The Experts below are selected from a list of 75 Experts worldwide ranked by ideXlab platform
J. Marius Zollner - One of the best experts on this subject based on the ideXlab platform.
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A Situation Context aware Dempster-Shafer fusion of digital maps and a road sign recognition system
2009 IEEE Intelligent Vehicles Symposium, 2009Co-Authors: Dennis Nienhuser, Thomas Gumpp, J. Marius ZollnerAbstract:Speed limit information systems solely based on one modality can hardly overcome their respective intrinsic disadvantages: Digital maps lack support for short-term changes brought by variable message signs and road works, while camera based systems cannot recognize implicit speed limits and may fail in adverse lighting scenarios. In this work we show a fusion approach that is able to overcome these limitations. It is specifically tailored to our task by adapting sensor reliability based on the perceived Situation Context. This enables the camera based system to easily outvote the digital map in a construction site or the digital map to veto against uncertain camera recognition results during nighttime. The proposed fusion approach was implemented and evaluated on a qualitative base showing very promising results: The Situation Context aware fusion is able to deduce the correct effective speed limit even when one of the sensors fails. Moreover, it reduces conflicts encountered between the sources compared to a not Situation Context aware fusion.
Michèle Rombaut - One of the best experts on this subject based on the ideXlab platform.
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IDRES: A rule-based system for driving Situation recognition with uncertainty management
Information Fusion, 2003Co-Authors: Jean-marc Nigro, Michèle RombautAbstract:This paper deals with the recognition of particular temporal sequences of a dynamic system, particularly in the driving Situation Context. The aim is to propose a method to recognise the manoeuvre performed by the driver from the data of sensors installed in a vehicle. The developed system named Intelligent Driving Recognition with Expert System is built on a two-level rule-based system that takes into account the measurements from the sensors and the sequence of the states that describes the manoeuvres. Because the inputs can be unreliable or/and inaccurate, a confidence notion is defined and modelled by a mass of evidence proposed in Dempster–Shafer’s theory. The final system recognises the current manoeuvre and evaluates the confidence of this recognition.
Dennis Nienhuser - One of the best experts on this subject based on the ideXlab platform.
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A Situation Context aware Dempster-Shafer fusion of digital maps and a road sign recognition system
2009 IEEE Intelligent Vehicles Symposium, 2009Co-Authors: Dennis Nienhuser, Thomas Gumpp, J. Marius ZollnerAbstract:Speed limit information systems solely based on one modality can hardly overcome their respective intrinsic disadvantages: Digital maps lack support for short-term changes brought by variable message signs and road works, while camera based systems cannot recognize implicit speed limits and may fail in adverse lighting scenarios. In this work we show a fusion approach that is able to overcome these limitations. It is specifically tailored to our task by adapting sensor reliability based on the perceived Situation Context. This enables the camera based system to easily outvote the digital map in a construction site or the digital map to veto against uncertain camera recognition results during nighttime. The proposed fusion approach was implemented and evaluated on a qualitative base showing very promising results: The Situation Context aware fusion is able to deduce the correct effective speed limit even when one of the sensors fails. Moreover, it reduces conflicts encountered between the sources compared to a not Situation Context aware fusion.
Charles Weissman - One of the best experts on this subject based on the ideXlab platform.
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Record completeness and data concordance in an anesthesia information management system using Context-sensitive mandatory data-entry fields
International Journal of Medical Informatics, 2012Co-Authors: Alexander Avidan, Charles WeissmanAbstract:Abstract Background Use of an anesthesia information management system (AIMS) does not insure record completeness and data accuracy. Mandatory data-entry fields can be used to assure data completeness. However, they are not suited for data that is mandatory depending on the clinical Situation (Context sensitive). For example, information on equal breath sounds should be mandatory with tracheal intubation, but not with mask ventilation. It was hypothesized that employing Context-sensitive mandatory data-entry fields can insure high data-completeness and accuracy while maintaining usability. Methods A commercial off-the-shelf AIMS was enhanced using its built-in VBScript programming tool to build event-driven forms with Context-sensitive mandatory data-entry fields. One year after introduction of the system, all anesthesia records were reviewed for data completeness. Data concordance, used as a proxy for accuracy, was evaluated using verifiable age-related data. Additionally, an anonymous satisfaction survey on general acceptance and usability of the AIMS was performed. Results During the initial 12 months of AIMS use, 12,241 (99.6%) of 12,290 anesthesia records had complete data. Concordances of entered data (weight, size of tracheal tubes, laryngoscopy blades and intravenous catheters) with patients' ages were 98.7–99.9%. The AIMS implementation was deemed successful by 98% of the anesthesiologists. Users rated the AIMS usability in general as very good and the data-entry forms in particular as comfortable. Limitations Due to the complexity and the high costs of implementation of an anesthesia information management system it was not possible to compare various system designs (for example with or without Context-sensitive mandatory data entry-fields). Therefore, it is possible that a different or simpler design would have yielded the same or even better results. This refers also to the evaluation of usability, since users did not have the opportunity to work with different design approaches or even different computer programs. Conclusions Using Context-sensitive mandatory fields in an anesthesia information management system was associated with high record completeness rate and data concordance. In addition, the system's usability was rated as very good by its users.
Jean-marc Nigro - One of the best experts on this subject based on the ideXlab platform.
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IDRES: A rule-based system for driving Situation recognition with uncertainty management
Information Fusion, 2003Co-Authors: Jean-marc Nigro, Michèle RombautAbstract:This paper deals with the recognition of particular temporal sequences of a dynamic system, particularly in the driving Situation Context. The aim is to propose a method to recognise the manoeuvre performed by the driver from the data of sensors installed in a vehicle. The developed system named Intelligent Driving Recognition with Expert System is built on a two-level rule-based system that takes into account the measurements from the sensors and the sequence of the states that describes the manoeuvres. Because the inputs can be unreliable or/and inaccurate, a confidence notion is defined and modelled by a mass of evidence proposed in Dempster–Shafer’s theory. The final system recognises the current manoeuvre and evaluates the confidence of this recognition.