A unified neurofuzzy model for classification

This work proposes a unified neurofuzzy modelling scheme. To begin with, the initial fuzzy base construction method is based on fuzzy clustering utilising a Gaussian mixture model (GMM) combined with the analysis of covariance (ANOVA) decomposition in order to obtain more compact univariate and bivariate membership functions over the subspaces of the input features. The mean and covariance of the Gaussian membership functions are found by the expectation maximisation (EM) algorithm with the merit of revealing the underlying density distribution of system inputs. The resultant set of membership functions forms the basis of the generalised fuzzy model (GFM) inference engine. The model structure and parameters of this neurofuzzy model are identified via the supervised subspace orthogonal least square (OLS) learning. Finally, instead of providing deterministic class label as model output by convention, a logistic regression model is applied to present the classifier’s output, in which the sigmoid type of logistic transfer function scales the outputs of the neurofuzzy model to the class probability. Experimental validation results are presented to demonstrate the effectiveness of the proposed neurofuzzy modelling scheme.

Pseudoclassical description of scalar particle in non-abelian background and path-integral representations

Path-integral representations for a scalar particle propagator in non-Abelian external backgrounds are derived. To this aim, we generalize the procedure proposed by Gitman and Schvartsman of path-integral construction to any representation of SU(N) given in terms of antisymmetric generators. And for arbitrary representations of SU(N), we present an alternative construction by means of fermionic coherent states. From the path-integral representations we derive pseudoclassical actions for a scalar particle placed in non-Abelian backgrounds. These actions are classically analyzed and then quantized to prove their consistency.

Robust strictly positive real synthesis of polynomial segments for discrete time systems

By using the result of robust strictly positive real synthesis of polynomial segments for continuous time systems, it is proved that, for any two n-th order polynomials a(z) and b(z), the Schur stability of their convex combination is necessary and sufficient for the existence of an n-th order polynomial c(z) such that c(z)/a(z) and c(z)/b(z) are both strictly positive real. We also provide the construction method of c(z). Illustrative examples are provided to show the effectiveness of this method.

Fuzzy entropy from weak fuzzy subsethood measures

In this paper, we propose a new construction method for fuzzy and weak fuzzy subsethood measures based on the aggregation of implication operators. We study the desired properties of the implication operators in order to construct these measures. We also show the relationship between fuzzy entropy and weak fuzzy subsethood measures constructed by our method.

Three types of monotonicity of averaging functions

Monotonicity with respect to all arguments is fundamental to the definition of aggregation functions, which are one of the basic tools in knowledge-based systems. The functions known as means (or averages) are idempotent and typically are monotone, however there are many important classes of means that are non-monotone. Weak monotonicity was recently proposed as a relaxation of the monotonicity condition for averaging functions. In this paper we discuss the concepts of directional and cone monotonicity, and monotonicity with respect to majority of inputs and coalitions of inputs. We establish the relations between various kinds of monotonicity, and illustrate it on various examples. We also provide a construction method for cone monotone functions.

Performance comparison of abrasion resistant textile motorcycle clothing

 Falling at speed onto a tarmac surface during cycling can cause abrasion and laceration of the skin and body tissue. Motorcycle clothing designed to reduce or avoid this type of injury has traditionally been made of animal leather as it has well known resistance to abrasion. In the last 20 years there has been an emergence of textile clothing reinforced with high performance/tenacity fibres such as those made from polyamides, aramids, ultra high molecular weight polyethylene and liquid crystal. Almost no comparative work has been undertaken to provide insight into the level of protection these clothing layers can provide.
This work has used a CE standard test method to evaluate a number of abrasion resistant textile pant products and compare them with a leather race product. It analysed the protective fabric layer structure for mass, thickness, construction method and resistance to abrasion.
Structures manufactured from high tenacity fibres performed better than those from lower tenacity ones. Fabric construction method and mass per unit area were the two key variables in providing an abrasion protective layer. Structures manufactured from knitted para-aramid fibres performed better than their woven counterparts due to the method of fabric failure. Several well designed protective layers performed at a similar level to that of leather; however, most garments tested failed to meet the lower level European standard of abrasion resistance (CE level 1), which may put their wearer at risk in the advent of a collision.

Uncertainty handling using neural network-based prediction intervals for electrical load forecasting

The complexity and level of uncertainty present in operation of power systems have significantly grown due to penetration of renewable resources. These complexities warrant the need for advanced methods for load forecasting and quantifying uncertainties associated with forecasts. The objective of this study is to develop a framework for probabilistic forecasting of electricity load demands. The proposed probabilistic framework allows the analyst to construct PIs (prediction intervals) for uncertainty quantification. A newly introduced method, called LUBE (lower upper bound estimation), is applied and extended to develop PIs using NN (neural network) models. The primary problem for construction of intervals is firstly formulated as a constrained single-objective problem. The sharpness of PIs is treated as the key objective and their calibration is considered as the constraint. PSO (particle swarm optimization) enhanced by the mutation operator is then used to optimally tune NN parameters subject to constraints set on the quality of PIs. Historical load datasets from Singapore, Ottawa (Canada) and Texas (USA) are used to examine performance of the proposed PSO-based LUBE method. According to obtained results, the proposed probabilistic forecasting method generates well-calibrated and informative PIs. Furthermore, comparative results demonstrate that the proposed PI construction method greatly outperforms three widely used benchmark methods. © 2014 Elsevier Ltd.

Mixed-norm sparse representation for multi view face recognition

Face recognition with multiple views is a challenging research problem. Most of the existing works have focused on extracting shared information among multiple views to improve recognition. However, when the pose variation is too large or missing, 'shared information' may not be properly extracted, leading to poor recognition results. In this paper, we propose a novel method for face recognition with multiple view images to overcome the large pose variation and missing pose issue. By introducing a novel mixed norm, the proposed method automatically selects candidates from the gallery to best represent a group of highly correlated face images in a query set to improve classification accuracy. This mixed norm combines the advantages of both sparse representation based classification (SRC) and joint sparse representation based classification (JSRC). A trade off between the ℓ1-norm from SRC and ℓ2,1-norm from JSRC is introduced to achieve this goal. Due to this property, the proposed method decreases the influence when a face image is unseen and has large pose variation in the recognition process. And when some face images with a certain degree of unseen pose variation appear, this mixed norm will find an optimal representation for these query images based on the shared information induced from multiple views. Moreover, we also address an open problem in robust sparse representation and classification which is using ℓ1-norm on the loss function to achieve a robust solution. To solve this formulation, we derive a simple, yet provably convergent algorithm based on the powerful alternative directions method of multipliers (ADMM) framework. We provide extensive comparisons which demonstrate that our method outperforms other state-of-the-arts algorithms on CMU-PIE, Yale B and Multi-PIE databases for multi-view face recognition.

Inference in differences-in-differences with few treated groups and heteroskedasticity

Differences-in-Differences (DID) is one of the most widely used identification strategies in applied economics. However, how to draw inferences in DID models when there are few treated groups remains an open question. We show that the usual inference methods used in DID models might not perform well when there are few treated groups and errors are heteroskedastic. In particular, we show that when there is variation in the number of observations per group, inference methods designed to work when there are few treated groups tend to (under-) over-reject the null hypothesis when the treated groups are (large) small relative to the control groups. This happens because larger groups tend to have lower variance, generating heteroskedasticity in the group x time aggregate DID model. We provide evidence from Monte Carlo simulations and from placebo DID regressions with the American Community Survey (ACS) and the Current Population Survey (CPS) datasets to show that this problem is relevant even in datasets with large numbers of observations per group. We then derive an alternative inference method that provides accurate hypothesis testing in situations where there are few treated groups (or even just one) and many control groups in the presence of heteroskedasticity. Our method assumes that we can model the heteroskedasticity of a linear combination of the errors. We show that this assumption can be satisfied without imposing strong assumptions on the errors in common DID applications. With many pre-treatment periods, we show that this assumption can be relaxed. Instead, we provide an alternative inference method that relies on strict stationarity and ergodicity of the time series. Finally, we consider two recent alternatives to DID when there are many pre-treatment periods. We extend our inference methods to linear factor models when there are few treated groups. We also derive conditions under which a permutation test for the synthetic control estimator proposed by Abadie et al. (2010) is robust to heteroskedasticity and propose a modification on the test statistic that provided a better heteroskedasticity correction in our simulations.

Inference in differences-in-differences with few treated groups and heteroskedasticity

Differences-in-Differences (DID) is one of the most widely used identification strategies in applied economics. However, how to draw inferences in DID models when there are few treated groups remains an open question. We show that the usual inference methods used in DID models might not perform well when there are few treated groups and errors are heteroskedastic. In particular, we show that when there is variation in the number of observations per group, inference methods designed to work when there are few treated groups tend to (under-) over-reject the null hypothesis when the treated groups are (large) small relative to the control groups. This happens because larger groups tend to have lower variance, generating heteroskedasticity in the group x time aggregate DID model. We provide evidence from Monte Carlo simulations and from placebo DID regressions with the American Community Survey (ACS) and the Current Population Survey (CPS) datasets to show that this problem is relevant even in datasets with large numbers of observations per group. We then derive an alternative inference method that provides accurate hypothesis testing in situations where there are few treated groups (or even just one) and many control groups in the presence of heteroskedasticity. Our method assumes that we know how the heteroskedasticity is generated, which is the case when it is generated by variation in the number of observations per group. With many pre-treatment periods, we show that this assumption can be relaxed. Instead, we provide an alternative application of our method that relies on assumptions about stationarity and convergence of the moments of the time series. Finally, we consider two recent alternatives to DID when there are many pre-treatment groups. We extend our inference method to linear factor models when there are few treated groups. We also propose a permutation test for the synthetic control estimator that provided a better heteroskedasticity correction in our simulations than the test suggested by Abadie et al. (2010).

Controle alternativo de Colletotrichum acutatum agente causal da queda prematura dos frutos cítricos

A queda prematura dos frutos cítricos (QPFC), causada por Colletotrichum acutatum, dados os grandes prejuízos que têm causado aos produtores, constitui-se numa doença de grande importância econômica. O controle da doença é feito predominantemente mediante uso de fungicidas, que eleva o custo de produção e afeta negativamente o meio ambiente. Diante disso, este trabalho teve por objetivo buscar um método alternativo de controle da QPFC, mediante o uso de agentes de biocontrole ou de biofertilizantes. Diferentes concentrações de biofertilizantes (originários de duas fontes distintas e denominados de Bio1 e Bio 2); três isolados de Bacillus subtilis (ACB-69; 72 e 77) e três isolados de Trichoderma spp. (ACB-14; 37 e 39) foram testados, isoladamente ou em combinação, sob condições de laboratório, quanto à capacidade inibitória da germinação de conídios de C. acutatum. Estudaram-se, ainda, a produção de metabólitos termoestáveis por B. subtilis e o efeito sobre a germinação do patógeno. Quinze isolados de B. subtilis foram testados quanto à capacidade de prevenir a infecção por C. acutatum em flores destacadas de lima- ácida 'Tahiti' e, no campo, foram instalados dois experimentos, visando a testar ACBs e biofertilizantes no controle da doença. Verificou-se que o isolado ACB-72 (B. subtilis) e ACB-37 (T. pseudokoningii) foram os que mais inibiram a germinação do patógeno. Quanto à produção de metabólitos termoestáveis, ACB-69 e 77 foram os mais eficientes em produzir substâncias antifúngicas, e em quantidades suficientes para inibirem a germinação do patógeno. A mistura dos quatro isolados de Bacillus (ACBs: 69; 72; 77 e AP3) foi o que apresentou maior porcentagem de inibição (73%). Os biofertilizantes (Bio1 e Bio2), em concentrações acima de 10% e, quando em associação com isolados de Trichoderma spp., promoveram maiores inibições na germinação de C. acutatum. em testes com flores destacadas, verificou-se que, onde foram aplicados os ACBs 69; 76; 74 e 77, as porcentagens de pétalas sem sintomas de infecção por C. acutatum foram de 83; 92; 92 e 97%, respectivamente. Mediante avaliações a campo, verificou-se a potencialidade de B. subtilis e de biofertilizantes em controlar a doença.

Prediction of Soil Properties from the Concepts of Energy Transfer in Dynamic Penetration Tests

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Theoretical analysis of the structural deformation in Mn-doped BaTiO3

An alternative theoretical method to simulate the structural deformation induced by Mn-doping in BaTiO3 is proposed. The periodic quantum-mechanical method is based on density functional theory at B3LYP level. The structural models were obtained from Rietveld refinement of the undoped and Mn doped BaTiO3 X-ray diffraction data. This modelization gives access to the dopant General effect on the electronic structure. In fact, the influence of the doing element itself on the electronic configuration is barely local: therefore, it is not included in the simulation. The simplicity of the model makes it available for working within a wide range of materials.(C) 2004 Published bv Elsevier B.V.

Método colorimétrico para determinação de dióxido de nitrogênio atmosférico com preconcentração em coluna de c-18

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Eletrodo modificado em filme de paládio para a determinação voltamétrica de fosfito

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)