published_or_final_version / Electrical Engineering / Master / Master of Science in Engineering

Many utility-scale wind turbine generators use wound-rotor induction machines. By adding an external rotor resistance to the rotor circuit it is possible to control the wind turbine output power and, with proper control, maintain a constant power for wind speeds between rated and cut-out. The external resistance modifies the generator torque-speed curve and changes the angular velocity of the rotor, resulting in a greater power extraction from the wind. A number of control strategies can achieve this objective. These include controlling the rotor resistance to maintain a constant generator equivalent circuit, and control based on the aerodynamic torque. It is also possible to use a lookup table instead of a feedback controller. These options all have the same steady-state result as direct output power control, but differing transient performance. Computer simulations and hardware experiments are used to investigate and characterize the different control methods. / text

<p>In this thesis a hybrid electrical powertrain called the Four Quadrant Transducer (4QT) has been evaluated through different driving simulations, which later resulted in the manufacture of a prototype.</p><p>The simulation of a 12 metric ton distribution truck showed that the 4QT system can reduce the fuel consumption by approximately 30 % during the FTP75 drive cycle. The reduction in fuel consumption is due to a more optimal control of the combustion engine and regenerative braking of the vehicle.</p><p>The prototype 4QT has been down scaled from the distribution truck size used in the simulations to a size suitable for a medium sized passenger car. This was done to fit the test rig in the electric machine laboratory.</p><p>The prototype was tested in the test bench to analyse performances such as efficiency, losses and thermal behaviour. These factors were investigated using both analytical models and the finite element method and later by measurements. The measured results were according to expectations.</p> / <p>I denna doktorsavhandling presenteras ett nytt elhybridsystem för vägfordon benämnt fyrkvadrant omvandlare, "Four Quadrant Transducer (4QT)". Detta system har simulerats under körcykler som t ex FTP75 för att kunna bilda sig en uppfattning om bränsleförbrukningen för hybridsystemet och för att kunna dimensionera elmaskinerna till systemet. En elmaskinprototyp för hybridsystemet har konstruerats och provats i momentvåg.</p><p>Enligt utförda simuleringar blir besparingen i bränsleförbrukning ca 30% för en tolv tons distributionslastbil utrustad med en 100kW dieselmotor under körcykeln FTP75. Denna minskning av bränsleförbrukning kommer främst från en mera optimal kontroll av förbränningsmotorn samt regenerativ bromsning av fordonet.</p><p>Den konstruerade prototypen är avsedd för en medelstor bil. Anledningen till att prototypen inte byggdes i en storlek passande för distributionslastbilen var att prototypen skulle passa i testutrustningen i elmaskinlaboratoriet.</p><p>Prototypen provades i momentvåg för att undersöka verkningsgrad, förluster och termiska prestanda. Resultaten är enligt förväntningarna.</p>

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Memory is a complex cognitive process which has been widely researched within the field of neuropsychology. In clinical studies of adults, the Wechsler Memory Scale (WMS) is widely used. At this time there is no comparable clinical tool within the child literature pertaining to memory. There are studies which have extended the age limits of the WMS, but the youngest sample ranged from 10 to 14 years of age. The present research was conducted in two studies. The first study concerns the development of a memory scale for use with children aged six to twelve. This scale, Wilson's Adapted Memory Scale for Children (WAMS-C), was constructed utilizing the basic structure and subtests of the WMS. The scale was administered to 33 normal children, ranging in age from 6 to 12 years. It was hypothesized that the scale would reflect the developmental nature of memory as well as the relationship between memory and intelligence. The second study compared the memory skills of a learning disabled (LD) sample of children to those of a sample of normal learning (NL) children. A specific profile of academic achievement was used to define the LD children who participated in this study. (Reading and Spelling impaired, and relatively better Arithmetic skills). Research conducted by Rourke and his associates identified this subtype of LD children and provided predictions pertaining to their differential performance on verbal and visual tasks. The WAMS-C contains both verbal and visual memory tasks. It was predicted that these children would (1) do less well than NL children on the memory scale and (2) that these LD children would be impaired on the verbal memory portion of the WAMS-C, compared to NL children, but would exhibit equivalent performance on the visual memory tasks. The results of the studies showed the WAMS-C to reflect the developmental nature of memory and the relationship with intelligence. Also, LD children had significantly lower scores on the WAMS-C. However, neither the verbal or visual subtests differentiated between groups. Rather, subtests which may reflect short-term memory deficits and/or attentional problems appeared responsible for the differences found.

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Within the field of pattern recognition (PR) a very active area is the clustering and classification of multispectral data, which basically aims to allocate the right class of ground category to a reflectance or radiance signal. Generally, the problem complexity is related to the incorporation of spatial characteristics that are complementary to the nonlinearities of land surface process heterogeneity, remote sensing effects and multispectral features. The present research describes the application of learning machine methods to accomplish the above task by inducting a relationship between the spectral response of farms’ land cover, and their farming system typology from a representative set of instances. Such methodologies are not traditionally used in crop-livestock studies. Nevertheless, this study shows that its application leads to simple and theoretically robust classification models. The study has covered the following phases: a)geovisualization of crop-livestock systems; b)feature extraction of both multispectral and attributive data and; c)supervised farm classification. The first is a complementary methodology to represent the spatial feature intensity of farming systems in the geographical space. The second belongs to the unsupervised learning field, which mainly involves the appropriate description of input data in a lower dimensional space. The last is a method based on statistical learning theory, which has been successfully applied to supervised classification problems and to generate models described by implicit functions. In this research the performance of various kernel methods applied to the representation and classification of crop-livestock systems described by multispectral response is studied and compared. The data from those systems include linear and nonlinearly separable groups that were labelled using multidimensional attributive data. Geovisualization findings show the existence of two well-defined farm populations within the whole study area; and three subgroups in relation to the Guarico section. The existence of these groups was confirmed by both hierarchical and kernel clustering methods, and crop-livestock systems instances were segmented and labeled into farm typologies based on: a)milk and meat production; b)reproductive management; c)stocking rate; and d)crop-forage-forest land use. The minimum set of labeled examples to properly train the kernel machine was 20 instances. Models inducted by training data sets using kernel machines were in general terms better than those from hierarchical clustering methodologies. However, the size of the training data set represents one of the main difficulties to be overcome in permitting the more general application of this technique in farming system studies. These results attain important implications for large scale monitoring of crop-livestock system; particularly to the establishment of balanced policy decision, intervention plans formulation, and a proper description of target typologies to enable investment efforts to be more focused at local issues.

Context. Light weight process virtualization has been used in the past e.g., Solaris zones, jails in Free BSD and Linux’s containers (LXC). But only since 2013 is there a kernel support for user namespace and process grouping control that make the use of lightweight virtualization interesting to create virtual environments comparable to virtual machines. Telecom providers have to handle the massive growth of information due to the growing number of customers and devices. Traditional databases are not designed to handle such massive data ballooning. NoSQL databases were developed for this purpose. Cassandra, with its high read and write throughputs, is a popular NoSQL database to handle this kind of data. Running the database using operating system virtualization or containerization would offer a significant performance gain when compared to that of virtual machines and also gives the benefits of migration, fast boot up and shut down times, lower latency and less use of physical resources of the servers. Objectives. This thesis aims to investigate the trade-off in performance while loading a Cassandra cluster in bare-metal and containerized environments. A detailed study of the effect of loading the cluster in each individual node in terms of Latency, CPU and Disk throughput will be analyzed. Methods. We implement the physical model of the Cassandra cluster based on realistic and commonly used scenarios or database analysis for our experiment. We generate different load cases on the cluster for bare-metal and Cassandra in docker scenarios and see the values of CPU utilization, Disk throughput and latency using standard tools like sar and iostat. Statistical analysis (Mean value analysis, higher moment analysis, and confidence intervals) are done on measurements on specific interfaces in order to increase the reliability of the results. Results.Experimental results show a quantitative analysis of measurements consisting Latency, CPU and Disk throughput while running a Cassandra cluster in Bare Metal and Container Environments.A statistical analysis summarizing the performance of Cassandra cluster is surveyed. Results.Experimental results show a quantitative analysis of measurements consisting Latency, CPU and Disk throughput while running a Cassandra cluster in Bare Metal and Container Environments.A statistical analysis summarizing the performance of Cassandra cluster is surveyed. Conclusions. With the detailed analysis, the resource utilization of the database was similar in both the bare-metal and container scenarios. Disk throughput is similar in the case of mixed load and containers have a slight overhead in the case of write loads for both the maximum load case and 66% of maximum load case. The latency values inside the container are slightly higher for all the cases. The mean value analysis and higher moment analysis helps us in doing a finer analysis of the results. The confidence intervals calculated show that there is a lot of variation in the disk performance which might be due to compactions happening randomly. Future work in the area can be done on compaction strategies.

The study explores binary classification with Support Vector Machines as means to predict a satisfaction score based on customer surveys in the customer supportdomain. Standard feature selection methods and their impact on results are evaluated and a feature scoring metric Log Odds Ratio is implemented for addressingasymmetrical class distributions. Results show that the feature selection andscoring methods implemented improve performance significantly. Results alsoshow that it is possible to get decent predictive values on test data based onlimited amount of training observations. However mixed results are presentedin a real-world application example as a there is a significant error rate fordiscriminating the minority class. We also show the negative effects of usingcommon metrics such as accuracy and f-measure for optimizing models whendealing with high-skew data in a classification context.

Computer-based drug design is a vital area of pharmaceutical chemistry; Quantitative Structure-Activity Relationships (QSARs), determined computationally from experimental observations, are crucial in identifying candidate drugs by early screening, saving time on synthesis and in vivo testing. This thesis investigates the viability and the practicalities of using Mass Spectra-based pseudo-molecular descriptors, in comparison with other molecular descriptor systems, to predict the carcinogenicity, mutagenicity and the Cltransport inhibiting ability of a variety of molecules, and in the first case, of chemotherapeutic drugs particularly. It does so by identifying a number of QSARs which link the physical properties of chemicals with their concomitant activities in a reliable and mathematical manner. First, this thesis confirms that carcinogenicity and mutagenicity are indeed predictable using a variety of Artificial Intelligence techniques, both supervised and unsupervised, information germane to pharmaceutical research groups interested in the preliminary screening of candidate anti-cancer drugs. Secondly, it demonstrates that Mass Spectral intensities possess great descriptive fidelity and shows that reducing the burden of dimensionality is not only important, but imperative; selecting this smaller set of orthogonal descriptors is best achieved using Principal Component Analysis as opposed to the selection of a set of the most frequent fragments, or the use of every peak up to a number determined by the boundaries of supervised learning. Thirdly, it introduces a novel system of backpropagation and demonstrates that it is more efficient than its principal competitor at monitoring a series of connection weights when applied to this area of research, which requires complex relationships. Finally, it promulgates some preliminary conclusions about which AI techniques are applicable to certain problem-scenarios, how these techniques might be applied, and the likelihood that that application will result in the identification of series of reliable QSARs.