Large-scale data for multiple-view stereopsis

The seminal multiple-view stereo benchmark evaluations from Middlebury and by Strecha et al. have played a major role in propelling the development of multi-view stereopsis (MVS) methodology. The somewhat small size and variability of these data sets, however, limit their scope and the conclusions that can be derived from them. To facilitate further development within MVS, we here present a new and varied data set consisting of 80 scenes, seen from 49 or 64 accurate camera positions. This is accompanied by accurate structured light scans for reference and evaluation. In addition all images are taken under seven different lighting conditions. As a benchmark and to validate the use of our data set for obtaining reasonable and statistically significant findings about MVS, we have applied the three state-of-the-art MVS algorithms by Campbell et al., Furukawa et al., and Tola et al. to the data set. To do this we have extended the evaluation protocol from the Middlebury evaluation, necessitated by the more complex geometry of some of our scenes. The data set and accompanying evaluation framework are made freely available online. Based on this evaluation, we are able to observe several characteristics of state-of-the-art MVS, e.g. that there is a tradeoff between the quality of the reconstructed 3D points (accuracy) and how much of an object’s surface is captured (completeness). Also, several issues that we hypothesized would challenge MVS, such as specularities and changing lighting conditions did not pose serious problems. Our study finds that the two most pressing issues for MVS are lack of texture and meshing (forming 3D points into closed triangulated surfaces).

Machine learning for large-scale wearable sensor data in Parkinson disease:concepts, promises, pitfalls, and futures

For the treatment and monitoring of Parkinson's disease (PD) to be scientific, a key requirement is that measurement of disease stages and severity is quantitative, reliable, and repeatable. The last 50 years in PD research have been dominated by qualitative, subjective ratings obtained by human interpretation of the presentation of disease signs and symptoms at clinical visits. More recently, “wearable,” sensor-based, quantitative, objective, and easy-to-use systems for quantifying PD signs for large numbers of participants over extended durations have been developed. This technology has the potential to significantly improve both clinical diagnosis and management in PD and the conduct of clinical studies. However, the large-scale, high-dimensional character of the data captured by these wearable sensors requires sophisticated signal processing and machine-learning algorithms to transform it into scientifically and clinically meaningful information. Such algorithms that “learn” from data have shown remarkable success in making accurate predictions for complex problems in which human skill has been required to date, but they are challenging to evaluate and apply without a basic understanding of the underlying logic on which they are based. This article contains a nontechnical tutorial review of relevant machine-learning algorithms, also describing their limitations and how these can be overcome. It discusses implications of this technology and a practical road map for realizing the full potential of this technology in PD research and practice. © 2016 International Parkinson and Movement Disorder Society.

A computational model of hydrogen production by steam reforming of dimethyl ether in a large scale CFB reactor. Part II:parametric analysis

This study presents a computational parametric analysis of DME steam reforming in a large scale Circulating Fluidized Bed (CFB) reactor. The Computational Fluid Dynamic (CFD) model used, which is based on Eulerian-Eulerian dispersed flow, has been developed and validated in Part I of this study [1]. The effect of the reactor inlet configuration, gas residence time, inlet temperature and steam to DME ratio on the overall reactor performance and products have all been investigated. The results have shown that the use of double sided solid feeding system remarkable improvement in the flow uniformity, but with limited effect on the reactions and products. The temperature has been found to play a dominant role in increasing the DME conversion and the hydrogen yield. According to the parametric analysis, it is recommended to run the CFB reactor at around 300 °C inlet temperature, 5.5 steam to DME molar ratio, 4 s gas residence time and 37,104 ml gcat -1 h-1 space velocity. At these conditions, the DME conversion and hydrogen molar concentration in the product gas were both found to be around 80%.

Large scale fabrication of nitrogen vacancy-embedded diamond nanostructures for single-photon source applications

Some color centers in diamond can serve as quantum bits which can be manipulated with microwave pulses and read out with laser, even at room temperature. However, the photon collection efficiency of bulk diamond is greatly reduced by refraction at the diamond/air interface. To address this issue, we fabricated arrays of diamond nanostructures, differing in both diameter and top end shape, with HSQ and Cr as the etching mask materials, aiming toward large scale fabrication of single-photon sources with enhanced collection efficiency made of nitrogen vacancy (NV) embedded diamond. With a mixture of O2 and CHF3 gas plasma, diamond pillars with diameters down to 45 nm were obtained. The top end shape evolution has been represented with a simple model. The tests of size dependent single-photon properties confirmed an improved single-photon collection efficiency enhancement, larger than tenfold, and a mild decrease of decoherence time with decreasing pillar diameter was observed as expected. These results provide useful information for future applications of nanostructured diamond as a single-photon source.

A theoretical and experimental account of n-tuple classifier performance

The n-tuple recognition method is briefly reviewed, summarizing the main theoretical results. Large-scale experiments carried out on Stat-Log project datasets confirm this method as a viable competitor to more popular methods due to its speed, simplicity, and accuracy on the majority of a wide variety of classification problems. A further investigation into the failure of the method on certain datasets finds the problem to be largely due to a mismatch between the scales which describe generalization and data sparseness.

The prediction of vibration in large electric machines

This thesis reports the development of a reliable method for the prediction of response to electromagnetically induced vibration in large electric machines. The machines of primary interest are DC ship-propulsion motors but much of the work reported has broader significance. The investigation has involved work in five principal areas. (1) The development and use of dynamic substructuring methods. (2) The development of special elements to represent individual machine components. (3) Laboratory scale investigations to establish empirical values for properties which affect machine vibration levels. (4) Experiments on machines on the factory test-bed to provide data for correlation with prediction. (5) Reasoning with regard to the effect of various design features. The limiting factor in producing good models for machines in vibration is the time required for an analysis to take place. Dynamic substructuring methods were adopted early in the project to maximise the efficiency of the analysis. A review of existing substructure- representation and composite-structure assembly methods includes comments on which are most suitable for this application. In three appendices to the main volume methods are presented which were developed by the author to accelerate analyses. Despite significant advances in this area, the limiting factor in machine analyses is still time. The representation of individual machine components was addressed as another means by which the time required for an analysis could be reduced. This has resulted in the development of special elements which are more efficient than their finite-element counterparts. The laboratory scale experiments reported were undertaken to establish empirical values for the properties of three distinct features - lamination stacks, bolted-flange joints in rings and cylinders and the shimmed pole-yoke joint. These are central to the preparation of an accurate machine model. The theoretical methods are tested numerically and correlated with tests on two machines (running and static). A system has been devised with which the general electromagnetic forcing may be split into its most fundamental components. This is used to draw some conclusions about the probable effects of various design features.

Managing risk in a large rural electrification programme in India

Rural electrification projects and programmes in many countries have suffered from design, planning, implementation and operational flaws as a result of ineffective project planning and lack of systematic project risk analysis. This paper presents a hierarchical risk-management framework for effectively managing large-scale development projects. The proposed framework first identifies, with the involvement of stakeholders, the risk factors for a rural electrification programme at three different levels (national, state and site). Subsequently it develops a qualitative risk prioritising scheme through probability and severity mapping and provides mitigating measures for most vulnerable risks. The study concludes that the hierarchical risk-management approach provides an effective framework for managing large-scale rural electrification programmes. © IAIA 2007.

Computer-aided biotechnology:from immuno-informatics to reverse vaccinology

Genome sequences from many organisms, including humans, have been completed, and high-throughput analyses have produced burgeoning volumes of 'omics' data. Bioinformatics is crucial for the management and analysis of such data and is increasingly used to accelerate progress in a wide variety of large-scale and object-specific functional analyses. Refined algorithms enable biotechnologists to follow 'computer-aided strategies' based on experiments driven by high-confidence predictions. In order to address compound problems, current efforts in immuno-informatics and reverse vaccinology are aimed at developing and tuning integrative approaches and user-friendly, automated bioinformatics environments. This will herald a move to 'computer-aided biotechnology': smart projects in which time-consuming and expensive large-scale experimental approaches are progressively replaced by prediction-driven investigations.

Characterisation of a harvesting step for monoclonal antibodies from mammalian cell culture

The focus of this research was defined by a poorly characterised filtration train employed to clarify culture broth containing monoclonal antibodies secreted by GS-NSO cells: the filtration train blinded unpredictably and the ability of the positively charged filters to adsorb DNA from process material was unknown. To direct the development of an assay to quantify the ability of depth filters to adsorb DNA, the molecular weight of DNA from a large-scale, fed-batch, mammalian cell culture vessel was evaluated as process material passed through the initial stages of the purification scheme. High molecular weight DNA was substantially cleared from the broth after passage through a disc stack centrifuge and the remaining low molecular weight DNA was largely unaffected by passage through a series of depth filters and a sterilising grade membrane. Removal of high molecular weight DNA was shown to be coupled with clarification of the process stream. The DNA from cell culture supernatant showed a pattern of internucleosomal cleavage of chromatin when fractionated by electrophoresis but the presence of both necrotic and apoptotic cells throughout the fermentation meant that the origin of the fragmented DNA could not be unequivocally determined. An intercalating fluorochrome, PicoGreen, was elected for development of a suitable DNA assay because of its ability to respond to low molecular weight DNA. It was assessed for its ability to determine the concentration of DNA in clarified mammalian cell culture broths containing pertinent monoclonal antibodies. Fluorescent signal suppression was ameliorated by sample dilution or by performing the assay above the pI of secreted IgG. The source of fluorescence in clarified culture broth was validated by incubation with RNase A and DNase I. At least 89.0 % of fluorescence was attributable to nucleic acid and pre-digestion with RNase A was shown to be a requirement for successful quantification of DNA in such samples. Application of the fluorescence based assay resulted in characterisation of the physical parameters governing adsorption of DNA by various positively charged depth filters and membranes in test solutions and the DNA adsorption profile of the manufacturing scale filtration train. Buffers that reduced or neutralised the depth filter or membrane charge, and those that impeded hydrophobic interactions were shown to affect their operational capacity, demonstrating that DNA was adsorbed by a combination of electrostatic and hydrophobic interactions. Production-scale centrifugation of harvest broth containing therapeutic protein resulted in the reduction of total DNA in the process stream from 79.8 μg m1-1 to 9.3 μg m1-1 whereas the concentration of DNA in the supernatant of pre-and post-filtration samples had only marginally reduced DNA content: from 6.3 to 6.0 μg m1-1 respectively. Hence the filtration train was shown to ineffective in DNA removal. Historically, blinding of the depth filters had been unpredictable with data such as numbers of viable cells, non-viable cells, product titre, or process shape (batch, fed-batch, or draw and fill) failing to inform on the durability of depth filters in the harvest step. To investigate this, key fouling contaminants were identified by challenging depth filters with the same mass of one of the following: viable healthy cells, cells that had died by the process of apoptosis, and cells that had died through the process of necrosis. The pressure increase across a Cuno Zeta Plus 10SP depth filter was 2.8 and 16.5 times more sensitive to debris from apoptotic and necrotic cells respectively, when compared to viable cells. The condition of DNA released into the culture broth was assessed. Necrotic cells released predominantly high molecular weight DNA in contrast to apoptotic cells which released chiefly low molecular weight DNA. The blinding of the filters was found to be largely unaffected by variations in the particle size distribution of material in, and viscosity of, solutions with which they were challenged. The exceptional response of the depth filters to necrotic cells may suggest the cause of previously noted unpredictable filter blinding whereby a number of necrotic cells have a more significant impact on the life of a depth filter than a similar number of viable or apoptotic cells. In a final set of experiments the pressure drop caused by non-viable necrotic culture broths which had been treated with DNase I or benzonase was found to be smaller when compared to untreated broths: the abilities of the enzyme treated cultures to foul the depth filter were reduced by 70.4% and 75.4% respectively indicating the importance of DNA in the blinding of the depth filter studied.

Damage analysis of bridge structures using vibrational techniques

Much research is currently centred on the detection of damage in structures using vibrational data. The work presented here examined several areas of interest in support of a practical technique for identifying and locating damage within bridge structures using apparent changes in their vibrational response to known excitation. The proposed goals of such a technique included the need for the measurement system to be operated on site by a minimum number of staff and that the procedure should be as non-invasive to the bridge traffic-flow as possible. Initially the research investigated changes in the vibrational bending characteristics of two series of large-scale model bridge-beams in the laboratory and these included ordinary-reinforced and post-tensioned, prestressed designs. Each beam was progressively damaged at predetermined positions and its vibrational response to impact excitation was analysed. For the load-regime utilised the results suggested that the infuced damage manifested itself as a function of the span of a beam rather than a localised area. A power-law relating apparent damage with the applied loading and prestress levels was then proposed, together with a qualitative vibrational measure of structural damage. In parallel with the laboratory experiments a series of tests were undertaken at the sites of a number of highway bridges. The bridges selected had differing types of construction and geometric design including composite-concrete, concrete slab-and-beam, concrete-slab with supporting steel-troughing constructions together with regular-rectangular, skewed and heavily-skewed geometries. Initial investigations were made of the feasibility and reliability of various methods of structure excitation including traffic and impulse methods. It was found that localised impact using a sledge-hammer was ideal for the purposes of this work and that a cartridge `bolt-gun' could be used in some specific cases.

Ground cracking and frost heaving associated with chilled gas pipeline operations in Britain

This thesis investigates the soil-pipeline interactions associated with the operation of large-diameter chilled gas pipelines in Britain, these are frost/pipe heave and ground cracking. The investigation was biased towards the definition of the mechanism of ground cracking and, the parameters which influence its generation and subsequent development, especially its interaction with frost heave. The study involved a literature review, questionnaire, large-scale test and small-scale laboratory model experiments. The literature review concentrated on soil-pipeline interactions and frost action, with frost/pipe heave often reported but ground cracking was seldom reported. A questionnaire was circulated within British Gas to gain further information on these interactions. The replies indicated that if frost/pipe heave was reported, ground cracking was also likely to be observed. These soil-pipeline interactions were recorded along 19% of pipelines in the survey and were more likely along the larger diameter, higher flow pipelines. A large-scale trial along a 900 mm pipeline was undertaken to assess the soil thermal, hydraulic and stress regimes, together with pipe and ground movements. Results indicated that cracking occurred intermittently along the pipeline during periods of rapid frost/pipe heave and ground movement and, that frozen annulus growth produced a ground surface profile was approximated by a normal probability distribution curve. This curve indicates maximum tensile strain directly over the pipe centre. Finally a small-scale laboratory model was operated to further define the ground cracking mechanism. Ground cracking was observed at small upward ground surface movement, and with continued movement the ground crack increased in width and depth. At the end of the experiments internal soil failure planes slanting upwards and away from the frozen annulus were noted. The suggested mechanism for ground cracking involved frozen annulus growth producing tensile strain in the overlying unfrozen soil, which when sufficient produced a crack.

Stock selection with full-scale optimization and differential evolution

When composing stock portfolios, managers frequently choose among hundreds of stocks. The stocks' risk properties are analyzed with statistical tools, and managers try to combine these to meet the investors' risk profiles. A recently developed tool for performing such optimization is called full-scale optimization (FSO). This methodology is very flexible for investor preferences, but because of computational limitations it has until now been infeasible to use when many stocks are considered. We apply the artificial intelligence technique of differential evolution to solve FSO-type stock selection problems of 97 assets. Differential evolution finds the optimal solutions by self-learning from randomly drawn candidate solutions. We show that this search technique makes large scale problem computationally feasible and that the solutions retrieved are stable. The study also gives further merit to the FSO technique, as it shows that the solutions suit investor risk profiles better than portfolios retrieved from traditional methods.

Weakly supervised joint sentiment-topic detection from text

Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework called joint sentiment-topic (JST) model based on latent Dirichlet allocation (LDA), which detects sentiment and topic simultaneously from text. A reparameterized version of the JST model called Reverse-JST, obtained by reversing the sequence of sentiment and topic generation in the modeling process, is also studied. Although JST is equivalent to Reverse-JST without a hierarchical prior, extensive experiments show that when sentiment priors are added, JST performs consistently better than Reverse-JST. Besides, unlike supervised approaches to sentiment classification which often fail to produce satisfactory performance when shifting to other domains, the weakly supervised nature of JST makes it highly portable to other domains. This is verified by the experimental results on data sets from five different domains where the JST model even outperforms existing semi-supervised approaches in some of the data sets despite using no labeled documents. Moreover, the topics and topic sentiment detected by JST are indeed coherent and informative. We hypothesize that the JST model can readily meet the demand of large-scale sentiment analysis from the web in an open-ended fashion.

Computationsl modelling of dimethyl ether separation and steam reforming in fluidized bed reactors

This study presents a computational fluid dynamic (CFD) study of Dimethyl Ether (DME) gas adsorptive separation and steam reforming (DME-SR) in a large scale Circulating Fluidized Bed (CFB) reactor. The CFD model is based on Eulerian-Eulerian dispersed flow and solved using commercial software (ANSYS FLUENT). Hydrogen is currently receiving increasing interest as an alternative source of clean energy and has high potential applications, including the transportation sector and power generation. Computational fluid dynamic (CFD) modelling has attracted considerable recognition in the engineering sector consequently leading to using it as a tool for process design and optimisation in many industrial processes. In most cases, these processes are difficult or expensive to conduct in lab scale experiments. The CFD provides a cost effective methodology to gain detailed information up to the microscopic level. The main objectives in this project are to: (i) develop a predictive model using ANSYS FLUENT (CFD) commercial code to simulate the flow hydrodynamics, mass transfer, reactions and heat transfer in a large scale dual fluidized bed system for combined gas separation and steam reforming processes (ii) implement a suitable adsorption models in the CFD code, through a user defined function, to predict selective separation of a gas from a mixture (iii) develop a model for dimethyl ether steam reforming (DME-SR) to predict hydrogen production (iv) carry out detailed parametric analysis in order to establish ideal operating conditions for future industrial application. The project has originated from a real industrial case problem in collaboration with the industrial partner Dow Corning (UK) and jointly funded by the Engineering and Physical Research Council (UK) and Dow Corning. The research examined gas separation by adsorption in a bubbling bed, as part of a dual fluidized bed system. The adsorption process was simulated based on the kinetics derived from the experimental data produced as part of a separate PhD project completed under the same fund. The kinetic model was incorporated in FLUENT CFD tool as a pseudo-first order rate equation; some of the parameters for the pseudo-first order kinetics were obtained using MATLAB. The modelling of the DME adsorption in the designed bubbling bed was performed for the first time in this project and highlights the novelty in the investigations. The simulation results were analysed to provide understanding of the flow hydrodynamic, reactor design and optimum operating condition for efficient separation. Bubbling bed validation by estimation of bed expansion and the solid and gas distribution from simulation agreed well with trends seen in the literatures. Parametric analysis on the adsorption process demonstrated that increasing fluidizing velocity reduced adsorption of DME. This is as a result of reduction in the gas residence time which appears to have much effect compared to the solid residence time. The removal efficiency of DME from the bed was found to be more than 88%. Simulation of the DME-SR in FLUENT CFD was conducted using selected kinetics from literature and implemented in the model using an in-house developed user defined function. The validation of the kinetics was achieved by simulating a case to replicate an experimental study of a laboratory scale bubbling bed by Vicente et al [1]. Good agreement was achieved for the validation of the models, which was then applied in the DME-SR in the large scale riser section of the dual fluidized bed system. This is the first study to use the selected DME-SR kinetics in a circulating fluidized bed (CFB) system and for the geometry size proposed for the project. As a result, the simulation produced the first detailed data on the spatial variation and final gas product in such an industrial scale fluidized bed system. The simulation results provided insight in the flow hydrodynamic, reactor design and optimum operating condition. The solid and gas distribution in the CFB was observed to show good agreement with literatures. The parametric analysis showed that the increase in temperature and steam to DME molar ratio increased the production of hydrogen due to the increased DME conversions, whereas the increase in the space velocity has been found to have an adverse effect. Increasing temperature between 200 oC to 350 oC increased DME conversion from 47% to 99% while hydrogen yield increased substantially from 11% to 100%. The CO2 selectivity decreased from 100% to 91% due to the water gas shift reaction favouring CO at higher temperatures. The higher conversions observed as the temperature increased was reflected on the quantity of unreacted DME and methanol concentrations in the product gas, where both decreased to very low values of 0.27 mol% and 0.46 mol% respectively at 350 °C. Increasing the steam to DME molar ratio from 4 to 7.68 increased the DME conversion from 69% to 87%, while the hydrogen yield increased from 40% to 59%. The CO2 selectivity decreased from 100% to 97%. The decrease in the space velocity from 37104 ml/g/h to 15394 ml/g/h increased the DME conversion from 87% to 100% while increasing the hydrogen yield from 59% to 87%. The parametric analysis suggests an operating condition for maximum hydrogen yield is in the region of 300 oC temperatures and Steam/DME molar ratio of 5. The analysis of the industrial sponsor’s case for the given flow and composition of the gas to be treated suggests that 88% of DME can be adsorbed from the bubbling and consequently producing 224.4t/y of hydrogen in the riser section of the dual fluidized bed system. The process also produces 1458.4t/y of CO2 and 127.9t/y of CO as part of the product gas. The developed models and parametric analysis carried out in this study provided essential guideline for future design of DME-SR at industrial level and in particular this work has been of tremendous importance for the industrial collaborator in order to draw conclusions and plan for future potential implementation of the process at an industrial scale.

An examination of the Prandtl mechanism in large dimension slope failures

An alternative explanation for the modes of failure of large scale failures of open pit walls to those of classical slope stability theory is proposed that makes use of the concept of a transition zone, which is described by a modified Prandtls prism.