Uma compacta linguagem descritora de páginas, destinada a impressoras não-impacto de estrutura raster é apresentada. Tal linguagem foi implementada usando o processador gráfico TMS4010, da Texas Instruments e possui uma estrutura muito similar à encontrada nas linguagens interpretativas encadeadas. A linguagem é totalmente modular e interativa, e se utiliza um modelo gráfico simples, visando simular as tarefas normalmente encontradas nas artes tipográficas. São efetuadas comparações com outras linguagens comerciais, visando avaliar suas possibilidades. Uma pequena introdução à tecnologia das impressoras laser é apresentada. / A small Page description language intended for raster non-impact printers is presented. The language is implemented using the Texas Instruments TMS4010 Graphics system processor and its structure is similar that encountered in threaded interpretative languages. The language is fully modular and interactive, and uses a simple graphic model to simulate the same common tasks encoutered in typographical arts. Comparison are made with other comercial languages to perform some evaluations on its possibilities. A small introduction on the laser printer technology is presented.

The design and evaluation of computer systems rely heavily upon simulation. Simulation is also a major bottleneck in the iterative design process. Applications that may be executed natively on physical systems in a matter of minutes may take weeks or months to simulate. As designs incorporate increasingly higher numbers of processor cores, it is expected the times required to simulate future systems will become an even greater issue. Simulation exhibits a tradeoff between speed and accuracy. By basing experimental procedures upon known statistical methods, the simulation of systems may be dramatically accelerated while retaining reliable methods to estimate error. This thesis focuses on the acceleration of simulation through statistical processes. The first two techniques discussed in this thesis focus on accelerating single-threaded simulation via cluster sampling. Cluster sampling extracts multiple groups of contiguous population elements to form a sample. This thesis introduces techniques to reduce sampling and non-sampling bias components, which must be reduced for sample measurements to be reliable. Non-sampling bias is reduced through the Reverse State Reconstruction algorithm, which removes ineffectual instructions from the skipped instruction stream between simulated clusters. Sampling bias is reduced via the Single Pass Sampling Regimen Design Process, which guides the user towards selected representative sampling regimens. Unfortunately, the extension of cluster sampling to include multi-threaded architectures is non-trivial and raises many interesting challenges. Overcoming these challenges will be discussed. This thesis also introduces thread skew, a useful metric that quantitatively measures the non-sampling bias associated with divergent thread progressions at the beginning of a sampling unit. Finally, the Barrier Interval Simulation method is discussed as a technique to dramatically decrease the simulation times of certain classes of multi-threaded programs. It segments a program into discrete intervals, separated by barriers, which are leveraged to avoid many of the challenges that prevent multi-threaded sampling.

Uma compacta linguagem descritora de páginas, destinada a impressoras não-impacto de estrutura raster é apresentada. Tal linguagem foi implementada usando o processador gráfico TMS4010, da Texas Instruments e possui uma estrutura muito similar à encontrada nas linguagens interpretativas encadeadas. A linguagem é totalmente modular e interativa, e se utiliza um modelo gráfico simples, visando simular as tarefas normalmente encontradas nas artes tipográficas. São efetuadas comparações com outras linguagens comerciais, visando avaliar suas possibilidades. Uma pequena introdução à tecnologia das impressoras laser é apresentada. / A small Page description language intended for raster non-impact printers is presented. The language is implemented using the Texas Instruments TMS4010 Graphics system processor and its structure is similar that encountered in threaded interpretative languages. The language is fully modular and interactive, and uses a simple graphic model to simulate the same common tasks encoutered in typographical arts. Comparison are made with other comercial languages to perform some evaluations on its possibilities. A small introduction on the laser printer technology is presented.

Testing of multi-threaded programs is a demanding work due to the many possible thread interleavings one should examine. The noise injection technique helps to increase the number of tested thread interleavings by noise injection to suitable program locations. This work optimizes meta-heuristics search techniques in the testing of concurrent programs by utilizing deterministic heuristic in the application of genetic algorithms in a space of legal program locations suitable for the noise injection. In this work, several novel deterministic noise injection heuristics without dependency on the random number generator are proposed in contrary to the most of currently used heuristic. The elimination of the randomness should make the search process more informed and provide better, more optimal, solutions thanks to increased stability in the results provided by novel heuristics. Finally, a benchmark of programs, used for the evaluation of novel noise injection heuristics is presented.

Finding concurrency bugs in complex software is difficult. As a contribution to coping with this problem the thesis proposes an architecture for a fully automated dynamic detection and healing of data races and atomicity violations in Java. Two distinct algorithms for detecting of data races are presented. One of them is a novel algorithm called AtomRace which detects data races as a special case of atomicity violations. The healing is based on suppressing a recurrence of the detected problem and can be performed by introducing an additional synchronization or by legally influencing the Java scheduler. Basically forces certain parts of the code  to be executed atomically. The proposed architecture uses bytecode instrumentation to be able to track and influence the execution. The architecture and algorithms were implemented and tested on multiple case studies.

The threaded fastener is by far the most common method for securing components together and plays a significant role in determining the quality of a product. Atlas Copco offers industrial tools for tightening these fasteners, which are today suffering from errors in the applied torque. These errors have been found to behave in periodic patterns which indicate that the errors can be predicted and therefore compensated for. However, this is only possible by knowing the rotational position of the tool. Atlas Copco is interested in the possibility of acquiring this rotational position without installing sensors inside the tools. To address this challenge, the thesis explores the feasibility of estimating the rotational position by analysing the behaviour of the errors and finding periodicities in the data. The objective is to determine whether these periodicities can be used to accurately estimate the rotation of the torque errors of unknown data relative to errors of data where the rotational position is known. The tool analysed in this thesis exhibits a periodic pattern in the torque error with a period of 11 revolutions.  Two methods for estimating the rotational position were evaluated: a simple nearest neighbour method that uses mean squared error (MSE) as distance measure, and a more complex circular fully convolutional network (CFCN). The project involved data collection from a custom-built setup. However, the setup was not fully completed, and the models were therefore evaluated on a limited dataset. The results showed that the CFCN method was not able to identify the rotational position of the signal. The insufficient size of the data is discussed to be the cause for this. The nearest neighbour method, however, was able to estimate the rotational position correctly with 100% accuracy across 1000 iterations, even when looking at a fragment of a signal as small as 40%. Unfortunately, this method is computationally demanding and exhibits slow performance when applied to large datasets. Consequently, adjustments are required to enhance its practical applicability. In summary, the findings suggest that the nearest neighbour method is a promising approach for estimating the rotational position and could potentially contribute to improving the accuracy of tools. / Skruvförband är den vanligaste typen av förband för att sammanfoga komponenter och är avgörande för en produkts kvalitet. Atlas Copco tillverkar industriverktyg avsedda för sådana skruvförband, som dessvärre lider av små avvikelser i åtdragningsmomentet. Avvikelserna uppvisar ett konsekvent periodiskt mönster, vilket indikerar att de är förutsägbara och därför möjliga att kompenseras för. Det är dock endast möjligt genom att veta verktygets vinkelposition. Atlas Copco vill veta om det är möjligt att erhålla vinkelpositionen utan att installera sensorer i verktygen. Denna uppsats undersöker möjligheten att uppskatta vinkelpositionen genom att analysera beteendet hos avvikelserna i åtdragningsmomentet och identifiera periodiciteter i datan, samt undersöka om dessa periodiciteter kan utnyttjas för att uppskatta rotationen hos avvikelserna hos okänd data i förhållande till tidigare data. Det verktyget som används i detta projekt uppvisar en tydlig periodicitet med en period på 11 varv. Två metoder för att uppskatta vinkelpositionen utvärderades: en simpel nearest neighbour-metod som använder mean squared error (MSE) som mått för avstånd, och ett mer komplext circular fully convolutional network (CFCN). Projektet innefattade datainsamling från en egendesignad testrigg som tyvärr aldrig blev färdigställd, vilket medförde att utvärderingen av modellerna utfördes på ett begränsat dataset.  Resultatet indikerade att CFCN-metoden kräver en större datamängd för att kunna uppskatta rotationen hos den okända datan. Nearest neighbour-metoden lyckades uppskatta rotationen med 100% noggrannhet över 1000 iterationer, även när endast ett segment så litet som 40% av signalen utvärderades. Tyvärr lider denna metod av hög beräkningsbelastning och kräver förbättringar för att vara praktiskt tillämpbar. Sammantaget visade resultaten att nearest neighbour-metoden har potential att vara ett lovande tillvägagångssätt för att uppskatta vinkelpositionen och kan på så sätt bidra till förbättring av verktygens noggrannhet.

Studying the preload in a screw joint has been the focus of today’s industry. The manufacturer reflects that demand by investigating different opportunities and techniques to develop this area. There are four different ways of controlling the tightening of bolts and joints to achieve the required clamp force that can hold for a specific preload. Torque control, angle control, gradient control, and ultrasonic clamp-force or elongation control. Many studies do exist about the first three mentioned techniques. However, there are a small number of studies for the ultrasonic clamp-force technique, and there is no study focusing on the usage of machine learning in that technique. This study investigates the use of machine learning to find the optimal frequency used to transmit the ultrasonic signals into the bolt for calculating the bolt elongation. Two machine learning models have been constructed, presenting two approaches: one for one-dimensional data (1D-CNN) and one for two-dimensional data (2D-CNN). The models classify the received signals (echos) with different frequencies into either accepted or non-accepted signals to get the optimal frequencies to be used later on, in the bolt elongation process. Both the 1D-CNN and 2D-CNN show an accepted performance of around 85% accuracy. The results indicate that there does exist a pattern in these ultrasonic signals that are useful for classifying them into accepted and non-accepted frequencies, so the usage of machine learning for the problem is feasible. / Att studera förspänningen i en skruvförband har varit i fokus för dagens industri. Tillverkaren speglar den efterfrågan genom att undersöka olika möjligheter och tekniker för att utveckla detta område. Det finns fyra olika sätt att kontroller åtdragningen av bultar för att uppnå den erforderliga klämkraften som kan hålla för en specifik förspänning. Vridmomentkontroll, vinkelkontroll, gradientkontroll och ultraljudskontroll av klämkraft. Det finns många studier om de tre förstnämnda teknologier. Det finns dock ett litet antal studier för ultraljudsklämkraftstekniken, och det finns ingen studie som fokuserar på användningen av maskininlärning i den tekniken. Denna studie undersöker användningen av maskininlärning för att hitta den optimala frekvensen som används för att beräkna bultens förlängning. Två maskininlärningsmodeller har konstruerats, som presenterar två metoder: en för endimensionell data (1D-CNN) och en för två-dimensionella data (2D-CNN). Modellerna klassificerar de mottagna signalerna (ekon) med olika frekvenser i antingen accepterade eller icke-accepterade signaler för att få de optimala frekvenserna att användas senare, i bultförlängningsprocessen. Både 1D-CNN och 2D-CNN visar en accepterad prestanda på cirka 85% noggrannhet. Resultaten indikerar att det finns ett mönster i dessa ultraljudssignaler som är användbara för att klassificera dem i accepterade och icke-accepterade frekvenser, så användningen av maskininlärning för problemet är genomförbar.

Sensor fault detection is an actively researched area and there are a plethora of studies on sensor fault detection in various applications such as nuclear power plants, wireless sensor networks, weather stations and nuclear fusion. However, there does not seem to be any study focusing on detecting sensor faults in the threaded fastening assembly application. Since the threaded fastening tools use torque and angle measurements to determine whether or not a screw or bolt has been fastened properly, faulty measurements from these sensors can have dire consequences. This study aims to investigate the use of machine learning to detect a subtle kind of sensor faults, common in this application, that are difficult to detect using canonical model-based approaches. Because of the subtle and infrequent nature of these faults, a two-stage system was designed. The first component of this system is given sensor data from a tightening and then tries to classify each data point in the sensor data as normal or faulty using a combination of low-pass filtering to generate residuals and a support vector machine to classify the residual points. The second component uses the output from the first one to determine if the complete tightening is normal or faulty. Despite the modest performance of the first component, with the best model having an F1-score of 0.421 for classifying data points, the design showed promising performance for classifying the tightening signals, with the best model having an F1-score of 0.976. These results indicate that there indeed exist patterns in these kinds of torque and angle multi-sensor signals that make machine learning a feasible approach to classify them and detect sensor faults. / Sensorfeldetektering är för nuvarande ett aktivt forskningsområde med mängder av studier om feldetektion i olika applikationer som till exempel kärnkraft, trådlösa sensornätverk, väderstationer och fusionskraft. Ett applikationsområde som inte verkar ha undersökts är det inom åtdragningsmontering. Eftersom verktygen inom åtdragningsmontering använder mätvärden på vridmoment och vinkel för att avgöra om en skruv eller bult har dragits åt tillräckligt kan felaktiga mätvärden från dessa sensorer få allvarliga konsekvenser. Målet med denna studie är att undersöka om det går att använda maskininlärning för att detektera en subtil sorts sensorfel som är vanlig inom åtdragningsmontering och har visat sig vara svåra att detektera med konventionella modell-baserade metoder. I och med att denna typ av sensorfel är både subtila och infrekventa designades ett system bestående av två komponenter. Den första får sensordata från en åtdragning och försöker klassificera varje datapunkt som antingen normal eller onormal genom att uttnyttja en kombination av lågpassfiltrering för att generera residualer och en stödvektormaskin för att klassificera dessa. Den andra komponenten använder resultatet från den första komponenten för att avgöra om hela åtdragningen ska klassificeras som normal eller onormal. Trots att den första komponenten hade ett ganska blygsamt resultat på att klassificera datapunkter så visade systemet som helhet mycket lovande resultat på att klassificera hela åtdragningar. Dessa resultat indikerar det finns mönster i denna typ av sensordata som gör maskininlärning till ett lämpligt verktyg för att klassificera datat och detektera sensorfel.

Nástroj Atomer je statický analyzátor založený na myšlence, že pokud jsou některé sekvence funkcí vícevláknového programu prováděny v některých bězích pod zámky, je pravděpodobně zamýšleno, že mají být vždy provedeny atomicky. Analyzátor Atomer se tudíž snaží takové sekvence hledat a poté zjišťovat, pro které z nich může být v některých jiných bězích programu porušena atomicita. Autor této diplomové práce ve své bakalářské práci navrhl a implementoval první verzi nástroje Atomer jako zásuvný modul aplikačního rámce Facebook Infer. V této diplomové práci je navržena nová a výrazně vylepšená verze analyzátoru Atomer. Cílem vylepšení je zvýšení jak škálovatelnosti, tak přesnosti. Kromě toho byla přidána podpora pro několik původně nepodporovaných programovacích vlastností (včetně např. možnosti analyzovat programy napsané v jazycích C++ a Java nebo podpory pro reentrantní zámky nebo stráže zámků, tzv. "lock guards"). Prostřednictvím řady experimentů (včetně experimentů s reálnými programy a reálnými chybami) se ukázalo, že nová verze nástroje Atomer je skutečně mnohem obecnější, přesnější a lépe škáluje.

This thesis work was conducted at Luleå University of Technology in collaboration with Sandvik Rock Tools. The aim of the work was to determine the influence of carbon content on the wear performance in carburized steel in the dry contact interface of threaded connections between drill rods. In order to investigate this, samples of drill rod steel were carburized to three different carbon concentrations and shot peened to replicate the production process of a drill rod. The samples were wear tested by utilizing a twin-disc wear tester with one disc rotating at 100 RPM and the other at 3000 RPM to mimic the operating conditions in the threaded connection between drill rods. The results was evaluated by wear rate, surface topography, hardness as well as optical analysis by light optical microscopy and scanning electron microscopy. The results show that an increased surface carbon content provide a decrease in wear rate and an increase in hardness in the surface layer that undergo microstructural changes due to the frictional heat and contact pressure during wear testing. The primary wear mechanisms were identified as plastic deformation, adhesive scratching and material removal through delamination.