2017 IEEE/AIAA 36th Digital Avionics Systems Conference (DASC)最新文献

{"title":"Aircraft conflict resolution cataloguer","authors":"C. Yao, A. Rusu, Andrew Danick, Ravina Hingorani, Ryan Toner","doi":"10.1109/DASC.2017.8102101","DOIUrl":"https://doi.org/10.1109/DASC.2017.8102101","url":null,"abstract":"Air route traffic control centers (ARTCCs) operated by the Federal Aviation Administration (FAA) are responsible for efficiently and safely managing United States en route air traffic at altitudes of 18,000 feet and above. To achieve the FAA's mission, the ARTCC's human air traffic controllers monitor air traffic and ensure aircraft safety within partitioned airspace, called sectors, located in each ARTCC's boundaries. In order to achieve this, air traffic controllers must resolve potential conflicts that are identified through methods including manual inspection through looking at their display, or air traffic automated systems alerts. A standard en route conflict occurs if two aircraft travel within five nautical miles in the horizontal plane, while simultaneously flying within 1000 feet in the vertical plane. The controllers request pilots make changes to their intended trajectories to prevent a risk of violating separation distances. The FAA collects and archives the air traffic automation data corresponding to the predicted conflict events and information about the aircraft during their flight, such as: ground speed, vertical phase, horizontal phase, minimum separation distances and time postings. This paper describes algorithms for cataloging (detect and characterize) aircraft conflict resolutions, utilizing the data that is archived by air traffic automated systems. The automated systems only alert the air traffic controllers of potential conflicts. It is the human air traffic controllers that either perform a conflict resolution or determine if the alert is false. Our algorithms identify the maneuvers cleared by the air traffic controllers that occurred. We verify and validate our algorithms using real air traffic data. Characteristics of the traffic scenarios we used are not factors impacting our algorithms.","PeriodicalId":130890,"journal":{"name":"2017 IEEE/AIAA 36th Digital Avionics Systems Conference (DASC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115795544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Verification scenarios of onboard databases under the RTCA DO-178C and the RTCA DO-200B","authors":"Johnny Cardoso Marques, Adilson Marques da Cunha","doi":"10.1109/DASC.2017.8102030","DOIUrl":"https://doi.org/10.1109/DASC.2017.8102030","url":null,"abstract":"According to the FAA Order 8110.49, there are two distinct types of databases used in airborne systems and equipment: Aeronautical Databases (AD) and Parameter Data Items (PDI). Although the database development processes in the RTCA DO-178C and the RTCA DO-200B have many similarities, the use of the DO-200B is limited to navigation, terrain, obstacle, and airport map databases. This paper provides some scenarios for database verification using the RTCA DO-178C and the RTCA DO-200B standards, including the usage of Tool Qualification, when processes are eliminated, reduced, or automated by the use of software tools without reviewing the output produced by such tools.","PeriodicalId":130890,"journal":{"name":"2017 IEEE/AIAA 36th Digital Avionics Systems Conference (DASC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117073518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive approach for validation of air traffic management security prototypes: A case study","authors":"T. Stelkens-Kobsch, M. Finke, Nils Carstengerdes","doi":"10.1109/DASC.2017.8102082","DOIUrl":"https://doi.org/10.1109/DASC.2017.8102082","url":null,"abstract":"Security in air traffic management is still a rather new challenge and receives increased interest during recent years. This implies that new security concepts and systems are developed. Usually all systems have to go through several validation cycles to reach a higher technical readiness level. As no well-established validation approach is available which considers the special aspects of security this forms an additional barrier when developing air traffic control security systems. This is true because suitable validation approaches have to be developed first. The latter includes the risk of forgetting something, when the development is not initiated in a structured way. Within the air traffic security project GAMMA such an approach has been developed and applied to a set of seven prototypes. Based on the European Operational Concept Validation Methodology and a Security Risk Assessment Methodology, this approach identifies additional security controls, system requirements, validation objectives and key performance indicators. These are the driving elements for the design of the validation setup and procedure The paper demonstrates the feasibility of this new approach using one specific example, the Secure Air Traffic Control Communications prototype. The paper describes the approach and the resulting validation setup and procedures in detail. It briefly describes the obtained results for the developed prototype as one specific use case of the approach.","PeriodicalId":130890,"journal":{"name":"2017 IEEE/AIAA 36th Digital Avionics Systems Conference (DASC)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116043320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Safety analysis paradigm for UAS: Development and use of a common architecture and fault tree model","authors":"J. Hammer, A. Murray, Alexa Lowman","doi":"10.1109/DASC.2017.8102039","DOIUrl":"https://doi.org/10.1109/DASC.2017.8102039","url":null,"abstract":"Unmanned Aerial Systems (UAS, a.k.a., drones) are a compelling technology with numerous possibilities for highly productive new airspace operations. Safety of operators and bystanders is of paramount concern, and a common, accepted, safety methodology is a pressing need to enable widespread adoption of UAS. This paper provides a methodology for safety analyses that can be conducted for multiple applications using common models and a suggested standardized architecture for small UAS. To date, in the US, safety analyses have been done on an individualized, custom basis, mainly in support of waivers for specific, limited UAS operations. For example, safety analyses have been conducted in the Federal Aviation Administration's (FAA) Pathfinder Program for use of drones in three focus areas: beyond visual line of sight for infrastructure inspections, extended visual line of sight in rural areas, and flight over people. In addition, approximately 400 waivers have been granted for multiple individual applicants [1]. The waiver process is highly specific to an individual applicant's operation and a special safety analysis must be conducted for each waiver request. This is an inefficient use of resources for both the FAA and industry. It would be more efficient if a common model for UAS safety analysis could be employed that was adaptable to varied applications. An important subclass of UAS operations which currently requires waivers are operations termed Beyond Visual Line of Sight (BVLOS). BVLOS operations allow for UAS flight operations which are out of the visual line of sight of the UAS operator. BVLOS will allow multiple economically beneficial applications, for example, infrastructure inspection and agriculture. Our approach seeks to begin providing an adaptable framework for analyses, focusing on Beyond Visual Line of Sight operations, that allows rapid assurance of operational safety. The benefits of this approach are twofold: first, in the near term, the workload involved in applying for waivers, both for the FAA and for applicants, would be significantly reduced, and second, the approach can be used to inform industry standards on key system requirements. This would give industry an important start in the development of common standards for equipment requirements, as is typically done in standards bodies such as RTCA. To provide a UAS reference model, a common small UAS architecture is proposed to conduct analyses across UAS platforms and operations. The architecture enables the safety model's inputs to be adapted to target UAS platforms and operational scenarios. This approach allows for large scale simulations that can analyze the impact of various vehicle performance configurations in differing operational scenarios. This paper also provides a fault-tree analysis model that is customizable to specific operations, and shows some initial results that help provide insights into tradeoffs and potential requirements. The paper explores these trade","PeriodicalId":130890,"journal":{"name":"2017 IEEE/AIAA 36th Digital Avionics Systems Conference (DASC)","volume":"267 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116383110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reduction of frames storage size in AFDX reception end-system using a lossless compression algorithm","authors":"Yohan Baga, Fakhreddine Ghaffari, D. Declercq, E. Zante, Michael Nahmiyace","doi":"10.1109/DASC.2017.8102086","DOIUrl":"https://doi.org/10.1109/DASC.2017.8102086","url":null,"abstract":"The growth of bandwidth needs and reliability requirements has determined Avionics Full-Duplex Switched Ethernet (AFDX) networks as the new generation of on-board communication mediums. AFDX belongs to the deterministic, real-time and Ethernet-based network family. The AFDX terminals are called End-Systems (ES). The frames arriving at an ES have to be stored in a reception buffer to avoid frames losses or corruptions due to slowdowns in the ES layers. Little attention is carried to the issue of buffer dimensioning which is generally set to a very large size. However, a too large buffer size leads to costs in terms of memory resources and energy. In this paper, we propose to reduce the reception buffer size by using an LZW-based compression algorithm implemented in hardware. To do that, we interpret frames as sequences of hexadecimal source symbols, and we use a set of 4 parallel dictionaries to encode sequences of source symbols in fix-length words. We realize compression gain measured on sets of frames comprising several millions of symbols, and we obtain until 22% of memory gain when the dictionaries sizes are optimally dimensioned.","PeriodicalId":130890,"journal":{"name":"2017 IEEE/AIAA 36th Digital Avionics Systems Conference (DASC)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122018375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel simulation environment for cognitive human factors engineering research","authors":"Yixiang Lim, A. Gardi, S. Ramasamy, J. Vince, H. Pongracic, Trevor Kistan, R. Sabatini","doi":"10.1109/DASC.2017.8102126","DOIUrl":"https://doi.org/10.1109/DASC.2017.8102126","url":null,"abstract":"The simulation environment used in cognitive Human Factors Engineering (HFE) research at RMIT University HFE-Lab is presented in this article. The simulation environment consists of Air Traffic Management (ATM) workstations including Unmanned Aircraft System (UAS) Traffic Management (UTM) features as well as pilot/remote pilot stations, including an immersive research flight simulator. Additional modules are used in cognitive HFE research for collecting and processing psycho-physiological data, and for scenario management. An overview of the simulation environment, including the network, modules and tools is presented. An experimental case study involving eye tracking and cardiorespiratory measures is presented to demonstrate the capabilities of the HFE-Lab as a research tool for cognitive ergonomics and HFE research.","PeriodicalId":130890,"journal":{"name":"2017 IEEE/AIAA 36th Digital Avionics Systems Conference (DASC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124764028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Use of touch screen display applications for aircraft flight control","authors":"W. Rouwhorst, R. Verhoeven, Marieke Suijkerbuijk, Tanja Bos, A. Maij, Mick Vermaat, R. Arents","doi":"10.1109/DASC.2017.8102060","DOIUrl":"https://doi.org/10.1109/DASC.2017.8102060","url":null,"abstract":"Touch screen technology is rapidly and progressively entering the world of commercial avionics and being introduced inside the cockpit. This paper presents the main results of a piloted experiment conducted by the Netherlands Aerospace Centre (NLR) as part of the ACROSS (Advanced Cockpit for Reduction Of StreSs and workload) project of the EU's 7th Frame Work Programme, see www.across-fp7.eu. The experiment focused on the use of novel touch screen applications in the cockpit of civil transport aircraft and investigated the potential for (peak-) workload reduction. Three different touch screen applications and associated experimental results will be discussed. Firstly the so-called tactical flight control operations of an aircraft is addressed, like changing the aircraft's speed, heading, altitude, flight level or vertical speed. Secondly a novel late runway change functionality was set up for supporting the crew decision to accept a new landing runway late in the approach while still allowing safely and easily configuring the aircraft cockpit systems. Similarly the third new application allowed for a fast and easy alternate airport selection process and subsequently a new route creation and selection towards the alternate airport. A piloted experiment was held in which ten airline crews participated on NLR's full motion flight simulator (GRACE). Baseline formed today's aircraft operations without touch screen functionality. Subjective workload and situation awareness ratings were used, as well as objective eye-tracking measurements and time-analysis. Also the effect of turbulence (intensity) was investigated. Main results for the tactical flight control application showed further room for design improvements in the field of workload reduction, especially under more severe turbulence. For the other two cockpit touchscreen applications the results supported the conclusions that pilot workload decreased, situation awareness improved and task execution was much faster and easier compared to the baseline.","PeriodicalId":130890,"journal":{"name":"2017 IEEE/AIAA 36th Digital Avionics Systems Conference (DASC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128913000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-layer model of swarm intelligence for resilient autonomous systems","authors":"J. Clifford, K. Garfield, Massood Towhidnejad, J. Neighbors, M. Miller, E. Verenich, G. Staskevich","doi":"10.1109/DASC.2017.8102147","DOIUrl":"https://doi.org/10.1109/DASC.2017.8102147","url":null,"abstract":"Embry-Riddle Aeronautical University (ERAU) is working with the Air Force Research Lab (AFRL) to develop a distributed multi-layer autonomous UAS planning and control technology for gathering intelligence in Anti-Access Area Denial (A2/AD) environments populated by intelligent adaptive adversaries. These resilient autonomous systems are able to navigate through hostile environments while performing Intelligence, Surveillance, and Reconnaissance (ISR) tasks, and minimizing the loss of assets. Our approach incorporates artificial life concepts, with a high-level architecture divided into three biologically inspired layers: cyber-physical, reactive, and deliberative. Each layer has a dynamic level of influence over the behavior of the agent. Algorithms within the layers act on a filtered view of reality, abstracted in the layer immediately below. Each layer takes input from the layer below, provides output to the layer above, and provides direction to the layer below. Fast-reactive control systems in lower layers ensure a stable environment supporting cognitive function on higher layers. The cyber-physical layer represents the central nervous system of the individual, consisting of elements of the vehicle that cannot be changed such as sensors, power plant, and physical configuration. On the reactive layer, the system uses an artificial life paradigm, where each agent interacts with the environment using a set of simple rules regarding wants and needs. Information is communicated explicitly via message passing and implicitly via observation and recognition of behavior. In the deliberative layer, individual agents look outward to the group, deliberating on efficient resource management and cooperation with other agents. Strategies at all layers are developed using machine learning techniques such as Genetic Algorithm (GA) or NN applied to system training that takes place prior to the mission.","PeriodicalId":130890,"journal":{"name":"2017 IEEE/AIAA 36th Digital Avionics Systems Conference (DASC)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122359603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Designing a future airborne capability environment (FACE) hypervisor for safety and security","authors":"S. Vanderleest","doi":"10.1109/DASC.2017.8102056","DOIUrl":"https://doi.org/10.1109/DASC.2017.8102056","url":null,"abstract":"A partitioning environment is one of the components of an avionics architecture aligned with the Future Airborne Capability Environment (FACE™). In this paper, we explore the design of a hypervisor to provide the partitioning specified in the FACE Technical Standard. The FACE Consortium is focused on military aviation software, with a dual emphasis on technical standards and business acquisition strategies. We provide an overview of the history and purpose of FACE, then briefly examine previous open avionics initiatives. Our hypervisor extends the Xen open source hypervisor to support the ARINC 653 partitioning standard. Adding the Application Program Interface for the ARINC 653 standard is relatively straightforward because the underlying architectural concepts align well (with a few interesting challenges). Our current work is to expand our hypervisor technology to provide the required interfaces under FACE safety and security profiles. We discuss the current state of the project, examining technical and business aspects of open source software. We conclude with a roadmap for our hypervisor technology to reach conformance with the FACE Technical Standard and eventually achieve flight and security certification.","PeriodicalId":130890,"journal":{"name":"2017 IEEE/AIAA 36th Digital Avionics Systems Conference (DASC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130879965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A machine learning approach for prediction of on-time performance of flights","authors":"Balasubramanian Thiagarajan, L. Srinivasan, Aditya Sharma, Dinesh Sreekanthan, Vineeth Vijayaraghavan","doi":"10.1109/DASC.2017.8102138","DOIUrl":"https://doi.org/10.1109/DASC.2017.8102138","url":null,"abstract":"One of the major business problems that airlines face is the significant costs that are associated with flights being delayed due to natural occurrences and operational shortcomings, which is an expensive affair for the airlines, creating problems in scheduling and operations for the end-users thus causing bad reputation and customer dissatisfaction. In our paper, a two-stage predictive model was developed employing supervised machine learning algorithms for the prediction of flight on-time performance. The first stage of the model performs binary classification to predict the occurrence of flight delays and the second stage does regression to predict the value of the delay in minutes. The dataset used for evaluating the model was obtained from historical data which contains flight schedules and weather data for 5 years. It was observed that, in the classification stage, Gradient Boosting Classifier performed the best and in the regression stage, Extra-Trees Regressor performed the best. The performance of the other algorithms is also extensively documented in the paper. Furthermore, a real-time Decision Support Tool was built using the model which utilizes features that are readily available before the departure of an airplane and can inform passengers and airlines about flight delays in advance, helping them reduce possible monetary losses.","PeriodicalId":130890,"journal":{"name":"2017 IEEE/AIAA 36th Digital Avionics Systems Conference (DASC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127639276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}