We discuss a formulation for active example selection for function learning problems. This formulation is obtained by adapting Fedorov's optimal experiment design to the learning problem. We specifically show how to analytically derive example selection algorithms for certain well defined function classes. We then explore the behavior and sample complexity of such active learning algorithms. Finally, we view object detection as a special case of function learning and show how our formulation reduces to a useful heuristic to choose examples to reduce the generalization error.

Viscoelastic materials can today be found in a wide range of practical applications. In order to make efficient use of these materials in construction, it is of importance to know how they behave when subjected to dynamic load. Characterization of viscoelastic materials is therefore an important topic, that has received a lot of attention over the years. This thesis treats different methods for identifying the complex modulus of an viscoelastic material. The complex modulus is a frequency dependent material function, that describes the deformation of the material when subjected to stress. With knowledge of this and other material functions, it is possible to simulate and predict how the material behaves under different kinds of dynamic load. The complex modulus is often identified through wave propagation testing, where the viscoelastic material is subjected to some kind of load and the response then measured. Models describing the wave propagation in the setups are then needed. In order for the identification to be accurate, it is important that these models can describe the wave propagation in an adequate way. A statistical test quantity is therefore derived and used to evaluate the wave propagation models in this thesis. Both nonparametric and parametric identification of the complex modulus is considered in this thesis.  An important aspect of the identification is the accuracy of the estimates.  Theoretical expressions for the variance of the estimates are therefore derived, both for the nonparametric and the parametric identification. In order for the identification to be as accurate as possible, it is also important that the experimental data contains as much valuable information as possible. Different experimental conditions, such as sensor locations and choice of excitation, can influence the amount of information in the data. The procedure of determining optimal values for such design parameters is known as optimal experiment design. In this thesis, both optimal sensor locations and optimal excitation are considered.

Identifying the parameters with the largest influence on the predicted outputs of a model revealswhich parameters need to be known more precisely to reduce the overall uncertainty on themodel output. A large improvement of such models would result when uncertainties in the keymodel parameters are reduced. To achieve this, new experiments could be very helpful,especially if the measurements are taken at the spatio-temporal locations that allow estimate the parameters in an optimal way. After evaluating the methodologies available for optimal sensor location, a few observations were drawn. The method based on the Gram determinant evolution can report results not according to what should be expected. This method is strongly dependent of the sensitivity coefficients behaviour. The approach based on the maximum angle between subspaces, in some cases, produced more that one optimal solution. It was observed that this method depends on the magnitude of outputs values and report the measurement positions where the outputs reached their extrema values. The D-optimal design method produces number and locations of the optimal measurements and it depends strongly of the sensitivity coefficients, but mostly of their behaviours. In general it was observed that the measurements should be taken at the locations where the extrema values (sensitivity coefficients, POD modes and/or outputs values) are reached. Further improvements can be obtained when a reduced model of the system is employed. This is computationally less expensive and the best estimation of the parameter is obtained, even with experimental data contaminated with noise. A new approach to calculate the time coefficients belonging to an empirical approximator based on the POD-modes derived from experimental data is introduced. Additionally, an artificial neural network can be used to calculate the derivatives but only for systems without complex nonlinear behaviour. The latter two approximations are very valuable and useful especially if the model of the system is unknown.

No description available.

The battery is a component with significant impact on both the cost and environmental footprint of a full electric vehicle (EV). Consequently, there is a strong motivation to maximize its degree of utilization. Usage limits are enforced by the battery management system (BMS) to ensure safe operation and limit battery degradation. The limits tend to be conservative to account for uncertainty in battery state estimation as well as changes in the battery's characteristics due to aging. To improve the utilization degree, aging sensitive battery control is necessary. This refers to control that a) adjusts during the battery's life based on its state and b) balances the trade-off between utilization and degradation according to requirements from the specific application.  In state-of-the-art battery installations, only three signals are measured; current, voltage and temperature. However, the battery's behaviour is governed by other states that must be estimated such as its state-of-charge (SOC) or local concentrations and potentials. The BMS therefore relies on models to estimate states and to perform control actions. In order to realize points a) and b), the models that are used for state estimation and control must be updated onboard. An updated model can also serve the purpose of diagnosing the battery, since it reflects the changing properties of an aging battery. This thesis investigates identification of physics-based and empirical battery models from operational EV data. The work is divided into three main studies. 1) A global sensitivity analysis was performed on the parameters of a high-order physics-based model. Measured current profiles from real EV:s were used as input and the parameters' impact on both modelled cell voltage and other internal states was assessed. The study revealed that in order to excite all model parameters, an input with high current rates, large SOC span and longer charge or discharge periods was required. This was only present in the data set from an electric truck with few battery packs. Data sets from vehicles with more packs (electric bus) and limited SOC operating window (plug-in hybrid truck) excited fewer model parameters. 2) Empirical linear-parameter-varying (LPV) dynamic models were identified on driving data. Model parameters were formulated as functions of the measured temperature, current magnitude and estimated open circuit voltage (OCV). To handle the time-scale differences in battery voltage response, continuous-time system identification was employed. We concluded that the proposed models had superior predictive abilities compared to discrete and time-invariant counterparts.  3) Instead of using driving data to parametrize models, we also investigated the possibility to design the charging current in order to increase its information content about model parameters. This was formulated as an optimal control problem with charging speed and information content as objectives. To also take battery degradation into account, constraints on polarization was included. The results showed that parameter information can be increased without significant increase in charge time nor aging related stress. / Elekriska fordon utgör en allt större andel av världens fordonsflotta. Batteriet är en komponent med betydande påverkan både på fordonets kostnadoch dess miljö- och klimatpåverkan. Det är därför viktigt att försöka maximera batteriets utnytjandegrad. Användargränser upprätthålls av batterietsstyrsystem, såkallad BMS, för att garantera säker drift samt för att begränsabatteriets åldrande. Användargränserna tenderar att vara konservativa för attta höjd för osäkerhet i tillståndsestimeringen samt batteriets förändrade egenskaper under dess livstid. För att utöka utnyttjandegraden är ålderskänsligstyrning nödvändig. Med detta avses styrning som a) justeras under batterietslivstid och b) balancerar utnyttjande och prestanda på ett sätt som passar enspecifik applikation. Ombord på fordon mäts typiskt tre signaler; ström, spänning och temperatur. Batteriets beteende bestäms dock av andra tillstånd som måste estimeras, såsom dess laddnivåeller lokala koncentrationer och potentialer. BMS:enförlitar sig därför på modeller för att estimera interna tillstånd och utföra styrning. För att uppfylla punkterna a) och b) måste modellerna som användsuppdateras ombord i takt med att batteriet åldras. En uppdaterad modellkan också fungera som ett diagnostiskt verktyg eftersom det speglar batteriets förändrade egenskaper. Den här avhandlingen undersöker identifieringav fysikbaserade och empiriska modeller från kördata. Arbetet delas in i treseparata studier. 1) En global känslighetsanalys utfördes på parametrarna i en fysikbaseradmodell av hög ordning. Som inputsignal användes uppmätt ström från riktigaelfordon i drift. Parametrarnas effekt på både cellspänning och interna batteritillstånd analyserades. Studien visade att alla modellparametrar exciteradesav strömmen från ett helelektriskt fordon. Anledningen var att batteriernaanvändes inom ett brett SOC spann samt att den dragna strömmen var stor.I fordon med snävare SOC span och lägre strömmar var inte alla parametrarkänsliga. 2) Dynamiska parametervarierande modeller formulerades och identifierades från kördata. Den uppmätta temperaturen, samt strömmens storlekoch den estimerade tomgångsspänningen (OCV) användes till parameterberoenden. För att hantera skillnader i tidsskala mellan spänningssvarets olikakomponenter användes systemidentifiering i kontinuerlig tid. Vi kunde draslutsatsen att de föreslagna modellerna var överlägsna motsvarande diskretaoch konstanta modeller. 3) Istället för att använda kördata för att parametrisera modeller undersökte vi också möjligheten att designa laddförloppet för att öka dess informationsinnheåll. Detta formulerades som ett optimeringsproblem med laddtidoch informationsinnehåll i kostnadsfunktionen. För att även ta batteriets åldrande i beaktning, ansattses bivillkor på polariseringsspänningen. / <p>QC 20220516</p>

Fundação Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro / O presente trabalho aborda o problema de identificação de danos em uma estrutura a partir de sua resposta impulsiva. No modelo adotado, a integridade estrutural é continuamente descrita por um parâmetro de coesão. Sendo assim, o Modelo de Elementos Finitos (MEF) é utilizado para discretizar tanto o campo de deslocamentos, quanto o campo de coesão. O problema de identificação de danos é, então, definido como um problema de otimização, cujo objetivo é minimizar, em relação a um vetor de parâmetros nodais de coesão, um funcional definido a partir da diferença entre a resposta impulsiva experimental e a correspondente resposta prevista por um MEF da estrutura. A identificação de danos estruturais baseadas no domínio do tempo apresenta como vantagens a aplicabilidade em sistemas lineares e/ou com elevados níveis de amortecimento, além de apresentar uma elevada sensibilidade à presença de pequenos danos. Estudos numéricos foram realizados considerando-se um modelo de viga de Euler-Bernoulli simplesmente apoiada. Para a determinação do posicionamento ótimo do sensor de deslocamento e do número de pontos da resposta impulsiva, a serem utilizados no processo de identificação de danos, foi considerado o Projeto Ótimo de Experimentos. A posição do sensor e o número de pontos foram determinados segundo o critério D-ótimo. Outros critérios complementares foram também analisados. Uma análise da sensibilidade foi realizada com o intuito de identificar as regiões da estrutura onde a resposta é mais sensível à presença de um dano em um estágio inicial. Para a resolução do problema inverso de identificação de danos foram considerados os métodos de otimização Evolução Diferencial e Levenberg-Marquardt. Simulações numéricas, considerando-se dados corrompidos com ruído aditivo, foram realizadas com o intuito de avaliar a potencialidade da metodologia de identificação de danos, assim como a influência da posição do sensor e do número de dados considerados no processo de identificação. Com os resultados obtidos, percebe-se que o Projeto Ótimo de Experimentos é de fundamental importância para a identificação de danos. / The present work deals with the damage identification problem in mechanical structures from their impulse response. In the adopted model, the structural integrity is continually described by a cohesion parameter and the finite element model (FEM) is used to spatially discretize both the displacement and cohesion fields. The damage identification problem is then posed as an optimization one, whose objective is to minimize, with respect to the vector of nodal cohesion parameters, a functional based on the difference between the experimentally obtained impulse response and the corresponding one predicted by an FEM of the structure. The damage identification problem built on the time domain presents some advantages, as the applicability in linear systems with high levels of damping an/or closed spaced modes, and in nonlinear systems. Besides, the time domain approaches present high sensitivities to the presence of small damages. Numerical studies were carried out considering a simply supported Euler-Bernoulli beam. Optimal experiment design techniques were considered with the aim at determining the optimal position of the displacement sensor and also the number of points of the impulse response to be considered in the identification process. The Differential Evolution optimization method and the Levenberg-Marquardt method were considered to solve the inverse problem of damage identification. Numerical analysis were carried out in order to assess the influence, on the identification results, of noise in the synthetic experimental data, of the sensor position, and of the number of points retained in the impulse response. The presented results shown the potentiality of the proposed damage identification approach and also the importance of the optimal experiment design for the quality of the identification. al importance for the identification of damage.

Fundação Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro / O presente trabalho aborda o problema de identificação de danos em uma estrutura a partir de sua resposta impulsiva. No modelo adotado, a integridade estrutural é continuamente descrita por um parâmetro de coesão. Sendo assim, o Modelo de Elementos Finitos (MEF) é utilizado para discretizar tanto o campo de deslocamentos, quanto o campo de coesão. O problema de identificação de danos é, então, definido como um problema de otimização, cujo objetivo é minimizar, em relação a um vetor de parâmetros nodais de coesão, um funcional definido a partir da diferença entre a resposta impulsiva experimental e a correspondente resposta prevista por um MEF da estrutura. A identificação de danos estruturais baseadas no domínio do tempo apresenta como vantagens a aplicabilidade em sistemas lineares e/ou com elevados níveis de amortecimento, além de apresentar uma elevada sensibilidade à presença de pequenos danos. Estudos numéricos foram realizados considerando-se um modelo de viga de Euler-Bernoulli simplesmente apoiada. Para a determinação do posicionamento ótimo do sensor de deslocamento e do número de pontos da resposta impulsiva, a serem utilizados no processo de identificação de danos, foi considerado o Projeto Ótimo de Experimentos. A posição do sensor e o número de pontos foram determinados segundo o critério D-ótimo. Outros critérios complementares foram também analisados. Uma análise da sensibilidade foi realizada com o intuito de identificar as regiões da estrutura onde a resposta é mais sensível à presença de um dano em um estágio inicial. Para a resolução do problema inverso de identificação de danos foram considerados os métodos de otimização Evolução Diferencial e Levenberg-Marquardt. Simulações numéricas, considerando-se dados corrompidos com ruído aditivo, foram realizadas com o intuito de avaliar a potencialidade da metodologia de identificação de danos, assim como a influência da posição do sensor e do número de dados considerados no processo de identificação. Com os resultados obtidos, percebe-se que o Projeto Ótimo de Experimentos é de fundamental importância para a identificação de danos. / The present work deals with the damage identification problem in mechanical structures from their impulse response. In the adopted model, the structural integrity is continually described by a cohesion parameter and the finite element model (FEM) is used to spatially discretize both the displacement and cohesion fields. The damage identification problem is then posed as an optimization one, whose objective is to minimize, with respect to the vector of nodal cohesion parameters, a functional based on the difference between the experimentally obtained impulse response and the corresponding one predicted by an FEM of the structure. The damage identification problem built on the time domain presents some advantages, as the applicability in linear systems with high levels of damping an/or closed spaced modes, and in nonlinear systems. Besides, the time domain approaches present high sensitivities to the presence of small damages. Numerical studies were carried out considering a simply supported Euler-Bernoulli beam. Optimal experiment design techniques were considered with the aim at determining the optimal position of the displacement sensor and also the number of points of the impulse response to be considered in the identification process. The Differential Evolution optimization method and the Levenberg-Marquardt method were considered to solve the inverse problem of damage identification. Numerical analysis were carried out in order to assess the influence, on the identification results, of noise in the synthetic experimental data, of the sensor position, and of the number of points retained in the impulse response. The presented results shown the potentiality of the proposed damage identification approach and also the importance of the optimal experiment design for the quality of the identification. al importance for the identification of damage.