Three-component tomographic flow imaging using artificial neural network reconstruction

Nooralahiyan, Amir Y.; Hoyle, Brian

doi:10.1016/s0009-2509(97)00040-7

Cited by 19 publications

(18 citation statements)

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“…Similar property of the neural network inverse was also reported in [7]. In the current approach the PCA projection of the potential signal and images contributes to the suppression of noise effects, as uncorrelated noise is also largely uncorrelated with the eigenvectors of the signals.…”

Section: Robustness To Noisesupporting

confidence: 75%

“…In [7] the reconstruction image was directly estimated by a neural network from the potential signals.The solution was demonstrated to be very robust against noise in input signals. However, the resolution of the image in such approach is in practice limited to some tens or hundreds of pixels, as networks with several hundreds of outputs are rather difficult to use and train, and often require non-standard regularization to smooth the results of neighboring pixels.…”

Section: Introductionmentioning

confidence: 99%

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Application of Bayesian neural network in electrical impedance tomography

Lampinen¹,

Vehtari²,

Leinonen³

IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339)

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Section: Robustness To Noisesupporting

confidence: 75%

Section: Introductionmentioning

confidence: 99%

Application of Bayesian neural network in electrical impedance tomography

Lampinen¹,

Vehtari²,

Leinonen³

IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339)

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“…Optimization algorithms are another type of iterative reconstruction methods , Nooralahiyan et al 1997. These algorithms are not based only on the least square error measure which contains no information about the nature of an accurate solution but on a set of objective functions.…”

Section: Reconstruction Techniquesmentioning

confidence: 99%

In-Situ Real Time Monitoring and Control of Mold Making and Filling Processes: Final Report

Abdelrahman¹,

Currie²

2010

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“…The third approach uses a single-modal sensing system with sound assumptions and/or a reconstruction technique capable of differentiating between three phases in the object domain. An example is the use of electrical capacitance tomography with a neural networkbased image reconstruction technique proposed by Nooralahiyan and Hoyle (1997). To enable the identification of gas bubbles and water drops in an oil environment, they used a singlelayer feed forward neural network with a double-step sigmoid function to replace the one-step sigmoid function in the neural network computing.…”

Section: Status Of Farm-based Digesters In the Unitedmentioning

confidence: 99%

Improved Biomass Utilization Through Remote Flow Sensing

2007

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The growth of the livestock industry provides a valuable source of affordable, sustainable, and renewable bioenergy, while also requiring the safe disposal of the large quantities of animal wastes (manure) generated at dairy, swine, and poultry farms. For example, the USA produces 1.8 billion tons of cow manure annually (USDA 2002(USDA /2003. If these biomass resources are mishandled and underutilized, major environmental problems will be created, such as surface and ground water contamination, odors, dust, ammonia leaching, and methane emission. Methane emission notably results in a greenhouse gas effect that is considered 22 times worse than carbon dioxide . Anaerobic digestion of animal wastes, in which microorganisms break down organic materials in the absence of oxygen, is one of the most promising waste treatment technologies. This process produces biogas typically containing ~65% methane and ~35% carbon dioxide. The production of biogas through anaerobic digestion from animal wastes, landfills, and municipal waste water treatment plants represents a large source of renewable and sustainable bio-fuel. Such bio-fuel can be combusted directly, used in internal combustion engines, converted into methanol, or partially oxidized to produce synthesis gas (a mixture of hydrogen and carbon monoxide) that can be converted to clean liquid fuels and chemicals via Fischer-Tropsch synthesis.Different design and mixing configurations of anaerobic digesters for treating cow manure have been utilized commercially and/or tested on a laboratory scale. These digesters include mechanically mixed, gas recirculation mixed, and slurry recirculation mixed designs, as well as covered lagoon digesters. Mixing is an important parameter for successful performance of anaerobic digesters. It enhances substrate contact with the microbial community; improves pH, temperature and substrate/microorganism uniformity; prevents stratification and scum accumulation; facilitates the removal of biogas from the digester; reduces or eliminates the formation of inactive zones (dead zones); prevents settling of biomass and inert solids; and aids in particle size reduction. Unfortunately, information and findings in the literature on the effect of mixing on anaerobic digestion are contradictory. All the published studies on cow manure anaerobic digestion have been performed using small laboratory scale digesters (~4 liter). No reported investigations have been conducted in pilot plant and/or commercial scale digesters. Hence, the impact of mixing intensity, operating conditions, and design variables on the performance of large digesters are unknown.One reason is the lack of measurement techniques for opaque systems such as digesters. Better understanding of the mixing and hydrodynamics of digesters will result in appropriate design, configuration selection, scale-up, and performance, which will ultimately enable avoiding digester failures. Anaerobic digesters are highly opaque systems and contain a large volume fraction of dispersed solids as well as ...

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Three-component tomographic flow imaging using artificial neural network reconstruction

Cited by 19 publications

References 1 publication

Application of Bayesian neural network in electrical impedance tomography

Application of Bayesian neural network in electrical impedance tomography

In-Situ Real Time Monitoring and Control of Mold Making and Filling Processes: Final Report

Improved Biomass Utilization Through Remote Flow Sensing

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