991 resultados para Machine components


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Jones, E. H. G., Thomas, Ned, and King, Alan, 'Machine Translation and the Internet', Mercator Media Forums (2001) 5(1) pp.84-98 RAE2008

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P.M. Hastie and W. Haresign (2006). A role for LH in the regulation of expression of mRNAs encoding components of the insulin-like growth factor (IGF) system in the ovine corpus luteum. Animal Reproduction Science, 96(1-2), 196-209. Sponsorship: DEFRA RAE2008

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Among the wide variety of materials employed in the manufacture of shoes, thermoplastic polyurethanes (TPUs) are one of the most widely used. Given its widespread use, and associated waste management problems, the development of more biodegradable and evironmentally compatible solutions is needed. In this work, a polyester-based TPU used in the footwear industry for outsoles production was modified by compounding with lignin, starch and cellulose at content of 4% (w/w). The biodegradability was evaluated by using agar plate tests with the fungi Aspergillus niger ATCC16404, the Gram-negative bacteria Pseudomonas aeruginosa ATCC9027 and an association of both (consortium), and soil tests at 37 °C and 58 °C. The obtained results evidenced a positive effect of the tested biobased additives, the most favourable results being registered with lignin. These results were corroborated by the structural modifications observed by FTIR analysis. Additionally, mechanical tests prove the suitability of using the lignin modified TPUs for footwear outsoles production.

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With the increased use of "Virtual Machines" (VMs) as vehicles that isolate applications running on the same host, it is necessary to devise techniques that enable multiple VMs to share underlying resources both fairly and efficiently. To that end, one common approach is to deploy complex resource management techniques in the hosting infrastructure. Alternately, in this paper, we advocate the use of self-adaptation in the VMs themselves based on feedback about resource usage and availability. Consequently, we define a "Friendly" VM (FVM) to be a virtual machine that adjusts its demand for system resources, so that they are both efficiently and fairly allocated to competing FVMs. Such properties are ensured using one of many provably convergent control rules, such as AIMD. By adopting this distributed application-based approach to resource management, it is not necessary to make assumptions about the underlying resources nor about the requirements of FVMs competing for these resources. To demonstrate the elegance and simplicity of our approach, we present a prototype implementation of our FVM framework in User-Mode Linux (UML)-an implementation that consists of less than 500 lines of code changes to UML. We present an analytic, control-theoretic model of FVM adaptation, which establishes convergence and fairness properties. These properties are also backed up with experimental results using our prototype FVM implementation.

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The Java programming language has been widely described as secure by design. Nevertheless, a number of serious security vulnerabilities have been discovered in Java, particularly in the component known as the Bytecode Verifier. This paper describes a method for representing Java security constraints using the Alloy modeling language. It further describes a system for performing a security analysis on any block of Java bytecodes by converting the bytes into relation initializers in Alloy. Any counterexamples found by the Alloy analyzer correspond directly to insecure code. Analysis of a real-world malicious applet is given to demonstrate the efficacy of the approach.

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This thesis is focused on the design and development of an integrated magnetic (IM) structure for use in high-power high-current power converters employed in renewable energy applications. These applications require low-cost, high efficiency and high-power density magnetic components and the use of IM structures can help achieve this goal. A novel CCTT-core split-winding integrated magnetic (CCTT IM) is presented in this thesis. This IM is optimized for use in high-power dc-dc converters. The CCTT IM design is an evolution of the traditional EE-core integrated magnetic (EE IM). The CCTT IM structure uses a split-winding configuration allowing for the reduction of external leakage inductance, which is a problem for many traditional IM designs, such as the EE IM. Magnetic poles are incorporated to help shape and contain the leakage flux within the core window. These magnetic poles have the added benefit of minimizing the winding power loss due to the airgap fringing flux as they shape the fringing flux away from the split-windings. A CCTT IM reluctance model is developed which uses fringing equations to accurately predict the most probable regions of fringing flux around the pole and winding sections of the device. This helps in the development of a more accurate model as it predicts the dc and ac inductance of the component. A CCTT IM design algorithm is developed which relies heavily on the reluctance model of the CCTT IM. The design algorithm is implemented using the mathematical software tool Mathematica. This algorithm is modular in structure and allows for the quick and easy design and prototyping of the CCTT IM. The algorithm allows for the investigation of the CCTT IM boxed volume with the variation of input current ripple, for different power ranges, magnetic materials and frequencies. A high-power 72 kW CCTT IM prototype is designed and developed for use in an automotive fuelcell-based drivetrain. The CCTT IM design algorithm is initially used to design the component while 3D and 2D finite element analysis (FEA) software is used to optimize the design. Low-cost and low-power loss ferrite 3C92 is used for its construction, and when combined with a low number of turns results in a very efficient design. A paper analysis is undertaken which compares the performance of the high-power CCTT IM design with that of two discrete inductors used in a two-phase (2L) interleaved converter. The 2L option consists of two discrete inductors constructed from high dc-bias material. Both topologies are designed for the same worst-case phase current ripple conditions and this ensures a like-for-like comparison. The comparison indicates that the total magnetic component boxed volume of both converters is similar while the CCTT IM has significantly lower power loss. Experimental results for the 72 kW, (155 V dc, 465 A dc input, 420 V dc output) prototype validate the CCTT IM concept where the component is shown to be 99.7 % efficient. The high-power experimental testing was conducted at General Motors advanced technology center in Torrence, Los Angeles. Calorific testing was used to determine the power loss in the CCTT IM component. Experimental 3.8 kW results and a 3.8 kW prototype compare and contrast the ferrite CCTT IM and high dc-bias 2L concepts over the typical operating range of a fuelcell under like-for-like conditions. The CCTT IM is shown to perform better than the 2L option over the entire power range. An 8 kW ferrite CCTT IM prototype is developed for use in photovoltaic (PV) applications. The CCTT IM is used in a boost pre-regulator as part of the PV power stage. The CCTT IM is compared with an industry standard 2L converter consisting of two discrete ferrite toroidal inductors. The magnetic components are compared for the same worst-case phase current ripple and the experimental testing is conducted over the operation of a PV panel. The prototype CCTT IM allows for a 50 % reduction in total boxed volume and mass in comparison to the baseline 2L option, while showing increased efficiency.

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Electronic signal processing systems currently employed at core internet routers require huge amounts of power to operate and they may be unable to continue to satisfy consumer demand for more bandwidth without an inordinate increase in cost, size and/or energy consumption. Optical signal processing techniques may be deployed in next-generation optical networks for simple tasks such as wavelength conversion, demultiplexing and format conversion at high speed (≥100Gb.s-1) to alleviate the pressure on existing core router infrastructure. To implement optical signal processing functionalities, it is necessary to exploit the nonlinear optical properties of suitable materials such as III-V semiconductor compounds, silicon, periodically-poled lithium niobate (PPLN), highly nonlinear fibre (HNLF) or chalcogenide glasses. However, nonlinear optical (NLO) components such as semiconductor optical amplifiers (SOAs), electroabsorption modulators (EAMs) and silicon nanowires are the most promising candidates as all-optical switching elements vis-à-vis ease of integration, device footprint and energy consumption. This PhD thesis presents the amplitude and phase dynamics in a range of device configurations containing SOAs, EAMs and/or silicon nanowires to support the design of all optical switching elements for deployment in next-generation optical networks. Time-resolved pump-probe spectroscopy using pulses with a pulse width of 3ps from mode-locked laser sources was utilized to accurately measure the carrier dynamics in the device(s) under test. The research work into four main topics: (a) a long SOA, (b) the concatenated SOA-EAMSOA (CSES) configuration, (c) silicon nanowires embedded in SU8 polymer and (d) a custom epitaxy design EAM with fast carrier sweepout dynamics. The principal aim was to identify the optimum operation conditions for each of these NLO device configurations to enhance their switching capability and to assess their potential for various optical signal processing functionalities. All of the NLO device configurations investigated in this thesis are compact and suitable for monolithic and/or hybrid integration.

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There has been an increased use of the Doubly-Fed Induction Machine (DFIM) in ac drive applications in recent times, particularly in the field of renewable energy systems and other high power variable-speed drives. The DFIM is widely regarded as the optimal generation system for both onshore and offshore wind turbines and has also been considered in wave power applications. Wind power generation is the most mature renewable technology. However, wave energy has attracted a large interest recently as the potential for power extraction is very significant. Various wave energy converter (WEC) technologies currently exist with the oscillating water column (OWC) type converter being one of the most advanced. There are fundemental differences in the power profile of the pneumatic power supplied by the OWC WEC and that of a wind turbine and this causes significant challenges in the selection and rating of electrical generators for the OWC devises. The thesis initially aims to provide an accurate per-phase equivalent circuit model of the DFIM by investigating various characterisation testing procedures. Novel testing methodologies based on the series-coupling tests is employed and is found to provide a more accurate representation of the DFIM than the standard IEEE testing methods because the series-coupling tests provide a direct method of determining the equivalent-circuit resistances and inductances of the machine. A second novel method known as the extended short-circuit test is also presented and investigated as an alternative characterisation method. Experimental results on a 1.1 kW DFIM and a 30 kW DFIM utilising the various characterisation procedures are presented in the thesis. The various test methods are analysed and validated through comparison of model predictions and torque-versus-speed curves for each induction machine. Sensitivity analysis is also used as a means of quantifying the effect of experimental error on the results taken from each of the testing procedures and is used to determine the suitability of the test procedures for characterising each of the devices. The series-coupling differential test is demonstrated to be the optimum test. The research then focuses on the OWC WEC and the modelling of this device. A software model is implemented based on data obtained from a scaled prototype device situated at the Irish test site. Test data from the electrical system of the device is analysed and this data is used to develop a performance curve for the air turbine utilised in the WEC. This performance curve was applied in a software model to represent the turbine in the electro-mechanical system and the software results are validated by the measured electrical output data from the prototype test device. Finally, once both the DFIM and OWC WEC power take-off system have been modeled succesfully, an investigation of the application of the DFIM to the OWC WEC model is carried out to determine the electrical machine rating required for the pulsating power derived from OWC WEC device. Thermal analysis of a 30 kW induction machine is carried out using a first-order thermal model. The simulations quantify the limits of operation of the machine and enable thedevelopment of rating requirements for the electrical generation system of the OWC WEC. The thesis can be considered to have three sections. The first section of the thesis contains Chapters 2 and 3 and focuses on the accurate characterisation of the doubly-fed induction machine using various testing procedures. The second section, containing Chapter 4, concentrates on the modelling of the OWC WEC power-takeoff with particular focus on the Wells turbine. Validation of this model is carried out through comparision of simulations and experimental measurements. The third section of the thesis utilises the OWC WEC model from Chapter 4 with a 30 kW induction machine model to determine the optimum device rating for the specified machine. Simulations are carried out to perform thermal analysis of the machine to give a general insight into electrical machine rating for an OWC WEC device.

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The concept of a biofuel cell takes inspiration from the natural capability of biological systems to catalyse the conversion of organic matter with a subsequent release of electrical energy. Enzymatic biofuel cells are intended to mimic the processes occurring in nature in a more controlled and efficient manner. Traditional fuel cells rely on the use of toxic catalysts and are often not easily miniaturizable making them unsuitable as implantable power sources. Biofuel cells however use highly selective protein catalysts and renewable fuels. As energy consumption becomes a global issue, they emerge as important tools for energy generation. The microfluidic platforms developed are intended to maximize the amount of electrical energy extracted from renewable fuels which are naturally abundant in the environment and in biological fluids. Combining microfabrication processes, chemical modification and biological surface patterning these devices are promising candidates for micro-power sources for future life science and electronic applications. This thesis considered four main aspects of a biofuel cell research. Firstly, concept of a miniature compartmentalized enzymatic biofuel cell utilizing simple fuels and operating in static conditions is verified and proves the feasibility of enzyme catalysis in energy conversion processes. Secondly, electrode and microfluidic channel study was performed through theoretical investigations of the flow and catalytic reactions which also improved understanding of the enzyme kinetics in the cell. Next, microfluidic devices were fabricated from cost-effective and disposable polymer materials, using the state-of-the-art micro-processing technologies. Integration of the individual components is difficult and multiple techniques to overcome these problems have been investigated. Electrochemical characterization of gold electrodes modified with Nanoporous Gold Structures is also performed. Finally, two strategies for enzyme patterning and encapsulation are discussed. Several protein catalysts have been effectively immobilized on the surface of commercial and microfabricated electrodes by electrochemically assisted deposition in sol-gel and poly-(o-phenylenediamine) polymer matrices and characterised with confirmed catalytic activity.

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Potatoes (Solanum Tuberosum L.) contain secondary metabolites that may have an impact on human health. The aim of this study was to assess the levels of some of these compounds in a wide range of varieties, including rare, heritage and commercial cultivars. Vitamin C, total carotenoids, phenolics, flavonoids, antioxidant activity and glycoalkaloids were determined, using spectroscopy and chromatography, in the skin and flesh of tubers grown in field trials. Transcript levels of key synthetic enzymes were assessed by qPCR. Accumulation of selected metabolites was higher in the skin than in the flesh of tubers, except ascorbate, which was undetected in the skin. Differences were on average 2.5 to 3-fold for carotenoids, 6-fold for phenolics, 15 to 16-fold for flavonoids, 21-fold for glycoalkaloids and 9 to 10-fold for antioxidant activity. Higher contents of carotenoids were associated with yellow skin or flesh, and higher values of phenolics, flavonoids and antioxidant activity with blue flesh. Variety ‘Burren’ had maxima values of carotenoids in skin and flesh, variety ‘Nicola’ of ascorbate, variety ‘Congo’ of phenolics, flavonoids and antioxidant activity in both tissues, except antioxidant activity in the skin, which was higher in ‘Edzell Blue’. Varieties ‘May Queen’ and ‘International Kidney’ had highest glycoalkaloid content in skin and flesh respectively. The effect of the environment was diverse: year of cultivation was significant for all metabolites, but site of cultivation was not for carotenoids and glycoalkaloids. Levels of expression of phenylalanine ammonia-lyase and chalcone synthase were higher in varieties accumulating high contents of phenolic compounds. However, levels of expression of phytoene synthase and L-galactono-1,4-lactone dehydrogenase were not different between varieties showing contrasting levels of carotenoids and ascorbate respectively. This work will help identify varieties that could be marketed as healthier and the most suitable varieties for extraction of high-value metabolites such as glycoalkaloids.

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A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilbert-Huang transform and machine learning algorithms. The random forests and gradient boosting trees learning techniques were examined. The decision tree techniques were used to rank the importance of variables employed in the forecasting models. The Mean Decrease Gini index is employed as an impurity function. The resulting hybrid forecasting models employ the radial basis function neural network and support vector regression. A part from introduction and references the paper is organized as follows. The second section presents the background and the review of several approaches for short-term forecasting of power system parameters. In the third section a hybrid machine learningbased algorithm using Hilbert-Huang transform is developed for short-term forecasting of power system parameters. Fourth section describes the decision tree learning algorithms used for the issue of variables importance. Finally in section six the experimental results in the following electric power problems are presented: active power flow forecasting, electricity price forecasting and for the wind speed and direction forecasting.

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An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions.

This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods.

On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.

In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.

We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,

and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy.

In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.

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In S. cerevisiae lacking SHR3, amino acid permeases specifically accumulate in membranes of the endoplasmic reticulum (ER) and fail to be transported to the plasma membrane. We examined the requirements of transport of the permeases from the ER to the Golgi in vitro. Addition of soluble COPII components (Sec23/24p, Sec13/31p, and Sar1p) to yeast membrane preparations generated vesicles containing the general amino acid permease. Gap1p, and the histidine permease, Hip1p. Shr3p was required for the packaging of Gap1p and Hip1p but was not itself incorporated into transport vesicles. In contrast, the packaging of the plasma membrane ATPase, Pma1p, and the soluble yeast pheromone precursor, glycosylated pro alpha factor, was independent of Shr3p. In addition, we show that integral membrane and soluble cargo colocalize in transport vesicles, indicating that different types of cargo are not segregated at an early step in secretion. Our data suggest that specific ancillary proteins in the ER membrane recruit subsets of integral membrane protein cargo into COPII transport vesicles.