Selection of Molybdenum-Filled Hip Implant Material Using Grey Relational Analysis Method

In this study, a series of implant material containing molybdenum of different weight percentages were fabricated via high temperature vertical vacuum casting induction furnace and examined their physical, mechanical and wear properties. The mechanical properties were tested by the micro-hardness tester and the compression testing machine, while the wear performance was analyzed through a pin-on-disc tribometer under different operating conditions at room temperature. Density, hardness, compressive strength and sliding wear were considered as criterions for this study. The proportions of alternatives consist of Co-30Cr as a base material and molybdenum as an alloying element which was varied from 0 to 4wt.%. Due to the conflict between the properties obtained, the Grey relational analysis method (GRA) was applied to choose the best material among the set of alternatives. From the results obtained, it was found that Co-30Cr implant material containing 4wt.%molybdenum provides the best combination of the properties for a given application (i.e. hip femoral head).

Optimization of Antenna Design Problems Using Binary Differential Evolution

Differential Evolution (DE) is a popular evolutionary algorithm that has been applied to several antenna design problems. However, DE is best suited for continuous search spaces. Therefore, in order to apply it to combinatorial optimization problems for antenna design a binary version of the DE algorithm has to be used. In this chapter, the author presents a design technique based on Novel Binary DE (NBDE). The main benefit of NBDE is reserving the DE updating strategy to binary space. This chapter presents results from design cases that include array thinning, phased array design with discrete phase shifters, and conformal array design with discrete excitations based on NBDE.

Sugeno Fuzzy-Inference-System-Based Land Cover Classification of Remotely Sensed Images

Accuracy of land cover classification in remotely sensed images relies on the features extracted and the classifier used. Texture features are significant in land cover classification. Traditional texture models capture only patterns with discrete boundaries whereas fuzzy patterns need to be classified by assigning due weightage to uncertainty. When remotely sensed image contains noise, the image may have fuzzy patterns characterizing land covers and fuzzy boundaries separating land covers. So a fuzzy texture model is proposed for effective classification of land covers in remotely sensed images and the model uses Sugeno Fuzzy Inference System (FIS). Support Vector Machine (SVM) is used for precise and fast classification of image pixels. Hence it is proposed to use a hybrid of fuzzy texture model and SVM for land cover classification of remotely sensed images. In this chapter, land cover classification of IRS-P6, LISS-IV remotely sensed image is performed using multivariate version of the proposed texture model.

Peer Group Situations and Games With Grey Uncertainty

In many economic and Operations Research (OR) situations the social configuration of the organization, influences the potential possibilities of all the groups of agents. The important group for an agent consists of the leader, the agent himself and all the intermediate agents that exist in the given hierarchy (peer group). In this study, peer group situations where grey uncertainty is included. Peer group grey situations are introduced and related peer group grey games are constructed. The grey Shapley value is specified as a solution concept with a proposition. As an application auction situations are considered. Finally, a conclusion is given.

Operations Research Problems for Airline Industry

Maintenance routing is one of the most complicated problems of operations research applications for airline industry. In this study, airline industry operations' main applications and subjects are basically mentioned and literature is briefly reviewed. This study is conducted under the headings of; Fleet Assignment, Aircraft Routing, Maintenance Routing and Crew Scheduling. Additionally, network models are explained basically on an example flight program. This study's purpose is to be a guide for the new researchers of this area through operations research applications for airline industry and to introduce maintenance routing problem literature.

Optimal Placement of Power Factor Correction Capacitors Using Taguchi Optimization Method

This chapter presents two methods to optimize the placement of capacitors in a distribution system and thus correcting power factor and reducing losses and costs. The first method uses ETAP software and its integrated algorithm of optimally placing power factor correction capacitors. The second uses the Taguchi Optimization method to solve the optimal capacitor placement problem in electric distribution systems. To test the efficiency of these methods, they were applied to various examples (different bus systems) and simulation results of the two methods are discussed.

Automatic Mask Alignment for Optical Lithography Using GA- and PSO-Based Image Registration Technique

Mask Alignment is a very important part of modern day VLSI fabrication process. To replicate the desired structure on the mask to the wafer, it is necessary to have some degree of accurate mask alignment procedure. However, present day mask alignment process is operated by manual inspection and hence may produce significant human errors. The objective of this work is to develop a novel mask alignment procedure based on image registration technique which is independent of manual inspection. For this purpose only requirement is a standard webcam to capture the images of mask and wafer to be registered. It is well known that registration is a technique by which one object is aligned geometrically with respect to other. Present study shows that genetic algorithm/particle swarm optimization based mask registration technique produces satisfactory results in a reasonable time. First section of this work describes the registration technique of mask and wafer images in details and following this registration values second part is hardware implementation of mask alignment procedure.

Intelligent Drones Improved With Algae Algorithm

Implement an optimal arrangement of equipment, instrumentation and medical personnel based on the weight and balance of the aircraft and transfer humanitarian aid in a Drone, by implementing artificial intelligence algorithms. This due to the problems presented by geographical layout of human settlements in southeast of the state of Chihuahua. The importance of this research is to understand from a Multivariable optimization associated with the path of a group of airplanes associated with different kind of aerial improve the evaluate flooding and send medical support and goods to determine the optimal flight route involve speed, storage and travel resources for determining the cost benefit have partnered with a travel plan to rescue people, which has as principal basis the orography airstrip restriction, although this problem has been studied on several occasions by the literature failed to establish by supporting ubiquitous computing for interacting with the various values associated with the achievement of the group of drones and their cost-benefit of each issue of the company and comparing their individual trips for the rest of group. There are several factors that can influence in the achievement of a group of Drones for our research we propose to use a Bioinspired Algorithm.

An Integrated Model of Data Envelopment Analysis and Artificial Neural Networks for Improving Efficiency in the Municipal Solid Waste Management

In the last decades, integral municipal solid waste management (IMSWM) has become one of the most challenging areas for local governmental authorities, which have struggled to lay down sustainable and financially stable policies for the sector. In this paper a model that evaluates the efficiency of IMSWMs through a combination of Data Envelopment Analysis (DEA) and an Artificial Neural Network (ANN) is presented. In a first stage, applying DEA, municipal administrations are classified according to the efficiency of their garbage processing systems. This is done in order to infer what modifications are necessary to make garbage handling more efficient. In a second stage, an ANN is used for predicting the necessary resources needed to make the waste processing system efficient. This methodology is applied on a toy model with 50 towns as well as on a real-world case of 21 cities. The results show the usefulness of the model for the evaluation of relative efficiency and for guiding the improvement of the system.

Decomposition-Based Multi-Objective Optimization of Energy Noise Trade-Off in a Wind Farm

A multi-objective optimization case study of maximization and minimization of energy generation and noise propagation is considered here. A novel hybrid methodology, as a combination of probabilistic variable decomposed multi-objective evolutionary algorithm (VdRBNSGA-II) and the newly developed deterministic gradient based Pareto frontier construction approach (nD-NNC), has been proposed to determine the optimum layout of turbines (numbers and locations) inside a wind farm. In contrast to previous case studies, the proposed approach is able to yield the alternative energy-noise solutions along with the additional information on corresponding turbine layouts (numbers and locations) on a single Pareto front. As a result, it provides a decision maker with an ample of choices to choose from different competing solutions based on the existing standards and guidelines.