Multi-coincidence System of Ionization Radiation Detectors Based on Advanced Position Sensitive Detectors
Husák, Miroslav; Jakůbek, Jan; Mašek, Petr
2017 - English Detection of ionizing radiation is an issue which affects many areas of a human life. Significant progress in detection capabilities was caused by the development of electronics in recent decades. This dissertation thesis, which deals with the design of the Multi-coincidence system of ionizing radiation detectors, follows this trend. The Multi-coincidence system of ionizing radiation detectors combines extraordinary technologies and due to using advanced electronics allows them to operate in a synchronous mode that brings new possibilities in radiation environment investigation, such as incoming direction or separation of particle types in a mixed radiation field. This dissertation thesis summarizes the development from the basic design, through hardware, firmware and software for user control as well as basic data processing. The functionality of the design is verified on a prototype and demonstrated by measurements whose results are also presented. The design is based on “sandwich” structure composed of two detection parts in closed geometry – a silicon pixel detector Timepix and a plastic scintillator covered by silicon photomultipliers. The pixel detector Timepix is a device with excellent parameters and unrivaled ability of detection and recognition of different types of radiation. The disadvantage is the detection material itself which does not allow to effectively detect certain types of radiation, such as neutrons. This is the domain of scintillation detectors. Recent optical sensors called silicon photomultipliers offers possibility for detecting of the scintillating light by small-size device. The Multi-coincidence system is a compact portable detection unit which can be used in applications where small dimensions are a significant requirement. Furthermore, it is insensitive to a magnetic field. Utilization of the system can be found in different areas from searching of radiation threats, through dosimetry to space weather monitoring. Keywords: Keywords: ionizing radiation; neutrons; pixel detector; scintillator; silicon photomultiplier; SiPM; FITPix; VATA64HDR16; dongle Available in digital repository of ČVUT. Multi-coincidence System of Ionization Radiation Detectors Based on Advanced Position Sensitive Detectors

Detection of ionizing radiation is an issue which affects many areas of a human life. Significant progress in detection capabilities was caused by the development of electronics in recent decades. ...

Husák, Miroslav; Jakůbek, Jan; Mašek, Petr
České vysoké učení technické v Praze, 2017

Methods for Balancing Electricity Generation from Renewables on the Electricity Markets
Vastl, Jaromír; Sokol, Radoslav
2016 - English Power generation mix of each country depends mainly on the availability of usable resources in its territory or the possibility of importing them. In connection with the fulfilment of ambitious objectives to be reached by 2020 power generation mix is changing rapidly and is heading toward low carbon electricity generation footprint. Power generation mix in individual countries is more and more affected by their geographical location and its potential for utilization of renewables. In the beginning of year 2016 OTC electricity prices for 2017 base-load delivery in Germany were attacking 20 EUR level while half a year ago it was traded for 30 EUR. [37] Subsidized renewables caused price curve distortion resulting in conventional hard coal and lignite power plants that accounted in 2015 for 42% of total electricity generation, to operate close to their variable costs. Share of Germany’s electricity generation from renewables reached in 2015 30% and still new projects are planned or already under construction. [13] Low prices are not only affecting electricity markets where renewables are installed but also adjacent electricity markets in the neighbouring countries that are well-interconnected. With increasing renewable power capacity serious problems are experienced in the whole meshed central European network that may eventually lead to blackout situations. For electricity market participants with renewables in their generation portfolio is crucial to have all possible means to stay balanced. Interconnected electricity markets with high liquidity covering every day 24 hours and offering short-term products that can be traded as close as possible to delivery have to be on place. In case of missing trading platform, low liquidity or long time lag between delivery and product trading deadline, market participants are unable to balance themselves and are consequently exposed to balancing costs that can account for big part of total costs endangering profitability. In any case balance responsible parties have to do everything to be balanced. In my thesis I am testing possibility of using neural networks to improve generation forecast for wind farm from three independent meteorological data providers. Results from test scenarios which differ in various combinations of inputs were analysed and compared with regard to the most important indicators from the perspective of the wind farm owner. Balancing cost saving criterion is the most important measure to evaluate whether obtained results are better compared to the results of the most accurate meteorological data provider. Test scenarios for both day ahead and intraday time frame have been tested. Test period of six months proved that cost savings for balancing deviations can be achieved. Available in digital repository of ČVUT. Methods for Balancing Electricity Generation from Renewables on the Electricity Markets

Power generation mix of each country depends mainly on the availability of usable resources in its territory or the possibility of importing them. In connection with the fulfilment of ambitious ...

Vastl, Jaromír; Sokol, Radoslav
České vysoké učení technické v Praze, 2016

Passivation of thin film silicon solar cells
Benda, Vítězslav; Fejfar, Antonín; Pikna, Peter
2016 - English Passivation of electronic defects is a necessary step in production of solar cells based on polycrystalline silicon thin film on glass. Standard passivation method is plasma hydrogenation, which however, represents the second most expensive production step. We used solid phase crystallized (SPC) silicon samples to explore an alternative cheaper passivation approach using annealing in water vapour. Open-circuit voltage VOC measured by Suns-VOC method was the key parameter to determine success of the treatment. Annealing of SPC Si thin film solar cells in water vapour was explored in the temperature range from 145°C to 650°C under steam pressure from atmospheric pressure to 1.0 MPa at the exposure times of 5-225 minutes. For this purpose a special passivation chamber enabling an independent control of a sample temperature and steam pressure was designed and built. We achieved the best VOC of 360 mV (from the starting 220 mV) demonstrating the annealing in water vapour as a possible low cost alternative passivation method. Some SPC poly-Si solar cells were annealed in hydrogen gas, a mixture of steam and hydrogen gas, or a mixture of steam and oxygen. These experiments uncovered that neither hydrogen gas nor the mixtures are able to passivate silicon as effectively as water vapour. While the plasma hydrogenation represents a saturation of silicon dangling bonds by hydrogen radicals, annealing in water vapour is an oxidation of silicon and hydrogen acts just as a catalyst. On the basis of the realized experiments and a review of scientific literature, principles of the water vapour passivation were described, explained and presented as a model of steam passivation. Still, the plasma hydrogenation can achieve better VOC for the same samples. We achieved the best VOC of 497 mV for the same SPC samples. This value resulted from optimization of the plasma hydrogenation parameters at the Helmholtz-Zentrum Berlin during which we suggested to use 1) the higher hydrogen pressure of 300-1,000 Pa in comparison with a commonly used 100 Pa, 2) the longer exposure time of 15-20 minutes, and mainly 3) to keep the usually omitted bias voltage Vbias constant during the whole passivation process up to the plasma termination. Since all experiments in the hydrogen plasma were realized as a closed system without a hydrogen flux with very satisfying results, the generally accepted necessity to run the plasma hydrogenation process with a continuous hydrogen flux was called into question. The development of the crystalline silicon on glass solar cells led to replacement of SPC by liquid phase crystallized (LPC) for which VOC over 600 mV can be achieved. We tested the effect of passivation for LPC poly-Si samples crystallized either by a laser or an electron beam (with the SiOx diffusion barrier deposited either by plasma enhanced chemical vapour deposition or by physical vapour deposition). In these experiments hydrogen plasma increased the VOC from the typical value of 535 mV to 570 mV for most of the parameter values. Some SPC Si samples treated in the hydrogen plasma were analyzed by both Suns-VOC method and also optical pump transient terahertz probe spectroscopy which represents optical method for measurement of photogenerated carrier transport at ultrafast time scales. While each of these methods characterizes the solar cell in a different state, a clear correlation between VOC and the lifetime of charge carriers was observed. Terahertz spectroscopy analyzes the sample before the photogenerated charge carriers can be redistributed by a space charge region and therefore VOC is not built up yet. In contrary to this, Suns-VOC method characterizes the cell at a quasi-steady state, when VOC is already built up. Available in digital repository of ČVUT. Passivation of thin film silicon solar cells

Passivation of electronic defects is a necessary step in production of solar cells based on polycrystalline silicon thin film on glass. Standard passivation method is plasma hydrogenation, which ...

Benda, Vítězslav; Fejfar, Antonín; Pikna, Peter
České vysoké učení technické v Praze, 2016

Cognitive Building Systems. Optimization Algorithms in Architecture from Design to Production
Achten, Henri; Kaftan, Martin; Kieferle, J. B.; Hirschberg, Urs
2016 - English Achitekti se musí vyporádávat pri navrhování s mnoha omezeními, které narustají jako dusledek zvyšujících se nároku na kvalitu a provoz budov z hlediska udržitelnosti. Na druhé strane technologický pokrok v oblasti softwaru usnadnuje návrh složitých geometrií. Proto v soucasné dobe platí více než predtím, že kvalita budovy závisí na schopnostech architekta nalézt optimální rešení pro všechny casto protikladná omezení. Toto je úkol, který s ohledem na složitost vyžaduje použití sofistikovaných optimalizacních algoritmu integrovaných do pracovního postupu. Tento výzkum navrhuje integraci optimalizacního modulu nazvaného "Kognitivní Kontrolní Systém" (CCS) do parametrického systému. Kognitivnost je zde formulována jako schopnost systému reagovat na performativní kritéria budovy tím, že najde její optimální rešení. CCS obsahuje sadu globálních a lokálních matematických rešitelu. Jeho soucástí je také grafické rozhraní, "Interaktivní Grafová Kontrola" (IGC), pomocí které muže uživatel optimalizacní proces rídit a prehledne jím navigovat. Tato interaktivní platforma prezentuje uživateli nejen nejlepší optimální rešení, ale zároven škálu dalších možných rešeních, i když méne optimálních. Práce zkoumá nekolik typu nelineárních algoritmu, jako jsou genetické algoritmy, neuronové síte, a numerické matematické rešitelé. Výzkum poukazuje na jejich výhody i nevýhody, a ukazuje, jak mohou být tyto algoritmy zacleneni do parametrického systému, aby zlepšily proces navrhování. Práce demonstruje, jak nastavit objektivní funkce pro více cílu a jak tato funkce ovlivnuje prubeh a kvalitu optimalizacního procesu. Funkcnost a použitelnost numerických rešitelu je demonstrována na nekolika príkladech. Príklady jsou organizovány podle jejich složitosti rešení. Zacínající jednoduchou studií, každá další studie je složitejší s více omezeními a cíli. Príklady pokrývají radu ruzných geometrických a konstrukcních témat, jako je generování geometricky složité strešní konstrukce, optimalizace rodinného domu smerem k nízké spotrebe energie, denního osvetlení a nákladu nebo návrh budovy muzea. Architects must deal with increasing amount of design constraints, which is the consequence of increasing demands on building’s performance in terms of sustainability and construction cost. On the other hand, complex geometries has become common part in architectural projects. Therefore, it is nowadays more true than before that the building’s qualities depend on architect’s ability to find the optimal solution for all, often contradicting constraints. This is a task for which due to the complexity necessitates the use of sophisticated solving algorithms integrated into the design workflow. The research proposes an integration of optimization apparatus called “ Cognitive Control System” (CCS) into a parametric design framework. Cognition or “ knowing ” is here defined in terms of the ability to respond to the performative criteria of a building by finding optimum solution. The CCS contains a set of global and local solvers. Its part is also an interface, the Interactive Graph Control (IGC) by which the user can steer and control the optimization process in a transparent fashion. This interactive platform presents the user not only the best optimal solution, but also the whole range of other possible solution, even if less optimal. The research examines several types of nonlinear solving algorithms, such as genetic algorithms, neural networks, and numerical mathematical solvers. The research reveals their pros and cons and demonstrates how these different types of algorithms can be integrated into parametric system to enhance the design process. The thesis presents how to set up an objective function for multiple objectives and how the function affects the optimization process. The functionality and usability of the solvers is demonstrated on several case studies. The case studies are performed on different scale projects with different solving complexity. The cases cover range of different geometrical and design topics, such as generating free-form roof structure with certain local height constraints, optimizing family house towards low energy consumption, daylight and cost or exploring the design options for museum building. v Available in digital repository of ČVUT. Cognitive Building Systems. Optimization Algorithms in Architecture from Design to Production

Achitekti se musí vyporádávat pri navrhování s mnoha omezeními, které narustají jako dusledek zvyšujících se nároku na kvalitu a provoz budov z hlediska udržitelnosti. Na druhé strane technologický ...

Achten, Henri; Kaftan, Martin; Kieferle, J. B.; Hirschberg, Urs
České vysoké učení technické v Praze, 2016

Models and Algorithms for Sustainable Journey Planning
Jakob, Michal; Hrnčíř, Jan
2016 - English The thesis focuses on models and algorithms for journey planning for sustainable transport, i.e., planning journeys from an origin to a destination that respect user preferences and utilise sustainable modes of transport. Our motivation is to provide people with intelligent tools that would help them discover routes that best suit their transport needs and, consequently, to facilitate the much needed shift towards sustainable mobility. In order to achieve our objectives, we rst de ne formal models that enable us to e ciently represent transport networks. On top of these network models, we then develop e cient algorithms that solve three important sustainable journey planning problems. Speci cally, we solve the problems of multi-criteria bicycle routing, intermodal journey planning, and ridesharing on timetabled transport services. We evaluate our implemented algorithms using real-world data. We then integrate our algorithms into prototype journey planning systems and validate them in real-world eld trials with thousands of users in total. Finally, based on our practical experience with the real-world deployment, we discuss key aspects of engineering realworld journey planning systems, including the quality assurance in journey planning and the e cient implementation of journey planning algorithms. Available in digital repository of ČVUT. Models and Algorithms for Sustainable Journey Planning

The thesis focuses on models and algorithms for journey planning for sustainable transport, i.e., planning journeys from an origin to a destination that respect user preferences and utilise ...

Jakob, Michal; Hrnčíř, Jan
České vysoké učení technické v Praze, 2016

Numerical Algorithms of Quadratic Programming for Model Predictive Control
Havlena, Vladimír; Šantin, Ondřej
2016 - English This dissertation thesis deals with the development of algorithms for the e ective solution of quadratic programming problems for the embedded application of Model Predictive Control (MPC). MPC is a modern multivariable control method which involves solution of quadratic programming problem at each sample instant. The presented algorithms combine the active set strategy with the proportioning test to decide when to leave the actual active set. For the minimization in the face, we use the Newton directions implemented by the Cholesky factors updates. The performance of the algorithms is illustrated by numerical experiments and the results are compared with the state-of-the-art solvers on benchmarks from MPC. The main contributions of this thesis are three new quadratic programming solvers together with their proof of convergence and properties analysis. Furthermore, the algorithm's implementation is described in detail showing how to exploit the structure of the face problem and resulting Newton direction to reduce the computational complexity of each iteration. Available in digital repository of ČVUT. Numerical Algorithms of Quadratic Programming for Model Predictive Control

This dissertation thesis deals with the development of algorithms for the e ective solution of quadratic programming problems for the embedded application of Model Predictive Control (MPC). MPC is a ...

Havlena, Vladimír; Šantin, Ondřej
České vysoké učení technické v Praze, 2016

Example-based Creation of Digital Imagery
Žára, Jiří; Sýkora, Daniel; Lukáč, Michal
2016 - English Available in digital repository of ČVUT. Example-based Creation of Digital Imagery

Žára, Jiří; Sýkora, Daniel; Lukáč, Michal
České vysoké učení technické v Praze, 2016

Combining Network Anomaly Detectors
Pevný, Tomáš; Grill, Martin
2016 - English The anomaly-based network intrusion detection systems (IDS) typically su er from high false alarm rate rendering them useless in practice as the subsequent analysis done by the network operator is costly and can be done only for a small number of raised alarms. This thesis introduces several novel anomaly detectors and develop techniques for their combination to achieve much smaller false positive rates. We propose an architecture of an IDS that uses a number of simple network anomaly detectors that are able identify anomalies relevant to malicious network communication using the NetFlow (CAMNEP IDS) or HTTP access log (Cisco Cognitive Threat Analytics|CTA) telemetry data. We introduce several novel network anomaly detection techniques that enrich the ensemble of the state-of-the-art network anomaly detection methods used in both detection systems. The detectors are designed to use di erent anomaly detection algorithms applied to di erent subsets of features to introduce diversity and detect wider range of malicious behaviors. The outputs of the anomaly detectors are combined using two parallel aggregation functions constructed in supervised and unsupervised manner. The unsupervised combination uses a state-of-the-art method that is robust to presence of low accuracy detectors. The supervised combination is created using a novel technique that nds a convex combination of outputs of the anomaly detectors maximizing the accuracy in -quantile of the most anomalous samples. An extensive experimental evaluation and comparison to prior art on real network data using anomaly detectors of both CAMNEP and CTA intrusion detection systems shows that the proposed method not only outperforms prior art, but is also more robust to noise in training data labels, which is another important feature for deployment in practice. Moreover, we propose to smooth the outputs of the ensembles by online Local Adaptive Multivariate Smoothing (LAMS) to further reduce the amount of the false positives. LAMS can reduce the number of false positives introduced by the anomaly detection by replacing the anomaly detector's output on a network event with an aggregate of its output on all similar network events observed in the past. The arguments are supported by extensive experimental evaluation involving ensembles of anomaly detectors of both CTA and CAMNEP intrusion detection systems. We also describe an e ective implementation of the proposed solution to process large streams of non-stationary data. Finally, the extensive experimental evaluation using real network data collected in a number of corporate networks with a large number of labeled samples shows that each of these techniques signi cantly improves the e cacy of the anomaly-based intrusion detection system. Available in digital repository of ČVUT. Combining Network Anomaly Detectors

The anomaly-based network intrusion detection systems (IDS) typically su er from high false alarm rate rendering them useless in practice as the subsequent analysis done by the network operator is ...

Pevný, Tomáš; Grill, Martin
České vysoké učení technické v Praze, 2016

System Imbalance Forecast
Starý, Oldřich; Kratochvíl, Štěpán
2016 - English This thesis deals with the optimization of the BRP imbalance in the opposite direction to the system imbalance by the change of the output of the power plant. BRP imbalance optimization is a part of the energy market together with a long-term trading future or forwards contracts, midd-term day-ahead market and short-term intra-day and balancing market as well. BRP imbalance optimization is special in the Czech Republic by the ability of the BRP to change its imbalance in order to gain profit from achieving the opposite direction to the system imbalance. Therefore, it is needed to forecast the average system imbalance value with the highest prediction for BRP optimization. The objective of the thesis is developing a forecast model, which recommends the optimization of the BRP imbalance in order to gain profit. Lack of the state-of-the-art papers is the reduced usage of the data inputs. There are a lot of factors that influence the system imbalance and often create sudden step changes in the system imbalance. Therefore, forecasted model includes multiple exogenous variables, which can explain and thus forecast these changes. Input exogenous variables are used both in their numeric values and in the differential values. Differential values can be obtained by deduction of the neighbouring values of the input variable or the difference between planned and actual value of the exogenous variable as well. The forecast of the system imbalance is not needed in the point forecast as the concrete value of the system imbalance is not necessary for the optimization of the BRP imbalance. Therefore, I define the intervals of the system imbalance, for which is be the forecast made. Thresholds of these intervals have to be optimized carefully to utilize all the information from the input variables. I calculate the profit and loss resulting from the optimization to evaluate the BRP imbalance optimization. Opportunity costs result from the keeping of the power reserve for the optimization. It has to be kept in mind as these costs can be higher than the profit from the optimization. Results of the forecasted model are compared with the state-of-art and widely spread used ARMA model, which is significantly overcome by our proposed model. Keywords: System imbalance; balance responsible party; exogenous variable; interval distribution; forecast Available in digital repository of ČVUT. System Imbalance Forecast

This thesis deals with the optimization of the BRP imbalance in the opposite direction to the system imbalance by the change of the output of the power plant. BRP imbalance optimization is a part of ...

Starý, Oldřich; Kratochvíl, Štěpán
České vysoké učení technické v Praze, 2016

Deep Brain Recordings in Parkinson's Disease: Processing, Analysis and Fusion with Anatomical Models
Novák, Daniel; Štěpánková, Olga; Bakštein, Eduard
2016 - English This thesis presents several novel techniques and tools for automatic classi cation and analysis of highly detailed invasive recordings of the brain activity in patients with Parkinson's disease (PD). By utilizing machine learning concepts, we approach three of the principal questions, central to modern treatment and understanding of the PD: i) What information about patient's state can be derived from recorded brain activity? By identifying patterns characteristic for tremor onset in signals recorded through deep brain stimulation electrodes, we show that an adaptive system, modifying treatment parameters to match current state of its bearer, is feasible. ii) How to obtain trustworthy answers to scienti c questions from noisy microelectrode activity recordings? We show that undesirable noise is highly prevalent in intraoperative microelectrode recordings and provide the sigInspect: a GUI tool for annotation of microelectrode signals. The tool includes a set of well-performing classi ers for automatic artifact identi cation, validated on an extensive multi-center database of manually labeled data. iii) Where exactly in the target nucleus were the signals recorded? This question is vital for appropriate stimulation electrode placement as well as for better understanding of possible side e ects. We propose a novel probabilistic model for tting a 3D anatomical atlas of the subthalamic nucleus based solely on the recorded electrophysiological activity and show that such approach may lead to more accurate localization of recording sites during and after the surgery. Available in digital repository of ČVUT. Deep Brain Recordings in Parkinson's Disease: Processing, Analysis and Fusion with Anatomical Models

This thesis presents several novel techniques and tools for automatic classi cation and analysis of highly detailed invasive recordings of the brain activity in patients with Parkinson's ...

Novák, Daniel; Štěpánková, Olga; Bakštein, Eduard
České vysoké učení technické v Praze, 2016