Aviation Safety Risk Analysis and Flight Technology Assessment Issues

• URL: http://arxiv.org/abs/2309.12324v1
• Date: Thu, 10 Aug 2023 14:13:49 GMT
• Title: Aviation Safety Risk Analysis and Flight Technology Assessment Issues
• Authors: Shuanghe Liu
• Abstract summary: It focuses on two main areas: analyzing exceedance events and statistically evaluating non-exceedance data. The proposed solutions involve data preprocessing, reliability assessment, quantifying flight control using neural networks, exploratory data analysis, and establishing real-time automated warnings.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This text highlights the significance of flight safety in China's civil aviation industry and emphasizes the need for comprehensive research. It focuses on two main areas: analyzing exceedance events and statistically evaluating non-exceedance data. The challenges of current approaches lie in insufficient cause analysis for exceedances. The proposed solutions involve data preprocessing, reliability assessment, quantifying flight control using neural networks, exploratory data analysis, flight personnel skill evaluation with machine learning, and establishing real-time automated warnings. These endeavors aim to enhance flight safety, personnel assessment, and warning mechanisms, contributing to a safer and more efficient civil aviation sector.

Related papers

• Applications of natural language processing in aviation safety: A review and qualitative analysis [0.0]
  This study explores using Natural Language Processing in aviation safety. It focuses on machine learning algorithms to enhance safety measures. There are currently 34 Scopus results from the keyword search natural language processing and aviation safety.
  arXiv  Detail & Related papers  (2025-01-03T07:36:10Z)
• Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress? [59.96471873997733]
  We propose an empirical foundation for developing more meaningful safety metrics and define AI safety in a machine learning research context. We aim to provide a more rigorous framework for AI safety research, advancing the science of safety evaluations and clarifying the path towards measurable progress.
  arXiv  Detail & Related papers  (2024-07-31T17:59:24Z)
• Risks of uncertainty propagation in Al-augmented security pipelines [5.692289204193087]
  The use of AI technologies is percolating into the secure development of software-based systems. No previous work has estimated the uncertainty of AI-augmented systems given the propagation of errors in the pipeline. We provide the formal underpinnings for capturing uncertainty propagation, develop a simulator to quantify uncertainty, and evaluate the simulation of propagating errors with two case studies.
  arXiv  Detail & Related papers  (2024-07-14T19:02:20Z)
• Machine Learning for Pre/Post Flight UAV Rotor Defect Detection Using Vibration Analysis [54.550658461477106]
  Unmanned Aerial Vehicles (UAVs) will be critical infrastructural components of future smart cities. In order to operate efficiently, UAV reliability must be ensured by constant monitoring for faults and failures. This paper leverages signal processing and Machine Learning methods to analyze the data of a comprehensive vibrational analysis to determine the presence of rotor blade defects.
  arXiv  Detail & Related papers  (2024-04-24T13:50:27Z)
• Topic Modeling Analysis of Aviation Accident Reports: A Comparative Study between LDA and NMF Models [0.0]
  This paper compares two prominent topic modeling techniques, Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF) LDA demonstrates higher topic coherence, indicating stronger semantic relevance among words within topics. NMF excelled in producing distinct and granular topics, enabling a more focused analysis of specific aspects of aviation accidents.
  arXiv  Detail & Related papers  (2024-03-04T01:41:07Z)
• The Last Decade in Review: Tracing the Evolution of Safety Assurance Cases through a Comprehensive Bibliometric Analysis [7.431812376079826]
  Safety assurance is of paramount importance across various domains, including automotive, aerospace, and nuclear energy. The use of safety assurance cases allows for verifying the correctness of the created systems capabilities, preventing system failure.
  arXiv  Detail & Related papers  (2023-11-13T17:34:23Z)
• Leveraging Traceability to Integrate Safety Analysis Artifacts into the Software Development Process [51.42800587382228]
  Safety assurance cases (SACs) can be challenging to maintain during system evolution. We propose a solution that leverages software traceability to connect relevant system artifacts to safety analysis models. We elicit design rationales for system changes to help safety stakeholders analyze the impact of system changes on safety.
  arXiv  Detail & Related papers  (2023-07-14T16:03:27Z)
• Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements [76.80453043969209]
  This survey presents a framework for safety research pertaining to large models. We begin by introducing safety issues of wide concern, then delve into safety evaluation methods for large models. We explore the strategies for enhancing large model safety from training to deployment.
  arXiv  Detail & Related papers  (2023-02-18T09:32:55Z)
• Anomaly Detection for Unmanned Aerial Vehicle Sensor Data Using a Stacked Recurrent Autoencoder Method with Dynamic Thresholding [0.3441021278275805]
  This paper proposes a system incorporating a Long Short-Term Memory (LSTM) Deep Learning Autoencoder based method with a novel dynamic thresholding algorithm and weighted loss function for anomaly detection of a UAV dataset. The dynamic thresholding and weighted loss functions showed promising improvements to the standard static thresholding method, both in accuracy-related performance metrics and in speed of true fault detection.
  arXiv  Detail & Related papers  (2022-03-09T14:16:14Z)
• The State of Aerial Surveillance: A Survey [62.198765910573556]
  This paper provides a comprehensive overview of human-centric aerial surveillance tasks from a computer vision and pattern recognition perspective. The main object of interest is humans, where single or multiple subjects are to be detected, identified, tracked, re-identified and have their behavior analyzed.
  arXiv  Detail & Related papers  (2022-01-09T20:13:27Z)
• Data-Driven Aerospace Engineering: Reframing the Industry with Machine Learning [49.367020832638794]
  The aerospace industry is poised to capitalize on big data and machine learning. Recent trends will be explored in context of critical challenges in design, manufacturing, verification and services.
  arXiv  Detail & Related papers  (2020-08-24T22:40:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.