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Record W2023239636 · doi:10.1109/oceans.2014.7003112

Development of ice profiler sonar (IPS) target sonar with a logarithmic detector

2014· article· en· W2023239636 on OpenAlexaff
R.A.J. Chave, David B. Fissel, David Lemon, Murray Clarke, Patrick Johnston

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsASL Environmental Sciences (Canada)
Fundersnot available
KeywordsSonarDetectorFirmwareRemote sensingEngineeringMarine engineeringComputer scienceAcousticsGeologyElectrical engineeringPhysicsComputer hardware

Abstract

fetched live from OpenAlex

Upward-looking sonar (ULS) instruments have become the primary source of data for high resolution and long duration measurements of sea ice drafts to support engineering requirements for oil and gas exploration projects in Arctic and other ice-infested areas. ULS instruments, in the form of ASL's Ice Profiler Sonar (IPS), provide accurate measurements for ice draft on a continuous year-long basis and allow detailed characterization of keel shapes and other ice features. The IPS instrument was originally developed in the 1990's and it was last upgraded by ASL Environmental Sciences Inc. in 2007-2008 through improved instrument design based on more capable microprocessors and more advanced on-board firmware. Another upgrade of the IPS instrument platform is presently underway with the design, testing and implementation of a logarithmic detector module in place of the previously used linear detector module which has been used for the past decade in the instrument. The linear detector module involves the use of an echo sounder detector which generates an analog voltage output from the raw transducer input supplied which is constant, i.e. independent of the time elapsed since the acoustic pulse was originally emitted. While this approach has proven reasonably serviceable, it has the disadvantage that the dynamic range of the instrument is curtailed from the alternative approach of using a logarithmic detector module which has previously been implemented in other ASL upward looking sonar instruments. The larger dynamic range of the log detector avoids using approximate TVG compensation. With the logarithmic sonar detector, the use of discrete threshold values for target detection is avoided and the resulting target detection capability is more robust. The project involved three principal components: (a) construction of a prototype 420 kHz log sonar card; (b) simulations of the response of the IPS log sonar instrument from previous IPS data sets which guided the development of operating firmware; and (c) assembly and field testing of a prototype IPS log sonar unit operated simultaneously with a standard IPS5. The simulations of the IPS5 log sonar outputs derived from previous standard IPS5 data indicate that there are occasional differences in the target detection for borderline cases, but they will not be significant. After iterations to improve the robustness of the target detection algorithm, development of the remaining functions of the IPS5 operating firmware was then carried out and further tested. Finally the prototype IPS5 log sonar instrument unit, along with a standard IPS5 instrument, was field tested in a deep open water environment (to 200 m water depth) in order to test the accuracy of the acoustic range of the sonar targets.

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)0.001
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.008
GPT teacher head0.180
Teacher spread0.172 · 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
GenreMethods

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

Citations2
Published2014
Admission routes1
Has abstractyes

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