The Experts below are selected from a list of 132 Experts worldwide ranked by ideXlab platform
Clive R. Frankish - One of the best experts on this subject based on the ideXlab platform.
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Speech Recognition Technology for Individuals with Disabilities
Augmentative and Alternative Communication, 1992Co-Authors: Jan M. Noyes, Clive R. FrankishAbstract:There are estimated to be about 15 million people in the United States alone who are disabled to some degree, and it has been hypothesized that at least 10% of the world's population experience some sort of physical impairment. Although such statistics are always open to debate, this does not detract from the suggestion that one of the most promising application areas for Automatic Speech Recognition (ASR) is in helping people with disabilities. It is within this context that the advantages of speech recognition are discussed, both in general and with specific relation to this user group. During the last decade, the development of more sophisticated techniques for analyzing incoming speech combined with the increased processing power of microcomputers has resulted in improved recognition performance. Consequently, speech recognizers are now either operational or being considered in a variety of industrial tasks and in office and Aerospace Environments. ASR applications specific to disabled users are revie...
Karen Margaret Holford - One of the best experts on this subject based on the ideXlab platform.
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A new methodology for automating acoustic emission detection of metallic fatigue fractures in highly demanding Aerospace Environments: An overview
Progress in Aerospace Sciences, 2017Co-Authors: Karen Margaret Holford, James J. Hensman, Sam L. Evans, Mark Jonathan Eaton, Rhys Pullin, Nikolaos Dervilis, Keith WordenAbstract:The acoustic emission (AE) phenomenon has many attributes that make it desirable as a structural health monitoring or non-destructive testing technique, including the capability to continuously and globally monitor large structures using a sparse sensor array and with no dependency on defect size. However, AE monitoring is yet to fulfil its true potential, due mainly to limitations in location accuracy and signal characterisation that often arise in complex structures with high levels of background noise. Furthermore, the technique has been criticised for a lack of quantitative results and the large amount of operator interpretation required during data analysis. This paper begins by introducing the challenges faced in developing an AE based structural health monitoring system and then gives a review of previous progress made in addresing these challenges. Subsequently an overview of a novel methodology for automatic detection of fatigue fractures in complex geometries and noisy Environments is presented, which combines a number of signal processing techniques to address the current limitations of AE monitoring. The technique was developed for monitoring metallic landing gear components during pre-flight certification testing and results are presented from a full-scale steel landing gear component undergoing fatigue loading. Fracture onset was successfully identify automatically at 49,000 fatigue cycles prior to final failure (validated by the use of dye penetrant inspection) and the fracture position was located to within 10 mm of the actual location.
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Energy Harvesting for Aerospace Structural Health Monitoring Systems
Modern Practice in Stress and Vibration Analysis 2012 (Mpsva 2012), 2012Co-Authors: Matthew Pearson, Karen Margaret Holford, Mark Jonathan Eaton, Rhys Pullin, IopAbstract:Recent research into damage detection methodologies, embedded sensors, wireless data transmission and energy harvesting in Aerospace Environments has meant that autonomous structural health monitoring (SHM) systems are becoming a real possibility. The most promising system would utilise wireless sensor nodes that are able to make decisions on damage and communicate this wirelessly to a central base station. Although such a system shows great potential and both passive and active monitoring techniques exist for detecting damage in structures, powering such wireless sensors nodes poses a problem. Two such energy sources that could be harvested in abundance on an aircraft are vibration and thermal gradients. Piezoelectric transducers mounted to the surface of a structure can be utilised to generate power from a dynamic strain whilst thermoelectric generators (TEG) can be used to generate power from thermal gradients. This paper reports on the viability of these two energy sources for powering a wireless SHM system from vibrations ranging from 20 to 400Hz and thermal gradients up to 50 degrees C. Investigations showed that using a single vibrational energy harvester raw power levels of up to 1mW could be generated. Further numerical modelling demonstrated that by optimising the position and orientation of the vibrational harvester greater levels of power could be achieved. However using commercial TEGs average power levels over a flight period between 5 to 30mW could be generated. Both of these energy harvesting techniques show a great potential in powering current wireless SHM systems where depending on the complexity the power requirements range from 1 to 180mW.
Jan M. Noyes - One of the best experts on this subject based on the ideXlab platform.
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Speech Recognition Technology for Individuals with Disabilities
Augmentative and Alternative Communication, 1992Co-Authors: Jan M. Noyes, Clive R. FrankishAbstract:There are estimated to be about 15 million people in the United States alone who are disabled to some degree, and it has been hypothesized that at least 10% of the world's population experience some sort of physical impairment. Although such statistics are always open to debate, this does not detract from the suggestion that one of the most promising application areas for Automatic Speech Recognition (ASR) is in helping people with disabilities. It is within this context that the advantages of speech recognition are discussed, both in general and with specific relation to this user group. During the last decade, the development of more sophisticated techniques for analyzing incoming speech combined with the increased processing power of microcomputers has resulted in improved recognition performance. Consequently, speech recognizers are now either operational or being considered in a variety of industrial tasks and in office and Aerospace Environments. ASR applications specific to disabled users are revie...
Rhys Pullin - One of the best experts on this subject based on the ideXlab platform.
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A new methodology for automating acoustic emission detection of metallic fatigue fractures in highly demanding Aerospace Environments: An overview
Progress in Aerospace Sciences, 2017Co-Authors: Karen Margaret Holford, James J. Hensman, Sam L. Evans, Mark Jonathan Eaton, Rhys Pullin, Nikolaos Dervilis, Keith WordenAbstract:The acoustic emission (AE) phenomenon has many attributes that make it desirable as a structural health monitoring or non-destructive testing technique, including the capability to continuously and globally monitor large structures using a sparse sensor array and with no dependency on defect size. However, AE monitoring is yet to fulfil its true potential, due mainly to limitations in location accuracy and signal characterisation that often arise in complex structures with high levels of background noise. Furthermore, the technique has been criticised for a lack of quantitative results and the large amount of operator interpretation required during data analysis. This paper begins by introducing the challenges faced in developing an AE based structural health monitoring system and then gives a review of previous progress made in addresing these challenges. Subsequently an overview of a novel methodology for automatic detection of fatigue fractures in complex geometries and noisy Environments is presented, which combines a number of signal processing techniques to address the current limitations of AE monitoring. The technique was developed for monitoring metallic landing gear components during pre-flight certification testing and results are presented from a full-scale steel landing gear component undergoing fatigue loading. Fracture onset was successfully identify automatically at 49,000 fatigue cycles prior to final failure (validated by the use of dye penetrant inspection) and the fracture position was located to within 10 mm of the actual location.
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Energy Harvesting for Aerospace Structural Health Monitoring Systems
Modern Practice in Stress and Vibration Analysis 2012 (Mpsva 2012), 2012Co-Authors: Matthew Pearson, Karen Margaret Holford, Mark Jonathan Eaton, Rhys Pullin, IopAbstract:Recent research into damage detection methodologies, embedded sensors, wireless data transmission and energy harvesting in Aerospace Environments has meant that autonomous structural health monitoring (SHM) systems are becoming a real possibility. The most promising system would utilise wireless sensor nodes that are able to make decisions on damage and communicate this wirelessly to a central base station. Although such a system shows great potential and both passive and active monitoring techniques exist for detecting damage in structures, powering such wireless sensors nodes poses a problem. Two such energy sources that could be harvested in abundance on an aircraft are vibration and thermal gradients. Piezoelectric transducers mounted to the surface of a structure can be utilised to generate power from a dynamic strain whilst thermoelectric generators (TEG) can be used to generate power from thermal gradients. This paper reports on the viability of these two energy sources for powering a wireless SHM system from vibrations ranging from 20 to 400Hz and thermal gradients up to 50 degrees C. Investigations showed that using a single vibrational energy harvester raw power levels of up to 1mW could be generated. Further numerical modelling demonstrated that by optimising the position and orientation of the vibrational harvester greater levels of power could be achieved. However using commercial TEGs average power levels over a flight period between 5 to 30mW could be generated. Both of these energy harvesting techniques show a great potential in powering current wireless SHM systems where depending on the complexity the power requirements range from 1 to 180mW.
Mark Jonathan Eaton - One of the best experts on this subject based on the ideXlab platform.
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A new methodology for automating acoustic emission detection of metallic fatigue fractures in highly demanding Aerospace Environments: An overview
Progress in Aerospace Sciences, 2017Co-Authors: Karen Margaret Holford, James J. Hensman, Sam L. Evans, Mark Jonathan Eaton, Rhys Pullin, Nikolaos Dervilis, Keith WordenAbstract:The acoustic emission (AE) phenomenon has many attributes that make it desirable as a structural health monitoring or non-destructive testing technique, including the capability to continuously and globally monitor large structures using a sparse sensor array and with no dependency on defect size. However, AE monitoring is yet to fulfil its true potential, due mainly to limitations in location accuracy and signal characterisation that often arise in complex structures with high levels of background noise. Furthermore, the technique has been criticised for a lack of quantitative results and the large amount of operator interpretation required during data analysis. This paper begins by introducing the challenges faced in developing an AE based structural health monitoring system and then gives a review of previous progress made in addresing these challenges. Subsequently an overview of a novel methodology for automatic detection of fatigue fractures in complex geometries and noisy Environments is presented, which combines a number of signal processing techniques to address the current limitations of AE monitoring. The technique was developed for monitoring metallic landing gear components during pre-flight certification testing and results are presented from a full-scale steel landing gear component undergoing fatigue loading. Fracture onset was successfully identify automatically at 49,000 fatigue cycles prior to final failure (validated by the use of dye penetrant inspection) and the fracture position was located to within 10 mm of the actual location.
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Energy Harvesting for Aerospace Structural Health Monitoring Systems
Modern Practice in Stress and Vibration Analysis 2012 (Mpsva 2012), 2012Co-Authors: Matthew Pearson, Karen Margaret Holford, Mark Jonathan Eaton, Rhys Pullin, IopAbstract:Recent research into damage detection methodologies, embedded sensors, wireless data transmission and energy harvesting in Aerospace Environments has meant that autonomous structural health monitoring (SHM) systems are becoming a real possibility. The most promising system would utilise wireless sensor nodes that are able to make decisions on damage and communicate this wirelessly to a central base station. Although such a system shows great potential and both passive and active monitoring techniques exist for detecting damage in structures, powering such wireless sensors nodes poses a problem. Two such energy sources that could be harvested in abundance on an aircraft are vibration and thermal gradients. Piezoelectric transducers mounted to the surface of a structure can be utilised to generate power from a dynamic strain whilst thermoelectric generators (TEG) can be used to generate power from thermal gradients. This paper reports on the viability of these two energy sources for powering a wireless SHM system from vibrations ranging from 20 to 400Hz and thermal gradients up to 50 degrees C. Investigations showed that using a single vibrational energy harvester raw power levels of up to 1mW could be generated. Further numerical modelling demonstrated that by optimising the position and orientation of the vibrational harvester greater levels of power could be achieved. However using commercial TEGs average power levels over a flight period between 5 to 30mW could be generated. Both of these energy harvesting techniques show a great potential in powering current wireless SHM systems where depending on the complexity the power requirements range from 1 to 180mW.
