For many companies in the manufacturing industry, attempts to find damages in their products is a vital process, especially during the production phase. Since applying different machine learning techniques can further aid the process of damage identification, it becomes a popular choice among companies to make use of these methods to enhance the production process even further. For some industries, damage identification can be heavily linked with anomaly detection of different measurements. In this thesis, the aim is to construct unsupervised machine learning models to identify anomalies on unlabeled measurements of pumps using high frequency sampled current and voltage time series data. The measurement can be split up into five different phases, namely the startup phase, three duty point phases and lastly the shutdown phase. The approach is based on clustering methods, where the main algorithms of use are the density-based algorithms DBSCAN and LOF. Dimensionality reduction techniques, such as feature extraction and feature selection, are applied to the data and after constructing the five models of each phase, it can be seen that the models identifies anomalies in the data set given.​ / För flera företag i tillverkningsindustrin är felsökningar av produkter en fundamental uppgift i produktionsprocessen. Då användningen av olika maskininlärningsmetoder visar sig innehålla användbara tekniker för att hitta fel i produkter är dessa metoder ett populärt val bland företag som ytterligare vill förbättra produktionprocessen. För vissa industrier är feldetektering starkt kopplat till anomalidetektering av olika mätningar. I detta examensarbete är syftet att konstruera oövervakad maskininlärningsmodeller för att identifiera anomalier i tidsseriedata. Mer specifikt består datan av högfrekvent mätdata av pumpar via ström och spänningsmätningar. Mätningarna består av fem olika faser, nämligen uppstartsfasen, tre last-faser och fasen för avstängning. Maskinilärningsmetoderna är baserade på olika klustertekniker, och de metoderna som användes är DBSCAN och LOF algoritmerna. Dessutom tillämpades olika dimensionsreduktionstekniker och efter att ha konstruerat 5 olika modeller, alltså en för varje fas, kan det konstateras att modellerna lyckats identifiera anomalier i det givna datasetet.

This thesis aims to investigate the acoustic properties of ultrasound contrast agents (UCAs) at high ultrasound frequencies. In recent years, there has been increasing development in the use of high frequency ultrasound in the fields of preclinical, intravascular, ophthalmology and superficial tissue imaging. Although research studying the acoustic response of UCAs at low diagnostic ultrasonic frequencies has been well documented, quantitative information on the acoustical properties of UCAs at high ultrasonic frequencies is limited. In this thesis, acoustical characterisation of three UCAs was performed using a preclinical ultrasound scanner (Vevo 770, VisualSonics Inc., Canada). Initially the acoustical characterisation of five high frequency transducers was measured using a membrane hydrophone with an active element of 0.2 mm in diameter to quantify the transmitting frequencies, pressures and spatial beam profiles of each of the transducers. Using these transducers and development of appropriate software, high frequency acoustical characterisation (speed and attenuation) of an agar-based tissue mimicking material (TMM) was performed using a broadband substitution technique. The results from this study showed that the acoustical attenuation of TMM varied nonlinearly with frequency and the speed of sound was approximately constant 1548m·s-1 in the frequency range 12-47MHz. The acoustical properties of three commercially available lipid encapsulated UCAs including two clinical UCAs Definity (Lantheus Medical Imaging, USA) and SonoVue (Bracco, Italy) and one preclinical UCAs MicroMarker (untargeted) (VisualSonics, Canada) were studied using the software and techniques developed for TMM characterisation. Attenuation, contrast-to-tissue ratio (CTR) and subharmonic to fundamental ratio were measured at low acoustic pressures. The results showed that large off-resonance and resonant MBs predominantly contributed to the fundamental response and MBs which resonated at half of the driven frequency predominantly contributed to subharmonic response. The effect of needle gauge, temperature and injection rate on the size distribution and acoustic properties of Definity and SonoVue was measured and was found to have significant impacts. Acoustic characterisations of both TMM and UCAs in this thesis extend our understanding from low frequency to high frequency ultrasound and will enable the further development of ultrasound imaging techniques and UCAs design specifically for high frequency ultrasound applications.

We present hereafter the development of the Liebherr Intelligent Hydraulic Cylinder, in which the hydraulic component is used as smart sensing element providing useful information for the system in which the cylinder is operated. The piston position and velocity are the most important signals derived from this new measuring approach. The performance under various load and temperature conditions (measured both on dedicated test facilities and in field in a real machine) will be presented. An integrated control electronics, which is performing the cylinder state processing, additionally allows the synchronized acquisition of external sensors. Providing comprehensive state information, such as temperature and system pressure, advanced control techniques or monitoring functions can be realized with a monolithic device. Further developments, trends and benefits for the system architecture will be briefly analyzed and discussed.

This dissertation addresses the fundamental question of what factors drive equity prices and investigates the mechanisms through which the drivers influence the price dynamics. The studies are based on the two different frequency levels of financial data. The first part aims to identify what systematic risk factors affect the expected return of stocks based on historical data with frequency being daily or monthly. The second part aims to explain how the hidden supply-demand of a stock affects the stock price dynamics based on market data observed at frequency levels generally between a millisecond and a second. With more and more financial market data becoming available, it greatly facilitates quantitative approaches for analyzing asset price dynamics and market microstructure problems. In the first part, we propose an econometric measure, terms as modularity, for characterizing the cluster structure in a universe of stocks. A high level of modularity implies that the cluster structure of the universe of stocks is highly evident, and low modularity implies a blurred cluster structure. The modularity measure is shown to be related to the cycle of the economy. In addition, individual stock's sensitivity to the modularity measure is shown to be related to its expected return. From 1992 to 2011, the average annual return of stocks with the lowest sensitivity exceeds that of the stocks with highest sensitivities by approximately 7.6%. Considerations of modularity as an asset pricing factor expand the investment opportunity set to passive investors. In the second part, we analyze the effect of hidden demands/supplies in equity trading market on the stock price dynamics. We propose a statistical estimation model for average hidden liquidity based on the limit orderbook data. Not only the estimated hidden liquidity explains the probabilistic property in market microstructure better, it also refines the existing price impact model and achieves higher explanation powers. Our enhanced price impact model offers a base for devising optimal order execution strategies. After we develop an optimal execution strategy based on the price impact function, the advantage of this strategy over benchmark strategies is tested on a simulated stock trading model calibrated by historical data. Simulation tests indicate that our strategy yields significant savings in transaction cost over the benchmark strategies.

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The High Frequency (HF) band, 3-30 MHz, is used when no infrastructure for long-range communications is available. New technology, such as digital signal processing enables higher data rate in the HF band, which in 2000s has resulted in increased commercial use. Reflection of radio waves in the ionosphere allows for beyond horizon communication, and are a unique property of the HF band. However, properties of the ionosphere are highly dependent of radiation from the sun, which varies with geographical location, season and time. The use of unmanned areal vehicle (UAV) has increased during the past years. In this project it is investigated if a HF transmitter can be placed on a small UAV platform. The objective is to get an estimation of the probabilities for successful HF transfer of real-time data from a small UAV. For example, the data could be sensor- or position data. When studying a complex problem having several parameters, such as a HF communication system, it is necessary to use the systems approach. This report illustrates the impact of size of the transmitting antenna, transmitter output power and bandwidth as well as different sources of noise and its levels. The results and analysis, made in this project, shows that there are feasible solutions for every tested case except at very high latitudes. Frequency planning, that is finding the less occupied channel, is almost as important as maximizing the signal to noise ratio. This project has been carried out on behalf of ÅF Technology in Solna, Sweden.

This study explored the evolution of Denton's VHF educational television assignment Channel 2, from its inception on April 14, 1952, to September 2, 1977. The problem was to discern why the channel remained inactive for twenty-five years. Chapters explore the attempts of broadcast interests to acquire control of the channel, and discuss why they were unsuccessful. The study concludes that a lack of finances, combined with the apathy and self-interests of Denton's educational leaders, prevented the channel's utilization. Federal Communications Commission policy allowed Denton's educators more time to raise money for a Denton station. Other conclusions suggest that the channel not be reassigned and that it be activated.

Cyanobacteria blooms have increased in Lake Champlain due to excessive nutrient loading, resulting in negative impacts on the local economy and environmental health. While climate warming is expected to promote increasingly severe cyanobacteria blooms globally, predicting the impacts of complex climate changes on individual lakes is complicated by the many physical, chemical, and biological processes which mediate nutrient dynamics and cyanobacteria growth across time and space. Furthermore, processes influencing bloom development operate on a variety of temporal scales (hourly, daily, seasonal, decadal, episodic), making it difficult to identify important factors controlling bloom development using traditional methods or coarse temporal resolution datasets. To resolve these inherent problems of scale, I use 4 years of high-frequency biological, hydrodynamic, and biogeochemical data from Missisquoi Bay, Lake Champlain; 23 years of lake-wide monitoring data; and integrated process-based climate-watershed-lake models driven by regional climate projections to answer the following research questions: 1) To what extent do external nutrient inputs or internal nutrient processing control nutrient concentrations and cyanobacteria blooms in Lake Champlain; 2) how do internal and external nutrient inputs interact with meteorological drivers to promote or suppress bloom development; and 3) how is climate change likely to impact these drivers and the risk of cyanobacteria blooms in the future? I find that cyanobacteria blooms are driven by specific combinations of meteorological and biogeochemical conditions in different areas of the lake, and that in the absence of strong management actions cyanobacteria blooms are likely to become more severe in the future due to climate change.

Correctly specified models to forecast returns of indices are important for in- vestors to minimize risk on financial markets. This thesis focuses on conditional Value at Risk modeling, employing flexible quantile regression framework and hence avoiding the assumption on the return distribution. We apply semi- parametric linear quantile regression (LQR) models with realized variance and also models with positive and negative semivariance which allows for direct modelling of the quantiles. Four European stock price indices are taken into account: Czech PX, Hungarian BUX, German DAX and London FTSE 100. The objective is to investigate how the use of realized variance influence the VaR accuracy and the correlation between the Central & Eastern and Western European indices. The main contribution is application of the LQR models for modelling of conditional quantiles and comparison of the correlation between European indices with use of the realized measures. Our results show that linear quantile regression models on one-step-ahead forecast provide better fit and more accurate modelling than classical VaR model with assumption of nor- mally distributed returns. Therefore LQR models with realized variance can be used as accurate tool for investors. Moreover we show that diversification benefits are...

This thesis examines an impact of high frequency trading on equity market qualities. As an indicator of market quality, stock prices realized volatility is used. To estimate the high frequency trading activity, we implement a special method of identification of high frequency orders from quote data. Study of relation between high frequency trading and market qualities is incited by growing concerns about the welfare impacts of high frequency trading and connected activities. In order to test the dependence and causality between high frequency trading activity and volatility, we implement time-scale estimation techniques. Wavelet coherence is used to study localized dependence. The analysis is amended by a robustness check, using wavelet correlation. Results show inconsistent dependence at short trading horizons and regions of significant continuous dependence at trading horizons within hours. Powered by TCPDF (www.tcpdf.org)