Demand response (DR) is one of the integral mechanisms of today's smart grids. It enables consumer energy assets such as flexible loads, standby generators and storage systems to add value to the grid by providing cost-effective flexibility. With increasing renewable generation and impending electric vehicle deployment, there is a critical need for large volumes of reliable and responsive flexibility through DR. This poses a new challenge for the electricity sector. Smart grid development has resulted in the availability of large amounts of data from different physical segments of the grid such as generation, transmission, distribution and consumption. For instance, smart meter data carrying valuable information is increasingly available from the consumers. Parallel to this, the domain of data analytics and machine learning (ML) is making immense progress. Data-driven modelling based on ML algorithms offers new opportunities to utilise the smart grid data and address the DR challenge. The thesis demonstrates the use of data-driven models for enhancing DR from large consumers such as commercial and industrial (C&I) buildings. A reliable, computationally efficient, cost-effective and deployable data-driven model is developed for large consumer building load estimation. The selection of data pre-processing and model development methods are guided by these design criteria. Based on this model, DR operational tasks such as capacity scheduling, performance evaluation and reliable operation are demonstrated for consumer energy assets such as flexible loads, standby generators and storage systems. Case studies are designed based on the frameworks of ongoing DR programs in different electricity markets. In these contexts, data-driven modelling shows substantial improvement over the conventional models and promises more automation in DR operations. The thesis also conceptualises an emissions-based DR program based on emissions intensity data and consumer load flexibility to demonstrate the use of smart grid data in encouraging renewable energy consumption. Going forward, the thesis advocates data-informed thinking for utilising smart grid data towards solving problems faced by the electricity sector.

The Hadoop framework has become the most deployed platform for processing Big Data. Despite its advantages, Hadoop s infrastructure is still deployed within the secured network perimeter because the framework lacks adequate inherent security mechanisms against various security threats. However, this approach is not sufficient for providing adequate security layer against attacks such as Distributed Denial of Service. Furthermore, current work to secure Hadoop s infrastructure against DDoS attacks is unable to provide a distributed node-level detection mechanism. This thesis presents a software agent-based framework that allows distributed, real-time intelligent monitoring and detection of DDoS attack at Hadoop s node-level. The agent s cognitive system is ingrained with cumulative sum statistical technique to analyse network utilisation and average server load and detect attacks from these measurements. The framework is a multi-agent architecture with transducer agents that interface with each Hadoop node to provide real-time detection mechanism. Moreover, the agents contextualise their beliefs by training themselves with the contextual information of each node and monitor the activities of the node to differentiate between normal and anomalous behaviours. In the experiments, the framework was exposed to TCP SYN and UDP flooding attacks during a legitimate MapReduce job on the Hadoop testbed. The experimental results were evaluated regarding performance metrics such as false-positive ratio, false-negative ratio and response time to attack. The results show that UDP and TCP SYN flooding attacks can be detected and confirmed on multiple nodes in nineteen seconds with 5.56% false-positive ration, 7.70% false-negative ratio and 91.5% success rate of detection. The results represent an improvement compared to the state-of the-art.

There are several ways in which a disability can occur. Strokes are a leading cause, affecting older people in particular, with an estimated annual incidence rate of 180, 125, 200, and 280 per 100,000 citizens in the USA, Europe, England, and Scotland, respectively. Muscle strengthening through resistance training has been reported to have a positive effect on the recovery of normal physiological functions after the occurrence of a neurological or traumatic injury. A number of studies have shown that resistance training results in improved mobility, a reduction in pain, and improved stability. Several rehabilitation devices have been developed and introduced for use in the healthcare sector, but a new generation of intelligent therapy-assisted machines is needed if there is to be a significant impact on the numbers of patients that can be treated under current staffing level. In this project, the design and performance of multi-degree-of-freedom smart balland-socket dampers and their application to fully-controllable rehabilitation training systems were investigated. A key feature of these dampers is the use of magnetorheological (MR) fluids which can exhibit dramatic changes in their rheological properties, such as yield stress, when subjected to external magnetic fields. These fast and reversible fluid rheological changes would permit the smart damper to provide the required impedance at orthotic arm joints, which are aimed for upper-limb rehabilitations and in accord with the exercise specifications prescribed by the physiotherapist. An exemplar upper-limb orthotic arm incorporating smart ball-and-socket dampers at its joints was assessed using SolidWorks software and the results confirmed the response of the dampers to variable excitation inputs under an input simulating a wheelchair driving motion. This study also enabled the estimation of the orthotic arm reach envelope, task performance and limitations in which important device design factors such as the angle of rotation of the smart dampers were taken into account. Although, three smart dampers with variable torque resistance capability are required at the shoulder, elbow and wrist joints of upper-limb rehabilitation orthoses, this project was focused on the development of a smart ball-and-socket damper aimed for the shoulder joint only. The target was to produce a compact smart electromagnetic damper that is capable to deliver the required torque resistance with the least power consumption. The efficient excitation of MR fluids requires a magnetic circuit, which consists of a source of magnetic flux and a path to deliver it to the fluid. Electromagnetic finite element analysis using Ansys software were carried out to achieve the optimum design of the damper’s electromagnetic circuit. The effects of the relative permeability of the damper’s materials on the generation of the magnetic field and its delivery to the MR fluid were examined. Other factors such as the coil shape, size, orientation and location in addition to the utilisation of non-magnetic materials in the electromagnetic circuit design were also investigated with the aim to optimise the performance of the smart damper. Furthermore, 3-D electromagnetic analyses were conducted, which confirmed the validity of the 2-D magnetic trials. Accordingly, the size of the MR fluid ball-and-socket damper was estimated with a ball diameter of 100 mm, which was found to produce a braking torque of about 50 N.m when the MR fluid is energised by about 1 Tesla. The performance of the ball-and-socket damper was estimated using theoretical, and numerical approaches. The theoretical model combines the viscous-friction and the controllable field-dependent characteristics of the MR fluid in which a Bingham plastic model was used to simulate the shear stress of the fluid under various input conditions. The numerical approach involved a special procedure to simulate the device performance using computational fluid dynamics techniques, which were performed using Ansys CFX code. Three commercial MR fluids were assessed and it was found that the simulated device torque compared well with the theoretical values. The mechanical design of the optimised ball-and-socket damper was accomplished using SolidWorks software when several important design and manufacturing factors were taken into account. These factors included the assembly of the ball and socket parts, the sealing of the MR fluid inside its designated gap, winding of the coil inside the socket part, maintaining a uniform MR fluid gap, and insertion of the nonmagnetic rings at their predesigned locations. Finally, a dedicated experimental rig was constructed which facilitated the assessment of the smart damper under both static and dynamic testing conditions. It was found that agreement between model predictions and experimental observations was excellent. Furthermore, this device performance was found to meet torque requirements expected in most upper-limb rehabilitation regimes.

Mok, King Keung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 77-79). / Abstracts in English and Chinese. / Abstract --- p.1 / Contents --- p.5 / List of Figures --- p.7 / List of Tables --- p.10 / Acknowledgments --- p.11 / Chapter 1. --- Introduction --- p.12 / Chapter 1.1. --- Low Power Design --- p.12 / Chapter 1.2. --- Power Consumption in Conventional CMOS Logic --- p.13 / Chapter 1.2.1. --- Dynamic Power --- p.13 / Chapter 1.2.2. --- Short-Circuit Power --- p.15 / Chapter 1.2.3. --- Leakage Power --- p.17 / Chapter 1.2.4. --- Static Power --- p.19 / Chapter 1.3. --- Smart Card / RFID --- p.21 / Chapter 1.3.1. --- Applications --- p.21 / Chapter 1.3.2. --- Operating Principle --- p.22 / Chapter 1.3.3. --- Conventional Architecture --- p.23 / Chapter 2. --- Adiabatic Logic --- p.25 / Chapter 2.1. --- Adiabatic Switching --- p.25 / Chapter 2.2. --- Energy Recovery --- p.27 / Chapter 2.3. --- Adiabatic Quasi-Static CMOS Logic --- p.29 / Chapter 2.3.1. --- Logic Structure --- p.29 / Chapter 2.3.2. --- Clocking Scheme --- p.31 / Chapter 2.3.3. --- Flip-flop --- p.33 / Chapter 2.3.4. --- Layout Techniques --- p.38 / Chapter 3. --- Adiabatic RFID --- p.41 / Chapter 3.1. --- System Architecture --- p.41 / Chapter 3.2. --- Circuit Design --- p.42 / Chapter 3.2.1. --- Voltage Limiter --- p.43 / Chapter 3.2.2. --- Substrate Bias Generation Circuit --- p.45 / Chapter 3.2.3. --- Ring Oscillator --- p.46 / Chapter 3.2.4. --- ROM and Control Logic --- p.48 / Chapter 3.2.5. --- Load Modulator --- p.52 / Chapter 3.2.6. --- Experimental Results --- p.53 / Chapter 4. --- Adiabatic Smart Card --- p.59 / Chapter 4.1. --- System Architecture --- p.59 / Chapter 4.2. --- Circuit Design --- p.61 / Chapter 4.2.1. --- ASK Demodulator --- p.61 / Chapter 4.2.2. --- Clock Recovery Circuit --- p.63 / Chapter 4.3. --- Experimental Results --- p.67 / Chapter 5. --- Conclusion --- p.74 / Chapter 6. --- Future Works --- p.76 / Reference --- p.77 / Appendix --- p.80

Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores (Major Telecomunicações). Faculdade de Engenharia. Universidade do Porto. 2009

We consider the optimal economic dispatch of power generators in a smart electric grid for allocating power between generators to meet load requirements at minimum total cost. We assume that each generator has a piece-wise linear cost function. We first present a polynomial time algorithm that achieves optimal dispatch. We then present a decentralized algorithm where, each generator independently adjusts its power output using only the aggregate power imbalance in the network, which can be observed by each generator through local measurements of the frequency deviation on the grid. The algorithm we propose exponentially erases the power imbalance, while eventually minimizing the generation cost.

NiTiHf alloys have emerged as potential materials for applications requiring high transformation temperatures (> 100 °C) with high strength and work output. Although they have high transformation temperatures, their low damping capacity, brittleness and poor superelastic responses (of Ti-rich NiTiHf) impedes their wider usage in many industrial applications. In this study, the quaternary alloying element of Pd has been added to NiTiHf alloys to improve and tailor their shape memory behavior,. NiTiHfPd alloys were systematically examined regarding the composition and heat treatments effects. Effects of substituting Hf with Ti on the shape memory behavior of NiTHfPd alloys were investigated. There compositions were selected as Ni40.3Ti34Hf20Pd5 Ni40.3Ti39.7Hf15Pd5 and Ni40.3Ti44.7Hf10Pd5 (at.%). Their transformation temperatures, microstructure and shape memory properties were revealed and compared with conventional shape memory alloys. It was revealed that their transformation temperatures increases but transformation strain decreases with the increment of Hf content. Additionally, superelastic responses of Ni45.3Ti29.7Hf20Pd5 andNi45.3Ti39.7Hf10Pd5 alloys were investigated. Transformation temperatures of polycrystalline Ni45.3Ti29.7Hf20Pd5are highly dependent on aging temperatures and they can be altered widely from room temperature to 250 oC. Finally, the damping capacity of the Ni45.3Ti39.7Hf10Pd5 polycrystal and [111]-oriented Ni45.3Ti29.7Hf20Pd5 single crystal were investigated. The damping capacities were found to be 16-25 J.cm-3, and 10-23 J.cm-3 for the Ni45.3Ti39.7Hf10Pd5 and [111]-oriented Ni45.3Ti29.7Hf20Pd5 alloys, respectively.

The main purpose of a vehicle information system using blockchain is to create a transparent and reliable information system which will help consumers buy a vehicle; it is a vehicle information system. The blockchain system will create a time sequence chain of events database for each vehicle from the original sale. It will include insurance, vehicle repair, and vehicle resale. This project is mainly divided into three parts. Part one is used by the administration who will create the blockchain and will give authentication to a different organization to create the blockchain. Part two will be used by the Organization to create a block in the blockchain. Part three will be used by customers who want to get information about the vehicle.

Building smart home environments which automatically or semi-automatically assist and comfort occupants is an important topic in the pervasive computing field, especially with the coming of cheap, easy-to-install sensors. This has given rise to the indispensable need for human activity recognition from ubiquitous sensors whose purpose is to observe and understand what occupants are trying to do from sensory data. The main approach to the problem of human activity recognition is a probabilistic one so as to handle the complication of uncertainty, the overlapping of human behaviours and environmental noise. This thesis develops a probabilistic model as a framework for human activity recognition using multiple multi-modal sensors in complex pervasive environments. The probabilistic model to be developed is adapted and based on the abstract hidden Markov model (AHMM) with one layer to fuse multiple sensors. The concept of factored state representation is employed in the model to parsimoniously represent the state transitions for reducing the number of required parameters. The exact method is used in learning the model’s parameters and performing inference. To be able to incorporate a large number of sensors, several more parsimonious representations including the mixtures of smaller multinomials and sigmoid functions are investigated to model the state transitions, resulting in a reduction of the number of parameters and time required for training. / We examine the approximate variational method to significantly reduce the time required for training the model instead of using the exact method. A system of fixed point equations is derived to iteratively update the free variational parameters. We also present the factored model in the case where all variables are continuous with the use of the conditional Gaussian distribution to model state transitions. The variational method is still employed in this case to speed up the model’s training process. The developed model is implemented and applied in recognizing daily activity in our smart home and the Nokia lab from multiple sensors. The experimental results show that the model is appropriate for fusing multiple sensors in activity recognition with a reasonable recognition performance.

<p>In wireless communication there is commonly much unnecessary communication made in directions not pointing towards the recipient. Normally omni directional antennas are being used which sends the same amount of energy in all directions equally. This waste of energy reduces the lifetime of battery powered units and causes more traffic collisions than necessary. One way of minimizing this wasted energy and traffic collisions, is to use another type of antenna called “smart antenna”. These antennas can use selectable radiation patterns depending on the situation and thus drastically minimize the unnecessary energy waste. Smart antennas also provide the ability to sense the direction of incoming signals which is favorable for physical layout mapping such as orientation.</p><p>This thesis presents the prototyping of a new type of smart antenna called the SPIDA smart antenna. This antenna is a cheap to produce smart antenna designed for the 2.4 GHz frequency band. The SPIDA smart antenna can use sixty-four different signal patterns with the control of six separate directional modes, amongst these patterns are six single direction patterns, an omni-directional signal pattern and fifty-six combi-direction patterns. The thesis presents complete building instructions, evaluation data and functional drivers for the SPIDA smart antenna.</p>