Application of data and information fusion

Ph.DDOCTOR OF PHILOSOPH

Congruent Weak Conformance

This research addresses the problem of verifying implementations against specifications through an innovative logic approach. Congruent weak conformance, a formal relationship between agents and specifications, has been developed and proven to be a congruent partial order. This property arises from a set of relations called weak conformations. The largest, called weak conformance, is analogous to Milner\u27s observational equivalence. Weak conformance is not an equivalence, however, but rather an ordering relation among processes. Weak conformance allows behaviors in the implementation that are unreachable in the specification. Furthermore, it exploits output concurrencies and allows interleaving of extraneous output actions in the implementation. Finally, reasonable restrictions in CCS syntax strengthen weak conformance to a congruence, called congruent weak conformance. At present, congruent weak conformance is the best known formal relation for verifying implementations against specifications. This precongruence derives maximal flexibility and embodies all weaknesses in input, output, and no-connect signals while retaining a fully replaceable conformance to the specification. Congruent weak conformance has additional utility in verifying transformations between systems of incompatible semantics. This dissertation describes a hypothetical translator from the informal simulation semantics of VHDL to the bisimulation semantics of CCS. A second translator is described from VHDL to a broadcast-communication version of CCS. By showing that they preserve congruent weak conformance, both translators are verified

Selection of an alternative production part approval process to improve weapon systems production readiness

This thesis conducted an examination related to the Department of Defense (DOD) weapons systems production approval practices. Current practices result in poor weapons system production outcomes that reduce fleet readiness in DOD weapons systems acquisition. The Government Accountability Office (GAO) has reported concerns related to a lack of manufacturing knowledge at production start as causal to poor production outcomes. A comparison of DOD practices against non-DOD industrial production approval processes addressing causality and improvement opportunity provided new insight not found in acquisition research. An analysis of alternatives identified best practices to improve production capability and readiness. Key findings revealed that the automotive production approval process followed industry best practices that fully addressed problems identified by the GAO. Non-DOD industries used a more prescriptive Quality Management System (QMS) that enabled a more disciplined manufacturing development and demonstration of production capability prior to production commitment. Commercial surveys in the literature confirmed the benefits of the automotive prescriptive QMS. The more successful QMS approach can be applied to DOD acquisition practices reducing costs and improving fleet readiness.http://archive.org/details/selectionofnlter1094556139Civilian, Department of the NavyApproved for public release; distribution is unlimited

The causes and consequences of divergence between the air traffic controller state awareness and actual system state

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2018.Cataloged from PDF version of thesis. "February 2018."Includes bibliographical references (pages 177-195).Divergence is an inconsistency between the human's system state awareness and the actual system state. This research investigated divergence potential in air traffic controllers and identified controller divergence causes and consequences. Based on this investigation, approaches to minimize controller divergence and its consequences were identified for current air traffic control systems and future systems where unmanned aircraft will be integrated. Prior studies identified pilot divergence as a factor in several recent aircraft accidents and could be a factor for controllers. The future addition of unmanned aircraft in national airspace is a significant change which will affect the pilot and controller relationship and presents an opportunity to consider divergence before procedures are developed. To understand how to minimize divergence and its consequences, this research developed a divergence cause and consequence framework and a cognitive process framework. The cause and consequence framework was developed using established risk analysis methods. The cognitive process framework was developed using established cognitive process and human error approaches. This research refined these frameworks and demonstrated their utility in an investigation of historical air traffic control accidents. They were then used to identify divergence vulnerabilities in a future unmanned aircraft-integrated national airspace. Air traffic control cases were analyzed between 2011 and 2015 using the framework to understand causes and consequences of controller divergence. Twenty-seven (sixty-four percent) of these cases contained controller divergence contributing to the hazardous consequence. Although divergence causes and states varied, the most common event sequence included a diverged controller inducing an aircraft-to-aircraft conflict. These cases provided insight for system mitigations to reduce divergence causes and the consequentiality should it occur. The potential emergence of controller divergence with the integration of unmanned aircraft in national airspace was then investigated. Field studies of controllers experienced managing unmanned aircraft identified important differences between manned and unmanned aircraft. The framework was then used to analyze these potential divergence vulnerabilities. Observables, specifically intent, appear more challenging to perceive yet crucial for controller projection of unmanned aircraft position due to their lack of onboard human perception, lost link, and automated operations. Hazardous consequences may be more likely due to the inability for unmanned aircraft to provide mitigations.Material is based upon work supported under Air Force Contract FA8721-05-C-0002 and/or FA8702-15-D-0001by Brandon R. Abel.Ph. D

Simplified Aircraft-Based Paired Approach: Concept Definition and Initial Analysis

Simplified Aircraft-based Parallel Approach (SAPA) is an advanced concept proposed by the Federal Aviation Administration (FAA) to support dependent parallel approach operations to runways with lateral spacing closer than 2500 ft. At the request of the FAA, NASA performed an initial assessment of the potential performance and feasibility of the SAPA concept, including developing and assessing an operational implementation of the concept and conducting a Monte Carlo wake simulation study to examine the longitudinal spacing requirements. The SAPA concept was shown to have significant operational advantages in supporting the pairing of aircraft with dissimilar final approach speeds. The wake simulation study showed that support for dissimilar final approach speeds could be significantly enhanced through the use of a two-phased altitudebased longitudinal positioning requirement, with larger longitudinal positioning allowed for higher altitudes out of ground effect and tighter longitudinal positioning defined for altitudes near and in ground effect. While this assessment is preliminary and there are a number of operational issues still to be examined, it has shown the basic SAPA concept to be technically and operationally feasible

4D Dynamic RNP Annual Interim Report-Year 1

This Annual Interim Report summarizes the activities led by Raytheon, in collaboration with GE Aviation and SAIC, and presents the results obtained during the first year of this research effort to expand the RNP concept to 4 dimensions relative to a dynamic frame of reference. Joint Program Development Office (JPDO)Concepts of Operations for the Next Generation Air Transportation System (NextGen) considers 4 Dimension Trajectory (4DT) procedures a key enabler to Trajectory Based Operations (TBO). The JPDO defines 4DT as a precise description of an aircraft path in space and time . While NextGen assumes that this path is defined within an Earth-reference frame, many 4DT procedure implementations will require an aircraft to precisely navigate relative to a moving reference such as another aircraft to form aggregate flows or a weather cell to allow for flows to shift. Current methods of implementing routes and flight paths rely on aircraft meeting a Required Navigation Performance (RNP) specification and being equipped with a monitoring and alerting capability to annunciate when the aircraft system is unable to meet the performance specification required for the operation. Since all aircraft today operate within the NAS relative to fixed reference points, the current RNP definition is deemed satisfactory. However, it is not well understood how the current RNP construct will support NextGen 4DT procedures where aircraft operate relative to each other or to other dynamic frames of reference. The objective of this research effort is to analyze candidate 4DT procedures from both an Air Navigation Service Provider (ANSP) and aircraft perspective, to identify their specific navigational requirements, assess the shortcomings of the current RNP construct to meet these requirements, to propose an extended 4 Dimensional Dynamic RNP (4D Dynamic RNP) construct that accounts for the dynamic spatial and temporal nature of the selected 4DT procedures, and finally, to design an experiment using the Airspace and Traffic Operations Simulation (ATOS) system to validate the 4D Dynamic RNP construct. This Annual Interim Report summarizes the activities led by Raytheon, in collaboration with GE Aviation and SAIC, and presents the results obtained during the first year of this research effort to expand the RNP concept to 4 dimensions relative to a dynamic frame of reference. A comprehensive assessment of the state-of-the-art international implementation of current RNP was completed and presented in the Contractor Report RNP State-of-the-Art Assessment, Version 4, 17 December 2008 . The team defined in detail two 4DT operations, Airborne Precision Spacing and Self-Separation, that are ideally suited to be supported by 4D Dynamic RNP and developed their respective conceptual frameworks, Required Interval Management Performance (RIMP) Version 1.1, 13 April 2009 and Required Self Separation Performance (RSSP) Version 1.1, 13 April 2009 . Finally, the team started the development of a mathematical model and simulation tool for RIMP and RSSP scheduled to be delivered during the second year of this research effort

Process Mapping a Diminishing Manufacturing Sources and Materiel Shortages Reactive Management Strategy: A Case Study

In order to handle its obligations, the Brazilian Ministry of Defense (MoD) will need an information system capable of managing logistics information from all military services. A project to develop an integrated information system to fit the requirements of different, but connected, organizations has inherent challenges. Differences in the organizational structures, cultures and political aspects, are key issues to be observed before the development to assure the project\u27s success. The same is applicable when trying to adapt an already existing information system to fill the needs of another organization. In the new organization, it is mandatory to assess the feasibility of the software\u27s alternatives available. Alternatives can be to adapt an existing information system or to develop a completely new system. This research sought to develop a method for assessing the organizational, cultural, and political considerations affecting the insertion of the Integrated Logistics Information System (SILOMS), developed by the Brazilian Air Force, into the MoD. The research develops a method for assisting decision makers in assessing the risks involved in the implementation of an information system in the MoD

An Integrated Cybersecurity Risk Management (I-CSRM) Framework for Critical Infrastructure Protection

Risk management plays a vital role in tackling cyber threats within the Cyber-Physical System (CPS) for overall system resilience. It enables identifying critical assets, vulnerabilities, and threats and determining suitable proactive control measures to tackle the risks. However, due to the increased complexity of the CPS, cyber-attacks nowadays are more sophisticated and less predictable, which makes risk management task more challenging. This research aims for an effective Cyber Security Risk Management (CSRM) practice using assets criticality, predication of risk types and evaluating the effectiveness of existing controls. We follow a number of techniques for the proposed unified approach including fuzzy set theory for the asset criticality, machine learning classifiers for the risk predication and Comprehensive Assessment Model (CAM) for evaluating the effectiveness of the existing controls. The proposed approach considers relevant CSRM concepts such as threat actor attack pattern, Tactic, Technique and Procedure (TTP), controls and assets and maps these concepts with the VERIS community dataset (VCDB) features for the purpose of risk predication. Also, the tool serves as an additional component of the proposed framework that enables asset criticality, risk and control effectiveness calculation for a continuous risk assessment. Lastly, the thesis employs a case study to validate the proposed i-CSRM framework and i-CSRMT in terms of applicability. Stakeholder feedback is collected and evaluated using critical criteria such as ease of use, relevance, and usability. The analysis results illustrate the validity and acceptability of both the framework and tool for an effective risk management practice within a real-world environment. The experimental results reveal that using the fuzzy set theory in assessing assets' criticality, supports stakeholder for an effective risk management practice. Furthermore, the results have demonstrated the machine learning classifiers’ have shown exemplary performance in predicting different risk types including denial of service, cyber espionage, and Crimeware. An accurate prediction can help organisations model uncertainty with machine learning classifiers, detect frequent cyber-attacks, affected assets, risk types, and employ the necessary corrective actions for its mitigations. Lastly, to evaluate the effectiveness of the existing controls, the CAM approach is used, and the result shows that some controls such as network intrusion, authentication, and anti-virus show high efficacy in controlling or reducing risks. Evaluating control effectiveness helps organisations to know how effective the controls are in reducing or preventing any form of risk before an attack occurs. Also, organisations can implement new controls earlier. The main advantage of using the CAM approach is that the parameters used are objective, consistent and applicable to CPS

An integrated cyber security risk management framework and risk predication for the critical infrastructure protection

Cyber security risk management plays an important role for today’s businesses due to the rapidly changing threat landscape and the existence of evolving sophisticated cyber attacks. It is necessary for organisations, of any size, but in particular those that are associated with a critical infrastructure, to understand the risks, so that suitable controls can be taken for the overall business continuity and critical service delivery. There are a number of works that aim to develop systematic processes for risk assessment and management. However, the existing works have limited input from threat intelligence properties and evolving attack trends, resulting in limited contextual information related to cyber security risks. This creates a challenge, especially in the context of critical infrastructures, since attacks have evolved from technical to socio-technical and protecting against them requires such contextual information. This research proposes a novel integrated cyber security risk management (i-CSRM) framework that responds to that challenge by supporting systematic identification of critical assets through the use of a decision support mechanism built on fuzzy set theory, by predicting risk types through machine learning techniques, and by assessing the effectiveness of existing controls. The framework is composed of a language, a process, and it is supported by an automated tool. The paper also reports on the evaluation of our work to a real case study of a critical infrastructure. The results reveal that using the fuzzy set theory in assessing assets' criticality, our work supports stakeholders towards an effective risk management by assessing each asset's criticality. Furthermore, the results have demonstrated the machine learning classifiers’ exemplary performance to predict different risk types including denial of service, cyber espionage and crimeware

USING IMAGERY PRACTICE TO IMPROVE AIRLINE PILOT SITUATIONAL AWARENESS

Pilot error remains the primary cause of airline airplane accidents (Federal Aviation Administration, n.d.). Airline pilots have relied on Crew Resource Management and Threat Error Management to reduce or eliminate errors (Helmreich & Foushee, 2019). Unfortunately, the worldwide accident rate continues to increase (International Air Transport Association, 2021), demonstrating the need for further research into improving aviation safety. Current regulations do not require imagery training for airline pilots to improve situational awareness (Federal Aviation Administration, 2017a). Athletes and other professionals, such as musicians and medical professionals, use imagery to improve performance (Munzert et al., 2009). Imagery practice may improve the situational awareness of airline pilots. This study examined the relationship between imagery practice and airline pilot situational awareness. The researcher used an experimental posttest design with a group of airline pilots that received imagery training and a practice period. The data analysis answered the research questions and objectives using data provided by the participants who completed an interactive video survey. The researcher compared the survey results with airline pilots without imagery practice, measuring Endsley\u27s (1995) three levels of situational awareness, including perception, comprehension, and projection. The study\u27s results produced three findings that emphasize the effects of the research. Pilots who practiced imagery more often had higher levels of situational awareness during the video survey than pilots who practiced less. Although there was an improvement in the group that practiced imaging a flight, further research may improve the effectiveness of imagery practice. More experienced pilots participated in the study compared to less experienced pilots. Further research regarding safety training experience and situational awareness could add to the findings of this study, along with Wang et al. (2021) findings regarding pilots using personal attributes such as emotional intelligence that replace inadequate training to maintain situational awareness