A Deep Learning Framework for Real-Time Bird Detection and Its Implications for Reducing Bird Strike Incidents.

Bird strikes are a substantial aviation safety issue that can result in serious harm to aircraft components and even passenger deaths. In response to this increased tendency, the implementation of new and more efficient detection and prevention technologies becomes urgent. The paper presents a novel deep learning model which is developed to detect and alleviate bird strike issues in airport conditions boosting aircraft safety. Based on an extensive database of bird images having different species and flight patterns, the research adopts sophisticated image augmentation techniques which generate multiple scenarios of aircraft operation ensuring that the model is robust under different conditions. The methodology evolved around the building of a spatiotemporal convolutional neural network which employs spatial attention structures together with dynamic temporal processing to precisely recognize flying birds. One of the most important features of this research is the architecture of its dual-focus model which consists of two components, the attention-based temporal analysis network and the convolutional neural network with spatial awareness. The model's architecture can identify specific features nested in a crowded and shifting backdrop, thereby lowering false positives and improving detection accuracy. The mechanisms of attention of this model itself enhance the model's focus by identifying vital features of bird flight patterns that are crucial. The results are that the proposed model achieves better performance in terms of accuracy and real time responses than the existing bird detection systems. The ablation study demonstrates the indispensable roles of each component, confirming their synergistic effect on improving detection performance. The research substantiates the model's applicability as a part of airport bird strike surveillance system, providing an alternative to the prevention strategy. This work benefits from the unique deep learning feature application, which leads to a large-scale and reliable tool for dealing with the bird strike problem.

An Orientation to Aviation Psychiatry: With 30 Case Examples and a 10-Year Follow-up.

ABSTRACT: This report orients general psychiatrists to assessment and care for "safety sensitive" aviation workers. Our case study of 30 sequential aviation patients consists of demographic, clinical, and aviation characteristics plus a 10-year follow-up. Relatively few pilots and other aviation workers self-identified their psychiatric condition. Aviation outcomes associated with psychiatric disorder included personnel injury and/or aircraft damage (three cases), imminent risk without injury or damage (nine cases), impaired aviation functioning without imminent risk (15 cases), and neither risk nor psychiatric disorder or impairment (three cases). Mood, anxiety, and substance use disorders comprised the most common diagnoses. Ten years later, 23 patients were employed (21 in aviation); seven were disabled or deceased. General psychiatrists will find aviation cases clinically familiar but requiring added evaluation for aviation safety and potential interaction with the Federal Aviation Agency and airline supervisors.

When Outcomes are not Enough: An Examination of Abductive and Deductive Logical Approaches to Risk Analysis in Aviation.

While airlines generate massive amounts of operational data every year, the ability to use the collected material to improve safety has begun to plateau. With the increasing demand for air travel, the aviation industry is continually growing while simultaneously being required to ensure the level of safety within the system remains constant. The purpose of this article is to explore whether the traditional analysis methods that have historically made aviation ultra-safe have reached their theoretical limits or merely practical ones. This analysis argues that the underlying logic governing the traditional (and current) approaches to assess safety and risk within aviation (and other safety critical systems) is abductive and therefore focused on creating explanations rather than predictions. While the current "fly-fix-fly" approach has, and will continue to be, instrumental in improving what (clearly) fails, alternative methods are needed to determine if a specific operation is more or less risky than others. As the system grows, so too does the number of ways it can fail, creating the possibility that more novel accidents may occur. The article concludes by proposing an alternative approach that explicitly adds temporality to the concepts of safety and risk. With this addition, a deductive analysis approach can be adopted which, while low in explanatory power, can be used to create predictions that are not bound to analyzing only outcomes that have occurred in the past but instead focuses on determining the deviation magnitude between the operation under analysis and historically commensurate operations.

Research on Aviation Safety Prediction Based on Variable Selection and LSTM.

Accurate prediction of aviation safety levels is significant for the efficient early warning and prevention of incidents. However, the causal mechanism and temporal character of aviation accidents are complex and not fully understood, which increases the operation cost of accurate aviation safety prediction. This paper adopts an innovative statistical method involving a least absolute shrinkage and selection operator (LASSO) and long short-term memory (LSTM). We compiled and calculated 138 monthly aviation insecure events collected from the Aviation Safety Reporting System (ASRS) and took minor accidents as the predictor. Firstly, this paper introduced the group variables and the weight matrix into LASSO to realize the adaptive variable selection. Furthermore, it took the selected variable into multistep stacked LSTM (MSSLSTM) to predict the monthly accidents in 2020. Finally, the proposed method was compared with multiple existing variable selection and prediction methods. The results demonstrate that the RMSE (root mean square error) of the MSSLSTM is reduced by 41.98%, compared with the original model; on the other hand, the key variable selected by the adaptive spare group lasso (ADSGL) can reduce the elapsed time by 42.67% (13 s). This shows that aviation safety prediction based on ADSGL and MSSLSTM can improve the prediction efficiency of the model while keeping excellent generalization ability and robustness.

Predicting and mitigating failures on the flight deck: an aircraft engine bird strike scenario.

Engine damage as a consequence of foreign object debris (FOD) during flight is frequently caused by birds. One approach to minimising disruption caused by this damage is to provide flight crew with accurate information relating to the continuing operational status of the aircraft's engines. Before designing such avionic systems however, understanding of current procedures is needed. Hierarchical Task Analysis (HTA) and Systematic Human Error Reduction and Prediction Approach (SHERPA) were used to identify potential failures that flight crew may make when managing an engine bird strike. Workshops with commercial pilots generated insights into current practice and a commercial pilot SME reviewed outputs for accuracy. Over 200 potential failures were identified, most commonly related to communication. Remedial measures, considering future avionic systems, are proposed to mitigate identified failures. This analysis provides a starting point for future design concepts for assisting flight crew in dealing with engine malfunction due to FOD strikes. Practitioner summary: Hierarchical Task Analysis was conducted to show all tasks involved in dealing with an in-flight aircraft engine bird strike. Systematic Human Error Reduction and Prediction Approach analysis was performed and over 200 possible failures were identified when managing this event. Remedial measures are proposed to help mitigate possible failures.

Human Factors Contributions to the Development of Standards for Displays of Unmanned Aircraft Systems in Support of Detect-and-Avoid.

OBJECTIVE: The aim is to provide a high-level synthesis of human factors research that contributed to the development of detect-and-avoid display requirements for unmanned aircraft systems (UAS). BACKGROUND: The integration of UAS into the U.S. National Airspace System is a priority under the Federal Aviation Administration's Modernization and Reform Act. For UAS to have routine access to the National Airspace System, UAS must have detect-and-avoid capabilities. One human factors challenge is to determine how to display information effectively to remote pilots for performing detect-and-avoid tasks. METHOD: A high-level review of research informing the display requirements for UAS detect-and-avoid is provided. In addition, description of the contributions of human factors researchers in the writing of the requirements is highlighted. RESULTS: Findings from human-in-the-loop simulations are used to illustrate how evidence-based guidelines and requirements were established for the display of information to assist pilots in performing detect-and-avoid. Implications for human factors are discussed. CONCLUSION: Human factors researchers and engineers made many contributions to generate the data used to justify the detect-and-avoid display requirements. Human factors researchers must continue to be involved in the development of standards to ensure that requirements are evidence-based and take into account human operator performance and human factors principles and guidelines. APPLICATION: The research presented in this paper is relevant to the design of UAS, the writing of standards and requirements, and the work in human-systems integration.

Blending Human and Machine: Feasibility of Measuring Fatigue Through the Aviation Headset.

OBJECTIVE: To determine viability of drowsiness detection, researchers study the feasibility of photoplethysmogram (PPG) data collection from the geography of the aviation headset, correlating to electrocardiogram (ECG) reference. BACKGROUND: Fatigue has been a probable cause, contributing factor, or a finding in 20% of transportation incidents and accidents studied between January 2001 and December 2012. This operational hazard is particularly troublesome within aviation and airline operations. METHOD: PPG and ECG data were collected synchronously from Federal Aviation Administration (FAA) commercially rated pilots during flight simulation in the window of circadian low (WOCL). Valid PPG and ECG data from 14 participants were analyzed, which yielded approximately 2 hr of data per participant for fatigue-related analysis. RESULTS: The results of the study demonstrate clear trends toward decreased heart rate for both ECG and PPG and suggest progression of drowsiness between four separate periods (T1, T2, T3, and T4) selected during the study; however, the mean heart rate change from T1 to T4 was statistically significant. CONCLUSION: The results suggest that ECG and PPG data can be an important tool to observe conditions where drowsiness or fatigue may add risk to the operation. In addition, the data show high correlation between ECG and PPG data, further suggesting that a simpler PPG sensor, mounted within the geography of the aviation headset, may streamline the operationalization of important physiological data. APPLICATION: Incorporation of PPG sensors and associated signal processing methods into facilitating equipment, such as the aviation headset, may add a layer to operational safety.

Eye tracking as a debriefing tool in upset prevention and recovery training (UPRT) for general aviation pilots.

Upset prevention and recovery training (UPRT) is intended to improve the ability of pilots to recognize and avoid situations that can lead to airplane upsets and to improve their ability to recover control of an airplane that has exceeded the normal flight envelope. To this end, a set of different training contents - from theoretical knowledge of aerodynamics and human factors to practice-based flight training - is necessary. In order to support the debriefing with an objective feedback, and because visual scanning is a core competence, two studies on subjective evaluation of aviation pilots - one conducted in a flight simulator and the other one in-flight - focussed on the practical application of eye tracking as a debriefing tool in UPRT. From a practitioner's perspective, eye tracking appeared to be a useful method in terms of visualising instrument scanning techniques, supporting the instructor with objective debriefing material and fostering self-awareness in human processes. The discussion recommends adjusted UPRT instructor training and further improvements to eye tracking hardware and software. Practitioner Summary: The article focuses on pilot evaluations of eye tracking as a debriefing tool in UPRT and the identification of critical elements in its use. Eye tracking is a promising debriefing tool for UPRT. The discussion points to desirable improvements of eye tracking hardware and software as well as adjustments to instructor training that are pertinent.

Using Neural Networks to predict HFACS unsafe acts from the pre-conditions of unsafe acts.

Human Factors Analysis and Classification System (HFACS) is based upon Reason's organizational model of human error which suggests that there is a 'one to many' mapping of condition tokens (HFACS level 2 psychological precursors) to unsafe act tokens (HFACS level 1 error and violations). Using accident data derived from 523 military aircraft accidents, the relationship between HFACS level 2 preconditions and level 1 unsafe acts was modelled using an artificial neural network (NN). This allowed an empirical model to be developed congruent with the underlying theory of HFACS. The NN solution produced an average overall classification rate of ca. 74% for all unsafe acts from information derived from their level 2 preconditions. However, the correct classification rate was superior for decision- and skill-based errors, than for perceptual errors and violations. Practitioner Summary: A model to predict unsafe acts (HFACS level 1) from their preconditions (HFACS level 2) was developed from the analysis of 523 military aircraft accidents using an artificial NN. The results could correctly predict approximately 74% of errors.

Modelling distributed crewing in commercial aircraft with STAMP for a rapid decompression hazard.

Changes to crewing configurations in commercial airlines are likely as a means of reducing operating costs. To consider the safety implications for a distributed crewing configuration, system theoretic accident model and processes (STAMP) was applied to a rapid decompression hazard. High level control structures for current operations and distributed crewing are presented. The CONOPS generated by STAMP-STPA for distributed crewing, and design constraints associated with unsafe control actions (UCAs) are offered to progress in the route to certification for distributed crewing, and improve safety in current operations. Control loops between stakeholders were created using system-theoretic process analysis (STPA). The factors leading to the Helios 255 incident demonstrated the redundancy that a ground station could offer without the risk of hypoxia, during a decompression incident. STPA analysis also highlighted initial UCAs that could occur within the hypothetical distributed crewing configuration, prompting consideration of design constraints and new CONOPS for ground station design. Practitioner Summary: SPO in commercial aircraft is likely as a means to reduce costs. This paper makes a case for distributed crewing using STAMP-STPA. Comparing current operations with a distributed crewing configuration, the redundancy offered by a ground station is demonstrated. Design constraints and new CONOPs for distributed crewing, and current operations are proposed.

What went right? An analysis of the protective factors in aviation near misses.

Learning from successful safety outcomes, or what went right, is an important emerging component of maintaining safe systems. Accordingly, there are increasing calls to study normal performance in near misses as a part of safety management activities. Despite this, there is limited guidance on how to accomplish this in practice. This article presents a study in which using Rasmussen's risk management framework to analyse 16 serious incidents from the aviation domain. The findings show that a network of protective factors prevents accidents with factors identified across the sociotechnical system. These protective networks share many properties with those identified in accidents. The article demonstrates that is possible to identify these networks of protective factors from incident investigation reports. The theoretical implications of these results and future research opportunities are discussed. Practitioner Statement: The analysis of near misses is an important part of safety management activities. This article demonstrates that Rasmussen?s risk management framework can be used to identify networks of protective factors which prevent accidents. Safety practitioners can use the framework described to discover and support the system-wide networks of protective factors.

Safety in the nonoperating room anesthesia suite is not an accident: lessons from the National Transportation Safety Board.

PURPOSE OF REVIEW: To review the findings of National Transportation Safety Board-related aviation near misses and catastrophes and apply these principles to the nonoperating room anesthesia (NORA) suite. RECENT FINDINGS: NORA is a specialty that has seen tremendous growth. In 2019, NORA contributes to a larger proportion of anesthesia practice than ever before. With this growth, the NORA anesthesiologist and team are challenged to provide safe, high-quality care for more patients, often with complex comorbidities, and are forced to utilize deeper levels of sedation and anesthesia than ever before. These added pressures create new avenues for human error and adverse outcomes. SUMMARY: Safety in modern anesthesia practice often draws comparison to the aviation industry. From distinct preoperational checklists, defined courses of action, safety monitoring and the process of guiding individuals through a journey, there are many similarities between the practice of anesthesia and flying an airplane. Consistent human performance is paramount to creating safe outcomes. Although human errors are inevitable in any complex process, the goal for both the pilot and physician is to ensure the safety of their passengers and patients, respectively. As the aviation industry has had proven success at managing human error with a dramatic improvement in safety, a deeper look at several key examples will allow for comparisons of how to implement these strategies to improve NORA safety.

Injury and fatality risks in aeromedical transport: focus on prevention.

BACKGROUND: Aeromedical transport (AMT) is a reliable and well-established life-saving option for rapid patient transfers to health care delivery hubs. However, owing to the very nature of AMT, fatal and nonfatal events may occur. This study reviews aeromedical incidents reported since the publication of the last definitive review in 2003, aiming to provide additional insight into a wide range of factors potentially associated with fatal and nonfatal AMT incidents (AMTIs). We hypothesized that weather and/or visual conditions, postcrash fire, aircraft make and/or type, and time of day all correlate with the risk of AMTI with injury or fatality. METHODS: Specialty databases were queried for AMTI between January 1, 2003 and July 31, 2015. Additional Internet-based resources were also used to find any additional AMTI (including non-US occurrences) to augment the event sample size available for analysis. Univariate analyses of the collected sample were then performed for association between "fatal crash or injury" (FCOI) and weather/visual conditions, aircraft type and/or make, pilot error, equipment failure, post-incident fire, time of day (6 am-7 pm versus 7 pm-6 am), weekend (Friday-Sunday) versus weekday (Monday-Thursday), season of the year, and presence of patient on board. Variables reaching significance level of P < 0.20 were included in multivariate analysis. RESULTS: A total of 59 AMTIs were identified. Helicopters were involved in 52 of 59 AMTIs, with 7 of 59 fixed-wing incidents. Comparing pre-2003 data with post-2003 data, we noted a significant decrease in AMTIs per month (0.70 versus 0.39, respectively, P = 0.048), whereas the number of fatalities per year increased slightly (7.20 versus 8.26, p = n/s). In univariate analyses, abnormal weather conditions, impaired visibility, time of incident (7 pm-6 am), aircraft model/make, and post-incident fire all reached statistical significance sufficient for inclusion in multivariate analysis (P < 0.20). Factors independently associated with FCOI included post-incident fire (odds ratio, 19.0; 95% confidence interval, 1.41-255.5) and time of incident between 7 pm and 6 am (odds ratio, 11.2; 95% confidence interval, 1.29-97.2). Weather conditions, impaired visibility, and aircraft model/make were not independently associated with FCOI. CONCLUSIONS: The present study supports previous observation that post-crash fire is independently associated with FCOI. However, our data do not support previous observations that weather conditions, impaired visibility, or aircraft model/make are independently predictive of fatal AMTI. In addition, this report demonstrates that flights between the hours of 7 pm-6 am may be associated with greater odds of FCOI. Efforts directed at identification, remediation, and active prevention of factors associated with AMTI and FCOI are warranted given the global increase in aeromedical transport.

A sonic net excludes birds from an airfield: implications for reducing bird strike and crop losses.

Collisions between birds and aircraft cause billions of dollars of damages annually to civil, commercial, and military aviation. Yet technology to reduce bird strike is not generally effective, especially over longer time periods. Previous information from our lab indicated that filling an area with acoustic noise, which masks important communication channels for birds, can displace European Starlings (Sturnus vulgaris) from food sources. Here we deployed a spatially controlled noise (termed a "sonic net"), designed to overlap with the frequency range of bird vocalizations, at an airfield. By conducting point counts, we monitored the presence of birds for four weeks before deployment of our sonic net, and for four weeks during deployment. We found an 82% reduction in bird presence in the sonic net area compared with change in the reference areas. This effect was as strong in the fourth week of exposure as in the first week. We also calculated the potential costs avoided resulting from this exclusion. We propose that spatially controlled acoustic manipulations that mask auditory communication for birds may be an effective long term and fairly benign way of excluding problem birds from areas of socioeconomic importance, such as airfields, agricultural sites, and commercial properties.

Airline Safety Improvement Through Experience with Near-Misses: A Cautionary Tale.

In recent years, the U.S. commercial airline industry has achieved unprecedented levels of safety, with the statistical risk associated with U.S. commercial aviation falling to 0.003 fatalities per 100 million passengers. But decades of research on organizational learning show that success often breeds complacency and failure inspires improvement. With accidents as rare events, can the airline industry continue safety advancements? This question is complicated by the complex system in which the industry operates where chance combinations of multiple factors contribute to what are largely probabilistic (rather than deterministic) outcomes. Thus, some apparent successes are realized because of good fortune rather than good processes, and this research intends to bring attention to these events, the near-misses. The processes that create these near-misses could pose a threat if multiple contributing factors combine in adverse ways without the intervention of good fortune. Yet, near-misses (if recognized as such) can, theoretically, offer a mechanism for continuing safety improvements, above and beyond learning gleaned from observable failure. We test whether or not this learning is apparent in the airline industry. Using data from 1990 to 2007, fixed effects Poisson regressions show that airlines learn from accidents (their own and others), and from one category of near-misses-those where the possible dangers are salient. Unfortunately, airlines do not improve following near-miss incidents when the focal event has no clear warnings of significant danger. Therefore, while airlines need to and can learn from certain near-misses, we conclude with recommendations for improving airline learning from all near-misses.

[Aeromedical Decision-Making in Psychiatry]. / Fliegerärztliche Entscheidungsfindung in der Psychiatrie.

This paper reviews aeromedical decision-making in psychiatry. It explains the "one-percent rule", the general medical criteria for fitness for flying and how they are applied to psychiatric disorders.

Hot Air Balloon: An Unusual Cause of Multicasualty Trauma Incident.

Hot air balloon incidents are few and far between compared with the total number of flights. Nevertheless, hot air balloon incidents may produce severe trauma involving several patients and are linked to significant mortality. The prehospital management of injured patients starts after having secured potential surrounding dangers, such as fire or explosion. In the context of a rescue by helicopter, close attention must be paid to potential obstacles, like trees or electrical wires, and the risk of aspiration of the balloon envelope into the rotor. Patients involved in such incidents are often split up in a closed perimeter around the crash point. The severity of the trauma depends essentially on the height of the fall. The most frequent traumatic lesions involve fractures of the lower limbs, the spine, and the pelvis as well as severe burns caused by the balloon fire. Because of the number of patients present, an initial triage is usually required at the site. The use of rescue helicopters can be helpful. They can perform aerial reconnaissance, provide on-site high-level resources, enable access to the patients even in hostile environments, and quickly transport them to trauma center hospitals.

Surgeons' attitudes are associated with reoperation and readmission rates.

BACKGROUND: Attitudes influence how people make decisions. In an effort to decrease pilot judgment-related accidents, the Federal Aviation Administration teaches new pilots about hazardous attitudes that are believed to be incompatible with safe flight: macho, impulsive, worry, resignation, self-confidence, and antiauthority. If these attitudes are hazardous for pilots and their passengers, they may also be incompatible with the reliable and safe delivery of surgical care. QUESTIONS/PURPOSES: The purposes of this study were (1) to ascertain to what extent surgeons harbor hazardous attitudes; and (2) to determine their relationship, if any, to reoperation and readmission rates. METHODS: We selected validated aviation psychology tools that are used to measure these attitudes in pilots. We converted the aviation scenarios to analogous situations for surgeons and invited all surgeons from one academic program to participate in this study. A total of 41 surgeons were eligible to participate; 37 (90%) completed the attitude prevalence protocol and 31 (76%) had complete reoperation and readmission data for the correlation and regression analysis. Attending orthopaedic surgeons completed the Modified Surgeon Hazardous Attitude Scale as well as a series of additional instruments. RESULTS: Levels of macho thought to be hazardous in pilots were present in nine (24%) surgeons. Similar, elevated levels of self-confidence were found in three (8%) surgeons. High levels of impulsivity were found in 5% (two surgeons) and high levels of antiauthority were found in 3% (one surgeon). Only one (3%) surgeon reported elevated levels of worry and no surgeon reported hazardous levels of resignation. Thirty percent (11 surgeons) of surgeons harbored at least one elevated attitude level. In a regression model, macho attitude levels predicted 19% of the variation in surgeons' rate of readmissions and reoperations. CONCLUSIONS: High levels of hazardous attitudes may not be consistent with the routine delivery of safe surgical care in a teamwork setting where human factors and safe systems are the key to success. Further research is needed to determine if abnormally high levels of these hazardous attitudes impact patient care. LEVEL OF EVIDENCE: Level II, prognostic study. See Guidelines for Authors for a complete description of levels of evidence.