Numera sker all industriell torkning av virke i sågverken i virkestorkar, som värms upp och därmed torkar virket till den optimala slutfuktkvot som tillönskas. Virkestorkar byggda i betong har länge varit ett stort problem för sågverksindustrin. Problematiken går tillbaka till 80-talet då man tvingades riva ett flertal torkar på grund av att betongtorkarna höll på att vittra sönder. Detta gällde för alla betongtorkar byggda fram till 70-talet. Det genomfördes i slutet på 90-talet en stor rapportundersökningen som behandlade de flesta typer av skador i betongtorkar. Fram tills idag har en närmare undersökning ej gjorts och det har fortfarande inte hittats standarder för reparation och underhåll som förlänger livslängden på betongtorkar. Sågverksindustrin förlorar varje år 10-tals miljoner kronor på reparationer och underhåll som i de flesta fall inte verkar fungera. Därför finns det en stor efterfrågan av tydliga instruktioner för val av material och hur dessa reparationer skall gå till. Rapporten behandlar den allmänna problematiken av skador som uppstår p.g.a. bland annat väldigt högt temperaturbelastade virkestorkar som är konstruerade i betong. Arbetet är uppdelat i två faser den ena fasen består av en teoridel som bland annat förklarar skadorna och dess uppkomst i betongtorkar. Den andra fasen behandlar en fältundersökning som genomfördes under rapportskrivningen samt de förslag till åtgärder som tagits fram. Denna rapport är en liten del av ett stort pågående projekt, där CBI Betonginstitutet och SP trä är projektutförare och samverkar med deltagande sågverk i Sverige och Norge samt leverantörer. Projektet ska mynna ut i en Guideline till sågverksindustrin, för att kunna utföra rätt reparationer med rätt materialval och därmed förlänga betongtorkarnas livslängd. Detta med utgångspunkt från fältundersökningens provtagningar. / Nowadays, all the process of industrial drying of lumber in sawmills are done in kilns, which the wood is heated and dried for the desired optimal moisture content. Timber drying kilns built in concrete has for a long time been a major issues for the sawmill industry. The problems goes as far back to the 80s when several wood kilns was forced to be demolished due to concrete kilns were about to crumble. This was mainly for all the concrete dries built up until the 70s. In the late 90's a report survey was carried out to investigate the damages that were inflicted in concrete dries. No other survey has been done since then and the report didn’t give tangible standards for repairs and maintenance that extends the service life in wood kilns. The sawmill industry loses each year tens of millions on repairs and maintenance that hasn’t shown any results of working. Therefore, there is a great demand of clear instructions for the selection of materials and how these repairs need to be done. The report deals with the general issues of very high temperature-loaded kilns constructed in concrete. The work is divided into two phases; one phase consists of a theoretical part including explaining of the damages and its emergence in the concrete kilns. The second phase deals with a field survey conducted during the report writing as well as the proposed measures have been developed. This report is one small part of a large ongoing project, where CBI and SP wood are the project implementers and interact with participating sawmills in Sweden and Norway and the suppliers. The project will culminate in a Guideline for the sawmill industry, to be able to perform the correct repair with proper materials and thereby extend the life of concrete kilns. This is based on field survey sampling. The project will culminate in a Guideline for the sawmill industry, to be able to perform the correct repair with proper materials and thereby extend the life of concrete drying. This is based on field survey sampling.

The thesis is aimed on the digital analysis and processing of micro- scopic image data with a focus on cross-section samples from the artworks which fall into cultural heritage domain. It contributes to solution of two different problems of image processing - image seg- mentation and image retrieval. The performance evaluation of differ- ent image segmentation methods on a data set of cross-section images is carried out in order to study the behavior of individual approaches and to propose guidelines how to choose suitable method for segmen- tation of microscopic images. Moreover, the benefit of segmenta- tion combination approach is studied and several distinct combination schemes are proposed. The evaluation is backed up by a large number of experiments where image segmentation algorithms are assessed by several segmentation quality measures. Applicability of achieved re- sults is shown on image data of different origin. In the second part, content-based image retrieval of cross-section samples is addressed and functional solution is presented. Its implementation is included in Nephele system, an expert system for processing and archiving the material research reports with image processing features, designed and implemented for the cultural heritage application area. 1

This thesis will cover the topic of cyber security in vehicles. Current vehicles contain many computers which communicate over a controller area network. This network has many vulnerabilities which can be leveraged by attackers. To combat these attackers, intrusion detection systems have been implemented. The latest research has mostly focused on the use of deep learning techniques for these intrusion detection systems. However, these deep learning techniques are not foolproof and possess their own security vulnerabilities. One such vulnerability comes in the form of adversarial samples. These are attacks that are manipulated to evade detection by these intrusion detection systems. In this thesis, the aim is to show that the known vulnerabilities of deep learning techniques are also present in the current state-of-the-art intrusion detection systems. The presence of these vulnerabilities shows that these deep learning based systems are still to immature to be deployed in actual vehicles. Since if an attacker is able to use these weaknesses to circumvent the intrusion detection system, they can still control many parts of the vehicles such as the windows, the brakes and even the engine. Current research regarding deep learning weaknesses has mainly focused on the image recognition domain. Relatively little research has investigated the influence of these weaknesses for intrusion detection, especially on vehicle networks. To show these weaknesses, firstly two baseline deep learning intrusion detection systems were created. Additionally, two state-of-the-art systems from recent research papers were recreated. Afterwards, adversarial samples were generated using the fast gradient-sign method on one of the baseline systems. These adversarial samples were then used to show the drop in performance of all systems. The thesis shows that the adversarial samples negatively impact the two baseline models and one state-of-the-art model. The state-of-the-art model’s drop in performance goes as high as 60% in the f1-score. Additionally, some of the adversarial samples need as little as 2 bits to be changed in order to evade the intrusion detection systems. / Detta examensarbete kommer att täcka ämnet cybersäkerhet i fordon. Nuvarande fordon innehåller många datorer som kommunicerar över ett så kallat controller area network. Detta nätverk har många sårbarheter som kan utnyttjas av angripare. För att bekämpa dessa angripare har intrångsdetekteringssystem implementerats. Den senaste forskningen har mestadels fokuserat på användningen av djupinlärningstekniker för dessa intrångsdetekteringssystem. Dessa djupinlärningstekniker är dock inte idiotsäkra och har sina egna säkerhetsbrister. En sådan sårbarhet kommer i form av kontradiktoriska prover. Dessa är attacker som manipuleras för att undvika upptäckt av dessa intrångsdetekteringssystem. I det här examensarbetet kommer vi att försöka visa att de kända sårbarheterna hos tekniker för djupinlärning också finns i de nuvarande toppmoderna systemen för intrångsdetektering. Förekomsten av dessa sårbarheter visar att dessa djupinlärningsbaserade system fortfarande är för omogna för att kunna användas i verkliga fordon. Eftersom om en angripare kan använda dessa svagheter för att kringgå intrångsdetekteringssystemet, kan de fortfarande kontrollera många delar av fordonet som rutorna, bromsarna och till och med motorn. Aktuell forskning om svagheter i djupinlärning har främst fokuserat på bildigenkänningsdomänen. Relativt lite forskning har undersökt inverkan av dessa svagheter för intrångsdetektering, särskilt på fordonsnätverk. För att visa dessa svagheter skapades först två baslinjesystem för djupinlärning intrångsdetektering. Dessutom återskapades två toppmoderna system från nya forskningsartiklar. Efteråt genererades motstridiga prover med hjälp av den snabba gradient-teckenmetoden på ett av baslinjesystemen. Dessa kontradiktoriska prover användes sedan för att visa nedgången i prestanda för alla system. Avhandlingen visar att de kontradiktoriska proverna negativt påverkar de två baslinjemodellerna och en toppmodern modell. Den toppmoderna modellens minskning av prestanda går så högt som 60% i f1-poängen. Dessutom behöver några av de kontradiktoriska samplen så lite som 2 bitar att ändras för att undvika intrångsdetekteringssystem.

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This dissertation focuses on the development of analytical methodology for the analysis of polycyclic aromatic hydrocarbons (PAHs) in water samples. Chemical analysis of PAHs is of great environmental and toxicological importance. Many of them are highly suspect as etiological agents in human cancer. Among the hundreds of PAHs present in the environment, the U.S. Environmental Protection Agency (EPA) lists sixteen as "Consent Decree" priority pollutants. Their routine monitoring in environmental samples is recommended to prevent human contamination risks. A primary route of human exposure to PAHs is the ingestion of contaminated water. The rather low PAH concentrations in water samples make the analysis of the sixteen priority pollutants particularly challenging. Current EPA methodology follows the classical pattern of sample extraction and chromatographic analysis. The method of choice for PAHs extraction and pre-concentration is solid-phase extraction (SPE). PAHs determination is carried out via high-performance liquid chromatography (HPLC) or gas chromatography/mass spectrometry (GC/MS). When HPLC is applied to highly complex samples, EPA recommends the use of GC/MS to verify compound identification and to check peak-purity of HPLC fractions. Although EPA methodology provides reliable data, the routine monitoring of numerous samples via fast, cost effective and environmentally friendly methods remains an analytical challenge. Typically, 1 L of water is processed through the SPE device in approximately 1 h. The rather large water volume and long sample processing time are recommended to reach detectable concentrations and quantitative removal of PAHs from water samples. Chromatographic elution times of 30 - 60 min are typical and standards must be run periodically to verify retention times. If concentrations of targeted PAHs are found to lie outside the detector's response range, the sample must be diluted (or concentrated), and the process repeated. In order to prevent environmental risks and human contamination, the routine monitoring of the sixteen EPA-PAHs is not sufficient anymore. Recent toxicological studies attribute a significant portion of the biological activity of PAH contaminated samples to the presence of high molecular weight (HMW) PAHs, i.e. PAHs with MW ≥ 300. Because the carcinogenic properties of HMW-PAHs differ significantly from isomer to isomer, it is of paramount importance to determine the most toxic isomers even if they are present at much lower concentrations than their less toxic isomers. Unfortunately, established methodology cannot always meet the challenge of specifically analyzing HMW-PAHs at the low concentration levels of environmental samples. The main problems that confront classic methodology arise from the relatively low concentration levels and the large number of structural isomers with very similar elution times and similar, possibly even virtually identical, fragmentation patterns. This dissertation summarizes significant improvements on various fronts. Its first original component deals with the unambiguous determination of four HMW-PAHs via laser-excited time-resolved Shpol'skii spectroscopy (LETRSS) without previous chromatographic separation. The second original component is the improvement of a relatively new PAH extraction method - solid-phase nanoextraction (SPNE) - which uses gold nanoparticles as extracting material for PAHs. The advantages of the improved SPNE procedure are demonstrated for the analysis of EPA-PAHs and HMW-PAHs in water samples via GC/MS and LETRSS, respectively.

In this work, we set the stage of a limited labelling budget and propose using a sample selector network to learn and select effective training samples, whose labels we would then acquire to train the target model performing the required machine learning task. We make the assumption that the sample features, the state of the target model and the training loss of the target model are informative for training the sample selector network. In addition, we approximate the state of the target model with its intermediate and final network outputs. We investigate if under a limited labelling budget, the sample selector network is capable of learning and selecting training samples that train the target model at least as effectively as using another training subset of the same size that is uniformly randomly sampled from the full training dataset, the latter being the common procedure used to train machine learning models without active learning. We refer to this common procedure as the traditional machine learning uniform random sampling method. We perform experiments on the MNIST and CIFAR-10 datasets; and demonstrate with empirical evidence that under a constrained labelling budget and some other conditions, active learning using a sample selector network enables the target model to learn more effectively. / I detta arbete sätter vi steget i en begränsad märkningsbudget och föreslår att vi använder ett provväljarnätverk för att lära och välja effektiva träningsprover, vars etiketter vi sedan skulle skaffa för att träna målmodellen som utför den nödvändiga maskininlärningsuppgiften. Vi antar att provfunktionerna, tillståndet för målmodellen och utbildningsförlusten för målmodellen är informativa för att träna provväljarnätverket. Dessutom uppskattar vi målmodellens tillstånd med dess mellanliggande och slutliga nätverksutgångar. Vi undersöker om provväljarnätverket enligt en begränsad märkningsbudget kan lära sig och välja utbildningsprover som tränar målmodellen minst lika effektivt som att använda en annan träningsdel av samma storlek som är enhetligt slumpmässigt samplad från hela utbildningsdatasystemet, det senare är det vanliga förfarandet som används för att utbilda maskininlärningsmodeller utan aktivt lärande. Vi hänvisar till denna vanliga procedur som den traditionella maskininlärning enhetliga slumpmässig sampling metod. Vi utför experiment på datasätten MNIST och CIFAR-10; och visa med empiriska bevis att under en begränsad märkningsbudget och vissa andra förhållanden, aktivt lärande med hjälp av ett provvalnätverk gör det möjligt för målmodellen att lära sig mer effektivt.

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