Relationship Systems Outside the Therapy Room| A Grounded Theory on Seasoned MFT Doctoral Graduates Utilizing Their Systemic Training in Human Service Team-based Organizations

Systematic design and analysis of fuzzy dynamic systems has been a problem which attracted much attention from researchers in recent years. In this dissertation, we propose a methodology for analysis and design of fuzzy dynamic systems. First, we introduce a new way to treat fuzzy sets: fuzzy sets as points in fuzzy state space. We investigate the relationship between membership functions and their corresponding fuzzy set points in fuzzy state space. We then examine the formulation and stability issues of fuzzy dynamic systems based on the geometric structure of fuzzy state space, resulting in the generalization and extension of classical stability definitions to fuzzy dynamic systems. We also introduce cellular structure to fuzzy state space, allowing a discrete cell-to-cell mapping method to be developed to approximate a fuzzy dynamic system model. This method leads to an efficient global behavior analysis algorithm based on a simple cell-to-cell mapping search. Finally, we outline the cellmapping-based search algorithm for fuzzy optimal control design and demonstrate its validity and advantages by applying it to time-optimal trajectory generation for coordinated manipulator systems with uncertain parameters.

The importance of effective and efficient distribution is evident from its associated costs. Transportation and shipping alone comprise roughly 15 percent of a product's sales in the U.S. Physical distribution is very energy and labor intensive, which have both become relatively more expensive in the last 10-15 years. Not surprisingly, there is a growing demand for automated planning systems that produce economical routes. Other than the cost savings, introduction of these systems enables companies to maintain a higher level of service for their customers, it makes them less dependent on human planners, it supplies better management information facilities and it makes distribution planning work faster and simpler.

A number of alternatives were considered to keep pecan processing economically competitive and sustainable. The industry needed a low risk evaluation technique for testing new high capital plant configurations. A simulation model was developed using the AweSimTM simulation system to form the model structure and framework. The Visual SLAMTM and Visual BASIC programming languages were used to build a network model that provided a mathematical-logical representation of the system. The model mathematically expresses all sub-processes including moisture conditioning, pasteurizing, cracking, shelling, sizing, manual and electronic sorting, resizing, resorting, and packaging. The pecan process simulation model consists of 24 RESOURCES, 353 ACTIVITIES, 48 AWAIT/QUEUE and FREE nodes, 83 BATCH and UNBATCH nodes, 79 ASSIGN nodes, 20 COLCT nodes, 39 GOON nodes, 10 other miscellaneous nodes and a graphic user interface (GUI). The model provides information on equipment utilization, delays, queues and bottlenecks for each process in the system. It also predicts total pecan cracked and total pecan packed, including details of production for each size class i.e. halves; large; medium; small; midget; fine; granule; and oil stock. The model was validated quantitatively by comparing output with actual production figures and qualitatively by plant management. Five options of process alternatives were simulated using the pecan simulation model. The first alternative (including 3 options) was a management proposed configuration for dual electronic sorting of pecan halves to reduce the shell pieces and ensure a lighter color product. Two options were found not viable as they required major capital investments and plant reconfiguration. The third option for dual sorting was found to be a viable process alternative with minor labor additions.

An efficient methodology for parameter identification is developed for general multi-degree of freedom linear or nonlinear systems in continuous time. The new methodology is based on a gradient flow algorithm and demonstrated to be useful in identifying unknown parameters for the systems defined by both linear and nonlinear differential equations. The new methodology identifies the unknown parameters by solving a system of differential equations rather than the conventional post-data fitting analysis. It is named the trajectory gradient integral flow (TGIF) algorithm. For the cases of stable, one-dimensional linear systems, the asymptotic stability of the TGIF algorithm is guaranteed in the neighborhood of the operating point. For higher order linear or nonlinear systems, certain criteria for stability are developed using the eigenvalue analysis and the Routh-Hurwitz stability criteria. A well-known system identification result is that any method works the best with non-steady, non-periodic data set that is driven by randomized inputs, however this is not an essential requirement with the TGIF algorithm. In fact, it is possible to perform efficient parameter identification with the TGIF algorithm using an unit step input or a simple sine input. Improvements over previous approaches include: (1) the new methodology is easy to apply for nonlinear systems, (2) it works well with a simple unit step or sinusoidal inputs as well as bounded (control) inputs, (3) it demonstrates a reasonable large "domain of attraction", (4) it can be applied for either "on-line" or "off-line" parameter identification processes.

This research, denoted MILOS (Multi-objective Integrated Large-scale Optimized ramp metering System) is a hierarchical structure for solution of the large-scale freeway management problem to address the key features of this problem (dynamic state changes, stochasticity, multi-dimensionality, unpredictability, partial-observability, and existence of multiple objectives). MILOS decomposes the freeway control problem into subproblems along temporal/spatial boundaries and is composed of three primary components: SPC-based anomaly detection and optimization scheduling, area-wide coordination layer, and predictive-cooperative real-time (PC-RT) optimization layer. The area-wide coordination component of the hierarchical control system considers the impact of queue growth on the adjacent interchanges in a quadratic programming optimization model with a multi-criterion objective function. The formulation of the area-wide optimization problem is augmented with overflow variables to guarantee a feasible solution. The nominal solution of the areawide coordination problem is then modified in real-time by the locally traffic-reactive, PC-RT algorithm based on a linear-program using a linearized dynamic difference equation implementation of the macroscopic FREFLO model. The PC-RT formulation pro-actively plans to utilize opportunities to disperse queues or hold back additional vehicles when freeway and ramp demand conditions are appropriate. The cost coefficients of this optimization problem is linked to the solution of the area-wide coordination problem by using information on the dual of the solution to the area-wide coordination problem. The optimization runs of the area-wide coordination problem and the PC-RT optimization problems at each ramp are scheduled by a demand/flow monitoring system based on statistical process control. A simulation experiment is executed to evaluate the MILOS hierarchical system against "no control", ADOT's current ramp metering policy, and an area-wide LP optimization problem resolved in 5-minute intervals on a small freeway network in the metropolitan Phoenix, AZ area. Three test cases are presented for a short "burst" of heavy-volume flows to all ramps, a 3-hour commuting peak, and a 3-hour commuting peak with a 30-minute incident occurring in the middle of the network. The performance results indicate that MILOS is able to reduce freeway travel time, increase freeway average speed, and improve recovery performance of the system when flow conditions become congested.

The purpose of this work is to provide a method for the automatic generation of test scenarios from the behavioral requirements of a system. The goal of the generated suite of test scenarios is to allow the system design to be validated against the requirements. The benefits of automatic test generation include improved efficiency, completeness (coverage), and objectivity (removal of human bias). The Model-based Codesign method is refined by defining a design process flow. This process flow includes the generation of test suites from requirements and the application of these tests across multiple levels of the design path. An approach is proposed that utilizes what is called a requirements model and a set of four algorithms. The requirements model is an executable model of the proposed system defined in a deterministic state-based modeling formalism. Each action in the requirements model that changes the state of the model is identified with a unique requirement identifier. The scenario generation algorithms perform controlled simulations of the requirements model in order to generate a suite of test scenarios applicable for black box testing. A process defining the generation and use of the test scenarios is developed. This process also includes the treatment of temporal requirements which are considered separately from the generation of the test scenarios. An algorithm is defined to combine the test scenarios with the environmental temporal requirements to produce timed test scenarios in the IEEE standard C/ATLAS test language. An algorithm is also defined to describe the behavior of the test environment as it interprets and applies the C/ATLAS test programs. Finally, an algorithm to analyze the test results logged while applying the test scenario is defined. Measurements of several metrics on the scenario generation algorithms have been collected using prototype tools. The results support the position that the algorithms are performing reasonably well, that the generated test scenarios are adequately efficient, and that the processing time needed for test generation grows slowly enough to support much larger systems.

Information systems development research has documented the importance and the difficulty of eliciting requirements from users. Research on the use of Group Support Systems (GSS) for requirements elicitation led to development of the Collaborative Software Engineering Methodology (CSEM) and identified the need for collaborative methods and tools to provide a dynamic picture of the business processes that a system must support. Recent research suggests that scenarios can fill this need. A review of the scenario literature showed that although there is widespread agreement on the usefulness of scenarios, there are many questions on how to implement a user-focused, scenario-based systems development process. The purpose of this research was to advance understanding in this area and to determine: What are the collaborative modeling processes, tools, and facilitation techniques needed to effectively elicit scenarios from users in a group environment? A two-phase, multi-method systems development research approach was used. The first phase focused on use of a general-purpose GSS for collaborative scenario elicitation. A conceptual framework and initial methodology were developed and then evaluated during exploratory case studies and a laboratory experiment. The second phase focused on development and evaluation of a special-purpose GSS and methodology. Phase I results showed that: users can easily define scenarios which provide rich pictures of the problem domain; an iterative, collaborative methodology with scenario and action prompts is needed to ensure scenario completeness; and limitations of general-purpose GSS negatively impacted productivity. The Collaborative Distributed Scenario and Process Analyzer (SPA) provides integrated textual scenario and graphical process modeling capabilities which successfully overcame these limitations. This research made several contributions. CSEM was extended to define scenario usage opportunities throughout development. Scenario content, form, group process and facilitation techniques were defined for collaborative scenario elicitation using a general-purpose GSS, which can be used now by practitioners. A special-purpose GSS tool (SPA) was developed and integrated into a comprehensive methodology which allows user groups to rapidly define and analyze scenarios in face-to-face and distributed settings. Finally, flexibility designed into SPA opens up opportunities for many other uses for SPA and serves as a first-step towards a build-your-own GSS tool.

The construction of lightweight manipulators with a larger speed range is one of the major goals in the design of well-behaving industrial robotic arms. Their use will lead to higher productivity and less energy consumption than is common with heavier, rigid arms. However, due to the flexibility involved with link deformation and the complexity of distributed parameter systems, modeling and control of flexible manipulators still remain a major challenge to robotic research. A compromise between modeling costs and control efficiency for real-time implementation is inevitable. The interdependency of subsystems results in a local optimal performance in the traditional design scheme. An important research topic in flexible manipulator design is the pursuit of better system performance while avoiding model-intensive or control-intensive work. This problem can be solved using the proposed mechatronic design approach. It treats the mechanical, electrical and control components of a flexible manipulator concurrently. The result is an improved design with an explicit link shape and controller parameters which result in the control problem and modeling accuracy no longer being critical for obtaining desired performance. Dynamics of flexible manipulators with rotatory inertia are derived, and state-space equations with the integration of DC motor dynamics are developed as a theoretical base for mechatronic designs. Two case studies based on LQR formula and Hinfinity control are considered. The beam shape and controller parameters are obtained using an adaptive iterative algorithm with the accommodation of various geometrical constraints. Also, different output feedback strategies are investigated to evaluate the impacts of various controller structures. Finally, a sensitivity analysis in terms of parameter variations and model uncertainties is conducted to reveal the robustness of this mechatronic design.

Object identification is very important for Robotic Manipulation of objects. A study on potential has been done. Three prime techniques of features extraction have been analyzed: Vision, Touch and Destruction of materials (NDE). The role of perceptual organization in aiding object identification is also discussed. A list of features has been obtained for each technique. Evaluation, based on cost of computation and accuracy of computation, of techniques for features extraction is presented. Some sample object identification systems have been designed using Classification Expert System Maker (CESM). Use of D-matrix (distiguishability matrix) is emphasized to get the optimal feature set used to generate a classification tree. The classification tree is transferred into CESM knowledge base to obtain an expert system. A comprehensive multisensor system for object identification is proposed.