Unsupervised visual object categorization

The number of digital images has been increasing exponentially in the last few years. People have problems managing their image collections and finding a specific image. An automatic image categorization system could help them to manage images and find specific images. In this thesis, an unsupervised visual object categorization system was implemented to categorize a set of unknown images. The system is unsupervised, and hence, it does not need known images to train the system which needs to be manually obtained. Therefore, the number of possible categories and images can be huge. The system implemented in the thesis extracts local features from the images. These local features are used to build a codebook. The local features and the codebook are then used to generate a feature vector for an image. Images are categorized based on the feature vectors. The system is able to categorize any given set of images based on the visual appearance of the images. Images that have similar image regions are grouped together in the same category. Thus, for example, images which contain cars are assigned to the same cluster. The unsupervised visual object categorization system can be used in many situations, e.g., in an Internet search engine. The system can categorize images for a user, and the user can then easily find a specific type of image.

Acidic dissolution of iron oxides and regeneration of a ceramic filter medium

The dewatering of iron ore concentrates requires large capacity in addition to producing a cake with low moisture content. Such large processes are commonly energy intensive and means to lower the specific energy consumption are needed. Ceramic capillary action disc filters incorporate a novel filter medium enabling the harnessing of capillary action, which results in decreased energy consumption in comparison to traditional filtration technologies. As another benefit, the filter medium is mechanically and chemically more durable than, for example, filter cloths and can, thus, withstand harsh operating conditions and possible regeneration better than other types of filter media. In iron ore dewatering, the regeneration of the filter medium is done through a combination of several techniques: (1) backwashing, (2) ultrasonic cleaning, and (3) acid regeneration. Although it is commonly acknowledged that the filter medium is affected by slurry particles and extraneous compounds, published research, especially in the field of dewatering of mineral concentrates, is scarce. Whereas the regenerative effect of backwashing and ultrasound are more or less mechanical, regeneration with acids is based on chemistry. The chemistry behind the acid regeneration is, naturally, dissolution. The dissolution of iron oxide particles has been extensively studied over several decades but those studies may not necessarily be directly applicable in the regeneration of the filter medium which has undergone interactions with the slurry components. The aim of this thesis was to investigate if free particle dissolution indeed correlates with the regeneration of the filter medium. For this purpose, both free particle dissolution and dissolution of surface adhered particles were studied. The focus was on acidic dissolution of iron oxide particles and on the study of the ceramic filter medium used in the dewatering of iron ore concentrates. The free particle dissolution experiments show that the solubility of synthetic fine grained iron oxide particles in oxalic acid could be explained through linear models accounting for the effects of temperature and acid concentration, whereas the dissolution of a natural magnetite is not so easily explained by such models. In addition, the kinetic experiments performed both support and contradict the work of previous authors: the suitable kinetic model here supports previous research suggesting solid state reduction to be the reaction mechanism of hematite dissolution but the formation of a stable iron oxalate is not supported by the results of this research. Several other dissolution mechanisms have also been suggested for iron oxide dissolution in oxalic acid, indicating that the details of oxalate promoted reductive dissolution are not yet agreed and, in this respect, this research offers added value to the community. The results of the regeneration experiments with the ceramic filter media show that oxalic acid is highly effective in removing iron oxide particles from the surface of the filter medium. The dissolution of those particles did not, however, exhibit the expected behaviour, i.e. complete dissolution. The results of this thesis show that although the regeneration of the ceramic filter medium with acids incorporates the dissolution of slurry particles from the surface of the filter medium, the regeneration cannot be assessed purely based upon free particle dissolution. A steady state, dependent on temperature and on the acid concentration, was observed in the dissolution of particles from the surface even though the limit of solubility of free iron oxide particles had not been reached. Both the regeneration capacity and efficiency, with regards to the removal of iron oxide particles, was found to be temperature dependent, but was not affected by the acid concentration. This observation further suggests that the removal of the surface adhered particles does not follow the dissolution of free particles, which do exhibit a dependency on the acid concentration. In addition, changes in the permeability and in the pore structure of the filter medium were still observed after the bulk concentration of dissolved iron had reached a steady state. Consequently, the regeneration of the filter medium continued after the dissolution of particles from the surface had ceased. This observation suggests that internal changes take place at the final stages of regeneration. The regeneration process could, in theory, be divided into two, possibly overlapping, stages: (1) dissolution of surface-adhered particles, and (2) dissolution of extraneous compounds from within the pore structure. In addition to the fundamental knowledge generated during this thesis, tools to assess the effects of parameters on the regeneration of the ceramic filter medium are needed. It has become clear that the same tools used to estimate the dissolution of free particles cannot be used to estimate the regeneration of a filter medium unless only a robust characterisation of the order of regeneration efficiency is needed.

Lujien hitsattavien terästen käyttäytyminen -60 °C:ssa

Opinnäytetyö on osa Arctic Materials Technologies Development -projektia, jonka tavoitteena on kehittää perusteita arktisten alueiden sovelluksiin suunnittelun ja valmistuksen kannalta. Arktisella alueella sijaitsee useita potentiaalisia öljy- ja maakaasuesiintymiä, joiden hyödyn-täminen tulee vuosi vuodelta kannattavammaksi ilmaston lämpenemisestä johtuvan merijään heikkenemisen vuoksi. Alin suunnittelulämpötila arktisilla alueilla on -60 °C, mikä aiheuttaa haasteita sekä materiaalinvalinnalle että hitsaukselle. Ferriittisillä teräksillä esiintyy lämpötilasta riippuvaa sitkeyden vaihtelua, jota kutsutaan transi-tiokäyttäytymiseksi. Lämpötilan laskiessa teräksen iskusitkeys sekä murtumissitkeys laske-vat. Arktisissa sovelluskohteissa käytetään yleisesti niukkaseosteisia, mikroseostettuja hie-noraeteräksiä, joille on ominaista erinomaiset sitkeys-, lujuus- sekä hitsattavuusominaisuudet vaativissakin olosuhteissa. Lujat termomekaanisesti valssatut ja nuorrutetut hienoraeteräkset kattavat myötölujuusluokat 355…700 MPa. Tutkimuksissa on saatu vaihtelevia tuloksia ma-teriaalien isku- ja murtumissitkeydestä -60 °C:ssa. Erityisesti sitkeysominaisuudet hitsiaineen ja muutosvyöhykkeen alueiden välillä ovat vaihtelevia. Pienemmällä lämmöntuonnilla ja seostetuilla lisäaineilla saavutetaan kuitenkin pääsääntöisesti parempia sitkeysarvoja. Asiku-laarinen ferriitti sekä alabainiitti ovat toivottavia mikrorakenteita liitoksessa, niiden pienen raekoon johdosta.

Simulation and material calibration of ultra high strength steel (UHSS) S960 welded joint under static tensile test utilizing finite element method (FEM)

The aim of this work was to calibrate the material properties including strength and strain values for different material zones of ultra-high strength steel (UHSS) welded joints under monotonic static loading. The UHSS is heat sensitive and softens by heat due to welding, the affected zone is heat affected zone (HAZ). In this regard, cylindrical specimens were cut out from welded joints of Strenx® 960 MC and Strenx® Tube 960 MH, were examined by tensile test. The hardness values of specimens’ cross section were measured. Using correlations between hardness and strength, initial material properties were obtained. The same size specimen with different zones of material same as real specimen were created and defined in finite element method (FEM) software with commercial brand Abaqus 6.14-1. The loading and boundary conditions were defined considering tensile test values. Using initial material properties made of hardness-strength correlations (true stress-strain values) as Abaqus main input, FEM is utilized to simulate the tensile test process. By comparing FEM Abaqus results with measured results of tensile test, initial material properties will be revised and reused as software input to be fully calibrated in such a way that FEM results and tensile test results deviate minimum. Two type of different S960 were used including 960 MC plates, and structural hollow section 960 MH X-joint. The joint is welded by BöhlerTM X96 filler material. In welded joints, typically the following zones appear: Weld (WEL), Heat affected zone (HAZ) coarse grained (HCG) and fine grained (HFG), annealed zone, and base material (BaM). Results showed that: The HAZ zone is softened due to heat input while welding. For all the specimens, the softened zone’s strength is decreased and makes it a weakest zone where fracture happens while loading. Stress concentration of a notched specimen can represent the properties of notched zone. The load-displacement diagram from FEM modeling matches with the experiments by the calibrated material properties by compromising two correlations of hardness and strength.

Comparison of Local Feature Detectors and Descriptors for Visual Object Categorization

Local features are used in many computer vision tasks including visual object categorization, content-based image retrieval and object recognition to mention a few. Local features are points, blobs or regions in images that are extracted using a local feature detector. To make use of extracted local features the localized interest points are described using a local feature descriptor. A descriptor histogram vector is a compact representation of an image and can be used for searching and matching images in databases. In this thesis the performance of local feature detectors and descriptors is evaluated for object class detection task. Features are extracted from image samples belonging to several object classes. Matching features are then searched using random image pairs of a same class. The goal of this thesis is to find out what are the best detector and descriptor methods for such task in terms of detector repeatability and descriptor matching rate.

Pretraining Convolutional Neural Networks for Visual Recognition

Convolutional Neural Networks (CNN) have become the state-of-the-art methods on many large scale visual recognition tasks. For a lot of practical applications, CNN architectures have a restrictive requirement: A huge amount of labeled data are needed for training. The idea of generative pretraining is to obtain initial weights of the network by training the network in a completely unsupervised way and then fine-tune the weights for the task at hand using supervised learning. In this thesis, a general introduction to Deep Neural Networks and algorithms are given and these methods are applied to classification tasks of handwritten digits and natural images for developing unsupervised feature learning. The goal of this thesis is to find out if the effect of pretraining is damped by recent practical advances in optimization and regularization of CNN. The experimental results show that pretraining is still a substantial regularizer, however, not a necessary step in training Convolutional Neural Networks with rectified activations. On handwritten digits, the proposed pretraining model achieved a classification accuracy comparable to the state-of-the-art methods.