Analysis of Part 135 Aircraft Accidents to Facilitate Flight Data Monitoring

The scheduled and on-demand air services with Title 14 Code of Federal Regulations (CFR) Part 135 certificate are operating with relatively less stringent safety program criteria compared to Part 121 operations. The Part 135 aircraft flight operation was identified as one of the most wanted transportation safety improvements by the National Transportation Safety Board (NTSB). Implementation of flight data monitoring (FDM) programs was recommended to mitigate the risk of Part 135 operations. The FDM program is the process of routinely collecting and analyzing aircraft operational data to provide insight into flight operations for safety improvement. The development of more practical and effective FDM programs for Part 135 operations depends on the identification of critical part of flight parameters. This study, focusing on the aircraft issue-related accidents in Part 135 aircraft operations recorded in the NTSB aircraft accident database, analyzed the common aircraft issues that caused aircraft accidents and the risk factors that are potentially associated with aircraft issue caused accidents in Part 135 operations. General descriptive analysis and Chi-square linear-by-linear association were adopted to provide insights into statistical characteristics of aircraft issue caused accidents, logistic regression was employed to explore the risk factors associated with such type of accidents. This study identified a list of common aircraft issues in Part 135 operations and risk factors that might contribute to aircraft issue caused mishaps. The findings are expected to facilitate better development and implementation of FDM programs for Part 135 operations by identifying critical aircraft issues and risk factors that should be monitored with more caution. Recommendations were proposed based upon the findings

Applying the ADS-B Out to Facilitate Flight Data Analysis for General Aviation

The International Civil Aviation Organization (ICAO) and major airlines believe that flight data analysis is an effective approach to mitigate the risk of aviation accidents (International Civil Aviation Organization, 2010; International Air Transport Association, 2016). In the United States, flight data analysis is encouraged by the Federal Aviation Administration (FAA) through the flight operational quality assurance (FOQA) program. Among all aviation activities, general aviation (GA) has the highest accident rate (National Transportation Safety Board, 2014). However, implementation of flight data analysis for GA not only requires expensive investment on flight data recording devices, but also increases long-term labor cost due to regular data collection and data analysis. Automatic Dependent Surveillance Broadcast Out (ADS-B Out) is a precise satellite-based surveillance system that periodically broadcasts flight data retrieved from satellites and onboard avionics of the ADS-B Out capable aircraft. Based on the standard technical provisions of the ADS-B Out, the use of ADS-B data is expected to be a possible approach to facilitate the flight data analysis for general aviation. This research explored the use of ADS-B data to facilitate flight data analysis for general aviation. Researchers started the current study phase from analyzing the structure and content of the ADSB message by referring to the ICAO technical provisions (2008) and the operational performance standard of ADS-B from the Radio Technical Commission for Aeronautics (RTCA) (2009). Based upon the findings of the ADS-B data structure and content, a set of retrievable aircraft parameters was identified, and additional aircraft parameters were derived from the basic ADS-B information. Furthermore, sets of flight metrics were developed using the aircraft parameters broadcasted by ADS-B Out. The development of flight metrics was expected to be essential for measuring flight operational performance to support flight data analysis. In addition, exceedance detection was adopted to analyze the flight metrics in flight data analysis. ADS-B data were collected using an ADS-B receiver, and 40 sets of ADS-B data were selected to detect five operational exceedances of the Cirrus SR-20 aircraft of the Purdue Fleet. Exceedances were detected from the 40 sets of data. However, researchers noticed that the sparse ADS-B data caused by the low reception rate might affect the exceedance detection. Therefore, a preliminary analysis was conducted to investigate the difference of exceedance detection using ADS-B data with different reception rates. The results of analysis indicated that sparse ADS-B data could affect the detection of exceedances, but some exceedances might be less sensitive to the sparse data. Based on the findings of this research, recommendations were proposed for future studies

ECGadv: Generating Adversarial Electrocardiogram to Misguide Arrhythmia Classification System

Deep neural networks (DNNs)-powered Electrocardiogram (ECG) diagnosis systems recently achieve promising progress to take over tedious examinations by cardiologists. However, their vulnerability to adversarial attacks still lack comprehensive investigation. The existing attacks in image domain could not be directly applicable due to the distinct properties of ECGs in visualization and dynamic properties. Thus, this paper takes a step to thoroughly explore adversarial attacks on the DNN-powered ECG diagnosis system. We analyze the properties of ECGs to design effective attacks schemes under two attacks models respectively. Our results demonstrate the blind spots of DNN-powered diagnosis systems under adversarial attacks, which calls attention to adequate countermeasures.Comment: Accepted by AAAI 202

Applications of ADS-B in General Aviation

ADS-B Applications for General Aviation Airports—ADS-B (Automatic Dependent Surveillance–Broadcast) Out will soon be required for aircraft operating in most assigned airspace categories. ADS-B data are more accessible for analysis than data from flight data monitoring devices and can potentially be used to improve airport operations. General aviation airports need low-cost, effective solutions. In this session we explore data from ADS-B technologies that support operations counting, landing/takeoff cycle characterization, and landing fee assessment, and that potentially identify areas of operational concern

Estimating Operations and Airport-Specific Landing & Take-off Cycles at GA Airports

Estimating Operations and AirportSpecific Landing Take-off Cycles at GA Airports—Estimating greenhouse gas and particulate emissions around airports is important when seeking to understand the impact on a community of existing or increasing aviation operations. Environmental consultants prepare models, and these models need estimates of the number of operations and landing/take-off (LTO) cycle characteristics. This information is difficult for GA airport managers to obtain. This presentation shows how to estimate LTO characteristics and the number of operations more easily by using sampling methods

Factorial Validity of the Flight Risk Assessment Tool in General Aviation Operations

The Flight Risk Assessment Tool (FRAT) was developed and is recommended by the Federal Aviation Administration to provide a solution of proactively identifying and mitigating risk before each flight. General aviation (GA) operators are encouraged to adapt the FRAT based upon specific operational characteristics. Currently, most safety management systems-compliant GA operators have implemented various versions of FRATs with different operational purposes. However, the FRAT could be inappropriately implemented because of the dynamic operational features of GA operations. The purpose of this study is to explore insights into potential approaches to validate the FRAT that is used for flight risk assessment in routine GA operations. A FRAT from a flight school regulated under Title 14 Code of Federal Regulations Part 141 was used as a study case. In total, 1,832 sets of FRAT data were collected from flight operations between November 2016 and February 2017. Confirmatory factor analysis (CFA) was adopted in this research. The CFA results indicated that the studied FRAT model did not provide good fit with the root mean square error of approximation (RMSEA) = 0.13, standardized root mean square residual (SRMR) = 0.08, comparative fit index (CFI) = 0.98, and Tucker–Lewis index (TLI) = 0.98. Based on the modification indices, the studied FRAT model was restructured by removing 11 risk items from the original 33 risk items. The new model fitted the data acceptably (RMSEA = 0.07, SRMR = 0.05, TLI = 0.76, CFI = 0.69). In addition, implications and directions for further study are discussed

Dynamic Bayesian Network-Based Escape Probability Estimation for Coach Fire Accidents

Coach emergency escape research is an effective measure to reduce casualties under serious vehicle fire accidents. A novel experiment method employing a wireless transducer was implemented and the head rotation speed, rotation moment and rotation duration were collected as the input variables for the classification and regression tree (CART) model. Based on this model, the classification result explicitly pointed out that the exit searching efficiency was evolving. By ignoring the last three unimportant factors from the Analytic Hierarchy Process (AHP), the ultimate Dynamic Bayesian Network (DBN) was built with the temporal part of the CART output and the time-independent part of the vehicle characteristics. Simulation showed that the most efficient exit searching period is the middle escape stage, which is 10 seconds after the emergency signal is triggered, and the escape probability clearly increases with the efficient exit searching. Furthermore, receiving emergency escape training contributes to a significant escape probability improvement of more than 10%. Compared with different failure modes, the emergency hammer layout and door reliability have a more significant influence on the escape probability improvement than aisle condition. Based on the simulation results, the escape probability will significantly drop below 0.55 if the emergency hammers, door, and aisle are all in a failure state

Keloids and Hypertrophic Scars: Update and Future Directions

Summary: The development of cutaneous pathological scars, namely, hypertrophic scars (HSs) and keloids, involves complex pathways, and the exact mechanisms by which they are initiated, evolved, and regulated remain to be fully elucidated. The generally held concepts that keloids and HSs represent “aberrant wound healing” or that they are “characterized by hyalinized collagen bundles” have done little to promote their accurate clinicopathological classification or to stimulate research into the specific causes of these scars and effective preventative therapies. To overcome this barrier, we review here the most recent findings regarding the pathology and pathogenesis of keloids and HSs. The aberrations of HSs and keloids in terms of the inflammation, proliferation, and remodeling phases of the wound healing process are described. In particular, the significant roles that the extracellular matrix and the epidermal and dermal layers of skin play in scar pathogenesis are examined. Finally, the current hypotheses of pathological scar etiology that should be tested by basic and clinical investigators are detailed. Therapies that have been found to be effective are described, including several that evolved directly from the aforementioned etiology hypotheses. A better understanding of pathological scar etiology and manifestations will improve the clinical and histopathological classification and treatment of these important lesions