Tuning of fuzzy inference systems through unconstrained optimization techniques

This paper presents a new methodology for the adjustment of fuzzy inference systems. A novel approach, which uses unconstrained optimization techniques, is developed in order to adjust the free parameters of the fuzzy inference system, such as its intrinsic parameters of the membership function and the weights of the inference rules. This methodology is interesting, not only for the results presented and obtained through computer simulations, but also for its generality concerning to the kind of fuzzy inference system used. Therefore, this methodology is expandable either to the Mandani architecture or also to that suggested by Takagi-Sugeno. The validation of the presented methodology is accomplished through an estimation of time series. More specifically, the Mackey-Glass chaotic time series estimation is used for the validation of the proposed methodology.

A constructive heuristic algorithm to Short Term Transmission Network Expansion Planning

In this paper a method for solving the Short Term Transmission Network Expansion Planning (STTNEP) problem is presented. The STTNEP is a very complex mixed integer nonlinear programming problem that presents a combinatorial explosion in the search space. In this work we present a constructive heuristic algorithm to find a solution of the STTNEP of excellent quality. In each step of the algorithm a sensitivity index is used to add a circuit (transmission line or transformer) to the system. This sensitivity index is obtained solving the STTNEP problem considering as a continuous variable the number of circuits to be added (relaxed problem). The relaxed problem is a large and complex nonlinear programming and was solved through an interior points method that uses a combination of the multiple predictor corrector and multiple centrality corrections methods, both belonging to the family of higher order interior points method (HOIPM). Tests were carried out using a modified Carver system and the results presented show the good performance of both the constructive heuristic algorithm to solve the STTNEP problem and the HOIPM used in each step.

A branch-and-bound algorithm for the multi-stage transmission expansion planning

This work presents a branch-and-bound algorithm to solve the multi-stage transmission expansion planning problem. The well known transportation model is employed, nevertheless the algorithm can be extended to hybrid models or to more complex ones such as the DC model. Tests with a realistic power system were carried out in order to show the performance of the algorithm for the expansion plan executed for different time frames. © 2005 IEEE.

Rainfall representativeness at TRMM & GPM PR resolution

Precipitation radar on board the TRMM satellite was a milestone in the rainfall observation capability in the large scale. Stemming from TRMM the new mission GPM (Global Precipitation Measurement) is to overcome some TRMM shortcomings like the high level of the PR MDZ. However, for major problems like the PR horizontal resolution, significant improvements are not foreseeable. This papers investigates the impact of TRMM PR resolution on the structure of tropical rainfall. The issue is approached by both gradient analysis and texture verification. Results indicate that the impact maybe significant, affecting important applications like in NWP. © 2005 IEEE.

Genetic algorithm of chu and beasley for static and multistage transmission expansion planning

In this paper the genetic algorithm of Chu and Beasley (GACB) is applied to solve the static and multistage transmission expansion planning problem. The characteristics of the GACB, and some modifications that were done, to efficiently solve the problem described above are also presented. Results using some known systems show that the GACB is very efficient. To validate the GACB, we compare the results achieved using it with the results using other meta-heuristics like tabu-search, simulated annealing, extended genetic algorithm and hibrid algorithms. © 2006 IEEE.

Optimal Boussinesq model for shallow-water waves interacting with a microstructure

In this paper, we consider the propagation of water waves in a long-wave asymptotic regime, when the bottom topography is periodic on a short length scale. We perform a multiscale asymptotic analysis of the full potential theory model and of a family of reduced Boussinesq systems parametrized by a free parameter that is the depth at which the velocity is evaluated. We obtain explicit expressions for the coefficients of the resulting effective Korteweg-de Vries (KdV) equations. We show that it is possible to choose the free parameter of the reduced model so as to match the KdV limits of the full and reduced models. Hence the reduced model is optimal regarding the embedded linear weakly dispersive and weakly nonlinear characteristics of the underlying physical problem, which has a microstructure. We also discuss the impact of the rough bottom on the effective wave propagation. In particular, nonlinearity is enhanced and we can distinguish two regimes depending on the period of the bottom where the dispersion is either enhanced or reduced compared to the flat bottom case. © 2007 The American Physical Society.

Intelligent alarm processing

This paper presents the analysis that have been carried out in the alarm system of the DCRanger EMS. The intention of this study is to present the problem of alarm processing in electric energy control centers, its various aspects and operational difficulties due to operator needs. Some tests are produced in order to identify the desirable features an alarm system should possess in order to be of effective help in the operative duty. © 2006 IEEE.

Heuristic algorithm to solve the short term transmission network expansion planning

In this paper, a method for solving the short term transmission network expansion planning problem is presented. This is a very complex mixed integer nonlinear programming problem that presents a combinatorial explosion in the search space. In order to And a solution of excellent quality for this problem, a constructive heuristic algorithm is presented in this paper. In each step of the algorithm, a sensitivity index is used to add a circuit (transmission line or transformer) or a capacitor bank (fixed or variable) to the system. This sensitivity index is obtained solving the problem considering the numbers of circuits and capacitors banks to be added (relaxed problem), as continuous variables. The relaxed problem is a large and complex nonlinear programming and was solved through a higher order interior point method. The paper shows results of several tests that were performed using three well-known electric energy systems in order to show the possibility and the advantages of using the AC model. ©2007 IEEE.

Distribution network reconfiguration using an efficient evolutionary algorithm

Network reconfiguration is an important tool to optimize the operating conditions of a distribution system. This is accomplished modifying the network structure of distribution feeders by changing the open/close status of sectionalizing switches. This not only reduces the power losses, but also relieves the overloading of the network components. Network reconfiguration belongs to a complex family of problems because of their combinatorial nature and multiple constraints. This paper proposes a solution to this problem, using a specialized evolutionary algorithm, with a novel codification, and a brand new way of implement the genetic operators considering the problem characteristics. The algorithm is presented and tested in a real distribution system, showing excellent results and computational efficiency. © 2007 IEEE.

Optimal power flow via interior-exterior method

This paper presents a new approach to the resolution of the Optimal Power Flow problem. In this approach the inequality constraints are treated by the Modified Barrier and Primal-Dual Logarithmic Barrier methods. The inequality constraints are transformed into equalities by introducing positive auxiliary variables, which are perturbed by the barrier parameter. A Lagrangian function is associated with the modified problem. The first-order necessary conditions are applied to the Lagrangian, generating a nonlinear system which is solved by Newton's method. The perturbation of the auxiliary variables results in an expansion of the feasible set of the original problem, allowing the limits of the inequality constraints to be reached. Numerical tests on the Brazilian CESP and South-Southeast systems and a comparative test indicated that the new approach efficiently resolves of the Optimal Power Flow problem. © 2007 IEEE.

Transmission network expansion planning considering uncertainty in generation and demand

This paper presents a mathematical model and a methodology to solve a transmission network expansion planning problem considering uncertainty in demand and generation. The methodology used to solve the problem, finds the optimal transmission network expansion plan that allows the power system to operate adequately in an environment with uncertainty. The model presented results in an optimization problem that is solved using a specialized genetic algorithm. The results obtained for known systems from the literature show that cheaper plans can be found satisfying the uncertainty in demand and generation. ©2008 IEEE.

Public-private partnerships for innovation and export development: the Irish model of development

Includes Bibliography

An enhanced genetic algorithm to solve the static and multistage transmission network expansion planning

An enhanced genetic algorithm (EGA) is applied to solve the long-term transmission expansion planning (LTTEP) problem. The following characteristics of the proposed EGA to solve the static and multistage LTTEP problem are presented, (1) generation of an initial population using fast, efficient heuristic algorithms, (2) better implementation of the local improvement phase and (3) efficient solution of linear programming problems (LPs). Critical comparative analysis is made between the proposed genetic algorithm and traditional genetic algorithms. Results using some known systems show that the proposed EGA presented higher efficiency in solving the static and multistage LTTEP problem, solving a smaller number of linear programming problems to find the optimal solutions and thus finding a better solution to the multistage LTTEP problem. Copyright © 2012 Luis A. Gallego et al.

Evaluación del impacto de una intervención educativa en atención farmacéutica y farmacia clínica

Introduction: The World Health Organization considers pharmaceutical care (PC) of fundamental importance for the patient and the community. Its exercise requires knowledge and skills, which can be acquired in academic and/or continuing educations, credited for effectiveness and impact evaluation. However, few manuscripts in the literature have showed the contribution of the educational interventions on the knowledge, skill and attitude of students and professionals who participate in scientifi c events related to PC. Objective: To evaluate the impact of an educational intervention (EI), and its degree of satisfaction, to pharmacists and pharmacy students. Method: A quasi-experimental study was performed, through an extension course with 40 hours of lectures approaching issues related to PC and clinical pharmacy (CP). Participants answered a survey which was handed out before and after the EI. The statistic tests of Sinais and Mann-Whitney were applied to evaluate the EI signifi cance. Results: Participants (n= 49) were mostly (n= 34) students and performing activities related to PC and CP (n= 20). Statistics differences, before and after the EI, were found in the scores of knowledge, skill and attitude (p <0.001). The evaluated item which showed the most improvement was the last one. Most (n= 30) had exceeded or met their expectations (n= 19). Conclusions: The analysis of the data led us to conclude that an EI of 40 h/week about knowledge, skill and attitude in PC using traditional methods, improves knowledge and problem-solving skills of participants. ©2012 Ediciones Mayo, S.A. All rights reserved.

Qualidade de vida de crianças e adolescentes asmáticos: Sua relação com estratégias de enfrentamento materno

Objective: To evaluate the quality of life of asthmatic children and adolescents, its relation with sociodemographic and clinical variables, and maternal coping strategies. Methods: Cross-sectional study in which children and adolescents with asthma answered a quality of life questionnaire, and their mothers did the same with a coping scale. Results: Out of the 42 children and adolescents investigated, 74% were classified as having mild/severe persistent asthma; 19%, mild persistent asthma; and 7%, intermittent asthma. A total of 69% of the participants showed impaired quality of life with mean scores ranging between 4.7 and 3.5, with greater harm in the domain of symptoms (score=3.6). There was a significant association between maternal schooling and the general index of quality of life, whereas maternal coping strategies were not associated with the severity of asthma. A large number of strategies used by mothers to cope with their children's crises were related to the management of stressors or to religious practices, and the latter presented negative correlation with the children's quality of life general index, showing that mothers whose children had worse quality of life used more religious coping. Conclusions: Asthmatic children, particularly those with moderate/severe persistent asthma, showed significant alterations as to quality of life. The high percentage of mothers using religious strategies, particularly in face of more severe clinical conditions, seem to indicate that they feel powerless to act, thus requiring concrete and useful orientation to low income families.