Ydinreaktorin painesäiliön ulkoisen jäähdytyksen lämmönsiirtokokeita

The purpose of this work was to design and carry out thermal-hydraulic experiments dealing with overcooling transients of a VVER-440-type nuclear reactor pressure vessel. Sudden overcooling accident could have negative effect on the mechanical strength of the pressure vessel. If part of the pressure vessel is compromised, the intense pressure inside a pressurized water reactor could cause the wall to fracture. Information on the heat transfer along the outside of the pressure vessel wall is necessary for stress analysis. Basic knowledge of the overcooling accident and heat transfer types on the outside of the pressure vessel is presented as background information. Test facility was designed and built based to study and measure heat transfer during specific overcooling scenarios. Two test series were conducted with the first one concentrating on the very beginning of the transient and the second one concentrating on steady state heat transfer. Heat transfer coefficients are calculated from the test data using an inverse method, which yields better results in fast transients than direct calculation from the measurement results. The results show that heat transfer rate varies considerably during the transient, being very high in the beginning and dropping to steady state in a few minutes. The test results show that appropriate correlations can be used in future analysis.

Korkeapainevesisumusuuttimen virtaustekninen laadunhallinta

Tämä työ vastaa tarpeeseen hallita korkeapainevesisumusuuttimen laatua virtausmekaniikan työkalujen avulla. Työssä tutkitaan suutinten testidatan lisäksi virtauksen käyttäytymistä suuttimen sisällä CFD-laskennan avulla. Virtausmallinnus tehdään Navier-Stokes –pohjaisella laskentamenetelmällä. Työn teoriaosassa käsitellään virtaustekniikkaa ja sen kehitystä yleisesti. Lisäksi esitetään suuttimen laskennassa käytettävää perusteoriaa sekä teknisiä ratkaisuja. Teoriaosassa käydään myös läpi laskennalliseen virtausmekaniikkaan (CFD-laskenta) liittyvää perusteoriaa. Tutkimusosiossa esitetään käsitellyt suutintestitulokset sekä mallinnetaan suutinvirtausta ajasta riippumattomaan virtauslaskentaan perustuvalla laskentamenetelmällä. Virtauslaskennassa käytetään OpenFOAM-laskentaohjelmiston SIMPLE-virtausratkaisijaa sekä k-omega SST –turbulenssimallia. Tehtiin virtausmallinnus kaikilla paineilla, joita suuttimen testauksessa myös todellisuudessa käytetään. Lisäksi selvitettiin mahdolliset kavitaatiokohdat suuttimessa ja suunniteltiin kavitaatiota ehkäisevä suutingeometria. Todettiin myös lämpötilan ja epäpuhtauksien vaikuttavan kavitaatioon sekä mallinnettiin lämpötilan vaikutusta. Luotiin malli, jolla suuttimen suunnitteluun liittyviin haasteisiin voidaan vastata numeerisella laskennalla.

Sensor communication in Smart cities and regions: An efficient IoT-based remote health monitoring system

Recent advances in Information and Communication Technology (ICT), especially those related to the Internet of Things (IoT), are facilitating smart regions. Among many services that a smart region can offer, remote health monitoring is a typical application of IoT paradigm. It offers the ability to continuously monitor and collect health-related data from a person, and transmit the data to a remote entity (for example, a healthcare service provider) for further processing and knowledge extraction. An IoT-based remote health monitoring system can be beneficial in rural areas belonging to the smart region where people have limited access to regular healthcare services. The same system can be beneficial in urban areas where hospitals can be overcrowded and where it may take substantial time to avail healthcare. However, this system may generate a large amount of data. In order to realize an efficient IoT-based remote health monitoring system, it is imperative to study the network communication needs of such a system; in particular the bandwidth requirements and the volume of generated data. The thesis studies a commercial product for remote health monitoring in Skellefteå, Sweden. Based on the results obtained via the commercial product, the thesis identified the key network-related requirements of a typical remote health monitoring system in terms of real-time event update, bandwidth requirements and data generation. Furthermore, the thesis has proposed an architecture called IReHMo - an IoT-based remote health monitoring architecture. This architecture allows users to incorporate several types of IoT devices to extend the sensing capabilities of the system. Using IReHMo, several IoT communication protocols such as HTTP, MQTT and CoAP has been evaluated and compared against each other. Results showed that CoAP is the most efficient protocol to transmit small size healthcare data to the remote servers. The combination of IReHMo and CoAP significantly reduced the required bandwidth as well as the volume of generated data (up to 56 percent) compared to the commercial product. Finally, the thesis conducted a scalability analysis, to determine the feasibility of deploying the combination of IReHMo and CoAP in large numbers in regions in north Sweden.

I must walk through the gate : an ontological necessity

This research is a self-study into my life as an athlete, elementary school teacher, leamer, and as a teacher educator/academic. Throughout the inquiry, I explore how my beliefs and values infused my lived experiences and ultimately influenced my constructivist, humanist, and ultimately my holistic teaching and learning practice which at times disrupted the status quo. I have written a collection of narratives (data generation) which embodied my identity as an unintelligent student/leamer, a teacher/learner, an experiential learner, a tenacious participant, and a change agent to name a few. As I unpack my stories and hermeneutically reconstruct their intent, I question their meaning as I explore how I can improve my teaching and learning practice and potentially effect positive change when instructing beginning teacher candidates at a Faculty of Education. At the outset I situate my story and provide the necessary political, social, and cultural background information to ground my research. I follow this with an in depth look at the elements that interconnect the theoretical framework of this self-study by presenting the notion of writing at the boundaries through auto ethnography (Ellis, 2000; Ellis & Bochner, 2004) and writing as a method of inquiry (Richardson, 2000). The emergent themes of experiential learning, identity, and embodied knowing surfaced during the data generation phase. I use the Probyn' s (1990) .. metaphor of locatedness to unpack these themes and ponder the question, Where is experience located? I deepen the exploration by layering Drake's (2007) KnowlDo/Be framework alongside locatedness and offer descriptions of learning moments grounded in pedagogical theories. In the final phase, I introduce thirdspace theory (Bhabha, 1994; Soja, 1996) as a space that allowed me to puzzle educational dilemmas and begin to reconcile the binaries that existed in my life both personally, and professionally. I end where I began by revisiting the questions that drove this study. In addition, Ireflect upon the writing process and the challenges that I encountered while immersed in this approach and contemplate the relevance of conducting a self-study. I leave the reader with what is waiting for me on the other side of the gate, for as Henry James suggested, "Experience is never limited, and it is never complete."

PERFORMANCE OF DIFFERENT CLASSIFIERS IN SPEECH RECOGNITION

Speech is the most natural means of communication among human beings and speech processing and recognition are intensive areas of research for the last five decades. Since speech recognition is a pattern recognition problem, classification is an important part of any speech recognition system. In this work, a speech recognition system is developed for recognizing speaker independent spoken digits in Malayalam. Voice signals are sampled directly from the microphone. The proposed method is implemented for 1000 speakers uttering 10 digits each. Since the speech signals are affected by background noise, the signals are tuned by removing the noise from it using wavelet denoising method based on Soft Thresholding. Here, the features from the signals are extracted using Discrete Wavelet Transforms (DWT) because they are well suitable for processing non-stationary signals like speech. This is due to their multi- resolutional, multi-scale analysis characteristics. Speech recognition is a multiclass classification problem. So, the feature vector set obtained are classified using three classifiers namely, Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Naive Bayes classifiers which are capable of handling multiclasses. During classification stage, the input feature vector data is trained using information relating to known patterns and then they are tested using the test data set. The performances of all these classifiers are evaluated based on recognition accuracy. All the three methods produced good recognition accuracy. DWT and ANN produced a recognition accuracy of 89%, SVM and DWT combination produced an accuracy of 86.6% and Naive Bayes and DWT combination produced an accuracy of 83.5%. ANN is found to be better among the three methods.

Mechanical Properties of Steel Fiber-Reinforced Concrete

This paper presents the results from an experimental program and an analytical assessment of the influence of addition of fibers on mechanical properties of concrete. Models derived based on the regression analysis of 60 test data for various mechanical properties of steel fiber-reinforced concrete have been presented. The various strength properties studied are cube and cylinder compressive strength, split tensile strength, modulus of rupture and postcracking performance, modulus of elasticity, Poisson’s ratio, and strain corresponding to peak compressive stress. The variables considered are grade of concrete, namely, normal strength 35 MPa , moderately high strength 65 MPa , and high-strength concrete 85 MPa , and the volume fraction of the fiber Vf =0.0, 0.5, 1.0, and 1.5% . The strength of steel fiber-reinforced concrete predicted using the proposed models have been compared with the test data from the present study and with various other test data reported in the literature. The proposed model predicted the test data quite accurately. The study indicates that the fiber matrix interaction contributes significantly to enhancement of mechanical properties caused by the introduction of fibers, which is at variance with both existing models and formulations based on the law of mixtures

Indicators of nitrate in wetland surface and soil-waters: interactions of vegetation and environmental factors

This paper describes a new bio-indicator method for assessing wetland ecosystem health: as such, the study is particularly relevant to current legislation such as the EU Water Framework Directive, which provides a baseline of the current status Of Surface waters. Seven wetland sites were monitored across northern Britain, with model construction data for predicting, eco-hydroloplical relationships collected from five sites during 1999, Two new sites and one repeat site were monitored during 2000 to provide model test data. The main growing season for the vegetation, and hence the sampling period, was May-August during both years. Seasonal mean concentrations of nitrate (NO3-) in surface and soil water samples during 1999 ranged from 0.01 to 14.07 mg N 1(-1), with a mean value of 1.01 mg N 1(-1). During 2000, concentrations ranged from trace level (<0.01 m- N 1(-1)) to 9.43 mg N 1(-1), with a mean of 2.73 mg N 1(.)(-1) Surface and soil-water nitrate concentrations did not influence plant species composition significantly across representative tall herb fen and mire communities. Predictive relationships were found between nitrate concentrations and structural characteristics of the wetland vegetation, and a model was developed which predicted nitrate concentrations from measures of plant diversity, canopy structure and density of reproductive structures. Two further models, which predicted stem density and density of reproductive structures respectively, utilised nitrate concentration as one of the independent predictor variables. Where appropriate, the models were tested using data collected during 2000. This approach is complementary to species-based monitoring, representing a useful and simple too] to assess ecological status in target wetland systems and has potential for bio-indication purposes.

Predicting feed quality - chemical analysis and in vitro evaluation

As the ideal method of assessing the nutritive value of a feedstuff, namely offering it to the appropriate class of animal and recording the production response obtained, is neither practical nor cost effective a range of feed evaluation techniques have been developed. Each of these balances some degree of compromise with the practical situation against data generation. However, due to the impact of animal-feed interactions over and above that of feed composition, the target animal remains the ultimate arbitrator of nutritional value. In this review current in vitro feed evaluation techniques are examined according to the degree of animal-feed interaction. Chemical analysis provides absolute values and therefore differs from the majority of in vitro methods that simply rank feeds. However, with no host animal involvement, estimates of nutritional value are inferred by statistical association. In addition given the costs involved, the practical value of many analyses conducted should be reviewed. The in sacco technique has made a substantial contribution to both understanding rumen microbial degradative processes and the rapid evaluation of feeds, especially in developing countries. However, the numerous shortfalls of the technique, common to many in vitro methods, the desire to eliminate the use of surgically modified animals for routine feed evaluation, paralleled with improvements in in vitro techniques, will see this technique increasingly replaced. The majority of in vitro systems use substrate disappearance to assess degradation, however, this provides no information regarding the quantity of derived end-products available to the host animal. As measurement of volatile fatty acids or microbial biomass production greatly increases analytical costs, fermentation gas release, a simple and non-destructive measurement, has been used as an alternative. However, as gas release alone is of little use, gas-based systems, where both degradation and fermentation gas release are measured simultaneously, are attracting considerable interest. Alternative microbial inocula are being considered, as is the potential of using multi-enzyme systems to examine degradation dynamics. It is concluded that while chemical analysis will continue to form an indispensable part of feed evaluation, enhanced use will be made of increasingly complex in vitro systems. It is vital, however, the function and limitations of each methodology are fully understood and that the temptation to over-interpret the data is avoided so as to draw the appropriate conclusions. With careful selection and correct application in vitro systems offer powerful research tools with which to evaluate feedstuffs. (C) 2003 Elsevier B.V. All rights reserved.

Constrained models of evolution lead to improved prediction of functional linkage from correlated gain and loss of genes

Motivation: We compare phylogenetic approaches for inferring functional gene links. The approaches detect independent instances of the correlated gain and loss of pairs of genes from species' genomes. We investigate the effect on results of basing evidence of correlations on two phylogenetic approaches, Dollo parsminony and maximum likelihood (ML). We further examine the effect of constraining the ML model by fixing the rate of gene gain at a low value, rather than estimating it from the data. Results: We detect correlated evolution among a test set of pairs of yeast (Saccharomyces cerevisiae) genes, with a case study of 21 eukaryotic genomes and test data derived from known yeast protein complexes. If the rate at which genes are gained is constrained to be low, ML achieves by far the best results at detecting known functional links. The model then has fewer parameters but it is more realistic by preventing genes from being gained more than once. Availability: BayesTraits by M. Pagel and A. Meade, and a script to configure and repeatedly launch it by D. Barker and M. Pagel, are available at http://www.evolution.reading.ac.uk .

Quantifying the residual creep life of polyester mooring ropes

This paper assesses the ARELIS (Assured Residual Life Span) method for estimating residual creep life of polyester rope used in deepwater mooring lines. A statistical model has been developed to quantify the uncertainties in the method, such as the scatter in creep rupture test data and load sharing between sub-ropes. This model can be used to determine the required test load, duration and number of ARELIS tests, in order to guarantee a minimum creep life for a mooring line at its service load. Creep rupture tests have been performed to provide input for the statistical model.

Dual-orthogonal radial basis function networks for nonlinear time series prediction

A new structure of Radial Basis Function (RBF) neural network called the Dual-orthogonal RBF Network (DRBF) is introduced for nonlinear time series prediction. The hidden nodes of a conventional RBF network compare the Euclidean distance between the network input vector and the centres, and the node responses are radially symmetrical. But in time series prediction where the system input vectors are lagged system outputs, which are usually highly correlated, the Euclidean distance measure may not be appropriate. The DRBF network modifies the distance metric by introducing a classification function which is based on the estimation data set. Training the DRBF networks consists of two stages. Learning the classification related basis functions and the important input nodes, followed by selecting the regressors and learning the weights of the hidden nodes. In both cases, a forward Orthogonal Least Squares (OLS) selection procedure is applied, initially to select the important input nodes and then to select the important centres. Simulation results of single-step and multi-step ahead predictions over a test data set are included to demonstrate the effectiveness of the new approach.

Superensembles of linear viscoelastic models of polymer melts

The idea of incorporating multiple models of linear rheology into a superensemble, to forge a consensus forecast from the individual model predictions, is investigated. The relative importance of the individual models in the so-called multimodel superensemble (MMSE) was inferred by evaluating their performance on a set of experimental training data, via nonlinear regression. The predictive ability of the MMSE model was tested by comparing its predictions on test data that were similar (in-sample) and dissimilar (out-of-sample) to the training data used in the calibration. For the in-sample forecasts, we found that the MMSE model easily outperformed the best constituent model. The presence of good individual models greatly enhanced the MMSE forecast, while the presence of some bad models in the superensemble also improved the MMSE forecast modestly. While the performance of the MMSE model on the out-of-sample training data was not as spectacular, it demonstrated the robustness of this approach.

Random Prism: a noise-tolerant alternative to Random Forests

Ensemble learning can be used to increase the overall classification accuracy of a classifier by generating multiple base classifiers and combining their classification results. A frequently used family of base classifiers for ensemble learning are decision trees. However, alternative approaches can potentially be used, such as the Prism family of algorithms that also induces classification rules. Compared with decision trees, Prism algorithms generate modular classification rules that cannot necessarily be represented in the form of a decision tree. Prism algorithms produce a similar classification accuracy compared with decision trees. However, in some cases, for example, if there is noise in the training and test data, Prism algorithms can outperform decision trees by achieving a higher classification accuracy. However, Prism still tends to overfit on noisy data; hence, ensemble learners have been adopted in this work to reduce the overfitting. This paper describes the development of an ensemble learner using a member of the Prism family as the base classifier to reduce the overfitting of Prism algorithms on noisy datasets. The developed ensemble classifier is compared with a stand-alone Prism classifier in terms of classification accuracy and resistance to noise.

User-friendly free-text keystroke dynamics authentication for practical applications

This paper introduces a novel approach for free-text keystroke dynamics authentication which incorporates the use of the keyboard’s key-layout. The method extracts timing features from specific key-pairs. The Euclidean distance is then utilized to find the level of similarity between a user’s profile data and his/her test data. The results obtained from this method are reasonable for free-text authentication while maintaining the maximum level of user relaxation. Moreover, it has been proven in this study that flight time yields better authentication results when compared with dwell time. In particular, the results were obtained with only one training sample for the purpose of practicality and ease of real life application.

Forecasting with difference and trend stationary models

Although difference-stationary (DS) and trend-stationary (TS) processes have been subject to considerable analysis, there are no direct comparisons for each being the data-generation process (DGP). We examine incorrect choice between these models for forecasting for both known and estimated parameters. Three sets of Monte Carlo simulations illustrate the analysis, to evaluate the biases in conventional standard errors when each model is mis-specified, compute the relative mean-square forecast errors of the two models for both DGPs, and investigate autocorrelated errors, so both models can better approximate the converse DGP. The outcomes are surprisingly different from established results.