Understanding Behavior of System of Systems Through Computational Intelligence Techniques

The world is facing an increasing level of systems integration leading towards systems of systems (SoS) that adapt to changing environmental conditions. The number of connections between components, the diversity of the components and the way the components are organized can lead to different emergent system behavior. Therefore, the need to focus on overall system behavior is becoming an unavoidable issue. The problem is to develop methodologies appropriate for better understanding behavior of system of systems before the design and implementation phase. This paper focuses on computational intelligence techniques used for analysis of complex adaptive systems with the aim of identifying areas that need methodology customization for SoS analysis

Testability of a swarm robot using a system of systems approach and discrete event simulation

A simulation framework using discrete event system specification (DEVS) and data encoded with Extensible Markup Language (XML) is presented to support agent-in-the-loop (AIL) simulations for large, complex, and distributed systems. A System of Systems (SoS) approach organizes the complex systems hierarchically. AIL simulations provide a necessary step in maintaining model continuity methods to achieve a greater degree of accuracy in systems analysis. The proposed SoS approach enables the simulation and analysis of these independent and cooperative systems by concentrating on the data transferred among systems to achieve interoperability instead of requiring the software modeling of global state spaces. The information exchanged is wrapped in XML to facilitate system integration and interoperability. A Groundscout is deployed as a real agent working cooperatively with virtual agents to form a robotic swarm in an example threat detection scenario. This scenario demonstrates the AIL framework\u27s ability to successfully test a swarm robot for individual performance and swarm behavior. Results of the testing process show an increase of robot team size increases the rate of successfully investigating a threat while critical violations of the algorithm remained low despite packet loss

A meta-architecture analysis for a coevolved system-of-systems

Modern engineered systems are becoming increasingly complex. This is driven in part by an increase in the use of systems-of-systems and network-centric concepts to improve system performance. The growth of systems-of-systems allows stakeholders to achieve improved performance, but also presents new challenges due to increased complexity. These challenges include managing the integration of asynchronously developed systems and assessing SoS performance in uncertain environments. Many modern systems-of-systems must adapt to operating environment changes to maintain or improve performance. Coevolution is the result of the system and the environment adapting to changes in each other to obtain a performance advantage. The complexity that engineered systems-of-systems exhibit poses challenges to traditional systems engineering approaches. Systems engineers are presented with the problem of understanding how these systems can be designed or adapted given these challenges. Understanding how the environment influences system-of-systems performance allows systems engineers to target the right set of capabilities when adapting the system for improved performance. This research explores coevolution in a counter-trafficking system-of-systems and develops an approach to demonstrate its impacts. The approach implements a trade study using swing weights to demonstrate the influence of coevolution on stakeholder value, develops a novel future architecture to address degraded capabilities, and demonstrates the impact of the environment on system performance using simulation. The results provide systems engineers with a way to assess the impacts of coevolution on the system-of-systems, identify those capabilities most affected, and explore alternative meta-architectures to improve system-of-systems performance in new environments --Abstract, page iii

Architecting system of systems: artificial life analysis of financial market behavior

This research study focuses on developing a framework that can be utilized by system architects to understand the emergent behavior of system architectures. The objective is to design a framework that is modular and flexible in providing different ways of modeling sub-systems of System of Systems. At the same time, the framework should capture the adaptive behavior of the system since evolution is one of the key characteristics of System of Systems. Another objective is to design the framework so that humans can be incorporated into the analysis. The framework should help system architects understand the behavior as well as promoters or inhibitors of change in human systems. Computational intelligence tools have been successfully used in analysis of Complex Adaptive Systems. Since a System of Systems is a collection of Complex Adaptive Systems, a framework utilizing combination of these tools can be developed. Financial markets are selected to demonstrate the various architectures developed from the analysis framework --Introduction, page 3

An Integrated Social Actor and Service Oriented Architecture (SOA) Approach for Improved Electronic Health Record (EHR) Privacy and Confidentiality in the US National Healthcare Information Network (NHIN)

The emerging US National Healthcare Information Network (NHIN) will improve healthcare’s efficacy, efficiency, and safety. The first-generation NHIN being developed has numerous advantages and limitations. One of the most difficult aspects of today’s NHIN is ensuring privacy and confidentiality for personal health data, because family and caregivers have multiple complex legal relationships to a patient. A Social Actor framework is suggested to organize and manage these legal roles, but the Social Actor framework would be very difficult to implement in today’s NHIN. Social Actor Security Management could, however, be effectively implemented using Service Oriented Architectures (SOAs), which are rapidly becoming accepted for supporting complex information exchange across heterogeneous information systems fabrics. The Department of Defense is applying SOA to all of its enterprises. It is using customized simulation and modeling tools to achieve security and robustness goals and to reduce the intrinsic design and implementation risks for SOA’s complex Systems of Systems environment. This paper integrates all of these approaches into a next-generation NHIN-2 design based on a specific Air Force SOA named MCSOA. This NHIN-2 design uses MCSOA to create Security Management, Service Discovery, and Presence Management agents to implement Social Actor support for improved confidentiality and privacy

Microstructural effects on the mechanical properties of carburized low-alloy steels

This study examined the effects of composition and initial microstructure on the physical, metallurgical, and mechanical properties of carburized SAE 8620 and PS-18 steels. Testing was performed on 8620 and PS-18 steels in the as-received and normalized conditions. Hardenability testing was conducted prior to additional heat treatments. Size and shape distortion, residual stress, retained austenite, and effective case depth measurements were obtained for specimens subjected to a carburizing heat treatment. Specimens subjected to a core thermal cycle heat treatment were tested to determine the tensile and Charpy impact properties of the core material of carburized components. Despite differences between the as-received and normalized materials prior to carburizing, testing revealed that normalizing did not have a significant effect on the properties of the carburized or core thermal cycle heat treated materials. PS-18 had a higher hardenability, effective case depth, and ultimate tensile strength and a lowerCharpy impact toughness than 8620

One-operator two-machine flow shop scheduling with setup times for machines and total completion time objective

In a manufacturing environment, when a worker or a machine switches from one type of operation to another, a setup time may be required. I propose a scheduling model with one operator and two machines. In this problem, a single operator completes a set of jobs requiring operations in a two-machine flow shop. The operator can perform only one operation at a time. When one machine is in use, the other is idle. Whenever the operator changes machine, a setup time is required. We consider the objective of total completion time. I formulate the problem as a linear integer programming with \u27 O\u27(\u27n\u273) 0-1 variables and \u27 O\u27(\u27n\u272) constraints. I also introduce some classes of valid inequalities. To obtain the exact solutions, Branch-and-Bound, Cut-and-Branch, Branch-and-Cut algorithms are used. For larger size problems, some heuristic procedures are proposed and the computational results are compared

Using sensor ontologies to create reasoning-ready sensor data for real-time hazard monitoring in a spatial decision support system

In order to protect at-risk communities and critical infrastructure, hazard managers use sensor networks to monitor the landscapes and phenomena associated with potential hazards. This strategy can produce large amounts of data, but when investigating an often unstructured problem such as hazard detection it can be beneficial to apply automated analysis routines and artificial intelligence techniques such as reasoning. Current sensor web infrastructure, however, is not designed to support this information-centric monitoring perspective. A generalized methodology to transform typical sensor data representations into a form that enables these analysis techniques has been created and is demonstrated through an implementation that bridges geospatial standards for sensor data and descriptions with an ontology-based monitoring environment. An ontology that describes sensors and measurements so they may be understood by an SDSS has also been developed. These tools have been integrated into a monitoring environment, allowing the hazard manager to thoroughly investigate potential hazards

Reconfigurable middleware architectures for large scale sensor networks

Wireless sensor networks, in an effort to be energy efficient, typically lack the high-level abstractions of advanced programming languages. Though strong, the dichotomy between these two paradigms can be overcome. The SENSIX software framework, described in this dissertation, uniquely integrates constraint-dominated wireless sensor networks with the flexibility of object-oriented programming models, without violating the principles of either. Though these two computing paradigms are contradictory in many ways, SENSIX bridges them to yield a dynamic middleware abstraction unifying low-level resource-aware task reconfiguration and high-level object recomposition. Through the layered approach of SENSIX, the software developer creates a domain-specific sensing architecture by defining a customized task specification and utilizing object inheritance. In addition, SENSIX performs better at large scales (on the order of 1000 nodes or more) than other sensor network middleware which do not include such unified facilities for vertical integration