<p>Presto Brandsäkerhet AB är marknadsledande inom brandsäkerhet i Norden och har en robotlina för montering av brandsläckare i Katrineholm. De senaste åren har brandsläckarförsäljningen ökat kraftigt och tros öka ännu mer. Med stigande produktionstakt ökar kraven på tillverkningsutrustningen. En viktig konkurrensprioritet är att erbjuda kunder snabba leveranser och därför har företaget valt att ha ett stort lager av de allra flesta modeller. Med kortare omställningstider skulle tillverkning av mindre partier vid behov bli mer fördelaktigt. Lagernivån skulle då kunna minskas och istället snabbt fyllas på vid behov. Detta examensarbete syftar därför till att kartlägga omställningsarbetet och ta fram förslag som reducerar omställningstiden för byte av brandsläckarvarianter i robotlinan. För att uppfylla syftet används SMED-metoden som grund, vilket är en universell metod för ställtidsreducering med ursprung från Japan.</p><p>Kartläggningen visar att omställningsarbetet är uppdelat i många små operationer, där inget enskilt moment upptar majoriteten av tiden. Trots detta har det framträtt att det finns goda möjligheter att få ner omställningstiden till en bråkdel av dagens tid. I denna rapport presenteras ett antal förbättringsförslag och rekommendationer. Det visas hur omställningstiden kan reduceras från dagens 45 – 60 minuter till ca 15 minuter. Det har uppnåtts genom att mesta möjliga arbete görs i förberedande syfte innan linan stoppas samt förbättring av själva operationerna och genom tillgång till en extra person.</p><p>Ställtidsreduktionen kommer att öka linans kapacitet och möjliggöra mindre partistorlekar av brandsläckare. Partistorlekarna kan exempelvis minskas med ca 70 % med bibehållen kapacitet. Detta bidrar till en lägre lagerhållningskostnad samt att det kommer bli mer fördelaktigt att tillverka vissa släckarmodeller direkt mot kundorder.</p> / <p>Presto Brandsäkerhet AB is the market leader within fire security in Scandinavia and has a robot line for the assembly of fire extinguishers in Katrineholm, Sweden. In recent years, sales of fire extinguishers have increase rapidly and are projected to increase even more. With rising production rate the requirements on the manufacturing equipment increases. An important competition priority is to offer customers fast deliveries and therefore the company has chosen to have a large stock of most models. With short setup times, production of smaller batches would become more beneficial. The stock level could be reduced and instead quickly replenished when necessary. This thesis aims to identify the setup work and to develop proposals that reduce the setup time for the change between different models of fire extinguishers. To fulfill the purpose SMED is used, which is a universal method for setup time reduction originating from Japan.</p><p>The survey shows that the setup work is divided into many small operations, where no single item occupies the majority of the time. Nevertheless, it appears that there are good opportunities to reduce the setup time to a fraction of today's time. In this report, a number of improvement suggestions and recommendations are presented. It is shown how the setup time can be reduced from today's 45 - 60 minutes to approximately 15 minutes. This has been achieved by having most of the preparatory work done before the line stops, by improvements of the operations themselves and through access to an additional person.</p><p>The setup time reduction will increase the capacity of the robot line and make smaller lot sizes of fire extinguishers possible. The lot sizes can for example be reduced by 70 % while still maintaining capacity. This contributes to a lower inventory cost, and that it will be more advantageous to produce some fire extinguisher models directly to customer orders.</p>

Presto Brandsäkerhet AB är marknadsledande inom brandsäkerhet i Norden och har en robotlina för montering av brandsläckare i Katrineholm. De senaste åren har brandsläckarförsäljningen ökat kraftigt och tros öka ännu mer. Med stigande produktionstakt ökar kraven på tillverkningsutrustningen. En viktig konkurrensprioritet är att erbjuda kunder snabba leveranser och därför har företaget valt att ha ett stort lager av de allra flesta modeller. Med kortare omställningstider skulle tillverkning av mindre partier vid behov bli mer fördelaktigt. Lagernivån skulle då kunna minskas och istället snabbt fyllas på vid behov. Detta examensarbete syftar därför till att kartlägga omställningsarbetet och ta fram förslag som reducerar omställningstiden för byte av brandsläckarvarianter i robotlinan. För att uppfylla syftet används SMED-metoden som grund, vilket är en universell metod för ställtidsreducering med ursprung från Japan. Kartläggningen visar att omställningsarbetet är uppdelat i många små operationer, där inget enskilt moment upptar majoriteten av tiden. Trots detta har det framträtt att det finns goda möjligheter att få ner omställningstiden till en bråkdel av dagens tid. I denna rapport presenteras ett antal förbättringsförslag och rekommendationer. Det visas hur omställningstiden kan reduceras från dagens 45 – 60 minuter till ca 15 minuter. Det har uppnåtts genom att mesta möjliga arbete görs i förberedande syfte innan linan stoppas samt förbättring av själva operationerna och genom tillgång till en extra person. Ställtidsreduktionen kommer att öka linans kapacitet och möjliggöra mindre partistorlekar av brandsläckare. Partistorlekarna kan exempelvis minskas med ca 70 % med bibehållen kapacitet. Detta bidrar till en lägre lagerhållningskostnad samt att det kommer bli mer fördelaktigt att tillverka vissa släckarmodeller direkt mot kundorder. / Presto Brandsäkerhet AB is the market leader within fire security in Scandinavia and has a robot line for the assembly of fire extinguishers in Katrineholm, Sweden. In recent years, sales of fire extinguishers have increase rapidly and are projected to increase even more. With rising production rate the requirements on the manufacturing equipment increases. An important competition priority is to offer customers fast deliveries and therefore the company has chosen to have a large stock of most models. With short setup times, production of smaller batches would become more beneficial. The stock level could be reduced and instead quickly replenished when necessary. This thesis aims to identify the setup work and to develop proposals that reduce the setup time for the change between different models of fire extinguishers. To fulfill the purpose SMED is used, which is a universal method for setup time reduction originating from Japan. The survey shows that the setup work is divided into many small operations, where no single item occupies the majority of the time. Nevertheless, it appears that there are good opportunities to reduce the setup time to a fraction of today's time. In this report, a number of improvement suggestions and recommendations are presented. It is shown how the setup time can be reduced from today's 45 - 60 minutes to approximately 15 minutes. This has been achieved by having most of the preparatory work done before the line stops, by improvements of the operations themselves and through access to an additional person. The setup time reduction will increase the capacity of the robot line and make smaller lot sizes of fire extinguishers possible. The lot sizes can for example be reduced by 70 % while still maintaining capacity. This contributes to a lower inventory cost, and that it will be more advantageous to produce some fire extinguisher models directly to customer orders.

Abstract In this thesis pre-stressed piezoelectric actuators for micro and fine mechanical applications have been developed. First, RAINBOW (Reduced And INternally Biased Oxide Wafer) and thick film actuators were manufactured and their electromechanical properties were characterised. In the second part, the novel pre-stressed piezoelectric actuator PRESTO (PRE-STressed electrOactive component by using a post-fired biasing layer) was developed and its electrical and electromechanical properties were measured. Commercial piezoelectric PZT 5A and PZT 5H discs were used in the RAINBOW and PRESTO actuators and PLZT paste for thick film actuators. The pre-stressing of the PRESTO actuators was based on the sintering shrinkage and different thermal expansion coefficient of the piezoelectric disc and passive material. Dielectric LTCC tape and AgPd paste were utilized as pre-stressing media and passive layer by using lamination and screen-printing, respectively. Different active and passive layer thicknesses and electrode materials were realized in order to obtain high displacements and good load bearing capability for actuators. The PRESTO actuators showed a significantly higher coercive electric field than their bulk counterparts, but a decreased remanent polarisation. The displacement as a function of load was measured under 0.3–3 N loads and electric fields up to ±0.75 MV/m. The highest displacement of 118 μm was obtained with a 250 μm thick PZT 5H actuator (Ø 25 mm) with LTCC tape (thickness ~96 μm) as the pre-stressing material. The corresponding actuator with AgPd pre-stressing material (thickness ~33 μm) produced 63 μm displacement. Additionally, PRESTO actuators were tested with a glued steel layer in a mechanical amplifier which obtained displacements up to 1.2 mm. Effective d31 coefficients of the PRESTO actuators were derived using an analysis based on unimorph model and measured displacement data. The actuators exhibited significantly enhanced effective d31 coefficients (d31eff = -690 pm/V and d31eff = -994 pm/V for PZT 5A and 5H, respectively) comparable to the RAINBOW actuators. Mass-producible PRESTO actuators with high displacement, moderate load bearing capabilities and integration possibilities can be utilised in various micro and fine mechanical devices e.g. dosing devices, electromechanical locks, regulators, positioners vibrators, speakers, adjusters, pumps, valves, relays, dispensers, micromanipulators, etc.

The PRESTO sequence is a well-known 3-D fMRI imaging sequence. In this sequence the echo planar imaging technique is merged with the echo-shift technique. This combination results in a very fast image acquisition, which is required for fMRI examinations of neural activation in the human brain. The aim of this work was to use the basic Cartesian PRESTO sequence as a framework when developing a novel trajectory using a non-Cartesian grid. Our new pulse sequence, PRESTO CAN, rotates the k-space profiles around the ky-axis in a non-Cartesian manner. This results in a high sampling density close to the centre of the k-space, and at the same time it provides sparser data collection of the part of the k-space that contains less useful information. This "can- or cylinder-like" pattern is expected to result in a much faster k-space acquisition without loosing important spatial information. A new reconstruction algorithm was also developed. The purpose was to be able to construct an image volume from data obtained using the novel PRESTO CAN sequence. This reconstruction algorithm was based on the gridding technique, and a Kaiser-Bessel window was also used in order to re-sample the data onto a Cartesian grid. This was required to make 3-D Fourier transformation possible. In addition, simulations were also performed in order to verify the function of the reconstruction algorithm. Furthermore, in vitro tests showed that the development of the PRESTO CAN sequence and the corresponding reconstruction algorithm were highly successful. In the future, the results can relatively easily be extended and generalized for in vivo investigations. In addition, there are numerous exciting possibilities for extending the basic techniques described in this thesis.

In recent years, there has been tremendous growth in both the volumes of data that is produced, stored, and queried by organizations. Organizations spend more money to investigate and obtain useful information or knowledge against terabytes and even petabytes of data. Large-scale data analysis is the key functionality provided by Big Data platforms. Previously, data platforms would get the information from unstructured data in the form of files, text, and videos. In recent times, the Hadoop stack has played a vital role in Big Data, becoming the defector open source software used to process and analyze Big Data. Hops is a Hadoop distribution developed by KTH and RISE SICS. Hops modifies the Hadoop stack by moving the meta-data for YARN and HDFS to NDB, an open-source in-memory distributed database. HopsWorks is the User Interface for Hops and provides support for multi-tenant users, as well as self-service, graphical access to frameworks such as Hadoop, Flink, Spark, Kafka, and Kibana. HopsWorks currently does not provide a SQL-on-Hadoop service, although work is ongoing for supporting Hive. Presto is one of the main SQL-on-Hadoop platform, but, currently, Presto does not provide multi-tenancy support for users. This thesis investigates providing multitenancy support to Presto with the help of HopsWorks, including both the security problem and the self-service UI requirements of HopsWorks. Presto is a distributed SQL query Engine which can run SQL queries against up to petabytes of data. As HopsWorks provides UI access to services, we decided to build our UI for Presto on an existing open-source UI for Presto, called Airpal, developed by Airbnb. This provided solution of the thesis divided into two functionalities. First one, maintain two separate Applications (HopsWorks and Airpal Applications) run by the help of two JVMs and maintain ProxyServlet to control traffic between them. Second one HopsWorks-Presto-service leverages HopsWorks access-control (Data owner and Data-scientist) and self-service security model. The evaluation of the thesis used qualitative approach by comparing HopsWorks-PrestoService with standalone PrestoDB and comparing HopsWorks-PrestoService with HopsWorks without Presto-Service. / De senaste åren, har det varit en avsevärd ökning vad gäller mängden av data som produceras, lagras och som används för analys av olika organisationer. Organisationer spenderar mer pengar för att undersöka och extrahera information och insikter i enorma datavolymer på flera terabyte eller petabyte. Storskalig dataanalys är en central funktionalitet som tillhandahålls av Big Data plattformar. I tidigare tillvägagångssätt hämtade data plattformaro-strukturerade data i form av filer, texter och videoklipp. I nutid, så har Hadoop-stacken spelat en kärnroll i Big Data, och blivit en viktig öppen källkod mjukvara som används för att processera och analysera Big Data. Hops är en Hadoop distribution som har utvecklats av KTH och RISE SICS. Hops tillför ändringar till Hadoop stacken genom att migrera metadata för YARN och HDFS till NDB, en öppen källkod i-minnet distribuerad databas. HopsWorks är ett användargränssnitt för Hops och tillför stöd för flera användare, med tillgång till självservice och tjänster såsom Hadoop, Flink, Spark, Kafka och Kibana. HopsWorks stödjer i nuläget inte någon SQL på Hadoop tjänst, även om arbete utförs i nuläget för att integrera Hive. Presto är en av de mest populära SQL på Hadoop plattformarna, men i nuläget så stödjer inte Presto flera användare. Den här uppsatsen utreder stöd för flera användare i Presto med hjälp av HopsWorks, både vad gäller säkerhetsproblem och självservice i HopsWorks. Presto är en distribuerad SQL frågespråk motor som kan ställa frågor mot upp till petabyte med data. Eftersom HopsWorks tillhandahåller ett gränssnitt för att interagera med tjänster, beslutade vi oss att bygga ett gränssnitt för Presto på det existerande öppen källkod gränssnittet för Presto, vid namn AirPal, utvecklat av Airbnb. Den utvecklade lösningen för uppsatsen kan delas in i två delar. Den första delen, att hantera två separata applikationer (HopsWorks och AirPal) som kör med hjälp av två Java virtuella maskiner och använder en ProxyServlet för att kontrollera trafik mellan dom. Den andra, HopsWorks-Presto-service som tillhandahåller HopsWorks åtkomstkontroll (Dataägare och Dataforskare) och en självservice säkerhetsmodell. Utvärderingen i uppsatsen är att genom ett kvalitativt tillvägagångssätt jämföra HopsWorks-Presto-service med en fristående PrestoDB och jämföra HopsWorks-Presto-service med HopsWorks utan Presto-service.

When patients move during an MRI examination, severe artifacts arise in the reconstructed image and motion correction is therefore often desired. An in-plane motion correction algorithm suitable for PRESTO-CAN, a new 3D functional MRI method where sampling of k-space is radial in kx-direction and kz-direction and Cartesian in ky-direction, was implemented in this thesis work. Rotation and translation movements can be estimated and corrected for sepa- rately since the magnitude of the data is only affected by the rotation. The data were sampled in a radial pattern and the rotation was estimated by finding the translation in angular direction using circular correlation. Correlation was also used when finding the translation in x-direction and z-direction. The motion correction algorithm was evaluated on computer simulated data, the motion was detected and corrected for, and this resulted in images with greatly reduced artifacts due to patient movements. / När patienter rör sig under en MRI-undersökning uppstår artefakter i den rekonstruerande bilden och därför är det önskvärt med rörelsekorrigering. En 2D- rörelsekorrigeringsalgoritm som är anpassad för PRESTO-CAN har tagits fram. PRESTO-CAN är en ny fMRI-metod för 3D där samplingen av k-rummet är radiell i (kx,kz)-planet och kartesisk i ky-riktningen. Rotations- och translationsrörelser kan estimeras separat då magnituden av signalen bara påverkas av rotationsrörelser. Eftersom data är samplat radiellt kan rotationen estimeras genom att hitta translationen i vinkelled med hjälp av cirkulär korrelation. Korrelation används även för att hitta translationen i i x- och z-riktningen. Test på simulerat data visar att rörelsekorrigeringsalgoritmen både detekterar och korrigerar för rörelser vilket leder till bilder med mycket mindre rörelseartefakter.

abstract: The processing of large volumes of RDF data require an efficient storage and query processing engine that can scale well with the volume of data. The initial attempts to address this issue focused on optimizing native RDF stores as well as conventional relational databases management systems. But as the volume of RDF data grew to exponential proportions, the limitations of these systems became apparent and researchers began to focus on using big data analysis tools, most notably Hadoop, to process RDF data. Various studies and benchmarks that evaluate these tools for RDF data processing have been published. In the past two and half years, however, heavy users of big data systems, like Facebook, noted limitations with the query performance of these big data systems and began to develop new distributed query engines for big data that do not rely on map-reduce. Facebook's Presto is one such example. This thesis deals with evaluating the performance of Presto in processing big RDF data against Apache Hive. A comparative analysis was also conducted against 4store, a native RDF store. To evaluate the performance Presto for big RDF data processing, a map-reduce program and a compiler, based on Flex and Bison, were implemented. The map-reduce program loads RDF data into HDFS while the compiler translates SPARQL queries into a subset of SQL that Presto (and Hive) can understand. The evaluation was done on four and eight node Linux clusters installed on Microsoft Windows Azure platform with RDF datasets of size 10, 20, and 30 million triples. The results of the experiment show that Presto has a much higher performance than Hive can be used to process big RDF data. The thesis also proposes an architecture based on Presto, Presto-RDF, that can be used to process big RDF data. / Dissertation/Thesis / Masters Thesis Computing Studies 2014

Truecaller is a mobile application with over 200 million unique users worldwide. Every day truecaller stores over 1 billion rows of data that they use to analyse for improving their product. The data is stored in Hadoop, which is a framework for storing and analysing large amounts of data on a distributed file system. In order to be able to analyse these large amounts of data the analytics team needs a new solution for more lightweight, ad-hoc analysis. This thesis evaluates the performance of the query engine Presto to see if it meets the requirements to help the data analytics team at truecaller gain efficiency. By using a design-science methodology, Presto’s pros and cons are presented. Presto is recommended as a solution to be used together with the tools today for specific lightweight use cases for users that are familiar with the data sets used by the analytics team. Other solutions for future evaluation are also recommended before taking a final decision.Keywords: Hadoop, Big Data, Presto, Hive, SQL on Hadoop / <p>Validerat; 20160819 (global_studentproject_submitter)</p>

Claudio Ambrosini’s (b. 1948) unpublished work for unaccompanied flute, A guisa di un arcier presto soriano (1981), although virtually unknown to the musical public and to connoisseurs alike, represents one of the most dazzling and impressive displays of extended techniques in the repertoire of solo flute music. The title, A guisa di un arcier presto soriano, comes from the seventh line of a sonnet by the Italian medieval poet Guido Cavalcanti (ca. 1250-1300) and translates as “just like a fast Syrian archer.” The archer in question is Eros, the Greek god of love. By the composer’s own admission, the form and expression of this piece is closely linked with the form and expression of Cavalcanti’s sonnet. In particular, Ambrosini intimates three elements specifically drawn from the poem: 1) moments of tension and suspense, as Eros silently approaches his target with bow and arrow in hand; 2) moments of love, even to the point of suggesting a love song; and 3) moments that suggest the fast passage of arrows. The purpose of this dissertation is to explore these three elements in Ambrosini’s work and to trace the correlations of the same elements in Cavalcanti’s sonnet. The expression of such concrete poetic imagery in Ambrosini’s music is at times easily deciphered through clear programmatic gestures or wordless madrigalisms; and at other times the symbolism of the poetry is developed in a hidden and metaphorical manner in its musical iteration. Further, Ambrosini’s use of a particularly colorful and vast array of extended techniques serves as the impetus for the formal structure that the music embodies, and I will show that this formal structure is itself a symbolic and metaphorical representation of the poetic significance of Cavalcanti’s sonnet.

Traditional relational database systems can not be efficiently used to analyze data with large volume and different formats, i.e. big data. Apache Hadoop is one of the first open-source tools that provides a distributed data storage system and resource manager. The space of big data processing has been growing fast over the past years and many technologies have been introduced in the big data ecosystem to address the problem of processing large volumes of data, and some of the early tools have become widely adopted, with Apache Hive being one of them. However,with the recent advances in technology, there are other tools better suited for interactive analytics of big data, such as Apache Spark and Presto. In this thesis these technologies are examined and benchmarked in order to determine their performance for the task of interactive business intelligence queries. The benchmark is representative of interactive business intelligence queries, and uses a star-shaped schema. The performance HiveTez, Hive LLAP, Spark SQL, and Presto is examined with text, ORC, Parquet data on different volume and concurrency. A short analysis and conclusions are presented with the reasoning about the choice of framework and data format for a system that would run interactive queries on bigdata. / Traditionella relationella databassystem kan inte användas effektivt för att analysera stora datavolymer och filformat, såsom big data. Apache Hadoop är en av de första open-source verktyg som tillhandahåller ett distribuerat datalagring och resurshanteringssystem. Området för big data processing har växt fort de senaste åren och många teknologier har introducerats inom ekosystemet för big data för att hantera problemet med processering av stora datavolymer, och vissa tidiga verktyg har blivit vanligt förekommande, där Apache Hive är en av de. Med nya framsteg inom området finns det nu bättre verktyg som är bättre anpassade för interaktiva analyser av big data, som till exempel Apache Spark och Presto. I denna uppsats är dessa teknologier analyserade med benchmarks för att fastställa deras prestanda för uppgiften av interaktiva business intelligence queries. Dessa benchmarks är representative för interaktiva business intelligence queries och använder stjärnformade scheman. Prestandan är undersökt för Hive Tex, Hive LLAP, Spark SQL och Presto med text, ORC Parquet data för olika volymer och parallelism. En kort analys och sammanfattning är presenterad med ett resonemang om valet av framework och dataformat för ett system som exekverar interaktiva queries på big data.