Made available in DSpace on 2017-07-10T18:08:16Z (GMT). No. of bitstreams: 1 Thiago DArisbo.pdf: 2108504 bytes, checksum: 7b8aad29ec7d75a6fd370e54a95cd849 (MD5) Previous issue date: 2011-02-24 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Ion exchange is a process that is used in the treatment of aqueous industrial effluents containing organic compounds and heavy metals. The fixed bed columns are longer applied by allowing the process to occur continuously (cycles of regeneration). The design and process optimization of the ion exchange column requires the use of mathematical models. Phenomenological models of these systems involve the solution of partial differential and algebraic equations. The equilibrium data for ion exchange processes are usually described by the Mass Action Law (MAL), which can be considered non-ideality of aqueous and solid phases. Artificial Neural Networks (ANN) are being used successfully for the study of equilibrium data because they are empirical models and don t demand a mathematical rigor. This work aimed to evaluate the applicability of the hybrid model to describe the dynamics of ion exchange in fixed beds of binary systems. This system consists of partial differential equations obtained from mass balance in fluid phases in the ion exchanger and ANN to describe the balance. LAM was adjusted to experimental data of ion exchange equilibrium and then were generated 4200 data sets for each binary pair studied, which served as training for RNA. We tested networks with different structures, with one and two input layers. The 3-3-2 structure was used in the simulations of the hybrid model because it was the best represented the systems during the training phase. The differential equations were solved by the lines method. A computer program in FORTRAN language was developed for solving the model equations. DASSL subroutine was used to solve the equations. The performance of the hybrid model was evaluated from the results obtained with the phenomenological model, in which case the equilibrium description was made with the use of MAL. It also was the analysis of results from the comparison of experimental data. To evaluate the model we used data from the literature of ion exchange in Amberlite IR 120 resin on the systems Cu-Na and Zn-Na and in NaY zeolite on Fe-Na and Zn-Na. Both models were efficient to describe the dynamics of ion-exchange fixed bed columns, and the hybrid model had the advantage of the reduced computational time (82% reduction on average) as a result of not needing to solve a nonlinear equation. / A troca iônica é um processo muito utilizado no tratamento de efluentes industriais aquosos contendo compostos orgânicos e metais pesados. As colunas de leito fixo são mais aplicadas por permitir que o processo ocorra de maneira contínua (ciclos de regeneração). O projeto e a otimização de processos de troca iônica em coluna requer o uso de modelos matemáticos. Os modelos fenomenológicos destes sistemas envolvem a resolução de equações diferenciais parciais e algébricas. Os dados de equilíbrio de processos de troca iônica geralmente são descritos pela Lei da Ação das Massas (LAM), na qual podem ser consideradas as não idealidades das fases aquosa e sólida. As Redes Neurais Artificiais (RNA) estão sendo utilizadas com sucesso para o estudo destes dados de equilíbrio por serem modelos empíricos e não demandarem tal rigor matemático. Esta dissertação teve por objetivo avaliar a aplicabilidade do modelo híbrido para descrever a dinâmica do processo de troca iônica em leito fixo de sistemas binários. Este sistema é constituído de equações diferenciais parciais obtidas por meio de balanço de massa nas fases fluida e no trocador iônico e de RNA para descrever o equilíbrio. A LAM foi ajustada a dados experimentais de equilíbrio de troca iônica e, então, foram gerados conjuntos de 4200 dados para cada par binário estudado, os quais serviram como treinamento para a RNA. Foram testadas redes com diferentes estruturas, com uma e com duas camadas de entrada. A estrutura 3-3-2 foi utilizada nas simulações do modelo híbrido, pois foi a que melhor representou os sistemas na etapa de treinamento. As equações diferenciais foram resolvidas pelo método das linhas. Um programa computacional em linguagem FORTRAN foi desenvolvido para a resolução das equações do modelo. Foi utilizada a sub-rotina DASSL para resolver as equações. O desempenho do modelo híbrido foi avaliada a partir dos resultados obtidos com o modelo fenomenológico, sendo que neste caso a descrição do equilíbrio foi feita pelo uso da LAM. Também foi feita a análise dos resultados a partir da comparação dos dados experimentais. Para avaliar o modelo foram utilizados dados da literatura de troca iônica em resina Amberlite IR 120 dos sistemas Cu-Na e Zn-Na e na zeólita NaY dos sistemas Fe-Na e Zn-Na. Ambos os modelos foram eficientes para descrever a dinâmica de troca iônica de colunas de leito fixo, sendo que o modelo híbrido apresentou como vantagem o menor tempo computacional (82% de redução em média) em decorrência de não necessitar resolver a equação não-linear.

Made available in DSpace on 2016-08-17T14:52:39Z (GMT). No. of bitstreams: 1 DISSERTACAO_DENIS FABRICIO SOUSA DE SA.pdf: 2409581 bytes, checksum: 4de5274676a1f75ffe2a1f6b46b1388c (MD5) Previous issue date: 2015-04-10 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The representation of dynamic systems or plants via mathematical models occupies an important position in control system design that allow the performance evaluation of the controller during his development stage. These models are also used as an alternative to solve the problem of the hardness or impracticability to install sensors that measure the controlled variables, the dynamic systems representations enable non-invasive measurement of these variables. As consequence the designer has an alternative way to perform adaptive and optimal sensorless control for a given process. In this dissertation is presented a proposal for control systems schemas and algorithms, based on recurrent neural networks (ANN) and Box-Jenkins models, that are dedicated to sensorless or indirect control of dynamic systems. The proposed models and algorithms are associated with the systems identification and recurrent ANN approaches. The algorithms developed for the AAN training are Backpropagation Accelerated and RLS types that are compared with classical methods and strategies to obtain it online parameters of indirect control of system for a thermal plant, where the actuator is Peltier cell. The performance the parametric models of the plant and adaptive PID digital controllers and linear quadratic regulator (DLQR) that are the main elements of the sensorless temperature control system, are evaluated by means of hybrid simulations, where the algorithms implemented in micro controllers and the plant represented by mathematical models. The performance results of the proposed sensorless control algorithms are promissory, not only, in terms of the control system performance, but also due to the reexibility to deploy it in other dynamic systems. / A representação de sistemas dinâmicos ou plantas por meio modelos matemáticos ocupa uma posição relevante no projeto de sistemas de controle, permitindo que o projetista avalie o desempenho dos controladores durante a fase de desenvolvimento do projeto. Estes modelos também são utilizados para resolver o problema da dificuldade ou impossibilidade da inserção de sensores em plantas para medição de variáveis controladas, onde os modelos viabilizam a mediação não invasiva destas variáveis, fornecendo uma alternativa para realização do controle indireto adaptativo e ótimo de um dado processo. Nesta dissertação apresenta-se o desenvolvimento de modelos propostos baseados em redes neurais artificiais recorrentes para o controle sensorless ou indireto da planta. Os modelos propostos estão associados com as abordagens de Identificação de Sistemas e de RNA's recorrentes. OS algoritmos desenvolvidos para o treinamento das RNAs são do tipo Backpropagation acelerado e RLS, que são comparados com estratégias e métodos clássicos, para obtenção online dos parâmetros do sistema de controle indireto de uma planta térmica, tendo como atuador uma célula Peltier. Para uns de avaliação de desempenho do sistema de controle indireto da planta, os modelos paramétricos e controladores digitais adaptativos do tipo PID e regulador linear quadrático (DLQR) são avaliados por meio de simulações híbridas, sendo os algoritmos dos controladores implementados em microcontroladores e a planta representada por modelos matemáticos. Os resultados apresentados são promissores, não são sentido do desempenho do sistema de controle, mas também nos custos reduzidos para seu desenvolvimento, operação e flexibilidade de aplicação em outros sistemas dinâmicos.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2016-06-09T12:11:11Z No. of bitstreams: 1 pedrodacruzloureiro.pdf: 1767688 bytes, checksum: 1fa1e4fbfaa6feaf5a5c88ea70df09d6 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2016-07-13T13:31:22Z (GMT) No. of bitstreams: 1 pedrodacruzloureiro.pdf: 1767688 bytes, checksum: 1fa1e4fbfaa6feaf5a5c88ea70df09d6 (MD5) / Made available in DSpace on 2016-07-13T13:31:22Z (GMT). No. of bitstreams: 1 pedrodacruzloureiro.pdf: 1767688 bytes, checksum: 1fa1e4fbfaa6feaf5a5c88ea70df09d6 (MD5) Previous issue date: 2012-04-16 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Neste trabalho é proposta a aplicação de redes neurais artificiais para ajuste de parâmetros de um compensador estático de reativos, para controle de tensão e harmônicos. Devido à intensa produção de correntes harmônicas e possíveis afundamentos de tensão em instalações industriais como o forno a arco, é necessário um sistema de controle eficiente e robusto. Além disso, os sistemas elétricos de potência se encontram em um cenário com a presença cada vez maior de geração distribuída, cargas não-lineares e forte tendência à operação no contexto das smartgrids e microgrids. Sendo assim, o suporte de reativos deve ser adequado a esses sistemas, podendo atuar de forma rápida, precisa e confiável. Uma possível solução é a utilização de um compensador estático de reativos (CER) com função adicional de filtragem no ponto onde se deseja controlar a tensão e a distorção harmônica. Entretanto, para o correto funcionamento, é necessário um sistema preciso para o ajuste dos parâmetros do CER, ou seja, determinar os ângulos de disparo dos tiristores e o número de bancos de capacitores a serem ligados. Neste trabalho é proposta uma estratégia de controle via redes neurais artificiais, treinadas para o reconhecimento de padrões de operação em regime permanente e definição da configuração do CER, conferindo inteligência ao equipamento. Os desenvolvimentos propostos foram implementados no ambiente MatLab®. A validação do método é feita através de simulações em sistemas-teste, presentes na literatura técnica, utilizando o fluxo de potência pelo método de injeção de correntes trifásico harmônico. Os resultados obtidos mostram as vantagens da utilização da estratégia proposta. / In this work, an artificial neural network-based static var compensator tuning is proposed for voltage and harmonic distortion control. Due to intense harmonic current injection and possible voltage sags produced by industrial facilities such as arc furnaces, an efficient robust control system is needed. Besides, electrical power systems face a new scenario with high penetration of distributed generation and non-linear loads and increased smart grid and microgrid trends. Therefore, the available reactive power sources must be able to provide system control in order to operate the system in a fast, accurate and reliable way. The application of a static var compensator (SVC) with additional filtering function at the controlled node is a possible solution. However, a precise SVC parameters tuning is needed, in order to make the system to work properly. In this work, a control strategy based on artificial neural networks is proposed. The neural networks are trained to recognize steadystate operating patterns and give the SVC adjustment. The proposed technique was implemented in the MatLab® environment. The methodology is validated by simulations in test-systems available in technical literature, using the three-phase harmonic current injection method power flow. Results show the advantages of the proposed methodology.

Lie-detection has been an evergreen and evolving subject. Polygraph techniques have been the most popular and successful technique till date. The main drawback of the polygraph is that good results cannot be attained without maintaining a physical contact, of the subject under test. In general, this physical contact would induce extra consciousness in the subject. Also, any sort of arousal in the subject triggers false positives while performing the traditional polygraph based tests. With all these drawbacks in the polygraph, also, due to rapid developments in the fields of computer vision and artificial intelligence, with newer and faster algorithms, have compelled mankind to search and adapt to contemporary methods in lie-detection. Observing the facial expressions of emotions in a person without any physical contact and implementing these techniques using artificial intelligence is one such method. The concept of magnifying a micro expression and trying to decipher them is rather premature at this stage but would evolve in future. Magnification using EVM technique has been proposed recently and it is rather new to extract these micro expressions from magnified EVM based on HOG features. Till date, HOG features have been used in conjunction with SVM, and generally for person/pedestrian detection. A newer, simpler and contemporary method of applying EVM with HOG features and Back-propagation Neural Network jointly has been introduced and proposed to extract and decipher the micro-expressions on the face. Micro-expressions go unnoticed due to its involuntary nature, but EVM is used to magnify them and makes them noticeable. Emotions behind the micro-expressions are extracted and recognized using the HOG features \& Back-Propagation Neural Network. One of the important aspects that has to be dealt with human beings is a biased mind. Since, an investigator is also a human and, he too, has to deal with his own assumptions and emotions, a Neural Network is used to give the investigator an unbiased start in identifying the true emotions behind every micro-expression. On the whole, this proposed system is not a lie-detector, but helps in detecting the emotions of the subject under test. By further investigation, a lie can be detected. / This thesis uses a magnification technique to magnify the subtle, faint and spontaneous facial muscle movements or more precisely, micro-expressions. This magnification would help a system in classifying them and estimating the emotion behind them. This technique additionally magnifies the color changes, which could be used to extract the pulse without a physical contact with the subject. The results are presented in a GUI. / Gautam: +46(0)739528573, +91-9701534064 Tushal: +46(0)723219833, +91-9000242241 Venu: +46(0)734780266, +91-9298653191 Sai: +91-9989410111

Methodologies designed to support new product development are receiving increasing interest in recent literature. A significant percentage of new product failure is attributed to a mismatch between designed product features and consumer liking. A variety of methodologies have been proposed and tested for consumer liking or preference prediction, ranging from statistical methodologies e.g. multiple linear regression (MLR) to non-statistical approaches e.g. artificial neural networks (ANN), support vector machines (SVM), and belief rule-based (BRB) systems. BRB has been previously tested for consumer preference prediction and target setting in case studies from the beverages industry. Results have indicated a number of technical and conceptual advantages which BRB holds over the aforementioned alternative approaches. This thesis focuses on presenting further advantages and applications of the BRB methodology for consumer liking prediction. The features and advantages are selected in response to challenges raised by three addressed case studies. The first case study addresses a novel industry for BRB application: the fast moving consumer goods industry, the personal care sector. A series of challenges are tackled. Firstly, stepwise linear regression, principal component analysis and AutoEncoder are tested for predictors’ selection and data reduction. Secondly, an investigation is carried out to analyse the impact of employing complete distributions, instead of averages, for sensory attributes. Moreover, the effect of modelling instrumental measurement error is assessed. The second case study addresses a different product from the personal care sector. A bi-objective prescriptive approach for BRB model structure selection and validation is proposed and tested. Genetic Algorithms and Simulated Annealing are benchmarked against complete enumeration for searching the model structures. A novel criterion based on an adjusted Akaike Information Criterion is designed for identifying the optimal model structure from the Pareto frontier based on two objectives: model complexity and model fit. The third case study introduces yet another novel industry for BRB application: the pastry and confectionary specialties industry. A new prescriptive framework, for rule validation and random training set allocation, is designed and tested. In all case studies, the BRB methodology is compared with the most popular alternative approaches: MLR, ANN, and SVM. The results indicate that BRB outperforms these methodologies both conceptually and in terms of prediction accuracy.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2016-01-08T10:36:58Z No. of bitstreams: 1 wanderantunesgasparvalente.pdf: 4197156 bytes, checksum: 5b667869c3bb237e570559ddf4cbb30d (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2016-01-25T16:56:26Z (GMT) No. of bitstreams: 1 wanderantunesgasparvalente.pdf: 4197156 bytes, checksum: 5b667869c3bb237e570559ddf4cbb30d (MD5) / Made available in DSpace on 2016-01-25T16:56:26Z (GMT). No. of bitstreams: 1 wanderantunesgasparvalente.pdf: 4197156 bytes, checksum: 5b667869c3bb237e570559ddf4cbb30d (MD5) Previous issue date: 2015-02-09 / O presente trabalho fundamenta-se no método das segmentações geométricas sucessivas (MSGS) para a construção de uma rede neural artificial capaz de gerar tanto a topologia da rede quanto o peso dos neurônios sem a especificação de parâmetros iniciais. O MSGS permite identificar um conjunto de hiperplanos no espaço Rn que, quando combinados adequadamente, podem separar duas ou mais classes de dados. Especificamente neste trabalho é empregado um aprimoramento ao MSGS com base em estimativas de densidade por kernel. Utilizando-se KDE, é possível encontrar novos hiperplanos de separação de forma mais consistente e, a partir daí, conduzir à classificação de dados com taxas de acerto superiores à técnica originalmente empregada. Neste trabalho, o MSGS aprimorado é empregado satisfatoriamente pela primeira vez para a identificação de padrões em sistemas de energia elétrica. O método foi ajustado para a classificação de faltas incipientes em transformadores de potência e os resultados apresentam índices de acerto superiores a trabalhos correlatos. O MSGS aprimorado também foi adaptado para classificar e localizar faltas inter-circuitos em linhas áreas de transmissão em circuito duplo, obtendo resultados positivos em comparação com a literatura científica. / This work is based on the method of successive geometric segmentations (SGSM) for the construction of an artificial neural network capable of generating both the network topology as the weight of neurons without specifying initial parameters. The MSGS allows to identify a set of hyperplanes in the Rn space that when properly combined, can separate two or more data classes. Specifically in this work is used an improvement to SGSM based on kernel density estimates (KDE). Using KDE, it is possible to find new hyperplanes of separation more consistently and, from there, lead to data classification with accuracy rates higher than originally technique. In this paper, the improved SGSM is first used satisfactorily to identify patterns in electrical power systems. The method has been adjusted to the classification of incipient faults in power transformers and the results have achieved rates above related work. The improved SGSM has also been adapted to classify and locate inter-circuit faults on double circuit overhead transmission lines with positive results compared with the scientific literature.

No description available.

<p>Istraživanja u okviru ove disertacije obuhvataju ispitivanje fizičkih, hemijskih i biolo&scaron;kih osobina svežeg i osu&scaron;enog ko&scaron;tičavog voća i razvoj inovativnog tehničkog re&scaron;enja su&scaron;enja voća u vakuumu. Dobijeni rezultati istraživanja daju doprinos formiranju baze naučnih znanja u vezi sa karakteristikama ko&scaron;tičavog voća osu&scaron;enog različitim tehnikama su&scaron;enja: konvektivnim su&scaron;enjem, vakuum su&scaron;enjem i liofilizacijom (su&scaron;enjem zamrzavanjem). Projektovan inovativni prototip vakuum su&scaron;are sa ejektorskim sistemom omogućava očuvanje biolo&scaron;ki aktivnih komponenata voća uz mogućnost smanjenja investicionih tro&scaron;kova i tro&scaron;kova održavanja opreme. Takođe je uspe&scaron;no prikazana mogućnost primene analize glavnih komponenata (eng. Principal Component Analysis, PCA), ve&scaron;tačke neuronske mreže (eng. Artificial Neural Networks, ANN) i matematičkog modelovanja za opisivanje funkcionalne zavisnosti primenjenih parametara su&scaron;enja i fizičkih, hemijskih i biolo&scaron;kih osobina osu&scaron;enog voća, optimizaciju procesa su&scaron;enja, kao i za pronalaženje modela koji najbolje opisuje proces su&scaron;enja.</p> / <p>Research in the framework of the thesis includes investigation of physical, chemical and biological properties of fresh and dried stone fruit and the development of an innovative technical solution for fruit vacuum drying. The obtained results contribute to the formation of the scientific base of knowledge, regarding the characteristics of stone fruit dried by different drying techniques: convective drying, vacuum drying and lyophilization (freeze drying). The designed innovative prototype of a vacuum dryer with an ejector system enables the preservation of biologically active fruit compounds with the possibility of reducing investment and equipment maintenance costs.<br />The possibility of application of Principal Component Analysis (PCA), Artificial Neural Networks (ANN) and mathematical modeling for describing the functional dependence between applied drying parameters and physical, chemical and biological properties of dried fruit, optimization of the drying process, as well for finding the model that best describes the drying process was presented in this work.</p>

Abstracts in English, Afrikaans and Northern Sotho / More time series forecasting methods were researched and made available in recent years. This is mainly due to the emergence of machine learning methods which also found applicability in time series forecasting. The emergence of a variety of methods and their variants presents a challenge when choosing appropriate forecasting methods. This study explored the performance of four advanced forecasting methods: autoregressive integrated moving averages (ARIMA); artificial neural networks (ANN); support vector machines (SVM) and regression models with ARIMA errors. To improve their performance, bagging was also applied. The performance of the different methods was illustrated using South African air passenger data collected for planning purposes by the Airports Company South Africa (ACSA). The dissertation discussed the different forecasting methods at length. Characteristics such as strengths and weaknesses and the applicability of the methods were explored. Some of the most popular forecast accuracy measures were discussed in order to understand how they could be used in the performance evaluation of the methods. It was found that the regression model with ARIMA errors outperformed all the other methods, followed by the ARIMA model. These findings are in line with the general findings in the literature. The ANN method is prone to overfitting and this was evident from the results of the training and the test data sets. The bagged models showed mixed results with marginal improvement on some of the methods for some performance measures. It could be concluded that the traditional statistical forecasting methods (ARIMA and the regression model with ARIMA errors) performed better than the machine learning methods (ANN and SVM) on this data set, based on the measures of accuracy used. This calls for more research regarding the applicability of the machine learning methods to time series forecasting which will assist in understanding and improving their performance against the traditional statistical methods / Die afgelope tyd is verskeie tydreeksvooruitskattingsmetodes ondersoek as gevolg van die ontwikkeling van masjienleermetodes met toepassings in die vooruitskatting van tydreekse. Die nuwe metodes en hulle variante laat ʼn groot keuse tussen vooruitskattingsmetodes. Hierdie studie ondersoek die werkverrigting van vier gevorderde vooruitskattingsmetodes: outoregressiewe, geïntegreerde bewegende gemiddeldes (ARIMA), kunsmatige neurale netwerke (ANN), steunvektormasjiene (SVM) en regressiemodelle met ARIMA-foute. Skoenlussaamvoeging is gebruik om die prestasie van die metodes te verbeter. Die prestasie van die vier metodes is vergelyk deur hulle toe te pas op Suid-Afrikaanse lugpassasiersdata wat deur die Suid-Afrikaanse Lughawensmaatskappy (ACSA) vir beplanning ingesamel is. Hierdie verhandeling beskryf die verskillende vooruitskattingsmetodes omvattend. Sowel die positiewe as die negatiewe eienskappe en die toepasbaarheid van die metodes is uitgelig. Bekende prestasiemaatstawwe is ondersoek om die prestasie van die metodes te evalueer. Die regressiemodel met ARIMA-foute en die ARIMA-model het die beste van die vier metodes gevaar. Hierdie bevinding strook met dié in die literatuur. Dat die ANN-metode na oormatige passing neig, is deur die resultate van die opleidings- en toetsdatastelle bevestig. Die skoenlussamevoegingsmodelle het gemengde resultate opgelewer en in sommige prestasiemaatstawwe vir party metodes marginaal verbeter. Op grond van die waardes van die prestasiemaatstawwe wat in hierdie studie gebruik is, kan die gevolgtrekking gemaak word dat die tradisionele statistiese vooruitskattingsmetodes (ARIMA en regressie met ARIMA-foute) op die gekose datastel beter as die masjienleermetodes (ANN en SVM) presteer het. Dit dui op die behoefte aan verdere navorsing oor die toepaslikheid van tydreeksvooruitskatting met masjienleermetodes om hul prestasie vergeleke met dié van die tradisionele metodes te verbeter. / Go nyakišišitšwe ka ga mekgwa ye mentši ya go akanya ka ga molokoloko wa dinako le go dirwa gore e hwetšagale mo mengwageng ye e sa tšwago go feta. Se k e k a le b a k a la g o t šwelela ga mekgwa ya go ithuta ya go diriša metšhene yeo le yona e ilego ya dirišwa ka kakanyong ya molokolokong wa dinako. Go t šwelela ga mehutahuta ya mekgwa le go fapafapana ga yona go tšweletša tlhohlo ge go kgethwa mekgwa ya maleba ya go akanya. Dinyakišišo tše di lekodišišitše go šoma ga mekgwa ye mene ya go akanya yeo e gatetšego pele e lego: ditekanyotshepelo tšeo di kopantšwego tša poelomorago ya maitirišo (ARIMA); dinetweke tša maitirelo tša nyurale (ANN); metšhene ya bekthara ya thekgo (SVM); le mekgwa ya poelomorago yeo e nago le diphošo tša ARIMA. Go kaonafatša go šoma ga yona, nepagalo ya go ithuta ka metšhene le yona e dirišitšwe. Go šoma ga mekgwa ye e fepafapanego go laeditšwe ka go šomiša tshedimošo ya banamedi ba difofane ba Afrika Borwa yeo e kgobokeditšwego mabakeng a dipeakanyo ke Khamphani ya Maemafofane ya Afrika Borwa (ACSA). Sengwalwanyaki šišo se ahlaahlile mekgwa ya kakanyo ye e fapafapanego ka bophara. Dipharologanyi tša go swana le maatla le bofokodi le go dirišega ga mekgwa di ile tša šomišwa. Magato a mangwe ao a tumilego kudu a kakanyo ye e nepagetšego a ile a ahlaahlwa ka nepo ya go kwešiša ka fao a ka šomišwago ka gona ka tshekatshekong ya go šoma ga mekgwa ye. Go hweditšwe gore mokgwa wa poelomorago wa go ba le diphošo tša ARIMA o phadile mekgwa ye mengwe ka moka, gwa latela mokgwa wa ARIMA. Dikutollo tše di sepelelana le dikutollo ka kakaretšo ka dingwaleng. Mo k gwa wa ANN o ka fela o fetišiša gomme se se bonagetše go dipoelo tša tlhahlo le dihlo pha t ša teko ya tshedimošo. Mekgwa ya nepagalo ya go ithuta ka metšhene e bontšhitše dipoelo tšeo di hlakantšwego tšeo di nago le kaonafalo ye kgolo go ye mengwe mekgwa ya go ela go phethagatšwa ga mešomo. Go ka phethwa ka gore mekgwa ya setlwaedi ya go akanya dipalopalo (ARIMA le mokgwa wa poelomorago wa go ba le diphošo tša ARIMA) e šomile bokaone go phala mekgwa ya go ithuta ka metšhene (ANN le SVM) ka mo go sehlopha se sa tshedimošo, go eya ka magato a nepagalo ya magato ao a šomišitšwego. Se se nyaka gore go dirwe dinyakišišo tše dingwe mabapi le go dirišega ga mekgwa ya go ithuta ka metšhene mabapi le go akanya molokoloko wa dinako, e lego seo se tlago thuša go kwešiša le go kaonafatša go šoma ga yona kgahlanong le mekgwa ya setlwaedi ya dipalopalo. / Decision Sciences / M. Sc. (Operations Research)

The proposed diploma thesis deals with the application of methodology and tools of inverse analysis for design of selected structural parameters using a fully probabilistic analysis to determine the level of its reliability. The method based on artificial neural networks is used to approximate the inverse function. The inverse analysis was carried out in two ways that differs in the method of obtaining reliability indicators. The structure analyzed in this work was an existing bridge. The year of construction is estimated approximately between the years 1955 to 1960. The bridge is located close to the Uherský Ostroh. It is a one-piece concrete slab made of MPD3 and MPD4 girders post-tensioned by tendons. Based on the 2006 and 2007 diagnostic surveys, laboratory tests, normative regulations and recommendations and, last but not least, sensitivity analyses, an inverse design of selected design parameters was performed for required limit states. Various load levels, different alternatives of design parameters and different neural network structures were studied.