Research in the field of Computer Aided Design (CAD) has long focused on reducing the time and effort required of engineers to define three dimensional digital product models. Parametric, feature-based modeling with inter-part associativity allows complex assembly designs to be defined and re-defined while maintaining the vital part-to-part interface relationships. The top-down modeling method which uses assembly level control structures to drive child level geometry has proved valuable in maintaining these interfaces. Creating robust parametric models like these, however, is very time consuming especially since there can be hundreds of features and thousands of mathematical expressions to create. Even if combinations of low-level features, known as User-Defined Features (UDFs), are used, this process still involves inserting individual features into individual components and creating all of the inter-part associativities by hand. This thesis shows that programmatic operations designed for a specific product type can streamline the assembly and component-level design process much further because a single programmatic operation can create an unlimited number of low-level features, modify geometry in multiple components, create new components, establish inter-part expressions, and define inter-part geometry links. Results from user testing show that a set of high-level programmatic operations can offer savings in time and effort of over 90% and can be general enough to support user-specified interface layouts and component cross sections while leaving the majority of the primary design decisions open to the engineer.

Rapid Prototyped part failure constitutes a major issue for both RP providers and customers. When parts fail the reputation of the vendor is heavily deteriorated, customer dissatisfaction increase and replacement of the broken parts is often necessary to avoid the loss of future business. Product design teams often run into situations where Rapid Prototyped parts are not able to withstand shipping and handling and delivered broken or while demonstrating and examining the parts. When done in the face of customers this builds a perception of poor quality and lack of aptitude on the design group as well as the RP processes. The rapid advance of the RP industry and technology has led users to employ RP parts for structural applications where the need to understand in great detail and accuracy the mechanical behavior of the product and its individual components is greater than ever. Models built on Rapid Prototyping (RP) equipment are most often made from polymers which frequently have mechanical properties that are inferior to those manufactured by traditional methods such as thermoforming or injection molding. Not only are the mechanical properties of RP models typically low, they are usually, at least in thin sections, directly dependent on the section or wall thickness of the models. This dependence of strength on wall thickness makes it difficult to predict a proper wall thickness for RP models, even when nominal values of material strength are known. The purpose of this work is to present and compare measured values of tensile strength and stiffness as a function of wall thickness for three RP processes and materials. These properties will assist designers estimating adequate minimum wall thicknesses for models built by the three processes. The three RP technologies included in the scope of this research are: Z Corporation (powder with polymer binder layup), Fuse Deposition Modeling and PolyJet Layup (Objet). The findings of this study establish that tensile strength and stiffness values are dependent upon wall thickness, building orientation and direction of the applied force of specimens created with the methods in consideration. It was also determined that the correlation between thickness and strength for all processes is non-linear. Due to these results a single tensile strength and modulus value for each material and all wall thicknesses do not accurately represent their behavior. However, these results will allow a designer to understand the relationship between the wall thickness and using the data provided in this work be able to model and then fabricate adequate 3D prototypes.

The present study replicated the research framework of a previous study (Biber, Gray, & Poonpon, 2011) that identifies the grammatical complexity of L1 professional academic prose as strongly favoring a dense use of phrasal nominal modifiers such as prepositional phrases as postmodifiers, attributive adjectives, and nouns as premodifiers which characterize its unique structurally compressed discourse style. The main purpose of the present study was to explore syntactic similarities and differences between L1 professional and L2 student academic writing in terms of their reliance on phrasal/nominal compression features to determine characteristics of the grammatical complexity of advanced ESL academic writing. To this end, the distributional patterns of use for 25 specific grammatical complexity features of structural elaboration and compression were investigated in a corpus of 128 short academic essays collected from 16 advanced ESL learners and 16 L1 university students (as comparison data).The results showed a heavier reliance of both the advanced ESL and L1 student academic writing on phrasal nominal modifiers (attributive adjectives and prepositional phrases as postmodifiers) of structural compression than on clausal elaboration features, which lent empirical support to Biber, Gray, and Poonpon’s (2011) findings. In addition to the phrasal compression features, both the advanced ESL and L1 student academic writing were also characterized by a prominent use of specific colloquial grammatical devices such as adverbs as adverbials. Compared to the advanced ESL writing, the L1 student academic writing showed a significantly more preference for one particular colloquial feature: ZERO relative clauses where relative pronouns replacing relativized objects are omitted. This combined reliance on both phrasal compression devices and colloquial features in both the advanced ESL and L1 student academic writing distinguished their grammatical complexities from that of L1 professional academic prose and signaled a possibility for recognizing them as a transitional developmental stage from more casual to more academic writing.

For more than three decades, researchers have been developping generation methods for the weather, energy, and economic domain. These methods provide generated datasets for reasons like system evaluation and data availability. However, despite the variety of approaches, there is no comparative and cross-domain assessment of generation methods and their expressiveness. We present a similarity measure that analyzes generation methods regarding general time series features. By this means, users can compare generation methods and validate whether a generated dataset is considered similar to a given dataset. Moreover, we propose a feature-based generation method that evolves cross-domain time series datasets. This method outperforms other generation methods regarding the feature-based similarity.

Prenumerationstjänster blir alltmer populära i dagens samhälle. En av nycklarna för att lyckas med en prenumerationsbaserad affärsmodell är att minimera kundbortfall (eng. churn), dvs. kunder som avslutar sin prenumeration inom en viss tidsperiod. I och med den ökande digitaliseringen, är det nu enklare att samla in data än någonsin tidigare. Samtidigt växer maskininlärning snabbt och blir alltmer lättillgängligt, vilket möjliggör nya infallsvinklar på problemlösning. Denna rapport kommer testa och utvärdera ett försök att förutsäga kundbortfall med hjälp av maskininlärning, baserat på kunddata från ett företag med en prenumerationsbaserad affärsmodell där prenumeranten får besöka live-event till en fast månadskostnad. De maskininlärningsmodeller som användes i testerna var Random Forests, Support Vector Machines, Logistic Regression, och Neural Networks som alla tränades med användardata från företaget. Modellerna gav ett slutligt träffsäkerhetsresultat i spannet mellan 73,7 % och 76,7 %. Därutöver tenderade modellerna att ge ett högre resultat för precision och täckning gällande att klassificera kunder som sagt upp sin prenumeration än för de som fortfarande var aktiva. Dessutom kunde det konstateras att de kundegenskaper som hade störst inverkan på klassifikationen var ”Använda Biljetter” och ”Längd på Prenumeration”. Slutligen kommer det i denna rapport diskuteras hur informationen angående vilka kunder som sannolikt kommer avsluta sin prenumeration kan användas ur ett mer affärsmässigt perspektiv. / In today’s world subscription-based online services are becoming increasingly popular. One of the keys to success in a subscription-based business model is to minimize churn, i.e. customer canceling their subscriptions. Due to the digitalization of the world, data is easier to collect than ever before. At the same time machine learning is growing and is made more available. That opens up new possibilities to solve different problems with the use of machine learning. This paper will test and evaluate a machine learning approach to churn prediction, based on the user data from a company with an online subscription service letting the user attend live shows to a fixed price. To perform the tests different machine learning models were used, both individually and combined. The models were Random Forests, Support Vector Machines, Logistic Regression and Neural Networks. In order to train them a data set containing either active or churned users was provided. Eventually the models returned accuracy results ranging from 73.7 % to 76.7 % when classifying churners based on their activity data. Furthermore, the models turned out to have higher scores for precision and recall for classifying the churners than the non-churners. In addition, the features that had the most impact on the model regarding the classification were Tickets Used and Length of Subscription. Moreover, this paper will discuss how churn prediction can be used from a business perspective.

As the web continues to evolve, so does our need for achieving an accessible web for people with disabilities. Content management systems (CMSs) have well observed accessibility problems with generated content, and in recent years, several features have been proposed in order to minimize or eliminate these problems. This study investigated CMSs in current use to find common accessibility problems and evaluated a set of features proposed by Acosta, T. et al. in 2018, targeting these problems. The study initially found a general lack of information, guidance and technical support provided by CMSs to editors promoting the generation of accessible content. The results indicate that even editors highly aware of accessibility may not be able to create accessible content due to the limitations of their systems. The study also received positive feedback towards the evaluated features from professionals, indicating that the features are of practical value and may help the editor by minimizing or eliminating common accessibility problems in content generated through CMSs. / Webben fortsätter att utvecklas, och det gör också vårt behov av att göra webben tillgänglig för personer med funktionshinder. Innehållshanteringssystem (CMS) har flera kända tillgänglighetsproblem med dess genererande innehåll och under de senaste åren så har ett antal tillgänglighetsfunktioner föreslagits för att minimera eller eliminera dessa problem. Den här studien undersökte CMS som används idag för att hitta vanliga tillgänglighetsproblem och evaluerade en samling av föreslagna funktioner av Acosta, T. et al. som riktade sig mot dessa problem. Studien fann i början en generell brist på information, vägledning och tekniskt stöd från CMS till redaktörer som främjar skapandet av tillgängligt innehåll. Resultaten visar att även redaktörer som är medvetna om tillgänglighet inte alltid har möjligheten att skapa tillgängligt innehåll på grund av begränsningarna i deras system. Studien fick också positiv återkoppling av de utvärderade funktionerna från yrkesverksamma inom området, vilket indikerar att funktionerna har ett praktiskt värde och kan hjälpa redaktören genom att minimera eller eliminera vanliga tillgänglighetsproblem i innehåll som skapats via CMS.

High hydrodynamic pressure (HDP) treatment is a novel non-thermal technology that improves tenderness in foods by subjecting foods to underwater shock waves. In this study non-destructive and destructive testing methods, along with two mathematical models were explored to predict biomechanical behavior of beef loins subjected to HDP-treament. The first study involved utilizing ultrasound and imaging techniques to predict textural changes in beef loins subjected to HDP-treatment using Warner-Braztler shear force (WBS) scores and texture profile analysis (TPA) features for correlation. Ultrasound velocity correlated very poorly with the WBS scores and TPA features, whereas the imaging features correlated better with higher r-values. The effect of HDP-treatment variables on WBS and TPA features indicated that amount of charge had no significant effects when compared to location of sample and container size during treatment. Two mathematical models were used to simulate deformational behavior in beef loins. The first study used a rheological based modeling of protein gel as a preliminary study. Results from the first modeling study indicated no viscous interactions in the model and complete deformation failure at pressures exceeding 50 kPa, which was contrary to the real-life process conditions which use pressures in the order of MPa. The second modeling study used a finite element method approach to model elastic behavior. Shock wave was modeled as a non-linear and linear propagating wave. The non-linear model indicated no deformation response, whereas the linear model indicated realistic deformation response assuming transverse isotropy of the model beef loin. The last study correlated small- and large-strain measurements using stress relaxation and elastic coefficients of the stiffness matrix as small-strain measures and results of the study indicated very high correlation between elastic coefficients c11, c22, and c44 with TPA cohesiveness (r > 0.9), and springiness (r > 0.85). Overall results of this study indicated a need for further research in estimating mechanical properties of beef loins in order to understand the dynamics of HDP-treatment process better. / Ph. D.

Space weather has become an international issue due to the catastrophic impact it can have on modern societies. Solar flares are one of the major solar activities that drive space weather and yet their occurrence is not fully understood. Research is required to yield a better understanding of flare occurrence and enable the development of an accurate flare prediction system, which can warn industries most at risk to take preventative measures to mitigate or avoid the effects of space weather. This thesis introduces novel technologies developed by combining advances in statistical physics, image processing, machine learning, and feature selection algorithms, with advances in solar physics in order to extract valuable knowledge from historical solar data, related to active regions and flares. The aim of this thesis is to achieve the followings: i) The design of a new measurement, inspired by the physical Ising model, to estimate the magnetic complexity in active regions using solar images and an investigation of this measurement in relation to flare occurrence. The proposed name of the measurement is the Ising Magnetic Complexity (IMC). ii) Determination of the flare prediction capability of active region properties generated by the new active region detection system SMART (Solar Monitor Active Region Tracking) to enable the design of a new flare prediction system. iii) Determination of the active region properties that are most related to flare occurrence in order to enhance understanding of the underlying physics behind flare occurrence. The achieved results can be summarised as follows: i) The new active region measurement (IMC) appears to be related to flare occurrence and it has a potential use in predicting flare occurrence and location. ii) Combining machine learning with SMART¿s active region properties has the potential to provide more accurate flare predictions than the current flare prediction systems i.e. ASAP (Automated Solar Activity Prediction). iii) Reduced set of 6 active region properties seems to be the most significant properties related to flare occurrence and they can achieve similar degree of flare prediction accuracy as the full 21 SMART active region properties. The developed technologies and the findings achieved in this thesis will work as a corner stone to enhance the accuracy of flare prediction; develop efficient flare prediction systems; and enhance our understanding of flare occurrence. The algorithms, implementation, results, and future work are explained in this thesis.

Malware is still one of the most prominent vectors through which computer networks and systems are compromised. A compromised computer system or network provides data and or processing resources to the world of cybercrime. With cybercrime projected to cost the world $6 trillion by 2021, malware is expected to continue being a growing challenge. Statistics around malware growth over the last decade support this theory as malware numbers enjoy almost an exponential increase over the period. Recent reports on the complexity of the malware show that the fight against malware as a means of building more resilient cyberspace is an evolving challenge. Compounding the problem is the lack of cyber security expertise to handle the expected rise in incidents. This thesis proposes advancing automation of the malware static analysis and detection to improve the decision-making confidence levels of a standard computer user in regards to a file’s malicious status. Therefore, this work introduces a framework that relies on two novel approaches to score the malicious intent of a file. The first approach attaches a probabilistic score to heuristic anomalies to calculate an overall file malicious score while the second approach uses fuzzy hashes and evidence combination theory for more efficient malware detection. The approaches’ resultant quantifiable scores measure the malicious intent of the file. The designed schemes were validated using a dataset of “clean” and “malicious” files. The results obtained show that the framework achieves true positive – false positive detection rate “trade-offs” for efficient malware detection.

Machine learning has achieved remarkable performance in many domains, now it promising to solve manufacturing problems — a new ongoing trend of using machine learning in industrial applications. Dealing with the material order demand in manufacturing as time-series sequences, making unsupervised time-series clustering possible to apply. This study aims to evaluate different time-series clustering approaches, algorithms, and distance measures in material flow data. Three different approaches are evaluated; statistical clustering approaches; raw based and shape-based approaches and at last feature-based approach. The objectives are to categorize the materials in the supermarket (intermediate storage area to store materials before assembling the products) into three different flows according to their time-series properties. The experimental shows that feature-based approach is performed best for the data. A features filter is applied to keep the relevant features, that catch the unique characteristics from the data the predicted output. As a conclusion data type, structure, the goal of the clustering task and the application domains are reasons that have to consider when choosing the suitable clustering approach.