The Experts below are selected from a list of 255 Experts worldwide ranked by ideXlab platform
Chao Yuan - One of the best experts on this subject based on the ideXlab platform.
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ICASSP - Robust sensor estimation using temporal information
2008 IEEE International Conference on Acoustics Speech and Signal Processing, 2008Co-Authors: Chao Yuan, Claus NeubauerAbstract:We propose a dynamic Bayesian framework for sensor estimation, a critical step of many machine condition monitoring systems. The temporal behavior of Normal sensor data is described by a stationary switching autoregressive (SSAR) model that possesses two advantages over traditional switching autoregressive (SAR) models. First, the SSAR model removes time dependency of signals during mode switching and fits sensor data better. Secondly, the SSAR model is stationary in that at each time, sensor data have the same distribution which represents the Normal Operating Range of a system; this ensures that estimates are accurate and are not distracted by deviations. During monitoring the deviation covariance is estimated adaptively, which effectively handles variable levels of deviations. Tests on gas turbine data are presented.
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Robust sensor estimation using temporal information
2008 IEEE International Conference on Acoustics Speech and Signal Processing, 2008Co-Authors: Chao Yuan, Claus NeubauerAbstract:We propose a dynamic Bayesian framework for sensor estimation, a critical step of many machine condition monitoring systems. The temporal behavior of Normal sensor data is described by a stationary switching autoregressive (SSAR) model that possesses two advantages over traditional switching autoregressive (SAR) models. First, the SSAR model removes time dependency of signals during mode switching and fits sensor data better. Secondly, the SSAR model is stationary in that at each time, sensor data have the same distribution which represents the Normal Operating Range of a system; this ensures that estimates are accurate and are not distracted by deviations. During monitoring the deviation covariance is estimated adaptively, which effectively handles variable levels of deviations. Tests on gas turbine data are presented.
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Bayesian Sensor Estimation for Machine Condition Monitoring
2007 IEEE International Conference on Acoustics Speech and Signal Processing - ICASSP '07, 2007Co-Authors: Chao Yuan, Claus NeubauerAbstract:We present a Bayesian framework to tackle the problem of sensor estimation, a critical step of fault diagnosis in machine condition monitoring. A Gaussian mixture model is employed to model the Normal Operating Range of the machine. A Gaussian random vector is introduced to model the possible deviations of the observed sensor values from their corresponding Normal values. Different levels of deviations are elegantly handled by the covariance matrix of this random vector, which is estimated adaptively for each input observation. Our algorithm doesn't require faulty operation training data, as desired by previous methods. Significant improvements over previous methods are achieved in our tests.
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Support vector methods and use of hidden variables for power plant monitoring
Proceedings. (ICASSP '05). IEEE International Conference on Acoustics Speech and Signal Processing 2005., 2005Co-Authors: Chao Yuan, C. Neubauer, Z. Cataltepe, H.-g. BrummelAbstract:This paper has three contributions to the fields of power plant monitoring. First, we differentiate out-of-Range detection from fault detection. An out-of-Range refers to a Normal Operating Range of a power plant unseen in the training data. In the case of an out-of-Range, instead of producing a fault alarm, the system should notify the operator to include more training data which capture this new Operating Range. Second, we apply a support vector one-class classifier to out-of-Range detection for its good volume modeling ability. Third, we propose to use hidden variables in regression models for fault detection. This is shown to be much better than prior work in terms of spillover reduction.
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ICASSP (5) - Support vector methods and use of hidden variables for power plant monitoring
Proceedings. (ICASSP '05). IEEE International Conference on Acoustics Speech and Signal Processing 2005., 2005Co-Authors: Chao Yuan, Claus Neubauer, Z. Cataltepe, H.-g. BrummelAbstract:This paper has three contributions to the fields of power plant monitoring. First, we differentiate out-of-Range detection from fault detection. An out-of-Range refers to a Normal Operating Range of a power plant unseen in the training data. In the case of an out-of-Range, instead of producing a fault alarm, the system should notify the operator to include more training data which capture this new Operating Range. Second, we apply a support vector one-class classifier to out-of-Range detection for its good volume modeling ability. Third, we propose to use hidden variables in regression models for fault detection. This is shown to be much better than prior work in terms of spillover reduction.
Joana Falcao Salles - One of the best experts on this subject based on the ideXlab platform.
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Normal Operating Range of bacterial communities in soil used for potato cropping
Applied and Environmental Microbiology, 2013Co-Authors: Ozgul Inceoglu, Joana Falcao Salles, Leo Van Overbeek, Jan Dirk Van ElsasAbstract:In this study, the impacts of six potato (Solanum tuberosum) cultivars with different tuber starch allocations (including one genetically modified [GM] line) on the bacterial communities in field soil were investigated across two growth seasons interspersed with 1 year of barley cultivation, using quantitative PCR, clone library, and PCR-denaturing gradient gel electrophoresis (DGGE) analyses. It was hypothesized that the modifications in the tuber starch contents of these plants, yielding changed root growth rates and exudation patterns, might have elicited altered bacterial communities in the soil. The data showed that bacterial abundances in the bulk soil varied over about 2 orders of magnitude across the 3 years. As expected, across all cultivars, positive potato rhizosphere effects on bacterial abundances were noted in the two potato years. The bulk soil bacterial community structures revealed progressive shifts across time, and moving-window analysis revealed a 60% change over the total experiment. Consistent with previous findings, the community structures in the potato rhizosphere compartments were mainly affected by the growth stage of the plants and, to a lesser extent, by plant cultivar type. The data from the soil under the non-GM potato lines were then taken to define the Normal Operating Range (NOR) of the microbiota under potatoes. Interestingly, the bacterial communities under the GM potato line remained within this NOR. In regard to the bacterial community compositions, particular bacterial species in the soil appeared to be specific to (i) the plant species under investigation (barley versus potato) or, with respect to potatoes, (ii) the plant growth stage. Members of the genera Arthrobacter, Streptomyces, Rhodanobacter, and Dokdonella were consistently found only at the flowering potato plants in both seasons, whereas Rhodoplanes and Sporosarcina were observed only in the soil planted to barley.
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microbe mediated processes as indicators to establish the Normal Operating Range of soil functioning
Soil Biology & Biochemistry, 2013Co-Authors: Michele De Cassia Pereira E Silva, Alexander V Semenov, Heike Schmitt, Jan Dirk Van Elsas, Joana Falcao SallesAbstract:Abstract Soils are major contributors to global nutrient cycling processes, which are indispensable for the healthy functioning of our ecosystems. In this study, we raise the question whether soil functioning can be captured in a concept denominated Normal Operating Range (NOR), or the Normal fluctuations in soil functioning under field conditions. We further examine how this concept could be effectively used to evaluate the impact of disturbances on agricultural ecosystems. We propose the establishment of a NOR on the basis of multiple parameters in the soil. These should include so-called sensitive processes, that is, those processes that are poorly redundant and easily deviate following a stress situation. The model that we built allowed to visualize the interplay of multiple soil parameters, under which the sensitive ones, which would be most indicative of a disturbance. Here we use the initial step of nitrification, i.e. ammonia oxidation, as an example of a sensitive process. By capturing the Normal fluctuations in ammonia oxidation-related parameters that take into account population dynamics, and implementing these in a mathematical model, a multidimensional representation of the NOR of soil function is created which is useful in tests of resilience in the context of disturbances.
Fu-liang Yang - One of the best experts on this subject based on the ideXlab platform.
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Characterization and modeling of SOI varactors at various temperatures
IEEE Transactions on Electron Devices, 2004Co-Authors: Kun-ming Chen, Guo-wei Huang, Yean-kuen Fang, Sheng-chun Wang, Fu-liang YangAbstract:Capacitance and quality factor of accumulation-mode and inversion-mode MOS varactors in silicon-on-insulator CMOS process were measured over a temperature Range of 0/spl deg/C/spl les/T/spl les/150/spl deg/C. The temperature coefficient of capacitance of inversion-mode devices is larger than that of accumulation-mode devices in the Normal Operating Range, because the threshold voltage is sensitive to temperature. Besides, the quality factor decreases with increasing temperature for these two types of varactors due to the increase of parasitic resistance. A device model based on BSIM3v3 model is proposed to simulate the temperature effect. The modeling results of capacitance, series resistance and quality factor for SOI varactors have excellent agreement with measured results.
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Characterization and modeling of SOI varactors at various temperatures
Proceedings of the 12th IEEE International Conference on Fuzzy Systems (Cat. No.03CH37442), 2003Co-Authors: Kun-ming Chen, Guo-wei Huang, Yean-kuen Fang, Fu-liang YangAbstract:SOI varactors have attracted attention for RF circuit applications due to the superior speed advantage of SOI technology. This paper presents the capacitance and the quality factor of MOS varactors in SOI CMOS process at various temperatures. The temperature coefficient of capacitance of inversion-mode device in larger than that of accumulation-mode devices in the Normal Operating Range. Besides, the quality factor decreases with increasing temperature for these varactors. A device model based on BSIM3v3 model is proposed to simulate the temperature effect. The modeled results of the capacitance, series resistance and quality factor for SOI varactors have excellent agreement with the measured results.
Claus Neubauer - One of the best experts on this subject based on the ideXlab platform.
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ICASSP - Robust sensor estimation using temporal information
2008 IEEE International Conference on Acoustics Speech and Signal Processing, 2008Co-Authors: Chao Yuan, Claus NeubauerAbstract:We propose a dynamic Bayesian framework for sensor estimation, a critical step of many machine condition monitoring systems. The temporal behavior of Normal sensor data is described by a stationary switching autoregressive (SSAR) model that possesses two advantages over traditional switching autoregressive (SAR) models. First, the SSAR model removes time dependency of signals during mode switching and fits sensor data better. Secondly, the SSAR model is stationary in that at each time, sensor data have the same distribution which represents the Normal Operating Range of a system; this ensures that estimates are accurate and are not distracted by deviations. During monitoring the deviation covariance is estimated adaptively, which effectively handles variable levels of deviations. Tests on gas turbine data are presented.
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Robust sensor estimation using temporal information
2008 IEEE International Conference on Acoustics Speech and Signal Processing, 2008Co-Authors: Chao Yuan, Claus NeubauerAbstract:We propose a dynamic Bayesian framework for sensor estimation, a critical step of many machine condition monitoring systems. The temporal behavior of Normal sensor data is described by a stationary switching autoregressive (SSAR) model that possesses two advantages over traditional switching autoregressive (SAR) models. First, the SSAR model removes time dependency of signals during mode switching and fits sensor data better. Secondly, the SSAR model is stationary in that at each time, sensor data have the same distribution which represents the Normal Operating Range of a system; this ensures that estimates are accurate and are not distracted by deviations. During monitoring the deviation covariance is estimated adaptively, which effectively handles variable levels of deviations. Tests on gas turbine data are presented.
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Bayesian Sensor Estimation for Machine Condition Monitoring
2007 IEEE International Conference on Acoustics Speech and Signal Processing - ICASSP '07, 2007Co-Authors: Chao Yuan, Claus NeubauerAbstract:We present a Bayesian framework to tackle the problem of sensor estimation, a critical step of fault diagnosis in machine condition monitoring. A Gaussian mixture model is employed to model the Normal Operating Range of the machine. A Gaussian random vector is introduced to model the possible deviations of the observed sensor values from their corresponding Normal values. Different levels of deviations are elegantly handled by the covariance matrix of this random vector, which is estimated adaptively for each input observation. Our algorithm doesn't require faulty operation training data, as desired by previous methods. Significant improvements over previous methods are achieved in our tests.
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ICASSP (5) - Support vector methods and use of hidden variables for power plant monitoring
Proceedings. (ICASSP '05). IEEE International Conference on Acoustics Speech and Signal Processing 2005., 2005Co-Authors: Chao Yuan, Claus Neubauer, Z. Cataltepe, H.-g. BrummelAbstract:This paper has three contributions to the fields of power plant monitoring. First, we differentiate out-of-Range detection from fault detection. An out-of-Range refers to a Normal Operating Range of a power plant unseen in the training data. In the case of an out-of-Range, instead of producing a fault alarm, the system should notify the operator to include more training data which capture this new Operating Range. Second, we apply a support vector one-class classifier to out-of-Range detection for its good volume modeling ability. Third, we propose to use hidden variables in regression models for fault detection. This is shown to be much better than prior work in terms of spillover reduction.
Jan Dirk Van Elsas - One of the best experts on this subject based on the ideXlab platform.
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Normal Operating Range of bacterial communities in soil used for potato cropping
Applied and Environmental Microbiology, 2013Co-Authors: Ozgul Inceoglu, Joana Falcao Salles, Leo Van Overbeek, Jan Dirk Van ElsasAbstract:In this study, the impacts of six potato (Solanum tuberosum) cultivars with different tuber starch allocations (including one genetically modified [GM] line) on the bacterial communities in field soil were investigated across two growth seasons interspersed with 1 year of barley cultivation, using quantitative PCR, clone library, and PCR-denaturing gradient gel electrophoresis (DGGE) analyses. It was hypothesized that the modifications in the tuber starch contents of these plants, yielding changed root growth rates and exudation patterns, might have elicited altered bacterial communities in the soil. The data showed that bacterial abundances in the bulk soil varied over about 2 orders of magnitude across the 3 years. As expected, across all cultivars, positive potato rhizosphere effects on bacterial abundances were noted in the two potato years. The bulk soil bacterial community structures revealed progressive shifts across time, and moving-window analysis revealed a 60% change over the total experiment. Consistent with previous findings, the community structures in the potato rhizosphere compartments were mainly affected by the growth stage of the plants and, to a lesser extent, by plant cultivar type. The data from the soil under the non-GM potato lines were then taken to define the Normal Operating Range (NOR) of the microbiota under potatoes. Interestingly, the bacterial communities under the GM potato line remained within this NOR. In regard to the bacterial community compositions, particular bacterial species in the soil appeared to be specific to (i) the plant species under investigation (barley versus potato) or, with respect to potatoes, (ii) the plant growth stage. Members of the genera Arthrobacter, Streptomyces, Rhodanobacter, and Dokdonella were consistently found only at the flowering potato plants in both seasons, whereas Rhodoplanes and Sporosarcina were observed only in the soil planted to barley.
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microbe mediated processes as indicators to establish the Normal Operating Range of soil functioning
Soil Biology & Biochemistry, 2013Co-Authors: Michele De Cassia Pereira E Silva, Alexander V Semenov, Heike Schmitt, Jan Dirk Van Elsas, Joana Falcao SallesAbstract:Abstract Soils are major contributors to global nutrient cycling processes, which are indispensable for the healthy functioning of our ecosystems. In this study, we raise the question whether soil functioning can be captured in a concept denominated Normal Operating Range (NOR), or the Normal fluctuations in soil functioning under field conditions. We further examine how this concept could be effectively used to evaluate the impact of disturbances on agricultural ecosystems. We propose the establishment of a NOR on the basis of multiple parameters in the soil. These should include so-called sensitive processes, that is, those processes that are poorly redundant and easily deviate following a stress situation. The model that we built allowed to visualize the interplay of multiple soil parameters, under which the sensitive ones, which would be most indicative of a disturbance. Here we use the initial step of nitrification, i.e. ammonia oxidation, as an example of a sensitive process. By capturing the Normal fluctuations in ammonia oxidation-related parameters that take into account population dynamics, and implementing these in a mathematical model, a multidimensional representation of the NOR of soil function is created which is useful in tests of resilience in the context of disturbances.
