Sharif Digital Repository

Statistical Process Control (SPC) is an approach that uses statistical techniques to monitor the process. The techniques of quality control are widely used in controlling any kinds of processes. One of these processes is the short processes. In short run processes often do not have enough data in each run to produce good estimates of the process parameters. This will cause the reduction of the performance and efficiency of control charts. A common solution to this problem is considering a single machine or process to produce many different parts, or different products. In this paper a new method based on non-conformity degree and fuzzy membership functions has been developed for controlling... 

In May 1, 2008, researchers at Hewlett Packard (HP) announced the first physical realization of a fundamental circuit element called memristor that attracted so much interest worldwide. This newly found element can easily be combined with crossbar interconnect technology which this new structure has opened a new field in designing configurable or programmable electronic systems. These systems in return can have applications in signal processing and artificial intelligence. In this paper, based on the simple memristor crossbar structure, we propose new and simple circuits for hardware implementation of fuzzy membership functions. In our proposed circuits, these fuzzy membership functions can... 

In Distributed detection systems, restricting the output of the local decision to one bit certainly implies a substantial information loss. In this paper, we consider the fuzzy detection, which uses a function called membership function for mapping the observation space of each local detector to a value between 0 and 1, indicating the degree of assurance about presence or absence of a signal. In this case, we examine the problem of distributed Maximum Likelihood (ML) and Order Statistic (OS) constant false alarm rate (CFAR) detections using fuzzy fusion rules such as "Algebraic Product"(AP), "Algebraic Sum"(AS), "Union"(Un) and " Intersection"(IS) in the fusion centre. For the Weibull... 

Mesh-based motion estimation is an important tool for video coding especially with low bit rate. In this paper, a new method for interpolating pixel motion from adjacent mesh nodes with the ability of omitting independent nodes is proposed. By exploiting fuzzy rules to determine the association of pixel and neighboring nodes, the proposed interpolation can detach pixels from some nodes. Consequently, it can deal with those critical patches on objects boundary which their nodes do not belong to one object. Updating the membership functions of each rule with specified strategy makes the interpolation adaptive with non-stationary conditions of image sequences and decreases sensitivity to... 

Differential Evolution (DE) and Genetic Algorithms (GA) are population based search algorithms that come under the category of evolutionary optimization techniques. In the present study, these evolutionary methods have been utilized to conduct the optimum design of a fuzzy controller for mobile robot trajectory tracking. Comparison between their performances has also been conducted. In this paper, we will present a fuzzy controller to the problem of mobile robot path tracking for a CEDRA rescue robot. After designing the fuzzy tracking controller, the membership functions will be optimized by evolutionary algorithms in order to obtain more acceptable results  

This paper describes a genetic-fuzzy system adapted to find efficient partitions on data domains for IDS (Ink Drop Spread). IDS is the engine of Active Learning Method (ALM), a methodology of soft computing. IDS extracts useful information from a system subjected to modeling. Proposed method, called GIDS (Genetic IDS), uses genetic algorithm which optimizes the parameters of membership functions that represent the partitions on data planes. Obtained Results showed that using genetic algorithm to find the partitions has better accuracy than the previous generic IDS methods. © 2008 IEEE  

In this paper a fuzzy logic based controller for STATCOM is used to improve power system transient stability. As opposed to the modern control theory, fuzzy logic design is not based on the mathematical model of the process. The controller designed using fuzzy logic implements human reasoning that can be programmed into fuzzy logic language (membership functions, rules and the rule interpretation). The nonlinear fuzzy controller is proposed to supply a supplementary control signal to STATCOM to increase the critical clearing time and overcome the uncertainties existing in the power systems. Proposed method is implemented in a single machine infinite bus system and the results are compared... 

Trip production has long been considered as a major element in trip demand estimation. Many models have been presented for this purpose. Models use socio-economic variables in order to predict trip production. This paper develops an Adaptive-Network-based Fuzzy Inference System (ANFIS) models to predict school trip production. ANFIS can construct an input-output mapping based on both human knowledge and stipulated input-output data pairs. In order to improve models' generalization capability, a heuristic algorithm is used to generate reasonable initial values for data loss in training data set. Models with different Membership Functions (MFs) were trained, validated and tested with real data... 

Differential Evolution (DE) and Genetic Algorithms (GA) are population based search algorithms that come under the category of evolutionary optimization techniques. In the present study, these evolutionary methods have been utilized to conduct the optimum design of a fuzzy controller for mobile robot trajectory tracking. Comparison between their performances has also been conducted. In this paper, we will present a fuzzy controller to the problem of mobile robot path tracking for a CEDRA rescue robot. After designing the fuzzy tracking controller, the membership functions will be optimized by evolutionary algorithms in order to obtain more acceptable results  

In this work a new method for analyzing nanostructured materials has been proposed to accelerate the simulations for solid crystalline materials. The proposed Structural Approximation Method (SAM) is based on Molecular Dynamics (MD) and the accuracy of the results can also be improved in a systematic manner by sacrificing the simulation speed. In this method a virtual material is used instead of the real one, which has less number of atoms and therefore fewer degrees of freedom, compared to the real material. The number of differential equations that must be integrated in order to specify the state of the system will decrease significantly, and the simulation speed increases. To generalize... 

Wind power is a promising source of electric power generation since it has tremendous environmental and social benefits. The generation scheduling (GS) problem encounters several uncertainties in terms of the system's parameters such as load, reserve and available wind power generation. The modeling of those uncertainties is an important issue in power system scheduling. A fuzzy based modeling approach can be used to develop the generation schedule under an uncertain environment. In this paper, the type-2 fuzzy membership function (MF) is implemented to model the linguistic uncertainty of type-1 MF of available wind power generation which stems from opinions of different experts. The... 

In this paper a two level supply chain system is studied, in which the final demand is assumed to be fuzzy with triangular membership function. The inventory control policy of (r, Q) is followed for this system and unsatisfied demand is assumed to be back ordered. The objective is to minimize the total cost of the system, including ordering, holding and shortage costs. The model happens to be a nonlinear programming. Considering the complexity arising from the model, we also develop a genetic algorithm to obtain a near-optimal solution. The method is illustrated through some numerical examples  

In this work a new method for analyzing nanostructured materials has been proposed to accelerate the simulations for solid crystalline materials. The proposed Structural Approximation Method (SAM) is based on Molecular Dynamics (MD) and the accuracy of the results can also be improved in a systematic manner by sacrificing the simulation speed. In this method a virtual material is used instead of the real one, which has less number of atoms and therefore fewer degrees of freedom, compared to the real material. The number of differential equations that must be integrated in order to specify the state of the system will decrease significantly, and the simulation speed increases. To generalize... 

Nowadays, discovery the association rules is an important and controversial area in data mining research studies. These rules, describe noticeable association relationships among different attributes. While most studies have focused on binary valued transaction data, in real world applications, there data usually consist of quantitative values. With that in mind, in this paper, we propose a fuzzy data mining algorithm for extracting membership functions from quantitative transactions. This is a hybrid genetic-pso algorithm for finding membership functions suitable for mining problems by a strong cooperation of GA and PSO. This algorithm integrates the two techniques entire run of simulation... 

We are concerned with the design of a model and an algorithm for computing a shortest path in a network having various types of fuzzy arc lengths. First, we develop a new technique for the addition of various fuzzy numbers in a path using α-cuts by proposing a linear least squares model to obtain membership functions for the considered additions. Then, using a recently proposed distance function for comparison of fuzzy numbers, we present a dynamic programming method for finding a shortest path in the network. Examples are worked out to illustrate the applicability of the proposed model  

This paper proposes an innovative robust-fuzzy method for multi-objective, multi-period supplier selection problem under multiple uncertainties. This approach integrates robust optimization and fuzzy programming. Uncertain parameters are modeled as random variables that take value within a symmetrical interval. However, due to the complexity or ambiguity of some real world problems and especially the nature of some of the available input data, the length of interval is also highly uncertain. This ambiguity motivated us to present a new approach, which could be applicable to multiple uncertainties conditions. Thus, in our approach, the half-length of these intervals is also represented by... 

This paper introduces a new supergraph, called the k-Semi-Yao graph (k-SYG), of the k-nearest neighbor graph (k-NNG) of a set P of points in Rd. We provide a kinetic data structure (KDS) to maintain the k-SYG on moving points, where the trajectory of each point is a polynomial function whose degree is bounded by some constant. Our technique gives the first KDS for the theta graph (i.e., 1-SYG) in Rd. It generalizes and improves on previous work on maintaining the theta graph in R2. As an application, we use the kinetic k-SYG to provide the first KDS for maintenance of all the k-nearest neighbors in Rd, for any k≥1. Previous works considered the k=1 case only. Our KDS for all the 1-nearest...