Includes supplemental material / Hostile acts of maritime piracy and terrorism have increased worldwide in recent years, and the global impacts of a successful attack on commercial shipping in the Straits of Malacca make it one of the most tempting target locations for maritime terrorism. In an attempt to develop a system of systems to defeat and prevent terrorism in the Straits of Malacca, this study developed three significant commercial shipping attack scenarios (Weapons of Mass Destruction (WMD) shipment, Ship As a Weapon (SAW), and Small Boat Attack (SBA)), and used a Systems Engineering Design Process (SEDP) to design alternative architectures that offered promising ways to defeat these attacks. Maritime Domain Protection (MDP) architecture alternatives combined five separate systems: a Land Inspection System, a Sensor System, a Command and Control, Communications, and Intelligence (C3I) System, a Response Force System, and a Sea Inspection System. Individual models for each system were developed and combined into overarching integrated architecture models to evaluate overall performance. The study results showed that solutions tended to be threat-specific, and current capabilities were mixed. While solutions were found to effectively reduce risk in all threat scenarios, these sometimes came at great expense. Alternatively, cost-effective solutions were also found for each scenario, but these sometimes gave limited performance.

The Energy Independence and Security Act of 2007 established that the current electric grid was inadequate to serve the United States needs. Congress mandated that the U.S. transition to a more intelligent grid for the future. The Department of Energy was tasked with making this goal a reality. Six years later in 2013, only marginal progress has been made. Outside of smart meter rollouts and pilots programs funded through the American Recovery and Reinvestment Act of 2009 (ARRA), many issues still need to be addressed in order to realize the U.S. Smart Grid vision. Most of the barriers to progress are not technological; the research and business community are rising to the occasion and meeting the challenge through innovation. However, policy issues present a large barrier to overcome. With issues ranging from vague Smart Grids goals issued by the Department of Energy to a general lack of consumer knowledge about the Smart Grid. This paper seeks to identify the gaps in the current electric grid and policy schema are inadequate and suggest recommendations to encourage and expedite the growth of the U.S. Smart Grid.

The total electricity consumption of plugged-in electric loads (PELs) currently accounts for more usage than any other single end-use service in residential and commercial buildings. Compared with other categories of electric loads, PELs possess significant potential to be efficiently controlled and managed in buildings. Therefore, accurate and reliable PEL identification methods that are used to collect identity and performance information are desired for many purposes. However, few existing electric load identification methods are designed for PELs to handle unique challenges such as the diversity within each type of PEL and similarity between different types of PELs equipped by similar front-end power supply units. The objective of this dissertation is to develop non-intrusive, accurate, robust, and applicable PEL identification algorithms utilizing voltage and current measurements. Based on the literature review of almost all existing features that describe electric loads and five types of existing methods for electric load identification, a two-level framework for PELs classification and identification is proposed. First, the supervised self-organizing map (SSOM) is adopted to classify a large number of PELs of different models and brands into several groups by their inherent similarities. Therefore, PELs with similar front-end power supply units or characteristics fall into the same group. The partitioned groups are verified by their power supply unit topology. That is, different groups should have different topologies. This dissertation proposes a novel combination of the SSOM framework and the Bayesian framework. Such a hybrid identifier can provide the probability of an unknown PEL belonging to a specific type of load. Within each classified group by the SSOM, both static and dynamic methods are proposed to distinguish PELs with similar characteristics. Static methods extract steady-state features from the voltage and current waveforms to train different computational intelligence algorithms such as the SSOM itself and the support vector machine (SVM). An unknown PEL is then presented to the trained algorithm for identification. In contrast to static methods, dynamic methods take into consideration the dynamics of long-term (minutes instead of milliseconds) waveforms of PELs and extract elements such as spikes, oscillations, steady-state operations, as well as similarly repeated patterns.

Shape memory materials have been known for many years to possess the unique ability of memorising their shape at some temperature. If these materials are pre-strained into the plastic range, they tend to recover their original un-strained shapes via phase transformation when subjected to heat stimulation. In recent years, this shape memory effect (SME) or strain recovery capability has been explored in aerospace structures for actuating the real-time movement of structural components. Among all the shape memory materials, the nickel-titanium based shape memory alloy (SMA) has by far received the most attention because of its high recovery capabilities. Since SMAs are usually drawn into the form of wires, they are particularly suitable for being integrated into fibre-reinforced composite structures. These integrated composite structures with SMA wires are thus called smart adaptive structures. To achieve the SME, these wires are normally embedded in the host composite structures. In returning to their unstrained shape upon heat application, they tend to exert internal stresses on the host composite structures in which they are embedded. This action could result in a controlled change in shape of the structural components. Although there has been a significant amount of research dedicated to characterising and modelling the SME of SMA wires, little experimental work had been done to offer an in-depth understanding of the mechanical behaviour of these smart adaptive polymeric composite structures. This project examined the deflection and shape change of carbon/epoxy and glass/epoxy cantilever beams through heating and cooling of internal nitinol SMA wires/strips. The heat damage mechanism and cyclic behaviour are major factors in the operation of such a system and need to be clearly understood in order to develop and gain confidence for the possible implementation of future smart actuating systems. Therefore, the objectives of the proposed research were to investigate (i) effect of embedding SMA, wires on mechanical properties of host composite, (ii) assessment of single-cycle and multiple-cycle actuation performance of smart beams, and (iii) thermal effects of excessive heat on the surrounding composite matrix.

Thesis (M.Eng.Sc.)--University of Adelaide, School of Mechanical Engineering, 2005. / Includes bibliographical references (leaves 131-137). Also available electronically as part of the Australian Digital Theses Program.

Thesis (M.Eng.Sc.)--University of Adelaide, School of Mechanical Engineering, 2005. / Title from screen page; viewed 16 Aug. 2005. Includes bibliographical references (leaves 131-137). Also available in print format.

Thesis (M.Eng.)--University of Wollongong, 2006. / Typescript. Includes bibliographical references: leaf 101-106.

Thesis (M.B.A.)--University of Hong Kong, 1996. / Includes bibliographical references (leaf 96-97).

This paper discusses the average energy savings of various smart devices in connection with their average price. By calculating the devices' payback times, a ranking of the tools can be given. The whole study focuses on the average household within the EU-28 in terms of climate as well as in terms of user behaviour. The purpose of the research was to provide a win-win situation for users' wallets and the environment by showing the device which suits both players best. As a result of the research, it was found that the greatest reduction in energy consumption can be reached by an interaction of the smart device and the inhabitants of a smart home. By giving users feedback on their energy consumption through smart meters, average savings of 7.5% are reached. As a smart meter is available for about Euro 80, it has a payback time of only 4.24 months.

abstract: Buildings (approximately half commercial and half residential) consume over 70% of the electricity among all the consumption units in the United States. Buildings are also responsible for approximately 40% of CO2 emissions, which is more than any other industry sectors. As a result, the initiative smart building which aims to not only manage electrical consumption in an efficient way but also reduce the damaging effect of greenhouse gases on the environment has been launched. Another important technology being promoted by government agencies is the smart grid which manages energy usage across a wide range of buildings in an effort to reduce cost and increase reliability and transparency. As a great amount of efforts have been devoted to these two initiatives by either exploring the smart grid designs or developing technologies for smart buildings, the research studying how the smart buildings and smart grid coordinate thus more efficiently use the energy is currently lacking. In this dissertation, a "system-of-system" approach is employed to develop an integrated building model which consists a number of buildings (building cluster) interacting with smart grid. The buildings can function as both energy consumption unit as well as energy generation/storage unit. Memetic Algorithm (MA) and Particle Swarm Optimization (PSO) based decision framework are developed for building operation decisions. In addition, Particle Filter (PF) is explored as a mean for fusing online sensor and meter data so adaptive decision could be made in responding to dynamic environment. The dissertation is divided into three inter-connected research components. First, an integrated building energy model including building consumption, storage, generation sub-systems for the building cluster is developed. Then a bi-level Memetic Algorithm (MA) based decentralized decision framework is developed to identify the Pareto optimal operation strategies for the building cluster. The Pareto solutions not only enable multiple dimensional tradeoff analysis, but also provide valuable insight for determining pricing mechanisms and power grid capacity. Secondly, a multi-objective PSO based decision framework is developed to reduce the computational effort of the MA based decision framework without scarifying accuracy. With the improved performance, the decision time scale could be refined to make it capable for hourly operation decisions. Finally, by integrating the multi-objective PSO based decision framework with PF, an adaptive framework is developed for adaptive operation decisions for smart building cluster. The adaptive framework not only enables me to develop a high fidelity decision model but also enables the building cluster to respond to the dynamics and uncertainties inherent in the system. / Dissertation/Thesis / Ph.D. Industrial Engineering 2012