The bi-directional dc/dc converter is a very popular and effective tool for alternative energy applications. One way it can be utilized is to charge and discharge batteries used in residential solar energy systems. In the day, excess power from the PV panels is used to charge the batteries. During the night, the charged batteries will power the dc bus for loads in the house such as home appliances. The dual active bridge (DAB) converter is very useful because of its high power capability and efficiency. Its symmetry is effective in transferring power in both directions. However, the DAB converter has drawbacks in the start-up stage. These drawbacks in boost mode include high in-rush current during start-up, and the fact that the high side voltage cannot be lower than the low side voltage. A popular existing method to alleviate this problem is the use of an active clamp and a flyback transformer in the circuit topology to charge the high side before the converter is switched into normal boost operation. The active clamp not only helps eliminate the transient spike caused by the transformer leakage, but also continues to be used during steady state. However, this method introduces a new current spike occurring when the converter transitions from start-up mode to boost mode. To alleviate this new setback, an additional transitional stage is proposed to significantly reduce the current spike without the use of any additional components. The converter is current-fed on the low side, and voltage-fed on the high side. A simple phase shift control is used in buck mode and PWM control is used during the boost mode for both the start-up mode and the normal boost operation. This thesis discusses the performance results of a 48-400 V dc/dc converter with 1000 W power output. / Master of Science

The Army-sponsored Institute for Soldier Nanotechnologies (ISN) was an entrepreneurial research institute established at the Massachusetts Institute of Technology (MIT) in 2002. Using Science and Technology Studies (STS) concepts from Actor-Network Theory, I study the founding era of this twenty-first century laboratory-based community, from 2002-2007. Actor-Network concepts of enrollment and translation, described by Bruno Latour, and heterogeneous engineering, described by John Law, are used as I 'follow the actors' founding this emergent institution. The operationalization of translation is traced through four case studies, structured around Defense funding constructs and Science and Technology communities: 6.0 Founding the Institute; 6.1 Building Basic Research Networks; 6.2 Shaping Applied Research for Cancer Research and Science Education to include non-users; and 6.3 Student Prototyping Teams Accelerating ISN Research for Traumatic Brain Injuries (TBI). Scientists, engineers, and transitioners partnered in new ways to transition innovative technologies to improve human protection, with soldiers as the first of many users. Using public information, I used qualitative and quantitative methodologies to assess the actor networks and research portfolio changes. These historical case studies extend STS with operationalization of translation and a new dynamic of bi-directional actor enrollment, as research teams transitioned nanotechnologies and prototypes. / Doctor of Philosophy / The Institute for Soldier Nanotechnologies (ISN) was an Army-sponsored entrepreneurial research institute established at the Massachusetts Institute of Technology (MIT) in 2002. This historical study examines the founding era, rarely described for start-up organizations. Science and Technology Studies (STS) concepts of Actor-Network Theory enrollment and translation are traced through four case studies: Founding the Institute; Building Basic Research Networks; Shaping Applied Research for Cancer Research and Science Education to include non-users; and Student Prototyping Teams Accelerating Research for Traumatic Brain Injuries (TBI). The scientists, engineers, and transitioners partnered in new ways to transition technologies to improve human protection, with soldiers as the first of many users. Using public information, I provide qualitative and quantitative methodologies to assess the social networks of actors, as well as the composition and changes in the research portfolio. These case studies show what the ISN members did and how the small teams innovated, operationalizing translation through enrollment, and transitioning nanotechnologies and other prototypes.

Traffic accidents are of concern for traffic safety; 1.25 million deaths are reported each year. Hence, it is crucial to have access to real-time data and rapidly detect or predict accidents. Predicting the occurrence of a highway car accident accurately any significant length of time into the future is not feasible since the vast majority of crashes occur due to unpredictable human negligence and/or error. However, rapid traffic incident detection could reduce incident-related congestion and secondary crashes, alleviate the waste of vehicles’ fuel and passengers’ time, and provide appropriate information for emergency response and field operation. While the focus of most previously proposed techniques is predicting the number of accidents in a certain region, the problem of predicting the accident occurrence or fast detection of the accident has been little studied. To address this gap, we propose a deep learning approach and build a deep neural network model based on long short term memory (LSTM). We apply it to forecast the expected speed values on freeways’ links and identify the anomalies as potential accident occurrences. Several detailed features such as weather, traffic speed, and traffic flow of upstream and downstream points are extracted from big datasets. We assess the proposed approach on a traffic dataset from Sacramento, California. The experimental results demonstrate the potential of the proposed approach in identifying the anomalies in speed value and matching them with accidents in the same area. We show that this approach can handle a high rate of rapid accident detection and be implemented in real-time travelers’ information or emergency management systems. / M.S. / Rapid traffic accident detection/prediction is essential for scaling down non-recurrent conges- tion caused by traffic accidents, avoiding secondary accidents, and accelerating emergency system responses. In this study, we propose a framework that uses large-scale historical traffic speed and traffic flow data along with the relevant weather information to obtain robust traffic patterns. The predicted traffic patterns can be coupled with the real traffic data to detect anomalous behavior that often results in traffic incidents in the roadways. Our framework consists of two major steps. First, we estimate the speed values of traffic at each point based on the historical speed and flow values of locations before and after each point on the roadway. Second, we compare the estimated values with the actual ones and introduce the ones that are significantly different as an anomaly. The anomaly points are the potential points and times that an accident occurs and causes a change in the normal behavior of the roadways. Our study shows the potential of the approach in detecting the accidents while exhibiting promising performance in detecting the accident occurrence at a time close to the actual time of occurrence.

Curved beams such as arches find ubiquitous applications in civil, mechanical and aerospace engineering, e.g., stiffened floors, fuselage, railway compartments, and wind turbine blades. The analysis of free vibrations of curved structures plays a critical role in their design to avoid transient loads with dominant frequencies close to their natural frequencies. One way to increase their areas of applications and possibly make them lighter without sacrificing strength is to make them of Functionally Graded Materials (FGMs) that are composites with continuously varying material properties in one or more directions. In this thesis, we study free vibrations of FGM circular beams by using a logarithmic shear deformation theory that incorporates through-the-thickness logarithmic variation of the circumferential displacement, and does not require a shear correction factor. The radial displacement of a point is assumed to depend only upon its angular position. Thus the beam theory can be regarded as a generalization of the Timoshenko beam theory. Equations governing transient deformations of the beam are derived by using Hamilton's principle. Assuming a time harmonic variation of the displacements, and by utilizing the generalized differential quadrature method (GDQM) the free vibration problem is reduced to solving an algebraic eigenvalue problem whose solution provides frequencies and the corresponding mode shapes. Results are presented for different spatial variations of the material properties, boundary conditions, and the aspect ratio. It is found that the radial and the circumferential gradation of material properties maintains their natural frequency within that of the homogeneous beam comprised of a constituent of the FGM beam. Furthermore, keeping every other variable fixed, the change in the beam opening angle results in very close frequencies of the first two modes of vibration, a phenomenon usually called mode transition. / Master of Science / Curved and straight beams of various cross-sections are one of the simplest and most fundamental structural elements that have been extensively studied because of their ubiquitous applications in civil, mechanical, biomedical and aerospace engineering. Many attempts have been made to enhance their material properties and designs for applications in harsh environments and reduce weight. One way of accomplishing this is to combine layerwise two or more distinct materials and take advantage of their directional properties. It results in a lightweight structure having overall specific strength superior to that of its constituents. Another possibility is to have volume fractions of two or more constituents gradually vary throughout the structure for enhancing its performance under anticipated applications. Functionally graded materials (FGMs) are a class of composites whose properties gradually vary along one or more space directions. In this thesis, we have numerically studied free vibrations of FGM circular beams to enhance their application domain and possibly use them for energy harvesting.

The popularity of the internal combustion engine (ICE) vehicles has contributed to global warming problem and degradation of air quality around the world. Furthermore, the vehicles??? massive demand on gas has played a role in the depletion of fossil fuel reserves and the considerable rise in the gas price over the past twenty years. Those existing challenges force the auto-industry to move towards the technology development of vehicle electrification. An electrified vehicle is driven by one or more electric motors. And the electricity comes from the onboard energy storage system (ESS). Currently, no single type of green energy source could meet all the requirements to drive a vehicle. A hybrid energy storage system (HESS), as a combination of battery and ultra-capacitor units, is expected to improve the overall performance of vehicles??? ESS. This thesis focuses on the design of HESS and the development of a HESS prototype for electric vehicles (EVs) and hybrid electric vehicles (HEVs). Battery unit (BU), ultra-capacitor unit (UC) and a DC/DC converter interfacing BU and UC are the three main components of HESS. The research work first reviews literatures regarding characteristics of BU, UC and power electronic converters. HESS design is then conducted based on the considerations of power capability, energy efficiency, size and cost optimization. Besides theoretical analysis, a HESS prototype is developed to prove the principles of operation as well. The results from experiment are compared with those from simulation.

Wave energy converter (WEC) harnesses energy from the ocean to produce electrical power. The electrical power produced by the WEC is fluctuating and is not maximized as well, due to the varying ocean conditions. As a consequence, without any intermediate power conversion stage, the output power from the WEC can not be fed into the grid. To feed WEC output power into the grid, a two-stage power conversion topology is used, where the WEC output power is first converted into DCpower through rectification, and then a DC-AC converter (inverter) is used to supply AC power into the grid. The main motive of this research is to extract maximum electrical power from the WEC by active rectification and smoothing the power fluctuation of the wave energy converter through a hybrid energy storage system consisting of battery and flywheel. This research also illustrates active and reactive power injection to the grid according to load demand through a voltage source inverter.

Negation is a common property of languages, in that there are few languages, if any, that lack means to revert the truth-value of a statement. A challenge to cross-lingual studies of negation lies in the fact that languages encode and use it in different ways. Although this variation has been extensively researched in linguistics, little has been done in automated language processing. In particular, we lack computational models of processing negation that can be generalized across language. We even lack knowledge of what the development of such models would require. These models however exist and can be built by means of existing cross-lingual resources, even when annotated data for a language other than English is not available. This thesis shows this in the context of detecting string-level negation scope, i.e. the set of tokens in a sentence whose meaning is affected by a negation marker (e.g. 'not'). Our contribution has two parts. First, we investigate the scenario where annotated training data is available. We show that Bi-directional Long Short Term Memory (BiLSTM) networks are state-of-the-art models whose features can be generalized across language. We also show that these models suffer from genre effects and that for most of the corpora we have experimented with, high performance is simply an artifact of the annotation styles, where negation scope is often a span of text delimited by punctuation. Second, we investigate the scenario where annotated data is available in only one language, experimenting with model transfer. To test our approach, we first build NEGPAR, a parallel corpus annotated for negation, where pre-existing annotations on English sentences have been edited and extended to Chinese translations. We then show that transferring a model for negation scope detection across languages is possible by means of structured neural models where negation scope is detected on top of a cross-linguistically consistent representation, Universal Dependencies. On the other hand, we found cross-lingual lexical information only to help very little with performance. Finally, error analysis shows that performance is better when a negation marker is in the same dependency substructure as its scope and that some of the phenomena related to negation scope requiring lexical knowledge are still not captured correctly. In the conclusions, we tie up the contributions of this thesis and we point future work towards representing negation scope across languages at the level of logical form as well.

Made available in DSpace on 2016-12-23T14:04:28Z (GMT). No. of bitstreams: 1 Thanira Thammy Bastos Vilches.pdf: 1736718 bytes, checksum: 0125ce265ba8a796bb53677bd39e82a4 (MD5) Previous issue date: 2013-03-11 / Suspended solids transport excess, caused by erosion, may limit water use and cause rivers siltation. Suspended solids concentration may be assessed by remote sensing because the reflectance of the scenes, in the visible and infrared ranges, is positively influenced. This work aims at the understanding of the variability of suspended sediments concentrations in a Doce river stretch located in Espírito Santo state, Brazil, through spectral analysis of satellite images data and analysis of field measurements results. There were analyzed suspended solids concentrations for samples collected in Regência and Colatina Doce river sections and images captured by LANDSAT 5 TM satellite. To obtain reflectance values, the following operations were performed: geometric correction, conversion of DN values to apparent FRB values, atmospheric correction, using the 6S model (FRB surface), and transformation of NCs values.The results showed that, for the images corrected from atmospheric effects, the values of the ND curve related to the apparent FRB are higher than the values of the surface s FRB curve in the visible region, due to the electromagnetic radiation scattering phenomenon. In the near-infrared regions, the reverse was observed, because of the absorption of electromagnetic radiation by the atmosphere. It was observed positive correlation between reflectance and suspended solids concentration, despite the difficulties in obtaining data from same satellite images and field sample collection dates. For Regência data, the best correlation was obtained for band 1, located in the visible range of the surface s FRB curve, suggesting less suspended sediment transport. For Colatina, the best correlation occurred for band 4, located in the near-infrared range of the same surface s FRB curve, indicating greater sediment transport in this part of the Doce river during the sampling dates. It was concluded that the surface reflectance best represents the water spectral curve and that the correlated data showed the following pattern: smaller reflectance corresponds to lower water suspended solids concentration / O transporte de sólidos em suspensão, causado por erosão, é um dos problemas que limitam o uso da água, podendo causar assoreamento nos rios. A concentração de sólidos suspensos pode ser avaliada através do sensoriamento remoto, pois a reflectância da cena, na faixa do visível e do infravermelho, é positivamente influenciada. Desta forma, este trabalho visa o conhecimento da variabilidade das concentrações de sedimentos em suspensão no trecho do rio Doce localizada no ES, através de análises de dados espectrais contidos nas imagens orbitais e de resultados de medições em campo. Para tanto, foram avaliados dados concentrações de sólidos suspensos em amostras coletadas em Regência e Colatina, e imagens TM do satélite LANDSAT 5. Para obtenção dos valores de reflectância, foram realizadas as seguintes operações: correção geométrica, conversão dos NDs para valores de FRB aparente, correção atmosférica com uso do modelo 6S (FRB superfície), e transformação de NCs em valores de reflectância. Os resultados mostraram que nas imagens corrigidas dos efeitos atmosféricos, os valores da curva de ND referentes aos FRBs aparentes são superiores aos valores da curva FRB de superfície na região do visível, devido ao fenômeno de espalhamento da radiação eletromagnética. Nas regiões do infravermelho próximo, ocorreu o inverso, uma vez que há absorção da radiação eletromagnética pela atmosfera. Quanto aos sólidos suspensos, foram encontradas correlações positivas entre a reflectância e a concentração dos sólidos suspensos, apesar das dificuldades de obtenção de dados concomitantes das datas de imagens orbitais com datas da coleta de campo. Para os dados de Regência, a melhor correlação ocorreu para banda 1, que se localiza na faixa do visível, da curva dos FRB de superfície, sugerindo menor transporte de sedimentos suspensos. Para Colatina, ocorreu para banda 4, que se encontra na faixa do infravermelho próximo, também para curva dos FRB de superfície, sugerindo maior transporte de sedimentos nesse trecho do rio Doce, para as datas de amostragem. Conclui-se que a reflectância que melhor representa a curva espectral da água é de superfície e os dados correlacionados seguiram o padrão de quanto menores as reflectâncias, menores as concentrações de sólidos suspensos na água

Recent years have seen a growing tendency that a large number of generators are connected to the electricity distribution networks, including renewables such as solar photovoltaics, wind turbines and biomass-fired power plants. Meanwhile, on the demand side, there are also some new types of electric loads being connected at increasing rates, with the most important of them being the electric vehicles (EVs). Uncertainties both from generation and consumption of electricity mentioned above are thereby being introduced, making the management of the system more challenging. With the proportion of electric vehicle ownership rapidly increasing, uncontrolled charging of large populations may bring about power system issues such as increased peak demand and voltage variations, while at the same time the cost of electricity generation, as well as the resulting Greenhouse Gases (GHG) emissions, will also rise. The work reported in this PhD Thesis aims to provide solutions to the three significant challenges related to EV integration, namely voltage regulation, generation cost minimisation and GHG emissions reduction. A novel, high-resolution, bottom-up probabilistic EV charging demand model was developed, that uses data from the UK Time Use Survey and the National Travel Survey to synthesise realistic EV charging time series based on user activity patterns. Coupled with manufacturers' data for representative EV models, the developed probabilistic model converts single user activity profiles into electrical demand, which can then be aggregated to simulate larger numbers at a neighbourhood, city or regional level. The EV charging demand model has been integrated into a domestic electrical demand model previously developed by researchers in our group at the University of Edinburgh. The integrated model is used to show how demand management can be used to assist voltage regulation in the distribution system. The node voltage sensitivity method is used to optimise the planning of EV charging based on the influence that every EV charger has on the network depending on their point of connection. The model and the charging strategy were tested on a realistic "highly urban" low voltage network and the results obtained show that voltage fluctuation due to the high percentage of EV ownership (and charging) can be significantly and maintained within the statutory range during a full 24-hour cycle of operation. The developed model is also used to assess the generation cost as well as the environmental impact, in terms of GHG emissions, as a result of EV charging, and an optimisation algorithm has been developed that in combination with domestic demand management, minimises the incurred costs and GHG emissions. The obtained results indicate that although the increased population of EVs in distribution networks will stress the system and have adverse economic and environmental effects, these may be minimised with careful off-line planning.

High-frequency dc-dc converters are nowadays widely used in a diversity of power electronic applications. High operating frequencies entail a reduction in size of the passive components, such as inductors, capacitors and power transformers. By operating the converter at higher frequencies with conventional hard-switching topologies, the transistor switching losses increase at both turn-on and turn-off. High-voltage converters in the power range of 1-10MW will therefore have excessive switching losses if the switching frequency is higher than 4 kHz. In order to achieve a high-frequency operation with moderate switching losses a number of soft-switched topologies have been studied in [Dem1]. The favourable DC-DC converter was found to be the Dual-Active Bridge when a bi-directional power flow is demanded. Additionally, the Single-Active Bridge (SAB) topology was introduced for the first time. In this thesis the two topologies are thoroughly studied. The dynamic small-signal models are presented and the dynamic behaviour of the converters is discussed in deep. Different control strategies are presented concerning the two converters and the advantages and the disadvantages of the different control strategies are stated. Critical issues as efficiency and stability are presented separately for the two converters. / QC 20101005