Multiobjective optimization of electromagnetic structures based on self-organizing migration

Práce se zabývá popisem nového stochastického vícekriteriálního optimalizačního algoritmu MOSOMA (Multiobjective Self-Organizing Migrating Algorithm). Je zde ukázáno, že algoritmus je schopen řešit nejrůznější typy optimalizačních úloh (s jakýmkoli počtem kritérií, s i bez omezujících podmínek, se spojitým i diskrétním stavovým prostorem). Výsledky algoritmu jsou srovnány s dalšími běžně používanými metodami pro vícekriteriální optimalizaci na velké sadě testovacích úloh. Uvedli jsme novou techniku pro výpočet metriky rozprostření (spread) založené na hledání minimální kostry grafu (Minimum Spanning Tree) pro problémy mající více než dvě kritéria. Doporučené hodnoty pro parametry řídící běh algoritmu byly určeny na základě výsledků jejich citlivostní analýzy. Algoritmus MOSOMA je dále úspěšně použit pro řešení různých návrhových úloh z oblasti elektromagnetismu (návrh Yagi-Uda antény a dielektrických filtrů, adaptivní řízení vyzařovaného svazku v časové oblasti…).This thesis describes a novel stochastic multi-objective optimization algorithm called MOSOMA (Multi-Objective Self-Organizing Migrating Algorithm). It is shown that MOSOMA is able to solve various types of multi-objective optimization problems (with any number of objectives, unconstrained or constrained problems, with continuous or discrete decision space). The efficiency of MOSOMA is compared with other commonly used optimization techniques on a large suite of test problems. The new procedure based on finding of minimum spanning tree for computing the spread metric for problems with more than two objectives is proposed. Recommended values of parameters controlling the run of MOSOMA are derived according to their sensitivity analysis. The ability of MOSOMA to solve real-life problems from electromagnetics is shown in a few examples (Yagi-Uda and dielectric filters design, adaptive beam forming in time domain…).

Adaptive strategy for neural network synthesis constant estimation

Neural Network Synthesis is a new innovative method for an artificial neural network learning and structural optimization. It is based on two other already very successful algorithms: Analytic Programming and Self-Organizing Migration Algorithm (SOMA). The method already recorded several theoretical as well as industrial application to prove itself as a useful tool of modelling and simulation. This paper explores promising possibility to farther improve the method by application of an adaptive strategy for SOMA. The new idea of adaptive strategy is explained here and tested on a theoretical experimental case for the first time. Obtained data are statistically evaluated and ability of adaptive strategy to improve neural network synthesis is proved in conclusion

A new MDA-SOA based framework for intercloud interoperability

Cloud computing has been one of the most important topics in Information Technology which aims to assure scalable and reliable on-demand services over the Internet. The expansion of the application scope of cloud services would require cooperation between clouds from different providers that have heterogeneous functionalities. This collaboration between different cloud vendors can provide better Quality of Services (QoS) at the lower price. However, current cloud systems have been developed without concerns of seamless cloud interconnection, and actually they do not support intercloud interoperability to enable collaboration between cloud service providers. Hence, the PhD work is motivated to address interoperability issue between cloud providers as a challenging research objective. This thesis proposes a new framework which supports inter-cloud interoperability in a heterogeneous computing resource cloud environment with the goal of dispatching the workload to the most effective clouds available at runtime. Analysing different methodologies that have been applied to resolve various problem scenarios related to interoperability lead us to exploit Model Driven Architecture (MDA) and Service Oriented Architecture (SOA) methods as appropriate approaches for our inter-cloud framework. Moreover, since distributing the operations in a cloud-based environment is a nondeterministic polynomial time (NP-complete) problem, a Genetic Algorithm (GA) based job scheduler proposed as a part of interoperability framework, offering workload migration with the best performance at the least cost. A new Agent Based Simulation (ABS) approach is proposed to model the inter-cloud environment with three types of agents: Cloud Subscriber agent, Cloud Provider agent, and Job agent. The ABS model is proposed to evaluate the proposed framework.Fundação para a Ciência e a Tecnologia (FCT) - (Referencia da bolsa: SFRH SFRH / BD / 33965 / 2009) and EC 7th Framework Programme under grant agreement n° FITMAN 604674 (http://www.fitman-fi.eu

Design and Optimization of High-Torque Ferrite Assisted Synchronous Reluctance Motor

Vysokomomentový asistovaný synchronní reluktanční motor může být, soudě podle nízkého počtu publikovaných článků, stále považován za relativně málo prozkoumané téma výzkumu. Tato ale i další výhody, jako nízká výrobní cena a vysoká hustota výkonu poutají pozornost výzkumných pracovníků. Navzdory tomu, že tento druh motoru je zajímavější z pohledu konvenčních nebo vysokootáčkových aplikací, tak se i trakční aplikace dostávají do popředí s tím, jak jsou objevovány vlastnosti tohoto motoru. Tato práce se zaměřuje na návrh tohoto typu motoru pro pohon lodi, který je navržen aby dosahoval vysokého momentu při nízkých otáčkách. Aplikace je definována výkonem 55 kW při 150 otáčkách za minutu a použitím levných feritových magnetů s cílem nízké ceny motoru. Návrh motoru je úzce propojen s optimalizačními algoritmy aby bylo dosaženo co nejlepšího výkonu v daném objemu stroje. Navzdory tomu, že návrh samotný je velice zajímavým tématem, tak práce deklaruje další teze, které jsou rovněž zajímavé a důležité. Vzhledem k tomu, že je práce zaměřena i na optimalizaci, tak prvním cílem práce je porovnání různých optimalizačních metod. V této práci jsou nejenom že různé druhy optimalizačních algoritmů, samoorganizující migrující algoritmus a genetický algoritmus, porovnány, ale jsou zde porovnány i různé optimalizační metody. Metoda založená na definování preferenčního vektoru a ideální multi-objektivní metody jsou v rovněž v této práci srovnány. Tyto algoritmy jsou srovnány v případě více optimalizovaných parametrů. Dalším scénářem pro porovnání ideálních multi-objektivních algoritmů je ten s menším počtem parametrů. Posledním cílem práce je laboratorní měření navrženého optimalizovaného stroje, které rovněž představuje další set výzev v této práci, které jsou diskutovány v poslední kapitole této práce.The high-torque assisted synchronous reluctance machine could be still considered, based on the relatively low amount of publications, as a rather unknown area of research. This and other main advantages, such as low manufacturing cost and a higher torque density of this machine type are driving researchers interest. Even though this machine type has become more interesting in the conventional or high-speed applications, the area of traction applications is slowly getting forward as the machine capabilities are discovered. This thesis is serving just this purpose of developing the ship propulsion driving motor, that is capable of sustaining the high-torque at low-speed. The application is defined by the 55 kW at 150 rpm using the low- cost ferrite magnets aiming to lower the cost. The design will be closely tied with optimization algorithms to deliver the best possible performance in the given volume. However the design challenge being difficult task on its own, the thesis is declaring other goals within, that are still very interesting and important. Since the optimization is included in the design process, the first goal, concluding from the given topic is to compare various optimization methods. Not only the two different optimization algorithms, self-organizing migrating algorithm and genetic algorithm, will be compared in the thesis, but even two multi-objective optimization approaches will be compared as well. The preference based vector and ideal multi-objective optimization techniques comparison will be demonstrated in one optimization scenario with a higher amount of optimized parameters. Other demonstrated goal within the thesis is the comparison of ideal multi-objective optimization with a lower number of parameters. The last goal will be the measurement of the designed and optimized machine, that introduced variety of challenges itself and all of them will be discussed within the last chapter.

Programming a paintable computer

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2002.Includes bibliographical references (p. 163-169).A paintable computer is defined as an agglomerate of numerous, finely dispersed, ultra-miniaturized computing particles; each positioned randomly, running asynchronously and communicating locally. Individual particles are tightly resource bound, and processing is necessarily distributed. Yet computing elements are vanishingly cheap and are regarded as freely expendable. In this regime, a limiting problem is the distribution of processing over a particle ensemble whose topology can vary unexpectedly. The principles of material self-assembly are employed to guide the positioning of "process fragments" - autonomous, mobile pieces of a larger process. These fragments spatially position themselves and reaggregate into a running process. We present the results of simulations to show that "process self-assembly" is viable, robust and supports a variety of useful applications on a paintable computer. We describe a hardware reference platform as an initial guide to the application domain. We describe a programming model which normatively defines the term process fragment and which provides environmental support for the fragment's mobility, scheduling and data exchange. The programming model is embodied in a simulator that supports development, test and visualization on a 2D particle ensemble. Experiments on simple combinations of fragments demonstrate robustness and explore the limits of scale invariance. Process fragments are shown interacting to approximate conservative fields, and using these fields to implement scaffolded and thermodynamic self-assembly.(cont.) Four applications demonstrate practical relevance, delineate the application domain and collectively illustrate the paintable's capacity for storage, communication and signal processing. These four applications are Audio Streaming, Holistic Data Storage, Surface Bus and Image Segmentation.by William Joseph Butera.Ph.D

ETL and analysis of IoT data using OpenTSDB, Kafka, and Spark

Master's thesis in Computer scienceThe Internet of Things (IoT) is becoming increasingly prevalent in today's society. Innovations in storage and processing methodologies enable the processing of large amounts of data in a scalable manner, and generation of insights in near real-time. Data from IoT are typically time-series data but they may also have a strong spatial correlation. In addition, many time-series data are deployed in industries that still place the data in inappropriate relational databases. Many open-source time-series databases exist today with inspiring features in terms of storage, analytic representation, and visualization. Finding an efficient method to migrate data into a time-series database is the first objective of the thesis. In recent decades, machine learning has become one of the backbones of data innovation. With the constantly expanding amounts of information available, there is good reason to expect that smart data analysis will become more pervasive as an essential element for innovative progress. Methods for modeling time-series data in machine learning and migrating time-series data from a database to a big data machine learning framework, such as Apache Spark, is explored in this thesis