Design and real time implementation of three-phase three switches three levels Vienna rectifier based on intelligent controllers

This paper treats the control of multilevel rectifier by applying the soft computing technique represented by the fuzzy logic. This latest, provides an inexpensive solution for controlling the ill-known complex systems, while relying on the imitation of human reasoning. In our case, the proposed intelligent controller aims at eliminating the current line harmonics, guaranteing an improved power factor, reducing the output voltage ripple and ensuring the balance between the two voltages across the two capacitors of Vienna rectifier. In order to evaluate the performance of the proposed control for Vienna rectifier, a comparative study in real time via the dSPACE card 1104 has been carried out between the traditional approach based on the conventional controllers and the new method using an intelligent controller. The experimental results confirm that the modified control approach has guaranteed a good quality of source currents in phase with the grid voltages and operation of the studied system with a power factor very close to unity. Furthermore, only one fuzzy controller used has ensured the regulation of the DC bus voltage, the minimization of the undulations for the two partial voltages and the improvement of the error of the voltage balance for the Vienna rectifier.     

Blood smear analyzer for white blood cell counting: A hybrid microscopic image analyzing technique

Total count and differential count of leukocytes or white blood cells (WBC) in blood samples are very important pathological factors for diagnosing a disease. There are not enough pathological infrastructures in the remote places of India and other developing countries. The objective of this work is to design a system, compatible with telemedicine, for automatic calculation of the total count and differential count of WBC from the blood smear slides. Hemocytometer based WBC counting provides more accurate result than manual counting, but hemocytometer preparation process needs expertise. As this device is targeted for remote places, blood smear technique is adopted to reduce the overhead of the operator. In the proposed system, microscopic images of blood smear sample are processed to highlight the WBC for segmentation. Region segmentation procedure involves background scaling and redundant region elimination from the region set. After segmentation, the more accurate region boundary is restored by using gradient based region growing with neighbourhood influence. Individual regions are separately classified on the basis of shape, size, color and texture features independently using different fuzzy and non-fuzzy techniques. A final decision is taken by combining these classification results, which is a kind of hybridization. A set of rules has been generated for making final classification decision based on outputs from various classifiers. The sensitivity and specificity of the system are found to be 96.4% and 79.6%, respectively on a database of 150 blood smear slides collected from different health centres of Kolkata Municipal Corporation, Kolkata, India.     

Induced continuous Choquet integral operators and their application to group decision making

With respect to multi-attribute group decision making, in this study two induced continuous Choquet integral operators named as the induced continuous Choquet weighted averaging (ICCWA) operator and the induced continuous Choquet geometric mean (ICCGM) operator are defined, which reflect the interactive characteristics between elements. Meantime, some associated desirable properties are studied to provide assurance in applications. In order to globally reflect the interactions between elements, we further define the probabilistic generalized semivalue ICCWA (PGS-ICCWA) operator and the probabilistic generalized semivalue ICCGM (PGS-ICCGM) operator. If the information about the weights of experts and attributes is incompletely known, the models for the optimal fuzzy measures on experts set and on attribute set based on consistency principle and TOPSIS method are respectively established. Moreover, an approach to uncertain multi-attribute group decision making with incomplete weight information and interactive conditions is developed. Finally, a numerical example is provided to illustrate the practicality and feasibility of the developed procedure.     

Optimization of bone drilling parameters using grey-based fuzzy algorithm

In this article a modified algorithm (grey based fuzzy algorithm) is used to optimize multiple performance characteristics in drilling of bone. Experiments have been performed with different cutting conditions using full factorial design. The quality parameters considered are temperature, force and surface roughness. Grey relational analysis (GRA) coupled with fuzzy logic is employed to obtain a grey fuzzy reasoning grade (GFRG) combining all the quality characteristics. The highest GFRG is obtained for the feed rate of 40 mm/min and the speed of 500 rpm and is the optimal level. Analysis of variance (ANOVA) carried out to find the significance of parameters on multiple performance characteristics revealed that the feed rate has the highest contribution on GFRG followed by the spindle speed. The optimum level of the process parameters obtained is validated by the confirmation experiment.     

FUZZY RELATIONAL EQUATIONS: BRIDGING THEORY,METHODOLOGY AND PRACTICE

This study delivers a general overview of the theory and practice of fuzzy relational equations. We discuss various methods leading to the solutions of these equations starting from analytical approaches, moving through semi-analytic methods and finally elaborating on the neural-like style of finding solutions to relational constructs. The paper addresses important aspects of knowledge representation worked out by these equations and proposes a number of structural enhancements of the existing relational architectures.     

Tuning fuzzy PD and PI controllers using reinforcement learning

In this paper, we propose a new auto-tuning fuzzy PD and PI controllers using reinforcement Q-learning (QL) algorithm for SISO (single-input single-output) and TITO (two-input two-output) systems. We first, investigate the design parameters and settings of a typical class of Fuzzy PD (FPD) and Fuzzy PI (FPI) controllers: zero-order Takagi–Sugeno controllers with equidistant triangular membership functions for inputs, equidistant singleton membership functions for output, Larsen’s implication method, and average sum defuzzification method. Secondly, the analytical structures of these typical fuzzy PD and PI controllers are compared to their classical counterpart PD and PI controllers. Finally, the effectiveness of the proposed method is proven through simulation examples.     

Identification and classification of dynamic event tree scenarios via possibilistic clustering: Application to a steam generator tube rupture event

This paper illustrates a method to identify and classify scenarios generated in a dynamic event tree (DET) analysis. Identification and classification are carried out by means of an evolutionary possibilistic fuzzy C-means clustering algorithm which takes into account not only the final system states but also the timing of the events and the process evolution. An application is considered with regards to the scenarios generated following a steam generator tube rupture in a nuclear power plant. The scenarios are generated by the accident dynamic simulator (ADS), coupled to a RELAP code that simulates the thermo-hydraulic behavior of the plant and to an operators’ crew model, which simulates their cognitive and procedures-guided responses.A set of 60 scenarios has been generated by the ADS DET tool. The classification approach has grouped the 60 scenarios into 4 classes of dominant scenarios, one of which was not anticipated a priori but was “discovered” by the classifier. The proposed approach may be considered as a first effort towards the application of identification and classification approaches to scenarios post-processing for real-scale dynamic safety assessments.     

Combining watershed models and knowledge-based models to predict local-scale impacts of climate change on endangered wildlife

Climate change is expected to have significant impacts on native, threatened and endangered wildlife. Understanding and modeling these impacts useful for wildlife managers, however, remain difficult due to complex climate change, and costly and high data requirements. Consequently, we proposed an easily-interpretable and data-efficient decision support approach to understand climate change impacts on the abundance of three endangered wetland birds (Hawaiian Stilt, Hawaiian Coot and Hawaiian Moorhen). We coupled a watershed model, AnnAGNPS, and ecological models using fuzzy-cognitive mapping software, Mental Modeler, in Hanalei watershed, Kauaʻi. Results suggested that increased precipitation would increase Stilt abundance, but decrease Coot and Moorhen abundance. Decreasing precipitation might have negative effects for all three species. Moreover, decision-makers should pay equal attention to controlling components (water depth, food availability and disease) with system-wide influence. Finally, besides being adaptable to similar environmental contexts, our approach captured both direct and indirect climate change impacts through ecological connectivity.     

Improved Bacterial Foraging Optimization Algorithm Based on Fuzzy Control Rule Base

Manual construction of a rule base for a fuzzy system is the hard and time-consuming task that requires expert knowledge. In this paper we proposed a method based on improved bacterial foraging optimization (IBFO), which simulates the foraging behavior of E.coli bacterium, to tune the Gaussian membership functions parameters of an improved Takagi-Sugeno-Kang fuzzy system (C-ITSKFS) rule base. To remove the defect of the low rate of convergence and prematurity, three modifications were produced to the standard bacterial foraging optimization (BFO). As for the low accuracy of finding out all optimal solutions with multi-method functions, the IBFO was performed. In order to demonstrate the performance of the proposed IBFO, multiple comparisons were made among the BFO, particle swarm optimization (PSO), and IBFO by MATLAB simulation. The simulation results show that the IBFO has a superior performance.