This project is developed with the University of Skövde and Volvo GTO. Purpose of the project is to complement and facilitate the quality insurance when gluing the engine frame. Quality defects in today’s industry is a major concern due to how costly it is to fix them. With competition rising and quality demands increasing, companies are looking for new and more efficient ways to ensure quality. Collaborative robots is a rising and unexplored technology in most industries. It is an upcoming field with great flexibility that could solve many issues and can assist its processes that are difficult to automate. The project aims to investigate if it is possible and beneficial to implement a vision system on a collaborative robot which ensures quality. Also, investigate if the collaborative robot could work with other tasks as well. This project also includes training and learning an artificial network with CAD generated models and real-life prototypes. The project had a lot of challenges with both training the AI and how the robot would communicate with it. The final results stated that a collaborative robot more specific UR10e could work with machine vision. This solution was based on using a camera which was compatible with the built-in robot software. However, this does not mean that other type of cameras cannot be used for this type of functions as well. Using machine vision based on artificial intelligence is a valid solution but requires further development and training to get a software function working in industry. Working with collaborative robots could change the industry for the better in many ways. Implementing collaborative robots could ease the work for the operators to aid in heavy lifting and repetitive work. Being able to combine a collaborative robot with a vision system could increase productivity and economic benefits.

Machine learning has been around for a long time, the applications range from a big variety of different subjects, everything from self driving cars to data mining. When a person takes a picture with its mobile phone it easily happens that the photo is a little bit crooked. It does also happen that people takes spontaneous photos with help of their phones, which can result in something irrelevant ending up in the corner of the image. This thesis combines machine learning with photo editing tools. It will explore the possibilities how machine learning can be used to automatically crop images in an aesthetically pleasing way and how machine learning can be used to create a portrait cropping tool. It will also go through how a straighten out function can be implemented with help of machine learning. At last, it is going to compare this tools with other software automatic cropping tools. / Maskinlärning har funnits en lång tid. Deras jobb varierar från flera olika ämnen. Allting från självkörande bilar till data mining. När en person tar en bild med en mobiltelefon händer det lätt att bilden är lite sned. Det händer också att en tar spontana bilder med sin mobil, vilket kan leda till att det kommer med något i kanten av bilden som inte bör vara där. Det här examensarbetet kombinerar maskinlärning med fotoredigeringsverktyg. Det kommer att utforska möjligheterna hur maskinlärning kan användas för att automatiskt beskära bilder estetsikt tilltalande samt hur maskinlärning kan användas för att skapa ett porträttbeskärningsverktyg. Det kommer även att gå igenom hur en räta-till-funktion kan bli implementerad med hjälp av maskinlärning. Till sist kommer det att jämföra dessa verktyg med andra programs automatiska beskärningsverktyg.

Although literature on flood risk and environmental justice investigates the link between race and ethnicity and vulnerability to floods, few studies examine the distribution of flood mitigation amenities. This study analyzes census tract proximity to flood mitigation projects (FMPs) completed between 2012 and 2016 in Harris County, Texas to determine if a) project location is biased towards economic growth and the urban core; b) areas most impacted by previous floods are prioritized for drainage assistance; and c) if low-income and Latinx populations are being neglected. A spatial error regression analysis indicates that FMPs are significantly proximate to the urban core, net of other factors. Results also indicate no significant relationship between census tract-level Latinx composition, income status, and proximity to FMPs. Finally, built environment characteristics and locations of previous flooding had no significant effect on where projects were placed.

University of Macau / Faculty of Science and Technology. / Department of Computer and Information Science

Syftet med detta projekt var att utveckla en testrigg för dentala implantat. Uppdraget kom från Institutionen för odontologi vid Umeå universitet. Beställare efterfrågade en testrigg som kunde testa hållfastheten i ett koncept där en tandbro installeras på två implantat istället för fyra. Inledningsvis var planen att utveckla ett pneumatiskt styrt system och automatisera detta med CODESYS. När det framgått att en elev på Tandläkarhögskolan var tänkt fortsätta med uppdraget under sitt kommande examensarbete så bytte fokus i projektet. Från detta tillfälle fortsatte arbetet med att utveckla sagda tandläkarelevs koncept. Detta baserades på att en DC-motor driver ett mekaniskt system, där en roterande kamnock ger upphov till kraftöverföring på testobjektet. Målet var att belasta testobjektet med en kraft på 177 N med ett varvtal på 89 rpm. För att få ett kvitto på överförd kraft inkorporerades en lastcell i systemet. Data från lastcellen skulle göras tillgänglig via en Raspberry Pi. Detta projekt resulterade i att delar till produkten undersöktes och införskaffades, men ingen färdig testrigg blev konstruerad. Arbetet med lastcellen resulterade inte i något färdigt program för att hantera data som önskat. / This project had the intention to develop a testing-machine for dental implants. The assignment came from the Department of Odontology at Umeå University. The sponsor asked for a testing-machine that could test the mechanical strength in a concept where a dental bridge was installed on two implants instead of four. Initially the plan was to develop a pneumatically powered system and automate this with CODESYS. When it turned out that a student at the dental program was thought to continue with the assignment as his upcoming degree project the focus of this project changed. From this moment the project instead continued by developing the concept that said dental student had started to develop. This concept was based on DC-engines to power a mechanical system, where a rotating camshaft gave cause to force loads on the test object. The goal was to introduce a force of 177 N with an rpm of 89. To make sure that the correct loads were applied, a load cell was incorporated into the concept. Data from the load cell was to be handled with a Raspberry Pi. This project resulted in parts necessary to construct the product researched and acquired. The work with the load cell did not result in a finished program to handle data as wished.

The amount of textual data that produce every minute on the internet is extremely high. Processing of this tremendous volume of mostly unstructured data is not a straightforward function. But the enormous amount of useful information that lay down on them motivate scientists to investigate efficient and effective techniques and algorithms to discover meaningful patterns. Social network applications provide opportunities for people around the world to be in contact and share their valuable knowledge, such as chat, comments, and discussion boards. People usually do not care about spelling and accurate grammatical construction of a sentence in everyday life conversations. Therefore, extracting information from such datasets are more complicated. Text mining can be a solution to this problem. Text mining is a knowledge discovery process used to extract patterns from natural language. Application of text mining techniques on social networking websites can reveal a significant amount of information. Text mining in conjunction with social networks can be used for finding a general opinion about any special subject, human thinking patterns, and group identification. In this study, we investigate machine learning methods in textual data in six chapters. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection

Healthcare is an integral component in peoples lives, especially for the rising elderly population, and must be affordable. The United States Medicare program is vital in serving the needs of the elderly. The growing number of people enrolled in the Medicare program, along with the enormous volume of money involved, increases the appeal for, and risk of, fraudulent activities. For many real-world applications, including Medicare fraud, the interesting observations tend to be less frequent than the normative observations. This difference between the normal observations and those observations of interest can create highly imbalanced datasets. The problem of class imbalance, to include the classification of rare cases indicating extreme class imbalance, is an important and well-studied area in machine learning. The effects of class imbalance with big data in the real-world Medicare fraud application domain, however, is limited. In particular, the impact of detecting fraud in Medicare claims is critical in lessening the financial and personal impacts of these transgressions. Fortunately, the healthcare domain is one such area where the successful detection of fraud can garner meaningful positive results. The application of machine learning techniques, plus methods to mitigate the adverse effects of class imbalance and rarity, can be used to detect fraud and lessen the impacts for all Medicare beneficiaries. This dissertation presents the application of machine learning approaches to detect Medicare provider claims fraud in the United States. We discuss novel techniques to process three big Medicare datasets and create a new, combined dataset, which includes mapping fraud labels associated with known excluded providers. We investigate the ability of machine learning techniques, unsupervised and supervised, to detect Medicare claims fraud and leverage data sampling methods to lessen the impact of class imbalance and increase fraud detection performance. Additionally, we extend the study of class imbalance to assess the impacts of rare cases in big data for Medicare fraud detection. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection

Developments in advanced technologies, such as DNA microarrays, have generated tremendous amounts of data available to researchers in the field of bioinformatics. These state-of-the-art technologies present not only unprecedented opportunities to study biological phenomena of interest, but significant challenges in terms of processing the data. Furthermore, these datasets inherently exhibit a number of challenging characteristics, such as class imbalance, high dimensionality, small dataset size, noisy data, and complexity of data in terms of hard to distinguish decision boundaries between classes within the data. In recognition of the aforementioned challenges, this dissertation utilizes a variety of machine-learning and data-mining techniques, such as ensemble classification algorithms in conjunction with data sampling and feature selection techniques to alleviate these problems, while improving the classification results of models built on these datasets. However, in building classification models researchers and practitioners encounter the challenge that there is not a single classifier that performs relatively well in all cases. Thus, numerous classification approaches, such as ensemble learning methods, have been developed to address this problem successfully in a majority of circumstances. Ensemble learning is a promising technique that generates multiple classification models and then combines their decisions into a single final result. Ensemble learning often performs better than single-base classifiers in performing classification tasks. This dissertation conducts thorough empirical research by implementing a series of case studies to evaluate how ensemble learning techniques can be utilized to enhance overall classification performance, as well as improve the generalization ability of ensemble models. This dissertation investigates ensemble learning techniques of the boosting, bagging, and random forest algorithms, and proposes a number of modifications to the existing ensemble techniques in order to improve further the classification results. This dissertation examines the effectiveness of ensemble learning techniques on accounting for challenging characteristics of class imbalance and difficult-to-learn class decision boundaries. Next, it looks into ensemble methods that are relatively tolerant to class noise, and not only can account for the problem of class noise, but improves classification performance. This dissertation also examines the joint effects of data sampling along with ensemble techniques on whether sampling techniques can further improve classification performance of built ensemble models. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection

Recent technological advances underpinned by high throughput sequencing have given new insights into the three-dimensional structure of mammalian genomes. Chromatin conformation assays have been the critical development in this area, particularly the Hi-C method which ascertains genome-wide patterns of intra and inter-chromosomal contacts. However many open questions remain concerning the functional relevance of such higher order structure, the extent to which it varies, and how it relates to other features of the genomic and epigenomic landscape. Current knowledge of nuclear architecture describes a hierarchical organisation ranging from small loops between individual loci, to megabase-sized self-interacting topological domains (TADs), encompassed within large multimegabase chromosome compartments. In parallel with the discovery of these strata, the ENCODE project has generated vast amounts of data through ChIP-seq, RNA-seq and other assays applied to a wide variety of cell types, forming a comprehensive bioinformatics resource. In this work we combine Hi-C datasets describing physical genomic contacts with a large and diverse array of chromatin features derived at a much finer scale in the same mammalian cell types. These features include levels of bound transcription factors, histone modifications and expression data. These data are then integrated in a statistically rigorous way, through a predictive modelling framework from the machine learning field. These studies were extended, within a collaborative project, to encompass a dataset of matched Hi-C and expression data collected over a murine neural differentiation timecourse. We compare higher order chromatin organisation across a variety of human cell types and find pervasive conservation of chromatin organisation at multiple scales. We also identify structurally variable regions between cell types, that are rich in active enhancers and contain loci of known cell-type specific function. We show that broad aspects of higher order chromatin organisation, such as nuclear compartment domains, can be accurately predicted in a variety of human cell types, using models based upon underlying chromatin features. We dissect these quantitative models and find them to be generalisable to novel cell types, presumably reflecting fundamental biological rules linking compartments with key activating and repressive signals. These models describe the strong interconnectedness between locus-level patterns of local histone modifications and bound factors, on the order of hundreds or thousands of basepairs, with much broader compartmentalisation of large, multi-megabase chromosomal regions. Finally, boundary regions are investigated in terms of chromatin features and co-localisation with other known nuclear structures, such as association with the nuclear lamina. We find boundary complexity to vary between cell types and link TAD aggregations to previously described lamina-associated domains, as well as exploring the concept of meta-boundaries that span multiple levels of organisation. Together these analyses lend quantitative evidence to a model of higher order genome organisation that is largely stable between cell types, but can selectively vary locally, based on the activation or repression of key loci.

In summary, the viewpoint of random graphs indeed provides us an opportunity of improving some existing machine learning algorithms. / In this thesis, we establish three machine learning models on random graphs: Heat Diffusion Models on Random Graphs, Predictive Random Graph Ranking, and Random Graph Dependency. The heat diffusion models on random graphs lead to Graph-based Heat Diffusion Classifiers (G-HDC) and a novel ranking algorithm on Web pages called DiffusionRank. For G-HDC, a random graph is constructed on data points. The generated random graph can be considered as the representation of the underlying geometry, and the heat diffusion model on them can be considered as the approximation to the way that heat flows on a geometric structure. Experiments show that G-HDC can achieve better performance in accuracy in some benchmark datasets. For DiffusionRank, theoretically we show that it is a generalization of PageRank when the heat diffusion coefficient tends to infinity, and empirically we show that it achieves the ability of anti-manipulation. / Predictive Random Graph Ranking (PRGR) incorporates DiffusionRank. PRGR aims to solve the problem that the incomplete information about the Web structure causes inaccurate results of various ranking algorithms. The Web structure is predicted as a random graph, on which ranking algorithms are expected to be improved in accuracy. Experimental results show that the PRGR framework can improve the accuracy of the ranking algorithms such as PageRank and Common Neighbor. / Three special forms of the novel Random Graph Dependency measure on two random graphs are investigated. The first special form can improve the speed of the C4.5 algorithm, and can achieve better results on attribute selection than gamma used in Rough Set Theory. The second special form of the general random graph dependency measure generalizes the conditional entropy because it becomes equivalent to the conditional entropy when the random graphs take their special form-equivalence relations. Experiments demonstrates that the second form is an informative measure, showing its success in decision trees on small sample size problems. The third special form can help to search two parameters in G-HDC faster than the cross-validation method. / Yang, haixuan. / "August 2007." / Advisers: Irwin King; Michael R. Lyu. / Source: Dissertation Abstracts International, Volume: 69-02, Section: B, page: 1125. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (p. 184-197). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract in English and Chinese. / School code: 1307.