General Electric

About: General Electric is a company organization based out in Boston, Massachusetts, United States. It is known for research contribution in the topics: Turbine & Rotor (electric). The organization has 76365 authors who have published 110557 publications receiving 1885108 citations. The organization is also known as: General Electric Company & GE.

Method and system for identifying repeatedly malfunctioning equipment

Abstract: Computerized method and system for identifying repeatedly malfunctioning equipment and root causes therefor are provided. The method allows to provide a database (e.g., 220) comprising detailed equipment data including data indicative of historical equipment malfunctions in a plurality of pieces of equipment. The equipment data includes a unique equipment identifier for uniquely relating each malfunction to respective equipment. The method further allows to analyze the data base for a selected time window to review equipment malfunctions logged in the database and resulting in servicing activities over that time window. An equipment malfunction threshold for the number of malfunctions occurring during a predetermining period of time is established. The database is configured to automatically issue a report identifying any respective equipment as a repeatedly-malfunctioning-equipment whenever the number of equipment malfunctions resulting in servicing activities over that time window exceeds the equipment malfunction threshold. An input/output device (e.g., 226) is provided to communicate with said database to receive the report from the database. A work order for the repeatedly malfunctioning-equipment is instantiated, wherein the order is configured to remain open at least until service personnel logs comment data into the work order indicative of possible root causes for the repeatedly malfunctioning equipment.

Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd

Abstract: Pedestrian detection in crowded scenes is a challenging problem since the pedestrians often gather together and occlude each other In this paper, we propose a new occlusion-aware R-CNN (OR-CNN) to improve the detection accuracy in the crowd Specifically, we design a new aggregation loss to enforce proposals to be close and locate compactly to the corresponding objects Meanwhile, we use a new part occlusion-aware region of interest (PORoI) pooling unit to replace the RoI pooling layer in order to integrate the prior structure information of human body with visibility prediction into the network to handle occlusion Our detector is trained in an end-to-end fashion, which achieves state-of-the-art results on three pedestrian detection datasets, ie, CityPersons, ETH, and INRIA, and performs on-pair with the state-of-the-arts on Caltech

Apparatus for segmenting encoded video signal for transmission

Abstract: A vide signal encoding system includes apparatus for segmenting encoded video data into transport blocks for signal transmission. The transport block format enhances signal recovery at the receiver by virtue of providing header data from which a receiver can determine re-entry points into the data stream on the occurrence of a loss or corruption of transmitted data. The number of re-entry points are maximized by providing secondary transport headers embedded within encoded video data in respective transport blocks.

Statistical monitoring of nonlinear product and process quality profiles

Abstract: In many quality control applications, use of a single (or several distinct) quality characteristic(s) is insufficient to characterize the quality of a produced item. In an increasing number of cases, a response curve (profile) is required. Such profiles can frequently be modeled using linear or nonlinear regression models. In recent research others have developed multivariate T2 control charts and other methods for monitoring the coefficients in a simple linear regression model of a profile. However, little work has been done to address the monitoring of profiles that can be represented by a parametric nonlinear regression model. Here we extend the use of the T2 control chart to monitor the coefficients resulting from a parametric nonlinear regression model fit to profile data. We give three general approaches to the formulation of the T2 statistics and determination of the associated upper control limits for Phase I applications. We also consider the use of non-parametric regression methods and the use of metrics to measure deviations from a baseline profile. These approaches are illustrated using the vertical board density profile data presented in Walker and Wright (Comparing curves using additive models. Journal of Quality Technology 2002; 34:118–129). Copyright © 2007 John Wiley & Sons, Ltd.