Brake system performance requirements of a lightweight electric/hybrid rear wheel drive vehicle

Investigates the braking performance requirements of the UltraCommuter, a lightweight series hybrid electric vehicle currently under development at the University of Queensland. With a predicted vehicle mass of 600 kg and two in-wheel motors each capable of 500 Nm of peak torque, decelerations up to 0.46 g are theoretically possible using purely regenerative braking. With 99% of braking demands less than 0.35 g, essentially all braking can be regenerative. The wheel motors have sufficient peak torque capability to lock the rear wheels in combination with front axle braking, eliminating the need for friction braking at the rear. Emergency braking levels approaching 1 g are achieved by supplementation with front disk brakes. This paper presents equations describing the peak front and rear axle braking forces which occur under straight line braking, including gradients. Conventionally, to guarantee stability, mechanical front/rear proportioning of braking effort ensures that the front axle locks first. In this application, all braking is initially regenerative at the rear, and an adaptive ''by-wire'' proportioning system presented ensures this stability requirement is still satisfied. Front wheel drive and all wheel drive systems are also discussed. Finally, peak and continuous performance measures, not commonly provided for friction brakes, are derived for the UltraCommuter's motor capability and range of operation.

Migrating to a sustainable energy system : distributed generation and storage, fuel cells and hypercars

This paper examines a number of issues in sustainable energy generation and distribution, and explores avenues that are available for integration of our society’s energy supplies. In particular, the paper presents a way in which transport vehicle energy supplies could be integrated with distributed generation schemes to achieve synergistic and beneficial outcomes. The worldwide energy system contains fundamental problems that result directly from the use of unsustainable fuels and a lack of energy system integration. There is a need to adopt an integrated, sustainable energy system for our society. The adoption of distributed generation could result in beneficial restructuring of the energy trade, and a change in the role of energy providers. Inherent benefits in distributed generation schemes would directly combat barriers to installation of renewable generation facilities, which might prove distributed renewable energy sources to be more feasible. The presence of fuel cells, batteries, power electronic inverters and intelligent controls in vehicles of the future provides many opportunities for the integration of vehicle energy supplies into a distributed generation scheme. In such a system, vehicles could play a major role in power generation and storage.

Migrating to a sustainable energy system : distributed generation and storage, fuel cells and hypercars

This paper examines a number of issues in sustainable energy generation and distribution, and explores avenues that are available for integration of our society’s energy supplies. In particular, the paper presents a way in which transport vehicle energy supplies could be integrated with distributed generation schemes to achieve synergistic and beneficial outcomes. The worldwide energy system contains fundamental problems that result directly from the use of unsustainable fuels and a lack of energy system integration. There is a need to adopt an integrated, sustainable energy system for our society. The adoption of distributed generation could result in beneficial restructuring of the energy trade, and a change in the role of energy providers. Inherent benefits in distributed generation schemes would directly combat barriers to installation of renewable generation facilities, which might prove distributed renewable energy sources to be more feasible. The presence of fuel cells, batteries, power electronic inverters and intelligent controls in vehicles of the future provides many opportunities for the integration of vehicle energy supplies into a distributed generation scheme. In such a system, vehicles could play a major role in power generation and storage.

Experiments with cooperative control of underwater robots

In this paper we describe cooperative control algorithms for robots and sensor nodes in an underwater environment. Cooperative navigation is defined as the ability of a coupled system of autonomous robots to pool their resources to achieve long-distance navigation and a larger controllability space. Other types of useful cooperation in underwater environments include: exchange of information such as data download and retasking; cooperative localization and tracking; and physical connection (docking) for tasks such as deployment of underwater sensor networks, collection of nodes and rescue of damaged robots. We present experimental results obtained with an underwater system that consists of two very different robots and a number of sensor network modules. We present the hardware and software architecture of this underwater system. We then describe various interactions between the robots and sensor nodes and between the two robots, including cooperative navigation. Finally, we describe our experiments with this underwater system and present data.

Robust vision-based underwater homing using self-similar landmarks

Next-generation autonomous underwater vehicles (AUVs) will be required to robustly identify underwater targets for tasks such as inspection, localization, and docking. Given their often unstructured operating environments, vision offers enormous potential in underwater navigation over more traditional methods; however, reliable target segmentation often plagues these systems. This paper addresses robust vision-based target recognition by presenting a novel scale and rotationally invariant target design and recognition routine based on self-similar landmarks that enables robust target pose estimation with respect to a single camera. These algorithms are applied to an AUV with controllers developed for vision-based docking with the target. Experimental results show that the system performs exceptionally on limited processing power and demonstrates how the combined vision and controller system enables robust target identification and docking in a variety of operating conditions.

An activity and resource advisory system for manufacturing process chains selection at the early stage of product development

Designers need to consider both the functional and production process requirements at the early stage of product development. A variety of the research works found in the literature has been proposed to assist designers in selecting the most viable manufacturing process chain. However, they do not provide any assistance for designers to evaluate the processes according to the particular circumstances of their company. This paper describes a framework of an Activity and Resource Advisory System (ARAS) that generates advice about the required activities and the possible resources for various manufacturing process chains. The system provides more insight, more flexibility, and a more holistic and suitable approach for designers to evaluate and then select the most viable manufacturing process chain at the early stage of product development.

A people-to-people matching system using graph mining techniques

A people-to-people matching system (or a match-making system) refers to a system in which users join with the objective of meeting other users with the common need. Some real-world examples of these systems are employer-employee (in job search networks), mentor-student (in university social networks), consume-to-consumer (in marketplaces) and male-female (in an online dating network). The network underlying in these systems consists of two groups of users, and the relationships between users need to be captured for developing an efficient match-making system. Most of the existing studies utilize information either about each of the users in isolation or their interaction separately, and develop recommender systems using the one form of information only. It is imperative to understand the linkages among the users in the network and use them in developing a match-making system. This study utilizes several social network analysis methods such as graph theory, small world phenomenon, centrality analysis, density analysis to gain insight into the entities and their relationships present in this network. This paper also proposes a new type of graph called “attributed bipartite graph”. By using these analyses and the proposed type of graph, an efficient hybrid recommender system is developed which generates recommendation for new users as well as shows improvement in accuracy over the baseline methods.

Precedent-free fault isolation in a diesel engine EGR valve system

In this paper, a framework for isolating unprecedented faults for an EGR valve system is presented. Using normal behavior data generated by a high fidelity engine simulation, the recently introduced Growing Structure Multiple Model System (GSMMS) is used to construct models of normal behavior for an EGR valve system and its various subsystems. Using the GSMMS models as a foundation, anomalous behavior of the entire system is then detected as statistically significant departures of the most recent modeling residuals from the modeling residuals during normal behavior. By reconnecting anomaly detectors to the constituent subsystems, the anomaly can be isolated without the need for prior training using faulty data. Furthermore, faults that were previously encountered (and modeled) are recognized using the same approach as the anomaly detectors.

Precedent-free fault isolation in a diesel engine exhaust gas recirculation system

In this paper, a recently introduced model-based method for precedent-free fault detection and isolation (FDI) is modified to deal with multiple input, multiple output (MIMO) systems and is applied to an automotive engine with exhaust gas recirculation (EGR) system. Using normal behavior data generated by a high fidelity engine simulation, the growing structure multiple model system (GSMMS) approach is used to construct dynamic models of normal behavior for the EGR system and its constituent subsystems. Using the GSMMS models as a foundation, anomalous behavior is detected whenever statistically significant departures of the most recent modeling residuals away from the modeling residuals displayed during normal behavior are observed. By reconnecting the anomaly detectors (ADs) to the constituent subsystems, EGR valve, cooler, and valve controller faults are isolated without the need for prior training using data corresponding to particular faulty system behaviors.

Growth and properties of a multilayer system based on Y1Ba 2Cu3Ox and amorphous Y-ZrO2

The growth of c-axis oriented Y1Ba2Cu 3Ox thin films on an amorphous buffer layer of Y-ZrO 2, deposited on sapphire substrates, was investigated. Both films were grown by a pulsed laser deposition technique. A strong correlation was observed between the properties of Y1Ba2Cu 3Ox and the thickness of the buffer layer. A Tc of 89 K was obtained for an optimal buffer layer thickness of 9 nm. A model that adequately describes the film growth process was developed. A multilayer system of Y1Ba2Cu3Ox and amorphous Y-ZrO2 was grown and a Tc of 87 K for the upper c-axis oriented layer was measured.

A social matching system : using implicit and explicit information for personalized recommendation in online dating service

Online dating websites enable a specific form of social networking and their efficiency can be increased by supporting proactive recommendations based on participants' preferences with the use of data mining. This research develops two-way recommendation methods for people-to-people recommendation for large online social networks such as online dating networks. This research discovers the characteristics of the online dating networks and utilises these characteristics in developing efficient people-to-people recommendation methods. Methods developed support improved recommendation accuracy, can handle data sparsity that often comes with large data sets and are scalable for handling online networks with a large number of users.

Localization of Mineralocorticoid Receptors at Mammalian Synapses

In the brain, membrane associated nongenomic steroid receptors can induce fast-acting responses to ion conductance and second messenger systems of neurons. Emerging data suggest that membrane associated glucocorticoid and mineralocorticoid receptors may directly regulate synaptic excitability during times of stress when adrenal hormones are elevated. As the key neuron signaling interface, the synapse is involved in learning and memory, including traumatic memories during times of stress. The lateral amygdala is a key site for synaptic plasticity underlying conditioned fear, which can both trigger and be coincident with the stress response. A large body of electrophysiological data shows rapid regulation of neuronal excitability by steroid hormone receptors. Despite the importance of these receptors, to date, only the glucocorticoid receptor has been anatomically localized to the membrane. We investigated the subcellular sites of mineralocorticoid receptors in the lateral amygdala of the Sprague-Dawley rat. Immunoblot analysis revealed the presence of mineralocorticoid receptors in the amygdala. Using electron microscopy, we found mineralocorticoid receptors expressed at both nuclear including: glutamatergic and GABAergic neurons and extra nuclear sites including: presynaptic terminals, neuronal dendrites, and dendritic spines. Importantly we also observed mineralocorticoid receptors at postsynaptic membrane densities of excitatory synapses. These data provide direct anatomical evidence supporting the concept that, at some synapses, synaptic transmission is regulated by mineralocorticoid receptors. Thus part of the stress signaling response in the brain is a direct modulation of the synapse itself by adrenal steroids.

Smart grid-demand side response model to mitigate prices and peak impact on the electrical system

The aims of this project is to develop demand side response model which assists electricity consumers who are exposed to the market price through aggregator to manage the air-conditioning peak electricity demand. The main contribution of this research is to show how consumers can optimise the energy cost caused by the air-conditioning load considering the electricity market price and network overload. The model is tested with selected characteristics of the room, Queensland electricity market data from Australian Energy Market Operator and data from the Bureau of Statistics on temperatures in Brisbane, during weekdays on hot days from 2011 - 2012.

Power system stability enhancement using flux control for excitation system

This paper proposes a new controller for the excitation system to improve rotor angle stability. The proposed controller uses energy function to predict desired flux for the generator to achieve improved first swing stability and enhanced system damping. The controller is designed through predicting the desired value of flux for the future step of the system and then obtaining appropriate supplementary control input for the excitation system. The simulations are performed on Single-Machine-Infinite-Bus system and the results verify the efficiency of the controller. The proposed method facilitates the excitation system with a feasible and reliable controller for severe disturbances.

The structured and practical approach in development of decision support system for consultant selection in public sector infrastructure project

Nowadays, most of the infrastructure development projects undertaken are complex in nature. Practically, public clients who do not have a good understanding of the design and management may suffer severe losses, especially for infrastructure projects. There is a need for luring the right consultant to secure client's investment in infrastructure developments. Throughout the project life cycle, consultants play vital role from the inception to completion stage of a project. A few studies in Malaysia show that infrastructure projects involving irrigation and drainage have experience problems such as poor workmanship, delay and cost overrun due to the consultant's inability or the client incompetence of recruiting consultants in time. This highlights the need of aided decision making and an efficient system to select the best consultant by using Decision Support System (DSS). On the other hand, recent trends reveal that most DSS in construction only concentrate on decision model development. These models are impractical and unused as they are complicated or difficult for laymen such as project managers to utilize. Thus, this research attempts to develop an efficient DSS for consultant selection namely consultDeSS. Driven by the motivation and research aims, this study deployed Design Science Research Methodology (DSRM) dominant with a combination of case studies at the Malaysian Department of Irrigation and Drainage (DID). Two real projects involving irrigation and drainage infrastructure were used to design, implement and evaluate the artefact. The 3-tier consultDeSS was revised after the evaluation and the design was significantly improved based on user feedback. By developing desirable tools that fit client's needs will enhance the productivity and minimize conflict within groups and organisations. The tool is more usable and efficient compared to previous studies in construction. Thus, this research has demonstrated a purposeful artefact with a practical and valid structured development approach that is applicable in a variety of problems in construction discipline.