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Study of the characteristics of error detection with Hamming codes, the consideration of which is appropriate for the synthesis of automatic devices with fault detection

2023· article· en· W4386569072 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTransport automation research · 2023
Typearticle
Languageen
FieldComputer Science
TopicCybersecurity and Information Systems
Canadian institutionsInternational Air Transport Association
Fundersnot available
KeywordsHamming codeFault detection and isolationHamming distanceComputer scienceError detection and correctionReliability engineeringFault (geology)AlgorithmArtificial intelligenceEngineeringBlock codeBiologyDecoding methods

Abstract

fetched live from OpenAlex

The peculiarities of using Hamming codes in the synthesis of automatic devices with fault detection are investigated. Such devices imply the organization of embedded control schemes to detect occurring faults indirectly based on the results of calculating the values of operational functions. Various methods can be used by the implementation of embedded control schemes. In this study, the focus is shifted to the issues of synthesizing embedded control schemes using the method of logical signal correction (the method of logical complementation). This method involves transforming all or part of the signals coming from the diagnostic object in the embedded control scheme in such a way that the code word generated after the signal correction block belongs to a preselected block uniform code. The study considers the application of classical Hamming codes for these purposes. The use of the method of logical signal correction allows obtaining the values of the informational symbols of the code words of the Hamming code directly as values at the operational outputs of the diagnostic object, while the check symbols are obtained by correcting signals from some of the operational outputs. However, it is also possible to use transformations of operational function values to obtain informational symbols, which expands the number of ways to organize the embedded control scheme. The article presents previously unknown absolute and relative error detection metrics in the code words of the Hamming code, taking into account their categorization based on types (according to the number of distortions in zero and one bits) and multiplicities. The experimental results with test combinational circuits confirm the effectiveness of using the method of logical signal correction with computation control using Hamming codes for synthesizing embedded control schemes. The results obtained in this study extend the theory of synthesis for self-checking digital devices and computational systems and can be practically applied in improving the methods for synthesizing automatic devices with fault detection.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.560
Threshold uncertainty score0.295

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.067
GPT teacher head0.320
Teacher spread0.254 · 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