Application of Fuzzy Logic and Fuzzy Optimization Techniques in Medical Image Processing

Biometrics ◽  
2017 ◽  
pp. 907-932 ◽  
Author(s):  
Niladri Sekhar Datta ◽  
Himadri Sekhar Dutta ◽  
Koushik Majumder
Keyword(s):  
Mathematical Model ◽  
Fuzzy Logic ◽  
Optimization Technique ◽  
Fuzzy Optimization ◽  
Fuzzy Model ◽  
Optimization Method ◽  
Fuzzy Modeling ◽  
Optimization Techniques ◽  
Fuzzy Variables ◽  
Truth Value

Fuzzy logic deals with approximate rather than fixed and exact reasoning. Fuzzy variables may have a truth value that ranges in degree between 0 and 1; extended to handle the concept of partial truth where the truth value may range between completely true or completely false. This computational logic uses truth degrees as a mathematical model of the vagueness phenomenon while probability is a mathematical model of ignorance. A huge number of complex problems may be solve using Fuzzy logic specifically Fuzzy modeling and optimization method. Fuzzy modeling is the understanding of the problem and analysis of the Fuzzy information where the Fuzzy optimization solves Fuzzy model optimally using optimization techniques via membership functions. In this research article authors describe the Fuzzy rules and its application and the different types of well known problems solved by the Fuzzy optimization technique.

Application of Fuzzy Logic and Fuzzy Optimization Techniques in Medical Image Processing

2016 ◽  
pp. 822-846
Author(s):  
Niladri Sekhar Datta ◽  
Himadri Sekhar Dutta ◽  
Koushik Majumder
Keyword(s):  
Mathematical Model ◽  
Fuzzy Logic ◽  
Optimization Technique ◽  
Fuzzy Optimization ◽  
Fuzzy Model ◽  
Optimization Method ◽  
Fuzzy Modeling ◽  
Optimization Techniques ◽  
Fuzzy Variables ◽  
Truth Value

Fuzzy logic deals with approximate rather than fixed and exact reasoning. Fuzzy variables may have a truth value that ranges in degree between 0 and 1; extended to handle the concept of partial truth where the truth value may range between completely true or completely false. This computational logic uses truth degrees as a mathematical model of the vagueness phenomenon while probability is a mathematical model of ignorance. A huge number of complex problems may be solve using Fuzzy logic specifically Fuzzy modeling and optimization method. Fuzzy modeling is the understanding of the problem and analysis of the Fuzzy information where the Fuzzy optimization solves Fuzzy model optimally using optimization techniques via membership functions. In this research article authors describe the Fuzzy rules and its application and the different types of well known problems solved by the Fuzzy optimization technique.

scholarly journals Data and Feature Reduction in Fuzzy Modeling through Particle Swarm Optimization

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
S. Sakinah S. Ahmad ◽  
Witold Pedrycz
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Fuzzy Model ◽  
Fuzzy Rule ◽  
Search Space ◽  
Fuzzy Modeling ◽  
Optimization Techniques ◽  
Feature Reduction ◽  
Data Sets ◽  
Swarm Optimization

The study is concerned with data and feature reduction in fuzzy modeling. As these reduction activities are advantageous to fuzzy models in terms of both the effectiveness of their construction and the interpretation of the resulting models, their realization deserves particular attention. The formation of a subset of meaningful features and a subset of essential instances is discussed in the context of fuzzy-rule-based models. In contrast to the existing studies, which are focused predominantly on feature selection (namely, a reduction of the input space), a position advocated here is that a reduction has to involve both data and features to become efficient to the design of fuzzy model. The reduction problem is combinatorial in its nature and, as such, calls for the use of advanced optimization techniques. In this study, we use a technique of particle swarm optimization (PSO) as an optimization vehicle of forming a subset of features and data (instances) to design a fuzzy model. Given the dimensionality of the problem (as the search space involves both features and instances), we discuss a cooperative version of the PSO along with a clustering mechanism of forming a partition of the overall search space. Finally, a series of numeric experiments using several machine learning data sets is presented.

Efficient Structural Design Using a Numerical Optimization Technique

2019 ◽  
Author(s):  
Qian Wang ◽  
Lucas Schmotzer ◽  
Yongwook Kim
Keyword(s):  
Structural Design ◽  
Numerical Optimization ◽  
Optimization Technique ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Convergence Criteria ◽  
Efficient Design ◽  
Concrete Building ◽  
Gradient Based ◽  
Structural Responses

<p>Structural designs of complex buildings and infrastructures have long been based on engineering experience and a trial-and-error approach. The structural performance is checked each time when a design is determined. An alternative strategy based on numerical optimization techniques can provide engineers an effective and efficient design approach. To achieve an optimal design, a finite element (FE) program is employed to calculate structural responses including forces and deformations. A gradient-based or gradient-free optimization method can be integrated with the FE program to guide the design iterations, until certain convergence criteria are met. Due to the iterative nature of the numerical optimization, a user programming is required to repeatedly access and modify input data and to collect output data of the FE program. In this study, an approximation method was developed so that the structural responses could be expressed as approximate functions, and that the accuracy of the functions could be adaptively improved. In the method, the FE program was not required to be directly looped in the optimization iterations. As a practical illustrative example, a 3D reinforced concrete building structure was optimized. The proposed method worked very well and optimal designs were found to reduce the torsional responses of the building.</p>

Optimization of Engine Mount Characteristics Using Experimental/Numerical Analysis

2003 ◽  
Vol 9 (10) ◽  
pp. 1121-1139 ◽  
Author(s):  
Mahmoud S. Foumani ◽  
Amir Khajepour ◽  
Mohammad Durali
Keyword(s):  
Mathematical Model ◽  
Numerical Technique ◽  
Optimization Method ◽  
Current Approach ◽  
Optimization Techniques ◽  
Trial And Error ◽  
Vibration Isolators ◽  
Engine Mounts ◽  
Quarter Car Model ◽  
Engine Mount

In this paper an experimental/numerical technique is developed for engine mount optimization. The method is general and can be applied to optimize active and passive vibration isolators or absorbers in any mechanical systems or civil structures. Engine mount optimization techniques mostly rely on an accurate mathematical model of the whole vehicle, which in most cases is not available or is too difficult to develop. As a result, the current approach for selecting engine mounts for a vehicle is based upon trial and error which is very time-consuming and expensive. The proposed technique counts upon experimental data for optimization and does not require any mathematical model of the vehicle or its components. The required experiments are similar to the current trial-and-error based experiments performed on a vehicle for mounts selection. The method is evaluated experimentally using a quarter car model and the results corroborate the proposed optimization method.

scholarly journals Study of Grey Fuzzy Optimization Techniques for FMS Scheduling Problem

2020 ◽  
Vol 5 (1) ◽  
pp. 01-16
Author(s):  
Reetik Kaushik ◽  
◽  
Nikita Deswal ◽  
Anurag Dudpuri ◽  
Sumit Chawla ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Flexible Manufacturing ◽  
Manufacturing Systems ◽  
Job Shop ◽  
Water Quality Management ◽  
Fuzzy Optimization ◽  
Optimization Techniques ◽  
Scheduling Problem ◽  
Fms Scheduling

Deep The research study focuses on the concept of Fuzzy Logic and its applications in diverse sectors like Behavioural Science, Machining, Business Operations, Medicinal Science, Water Quality Management, Manufacturing, Anti-Braking systems, etc. Fuzzy Logic is regarded as the best option to deal with complex problems which arise due to uncertainty, incomplete information, and lack of preciseness. The major focus of this research is then divided into a case study on the application of Grey Fuzzy Optimization in the Manufacturing and Machining sectors, using the Fuzzy Logic Toolbox of MATLAB. The case study deals with a Job-Shop Scheduling Problem in a Flexible Manufacturing System (FMS). Scheduling of Jobs is a crucial task for meeting and exceeding the market demands. The decision of which part will be the next to be processed considers some criteria that influence the performance of a job shop system. The job shop system is taken from previous literature and the proposed approach is applied to get the optimal scheduling of products. Within the vast field of production and operation, its applicability was tested in scheduling conflicts in flexible manufacturing systems (FMS). All the output values were in the range of single-digit percentage error owing to different sets of rule formations but nonetheless, the proximity of the actual and calculated values reaffirmed the confidence that the verification was justified.

Traditional and Soft Computing Techniques for Image Enhancement

2020 ◽  
Vol 9 (6) ◽  
pp. 928-936
Keyword(s):  
Neural Network ◽  
Image Enhancement ◽  
Soft Computing ◽  
Optimization Technique ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Result Section ◽  
Image Quality Enhancement ◽  
Bee Colony ◽  
Soft Computing Techniques

Now-a-days, there is a growing demand for image processing. The target of image enhancement is to find details present in images having low luminance for better image quality. Enhancement is required to improve the picture quality. In this process, we can enhance an image, by applying the suitable technique. In enhancement, there is a conversion in image contrast, quality, color vision, brightness, clarity etc. So we need image enhancement. A comparative survey is carried out in this paper, explaining traditional and soft computing techniques. This paper clarifies a study of traditional such as edge detection of an image and fuzzy logic based soft computing for improvement of an image. In the result section output of image is shown as edges using traditional as well as fuzzy. A small description is also study for picture improvement using different soft computing and optimization techniques such as Neural network, Convolution Neural Network, Ant Bee Colony, Particle Swarm Optimization etc. in literature survey and in comparative table. It is concluded that Image enhancement can be done by traditional method, soft computing and optimization method. Image enhancement has found various vision applications that have the ability to enhance the visibility of images. To enhance an image it is very important that image should be clear, so before using the enhancement techniques we should need to learn about the enhancement. So this paper described a survey of image enhancement with different techniques. In future scope of this paper we can find out different type of parameters like PSNR, MSE and execution time, also we can use optimization technique. We are also showing a comparison table of image enhancement based on traditional, soft computing and optimization techniques with its open scope.

Calibration of the Parameters of a Model of an Engineering System Using the Global Optimization Method

2014 ◽  
Vol 5 (3) ◽  
pp. 14-41 ◽  
Author(s):  
Marwa Elhajj ◽  
Rafic Younes ◽  
Sebastien Charles ◽  
Eric Padiolleau
Keyword(s):  
Genetic Algorithm ◽  
Global Optimization ◽  
Optimization Technique ◽  
Optimization Method ◽  
Simulation Software ◽  
Optimization Techniques ◽  
Engineering System ◽  
Global Optimization Method ◽  
Unknown Parameters ◽  
Simulated Model

The calibration of the model is one of the most important steps in the development of models of engineering systems. A new approach is presented in this study to calibrate a complex multi-domain system. This approach respects the real characteristics of the circuit, the accuracy of the results, and minimizes the cost of the experimental phase. This paper proposes a complete method, the Global Optimization Method for Parameter Calibration (GOMPC). This method uses an optimization technique coupled with the simulated model on simulation software. In this paper, two optimization techniques, the Genetic Algorithm (GA) and the two-level Genetic Algorithm, are applied and then compared on two case studies: a theoretical and a real hydro-electromechanical circuit. In order to optimize the number of measured outputs, a sensitivity analysis is used to identify the objective function (OBJ) of the two studied optimization techniques. Finally, results concluded that applying GOMPC by combining the two-level GA with the simulated model was an efficient solution as it proves its accuracy and efficiency with less computation time. It is believed that this approach is able to converge to the expected results and to find the system's unknown parameters faster and with more accuracy than GA.

scholarly journals Development of a fuzzy logic based software for automation of a single pool irrigation canal

2013 ◽  
pp. 226-232
Author(s):  
Dr. R. Gopakumar ◽  
Reena Nair ◽  
Vinuraj R. ◽  
Sony Davis ◽  
Bijeesh V. ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Water Level ◽  
Fuzzy Inference ◽  
Optimization Technique ◽  
Research Work ◽  
Fuzzy Model ◽  
Irrigation Canal ◽  
Level Control ◽  
Doctoral Research ◽  
Fuzzy Control Algorithm

A fuzzy logic based software for automation of a single pool irrigation canal is presented. Purpose of the software is to control downstream discharge and water level of the canal, by adjusting discharge release from the upstream end and upstream gate settings. The software is developed on a fuzzy control algorithm proposed by the first author during his doctoral research work and published in literature. Details of the algorithm are given. The algorithm was originally developed using fuzzy logic tool box of MATLAB, which is proprietary software not available freely and hence cannot be adopted for general use. Present study describes development of a canal automation software based on this algorithm using open source tools, which are freely available. The software is transparent and intuitive, which can be easily applied by field engineers. The effort required in tuning the fuzzy model has been reduced by including an optimization technique. Also, a new procedure has been introduced for fuzzy inference based on the Mamdani Implication method. The software is tested by applying it to water level control problem in a canal with a single pool, as reported in literature, and satisfactory results are obtained.

Heater Configuration Design of a Pultrusion Die Using Design Optimization Techniques

1992 ◽  
Author(s):  
T. W. Awa ◽  
R. L. West ◽  
H. L. Price
Keyword(s):  
Control System ◽  
Design Optimization ◽  
Temperature Profile ◽  
Optimization Technique ◽  
Power Input ◽  
Optimization Method ◽  
Input Power ◽  
Optimization Techniques ◽  
Pultrusion Process ◽  
Production Temperature

Abstract The goal of the pultrusion process is to produce a composite material having the desired properties with minimal variation. This requires reliability, uniformity and consistency in production. Key to achieving these requirements is the design of the die and an automated control system. A consistent quality control system can result in greater confidence in the predicted composite properties of the products. The objective of this study is to design the heating configuration for an existing pultrusion die. The design optimization technique is used to synthesize the heating configuration of a laboratory scale die to produce the required production temperature profile for a pultrusion process. The heating configuration includes three variables, which are the number of heaters, power input and location of the heaters. The scope of the design optimization method is to choose the number of heaters, the input power and heater location to minimize the error between the calculated and desired production temperature profile.

scholarly journals Solution of Single and Multiobjective Stochastic Inventory Models with Fuzzy Cost Components by Intuitionistic Fuzzy Optimization Technique

2010 ◽  
Vol 2010 ◽  
pp. 1-19 ◽  
Author(s):  
S. Banerjee ◽  
T. K. Roy
Keyword(s):  
Annual Cost ◽  
Lead Time ◽  
Optimization Technique ◽  
Fuzzy Optimization ◽  
Inventory Model ◽  
Optimization Techniques ◽  
Stochastic Inventory ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Cost ◽  
Cost Components

Paknejad et al.'s model is considered in this paper. Itemwise multiobjective models for both exponential and uniform lead-time demand are taken and the results are compared numerically both in fuzzy optimization and intuitionistic fuzzy optimization techniques. Objective of this paper is to establish that intuitionistic fuzzy optimizaion method is better than usual fuzzy optimization technique as expected annual cost of this inventory model is more minimized in case of intuitionistic fuzzy optimization method. As a single objective stochastic inventory model where the lead-time demand follows normal distribution and with varying defective rate, expected annual cost is also measured. Finally the model considers for fuzzy cost components, which make the model more realistic, and numerical values for uniform, exponential, and normal lead-time demand are compared. Necessary graphical presentations are also given besides numerical illustrations.

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