A Computational Intelligence Approach to System-of-Systems Architecting Incorporating Multi-Objective Optimization

A computational intelligence approach to system-of-systems architecting is developed using multi-objective optimization. Such an approach yields a set of optimal solutions (the Pareto set) which has both advantages and disadvantages. The primary benefit is that a set of solutions provides a picture of the optimal solution space that a single solution cannot. The primary difficulty is making use of a potentially infinite set of solutions. Therefore, a significant part of this approach is the development of a method to model the solution set with a finite number of points allowing the architect to intelligently choose a subset of optimal solutions based on criteria outside of the given objectives. The approach developed incorporates a meta-architecture, multi-objective genetic algorithm, and a corner search to identify points useful for modeling the solution space. This approach is then applied to a network centric warfare problem seeking the optimum selection of twenty systems. Finally, using the same problem, it is compared to a hybrid approach using single-objective optimization with a fuzzy logic assessor to demonstrate the advantage of multi-objective optimization

Establishing Rules for Self-Organizing Systems-Of-Systems

Self-organizing systems-of-systems offer the possibility of autonomously adapting to new circumstances and tasking. This could significantly benefit large endeavors such as smart cities and national defense by increasing the probability that new situations are expediently handled. Complex self-organizing behaviors can be produced by a large set of individual agents all following the same simple set of rules. While biological rule sets have application in achieving human goals, other rules sets may be necessary as these goals are not necessarily mirrored in nature. To this end, a set of system, rather than biologically, inspired rules is introduced and an agent-based model is used to simulate and analyze the behavior produced with various parameters. Agents represent systems and their decisions are defined by the given rule set and parameters. The environment provides a variety of time-critical missions on an ongoing basis. The effectiveness of a particular rule or set of rules is measured by a set of key performance metrics such as the rate at which missions achieve their required capabilities within a given deadline and the average time required to do so. Different rules will be compared using this criterion along with an assessment of their ability to demonstrate beneficial self-organizing behavior

A Hierarchial Neural Network Implementation for Forecasting

In this paper, a hierarchical neural network architecture for forecasting time series is presented. The architecture is composed of two hierarchical levels using a maximum likelihood competitive learning algorithm. The first level of the system has three experts each using backpropagation and a gating network to partition the input space in order to map the input vectors to the output vectors. The second level of the hierarchical network has an expert using fuzzy ART for producing the correct trend coming from the first level. The experiments show that the resulting network is capable of forecasting the changes in the input and identifying the trends correctl

Implementing an Architectural Framework to Define and Deliver Net-Centric Capability to Legacy Military Air Assets Operating within a System of Systems

The United States Air Force (USAF) is implementing an integrated net-centric system of systems for airborne operations in support of the global war on terror (GWOT). The GWOT demands that a successful architecture framework transforms and delivers net-centric assets to the war-fighter in a timely manner. A critical component of this implementation is the transformation of legacy strategic air platforms into net-centric air power assets operating within a system of systems. The System Architectural (SA) framework, and the Department of Defense Architectural Framework (DoDAF) are ways of managing complexity and organizing information within a system of systems network. This paper will explore and compare these architectural frameworks; show examples used in a system of systems network; and illustrate how the DoDAF can successfully define the transformation of a legacy weapon system into a net-centric asset

Genetically modified animals for use in research and biotechnology

Transgenic animals are used extensively in the study of in vivo gene function, as models for human diseases and in the production of biopharmaceuticals. The technology behind obtaining these animals involves molecular biology techniques, cell culture and embryo manipulation; the mouse is the species most widely used as an experimental model. In scientific research, diverse models are available as tools for the elucidation of gene function, such as transgenic animals, knockout and conditional knockout animals, knock-in animals, humanized animals, and knockdown animals. We examined the evolution of the science for the development of these animals, as well as the techniques currently used in obtaining these animal models. We review the phenotypic techniques used for elucidation of alterations caused by genetic modification. We also investigated the role of genetically modified animals in the biotechnology industry, where they promise a revolution in obtaining heterologous proteins through natural secretions, such as milk, increasing the scale of production and facilitating purification, thereby lowering the cost of production of hormones, growth factors and enzyme

Electro-Optic and Electro-absorption characterization of InAs quantum dot waveguides

Cataloged from PDF version of article.Abstract Optical properties of multilayer InAs quantum dot waveguides, grown by molecular beam epitaxy, have been studied under applied electric field. Fabry-Perot measurements at 1515 nm on InAs/GaAs quantum dot structures yield a significantly enhanced linear electro-optic efficiency compared to bulk GaAs. Electro-absorption measurements at 1300 nm showed increased absorption with applied field accompanied with red shift of the spectra. Spectral shifts of up to 21% under 18 Volt bias was observed at 1320 nm. (C) 2008 Optical Society of America

Understanding System of Systems Development Using an Agent- Based Wave Model

System of Systems (SoS) development is a complex process that depends on the cooperation of various independent Systems[1]. SoS acquisition and development differs from that typical for a single System; it has been shown to follow a wave paradigm known as the Wave Model[2]. Agent based models (ABMs) consist of a set of abstracted entities referred to as agents, and a framework using simplified rules for simulating agent decisions and interactions. Agents have their own goals and are capable of perceiving changes in the environment. Systemic (global) behavior emerges from the decisions and interactions of the agents. This research provides a generic model of SoS development with a genetic algorithm and fuzzy assessor implemented in an agent based model. The generic SoS development follows the Wave Model. The genetic algorithm provides an initial SoS meta- architecture. The fuzzy assessor qualitatively evaluates SoS meta-architectures. The agent-based model implements the generic SoS development, the genetic algorithm, the fuzzy assessor, and independent SoS and system agents and shows the SoS development based on an initial set of conditions. A prototype model is developed to test the concept on a sample from the DoD Intelligence, Surveillance, and Reconnaissance (ISR) domain

A Fuzzy Evaluation Method for System of Systems Meta-architectures

A method is proposed for evaluating a range of System of Systems (SoS) meta-architecture alternatives. SoS are composed through combination of existing, fully functioning Systems, possibly with minor functional changes, but certainly by using the combined Systems to achieve a new capability, not available from the Systems alone. The meta-architecture describes how all possible subsets of Systems can be combined to create an SoS. The fitness of a realizable SoS architecture may be characterized by terms such as unacceptable, marginal, above average, or excellent. While these terms provide little information about the SoS when used alone and informally, they readily fit into fuzzy membership sets that overlap at their boundaries. More descriptive attributes such as “ease of use,” which might depend on individual user and a set of conditions, “mission effectiveness” over a particular suite of missions, and “affordability,” which may change over time with changing business climate, etc., lend themselves readily to fuzzy evaluation as well. An approach to defining the fuzzy concepts and establishing rule sets to provide an overall SoS evaluation for many sets of participating individual Systems represented by the meta-architecture is discussed. An application of the method is discussed within the framework of developing and evaluating a hypothetical Intelligence, Surveillance and Reconnaissance (ISR) SoS capability