A Reinforcement-Learning Approach to Power Management

We describe an adaptive, mid-level approach to the wireless device power management problem. Our approach is based on reinforcement learning, a machine learning framework for autonomous agents. We describe how our framework can be applied to the power management problem in both infrastructure and ad~hoc wireless networks. From this thesis we conclude that mid-level power management policies can outperform low-level policies and are more convenient to implement than high-level policies. We also conclude that power management policies need to adapt to the user and network, and that a mid-level power management framework based on reinforcement learning fulfills these requirements.

A HOLISTIC APPROACH TO MANUFACTURING SYSTEM DESIGN IN THE DEFENSE AEROSPACE INDUSTRY

Manufacturing has evolved to become a critical element of the competitive skill set of defense aerospace firms. Given the changes in the acquisition environment and culture; traditional “thrown over the wall” means of developing and manufacturing products are insufficient. Also, manufacturing systems are complex systems that need to be carefully designed in a holistic manner and there are shortcomings with available tools and methods to assist in the design of these systems. This paper outlines the generation and validation of a framework to guide this manufacturing system design process.

Context Interchange as a Scalable Solution to Interoperating Amongst Heterogeneous Dynamic Services

Many online services access a large number of autonomous data sources and at the same time need to meet different user requirements. It is essential for these services to achieve semantic interoperability among these information exchange entities. In the presence of an increasing number of proprietary business processes, heterogeneous data standards, and diverse user requirements, it is critical that the services are implemented using adaptable, extensible, and scalable technology. The COntext INterchange (COIN) approach, inspired by similar goals of the Semantic Web, provides a robust solution. In this paper, we describe how COIN can be used to implement dynamic online services where semantic differences are reconciled on the fly. We show that COIN is flexible and scalable by comparing it with several conventional approaches. With a given ontology, the number of conversions in COIN is quadratic to the semantic aspect that has the largest number of distinctions. These semantic aspects are modeled as modifiers in a conceptual ontology; in most cases the number of conversions is linear with the number of modifiers, which is significantly smaller than traditional hard-wiring middleware approach where the number of conversion programs is quadratic to the number of sources and data receivers. In the example scenario in the paper, the COIN approach needs only 5 conversions to be defined while traditional approaches require 20,000 to 100 million. COIN achieves this scalability by automatically composing all the comprehensive conversions from a small number of declaratively defined sub-conversions.

A Holistic Approach to Manufacturing System Design in the Defense Aerospace Industry

Manufacturing has evolved to become a critical element of the competitive skill set of defense aerospace firms. Given the changes in the acquisition environment and culture; traditional “thrown over the wall” means of developing and manufacturing products are insufficient. Also, manufacturing systems are complex systems that need to be carefully designed in a holistic manner and there are shortcomings with available tools and methods to assist in the design of these systems. This paper outlines the generation and validation of a framework to guide this manufacturing system design process.