Project report (M.S.)--Virginia Polytechnic Institute and State University. M.S. 1992. / Abstract. Includes bibliographical references (leaves 101-106). Also available via the Internet.

Thesis (M.S. in Engineering Science)--Naval Postgraduate School, December 1990. / Thesis Advisor(s): Lee, Yuh-jeng. Second Reader: Giannotti, B. B. "December 1990." Description based on title screen as viewed on March 30, 2010. DTIC Descriptor(s): Weapons, Simulation, Detectors, Decision Making, Defense Systems, Accuracy, Efficiency, Theses, Targets, Three Dimensional, Shipboard, Expert Systems, Systems Approach, Enemy, Timeliness, Battles, Naval Vessels(Combatant), Preprocessing, Decision Support Systems, Input. DTIC Identifier(s): Rule Based Systems, Decision Aids, Weapon System Effectiveness, Shipboard, KEE(Knowledge Engineering Environment), WSS(Weapon Suggestion System), Target Detection, Threat Evaluation, Naval Vessels(Combatant), Tactical Analysis, Theses. Includes bibliographical references (p. 74-76). Also available in print.

Thesis (Ph. D.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 187-198).

The failure of many System of Systems (SoS) enterprises can be attributed to the inappropriate application of traditional Systems Engineering (SE) processes within the SoS domain, because of the mistaken belief that a SoS can be regarded as a single large, or complex, system. SoS Engineering (SoSE) is a sub-discipline of SE; Risk Management and Modelling and Simulation (M&S) are key areas within SoSE, both of which also lie within the traditional SE domain. Risk Management of SoS requires a different approach to that currently taken for individual systems; if risk is managed for each component system then it cannot be assumed that the aggregated affect will be to mitigate risk at the SoS level. A literature review was undertaken examining three themes: (1) SoS Engineering (SoSE), (2) M&S and (3) Risk. Theme 1 of the literature provided insight into the activities comprising SoSE and its difference from traditional SE with risk management identified as a key activity. The second theme discussed the application of M&S to SoS, providing an output, which supported the identification of appropriate techniques and concluding that, the inherent complexity of a SoS required the use of M&S in order to support SoSE activities. Current risk management approaches were reviewed in theme 3 as well as the management of SoS risk. Although some specific examples of the management of SoS risk were found, no mature, general approach was identified, indicating a gap in current knowledge. However, it was noted most of these examples were underpinned by M&S approaches. It was therefore concluded a general approach SoS risk management utilising M&S methods would be of benefit. In order to fill the gap identified in current knowledge, this research proposed a new model based approach to Risk Management where risk identification was supported by a framework, which combined SoS system of interest dimensions with holistic risk types, where the resulting risks and contributing factors are captured in a causal network. Analysis of the causal network using a model technique selection tool, developed as part of this research, allowed the causal network to be simplified through the replacement of groups of elements within the network by appropriate supporting models. The Bayesian Belief Network (BBN) was identified as a suitable method to represent SoS risk. Supporting models run in Monte Carlo Simulations allowed data to be generated from which the risk BBNs could learn, thereby providing a more quantitative approach to SoS risk management. A method was developed which provided context to the BBN risk output through comparison with worst and best-case risk probabilities. The model based approach to Risk Management was applied to two very different case studies: Close Air Support mission planning and the Wheat Supply Chain, UK National Food Security risks, demonstrating its effectiveness and adaptability. The research established that the SoS SoI is essential for effective SoS risk identification and analysis of risk transfer, effective SoS modelling requires a range of techniques where suitability is determined by the problem context, the responsibility for SoS Risk Management is related to the overall SoS classification and the model based approach to SoS risk management was effective for both application case studies.

The surveillance of geographic borders and critical infrastructures using limited sensor capability has always been a challenging task in many homeland security applications. While geographic borders may be very long and may go through isolated areas, critical assets may be large and numerous and may be located in highly populated areas. As a result, it is virtually impossible to secure each and every mile of border around the country, and each and every critical infrastructure inside the country. Most often, a compromise must be made between the percentage of border or critical asset covered by surveillance systems and the induced cost. Although threats to homeland security can be conceived to take place in many forms, those regarding illegal penetration of the air, land, and maritime domains under the cover of day-to-day activities have been identified to be of particular interest. For instance, the proliferation of drug smuggling, illegal immigration, international organized crime, resource exploitation, and more recently, modern piracy, require the strengthening of land border and maritime awareness and increasingly complex and challenging national security environments. The complexity and challenges associated to the above mission and to the protection of the homeland may explain why a methodology enabling the design and optimization of distributed detection systems architectures, able to provide accurate scanning of the air, land, and maritime domains, in a specific geographic and climatic environment, is a capital concern for the defense and protection community. This thesis proposes a methodology aimed at addressing the aforementioned gaps and challenges. The methodology particularly reformulates the problem in clear terms so as to facilitate the subsequent modeling and simulation of potential operational scenarios. The needs and challenges involved in the proposed study are investigated and a detailed description of a multidisciplinary strategy for the design and optimization of detection architectures in terms of detection performance and cost is provided. This implies the creation of a framework for the modeling and simulation of notional scenarios, as well as the development of improved methods for accurate optimization of detection architectures. More precisely, the present thesis describes a new approach to determining detection architectures able to provide effective coverage of a given geographical environment at a minimum cost, by optimizing the appropriate number, types, and locations of surveillance and detection systems. The objective of the optimization is twofold. First, given the topography of the terrain under study, several promising locations are determined for each sensor system based on the percentage of terrain it is covering. Second, architectures of sensor systems able to effectively cover large percentages of the terrain at minimal costs are determined by optimizing the number, types and locations of each detection system in the architecture. To do so, a modified Genetic Algorithm and a modified Particle Swarm Optimization are investigated and their ability to provide consistent results is compared. Ultimately, the modified Particle Swarm Optimization algorithm is used to obtain a Pareto frontier of detection architectures able to satisfy varying customer preferences on coverage performance and related cost.

The Ph.D. project has evolved from focusing on the technical problem of the integration and interoperability of an assemblage of complex systems and SoS within a flight training system to development of a workflow process using frameworks to aid the decision making process for the selection of optimal flight training blending mixes. The focus of the research involved developing a methodology to satisfy research project proposal requirements agreed upon with the industrial sponsor. This thesis investigates the complexity of a modern flight training systems and the need for understanding that it is supported by a complex Family of Systems (FoS) including Virtual Reality Training Environments such as flight simulators, to live training aircraft with various configurations of avionic controls. One of the key technical problems today is how best to develop and assemble a family of flight training system into an integrated Live/Synthetic mix for aircrew training to optimise organisation and training objectives. With the increased use of emulation/synthetic data on aircraft for live training, the synthetic boundary is becoming increasingly blurred. Systematic consideration of the most appropriate blend is needed. The methodology used in the research is model driven and the architecture produced is described at a level of abstraction to enable communication to all stakeholders for the means of understanding the structure involved in the system design process. Relational Oriented Systems Engineering and Technology Trade-Off Analysis (ROSETTA) frameworks are described using Model Based Systems Engineering (MBSE) techniques for supporting capability based trade-off decisions for selection of optimal flight training FoS mixes dependent on capability. The research proposes a methodology and associated methods including a high-level systematic closed loop information management structure for blended device/tool aircrew training and a modelling and analysis approach for the FoS aviation training problem to enhance the existing training programmes to provide a more efficient and agile training environment. The mathematical formalisms used provide a method of quantifying subjective opinions and judgements for trade studies to be accomplished on the suitability of technology for each student pilot in relation to training and organisational objectives. The methodology presented is by no means a final solution, but a path for further research to enable a greater understanding of the suitability of training tools/technology used to train individual pilots at various stages throughout the training pipeline lifecycle(s).

Approved for public release; distribution in unlimited. / This thesis represents a cross Systems Command (NAVSEA/NAVAIR) developed product. The product - the Sunset Supply Base (SSB) system - provides a complete system for addressing the risks and supportability issues involved with Commercial Off the Shelf (COTS) products in Navy combat and support systems. The SSB system was implemented on three Navy combat weapon systems at various phases of the product development life cycle. The main body provides to the Program Management Offices (PMO) and other decision makers, a high level summary of performance expectations. Appendix A - The Sunset Supply Base Architecture - identifies at a high level of abstraction a collaborative architecture providing a roadmap for design and development of the SSB system. Appendix B - The Systems Engineering Development and Implementation (SEDI) plan - is a prescriptive or "How to" manual describing activities that have been used to successfully implement the SSB system. Appendix C - Business Case Analysis (BCA) - presents the data collected as a result of SEDI plan implementation then addresses the business/programmatic attributes showing the viability and value proposition possible through the SSB system. Appendix D - The Marketing Plan for the SSB system - defines methods and practices necessary to establish the SSB system as the alternative of choice. / Chemical Engineer, United States Navy / Systems Engineer, United States Navy