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Uncertainty: Overview00:59

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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
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Towards an uncertainty evaluation model for marine track considering local sampling cloud and sample information.

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A new model evaluates ship navigation skills using normal cloud charts and track belt division. This method effectively assesses crew performance during port entry, aligning with expert judgments.

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Area of Science:

  • Maritime Safety
  • Navigational Engineering
  • Artificial Intelligence in Maritime

Background:

  • Assessing ship crew practical abilities during navigation, especially in complex port approaches, remains challenging.
  • Traditional methods often rely on subjective expert judgment, lacking quantitative and objective evaluation metrics.
  • The need for intelligent systems to evaluate navigational performance under uncertainty is growing.

Purpose of the Study:

  • To develop a novel track evaluation model for assessing single ship inward-port navigation.
  • To introduce a track belt division method to handle divergent track sampling data.
  • To establish a comprehensive evaluation scheme integrating these methods for intelligent assessment.

Main Methods:

  • Development of a track evaluation model using forward and backward normal cloud generators on a planar chart.
  • Proposal of a track belt division method based on normal cloud drop contributions.
  • Integration of the evaluation model and division method for a comprehensive sampling information-based scheme.

Main Results:

  • The developed model effectively evaluated the practical abilities of crews during inward-port navigation.
  • An example using M.V. DAQING 257 into Dalian Port demonstrated the model's practical effectiveness.
  • Evaluation results showed consistency with subjective expert assessments, validating the model's reliability.

Conclusions:

  • The proposed track evaluation model and track belt division method offer a robust approach for navigational performance assessment.
  • This integrated scheme provides an intelligent evaluation method under sample information, addressing uncertainty.
  • The findings contribute a new, data-driven approach to maritime safety and crew training evaluation.