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Automated Multi-Objective Parallel Evolutionary Circuit Design and Approximation
Hrbáček, Radek ; Fišer, Petr (oponent) ; Trefzer,, Martin (oponent) ; Sekanina, Lukáš (vedoucí práce)
Recently, energy efficiency has become one of the most important properties of computing platforms, especially because of limited power supply capacity of battery-power devices and very high consumption of growing data centers and cloud infrastructure. At the same time, in an increasing number of applications users are able to tolerate inaccurate or incorrect computations to a certain extent due to the imperfections of human senses, statistical nature of data processing, noisy input data etc. Approximate computing, an emerging paradigm in computer engineering, takes advantage of relaxed functionality requirements to make computer systems more efficient in terms of energy consumption, computing performance or complexity. Error resilient applications can achieve significant savings while still serving their purpose with the same or a slightly degraded quality. Even though new design methods for approximate computing are emerging, there is a lack of methods for automated approximate HW/SW design offering a rich set of compromise solutions. Conventional methods often produce solutions that are far from an optimum. Evolutionary algorithms have been shown to bring innovative solutions to complex design and optimization problems. However, these methods suffer from several problems, such as the scalability or a high number of fitness evaluations needed to evolve competitive results. Finally, existing methods are usually single-objective whilst multi-objective approach is more suitable in the case of approximate computing. In this thesis, a new automated multi-objective parallel evolutionary algorithm for circuit design and approximation is proposed. The method is based on Cartesian Genetic Programming. In order to improve the scalability of the algorithm, a brand new highly parallel implementation was proposed. The principles of the NSGA-II algorithm were used to provide the multi-objective design and approximation capability. The performance of the implementation was evaluated in multiple different applications, in particular (approximate) combinational arithmetic circuits design, bent Boolean functions discovery and approximate logic circuits for TMR schema. In these cases, important improvements with respect to the state of the art were obtained.
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Multi-objective genetic algorithms in road traffic prediction
Petrlík, Jiří ; Brandejský, Tomáš (oponent) ; Snášel,, Václav (oponent) ; Sekanina, Lukáš (vedoucí práce)
The understanding of the road traffic behavior is a key to effective traffic control, management and organization. This task is becoming more and more important with increasing traffic demands and the number of registered vehicles. The information about the current and future traffic situation is very important for drivers and traffic operators. Fortunately, there was a huge progress in technologies for traffic data acquisition in the last few decades. Stationary sensors, such as loop detectors, radars, cameras and infrared sensors can be installed on important locations of the roads and measure various microscopic and macroscopic traffic variables. However, some measurements can lead to an incorrect data which cannot further be used in the subsequent processing tasks such as traffic prediction or intelligent control. For example, this can be caused by equipment failures or data transmission problems. It is highly desirable to have a framework, which is capable of estimating the missing values in traffic data. It is also very important to provide a reliable short-time prediction of the traffic state. In this thesis, we focus on selected problems from this domain - the imputation of missing traffic data, short time traffic forecasting and travel times estimation. The proposed solution is based on combining the state-of-the art machine learning methods such as support vector regression (SVR) with the multi-objective evolutionary optimization. SVR has various meta-parameters which should be properly set in order to achieve the best performance. The performance also strongly depends on the selection of the input variables for SVR. We used the multi-objective optimization to find the proper settings of SVR meta-parameters and input variables. Using the multi-objective optimization, we obtained many different non-dominated solutions from Pareto front. These solutions can dynamically be switched according to the traffic data which are currently available, in order to maximize the quality of prediction. The proposed methods are specially designed for environments with many missing values in traffic data. We evaluated the proposed methods using real world data and compared them with the state of the art methods for the traffic data imputation and short term prediction such as the probabilistic principal component analysis and support vector regression optimized by a single objective optimization. The proposed methods provide better results than these state of the art methods especially in the cases where there are many missing values in the traffic data.
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Optimalizace tvaru výfukových svodů
Navrátil, Dušan ; Rozman, Jaroslav (oponent) ; Zbořil, František (vedoucí práce)
V této práci je vyvinut systém pro multikriteriální optimalizaci tvaru výfukových svodů včetně počátečního návrhu svodů. Prostor řešení je prohledáván na základě evolučních algoritmů. Ohodnocení tvaru výfukových svodů vychází z délky svodů a součtu obloukových úhlů. Zárověň svody nesmí zasahovat do okolních dílů. Systém je otestován na sadě vstupních dat vycházejících z praxe. Dále je vyhodnocena výkonnost navrženého evolučního algoritmu.
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Antenna Arrays with Synthesized Frequency Response of Gain
Všetula, Petr ; Polívka,, MIlan (oponent) ; Bonefačic, Davor (oponent) ; Raida, Zbyněk (vedoucí práce)
In the thesis, we present a method of the synthesis of a dipole antenna array with prescribed spectral and spatial filtering capabilities. Thanks to the spatial filtering capabilities, the main lobe direction and the value of gain vary negligibly over the operating band. Thanks to the spectral filtering capabilities, the value of gain is maximal in the operating band and minimal out of the operating band. In order to synthesize a dipole array with prescribed filtering capabilities, amplitudes, phases and dimensions of antenna elements are optimized. The initial optimization is speeded up by considering an idealized antenna array when evaluating objective functions. Since the optimization comprises requirements on the main lobe direction, the value of gain and impedance matching, a multi-objective optimization is used. The optimized antenna array is analyzed by a full-wave simulator to verify results of the synthesis. Finally, the synthesized dipole array is manufactured and its performance is experimentally verified.
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Multikriteriální optimalizace v EMC
Olivová, Jana ; Sekanina, Lukáš (oponent) ; Křesálek,, Vojtěch (oponent) ; Raida, Zbyněk (vedoucí práce)
Cílem práce je navrhnout metodiku pro vytvo°ení homogenních náhrad kompozitn ích materiál· pouoívaných na konstrukci letadel. Tyto náhrady by mYly umoonit vytvo°ení numerických model· letadel pro simulace p°edcerti- kaLních test· jejich elektromagnetické odolnosti proti zásahu blesku. Eliminace situací ohrooujících letadlo i pasaoéry jio v poLáteLních krocích návrhu letadla vede k úspo°e výrobních náklad· a p°ispYje k bezpeLnosti letecké dopravy. Pro nalezení ekvivalentních náhrad kompozitních materiál· p°i inverzn í úloze je v práci vyuoito globálních optimalizaLních metod.
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