MétaCan
Menu
Back to cohort
Record W7029357916

Improved antenna design for grain bin electromagnetic imaging

2022· dissertation· en· W7029357916 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2022
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsnot available
Fundersnot available
KeywordsReflection (computer programming)Noise (video)HyporeflexiaPoint (geometry)NucleofectionFerroresonance in electricity networks
DOInot available

Abstract

fetched live from OpenAlex

Grain such as wheat and canola is usually stored in silos or bins, often for long periods of time. Grain can and regularly does spoil in such bins, thus monitoring the grain quality and quantity inside the bin is crucial. It is possible to monitor grain within metallic grain bins using three-dimensional ElectroMagnetic Imaging (EMI). To acquire data in EMI, multiple antennas must transmit and receive ElectroMagnetic (EM) energy within the imaging bin. These antennas subjected to significant mechanical forces, they must have a low profile. These antennas should also be easy to model in computational EM software (using a point source). Moreover, since the EMI algorithm uses linear polarization of EM fields, it is common to choose antennas that can detect a linear polarization and reject others, such as antennas that measure magnetic fields normal to the antenna’s loop and reject electric fields. This can be done through using Shielded Loop Antennas. Given their desirable physical and EM characteristics, previous grain bin imaging systems have been designed with Shielded Half Loop Antennas (SHLA). While capable of detecting primarily the tangential magnetic field at the bin wall, existing SHLA suffer from a large reflection coefficient at the antenna terminal, meaning they can be drastically improved by reducing this reflection coefficient, thus reducing noise and receiving more desired signal. Herein, we consider two methods of improving upon this design: matching circuits and improved antennas. For matching techniques, we consider passive and active circuits. Through simulation, we show how these methods can improve antenna performance. For an improved antenna design, we show that loading a SHLA with ferrite material can enhance its performance. To model the behavior of a ferrite-loaded shielded loop antenna, we develop an approximate lumped circuit model which can predict antenna resonance frequency, its reflection coefficient, and its efficiency. This model aids in the design of larger sized ferrite-backed antennas. An antenna at resonance frequency of 260 MHz is fabricated and tested in a small (labratorysized) grain bin. Its sensitivity to magnetic field and rejecting electric fields is tested in Gigahertz Transverse EM (GTEM) cell at the Electromagnetic Imaging Laboratory (EIL) of the University of Manitoba, and show that the ferrite-backed antenna increases signal strength while maintaining the rejection of normal-E fields. The small ferrite loaded shielded half-loop antenna provided a 6 􀀀 18 dB improvement in signal level over existing half-loop antennas over the imaging frequencies of interest (approx 200􀀀600 MHz). Furthermore, experimental laboratory imaging tests with hard-red winter wheat indicate that the proposed antennas can be modeled more accurately using simple polarized point sources. A full 3D image of a wet-grain target set within a dry-grain background inside the enclosure shows that the improved antennas enhance the quality and accuracy of the reconstructed images, mainly where the antenna performance improvements are prominent. Based on these promising results, we designed and constructed a scaled up ferrite-backed halfloop antenna suitable for a larger industrial grain bin and at a lower frequency (a resonance of 120 MHz). Preliminary measurements of these antennas show that these larger versions not only can detect magnetic field and reject electric fields, but also increase the signal strength in the grain bin.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.208
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueMspace (University of Manitoba)Same topicGeophysical and Geoelectrical MethodsFrench-language works237,207