Here Global B.v.

Drexel University

Traffic.com, Inc.

Near-miss Management LLC

Dmitriy Bespalov

Ali Shokoufandeh

James F Carroll, I

William C Regli

Dean Zimmerman

Ulku G Oktem

Ankur Pariyani

Luis Cielak

The dynamic risk analyzer (DRA) provided by the present invention periodically assesses real-time or historic process data, or both, associated with an operations site, such as a manufacturing, production, or processing facility, including a plant's operations, and identifies hidden near-misses of such operation, when in real time the process data appears otherwise normal. DRA assesses the process data in a manner that enables operating personnel including management at a facility to have a comprehensive understanding of the risk status and changes in both alarm and non-alarm based process variables. The hidden process near-miss data may be analyzed alone or in combination with other process data and/or data resulting from prior near-miss situations to permit strategic action to be taken to reduce or avert the occurrence of adverse incidents or catastrophic failure of a facility operation.

Dynamic prediction of risk levels for manufacturing operations through leading risk indicators: dynamic risk analyzer engine

Actual conditions of a roadway segment are estimated by providing roadway condition data to a processor for the roadway segment from a plurality of different types of sources of the roadway condition data, assigning a quality to each of the plurality of different types of sources of the roadway condition data, and estimating in the processor the actual conditions of the roadway segment by using the roadway condition data and the quality of each of the plurality of different types of sources of the roadway data. The quality determines weightings given to each of the plurality of different types of sources.

Estimation of actual conditions of a roadway segment by weighting roadway condition data with the quality of the roadway condition data

A scale-Space feature extraction technique is based on recursive decomposition of polyhedral surfaces into surface patches. The experimental results show that this technique can be used to perform matching based on local model structure. Scale-space techniques can be parameterized to generate decompositions that correspond to manufacturing, assembly or surface features relevant to mechanical design. One application of these techniques is to support matching and content-based retrieval of solid models. Scale-space technique can extract features that are invariant with respect to the global structure of the model as well as small perturbations that 3D laser scanning may introduce. A new distance function defined on triangles instead of points is introduced. This technique offers a new way to control the feature decomposition process, which results in extraction of features that are more meaningful from an engineering viewpoint. The technique is computationally practical for use in indexing large models.

Multi-scale segmentation and partial matching 3D models

Actual traffic conditions of a roadway segment are predicted by providing a plurality of historical roadway condition patterns of the roadway segment in a database, obtaining an electronic representation of a current roadway condition pattern of the roadway segment, identifying one or more of the historical roadway condition patterns that closely matches the current roadway condition pattern, and predicting the future actual traffic conditions of the roadway segment by using the conditions associated with the one or more identified historical patterns.

Use of pattern matching to predict actual traffic conditions of a roadway segment

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Philadelphia, PA, United States of America

Dmitriy Bespalov