A Study Of Factors Contributing To Self-reported Anomalies In Civil Aviation

A study investigating what factors are present leading to pilots submitting voluntary anomaly reports regarding their flight performance was conducted. The study employed statistical methods, text mining, clustering, and dimensional reduction techniques in an effort to determine relationships between factors and anomalies. A review of the literature was conducted to determine what factors are contributing to these anomalous incidents, as well as what research exists on human error, its causes, and its management. Data from the NASA Aviation Safety Reporting System (ASRS) was analyzed using traditional statistical methods such as frequencies and multinomial logistic regression. Recently formalized approaches in text mining such as Knowledge Based Discovery (KBD) and Literature Based Discovery (LBD) were employed to create associations between factors and anomalies. These methods were also used to generate predictive models. Finally, advances in dimensional reduction techniques identified concepts or keywords within records, thus creating a framework for an unsupervised document classification system. Findings from this study reinforced established views on contributing factors to civil aviation anomalies. New associations between previously unrelated factors and conditions were also found. Dimensionality reduction also demonstrated the possibility of identifying salient factors from unstructured text records, and was able to classify these records using these identified features

Development and Initial Evaluation of a Reinforced Cue Detection Model to Assess Situation Awareness in Commercial Aircraft Cockpits

Commercial transport aircraft of today vary greatly from early aircraft with regards to how the aircraft are controlled and the feedback provided from the machine to the human operator. Over time, as avionics systems became more automated, pilots had less direct control over their aircraft. Much research exists in the literature about automation issues, and several major accidents over the last twenty years spurred interest about how to maintain the benefits of automation while improving the overall human-machine interaction as the pilot is considered the last line of defense. An important reason for maintaining or even improving overall pilot situation awareness is that the resulting improved situation awareness can assist the human pilot in rapidly solving unanticipated, novel problems for which no computer logic has been written. It is essential for the pilots to obtain cues to make appropriate decisions under time pressure. However, to date, no studies have directly examined the approach of reinforcing the relevant flight and automation status cues during flight to increase the pilot’s situation awareness when a failure unexpectedly occurs. Attitudes toward, and issues with automated systems from the pilots’ perspectives were studied using a survey completed by commercial air transport pilots. The survey results were used as the framework for designing a simulation analysis, using a small group of commercial airline pilots, to assess the benefits of a reinforced cue detection model. A phenomenological assessment of open ended questions asked at the conclusion of each simulation showed, subject to the limits of the relatively small sample size, that the “Reinforced Cue Detection Model” implemented in the form of asking the pilots situational awareness questions during the flight, can help to reduce pilot’s complacency, increase situation awareness, and make automation a better team member. Pilots also found reinforced cues to be helpful in the event of unexpected system failure. The current research supports literature regarding pilots’ opinions towards automated systems and indicates that there are benefits to be gained from improving the pilot automation integration. The Reinforced Cue Detection Model, albeit tested on a small sample size, supported improvement of the pilots’ situation awareness

Pilot Your Life Decisively for Well-Being and Flourishing

I have been a pilot, aviation instructor and FAA Pilot Examiner for over 40 years. Aviation requires a “pilot in command” mindset consistent with the tenets of positive psychology. This paper explains and advocates for this daily empowered, adaptive decision making process used by pilots in aviation as a necessary life skill to eliminate mind wandering and disengagement and optimize human performance consistent with the goals of positive psychology. Exploring the concepts of “pilot-in-command” (decisive control and self-efficacy) and “situational awareness” (alert mental functioning) I will offer techniques and suggestions for developing and deploying these critical skills in everyday life. I will examine the heuristic-based, “fast and frugal” (time and data limited) decision-making used every day in aviation and apply this to life for optimal performance and flourishing for individual lives and organizational effectiveness

Aerospace medicine and biology: A cumulative index to a continuing bibliography (supplement 345)

This publication is a cumulative index to the abstracts contained in Supplements 333 through 344 of Aerospace Medicine and Biology: A Continuing Bibliography. Seven indexes are included -- subject, personal author, corporate source, foreign technology, contract number, report number, and accession number

Management: A bibliography for NASA managers

This bibliography lists 731 reports, articles and other documents introduced into the NASA Scientific and Technical Information System in 1990. Items are selected and grouped according to their usefulness to the manager as manager. Citations are grouped into ten subject categories: human factors and personnel issues; management theory and techniques; industrial management and manufacturing; robotics and expert systems; computers and information management; research and development; economics, costs and markets; logistics and operations management; reliability and quality control; and legality, legislation, and policy

IIRC : Incremental Implicitly-Refined Classification

Nous introduisons la configuration de la "Classification Incrémentale Implicitement Raffinée / Incremental Implicitly-Refined Classification (IIRC)", une extension de la configuration de l'apprentissage incrémental des classes où les lots de classes entrants possèdent deux niveaux de granularité, c'est-à-dire que chaque échantillon peut avoir une étiquette (label) de haut niveau (brute), comme "ours”, et une étiquette de bas niveau (plus fine), comme "ours polaire". Une seule étiquette (label) est fournie à la fois, et le modèle doit trouver l’autre étiquette s’il l’a déjà apprise. Cette configuration est plus conforme aux scénarios de la vie réelle, où un apprenant aura tendance à interagir avec la même famille d’entités plusieurs fois, découvrant ainsi encore plus de granularité à leur sujet, tout en essayant de ne pas oublier les connaissances acquises précédemment. De plus, cette configuration permet d’évaluer les modèles pour certains défis importants liés à l’apprentissage tout au long de la vie (lifelong learning) qui ne peuvent pas être facilement abordés dans les configurations existantes. Ces défis peuvent être motivés par l’exemple suivant: “si un modèle a été entraîné sur la classe ours dans une tâche et sur ours polaire dans une autre tâche; oubliera-t-il le concept d’ours, déduira-t-il à juste titre qu’un ours polaire est également un ours ? et associera-t-il à tort l’étiquette d’ours polaire à d’autres races d’ours ?” Nous développons un benchmark qui permet d’évaluer les modèles sur la configuration de l’IIRC. Nous évaluons plusieurs algorithmes d’apprentissage ”tout au long de la vie” (lifelong learning) de l’état de l’art. Par exemple, les méthodes basées sur la distillation sont relativement performantes mais ont tendance à prédire de manière incorrecte un trop grand nombre d’étiquettes par image. Nous espérons que la configuration proposée, ainsi que le benchmark, fourniront un cadre de problème significatif aux praticiens.We introduce the "Incremental Implicitly-Refined Classification (IIRC)" setup, an extension to the class incremental learning setup where the incoming batches of classes have two granularity levels. i.e., each sample could have a high-level (coarse) label like "bear" and a low-level (fine) label like "polar bear". Only one label is provided at a time, and the model has to figure out the other label if it has already learned it. This setup is more aligned with real-life scenarios, where a learner usually interacts with the same family of entities multiple times, discovers more granularity about them, while still trying not to forget previous knowledge. Moreover, this setup enables evaluating models for some important lifelong learning challenges that cannot be easily addressed under the existing setups. These challenges can be motivated by the example "if a model was trained on the class bear in one task and on polar bear in another task, will it forget the concept of bear, will it rightfully infer that a polar bear is still a bear? and will it wrongfully associate the label of polar bear to other breeds of bear?". We develop a standardized benchmark that enables evaluating models on the IIRC setup. We evaluate several state-of-the-art lifelong learning algorithms and highlight their strengths and limitations. For example, distillation-based methods perform relatively well but are prone to incorrectly predicting too many labels per image. We hope that the proposed setup, along with the benchmark, would provide a meaningful problem setting to the practitioners

A multi-agent approach for design consistency checking

The last decade has seen an explosion of interest to advanced product development methods, such as Computer Integrated Manufacture, Extended Enterprise and Concurrent Engineering. As a result of the globalization and future distribution of design and manufacturing facilities, the cooperation amongst partners is becoming more challenging due to the fact that the design process tends to be sequential and requires communication networks for planning design activities and/or a great deal of travel to/from designers' workplaces. In a virtual environment, teams of designers work together and use the Internet/Intranet for communication. The design is a multi-disciplinary task that involves several stages. These stages include input data analysis, conceptual design, basic structural design, detail design, production design, manufacturing processes analysis, and documentation. As a result, the virtual team, normally, is very changeable in term of designers' participation. Moreover, the environment itself changes over time. This leads to a potential increase in the number of design. A methodology of Intelligent Distributed Mismatch Control (IDMC) is proposed to alleviate some of the related difficulties. This thesis looks at the Intelligent Distributed Mismatch Control, in the context of the European Aerospace Industry, and suggests a methodology for a conceptual framework based on a multi-agent architecture. This multi-agent architecture is a kernel of an Intelligent Distributed Mismatch Control System (IDMCS) that aims at ensuring that the overall design is consistent and acceptable to all participating partners. A Methodology of Intelligent Distributed Mismatch Control is introduced and successfully implemented to detect design mismatches in complex design environments. A description of the research models and methods for intelligent mismatch control, a taxonomy of design mismatches, and an investigation into potential applications, such as aerospace design, are presented. The Multi-agent framework for mismatch control is developed and described. Based on the methodology used for the IDMC application, a formal framework for a multi-agent system is developed. The Methods and Principles are trialed out using an Aerospace Distributed Design application, namely the design of an A340 wing box. The ontology of knowledge for agent-based Intelligent Distributed Mismatch Control System is introduced, as well as the distributed collaborative environment for consortium based projects

Effects of Acute Stress on Aircrew Performance: Literature Review and Analysis of Operational Aspects

Situational stress can adversely affect the cognition and skilled performance of pilots, as well as experts in other domains. Emergencies and other threatening situations require pilots to execute infrequently practiced procedures correctly and to use their skills and judgment to select an appropriate course of action, often under high workload, time pressure, and ambiguous indications, all of which can be stressful. Our current study, consited of three parts, starting with a critical review of the research literature on the effects of stress on skilled performance, going back to World War II and continuing to recent and more sophisticated studies of the cognitive effects of anxiety. In the second part we analyzed the specific ways stress may have impaired the performance of airline crews in twelve major accidents, selected for diversity of the situations the crews encountered. The third part examined the operational significance and practical implications of the findings from the first two parts, suggested specific ways to reduce the harmful effects of stress on flight crews, and identified aspects requiring further research. Even thought this study focused on flight crews, the findings apply to the effects of stress on the skilled performance of experts in almost any domain