Difference equation sensor characterization algorithms for two-thermocouple probes

The characterization of thermocouple sensors for temperature measurement in variable flow environments is a challenging problem. In this paper, novel difference equation-based algorithms are presented that allow in situ characterization of temperature measurement probes consisting of two-thermocouple sensors with differing time constants. Linear and non-linear least squares formulations of the characterization problem are introduced and compared in terms of their computational complexity, robustness to noise and statistical properties. With the aid of this analysis, least squares optimization procedures that yield unbiased estimates are identified. The main contribution of the paper is the development of a linear two-parameter generalized total least squares formulation of the sensor characterization problem. Monte-Carlo simulation results are used to support the analysis.

Biosorption of toxic chromium from aqueous phase by lignin: mechanism, effect of other metal ions and salts

Surface reaction methodology was implicated in the optimization of hexavalent chromium removal onto lignin with respect to the process parameters. The influence of altering the conditions for removal of chromium(VI), for instance; solution pH, ionic strength, initial concentration, the dose of biosorbent, presence of other metals (Zn and Cu), presence of salts and biosorption-desorption studies, were investigated. It was found that the biosorption capacity of lignin depends on solution pH, with a maximum biosorption capacity for chromium at pH 2. Experimental equilibrium data were fitted to five different isotherm models by non-linear regression method, however, the biosorption equilibrium data were well interpreted by the Freundlich isotherm. The maximum biosorption capacities (q(max)) obtained using Dubinin-Radushkevich and Khan isotherms for Cr(VI) biosorption are 31.6 and 29.1 mg/g. respectively. Biosorption showed pseudo second order rate kinetics at different initial concentrations of Cr(VI). The intraparticle diffusion study indicated that film diffusion may be involved in the current study. The percentage removal of chromium on lignin decreased significantly in the presence of NaHCO3 and K2P2O7 salts. Desorption data revealed that nearly 70% of the Cr(VI) adsorbed on lignin could be desorbed using 0.1 M NaOH. It was evident that the biosorption mechanism involves the attraction of both hexavalent chromium (anionic) and trivalent chromium (cationic) onto the surface of lignin. (C) 2011 Elsevier B.V. All rights reserved.

Hierarchical Bayesian models in ecology: Reconstructing species interaction networks from non-homogeneous species abundance data

The relationships among organisms and their surroundings can be of immense complexity. To describe and understand an ecosystem as a tangled bank, multiple ways of interaction and their effects have to be considered, such as predation, competition, mutualism and facilitation. Understanding the resulting interaction networks is a challenge in changing environments, e.g. to predict knock-on effects of invasive species and to understand how climate change impacts biodiversity. The elucidation of complex ecological systems with their interactions will benefit enormously from the development of new machine learning tools that aim to infer the structure of interaction networks from field data. In the present study, we propose a novel Bayesian regression and multiple changepoint model (BRAM) for reconstructing species interaction networks from observed species distributions. The model has been devised to allow robust inference in the presence of spatial autocorrelation and distributional heterogeneity. We have evaluated the model on simulated data that combines a trophic niche model with a stochastic population model on a 2-dimensional lattice, and we have compared the performance of our model with L1-penalized sparse regression (LASSO) and non-linear Bayesian networks with the BDe scoring scheme. In addition, we have applied our method to plant ground coverage data from the western shore of the Outer Hebrides with the objective to infer the ecological interactions. (C) 2012 Elsevier B.V. All rights reserved.

Implementation of a non-orthogonal model for the finite element simulation of textile composite draping

In the pursuit of producing high quality, low-cost composite aircraft structures, out-of-autoclave manufacturing processes for textile reinforcements are being simulated with increasing accuracy. This paper focuses on the continuum-based, finite element modelling of textile composites as they deform during the draping process. A non-orthogonal constitutive model tracks yarn orientations within a material subroutine developed for Abaqus/Explicit, resulting in the realistic determination of fabric shearing and material draw-in. Supplementary material characterisation was experimentally performed in order to define the tensile and non-linear shear behaviour accurately. The validity of the finite element model has been studied through comparison with similar research in the field and the experimental lay-up of carbon fibre textile reinforcement over a tool with double curvature geometry, showing good agreement.

Improving parameter learning of Bayesian nets from incomplete data

This paper addresses the estimation of parameters of a Bayesian network from incomplete data. The task is usually tackled by running the Expectation-Maximization (EM) algorithm several times in order to obtain a high log-likelihood estimate. We argue that choosing the maximum log-likelihood estimate (as well as the maximum penalized log-likelihood and the maximum a posteriori estimate) has severe drawbacks, being affected both by overfitting and model uncertainty. Two ideas are discussed to overcome these issues: a maximum entropy approach and a Bayesian model averaging approach. Both ideas can be easily applied on top of EM, while the entropy idea can be also implemented in a more sophisticated way, through a dedicated non-linear solver. A vast set of experiments shows that these ideas produce significantly better estimates and inferences than the traditional and widely used maximum (penalized) log-likelihood and maximum a posteriori estimates. In particular, if EM is adopted as optimization engine, the model averaging approach is the best performing one; its performance is matched by the entropy approach when implemented using the non-linear solver. The results suggest that the applicability of these ideas is immediate (they are easy to implement and to integrate in currently available inference engines) and that they constitute a better way to learn Bayesian network parameters.

Computational scheduling methods for integrating plug-in electric vehicles with power systems: a review

Traditional internal combustion engine vehicles are a major contributor to global greenhouse gas emissions and other air pollutants, such as particulate matter and nitrogen oxides. If the tail pipe point emissions could be managed centrally without reducing the commercial and personal user functionalities, then one of the most attractive solutions for achieving a significant reduction of emissions in the transport sector would be the mass deployment of electric vehicles. Though electric vehicle sales are still hindered by battery performance, cost and a few other technological bottlenecks, focused commercialisation and support from government policies are encouraging large scale electric vehicle adoptions. The mass proliferation of plug-in electric vehicles is likely to bring a significant additional electric load onto the grid creating a highly complex operational problem for power system operators. Electric vehicle batteries also have the ability to act as energy storage points on the distribution system. This double charge and storage impact of many uncontrollable small kW loads, as consumers will want maximum flexibility, on a distribution system which was originally not designed for such operations has the potential to be detrimental to grid balancing. Intelligent scheduling methods if established correctly could smoothly integrate electric vehicles onto the grid. Intelligent scheduling methods will help to avoid cycling of large combustion plants, using expensive fossil fuel peaking plant, match renewable generation to electric vehicle charging and not overload the distribution system causing a reduction in power quality. In this paper, a state-of-the-art review of scheduling methods to integrate plug-in electric vehicles are reviewed, examined and categorised based on their computational techniques. Thus, in addition to various existing approaches covering analytical scheduling, conventional optimisation methods (e.g. linear, non-linear mixed integer programming and dynamic programming), and game theory, meta-heuristic algorithms including genetic algorithm and particle swarm optimisation, are all comprehensively surveyed, offering a systematic reference for grid scheduling considering intelligent electric vehicle integration.

Optimal Scheduling Methods to Integrate Plug-In Electric Vehicles with the Power System: A Review

The introduction of the Tesla in 2008 has demonstrated to the public of the potential of electric vehicles in terms of reducing fuel consumption and green-house gas from the transport sector. It has brought electric vehicles back into the spotlight worldwide at a moment when fossil fuel prices were reaching unexpected high due to increased demand and strong economic growth. The energy storage capabilities from of fleets of electric vehicles as well as the potentially random discharging and charging offers challenges to the grid in terms of operation and control. Optimal scheduling strategies are key to integrating large numbers of electric vehicles and the smart grid. In this paper, state-of-the-art optimization methods are reviewed on scheduling strategies for the grid integration with electric vehicles. The paper starts with a concise introduction to analytical charging strategies, followed by a review of a number of classical numerical optimization methods, including linear programming, non-linear programming, dynamic programming as well as some other means such as queuing theory. Meta-heuristic techniques are then discussed to deal with the complex, high-dimensional and multi-objective scheduling problem associated with stochastic charging and discharging of electric vehicles. Finally, future research directions are suggested.

An integrated approach for real-time model-based state-of-charge estimation of lithium-ion batteries

Lithium-ion batteries have been widely adopted in electric vehicles (EVs), and accurate state of charge (SOC) estimation is of paramount importance for the EV battery management system. Though a number of methods have been proposed, the SOC estimation for Lithium-ion batteries, such as LiFePo4 battery, however, faces two key challenges: the flat open circuit voltage (OCV) vs SOC relationship for some SOC ranges and the hysteresis effect. To address these problems, an integrated approach for real-time model-based SOC estimation of Lithium-ion batteries is proposed in this paper. Firstly, an auto-regression model is adopted to reproduce the battery terminal behaviour, combined with a non-linear complementary model to capture the hysteresis effect. The model parameters, including linear parameters and non-linear parameters, are optimized off-line using a hybrid optimization method that combines a meta-heuristic method (i.e., the teaching learning based optimization method) and the least square method. Secondly, using the trained model, two real-time model-based SOC estimation methods are presented, one based on the real-time battery OCV regression model achieved through weighted recursive least square method, and the other based on the state estimation using the extended Kalman filter method (EKF). To tackle the problem caused by the flat OCV-vs-SOC segments when the OCV-based SOC estimation method is adopted, a method combining the coulombic counting and the OCV-based method is proposed. Finally, modelling results and SOC estimation results are presented and analysed using the data collected from LiFePo4 battery cell. The results confirmed the effectiveness of the proposed approach, in particular the joint-EKF method.

A hybrid harmony search with arithmetic crossover operation for economic dispatch

Economic dispatch (ED) problems often exhibit non-linear, non-convex characteristics due to the valve point effects. Further, various constraints and factors, such as prohibited operation zones, ramp rate limits and security constraints imposed by the generating units, and power loss in transmission make it even more challenging to obtain the global optimum using conventional mathematical methods. Meta-heuristic approaches are capable of solving non-linear, non-continuous and non-convex problems effectively as they impose no requirements on the optimization problems. However, most methods reported so far mainly focus on a specific type of ED problems, such as static or dynamic ED problems. This paper proposes a hybrid harmony search with arithmetic crossover operation, namely ACHS, for solving five different types of ED problems, including static ED with valve point effects, ED with prohibited operating zones, ED considering multiple fuel cells, combined heat and power ED, and dynamic ED. In this proposed ACHS, the global best information and arithmetic crossover are used to update the newly generated solution and speed up the convergence, which contributes to the algorithm exploitation capability. To balance the exploitation and exploration capabilities, the opposition based learning (OBL) strategy is employed to enhance the diversity of solutions. Further, four commonly used crossover operators are also investigated, and the arithmetic crossover shows its efficiency than the others when they are incorporated into HS. To make a comprehensive study on its scalability, ACHS is first tested on a group of benchmark functions with a 100 dimensions and compared with several state-of-the-art methods. Then it is used to solve seven different ED cases and compared with the results reported in literatures. All the results confirm the superiority of the ACHS for different optimization problems.

An efficient harmony search with new pitch adjustment for dynamic economic dispatch

A simple yet efficient harmony search (HS) method with a new pitch adjustment rule (NPAHS) is proposed for dynamic economic dispatch (DED) of electrical power systems, a large-scale non-linear real time optimization problem imposed by a number of complex constraints. The new pitch adjustment rule is based on the perturbation information and the mean value of the harmony memory, which is simple to implement and helps to enhance solution quality and convergence speed. A new constraint handling technique is also developed to effectively handle various constraints in the DED problem, and the violation of ramp rate limits between the first and last scheduling intervals that is often ignored by existing approaches for DED problems is effectively eliminated. To validate the effectiveness, the NPAHS is first tested on 10 popular benchmark functions with 100 dimensions, in comparison with four HS variants and five state-of-the-art evolutionary algorithms. Then, NPAHS is used to solve three 24-h DED systems with 5, 15 and 54 units, which consider the valve point effects, transmission loss, emission and prohibited operating zones. Simulation results on all these systems show the scalability and superiority of the proposed NPAHS on various large scale problems.

Study of oxidation and non-enzymatic glycosylation posttranslational modifications using a proteomic approach

A glicosilação não-enzimática e o stress oxidativo representam dois processos importantes visto desempenharem um papel importante no que respeita às complicações de vários processos patofisiológicos. No presente, a associação entre a glicosilação não-enzimática e a oxidação de proteínas é reconhecida como sendo um dos principais responsáveis pela acumulação de proteínas não-funcionais que, por sua vez, promove uma contínua sensibilização para um aumento do stress oxidativo ao nível celular. Embora esteja disponível bastante informação no que respeita aos dois processos e suas consequências ao nível estrutural e funcional, permanecem questões por esclarecer acerca do que se desenvolve ao nível molecular. Com o objectivo de contribuir para uma melhor compreensão da relação entre a glicosilação não-enzimática e a oxidação, proteínas modelo (albumina, insulina e histonas H2B e H1) foram submetidas a sistemas in vitro de glicosilação não-enzimática e oxidação em condições controladas e durante um período de tempo específico. A identificação dos locais de glicosilação e oxidação foi realizada através de uma abordagem proteómica, na qual após digestão enzimática se procedeu à análise por cromatografia líquida acoplada a espectrometria de massa tandem (MALDI-TOF/TOF). Esta abordagem permitiu a obtenção de elevadas taxas de cobertura das sequências proteicas, permitindo a identificação dos locais preferenciais de glicosilação e oxidação nas diferentes proteínas estudadas. Como esperado, os resíduos de lisina foram os preferencialmente glicosilados. No que respeita à oxidação, além das modificações envolvendo hidroxilações e adições de oxigénio, foram identificadas deamidações, carbamilações e conversões oxidativas específicas de vários aminoácidos. No geral, os resíduos mais afectados pela oxidação foram os resíduos de cisteína, metionina, triptofano, tirosina, prolina, lisina e fenilalanina. Ao longo do período de tempo estudado, os resultados indicaram que a oxidação teve início em zonas expostas da proteína e/ou localizadas na vizinhança de resíduos de cisteína e metionina, ao invés de exibir um comportamente aleatório, ocorrendo de uma forma nãolinear por sua vez dependente da estabilidade conformacional da proteína. O estudo ao longo do tempo mostrou igualmente que, no caso das proteínas préglicosiladas, a oxidação das mesmas ocorreu de forma mais rápida e acentuada, sugerindo que as alterações estruturais induzidas pela glicosilação promovem um estado pro-oxidativo. No caso das proteínas pré-glicosiladas e oxidadas, foi identificado um maior número de modificações oxidativas assim como de resíduos modificados na vizinhança de resíduos glicosilados. Com esta abordagem é realizada uma importante contribuição na investigação das consequências do dano ‘glico-oxidativo’ em proteínas ao nível molecular através da combinação da espectrometria de massa e da bioinformática.

Biaxial seismic behaviour of reinforced concrete columns

A análise dos efeitos dos sismos mostra que a investigação em engenharia sísmica deve dar especial atenção à avaliação da vulnerabilidade das construções existentes, frequentemente desprovidas de adequada resistência sísmica tal como acontece em edifícios de betão armado (BA) de muitas cidades em países do sul da Europa, entre os quais Portugal. Sendo os pilares elementos estruturais fundamentais na resistência sísmica dos edifícios, deve ser dada especial atenção à sua resposta sob ações cíclicas. Acresce que o sismo é um tipo de ação cujos efeitos nos edifícios exige a consideração de duas componentes horizontais, o que tem exigências mais severas nos pilares comparativamente à ação unidirecional. Assim, esta tese centra-se na avaliação da resposta estrutural de pilares de betão armado sujeitos a ações cíclicas horizontais biaxiais, em três linhas principais. Em primeiro lugar desenvolveu-se uma campanha de ensaios para o estudo do comportamento cíclico uniaxial e biaxial de pilares de betão armado com esforço axial constante. Para tal foram construídas quatro séries de pilares retangulares de betão armado (24 no total) com diferentes características geométricas e quantidades de armadura longitudinal, tendo os pilares sido ensaiados para diferentes histórias de carga. Os resultados experimentais obtidos são analisados e discutidos dando particular atenção à evolução do dano, à degradação de rigidez e resistência com o aumento das exigências de deformação, à energia dissipada, ao amortecimento viscoso equivalente; por fim é proposto um índice de dano para pilares solicitados biaxialmente. De seguida foram aplicadas diferentes estratégias de modelação não-linear para a representação do comportamento biaxial dos pilares ensaiados, considerando não-linearidade distribuída ao longo dos elementos ou concentrada nas extremidades dos mesmos. Os resultados obtidos com as várias estratégias de modelação demonstraram representar adequadamente a resposta em termos das curvas envolventes força-deslocamento, mas foram encontradas algumas dificuldades na representação da degradação de resistência e na evolução da energia dissipada. Por fim, é proposto um modelo global para a representação do comportamento não-linear em flexão de elementos de betão armado sujeitos a ações biaxiais cíclicas. Este modelo tem por base um modelo uniaxial conhecido, combinado com uma função de interação desenvolvida com base no modelo de Bouc- Wen. Esta função de interação foi calibrada com recurso a técnicas de otimização e usando resultados de uma série de análises numéricas com um modelo refinado. É ainda demonstrada a capacidade do modelo simplificado em reproduzir os resultados experimentais de ensaios biaxiais de pilares.

Optimal alarms systems and its application to financial time series

This thesis focuses on the application of optimal alarm systems to non linear time series models. The most common classes of models in the analysis of real-valued and integer-valued time series are described. The construction of optimal alarm systems is covered and its applications explored. Considering models with conditional heteroscedasticity, particular attention is given to the Fractionally Integrated Asymmetric Power ARCH, FIAPARCH(p; d; q) model and an optimal alarm system is implemented, following both classical and Bayesian methodologies. Taking into consideration the particular characteristics of the APARCH(p; q) representation for financial time series, the introduction of a possible counterpart for modelling time series of counts is proposed: the INteger-valued Asymmetric Power ARCH, INAPARCH(p; q). The probabilistic properties of the INAPARCH(1; 1) model are comprehensively studied, the conditional maximum likelihood (ML) estimation method is applied and the asymptotic properties of the conditional ML estimator are obtained. The final part of the work consists on the implementation of an optimal alarm system to the INAPARCH(1; 1) model. An application is presented to real data series.

A soft-computing approach for non-invasive temperature estimation

The domain of thermal therapies applications can be improved with the development of accurate non-invasive timespatial temperature models. These models should represent the non-linear tissue thermal behaviour and be capable of tracking temperature at both time-instant and spatial position. If such estimators exist then efficient controllers for the therapeutic instrumentation could be developed, and the desired safety and effectiveness reached.

Simulated annealing metaheuristic to solve the optimal power flow

The optimal power flow problem has been widely studied in order to improve power systems operation and planning. For real power systems, the problem is formulated as a non-linear and as a large combinatorial problem. The first approaches used to solve this problem were based on mathematical methods which required huge computational efforts. Lately, artificial intelligence techniques, such as metaheuristics based on biological processes, were adopted. Metaheuristics require lower computational resources, which is a clear advantage for addressing the problem in large power systems. This paper proposes a methodology to solve optimal power flow on economic dispatch context using a Simulated Annealing algorithm inspired on the cooling temperature process seen in metallurgy. The main contribution of the proposed method is the specific neighborhood generation according to the optimal power flow problem characteristics. The proposed methodology has been tested with IEEE 6 bus and 30 bus networks. The obtained results are compared with other wellknown methodologies presented in the literature, showing the effectiveness of the proposed method.