Erkan Ülker

An artificial immune system approach to CNC tool path generation

Reduced machining time and increased accuracy for a sculptured surface are both very important when producing complicated parts, so, the step-size and tool-path interval are essential components in high-speed and high-resolution machining. If they are too small, the machining time will increase, whereas if they are too large, rough surfaces will result. In particular, the machining time, which is a key factor in high-speed machining, is affected by the tool-path interval more than the step size. The present paper introduces a ‘system software’ developed to reduce machining time and increased accuracy for a sculptured surface with Non-Uniform Rational B-Spline (NURBS) patches. The system is mainly based on a new and a powerful artificial intelligence (AI) tool, called artificial immune systems (AIS). It is implemented using C programming language on a PC. It can be used as stand alone system or as the integrated module of a CNC machine tool. With the use of AIS, the impact and power of AI techniques have been reflected on the performance of the tool path optimization system. The methodology of the developed tool path optimization system is illustrated with practical examples in this paper.

An Improved Marriage in Honey Bees Optimization Algorithm for Single Objective Unconstrained Optimization

Marriage in honey bees optimization (MBO) is a metaheuristic optimization algorithm developed by inspiration of the mating and fertilization process of honey bees and is a kind of swarm intelligence optimizations. In this study we propose improved marriage in honey bees optimization (IMBO) by adding Levy flight algorithm for queen mating flight and neighboring for worker drone improving. The IMBO algorithms performance and its success are tested on the well-known six unconstrained test functions and compared with other metaheuristic optimization algorithms.

B-Spline Curve Approximation Using Pareto Envelope- Based Selection Algorithm - PESA

The most common problems which constitute the subject of research in the field of computer-based modeling are to predict the desired shape of the curve for a set of given points. Many approaches and methods were developed for different types of curve and techniques are used for solution of the problem. In this study similarly, Pareto Envelope-based Selection Algorithm (PESA) is proposed for estimated curves. An Application environment which is collected reverse engineering methods and approaches of the algorithm was improved and the node estimation process of B-spline curves is introduced using PESA. Index Terms—PESA, B-Spline Curve, Approximation, reverse engineering.

An approach to geometrical modelling of slope curves and discontinuities

Abstract Conventional geometrical methods for mining applications such as geologic mapping, cross-sections, logging, stereonets, contours and others are so sophisticated that often additional explanations are needed. After studying conventional geometrical methods, we decided to analyze both general slope curves and discontinuities of open pit mines used in excavations in rock mass to achieve meaningful 3D results by using linear mathematical transformation and isometric perspective methods. In addition, a new reorganization of discontinuities as a construction method has been developed according to their spatial orientations. In the developed method, slope curves and discontinuities were geometrically analyzed in a rectangular prism. An example of a detailed structural study of the Dogankuzu South Block Bauxite Open Pit Mine in Seydisehir, Turkey is given. Results indicate that there is a high similarity in both slope curves and discontinuity traces on free surfaces. The main aim of this study is to present a fast and practical visualization of these structures using directly conventional surveying instruments such as theodolite and compass-clinometer or periodic product maps. Present limitations of the method and further studies are also stipulated.

The Analysis of Migrating Birds Optimization Algorithm with Neighborhood Operator on Traveling Salesman Problem

Migrating birds optimization (MBO) algorithm is a new meta-heuristic algorithm inspired from behaviors of migratory birds during migration. Basic MBO algorithm is designed for quadratic assignment problems (QAP) which are known as discrete problems, and the performance of MBO algorithm for solving QAP is shown successfully. But MBO algorithm could not achieve same performance for some other benchmark problems like traveling salesman problem (TSP) and asymmetric traveling salesman problem (ATSP). In order to deal with these kinds of problems, neighborhood operators of MBO is focused in this paper. The performance of MBO algorithm is evaluated with seven varieties of neighborhood operators on symmetric and asymmetric TSP problems. Experimental results show that the performance of MBO algorithm is improved up to 36% by utilizing different neighborhood operators.

The calculation of parametric NURBS surface interval values using neural networks

Three dimensional coordinate values of parametric NURBS (Non-Uniform Rational B-Splines) surfaces are obtained from two dimensional parameters u and v. An approach for generating surfaces produces a model by giving a fixed increase to u and v values. However, the ratio of three dimensional parameters increases and fixed increase of u and v values is not always the same. This difference of ratio costs unrequired sized breaks. In this study an artificial neural network method for simulation of a NURBS surface is proposed. Free shaped NURBS surfaces and various three dimensional object simulations with different patches can be produced using a method projected as network training with respect to coordinates which are found from interval scaled parameters. Experimental results show that this method in imaging modeled surface can be used as a simulator.

Watermarking schema using an artificial immune system in spatial domain

In this paper, we propose a robust watermarking method for image copyright protection in spatial domain based on artificial immune system (AIS). Our method optimizes robustness and imperceptibility which are known to be inversely proportional to each other. The robustness of our watermark method was tested extensively against attacks by lossy JPEG compression. Performance of proposed method was evaluated by comparing with the genetic watermarking method and also the randomly chosen block based watermark method. The experiments indicate that our method is better at robustness over genetic watermarking method. We have shown that our method improves the robustness and imperceptibility comparing with the randomly chosen block based watermarking.

B-Spline Curve Knot Estimation by Using Niched Pareto Genetic Algorithm (NPGA)

In this paper, estimated curve Knot points are found for B- Spline Curve by using Niched (Celled) Pareto Genetic Algorithm which is one of the multi objective genetic algorithms. It is necessary to know degree of the curve, control points and knot vector for drawing B-Spline curve. Some knot points are of very few or no effect at all on the drawing of B-Spline curve drawing. Omitting such points will not effect the shape of curve in curve drawing. In this study, it is aimed to find and omit these ineffective curve points from drove of curve. Performance of proposed method are compared with selected studies from literature.

Effects of discretization on determination of coronary artery disease using support vector machine

In this paper, the effect of discretization on determination of coronary artery disease using exercise stress test data by support vector machine classification method is investigated. The study dataset is obtained from cardiology department of Meram faculty of medicine including 480 patients having 23 features. Four classification models are composed. In the first model, the data is classified simply by normalizing it into [-1,1] range. In the second, third and fourth models, the data is classified by employing entropy-MDL, equal width and equal frequency discretization methods on it respectively. Support vector machine is used as the classifier for all classification models. The results show that classification performance of the model implemented by entropy-MDL discretization has the best value.

Migrating Birds Optimization (MBO) algorithm to solve 0-1 multidimensional knapsack problem

This study presents Migrating Birds Optimization (MBO) which is a novel meta-heuristic algorithm for the solution of 0-1 multidimensional knapsack problem. In the study, the basic migrating birds optimization algorithm is used and change is made to the only neighborhood structure of this algorithm for adapting to the addressed problem. The performance of the algorithm is examined on the test problems that selected from OR-library. The obtained results show that the migrating birds optimization algorithm has achieved successful results in 0-1 multidimensional backpack problems.