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…).

Optimal Timing of TV Commercials: Symmetrical Model

In this paper I study the behavior of free-good producers (TV broadcasters) on a market where every consumer (TV viewer) perpetually makes a decision whether to consume and which product (TV channel) to consume contingent on the attractiveness of the currently consumed product. Every producer optimally allocates a time period where a product with higher attractiveness (TV program) is replaced by a product with lower attractiveness (advertising). While products with higher attractiveness represent producers’ costs, products with lower attractiveness bring in revenue that is proportional to the audience reach. I assume that consumers choose among products and the outside option following a Markov process where probabilities of transition reflect various attractiveness of the products. Given symmetrical positions of the producers, I prove that their optimal strategy is to put their commercial breaks into the same or very close times. For some setting of the parameters, the breaks will overlap perfectly. Given the perfect overlap, both broadcasters are better off if they fragment their breaks into shorter breaks keeping the total amount of commercial time the same.

Application of Computational Intelligence Techniques to Process Industry Problems

In the last two decades there has been a large progress in the computational intelligence research field. The fruits of the effort spent on the research in the discussed field are powerful techniques for pattern recognition, data mining, data modelling, etc. These techniques achieve high performance on traditional data sets like the UCI machine learning database. Unfortunately, this kind of data sources usually represent clean data without any problems like data outliers, missing values, feature co-linearity, etc. common to real-life industrial data. The presence of faulty data samples can have very harmful effects on the models, for example if presented during the training of the models, it can either cause sub-optimal performance of the trained model or in the worst case destroy the so far learnt knowledge of the model. For these reasons the application of present modelling techniques to industrial problems has developed into a research field on its own. Based on the discussion of the properties and issues of the data and the state-of-the-art modelling techniques in the process industry, in this paper a novel unified approach to the development of predictive models in the process industry is presented

Learnt Topology Gating Artificial Neural Networks

This work combines several established regression and meta-learning techniques to give a holistic regression model and presents the proposed Learnt Topology Gating Artificial Neural Networks (LTGANN) model in the context of a general architecture previously published by the authors. The applied regression techniques are Artificial Neural Networks, which are on one hand used as local experts for the regression modelling and on the other hand as gating networks. The role of the gating networks is to estimate the prediction error of the local experts dependent on the input data samples. This is achieved by relating the input data space to the performance of the local experts, and thus building a performance map, for each of the local experts. The estimation of the prediction error is then used for the weighting of the local experts predictions. Another advantage of our approach is that the particular neural networks are unconstrained in terms of the number of hidden units. It is only necessary to define the range within which the number of hidden units has to be generated. The model links the topology to the performance, which has been achieved by the network with the given complexity, using a probabilistic approach. As the model was developed in the context of process industry data, it is evaluated using two industrial data sets. The evaluation has shown a clear advantage when using a model combination and meta-learning approach as well as demonstrating the higher performance of LTGANN when compared to a standard combination method

Gating Artificial Neural Network Based Soft Sensor

This work proposes a novel approach to Soft Sensor modelling, where the Soft Sensor is built by a set of experts which are artificial neural networks with randomly generated topology. For each of the experts a meta neural network is trained, the gating Artificial Neural Network. The role of the gating network is to learn the performance of the experts in dependency on the input data samples. The final prediction of the Soft Sensor is a weighted sum of the individual experts predictions. The proposed meta-learning method is evaluated on two different process industry data sets

Self-Adapting Soft Sensor for On-Line Prediction

When it comes to application of computational learning techniques in practical scenarios, like for example adaptive inferential control, it is often difficult to apply the state-of-the-art techniques in a straight forward manner and usually some effort has to be dedicated to tuning either the data, in a form of data pre-processing, or the modelling techniques, in form of optimal parameter search or modification of the training algorithm. In this work we present a robust approach to on-line predictive modelling which is focusing on dealing with challenges like noisy data, data outliers and in particular drifting data which are often present in industrial data sets. The approach is based on the local learning approach, where models of limited complexity focus on partitions of the input space and on an ensemble building technique which combines the predictions of the particular local models into the final predicted value. Furthermore, the technique provides the means for on-line adaptation and can thus be deployed in a dynamic environment which is demonstrated in this work in terms of an application of the presented approach to a raw industrial data set exhibiting drifting data, outliers, missing values and measurement noise

Atmospheric temperature gradients related to clear air turbulence in the upper troposphere and lower stratosphere

Relationship between atmospheric temperature gradients and clear air turbulence of lower atmospher

Nature-Inspired Adaptive Architecture for Soft Sensor Modelling

This paper gives a general overview of the challenges present in the research field of Soft Sensor building and proposes a novel architecture for building of Soft Sensors, which copes with the identified challenges. The architecture is inspired and making use of nature-related techniques for computational intelligence. Another aspect, which is addressed by the proposed architecture, are the identified characteristics of the process industry data. The data recorded in the process industry consist usually of certain amount of missing values or sample exceeding meaningful values of the measurements, called data outliers. Other process industry data properties causing problems for the modelling are the collinearity of the data, drifting data and the different sampling rates of the particular hardware sensors. It is these characteristics which are the source of the need for an adaptive behaviour of Soft Sensors. The architecture reflects this need and provides mechanisms for the adaptation and evolution of the Soft Sensor at different levels. The adaptation capabilities are provided by maintaining a variety of rather simple models. These particular models, called paths in terms of the architecture, can for example focus on different partition of the input data space, or provide different adaptation speeds to changes in the data. The actual modelling techniques involved into the architecture are data-driven computational learning approaches like artificial neural networks, principal component regression, etc

Alien plants in urban nature reserves : from red-list species to future invaders?

Urban reserves, like other protected areas, aim to preserve species richness but conservation efforts in these protected areas are complicated by high proportions of alien species. We examined which environmental factors determine alien species presence in 48 city reserves of Prague, Czech Republic. We distinguished between archaeophytes, i.e. alien species introduced since the beginning of Neolithic agriculture up to 1500 A. D., and neophytes, i.e. modern invaders introduced after that date, with the former group separately analysed for endangered archaeophytes (listed as C1 and C2 categories on national red list). Archaeophytes responded positively to the presence of arable land that was in place at the time of the reserve establishment, and to a low altitudinal range. In addition to soil properties, neophytes responded to recent human activities with the current proportion of built-up area in reserves serving as a proxy. Endangered archaeophytes, with the same affinity for past arable land as other archaeophytes, were also supported by the presence of current shrubland in the reserve. This suggests that for endangered archaeophytes it may have been difficult to adapt to changing agricultural practices, and shrublands might act as a refugium for them. Forty-six of the 155 neophytes recorded in the reserves are classified as invasive. The reserves thus harbour 67% of the 69 invasive neophytes recorded in the country, and particularly worrisome is that many of the most invasive species are shrubs and trees, a life form that is known to account for widespread invasions with high impacts. Our results thus strongly suggest that in Prague nature reserves there is a high potential for future invasions

Knowledge Base Completion: Baselines Strike Back

Many papers have been published on the knowledge base completion task in the past few years. Most of these introduce novel architectures for relation learning that are evaluated on standard datasets such as FB15k and WN18. This paper shows that the accuracy of almost all models published on the FB15k can be outperformed by an appropriately tuned baseline - our reimplementation of the DistMult model. Our findings cast doubt on the claim that the performance improvements of recent models are due to architectural changes as opposed to hyper-parameter tuning or different training objectives. This should prompt future research to re-consider how the performance of models is evaluated and reported