Individual Professional Practice in the Company

Tato práce popisuje průběh konání odborné praxe ve firmě Siemens, s. r. o. Zaměřuje se především na oblast vývoje webových aplikací, analýzu dat ve firemním informačním systému AM/IT a využití rozšířeného podnikového informačního systému SAP. Soustředí se především na ukázky základních principů vývoje webových aplikací v ASP.NET i za pomocí Kendo UI. Většina problémů, které byly řešeny, prošly celým procesem vývoje od jednání se zákazníkem,analýzou, vývojem až po samotné nasazení do produkce. V závěru práce jsou shrnuty znalosti a principy, kterých bylo při absolvování praxe nabyto.This thesis describes the practice at the Siemens, s. r. o. It focuses mostly on the field of web development, data analysis on the internal information system called AM/IT and wide used corporate information system SAP. The thesis mostly displays parts of the main principles of web development in ASP.NET framework with the use of Kendo UI. Most of the problems, that were solved, went through the whole process from the talk with the client, analysis, development and to the deployment to the production. In the end of the thesis, there are summarized principles and knowledge, which were gained in the process of the practice.460 - Katedra informatikyvýborn

Lithium Ion Cell Development for Photovoltaic Energy Storage Applications

The overall project goal is to reduce the cost of home and neighborhood photovoltaic storage systems by reducing the single largest cost component â the energy storage cells. Solar power is accepted as an environmentally advantaged renewable power source. Its deployment in small communities and integrated into the grid, requires a safe, reliable and low cost energy storage system. The incumbent technology of lead acid cells is large, toxic to produce and dispose of, and offer limited life even with significant maintenance. The ideal PV storage battery would have the safety and low cost of lead acid but the performance of lithium ion chemistry. Present lithium ion batteries have the desired performance but cost and safety remain the two key implementation barriers. The purpose of this project is to develop new lithium ion cells that can meet PVES cost and safety requirements using A123Systems phosphate-based cathode chemistries in commercial PHEV cell formats. The cost target is a cell design for a home or neighborhood scale at <$25/kWh. This DOE program is the continuation and expansion of an initial MPSC (Michigan Public Service Commission) program towards this goal. This program further pushes the initial limits of some aspects of the original program â even lower cost anode and cathode actives implemented at even higher electrode loadings, and as well explores new avenues of cost reduction via new materials â specifically our higher voltage cathode. The challenge in our materials development is to achieve parity in the performance metrics of cycle life and high temperature storage, and to produce quality materials at the production scale. Our new cathode material, M1X, has a higher voltage and so requires electrolyte reformulation to meet the high temperature storage requirements. The challenge of thick electrode systems is to maintain adequate adhesion and cycle life. The composite separator has been proven in systems having standard loading electrodes; the challenge with this material will be to maintain proven performance when this composite is coated onto a thicker electrode; as well the high temperature storage must meet application requirements. One continuing program challenge was the lack of specific performance variables for this PV application and so the low power requirements of PHEV/EV transportation markets were again used

Use of Autoregressive Predictor in Echo State Neural Networks

&quot;Echo State&quot; neural networks (ESN), which are a special case of recurrent neural networks, are studied with the goal to achieve their greater predictive ability by the correction of their output signal. In this paper standard ESN was supplemented by a new correcting neural network which has served as an autoregressive predictor. The main task of this special neural network was output signal correction and therefore also a decrease of the prediction error. The goal of this paper was to compare the results achieved by this new approach with those achieved by original one-step learning algorithm. This approach was tested in laser fluctuations and air temperature prediction. Its prediction error decreased substantially in comparison to the standard approach

Role of Dim Artificial Light at Night (dALAN) on Body Weight Percentage Increase of Mus musculus

Light pollution at night is a growing issue in many suburban and urban settings, commonly referred to as artificial light at night (ALAN). Many studies have been conducted as to how the intensity or wavelength of this lighting can disrupt the circadian rhythm but none have evaluated how the timing of this light could affect it. It is hypothesized that those that chronically experience dim artificial light at night (dALAN) after biological day will lead to a more pronounced disruption in the metabolic system and therefore will cause an increased level of weight gain. Mice were used as test subjects and were split into four groups: 1) 12 light, 12 dark (L:N); 2) 12 light, 4 dim, 8 off (L:D:N); 3) 12 light, 4 off, 4 dim, 4 off (L:N:D:N); and 4) 12 light, 8 off, 4 dim (L:N:D). The weight of these mice was tracked weekly to obtain the necessary data. This data were then analyzed for percent body weight increase and an ANOVA was run, obtaining a p-value of 0.000053. A Scheffe test was then run, finding a significant difference between L:N and L:D:N, L:N and L:N:D, and L:D:N and L:N:D:N. These results support that chronic dALAN exposure can lead to increased percent body weight changes. Future studies can further examine the possibilities as to why this is

Dilemmas in accounts of medical drugs: consequences for drug takers negotiation of identity

Bibliography: p. 117-12

Modular Echo State Neural Networks in Time Series Prediction

Echo State neural networks (ESN), which are a special case of recurrent neural networks, are studied from the viewpoint of their learning ability, with a goal to achieve their greater predictive ability. In this paper we study the influence of the memory length on predictive abilities of Echo State neural networks. The conclusion is that Echo State neural networks with fixed memory length can have troubles with adaptation of its intrinsic dynamics to dynamics of the prediction task. Therefore, we have tried to create complex prediction system as a combination of the local expert Echo State neural networks with different memory length and one special gating Echo State neural network. This approach was tested in laser fluctuations and turbojet gas temperature prediction. The prediction error achieved by this approach was substantially smaller in comparison with prediction error achieved by standard Echo State neural networks

Fabrication and characterization of high quality factor silicon nitride nanobeam cavities

Si3N4 is an excellent material for applications of nanophotonics at visible wavelengths due to its wide bandgap and moderately large refractive index (n $\approx$ 2.0). We present the fabrication and characterization of Si3N4 photonic crystal nanobeam cavities for coupling to diamond nanocrystals and Nitrogen-Vacancy centers in a cavity QED system. Confocal micro-photoluminescence analysis of the nanobeam cavities demonstrates quality factors up to Q ~ 55,000, which is limited by the resolution of our spectrometer. We also demonstrate coarse tuning of cavity resonances across the 600-700nm range by lithographically scaling the size of fabricated devices. This is an order of magnitude improvement over previous SiNx cavities at this important wavelength range

Plasmonic resonators for enhanced diamond NV- center single photon sources

We propose a novel source of non-classical light consisting of plasmonic aperture with single-crystal diamond containing a single Nitrogen-Vacancy (NV) color center. Theoretical calculations of optimal structures show that these devices can simultaneously enhance optical pumping by a factor of 7, spontaneous emission rates by Fp ~ 50 (Purcell factor), and offer collection efficiencies up to 40%. These excitation and collection enhancements occur over a broad range of wavelengths (~30nm), and are independently tunable with device geometry, across the excitation (~530nm) and emission (~600-800nm) spectrum of the NV center. Implementing this system with top-down techniques in bulk diamond crystals will provide a scalable architecture for a myriad of diamond NV center applications.Comment: 9 pages, 7 figure