This thesis deals with the optimization of distribution of bakery products. Firstly there are the fundamental types of vehicle routing problems and their optimization models introduced. Next part is dedicated to heuristic algorithms. The heuristic methods are introduced in general, then there are the chosen methods described. Later there are two chosen algorithms formulated. First one based on the nearest neighbour method and another one based on the savings algorithm. Both of algorithms were programmed in the Visual Basic of Applications MS Excel 2010. These algorithms were applied for the solution of the real problem dealing with the distribution of goods. The bakery company has provided the data about its customers for this purpose. The last part of this thesis is dedicated to the summary and comparison of the solution of the assigned problem that was gained by the proposed algorithms with the solution that the bakery company has put into practice.

Lattice Density Functional Theory is a powerful method to treat equilibrium structural properties and non-equilibrium kinetics of condensed matter systems. In this thesis an approach based on Markov chains is followed to derive exact density functionals for interacting particles in one-dimension. First, hard rod mixtures on a lattice are considered. For the treatment of this system, certain sets of site occupation numbers are introduced. These sets reflect zero-dimensional or one-particle cavities in continuum treatments, which can hold at most one particle. The exact functional follows from rather simple probabilistic arguments. Thereby the derivation simplifies an earlier, more complicated treatment. A rearrangement of the functional casts it into a form according to lattice fundamental measure theory. This makes it possible to systematically setup approximate density functionals in higher dimensions, which become exact under dimensional reduction. In the next step, the theory is extended to hard rod mixtures with contact interactions. Finally, hard rods with arbitrary nearest-neighbor interactions extending over two rod lengths are studied. For those interactions, two types of zero-dimensional cavities need to be introduced. The first one is a one-particle cavity corresponding to a set of occupation numbers with at most one occupation number being nonzero. The second type is a two-particle cavity, which is a cavity that cannot hold more than two particles, that means at most two occupation numbers can be one in the corresponding set. In order to account for time-dependent kinetics, a lattice version of Time-Dependent Density Functional Theory is followed and applied to hard rods with contact interactions.

In this report it is evaluated whether a higher dimensional fingerprint vector increases accuracy of an algorithm for indoor localisation. Many solutions use a Received Signal Strength Indicator (RSSI) to estimate a position. It was studied if the use of the Channel State Information (CSI), i.e. the channel’s frequency response, is beneficial for the accuracy.The localisation algorithm estimates the position of a new measurement by comparing it to previous measurements using k-Nearest Neighbour (k-NN) regression. The mean power was used as RSSI and 100 samples of the frequency response as CSI. Reduction of the dimension of the CSI vector with statistical moments and Principal Component Analysis (PCA) was tested. An improvement in accuracy could not be observed by using a higher dimensional fingerprint vector than RSSI. A standardised Euclidean or Mahalanobis distance measure in the k-NN algorithm seemed to perform better than Euclidean distance. Taking the logarithm of the frequency response samples before doing any calculation also seemed to improve accuracy. / I denna rapport utvärderas huruvida data av högre dimension ökar noggrannheten hos en algoritm för inomhuspositionering. Många lösningar använder en indikator för mottagen signalstyrka (RSSI) för att skatta en position. Det studerades studerade om användningen av kanalens fysikaliska tillstånd (CSI), det vill säga kanalens frekvenssvar, är fördelaktig för noggrannheten.Positioneringsalgoritmen skattar positionen för en ny mätning genom att jämföra den med tidigare mätningar med k-Nearest Neighbour (k-NN)-regression. Medeleffekten användes som RSSI och 100 sampel av frekvenssvaret som CSI. Reducering av CSI vektornsdimension med statistiska moment och Principalkomponentanalys(PCA) testades. En förbättring av noggrannheten kunde inte observeras genom att använda data med högre dimension än RSSI. Ett standardiserat Euklidiskt eller Mahalanobis avståndsåatt i k-NN-algoritmen verkade prestera bättre än Euklidiskt avstånd. Att ta logaritmen av frekvenssvarets sampel innan andra beräkningar gjordes verkade också förbättra noggrannheten.

Machine learning is a central technology in data-driven decision making. In this study, machine learning in the context of demand forecasting in the motion picture industry from film exhibitors’ perspective is investigated. More specifically, it is investigated to what extent the technology can assist estimation of public interest in terms of revenue levels of unreleased movies. Three machine learning models are implemented with the aim to forecast cumulative revenue levels during the opening weekend of various movies which were released in 2010-2017 in Sweden. The forecast is based on ten attributes which range from public online user-generated data to specific movie characteristics such as production budget and cast. The results indicate that the choice of attributes as well as models in this study were not optimal on the Swedish market as the retrieved values from relevant precision metrics were inadequate, however with valid underlying reasons. / Maskininlärning är en central teknik i datadrivet beslutsfattande. I den här rapporten utreds maskininlärning isammanhanget av efterfrågeprediktion i filmbranschen från biografers perspektiv. Närmare bestämt undersöks det i vilken utsträckningtekniken kan bistå uppskattning av publikintresse i termer av intäkter vad gäller osläppta filmer hos biografer. Tremaskininlärningsmodeller implementeras i syfte att göra en prognos på kumulativa intäktsnivåer under premiärhelgen för filmer vilkahade premiär 2010-2017 i Sverige. Prognostiseringen baseras på varierande attribut som sträcker sig från publik användargenererad data på nätet till filmspecifika variabler så som produktionsbudget och uppsättning av skådespelare. De erhållna resultaten visar att valen av attribut och modeller inte var optimala på den svenska marknaden då erhållna precisionsmått från modellerna antog låga värden, med relevanta underliggande skäl.

A good image representation is vital for image comparision and classification; it may affect the classification accuracy and efficiency. The purpose of this thesis was to explore novel and appropriate image representations. Another aim was to investigate these representations for image classification. Finally, novel features were examined for improving image classification accuracy. Images of interest to this thesis were textile design images. The motivation of analysing textile design images is to help designers browse images, fuel their creativity, and improve their design efficiency. In recent years, bag-of-words model has been shown to be a good base for image representation, and there have been many attempts to go beyond this representation. Bag-of-words models have been used frequently in the classification of image data, due to good performance and simplicity. “Words” in images can have different definitions and are obtained through steps of feature detection, feature description, and codeword calculation. The model represents an image as an orderless collection of local features. However, discarding the spatial relationships of local features limits the power of this model. This thesis exploited novel image representations, bag of shapes and region label graphs models, which were based on bag-of-words model. In both models, an image was represented by a collection of segmented regions, and each region was described by shape descriptors. In the latter model, graphs were constructed to capture the spatial information between groups of segmented regions and graph features were calculated based on some graph theory. Novel elements include use of MRFs to extract printed designs and woven patterns from textile images, utilisation of the extractions to form bag of shapes models, and construction of region label graphs to capture the spatial information. The extraction of textile designs was formulated as a pixel labelling problem. Algorithms for MRF optimisation and re-estimation were described and evaluated. A method for quantitative evaluation was presented and used to compare the performance of MRFs optimised using alpha-expansion and iterated conditional modes (ICM), both with and without parameter re-estimation. The results were used in the formation of the bag of shapes and region label graphs models. Bag of shapes model was a collection of MRFs' segmented regions, and the shape of each region was described with generic Fourier descriptors. Each image was represented as a bag of shapes. A simple yet competitive classification scheme based on nearest neighbour class-based matching was used. Classification performance was compared to that obtained when using bags of SIFT features. To capture the spatial information, region label graphs were constructed to obtain graph features. Regions with the same label were treated as a group and each group was associated uniquely with a vertex in an undirected, weighted graph. Each region group was represented as a bag of shape descriptors. Edges in the graph denoted either the extent to which the groups' regions were spatially adjacent or the dissimilarity of their respective bags of shapes. Series of unweighted graphs were obtained by removing edges in order of weight. Finally, an image was represented using its shape descriptors along with features derived from the chromatic numbers or domination numbers of the unweighted graphs and their complements. Linear SVM classifiers were used for classification. Experiments were implemented on data from Liberty Art Fabrics, which consisted of more than 10,000 complicated images mainly of printed textile designs and woven patterns. Experimental data was classified into seven classes manually by assigning each image a text descriptor based on content or design type. The seven classes were floral, paisley, stripe, leaf, geometric, spot, and check. The result showed that reasonable and interesting regions were obtained from MRF segmentation in which alpha-expansion with parameter re-estimation performs better than alpha-expansion without parameter re-estimation or ICM. This result was not only promising for textile CAD (Computer-Aided Design) to redesign the textile image, but also for image representation. It was also found that bag of shapes model based on MRF segmentation can obtain comparable classification accuracy with bag of SIFT features in the framework of nearest neighbour class-based matching. Finally, the result indicated that incorporation of graph features extracted by constructing region label graphs can improve the classification accuracy compared to both bag of shapes model and bag of SIFT models.

No description available.

From a fresh data science perspective, this thesis discusses the prediction of coronary artery disease based on Single-Nucleotide Polymorphisms (SNPs) from the Ontario Heart Genomics Study (OHGS). First, the thesis explains the k-Nearest Neighbour (k-NN) and Random Forest learning algorithms, and includes a complete proof that k-NN is universally consistent in finite dimensional normed vector spaces. Second, the thesis introduces two dimensionality reduction techniques: Random Projections and a new method termed Mass Transportation Distance (MTD) Feature Selection. Then, this thesis compares the performance of Random Projections with k-NN against MTD Feature Selection and Random Forest for predicting artery disease. Results demonstrate that MTD Feature Selection with Random Forest is superior to Random Projections and k-NN. Random Forest is able to obtain an accuracy of 0.6660 and an area under the ROC curve of 0.8562 on the OHGS dataset, when 3335 SNPs are selected by MTD Feature Selection for classification. This area is considerably better than the previous high score of 0.608 obtained by Davies et al. in 2010 on the same dataset.

Although a number of definitions of mixing have been proposed in the literature, no single definition accurately and clearly describes the full range of problems in the field of industrial mixing. Based on the review of mixing and segregation characterization techniques in chemical engineering, spatial statistics and population studies, a definition of industrial mixing is proposed in this thesis, based on three separate dimensions of segregation. The first dimension is the intensity of segregation which quantifies the uniformity of concentration; the second dimension is the scale of segregation or clustering; and the last dimension is the exposure or the potential to reduce segregation. The first dimension focuses on the instantaneous concentration variance; the second on the instantaneous length scales in the mixing field; and the third on the driving force for change, i.e. the mixing time scale, or the instantaneous rate of reduction in segregation. The definition is introduced using concepts, theory and mathematical equations. This definition provides a theoretical framework for the rigorous analysis of mixing problems, encompassing all industrial mixing processes and allowing a clear evaluation of experimental methods. In this work, the three dimensions of segregation are presented and defined in the context of previous definitions of mixing, and then applied to a range of industrial mixing problems to test their accuracy and robustness. Suitable quantities for direct measurement of the dimensions of segregation are then investigated in detail. The result is a toolkit of ready-to-use methods for the measurement of the intensity (CoV) and the scale of segregation (maximum striation thickness on a transect, point-to-nearest neighbour distributions and variogram), provided as Matlab codes. The chosen methods are thoroughly investigated by testing their applicability, limitations, sampling strategies and meaningfulness of the results using selected sets of mixing data, resulting in creation of guidelines for the use of each of the provided methods. The developed definition of mixing, together with tools and guidelines for measurement of mixing will help researches to further develop the field of mixing, engineers to solve practical industrial mixing problems, and instructors of chemical engineering courses to introduce mixing concepts more easily. / Chemical Engineering

Approximate Nearest-Neighbour Field (ANNF\ maps between two related images are commonly used by computer vision and graphics community for image editing, completion, retargetting and denoising. In this work we generalize ANNF computation to unrelated image pairs. For accurate ANNF map computation we propose Feature Match, in which the low-dimensional features approximate image patches along with global colour adaptation. Unlike existing approaches, the proposed algorithm does not assume any relation between image pairs and thus generalises ANNF maps to any unrelated image pairs. This generalization enables ANNF approach to handle a wider range of vision applications more efficiently. The following is a brief description of the applications developed using the proposed Feature Match framework. The first application addresses the problem of detecting the optic disk from retinal images. The combination of ANNF maps and salient properties of optic disks leads to an efficient optic disk detector that does not require tedious training or parameter tuning. The proposed approach is evaluated on many publicly available datasets and an average detection accuracy of 99% is achieved with computation time of 0.2s per image. The second application aims to super-resolve a given synthetic image using a single source image as dictionary, avoiding the expensive training involved in conventional approaches. In the third application, we make use of ANNF maps to accurately propagate labels across video for segmenting video objects. The proposed approach outperforms the state-of-the-art on the widely used benchmark SegTrack dataset. In the fourth application, ANNF maps obtained between two consecutive frames of video are enhanced for estimating sub-pixel accurate optical flow, a critical step in many vision applications. Finally a summary of the framework for various possible applications like image encryption, scene segmentation etc. is provided.

From a fresh data science perspective, this thesis discusses the prediction of coronary artery disease based on Single-Nucleotide Polymorphisms (SNPs) from the Ontario Heart Genomics Study (OHGS). First, the thesis explains the k-Nearest Neighbour (k-NN) and Random Forest learning algorithms, and includes a complete proof that k-NN is universally consistent in finite dimensional normed vector spaces. Second, the thesis introduces two dimensionality reduction techniques: Random Projections and a new method termed Mass Transportation Distance (MTD) Feature Selection. Then, this thesis compares the performance of Random Projections with k-NN against MTD Feature Selection and Random Forest for predicting artery disease. Results demonstrate that MTD Feature Selection with Random Forest is superior to Random Projections and k-NN. Random Forest is able to obtain an accuracy of 0.6660 and an area under the ROC curve of 0.8562 on the OHGS dataset, when 3335 SNPs are selected by MTD Feature Selection for classification. This area is considerably better than the previous high score of 0.608 obtained by Davies et al. in 2010 on the same dataset.