The Experts below are selected from a list of 177255 Experts worldwide ranked by ideXlab platform
James S Albus - One of the best experts on this subject based on the ideXlab platform.
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4 d rcs reference model architecture for unmanned ground vehicles
International Conference on Robotics and Automation, 2000Co-Authors: James S AlbusAbstract:4D/RCS is the reference model architecture currently being developed for the Demo III Experimental Unmanned Vehicle program. 4D/RCS integrates the NIST (National Institute of Standards and Technology) RCS (real-time control system) with the German (Universitat der Bundeswehr Munchen) VaMoRs 4D approach (3 dimensions+time) to dynamic machine vision. The 4D/RCS architecture consists of a hierarchy of computational nodes each of which contains behavior generation (BG), world modeling (WM), sensory processing (SP), and value judgement (VJ) processes. Each node also contains a Knowledge Database (KD) and an operator interface. These computational nodes are arranged such that the BG processes represent organizational units within a command and control hierarchy.
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4-D/RCS Reference Model Architecture for Unmanned Ground Vehicles
1999Co-Authors: James S AlbusAbstract:4-D/RCS is the reference model architecture currently being developed for the Demo m Experimental Unmanned Vehicle program. 4-D/RCS integrates the NIST (National Institute of Standards and Technology) RCS (Real-time Control System) with the German (Universitat der Bundeswehr Munchen) VaMoRs 4-D approach to dynamic machine vision. The 4-D/RCS architecture consists of a hierarchy of computational nodes each of which contains behavior generation (BG), world modeling (WM), sensory processing (SP), and value judgment (VJ) processes. Each node also contains a Knowledge Database (KD) and an operator interface. These computational nodes are arranged such that the BG processes represent organizational units within a command and control hierarchy.
Rene De Jesus Romerotroncoso - One of the best experts on this subject based on the ideXlab platform.
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incremental novelty detection and fault identification scheme applied to a kinematic chain under non stationary operation
Isa Transactions, 2020Co-Authors: Jesus A Carino, Miguel Delgadoprieto, Daniel Zurita, Antoine Picot, J A Ortega, Rene De Jesus RomerotroncosoAbstract:Abstract Classical methods for monitoring electromechanical systems lack two critical functions for effective industrial application: management of unexpected events and the incorporation of new patterns into the Knowledge Database. This study presents a novel, high-performance condition-monitoring method based on a four-stage incremental learning approach. First, non-stationary operation is characterised using normalised time-frequency maps. Second, operating novelties are detected using multivariate kernel density estimators. Third, the operating novelties are characterised and labelled to increase the Knowledge available for subsequent diagnosis. Fourth, operating faults are diagnosed and classified using neural networks. The proposed method is validated experimentally with an industrial camshaft-based machine under a variety of operating conditions.
D. Cooney - One of the best experts on this subject based on the ideXlab platform.
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caa paper 2006 01 a Database to record human experience of evacuation in aviation accidents the aircraft accident statistics and Knowledge Database aask
2008Co-Authors: Edwin R. Galea, K.m. Finney, A. J. P. Dixon, Asim Siddiqui, D. CooneyAbstract:This report concerns the development of the AASK V4.0 Database (CAA Project 560/SRG/R+AD). AASK is the Aircraft Accident Statistics and Knowledge Database, which is a repository of survivor accounts from aviation accidents. Its main purpose is to store observational and anecdotal data from interviews of the occupants involved in aircraft accidents. The AASK Database has wide application to aviation safety analysis, being a source of factual data regarding the evacuation process. It is also key to the development of aircraft evacuation models such as airEXODUS, where insight into how people actually behave during evacuation from survivable aircraft crashes is required. With support from the UK CAA (Project 277/SRG/R&AD), AASK V3.0 was developed. This was an on-line prototype system available over the internet to selected users and included a significantly increased number of passenger accounts compared with earlier versions, the introduction of cabin crew accounts, the introduction of fatality information and improved functionality through the seat plan viewer utility. The most recently completed AASK project (Project 560/SRG/R+AD) involved four main components: a) analysis of the data collected in V3.0; b) continued collection and entry of data into AASK; c) maintenance and functional development of the AASK Database; and d) user feedback survey. All four components have been pursued and completed in this two-year project. The current version developed in the last year of the project is referred to as AASK V4.0. This report provides summaries of the work done and the results obtained in relation to the project deliverables.
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Aircraft Accident Statistics and Knowledge Database: Analyzing Passenger Behavior in Aviation Accidents
Journal of Aircraft, 2006Co-Authors: Edwin R. Galea, K.m. Finney, A. J. P. Dixon, Asim Siddiqui, D. CooneyAbstract:The Aircraft Accident Statistics and Knowledge (AASK) Database is a repository of passenger accounts from survivable aviation accidents/incidents compiled from interview data collected by agencies such as the US NTSB. Its main purpose is to store observational and anecdotal data from the actual interviews of the occupants involved in aircraft accidents. The Database has wide application to aviation safety analysis, being a source of factual data regarding the evacuation process. It also plays a significant role in the development of the airEXODUS aircraft evacuation model, where insight into how people actually behave during evacuation from survivable aircraft crashes is required. This paper describes the latest version of the Database (Version 4.0) and includes some analysis of passenger behavior during actual accidents/incidents.
Jesus A Carino - One of the best experts on this subject based on the ideXlab platform.
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incremental novelty detection and fault identification scheme applied to a kinematic chain under non stationary operation
Isa Transactions, 2020Co-Authors: Jesus A Carino, Miguel Delgadoprieto, Daniel Zurita, Antoine Picot, J A Ortega, Rene De Jesus RomerotroncosoAbstract:Abstract Classical methods for monitoring electromechanical systems lack two critical functions for effective industrial application: management of unexpected events and the incorporation of new patterns into the Knowledge Database. This study presents a novel, high-performance condition-monitoring method based on a four-stage incremental learning approach. First, non-stationary operation is characterised using normalised time-frequency maps. Second, operating novelties are detected using multivariate kernel density estimators. Third, the operating novelties are characterised and labelled to increase the Knowledge available for subsequent diagnosis. Fourth, operating faults are diagnosed and classified using neural networks. The proposed method is validated experimentally with an industrial camshaft-based machine under a variety of operating conditions.
Ori Mashiach - One of the best experts on this subject based on the ideXlab platform.
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Adaptation logic for HTTP dynamic adaptive streaming using geo-predictive crowdsourcing for mobile users
Multimedia Systems, 2016Co-Authors: Ran Dubin, Itay Katz, Ofir Pele, Amit Dvir, Ofer Hadar, Ori MashiachAbstract:The increasing demand for video streaming services with a high Quality of Experience (QoE) has prompted considerable research on client-side adaptation logic approaches. However, most algorithms use the client’s previous download experience and do not use a crowd Knowledge Database generated by users of a professional service. We propose a new crowd algorithm that maximizes the QoE. We evaluate our algorithm against state-of-the-art algorithms on large, real-life, crowdsourcing datasets. There are six datasets, each of which contains samples of a single operator (T-Mobile, AT&T or Verizon) from a single road (I100 or I405). All measurements were from Android cellphones. The datasets were provided by WeFi LTD and are public for academic users. Our new algorithm outperforms all other methods in terms of QoE (eMOS).
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adaptation logic for http dynamic adaptive streaming using geo predictive crowdsourcing
arXiv: Multimedia, 2016Co-Authors: Ran Dubin, Itay Katz, Ofir Pele, Amit Dvir, Ofer Hadar, Ori MashiachAbstract:The increasing demand for video streaming services with high Quality of Experience (QoE) has prompted a lot of research on client-side adaptation logic approaches. However, most algorithms use the client's previous download experience and do not use a crowd Knowledge Database generated by users of a professional service. We propose a new crowd algorithm that maximizes the QoE. Additionally, we show how crowd information can be integrated into existing algorithms and illustrate this with two state-of-the-art algorithms. We evaluate our algorithm and state-of-the-art algorithms (including our modified algorithms) on a large, real-life crowdsourcing dataset that contains 336,551 samples on network performance. The dataset was provided by WeFi LTD. Our new algorithm outperforms all other methods in terms of QoS (eMOS).
