Study on safety mode of dragon boat sports physical fitness training based on machine learning
مطالعه نحوه ایمنی تمرینات آمادگی جسمانی ورزشی قایق اژدها بر اساس یادگیری ماشین-2019
In order to improve the safety control ability of dragon boat sports physical fitness training, this paper uses the advantages of machine learning in data analysis and feature mining in the training of dragon boat sports. A machine learning-based safety mode control model for dragon boat sports physical fitness training was proposed. Big data statistical analysis method was used to analyze the constraint parameters of the safety mode of dragon boat sports physical fitness training, and combined with the joint association rule mining method, the dragon boat sports physical fitness training safety mode training was carried out. The correlation feature quantity which constrains the safety of dragon boat physical ability training is extracted. The fuzzy clustering technique is used to classify and study the safety management data of dragon boat sport physical ability training. The method of spectral density analysis and fuzzy fusion clustering analysis is used to realize automatic mining of safety association feature data of dragon boat physical fitness training, and machine learning algorithm is combined to realize the optimization of safety pattern of dragon boat sports physical fitness training. The simulation results show that the feature extraction of the safety model of dragon boat sports physical fitness training is better and the ability of feature resolution is stronger, which improves the safety management ability of dragon boat sports physical fitness training.
Keywords: Machine learning | Dragon boat sports | Physical training | Safety mode
Systems Theoretic Accident Model and Process (STAMP) safety modelling applied to an aircraft rapid decompression event
سیستم های نظری مدل حوادث و مدل سازی (STAMP) ایمنی فرآیند اعمال شده به یک رویداد رفع فشار سریع هواپیما-2017
Understanding a crew’s response to a rapid decompression, and factors which can influence the decisions crew members make, can facilitate a safe resolution of a potentially life threatening hazard. Anticipating the Human Factors issues is an appropriate way to assess potential risk factors before such an event hap pens. The Systems Theoretic Accident Model and Process (STAMP) and its predictive risk assessment method, System-Theoretic Process Analysis (STPA), is a systemic approach to safety analysis. This approach is ideal when considering complex systems, such as aviation. The scenario of an aircraft expe riencing a decompression event was analysed using STAMP-STPA across a series of workshops during which key safety elements were identified and reflected upon. It was found that the use of the STAMP-STPA methodology successfully identified factors central to the Helios 522 accident. Based on the outputs of this research, it is suggested that, due to its inherent utility, the STAMP-STPA method can be used to elicit a variety of safety critical insights, and does so in a way that considers individuals, organisations and technology at the same level of granularity, in a way that does not attribute blame to any single agent.
Keywords: STAMP | Systemic safety | Aviation | Systems thinking
A systemic modelling of ground handling services using the functional resonance analysis method
مدلسازی سیستماتیک خدمات زمینی با استفاده از روش تجزیه و تحلیل رزونانس تابعی-2017
In contrast to air transport safety, safety in ground handling is not concerned only with air craft accidents but also the Occupational Health and Safety of the employees who work at airport aprons. Ground handling safety costs the aviation industry tens of billions USD every year which raises the questions about the effectiveness of linear safety risk manage ment of Ground Handling Services (GHS). This paper uses the state-of-the-art safety theory to justify and highlight the need for a systemic approach to safety risk management of GHS on the apron. A hybrid Total Apron Safety Management (TASM) framework, based on the combination of Functional Resonance Analysis Method (FRAM), Grounded Theory, Template Analysis and Goals-Means Task Analysis (GMTA) was developed to support sys temic safety modelling of GHS. The data that underpins the TASM framework includes extensive literature review, 15 observations, 43 interviews and expert judgement across five international airports. While the TASM framework can be applied in retrospective, prospective and system design analysis to improve both the safety management and the efficiency of apron operations, this paper showcases only one of its application on a case study of a historical safety occurrence. The results of the investigation carried out in this paper clearly demonstrate the benefits of the systemic as opposed to the existing linear approaches to retrospective safety analyses and the suitability of the TASM framework for occurrence analysis and prevention.
Keywords: Ground handling | Safety | Apron | FRAM | TASM | Occurrence investigation