A Game for Apple TV

Táto práca pozostáva z návrhu a implementácie hry pre Apple TV a popisu použitých technológií. Výsledkom je hra napísaná v jazyku Swift, spustiteľná na zariadení Apple TV s operačným systémom tvOS. Základom hry je knižnica SpriteKit, ktorá umožňuje tvorbu jednoduchých hier pre systém tvOS. Hra je otestovaná a plne funkčná na reálom zariadení. Prednosťami hry sú nízke hardwarové nároky a jednoduchý koncept hry.This thesis consists of the design and implementation of the Apple TV game and a description of the technologies used. The game was programmed in Swift, executable on Apple TV with the tvOS operating system. The game is based on SpriteKit, which allows you to develop simple games for tvOS. The game is tested and fully functional on real devices. The advantages of the game are low hadrware requirements and a simple concept of the game.

Direct Torque Control Simulation of Induction Motor

Tématem této diplomové práce je simulování přímého řízení momentu asynchronního motoru, jenž je napájen z nepřímého měniče kmitočtu s napěťovým meziobvodem. V této práci jsou uvedeny možnosti řízení otáček asynchronního motoru, detailněji je probrán princip přímého řízení momentu a magnetického toku statoru a jeho konkrétní metody. Práce je zaměřena především na Takahashiho metodu a metodu s PWM modulátorem. Tyto dvě metody jsou použity v simulačním modelu regulace rychlosti. Simulační model je sestaven v softwaru Matlab Simulink verze R2014b. Jsou zde zobrazeny časové průběhy důležitých veličin v různých oblastech rychlosti (nízká, střední, vysoká – oblast odbuzování). Práce se zabývá i vlivem použití podprogramu nabuzení, jenž slouží k omezení velikosti statorových proudů. Simulované výsledky prezentují očekávané vlastnosti řízení asynchronního motoru.The theme of this diploma thesis is simulation of the direct torque control of induction motor. This motor is supplied by an indirect frequency converter with DC voltage link. In this thesis are mentioned the possibilities of the control of the induction motor speed. There is also explained the principle of direct control of torque and stator magnetic flux in more detail and methods of this control. This thesis is focused mainly on the Takahashi method and the method with PWM modulator. These two methods are used in the speed regulation simulation model. The simulation model is built in Matlab Simulink software. Version of software is R2014b. There in this model are the time courses of important quantities in various areas of speed (low, medium, high – de-excitation area) there. This thesis deals with the effect of using of excitation subroutine. Subroutine is used for decrease of the stator current. The simulated results presents the expected properties of the induction motor.430 - Katedra elektronikyvýborn

JOINIG OF MG ALLOY AZ31B BY SELECTED TECHNOLOGIES

This contribution presents metallurgical joining of Mg alloy AZ 31B by several technologies - Friction Stir Welding - FSW, Laser Beam Welding - LBW, soldering and their mutual comparison. The difficulty of joining Mg alloys is connected with the strong MgO layer, low ductility of the weld metal (WM), a presence of intermetallic phases connected with the hardness increase. To successfully join Mg alloys, a precise setting of welding parameters is needed. The welds are susceptible to low mechanical properties as all samples fractured at WM. From the tested methods, laser beam welding proved the best results, because laser welding is the fastest and weld strength reached the highest values, i.e. 87% of BM (base metal) strength

Explaining grokking through circuit efficiency

One of the most surprising puzzles in neural network generalisation is grokking: a network with perfect training accuracy but poor generalisation will, upon further training, transition to perfect generalisation. We propose that grokking occurs when the task admits a generalising solution and a memorising solution, where the generalising solution is slower to learn but more efficient, producing larger logits with the same parameter norm. We hypothesise that memorising circuits become more inefficient with larger training datasets while generalising circuits do not, suggesting there is a critical dataset size at which memorisation and generalisation are equally efficient. We make and confirm four novel predictions about grokking, providing significant evidence in favour of our explanation. Most strikingly, we demonstrate two novel and surprising behaviours: ungrokking, in which a network regresses from perfect to low test accuracy, and semi-grokking, in which a network shows delayed generalisation to partial rather than perfect test accuracy