As the spatial information industry moves from an era of data collection to one of data maintenance, new integration methods to consolidate or to update datasets are required. These must reduce the discrepancies that are becoming increasingly apparent when spatial datasets are overlaid. It is essential that any such methods consider the quality characteristics of, firstly, the data being integrated and, secondly, the resultant data. This thesis develops techniques that give due consideration to data quality during the integration process.

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This thesis deals with training dataset and validation dataset for Earth observation classification accuracy improvement. Experiments with training data and validation data for two classification algorithms (Maximum Likelihood - MLC and Support Vector Machine - SVM) are carried out from the forest-meadow landscape located in the foothill of the Giant Mountains (Podkrkonoší). The thesis is base on the assumption that 1/3 of training data and 2/3 of validation data is an ideal ratio to achieve maximal classification accuracy (Foody, 2009). Another hypothesis was that in a case of SVM classification, a lower number of training point is required to achieve the same or similar accuracy of classification, as in the case of the MLC algorithm (Foody, 2004). The main goal of the thesis was to test the influence of proportion / amount of training and validation data on the classification accuracy of Sentinel - 2A multispectral data using the MLC algorithm. The highest overal accuracy using the MLC classification algorithm was achieved for 375 training and 625 validation points. The overal accuracy for this ratio was 72,88 %. The theory of Foody (2009) that 1/3 of training data and 2/3 of validation data is an ideal ratio to achieve the highest classification accuracy, was confirmed by the overal accuracy and...

Network traffic exhibits a high level of variability over short periods of time. This variability impacts negatively on the performance (accuracy) of anomaly-based network Intrusion Detection Systems (IDS) that are built using predictive models in a batch-learning setup. This thesis investigates how adapting the discriminating threshold of model predictions, specifically to the evaluated traffic, improves the detection rates of these Intrusion Detection models. Specifically, this thesis studied the adaptability features of three well known Machine Learning algorithms: C5.0, Random Forest, and Support Vector Machine. The ability of these algorithms to adapt their prediction thresholds was assessed and analysed under different scenarios that simulated real world settings using the prospective sampling approach. A new dataset (STA2018) was generated for this thesis and used for the analysis. This thesis has demonstrated empirically the importance of threshold adaptation in improving the accuracy of detection models when training and evaluation (test) traffic have different statistical properties. Further investigation was undertaken to analyse the effects of feature selection and data balancing processes on a model's accuracy when evaluation traffic with different significant features were used. The effects of threshold adaptation on reducing the accuracy degradation of these models was statistically analysed. The results showed that, of the three compared algorithms, Random Forest was the most adaptable and had the highest detection rates. This thesis then extended the analysis to apply threshold adaptation on sampled traffic subsets, by using different sample sizes, sampling strategies and label error rates. This investigation showed the robustness of the Random Forest algorithm in identifying the best threshold. The Random Forest algorithm only needed a sample that was 0.05% of the original evaluation traffic to identify a discriminating threshold with an overall accuracy rate of nearly 90% of the optimal threshold.

The industrial robot is a flexible and cheap standard component that can becombined with a milling head to complete low accuracy milling tasks. Thefuture goal for researchers and industry is to increase the milling accuracy, suchthat it can be introduced to more high value added operations.The serial build up of an industrial robot bring non-linear compliance andchallenges in vibration mitigation due to the member and reducer design. WithAdditive Manufacturing (AM), the traditional cast aluminum structure couldbe revised and, therefore, milling accuracy gain could be made possible due tostructural changes.This thesis proposes the structural changes that would improve the millingaccuracy for a specific trajectory. To quantify the improvement, first the robothad to be reverse engineered and a kinematic simulation model be built. Nextthe kinematic simulation process was automated such that multiple input parameterscould be varied and a screening conducted that proposed the mostprofitable change.It was found that a mass decrease in any member did not affect the millingaccuracy and a stiffness increase in the member of the second axis would increasethe milling accuracy the most, without changing the design concept. To changethe reducer in axis 1 would reduce the mean position error by 7.5 % and themean rotation error by 4.5 % approximately, but also reduces the maximumspeed of the robot. The best structural change would be to introduce twosupport bearings for axis two and three, which decreased the mean positioningerror and rotation error by approximately 8 % and 13 % respectively. / En industrirobot är en anpassningsbar och relativt billig standardkomponent.Den kan utrustas med ett fräshuvud för att genomföra fräsoperationer med låg noggrannhet. Det framtida målet för forskare och industri är att öka noggrannheten vid fräsning så att dess användningsområde kan utökas till ändamål som kräver högre precision.Den seriella uppbyggnaden av en industrirobot medför icke-linjär styvhet och därmed utmaningar vid vibrationsdämpning. Detta på grund av den strukturella uppbyggnaden då en industrirobot kan förenklat sägas vara uppbyggd av balkelement som i ledpunkterna kopplas samman av växellådor. Med friformsframställning kan en mer komplex struktur erhållas jämfört med traditionellt gjuten aluminiumkonstruktion därmed skulle en ökad noggrannhet vid fräsning kunna uppnås.Det här examensarbetet föreslår strukturella ändringar som skulle kunna öka noggrannheten vid fräsning för en specifik fräsbana. För att kvantifiera förbättringen, var det först nödvändigt att utgående från tillgänglig data konstruktion en specific robot samt att bygga en kinematisk modell. Därefter automatiserades beräkningsflödet så att ett flertal indata kunde varieras. Detta resulterande i en kombinationsstudie som visade den mest gynsamma strukturella förändringen.Det visade sig att en minskning av balkelementens massa inte påverkade nogrannheten. Att öka styvheten i balkelementet från den andra axeln skulle d¨aremot öka nogrannheten mest utan att behöva ändra robotens uppbyggnad.Att byta växellåda i första axeln kan öka positionsnogrannheten med nära 7.5 % och rotationsnoggrannheten med cirka 4.5 % men ändringen sänker samtidigt den maximala hastigheten. Den bästa strukturella förändringen vore att introducera ett stödlager vid axel två respektive tre, vilket skulle förbättra positionsnogrannheten med cirka 8 % och rotationsnogrannheten med nära 13 %.

Submitted by SANTIAGO BURGADA (sburgada@maxam.net) on 2015-01-25T12:10:08Z No. of bitstreams: 1 DISSERTATION SANTIAGO BURGADA CORPORATE INTERNATIONAL MASTERS SUBMISSION VERSION.pdf: 3588309 bytes, checksum: b70385fd690a43ddea32379f34b4afe9 (MD5) / Approved for entry into archive by Janete de Oliveira Feitosa (janete.feitosa@fgv.br) on 2015-02-04T19:27:15Z (GMT) No. of bitstreams: 1 DISSERTATION SANTIAGO BURGADA CORPORATE INTERNATIONAL MASTERS SUBMISSION VERSION.pdf: 3588309 bytes, checksum: b70385fd690a43ddea32379f34b4afe9 (MD5) / Approved for entry into archive by Marcia Bacha (marcia.bacha@fgv.br) on 2015-02-11T13:27:32Z (GMT) No. of bitstreams: 1 DISSERTATION SANTIAGO BURGADA CORPORATE INTERNATIONAL MASTERS SUBMISSION VERSION.pdf: 3588309 bytes, checksum: b70385fd690a43ddea32379f34b4afe9 (MD5) / Made available in DSpace on 2015-02-11T13:34:18Z (GMT). No. of bitstreams: 1 DISSERTATION SANTIAGO BURGADA CORPORATE INTERNATIONAL MASTERS SUBMISSION VERSION.pdf: 3588309 bytes, checksum: b70385fd690a43ddea32379f34b4afe9 (MD5) Previous issue date: 2014-10-29 / Industrial companies in developing countries are facing rapid growths, and this requires having in place the best organizational processes to cope with the market demand. Sales forecasting, as a tool aligned with the general strategy of the company, needs to be as much accurate as possible, in order to achieve the sales targets by making available the right information for purchasing, planning and control of production areas, and finally attending in time and form the demand generated. The present dissertation uses a single case study from the subsidiary of an international explosives company based in Brazil, Maxam, experiencing high growth in sales, and therefore facing the challenge to adequate its structure and processes properly for the rapid growth expected. Diverse sales forecast techniques have been analyzed to compare the actual monthly sales forecast, based on the sales force representatives’ market knowledge, with forecasts based on the analysis of historical sales data. The dissertation findings show how the combination of both qualitative and quantitative forecasts, by the creation of a combined forecast that considers both client´s demand knowledge from the sales workforce with time series analysis, leads to the improvement on the accuracy of the company´s sales forecast.