can benefit from the use of knowledge discovery methods and statistical machine learning for preventive maintenance. We introduce a general process for transforming historical electrical grid data into models that aim to predict the risk of failures for components and systems. These models can be used directly by power companies to assist with prioritization of maintenance and repair work. Specialized versions of this process are used to produce (1) feeder failure rankings, (2) cable, joint, terminator, and transformer rankings, (3) feeder Mean Time Between Failure (MTBF) estimates, and (4) manhole events vulnerability rankings. The process in its most general form can handle diverse, noisy, sources that are historical (static), semi-real-time, or real-time, incorporates state-of-the-art machine learning algorithms for prioritization (supervised ranking or MTBF), and includes an evaluation of results via cross-validation and blind test. Above and beyond the ranked lists and MTBF estimates are business management interfaces that allow the prediction capability to be integrated directly into corporate planning and decision support; such interfaces rely on several important properties of our general modeling approach: that machine learning features are meaningful to domain experts, that the processing of data is transparent, and that prediction results are accurate enough to support sound decision making. We discuss the challenges in working with historical electrical grid data that were not designed for predictive purposes. The “rawness” of these data contrasts with the accuracy of the statistical models that can be obtained from the process; these models are sufficiently accurate to assist in maintaining New York City's electrical grid

Assessing the hydrological impacts of agricultural changes upstream of the Tunisian World Heritage sea-connected Ichkeul Lake

The impact of changes in agricultural land use and practices as a controlling driver of hydrologic response and as a source of diffuse pollution, are studied in the Joumine River basin, discharging into the Ichkeul Lake, northern Tunisia, a UNESCO World Heritage site since 1979. The lake is characterized by a very specific hydrological functioning based on a seasonal alternation of water levels and salinity through its link to the Mediterranean Sea. Three Landsat images, in situ surveys and SWAT modelling were used to simulate and assess streamflows and nitrate loads under retrospective land uses

Toward the Discovery of Itemsets with Significant Variations in Gene Expression Matrices

International audienc

Automated specification extraction and analysis with specstractor

This paper presents Specstractor, a tool chain for the extraction and analysis of system specifications in the form of collections of invariants. Such invariants convey valuable information about the behavior of a software system and are also useful in identifying missing or defective parts of existing specifications. Using data-mining techniques, Specstractor derives likely invariants from test data that it automatically generates from the system under analysis, using an iterative approach to refine the set of proposed invariants and eliminate false positives. The paper describes the Spectstractor technology and evaluates it on real-world artifacts from automotive-control and medical-device applications