Optimal reactive power flow via the modified barrier Lagrangian function approach

A new approach called the Modified Barrier Lagrangian Function (MBLF) to solve the Optimal Reactive Power Flow problem is presented. In this approach, the inequality constraints are treated by the Modified Barrier Function (MBF) method, which has a finite convergence property: i.e. the optimal solution in the MBF method can actually be in the bound of the feasible set. Hence, the inequality constraints can be precisely equal to zero. Another property of the MBF method is that the barrier parameter does not need to be driven to zero to attain the solution. Therefore, the conditioning of the involved Hessian matrix is greatly enhanced. In order to show this, a comparative analysis of the numeric conditioning of the Hessian matrix of the MBLF approach, by the decomposition in singular values, is carried out. The feasibility of the proposed approach is also demonstrated with comparative tests to Interior Point Method (IPM) using various IEEE test systems and two networks derived from Brazilian generation/transmission system. The results show that the MBLF method is computationally more attractive than the IPM in terms of speed, number of iterations and numerical conditioning. (C) 2011 Elsevier B.V. All rights reserved.

A New Approach for Evaluating the BER of a Multirate, Multiclass OFFH-CDMA System

In this letter, we propose a new approach to evaluate the bit error rate (BER) of a multirate, multiclass optical fast frequency hopping code-division multiple-access (OFFH-CDMA) system. This proposed approach does not require knowledge of the generated users' code sequences, which makes the system analysis straightforward. Furthermore, the presented formalism can also be successfully applied to most multi-weight multi-length family of codes, as long as the corresponding code parameters are employed.

A Bayesian Approach for Decision Making on the Identification of Genes with Different Expression Levels: An Application to Escherichia coli Bacterium Data

A common interest in gene expression data analysis is to identify from a large pool of candidate genes the genes that present significant changes in expression levels between a treatment and a control biological condition. Usually, it is done using a statistic value and a cutoff value that are used to separate the genes differentially and nondifferentially expressed. In this paper, we propose a Bayesian approach to identify genes differentially expressed calculating sequentially credibility intervals from predictive densities which are constructed using the sampled mean treatment effect from all genes in study excluding the treatment effect of genes previously identified with statistical evidence for difference. We compare our Bayesian approach with the standard ones based on the use of the t-test and modified t-tests via a simulation study, using small sample sizes which are common in gene expression data analysis. Results obtained report evidence that the proposed approach performs better than standard ones, especially for cases with mean differences and increases in treatment variance in relation to control variance. We also apply the methodologies to a well-known publicly available data set on Escherichia coli bacterium.

A new approach for modeling and control of nonlinear systems via norm-bounded linear differential inclusions

A systematic approach to model nonlinear systems using norm-bounded linear differential inclusions (NLDIs) is proposed in this paper. The resulting NLDI model is suitable for the application of linear control design techniques and, therefore, it is possible to fulfill certain specifications for the underlying nonlinear system, within an operating region of interest in the state-space, using a linear controller designed for this NLDI model. Hence, a procedure to design a dynamic output feedback controller for the NLDI model is also proposed in this paper. One of the main contributions of the proposed modeling and control approach is the use of the mean-value theorem to represent the nonlinear system by a linear parameter-varying model, which is then mapped into a polytopic linear differential inclusion (PLDI) within the region of interest. To avoid the combinatorial problem that is inherent of polytopic models for medium- and large-sized systems, the PLDI is transformed into an NLDI, and the whole process is carried out ensuring that all trajectories of the underlying nonlinear system are also trajectories of the resulting NLDI within the operating region of interest. Furthermore, it is also possible to choose a particular structure for the NLDI parameters to reduce the conservatism in the representation of the nonlinear system by the NLDI model, and this feature is also one important contribution of this paper. Once the NLDI representation of the nonlinear system is obtained, the paper proposes the application of a linear control design method to this representation. The design is based on quadratic Lyapunov functions and formulated as search problem over a set of bilinear matrix inequalities (BMIs), which is solved using a two-step separation procedure that maps the BMIs into a set of corresponding linear matrix inequalities. Two numerical examples are given to demonstrate the effectiveness of the proposed approach.

Color texture analysis based on fractal descriptors

Color texture classification is an important step in image segmentation and recognition. The color information is especially important in textures of natural scenes, such as leaves surfaces, terrains models, etc. In this paper, we propose a novel approach based on the fractal dimension for color texture analysis. The proposed approach investigates the complexity in R, G and B color channels to characterize a texture sample. We also propose to study all channels in combination, taking into consideration the correlations between them. Both these approaches use the volumetric version of the Bouligand-Minkowski Fractal Dimension method. The results show a advantage of the proposed method over other color texture analysis methods. (C) 2011 Elsevier Ltd. All rights reserved.

Efficient Kernel Computation for Volterra Filter Structure Evaluation

Despite their generality, conventional Volterra filters are inadequate for some applications, due to the huge number of parameters that may be needed for accurate modelling. When a state-space model of the target system is known, this can be assessed by computing its kernels, which also provides valuable information for choosing an adequate alternate Volterra filter structure, if necessary, and is useful for validating parameter estimation procedures. In this letter, we derive expressions for the kernels by using the Carleman bilinearization method, for which an efficient algorithm is given. Simulation results are presented, which confirm the usefulness of the proposed approach.

Linear matrix inequality-based robust model predictive control for time-delayed systems

This work addresses the solution to the problem of robust model predictive control (MPC) of systems with model uncertainty. The case of zone control of multi-variable stable systems with multiple time delays is considered. The usual approach of dealing with this kind of problem is through the inclusion of non-linear cost constraint in the control problem. The control action is then obtained at each sampling time as the solution to a non-linear programming (NLP) problem that for high-order systems can be computationally expensive. Here, the robust MPC problem is formulated as a linear matrix inequality problem that can be solved in real time with a fraction of the computer effort. The proposed approach is compared with the conventional robust MPC and tested through the simulation of a reactor system of the process industry.

Class-specific metrics for multidimensional data projection applied to CBIR

Content-based image retrieval is still a challenging issue due to the inherent complexity of images and choice of the most discriminant descriptors. Recent developments in the field have introduced multidimensional projections to burst accuracy in the retrieval process, but many issues such as introduction of pattern recognition tasks and deeper user intervention to assist the process of choosing the most discriminant features still remain unaddressed. In this paper, we present a novel framework to CBIR that combines pattern recognition tasks, class-specific metrics, and multidimensional projection to devise an effective and interactive image retrieval system. User interaction plays an essential role in the computation of the final multidimensional projection from which image retrieval will be attained. Results have shown that the proposed approach outperforms existing methods, turning out to be a very attractive alternative for managing image data sets.

Dirac fermions in strong electric field and quantum transport in graphene

Our previous results on the nonperturbative calculations of the mean current and of the energy-momentum tensor in QED with the T-constant electric field are generalized to arbitrary dimensions. The renormalized mean values are found, and the vacuum polarization contributions and particle creation contributions to these mean values are isolated in the large T limit; we also relate the vacuum polarization contributions to the one-loop effective Euler-Heisenberg Lagrangian. Peculiarities in odd dimensions are considered in detail. We adapt general results obtained in 2 + 1 dimensions to the conditions which are realized in the Dirac model for graphene. We study the quantum electronic and energy transport in the graphene at low carrier density and low temperatures when quantum interference effects are important. Our description of the quantum transport in the graphene is based on the so-called generalized Furry picture in QED where the strong external field is taken into account nonperturbatively; this approach is not restricted to a semiclassical approximation for carriers and does not use any statistical assumptions inherent in the Boltzmann transport theory. In addition, we consider the evolution of the mean electromagnetic field in the graphene, taking into account the backreaction of the matter field to the applied external field. We find solutions of the corresponding Dirac-Maxwell set of equations and with their help we calculate the effective mean electromagnetic field and effective mean values of the current and the energy-momentum tensor. The nonlinear and linear I-V characteristics experimentally observed in both low-and high-mobility graphene samples are quite well explained in the framework of the proposed approach, their peculiarities being essentially due to the carrier creation from the vacuum by the applied electric field. DOI: 10.1103/PhysRevD.86.125022

Paper-based diffusive gradients in thin films technique coupled to energy dispersive X-ray fluorescence spectrometry for the determination of labile Mn, Co, Ni, Cu, Zn and Pb in river water

The diffusive gradients in thin films (DGT) technique has shown enormous potential for labile metal monitoring in fresh water due to the preconcentration, time-integrated, matrix interference removal and speciation analytical features. In this work, the coupling of energy dispersive X-ray fluorescence (EDXRF) with paper-based DGT devices was evaluated for the direct determination of Mn, Co. Ni, Cu, Zn and Pb in fresh water. The DGT samplers were assembled with cellulose (Whatman 3 MM chromatography paper) as the diffusion layer and a cellulose phosphate ion exchange membrane (Whatman P 81 paper) as the binding agent. The diffusion coefficients of the analytes on 3 MM chromatography paper were calculated by deploying the DGT samplers in synthetic solutions containing 500 mu g L-1 of Mn. Co, Ni, Cu, Zn and Pb (4 L at pH 5.5 and ionic strength at 0.05 mol L-1). After retrieval, the DGT units were disassembled and the P81 papers were dried and analysed by EDXRF directly. The 3 MM chromatographic paper diffusion coefficients of the analytes ranged from 1.67 to 1.87 x 10(-6) cm(2) s(-1). The metal retention and phosphate group homogeneities on the P81 membrane was studied by a spot analysis with a diameter of 1 mm. The proposed approach (DGT-EDXRF coupling) was applied to determine the analytes at five sampling sites (48 h in situ deployment) on the Piracicaba river basin, and the results (labile fraction) were compared with 0.45 mu m dissolved fractions determined by synchrotron radiation-excited total reflection X-ray fluorescence (SR-TXRF). The limits of detection of DGT-EDXRF coupling for the analytes (from 7.5 to 26 mu g L-1) were similar to those obtained by the sensitive SR-TXRF technique (3.8 to 9.1 mu g L-1). (C) 2012 Elsevier B.V. All rights reserved.

Identifying regulational alterations in gene regulatory networks by state space representation of vector autoregressive models and variational annealing

Background: In the analysis of effects by cell treatment such as drug dosing, identifying changes on gene network structures between normal and treated cells is a key task. A possible way for identifying the changes is to compare structures of networks estimated from data on normal and treated cells separately. However, this approach usually fails to estimate accurate gene networks due to the limited length of time series data and measurement noise. Thus, approaches that identify changes on regulations by using time series data on both conditions in an efficient manner are demanded. Methods: We propose a new statistical approach that is based on the state space representation of the vector autoregressive model and estimates gene networks on two different conditions in order to identify changes on regulations between the conditions. In the mathematical model of our approach, hidden binary variables are newly introduced to indicate the presence of regulations on each condition. The use of the hidden binary variables enables an efficient data usage; data on both conditions are used for commonly existing regulations, while for condition specific regulations corresponding data are only applied. Also, the similarity of networks on two conditions is automatically considered from the design of the potential function for the hidden binary variables. For the estimation of the hidden binary variables, we derive a new variational annealing method that searches the configuration of the binary variables maximizing the marginal likelihood. Results: For the performance evaluation, we use time series data from two topologically similar synthetic networks, and confirm that our proposed approach estimates commonly existing regulations as well as changes on regulations with higher coverage and precision than other existing approaches in almost all the experimental settings. For a real data application, our proposed approach is applied to time series data from normal Human lung cells and Human lung cells treated by stimulating EGF-receptors and dosing an anticancer drug termed Gefitinib. In the treated lung cells, a cancer cell condition is simulated by the stimulation of EGF-receptors, but the effect would be counteracted due to the selective inhibition of EGF-receptors by Gefitinib. However, gene expression profiles are actually different between the conditions, and the genes related to the identified changes are considered as possible off-targets of Gefitinib. Conclusions: From the synthetically generated time series data, our proposed approach can identify changes on regulations more accurately than existing methods. By applying the proposed approach to the time series data on normal and treated Human lung cells, candidates of off-target genes of Gefitinib are found. According to the published clinical information, one of the genes can be related to a factor of interstitial pneumonia, which is known as a side effect of Gefitinib.

Development of an Autonomous Robot for Gas Storage Spheres Inspection

Inspection for corrosion of gas storage spheres at the welding seam lines must be done periodically. Until now this inspection is being done manually and has a high cost associated to it and a high risk of inspection personel injuries. The Brazilian Petroleum Company, Petrobras, is seeking cost reduction and personel safety by the use of autonomous robot technology. This paper presents the development of a robot capable of autonomously follow a welding line and transporting corrosion measurement sensors. The robot uses a pair of sensors each composed of a laser source and a video camera that allows the estimation of the center of the welding line. The mechanical robot uses four magnetic wheels to adhere to the sphere's surface and was constructed in a way that always three wheels are in contact with the sphere's metallic surface which guarantees enough magnetic atraction to hold the robot in the sphere's surface all the time. Additionally, an independently actuated table for attaching the corrosion inspection sensors was included for small position corrections. Tests were conducted at the laboratory and in a real sphere showing the validity of the proposed approach and implementation.

Designing piezoresistive plate-based sensors with distribution of piezoresistive material using topology optimization

Piezoresistive sensors are commonly made of a piezoresistive membrane attached to a flexible substrate, a plate. They have been widely studied and used in several applications. It has been found that the size, position and geometry of the piezoresistive membrane may affect the performance of the sensors. Based on this remark, in this work, a topology optimization methodology for the design of piezoresistive plate-based sensors, for which both the piezoresistive membrane and the flexible substrate disposition can be optimized, is evaluated. Perfect coupling conditions between the substrate and the membrane based on the `layerwise' theory for laminated plates, and a material model for the piezoresistive membrane based on the solid isotropic material with penalization model, are employed. The design goal is to obtain the configuration of material that maximizes the sensor sensitivity to external loading, as well as the stiffness of the sensor to particular loads, which depend on the case (application) studied. The proposed approach is evaluated by studying two distinct examples: the optimization of an atomic force microscope probe and a pressure sensor. The results suggest that the performance of the sensors can be improved by using the proposed approach.

On response time reduction of electrothermomechanical MEMS using topology optimization

Electrothermomechanical MEMS are essentially microactuators that operate based on the thermoelastic effect induced by the Joule heating of the structure. They can be easily fabricated and require relatively low excitation voltages. However, the actuation time of an electrothermomechanical microdevice is higher than the actuation times related to electrostatic and piezoelectric actuation principles. Thus, in this research, we propose an optimization framework based on the topology optimization method applied to transient problems, to design electrothermomechanical microactuators for response time reduction. The objective is to maximize the integral of the output displacement of the actuator, which is a function of time. The finite element equations that govern the time response of the actuators are provided. Furthermore, the Solid Isotropic Material with Penalization model and Sequential Linear Programming are employed. Finally, a smoothing filter is implemented to control the solution. Results aiming at two distinct applications suggest the proposed approach can provide more than 50% faster actuators. (C) 2012 Elsevier B.V. All rights reserved.

Nondetection Index of Anti-Islanding Passive Protection of Synchronous Distributed Generators

Synchronous distributed generators are prone to operate islanded after contingencies, which is usually not allowed due to safety and power-quality issues. Thus, there are several anti-islanding techniques; however, most of them present technical limitations so that they are likely to fail in certain situations. Therefore, it is important to quantify and determine whether the scheme under study is adequate or not. In this context, this paper proposes an index to evaluate the effectiveness of anti-islanding frequency-based relays commonly used to protect synchronous distributed generators. The method is based on the calculation of a numerical index that indicates the time period that the system is unprotected against islanding considering the global period of analysis. Although this index can precisely be calculated based on several electromagnetic transient simulations, a practical method is also proposed to calculate it directly from simple analytical formulas or lookup tables. The results have shown that the proposed approach can assist distribution engineers to assess and set anti-islanding protection schemes.