The diploma thesis is focused on verifying the success of prediction models mean monthly flows for strategy control storage function of water management system. The success of prediction models is tested during three different periods. To determine the predicted inflows into the reservoirs is applied to the generator artificial flow series LTMA using the Monte Carlo method and the classical model of neuron networks. During the solution is applying the principles of adaptive control. The adaptive control is compared with simple control for improved outflow.

This thesis is dealing with optimization by using artificial intelligence of an induction machine rotor slot. The one optimized is commonly manufactured induction motor with simple deep bar rotor. Goal is to design an optimization method and achieve the highest possible value of efficiency and power factor of chosen machine. Work contains the parametric model construction in Ansys Maxwell software, the optimization algorithm assembly and its setup for desirable output, and processing of the results.

<p>Centralna tema istraživanja se zasniva na proučavanju procesa<br />optimizacije određivanja arhitektonskih programa tržnih centara<br />primenom savremenih tehnologija. Istraživanje dominantno obrađuje<br />teme vezane za značaj dispozicije objekata tržnih centara i raspodele<br />njihovih programskih sadržaja.</p> / <p>The central theme of this research is based on the study of the optimization<br />for determining architectural programmes of the chopping centers process<br />by means of contemporary technologies. The research mainly deals with<br />topics related to the importance of disposition of shopping centers and the<br />distribution of their programmes.</p>

Abstract This work addresses the problems related to three-dimensional modeling of scenes and objects and model evaluation. The work is divided into four main parts. At first, the work concentrates on purely image-based reconstruction while the second part presents a modeling pipeline based on an active depth sensor. Then, the work introduces methods for producing surface meshes from point clouds, and finally, a novel approach for model evaluation is presented. In the first part, this work proposes a multi-view stereo (MVS) reconstruction method that takes a set of images as an input and outputs a model represented as a point cloud. The method is based on match propagation, where a set of initial corresponding points between images is expanded iteratively into larger regions by searching new correspondences in the spatial neighborhood of the existing ones. The expansion is implemented using a best-first strategy, where the most reliable match is always expanded first. The method produces comparable results with the state-of-the-art but significantly faster. In the second part, this work presents a method that merges a sequence of depth maps into a single non-redundant point cloud. In the areas, where the depth maps overlap, the method fuses points together by giving more weight to points which seem to be more reliable. The method overcomes its predecessor both in accuracy and robustness. In addition, this part introduces a method for depth camera calibration. The method develops on an existing calibration approach which was originally designed for the first generation Microsoft Kinect device. The third part of the thesis addresses the problem of converting the point clouds to surface meshes. The work briefly reviews two well-known approaches and compares their ability to produce sparse mesh models without sacrificing accuracy. Finally, the fourth part of this work describes the development of a novel approach for performance evaluation of reconstruction algorithms. In addition to the accuracy and completeness, which are the metrics commonly used in existing evaluation benchmarks, the method also takes the compactness of the models into account. The metric enables the evaluation of the accuracy-compactness trade-off of the models. / Tiivistelmä Tässä työssä käsitellään näkymän tai esineen kolmiulotteista mallintamista ja tulosten laadun arviointia. Työ on jaettu neljään osaan. Ensiksi keskitytään pelkästään valokuvia hyödyntävään mallinnukseen ja sitten esitellään menetelmä syvyyskamerapohjaiseen mallinnukseen. Kolmas osa kuvaa menetelmiä verkkomallien luomiseen pistepilvestä ja lopuksi esitellään menetelmä mallien laadun arviointiin. Ensimmäisessä osassa esitellään usean kuvan stereoon perustuva mallinnusmenetelmä, joka saa syötteenä joukon valokuvia ja tuottaa kuvissa näkyvästä kohteesta pistepilvimallin. Menetelmä perustuu vastinpisteiden laajennukseen, jossa kuvien välisiä pistevastaavuuksia laajennetaan iteratiivisesti suuremmiksi vastinalueiksi hakemalla uusia vastinpistepareja jo löydettyjen läheisyydestä. Laajennus käyttää paras ensin -menetelmää, jossa luotettavin pistevastaavuus laajennetaan aina ensin. Menetelmä tuottaa vertailukelpoisia tuloksia johtaviin menetelmiin verrattuna, mutta merkittävästi nopeammin. Toisessa osassa esitellään menetelmä, joka yhdistää joukon syvyyskameralla kaapattuja syvyyskarttoja yhdeksi pistepilveksi. Alueilla, jotka sisältävät syvyysmittauksia useasta syvyyskartasta, päällekkäiset mittaukset yhdistetään painottamalla luotettavammalta vaikuttavaa mittausta. Menetelmä on tarkempi kuin edeltäjänsä ja toimii paremmin kohinaisemmalla datalla. Lisäksi tässä osassa esitellään menetelmä syvyyskameran kalibrointiin. Menetelmä kehittää jo olemassa olevaa kalibrointityökalua, joka alun perin kehitettiin ensimmäisen sukupolven Microsoft Kinect laitteelle. Väitöskirjan kolmas osa käsittelee pintamallin luomista pistepilvestä. Työ esittelee kaksi hyvin tunnettua menetelmää ja vertailee niiden kykyä luoda harvoja, mutta edelleen tarkkoja malleja. Lopuksi esitellään uudenlainen menetelmä mallinnusmenetelmien arviointiin. Tarkkuuden ja kattavuuden lisäksi, jotka ovat yleisimmät arvioinnissa käytetyt metriikat, menetelmä ottaa huomioon myös mallin pistetiheyden. Metriikan avulla on mahdollista arvioida kompromissia mallin tarkkuuden ja tiheyden välillä.

Managerial simulators enable us to experiment easily with all possible variants of alternative decisions. This facilitates the process of assessing the effective policies and strategies formed by the managers. The creation of simulators based on the principles of system dynamics in the background is the main point of interest of this diploma thesis. This paper is focused on building a simulator which would simulate policies of communication from the position of Information manager, depending on the variable settings of its parameters. The structure of the model and the parametrization of the simulator comes from practical experiences from the company called NETservis s.r.o., which specializes on the development of internet applications for large international companies. The simulator is therefore intended for the simulation of policies in this particular company, however can also serve as a referential model and be used by other companies.

This theses is related to design of synchronous reluctance motors of the transverse-laminated type. The first part describes synchronous reluctance machine via space-vector theory. Furthermore this part briefly discuss the main design aspects of synchronous reluctance machines. The main part of this thesis is related to practical electromagnetic rotor design for synchronous reluctance machine by means of finite element method. On this base the rotor has been fabricated and placed inside of induction motor instead of his original rotor. On this prototype was made several measurements which were compared with results from finite element method analysis. The last part of this theses is related to short study of synchronous reluctance machine dynamical behaviour under scalar and vector control.

Advancements in batteries, microprocessors as well as an extra emphasis being put on the environment has pushed electric vehicles to the forefront of today. Despite the many benefits of electric vehicles, range anxiety and long charge times are hurdles to overcome. These shortfalls are a result of the current battery technology regardless of the many breakthroughs over the last decade. Lithium-ion Batteries and other modern chemistries pose a number of challenges in testing and research when compared to the traditional lead acid batteries. Current test systems fall short in providing a complete testing solution with. The focus of this thesis is to develop a complete software framework for battery characterization: testing, modelling and characterization to accompany battery testing hardware developed by D&V Electronics. The first step in battery characterization, involves battery testing in order to obtain data. This required development of the test software and a number of battery tests, including: Charge and discharge, state of charge vs. open circuit voltage curve generation, Electro-Impedance Spectroscopy, and capacity test. Research was done in order to ensure developed test procedures lined up with that of other publications. All data from the testing data is logged to a central database, allowing for the second major development, the model framework. The model framework is composed of seven different battery models that can be parameterized with the touch of a button, using data collected from the tester. It is a software framework that is meant to be expandable by abstracting the details of a model from the tester. This allows for new models and parameterization techniques to be integrated into the software without the need of new software development. Lastly, all development was used to do a battery characterization of a prismatic battery cell. All tests were conducted on a battery over two hundred cycles, followed by battery parameterization using the mode framework. The battery models were then used to simulate a US06 drive profile and compared to the same profile with measurements taken from the tester. With an average root mean square error of 8 millivolts, the battery characterization using the framework proved to be a success. / Thesis / Master of Applied Science (MASc)

Surrogate-model-based optimization is widely used to solve black-box optimization problems if the evaluation of a target system is expensive. However, when the optimization budget is limited to a single or several evaluations, surrogate-model-based optimization may not perform well due to the lack of knowledge about the search space. In this case, transfer learning helps to get a good optimization result due to the usage of experience from the previous optimization runs. And if the budget is not strictly limited, transfer learning is capable of improving the final results of black-box optimization. The recent work in surrogate-model-based optimization showed that using multiple surrogates (i.e., applying multi-surrogate-model optimization) can be extremely efficient in complex search spaces. The main assumption of this thesis suggests that transfer learning can further improve the quality of multi-surrogate-model optimization. However, to the best of our knowledge, there exist no approaches to transfer learning in the multi-surrogate-model context yet. In this thesis, we propose an approach to transfer learning for multi-surrogate-model optimization. It encompasses an improved method of defining the expediency of knowledge transfer, adapted multi-surrogate-model recommendation, multi-task learning parameter tuning, and few-shot learning techniques. We evaluated the proposed approach with a set of algorithm selection and parameter setting problems, comprising mathematical functions optimization and the traveling salesman problem, as well as random forest hyperparameter tuning over OpenML datasets. The evaluation shows that the proposed approach helps to improve the quality delivered by multi-surrogate-model optimization and ensures getting good optimization results even under a strictly limited budget.:1 Introduction 1.1 Motivation 1.2 Research objective 1.3 Solution overview 1.4 Thesis structure 2 Background 2.1 Optimization problems 2.2 From single- to multi-surrogate-model optimization 2.2.1 Classical surrogate-model-based optimization 2.2.2 The purpose of multi-surrogate-model optimization 2.2.3 BRISE 2.5.0: Multi-surrogate-model-based software product line for parameter tuning 2.3 Transfer learning 2.3.1 Definition and purpose of transfer learning 2.4 Summary of the Background 3 Related work 3.1 Questions to transfer learning 3.2 When to transfer: Existing approaches to determining the expediency of knowledge transfer 3.2.1 Meta-features-based approaches 3.2.2 Surrogate-model-based similarity 3.2.3 Relative landmarks-based approaches 3.2.4 Sampling landmarks-based approaches 3.2.5 Similarity threshold problem 3.3 What to transfer: Existing approaches to knowledge transfer 3.3.1 Ensemble learning 3.3.2 Search space pruning 3.3.3 Multi-task learning 3.3.4 Surrogate model recommendation 3.3.5 Few-shot learning 3.3.6 Other approaches to transferring knowledge 3.4 How to transfer (discussion): Peculiarities and required design decisions for the TL implementation in multi-surrogate-model setup 3.4.1 Peculiarities of model recommendation in multi-surrogate-model setup 3.4.2 Required design decisions in multi-task learning 3.4.3 Few-shot learning problem 3.5 Summary of the related work analysis 4 Transfer learning for multi-surrogate-model optimization 4.1 Expediency of knowledge transfer 4.1.1 Experiments’ similarity definition as a variability point 4.1.2 Clustering to filter the most suitable experiments 4.2 Dynamic model recommendation in multi-surrogate-model setup 4.2.1 Variable recommendation granularity 4.2.2 Model recommendation by time and performance criteria 4.3 Multi-task learning 4.4 Implementation of the proposed concept 4.5 Conclusion of the proposed concept 5 Evaluation 5.1 Benchmark suite 5.1.1 APSP for the meta-heuristics 5.1.2 Hyperparameter optimization of the Random Forest algorithm 5.2 Environment setup 5.3 Evaluation plan 5.4 Baseline evaluation 5.5 Meta-tuning for a multi-task learning approach 5.5.1 Revealing the dependencies between the parameters of multi-task learning and its performance 5.5.2 Multi-task learning performance with the best found parameters 5.6 Expediency determination approach 5.6.1 Expediency determination as a variability point 5.6.2 Flexible number of the most similar experiments with the help of clustering 5.6.3 Influence of the number of initial samples on the quality of expediency determination 5.7 Multi-surrogate-model recommendation 5.8 Few-shot learning 5.8.1 Transfer of the built surrogate models’ combination 5.8.2 Transfer of the best configuration 5.8.3 Transfer from different experiment instances 5.9 Summary of the evaluation results 6 Conclusion and Future work

In my thesis I~ deal with the design, implementation and testing of the advanced parallel Estimation of Distribution Algorithm (EDA) utilizing copula theory to create a~ probabilistic model. A~new population is created by the process of sampling the joint distribution function, which models the current distribution of the subpopulation of promising individuals . The usage of copulas increases the efficiency of the learning process and sampling the probabilistic model. It can be separated into mutually independent marginal distributions and the copula , which represents the correlations between the variables of the solved problem. This concept initiated the usage of the parallel island architecture , in which the migration of probabilistic models belonging to individual islands ' subpopulations was used instead of the migration of individuals . The statistical tests used in the comparison of the proposed algorithm ( mCEDA = migrating Copula - based Estimation of Distribution Algorithm ) and the algorithms of other authors confirmed the effectiveness of the proposed concept .

This thesis covers the process of automating the design of coolant nozzles used for cylindrical grinding. Coolant nozzles are used to supply coolant, an oil and water mixture used to cool the metal workpiece and lubricate the grinding wheel. In the automotive industry, grinding is used to reduce the surface roughness of the workpiece. However, a large amount of heat is generated, risking the heat treatment of the steel to be compromised, for this, coolant is supplied to minimize the heat caused by friction. A nozzle is used, aiming a jet to the zone that generates heat. Commonly used nozzles are adjustable, leading to variation in cooling performance if misaligned. The design of fixed nozzles is developed in this thesis to reduce variation and automatise the design for multiple applications. The automatically designed nozzles are fused deposition modeled and tested. The design automation tool is tested repeatedly and improved successively in the span of the thesis. This lead to a great extent of implementation of design automation. Which lead to a facilitation in reaching of the work zone and avoid obstacles. Also, the tool managed to create nozzle tubes for a multitude of machines. The tool is able to generate, aim, orient, and individually dimension multi-nozzle tubes. Design of Experiment methodology is implemented to find nozzle designs with improved velocity and flow rate and minimize the air mixture with the coolant. Several nozzle designs are tested and fitted into a surrogate model that is, in turn, optimized. The results of the tests led to a greater understanding of how the nozzle geometry restricts the flow rate when attempts of reaching higher velocities of the coolant jet are made. The surrogate models created, also made it possible to find the range of designs which best suits different applications, whereby a Pareto front was able to be populated with a range of different designs alternating in flow rate, velocity and coherency ratio.