Spectro-tomography

Hoyle, Brian

doi:10.1016/b978-1-78242-118-4.00010-1

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…In this case, particularly at a higher frequency (10 4 –10 5 Hz), the relationship between the phase angle and the moisture content of the material becomes meaningful. This gives the opportunity to use, at a later stage of the research, both the resistive and capacitive properties of the system by proposing a dual modality tomography [34] method or spectro-tomography [14,15,16]. Further research should obviously include carrying out measurements at different temperatures because, a significant impact on the results might be expected.…”

Section: Resultsmentioning

confidence: 99%

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Assessment of the Spatial Distribution of Moisture Content in Granular Material Using Electrical Impedance Tomography

Porzuczek

2019

Sensors

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This paper presents a method for the online determination of the spatial distribution of the moisture content in granular material. It might be essential for the monitoring and optimal control of, for example, drying processes. The proposed method utilizes Electrical Impedance Tomography (EIT). As an exemplary material for experimental research, the black chokeberry (Aronia melanocarpa) was used. The relationship between the electrical impedance of the chokeberry and its moisture content was determined for a wide range of frequencies (20 Hz–200 kHz). The EIT research consisted of both simulation and experimental investigation. Experimental studies of the spatial distribution of the moisture content were performed in a cylindrical vessel equipped with 8 electrodes circumferentially arranged. The voltage signal from the electrodes was acquired simultaneously using the data acquisition module. Due to the high impedance of the chokeberries, exceeding 109 Ω for the dried matter, extraordinary instrumentation was necessary to be applied. On the other hand, raw chokeberry was characterized by a several orders of magnitude lower impedance (103–104 Ω), especially for high frequencies. The wide range of the observed impedance was able to be measured owing to its use of the voltage stimulation instead of the current stimulation (which is most common for EIT). The image reconstruction problem was solved using an iterative Gauss–Newton algorithm and the EIDORS (Electrical Impedance Tomography and Diffuse Optical Tomography Reconstruction Software) package. The obtained results showed a satisfactory ability to localize an insufficiently dried part of the material. Prospective ways to improve the imaging quality are also discussed.

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Section: Resultsmentioning

confidence: 99%

“…Particular attention must be given to ensuring the best possible contact between the material and the sensor electrodes. For future research, the use of multifrequency impedance tomography or spectro-tomography [14,15,16] seems to be promising.…”

Section: Introductionmentioning

confidence: 99%

Assessment of the Spatial Distribution of Moisture Content in Granular Material Using Electrical Impedance Tomography

Porzuczek

2019

Sensors

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“…Hoyle et al. (2015) used a tree-based data augmentation method for apparent magnitudes, which provided low-bias redshift estimation of galaxies. Hosenie et al.…”

Section: Introductionmentioning

confidence: 99%

A novel approach for variable star classification based on imbalanced learning

Zhang

Kang

et al. 2023

Publ. Astron. Soc. Aust.

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The advent of time-domain sky surveys has generated a vast amount of light variation data, enabling astronomers to investigate variable stars with large-scale samples. However, this also poses new opportunities and challenges for the time-domain research. In this paper, we focus on the classification of variable stars from the Catalina Surveys Data Release 2 and propose an imbalanced learning classifier based on Self-paced Ensemble (SPE) method. Compared with the work of Hosenie et al. (2020), our approach significantly enhances the classification Recall of Blazhko RR Lyrae stars from 12% to 85%, mixed-mode RR Lyrae variables from 29% to 64%, detached binaries from 68% to 97%, and LPV from 87% to 99%. SPE demonstrates a rather good performance on most of the variable classes except RRab, RRc, and contact and semi-detached binary. Moreover, the results suggest that SPE tends to target the minority classes of objects, while Random Forest is more effective in finding the majority classes. To balance the overall classification accuracy, we construct a Voting Classifier that combines the strengths of SPE and Random Forest. The results show that the Voting Classifier can achieve a balanced performance across all classes with minimal loss of accuracy. In summary, the SPE algorithm and Voting Classifier are superior to traditional machine learning methods and can be well applied to classify the periodic variable stars. This paper contributes to the current research on imbalanced learning in astronomy and can also be extended to the time-domain data of other larger sky survey projects (LSST, etc.).

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Spectro-tomography

Cited by 2 publications

References 9 publications

Assessment of the Spatial Distribution of Moisture Content in Granular Material Using Electrical Impedance Tomography

Assessment of the Spatial Distribution of Moisture Content in Granular Material Using Electrical Impedance Tomography

A novel approach for variable star classification based on imbalanced learning

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