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

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

A Motif-based Mission Planning Method for UAV Swarms Considering Dynamic Reconfiguration

Influenced by complex terrain conditions of combat environments and constrained by the level of communication technology, communication among unmanned aerial vehicles (UAV) is greatly restricted. In light of this situation, mission planning for UAV swarms under limited communication has become a difficult problem. This paper introduces motifs as the basic unit of configuration and proposes a motif-based mission planning method considering dynamic reconfiguration. This method uses multidimensional dynamic list scheduling algorithm to generate a mission planning scheme based on the motif-based swarm configuration solution. Then it incorporates order preserved operators with NSGA-III algorithm to find Pareto front solutions of all possible mission planning schemes. The feasibility of this mission planning method is validated through a case study

A case for an international consortium on system-of-systems engineering

A system-of-systems (SoS) conceptualization is essential in resolving issues involving heterogeneous independently operable systems to achieve a unique purpose. Successful operation as an SoS requires communication among appropriate individuals and groups across enterprises through an effective protocol. This paper presents a position on the creation of a consortium of concerned system engineers and scientists worldwide to examine the problems and solutions strategies associated with SoS. The consortium could lead efforts in clarifying ambiguities and in seeking remedies to numerous open questions with respect to SoS analysis, SoS engineering (SoSE), as well as differences between systems engineering (SE) and SoSE. The mission of this consortium is envisioned to: 1) act as a neutral party; 2) provide a forum to put forth Calls to Action; and 3) establish a community of interest to recommend a set of solutions. <br /

Flexible and Intelligent Learning Architectures for SOS (FILA-SoS)

Multi-faceted systems of the future will entail complex logic and reasoning with many levels of reasoning in intricate arrangement. The organization of these systems involves a web of connections and demonstrates self-driven adaptability. They are designed for autonomy and may exhibit emergent behavior that can be visualized. Our quest continues to handle complexities, design and operate these systems. The challenge in Complex Adaptive Systems design is to design an organized complexity that will allow a system to achieve its goals. This report attempts to push the boundaries of research in complexity, by identifying challenges and opportunities. Complex adaptive system-of-systems (CASoS) approach is developed to handle this huge uncertainty in socio-technical systems

Developing Executable Digital Models with Model-Based Systems Engineering – An Unmanned Aerial Vehicle Surveillance Scenario Example

There is an increase in complexity in modern systems that causes inconsistencies in the iterative exchange loops of the system design process and in turn, demands greater quality of system organization and optimization techniques. A recent transition from document-centric systems engineering to Model-Based Systems Engineering (MBSE) is being documented in literature from various industries to address these issues. This study aims to investigate how MBSE can be used as a starting point in developing digital twins (DT). Specifically, the adoption of MBSE for realizing DT has been investigated, resulting in various literature reviews that indicate the most prevalent methodologies and tools used to enhance and validate existing and future systems. An MBSE-enabled template for virtual model development was executed for the creation of executable models, which can serve as a research testbed for DT and system and system-of-systems optimization. This study explores the feasibility of this MBSE-enabled template by creating and simulating a surveillance system that monitors and reports on the health status and performance of an armored fighting vehicle via an Unmanned Aerial Vehicle (UAV). The objective of this template is to demonstrate how executable SysML diagrams are used to establish a collaborative working environment between multiple platforms to better convey system behavior, modifications, and analytics for various system stakeholders

Application of computational intelligence to explore and analyze system architecture and design alternatives

Systems Engineering involves the development or improvement of a system or process from effective need to a final value-added solution. Rapid advances in technology have led to development of sophisticated and complex sensor-enabled, remote, and highly networked cyber-technical systems. These complex modern systems present several challenges for systems engineers including: increased complexity associated with integration and emergent behavior, multiple and competing design metrics, and an expansive design parameter solution space. This research extends the existing knowledge base on multi-objective system design through the creation of a framework to explore and analyze system design alternatives employing computational intelligence. The first research contribution is a hybrid fuzzy-EA model that facilitates the exploration and analysis of possible SoS configurations. The second contribution is a hybrid neural network-EA in which the EA explores, analyzes, and evolves the neural network architecture and weights. The third contribution is a multi-objective EA that examines potential installation (i.e. system) infrastructure repair strategies. The final contribution is the introduction of a hierarchical multi-objective evolutionary algorithm (MOEA) framework with a feedback mechanism to evolve and simultaneously evaluate competing subsystem and system level performance objectives. Systems architects and engineers can utilize the frameworks and approaches developed in this research to more efficiently explore and analyze complex system design alternatives --Abstract, page iv

Building Enterprise Transition Plans Through the Development of Collapsing Design Structure Matrices

The United States Air Force (USAF), like many other large enterprises, has evolved over time, expanded its capabilities and has developed focused, yet often redundant, operational silos, functions and information systems (IS). Recent failures in enterprise integration efforts herald a need for a new method that can account for the challenges presented by decades of increases in enterprise complexity, redundancy and Operations and Maintenance (O&M) costs. Product or system-level research has dominated the study of traditional Design Structure Matrices (DSMs) with minimal coverage on enterprise-level issues. This research proposes a new method of collapsing DSMs (C-DSMs) to illustrate and mitigate the problem of enterprise IS redundancy while developing a systems integration plan. Through the use of iterative user constraints and controls, the C-DSM method employs an algorithmic and unbiased approach that automates the creation of a systems integration plan that provides not only a roadmap for complexity reduction, but also cost estimates for milestone evaluation. Inspired by a recent large IS integration program, an example C-DSM of 100 interrelated legacy systems was created. The C-DSM method indicates that if a slow path to integration is selected then cost savings are estimated to surpass integration costs after several iterations

CINELDI Annual Report 2020

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