In early 1973 Professor Fred O. Simons Jr., of Florida Technological University, suggested the topic for this research report. During control engineering discussions, it was proposed that a D.C. Shunt Motor could be constrained to perform in an idealized sense for the static case. The purpose of this report is to provide a reasonably comprehensive examination of a control which linearizes the torque-speed relationship of a dc servo system. The fundamental goal is to determine a control which linearizes the torque-speed output relation of a system, in which a dc shunt motor is under basic armature control. Consequently, the emphasis is on a concept and technique rather than the design of the system process. In this regard, the report does not delve into performance analysis, or design of the system to constrain the torque and speed parameters to operate along an idealized curve.

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This report discusses one application of a flight software design for a spacecraft in which the software executes from a database that can be managed by systems engineering. This report gives an overview of how such a software design can be developed and implemented. It also discusses why this approach is beneficial to the systems engineering program.

No description available.

The developing world often relies on low-water crossings on unpaved roads in place of bridges for vehicle river crossings and impoverished communities suffer disproportionately more from road shut-downs. This project defines a modular and deployable bridge solution to combat low-water crossing hazards in undeveloped road systems in year-round conditions using Systems Engineering and related methodologies to architect the solution.

Over the past few decades, not only have systems continued to increase in complexity, but they are expected to be delivered in the same timeframe and cost range. Technology has advanced us into what some refer to as the 4th Industrial Revolution. Digital is becoming the expectation in all areas of people's lives. Model-Based Systems Engineering (MBSE) represents the transition of systems into this new digital age, promising many improvements over the previous Document-Based Systems Engineering. This transition, however, is not simple. MBSE is a major paradigm shift for systems engineers, especially for those who have been in this field for many years. In order to work as intended, MBSE requires the participation of many different disciplines and functionalities in an organization. Gaining this level of organizational collaboration, however, is no easy task. Organizational structure and culture have intuitively been believed to be critical barriers to the successful adoption of MBSE, but little work has been done to discover what the impacts of these organizational factors are. The purpose of this research is to further explore the MBSE adoption process in the context of the organization. There were three research objectives designed to address the research question: how does organizational structure influence the adoption and implementation of MBSE? Research objective one was: relate organizational structure characteristics to MBSE adoption and implementation measures. Research objective two was: discover how organizational factors contribute to decisions made and other aspects of the MBSE adoption process. Research objective three was: connect different organizational structure and adoption variables together to derive critical variables in the adoption process. Research objective one was carried out using a survey as the instrument. The objective of the survey was to examine what the effects of organizational structure are on MBSE adoption and implementation. Organizational structure was represented by seven variables: Size, Formalization, Centralization, Specialization, Vertical Differentiation, Flexibility, and Interconnectedness. These are different characteristics of organizational structure that can be measured on a scale. MBSE adoption and implementation was represented by one adoption and three implementation variables. These include Adoption Process, Maturity of MBSE, Use of MBSE, and Influence on organizational outcomes. A total of 51 survey responses were received that met the inclusion criteria. Factor analysis was done for variables with multi-item measures. The factors were then analyzed using pairwise correlations to determine which relationships were significant. Formalization, Flexibility, and Interconnectedness were found to have positive correlations with adoption and implementation variables. Size and Vertical Differentiation had a negative correlation with Use of MBSE (implementation). Centralization was found to have negative correlations with adoption and implementation. Specialization did not have any significant correlations. Research objective two utilized semi-structured interviews as the main instrument. Survey participants had the opportunity to provide more detailed explanations of their organizations' experiences in the form of follow-up interviews. Eighteen survey participants agreed to this follow-up interview focused on MBSE adoption. Two of the participants shared failed adoption experiences, with the rest were at various stages of the adoption process. One of the most emergent themes out of the interviews was the idea of integration. Integration needs to occur at the organizational level, and the technical level. The technical level refers to the fact that tools, models, and/or data repositories need to be linked together in some way. Integration also has to occur at the organizational level, because a lot of different functional areas need to come together for MBSE. The way that organizations can address the issue of integration is through coordination mechanisms. The ultimate goal is to achieve implicit coordination through the use of connected models, but getting to that point will require coordination between different subunits. Interview responses were evaluated for coordination mechanisms, or situations that showed a distinct lack of a coordination mechanism. The lack of coordination mechanisms largely consists of a lack of standardization, lack of communication between subunits, and issues of authority. The final research objective of this work was carried out through a causal analysis using the data obtained from the survey and interviews. The purpose of this analysis was to visualize and better understand the adoption process. According to the calculated measures of centrality, the important nodes in this model are Improved organizational outcomes, Coordination between subunits, Projects use tools/methods, and People willing to use tools. Improved organizational outcomes is part of a key loop in the causal model. Improved organizational outcomes contributes to leaders and employees' willingness to support and use MBSE methods and tools, which contribute to actual use of tools and methods. This creates more Improved organizational outcomes, completing the loop. The survey results showed that Formalization, Decentralization, Flexibility, and Interconnectedness all have positive correlations with the Influence on organizational outcomes. So these organizational structure components are external factors that can be used to positively impact the adoption loop. Overall, this work provided several contributions to the field regarding the MBSE adoption process in an organizational setting. Organizational structure was shown to have significant correlations with adoption and implementation of MBSE. Coordination mechanisms were identified as a method to achieve integration across different functional areas of the organization. Improved organizational outcomes was shown to be a critical variable in the adoption process as an avenue for organizational structure factors to have a positive effect on the adoption process. / Doctor of Philosophy / Over the past few decades, not only have systems continued to increase in complexity, but they are expected to be delivered in the same timeframe and cost range. Technology has advanced us into what some refer to as the 4th Industrial Revolution. Digital is becoming the expectation in all areas of people's lives. Model-Based Systems Engineering (MBSE) represents the transition of systems into this new digital age, promising many improvements over the previous Document-Based Systems Engineering. This transition, however, is not simple. MBSE is a major mindset change for systems engineers, especially for those who have been in this field for many years. In order to work as intended, MBSE requires the participation of many different disciplines and functionalities in an organization. Gaining this level of organizational collaboration, however, is no easy task. Organizational structure and culture have intuitively been believed to be critical barriers to the successful adoption of MBSE, but little work has been done to discover what the impacts of these organizational factors are. This research looks into how organizational structure may have an impact on MBSE adoption and implementation. This research was carried out with the use of three different methods: an online survey, semi-structured interviews, and a causal analysis. The data obtained from the survey and interviews was used to construct a causal model depicting the MBSE adoption process. Overall, this work provided several contributions to the field regarding the MBSE adoption process in an organizational setting. Organizational structural variables were shown to have significant correlations with adoption and implementation of MBSE. Formalization, Flexibility, and Interconnectedness were found to have positive correlations with adoption and implementation variables, while Centralization had negative correlations with adoption and implementation. Coordination mechanisms were identified as a method to achieve integration across different functional areas of the organization. Interview responses were evaluated for coordination mechanisms, or situations that showed a distinct lack of a coordination mechanism. The lack of coordination mechanisms largely consists of a lack of standardization, lack of communication between subunits, and issues of authority. The causal analysis showed that Improved organizational outcomes, Coordination between subunits, Projects use tools/methods, and People willing to use tools were the critical variables in the MBSE adoption process.

No description available.

Past research into the phenomenon of reliability growth has emphasised modeling a major reliability characteristic in terms of a specific parametric function. In addition, the time-to-failure distribution of the system was generally assumed to be exponential. The result was that in most cases the improvement was modeled as a nonhomogeneous Poisson process with intensity λ(t). Major differences among models centered on the particular functional form of the intensity function. The popular Duane model, for example, assumes that λ(t) = β(1 – α)t ⁻ᵅ. The inability of any one family of distributions or parametric form to describe the growth process resulted in a multitude of models, each directed toward answering problems encountered with a particular test situation. This thesis proposes two new growth models, neither requiring the assumption of a specific function to describe the intensity λ(t). Further, the first of the models only requires that the time-to-failure distribution be unimodal and that the reliability become no worse as development progresses. The second model, while requiring the assumption of an exponential failure distribution, remains significantly more flexible than past models. Major points of this Bayesian model include: (1) the ability to encorporate data from a number of test sources (e.g. engineering judgement, CERT testing, etc.), (2) the assumption that the failure intensity is stochastically decreasing, and (3) accountability of changes that are incorporated into the design after testing is completed. These models were compared to a number of existing growth models and found to be consistently superior in terms of relative error and mean-square error. An extension to the second model is also proposed that allows system level growth analysis to be accomplished based on subsystem development data. This is particularly significant, in that, as systems become larger and more complex, development efforts concentrate on subsystem levels of design. No analysis technique currently exists that has this capability. The methodology is applied to data sets from two actual test situations.

This thesis explores the ability of Wi-Fi technology and the Institute of Electrical and Electronic Engineers (IEEE) 802.11 capability to disseminate various forms of information through densely vegetated, high humidity and high temperature environments. Using a lighter-than-air vehicle (balloon) and existing commercial-off-the- shelf, 802.11b and 802.16 wireless components, real-time information can be brought to the war-fighter. In particular, this thesis experiments with the use of commercially available wireless equipment and various antennae all attached to a helium-filled balloon to send and receive video, audio and digital information. This information is then disbursed to individual members of an established network over a specified land-mass. The balloon plays an important role in connecting network members to information that helps local and national commanders in making tactical decisions. These decisions consist of deploying forces, identifying and targeting the enemy, and deterring hostilities. Identifying the best method to supply real-time data to facilitate the movement of military assets and enhance a military s ability to engage an enemy decisively. Employing commercial-off-the-shelf (COTS) systems to disseminate real-time information is a potentially inexpensive solution to enable air and ground components to survey and target adversaries instantaneously. The ability to provide actionable information to the soldier serves as a force multiplier and increases the probability of mission success.

This thesis examines the following hypothesis: Through the combined use of common training and collaborative planning, a decision maker may sufficiently alleviate the harmful effects of an environment of information so that he/she can continue to make effective decisions. An environment of extreme information ambiguity, a dependent variable, is one of the most difficult components of a battle where the decision maker may reach a confusing and debilitating point where surviving seems less and less likely. Common training, an independent variable, purports that everyone who is relevant to the situation in the battlespace has similar skills, education, doctrine, and standards of performance coupled with comparable experiences. Collaborative planning, an independent variable, connotes a sharing of ideas; synchronization of assets, use of information technology, global real-time mission planning, face-to-face meetings, and other information sharing techniques for situations of collective concern. This thesis is a unique and in depth exploration of the relation of these three variables. Until now, no other research has looked at the relation of common training and collaborative planning with respect to decision making in environments of extreme information ambiguity. In order to explore the model the researcher analyzed two historical military battles: the Battle of Trafalgar and the Battle of Midway. Detailed research using a case study method was conducted to determine if the battles substantiated the thesis model. Research results indicate that for the maritime battles studied, the model appears to be a useful tool for interpretation and description of events and their outcomes. However, future studies should also increase the number and type of battles and other factors such as time and leadership should be considered.