Disertacijoje nagrinėjamos terminių srautų paieškos ir prognozavimo autonominio orlaivio skrydžio metu problemos. Pagrindinis tyrimų objektas yra terminių srautų aparatinis aptikimas ir prognozavimas. Terminiai srautai yra labai svarbus autonominio orlaivio skrydžio charakteristikų, kaip antai skrydžio laikas ir trukmė, gerinimo šaltinis. Pagrindinis disertacijos tikslas – sukurti metodikas ir algoritmus, leidžiančius aptikti terminį srautą, nustatyti bei sėkmingai prognozuoti jo parametrus. Sukurtų metodų ir algoritmų taikymo sritis – autonominių orlaivių valdymo sistemų sintezė, meteorologiniai mezomastelinių meteorologinių reiškinių tyrimai, biologinius skaičiavimo modelius naudojančių sistemų sintezė. Darbe sprendžiami keli uždaviniai: terminio srauto aptikimas naudojant orlaivio navigacinių parametrų matavimo duomenis, terminio srauto modeliavimas bei modeliui reikalingų duomenų pateikimas. Disertaciją sudaro įvadas, keturi skyriai, rezultatų apibendrinimas, naudotos literatūros ir autoriaus publikacijų disertacijos tema sąrašai ir tris priedai. Įvadiniame skyriuje aptariama tiriamoji problema, darbo aktualumas, aprašomas tyrimų objektas, formuluojamas darbo tikslas bei uždaviniai, aprašoma tyrimų metodika, darbo mokslinis naujumas, darbo rezultatų praktinė reikšmė, ginamieji teiginiai. Įvado pabaigoje pristatomos disertacijos tema autoriaus paskelbtos publikacijos ir konferencijų pranešimai bei disertacijos struktūra. Pirmajame skyriuje pateikiama su disertacijos... [toliau žr. visą tekstą] / The dissertation examines the thermal flow detection and prediction prob-lems during an autonomous aircraft flight. The main research object is the thermal flows and artificial neural networks. Thermal flows are a very im-portant source for improving autonomous aircraft flight parameters, such as flight time and duration. The primary aim of the dissertation is to create methodologies and algorithms to detect, identify and to successfully predict the parameters the thermal flows. The application are of the methods and algorithms developed is autonomous aircraft control system synthesis, research on mesoscale meteorological phenomena and synthesis of computing systems using biological models. The following objectives are carried out: thermal flow sensing using aircraft navigational parameters measurement data, thermal flow simulation modeling and data input necessary for modeling. The dissertation consists of an introduction, four chapters, conclusions, bibliography, and list of author publications on the topic as well as three annexes. The introductory chapter discusses the research problem and the relevance of the research described in the thesis, formulates the goal and objectives, describes the research methodology, scientific novelty, the practical significance of the results, hypotheses. In the end of the introduction a list of author's publications on the topic and the structure of the dissertation are presented. The first section provides a review of previous... [to full text]

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Data about the spatial variation of soil attributes is required to address a great number of environmental issues, such as improving water quality, flood mitigation, and determining the effects of the terrestrial carbon cycle. The need for a continuum of soils data is problematic, as it is only possible to observe soil attributes at a limited number of locations, beyond which, prediction is required. There is, however, disparity between the way in which much of the existing information about soil is recorded and the format in which the data is required. There are two primary methods of representing the variation in soil properties, as a set of distinct classes or as a continuum. The former is how the variation in soils has been recorded historically by the soil survey, whereas the latter is how soils data is typically required. One solution to this issue is to use a soil-landscape modelling approach which relates the soil to the wider landscape (including topography, land-use, geology and climatic conditions) using a statistical model. In this study, the soil-landscape modelling approach has been applied to the prediction of soil bulk density (Db). The original contribution to knowledge of the study is demonstrating that producing a continuous surface of Db using a soil-landscape modelling approach is that a viable alternative to the ‘classification’ approach which is most frequently used. The benefit of this method is shown in relation to the prediction of soil carbon stocks, which can be predicted more accurately and with less uncertainty. The second part of this study concerns the inclusion of expert knowledge within the soil-landscape modelling approach. The statistical modelling approaches used to predict Db are data driven, hence it is difficult to interpret the processes which the model represents. In this study, expert knowledge is used to predict Db within a Bayesian network modelling framework, which structures knowledge in terms of probability. This approach creates models which can be more easily interpreted and consequently facilitate knowledge discovery, it also provides a method for expert knowledge to be used as a proxy for empirical data. The contribution to knowledge of this section of the study is twofold, firstly, that Bayesian networks can be used as tools for data-mining to predict a continuous soil attribute such as Db and that in lieu of data, expert knowledge can be used to accurately predict landscape-scale trends in the variation of Db using a Bayesian modelling approach.

A hybrid model integrating predictive capabilities of Artificial Neural Network (ANN) and optimization feature of Genetic Algorithm (GA) is developed for the purpose of inverse modeling. The proposed approach is applied to Superplastic forming of materials to predict the material properties which characterize the performance of a material. The study is carried out on two problems. For the first problem, ANN is trained to predict the strain rate sensitivity index m given the temperature and the strain rate. The performance of different gradient search methods used in training the ANN model is demonstrated. Similar approach is used for the second problem. The objective of which is to predict the input parameters, i.e. strain rate and temperature corresponding to a given flow stress value. An attempt to address one of the major drawbacks of ANN, which is the black box behavior of the model, is made by collecting information about the weights and biases used in training and formulating a mathematical expression. The results from the two problems are compared to the experimental data and validated. The results indicated proximity to the experimental data.

This thesis focuses on detecting, classifying and locating faults on electric power transmission lines. Fault detection, fault classification and fault location have been achieved by using artificial neural networks. Feedforward networks have been employed along with backpropagation algorithm for each of the three phases in the Fault location process. Analysis on neural networks with varying number of hidden layers and neurons per hidden layer has been provided to validate the choice of the neural networks in each step. Simulation results have been provided to demonstrate that artificial neural network based methods are efficient in locating faults on transmission lines and achieve satisfactory performances.

Darbe aprašoma tyrimas, kurio metu buvo sukurta programa, naudojantis OpenCV ir DNT klaidos skleidimo atgal algoritmu, gebanti aptikti ir bandanti klasifikuoti veidus. Darbo eigoje: • Įsigilinta į OpenCV funkcijų biblioteką; • Išanalizuota DNT teorinė medžiaga; • Sukurta programinė įranga, kuri, naudojantis „webcam“, geba aptikti ir bando klasifikuoti veidus; • Atliktas eksperimentinis tyrimas; • Nustatyti programos trūkumai; • Pateikti kiti sprendimo būdai; Realizuota programinė įranga gali būti naudojama edukaciniais tikslais. / The work describes an experiment,in which progress was created a software,by using OpenCV and ANN error back propagation algorithm capable of detecting and attempting to classify the faces. Workflow: • Delved deeply into the OpenCV library functions; • Analyzed the theoretical material of ANN • Developed the software, which, using webcam, is capable of detecting and trying to classify the faces; • Made an experimental study; • Determined the weaknesses of the program; • The other methods; created software can be used for educational purposes.

Electricity costs in South Africa have risen steeply; there are a number of factors that have contributed to this increase. The increased costs have a considerable inuence on the mines and mining sector in general. It requires considerable planning to assist mines in such management. The present study addresses the development of a way to predict both electricity consumption and costs, which general involves a large range of personnel. The majority of planning personnel can be more usefully employed in other ways. The goal is not to replace such planners but make them more task e_ective. Automation, which will reduce their workload, may have little or no e_ect on performance. In some cases, however, automation may produce better results. There is a complex system to be analysed in the prediction of electricity consumption and costs. The existing prediction methodology is investigated in this study; the investigation highlights the need for a new methodology. The new method should be automated, easier to use and more accurate. Such a model is developed. The new prediction methodology extracts data from the monthly Eskom bills and stores it in a database. The data is grouped according to a new model and then normalised. An arti_cial neural network is used to \learn" the dynamics of the data to calculate new future electricity consumption. Electricity costs are predicted by multiplying the predicted electrical consumption with a calculated factor based on cost per electricity unit of the previous year with the expected increase added. The new methodology is integrated in a commercial energy management platform named Management Toolbox, which o_ers a range of functionality. In this study the prediction of electricity consumption and costs are implemented. The implementation is executed with simplicity in mind and care is taken to present the user with the optimal amount of data. The performance of the electricity consumption prediction is sensitive to production changes and the quality of the data history. Performance of the electricity costs prediction model is an improvement over the existing prediction method. The proposed methodology has greater accuracy and uses less personnel, which can lead to using most of the personnel on more important tasks. / Thesis (MIng (Computer and Electronic Engineering))--North-West University, Potchefstroom Campus, 2013.

Electricity costs in South Africa have risen steeply; there are a number of factors that have contributed to this increase. The increased costs have a considerable inuence on the mines and mining sector in general. It requires considerable planning to assist mines in such management. The present study addresses the development of a way to predict both electricity consumption and costs, which general involves a large range of personnel. The majority of planning personnel can be more usefully employed in other ways. The goal is not to replace such planners but make them more task e_ective. Automation, which will reduce their workload, may have little or no e_ect on performance. In some cases, however, automation may produce better results. There is a complex system to be analysed in the prediction of electricity consumption and costs. The existing prediction methodology is investigated in this study; the investigation highlights the need for a new methodology. The new method should be automated, easier to use and more accurate. Such a model is developed. The new prediction methodology extracts data from the monthly Eskom bills and stores it in a database. The data is grouped according to a new model and then normalised. An arti_cial neural network is used to \learn" the dynamics of the data to calculate new future electricity consumption. Electricity costs are predicted by multiplying the predicted electrical consumption with a calculated factor based on cost per electricity unit of the previous year with the expected increase added. The new methodology is integrated in a commercial energy management platform named Management Toolbox, which o_ers a range of functionality. In this study the prediction of electricity consumption and costs are implemented. The implementation is executed with simplicity in mind and care is taken to present the user with the optimal amount of data. The performance of the electricity consumption prediction is sensitive to production changes and the quality of the data history. Performance of the electricity costs prediction model is an improvement over the existing prediction method. The proposed methodology has greater accuracy and uses less personnel, which can lead to using most of the personnel on more important tasks. / Thesis (MIng (Computer and Electronic Engineering))--North-West University, Potchefstroom Campus, 2013.

This thesis proposes an integrated artificial neural network based approach to classify the position of a wireless device in an indoor protected area. Our experiments are conducted in two different types of interference affected indoor locations. We found that the environment greatly influences the received signal strength. We realized the need of incorporating a complexity measure of the Wi-Fi signal as additional information in our localization algorithm. The inputs to the integrated artificial neural network were comprised of an integer dimension representation and a fractional dimension representation of the Wi-Fi signal. The integer dimension representation consisted of the raw signal strength, whereas the fractional dimension consisted of a variance fractal dimension of the Wi-Fi signal. The results show that the proposed approach performed 8.7% better classification than the “one dimensional input” ANN approach, achieving an 86% correct classification rate. The conventional Trilateration method achieved only a 47.97% correct classification rate.