A. Egemen Yilmaz

Bio: A. Egemen Yilmaz is an academic researcher from Ankara University. The author has contributed to research in topics: Particle swarm optimization & Optimization problem. The author has an hindex of 7, co-authored 43 publications receiving 158 citations.

A review on motion control of the Underwater Vehicles

Abstract: Unmanned Underwater Vehicles have gained popularity for the last decades, especially for the purpose of not risking human life in dangerous operations. On the other hand, underwater environment introduces numerous challenges in control, navigation and communication of such vehicles. Certainly, this fact makes the development of these vehicles more interesting and engineering-wise more attractive. In this review study, among the mentioned problems, we focus on the control of underwater vehicles, particularly the motion control. We try to summarize the evolution of the underwater vehicle motion control studies throughout the last two decades, and classify them.

Multiobjective Hooke–Jeeves algorithm with a stochastic Newton–Raphson-like step-size method

Abstract: Computational optimization algorithms are focused on the improvement of meta-heuristic algorithms in a way that they can able to handle problems with more than one objective; such improved algorithms are called multiobjective optimization algorithms As the number of objectives is increased, the complexity of the algorithm is increased with respect to the computational cost Because classical optimization algorithms follow the direction of descending values by calculating derivations of the function, it is possible to evaluate a classical optimization algorithm as the core of a novel multiobjective optimization algorithm Among the classical optimization algorithms, in this study, the Hooke–Jeeves (HJ) algorithm is selected as the basis of the proposed multiobjective optimization algorithm, in which members of the proposed population-based HJ algorithm move to the Pareto front by checking two neighborhood solutions at each dimension, with a dynamic distance that is calculated by using the Newton–Raphson-like stochastic step-size method Unlike various multiobjective optimization algorithms, the performance of the proposed algorithm is greatly dependent on the decision space dimension instead of the number of objectives As the number of objectives increases without changing the decision dimension, the computational cost almost remains the same In addition, the proposed algorithm can be applied to single, multiple and many objective optimization problems In this study, initially, the behaviors of the HJ and proposed multiobjective HJ algorithms are evaluated by theoretical and graphical demonstrations Next, the performance of the proposed method is evaluated on well-known benchmark problems, and the performance of this algorithm is compared with the Nondominated Sorting Genetic Algorithm-II (NSGA-II) algorithm by using three different metric calculations Finally, the algorithm is applied to many-objective optimization problems, and the performance of the proposed algorithm is evaluated based on the obtained results

Application of Chaos Embedded PSO for PID Parameter Tuning

Abstract: Proportional-Integral-Derivative (PID) control is the most common method applied in the industry due to its simplicity. On the other hand, due to its difficulties, parameter tuning of the PID controllers are usually performed poorly. Generally, the design objectives are obtained by adjusting the controller parameters repetitively until the desired closed-loop system performance is achieved. This allows researchers to use more advanced and even some heuristic methods to achieve the optimal PID parameters. This paper focuses on application of the chaos embedded particle swarm optimization algorithm (CPSO) for PID controller tuning, and demonstrates how to employ the CPSO method to find optimal PID parameters in details. The method is applied to optimal PID parameter tuning for three typical systems with various ordered, and comparisons with the conventional PSO and the Ziegler-Nichols methods are performed. The numerical results from the simulations verify the performance of the proposed scheme.

Natural Language Processing Techniques in Requirements Engineering

Abstract: Requirement analysis is the very first and crucial step in the software development processes. Stating the requirements in a clear manner, not only eases the following steps in the process, but also reduces the number of potential errors. In this chapter, techniques for the improvement of the requirements expressed in the natural language are revisited. These techniques try to check the requirement quality attributes via lexical and syntactic analysis methods sometimes with generic, and sometimes domain and application specific knowledge bases.

Reduction of specific absorption rate with artificial magnetic conductors

Abstract: This study presents an artificial magnetic conductor (AMC) design in order to be used for reducing the specific absorption rate (SAR) for the portion of the electromagnetic spectrum in which the electromagnetic pollution is commonly observed. The proposed design has a bandwidth of 0.4609 GHz and it is evaluated by using three different body tissues consisting of planar, cylindrical, and CST SAM phantom head model, and a significant reduction is observed via the usage of AMC. The proposed design, which has an AMC property between 2.8 GHz and 3.3 GHz, achieved a reduction in the SAR value with a percentage of 9% in planar human tissue, 58.9% for the cylindrical tissue, 18.4% for the phantom head model, at 3 GHz. © 2016 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2016.

Software engineering economics

Abstract: This paper summarizes the current state of the art and recent trends in software engineering economics. It provides an overview of economic analysis techniques and their applicability to software engineering and management. It surveys the field of software cost estimation, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.

Fast and Accurate Annotation of Short Texts with Wikipedia Pages

Abstract: Several recent software systems have been designed to obtain novel annotation of cross-referencing text fragments and Wikipedia pages. Tagme is state of the art in this setting and can accurately manage short textual fragments (such as snippets of search engine results, tweets, news, or blogs) on the fly.

Modeling and control of autonomous underwater vehicle (AUV) in heading and depth attitude via self-adaptive fuzzy PID controller

Abstract: This paper focuses on design of a new self-adaptive fuzzy PID controller based on nonlinear MIMO structure for an AUV. Complexity and highly coupled dynamics, time-variance, and difficulty in hydrodynamic modeling and simulation, complicates the AUV modeling process and the design of proper and acceptable controller. In this work, the comprehensive nonlinear model of AUV is derived through kinematics and dynamic equations and then its treatment in open-loop is verified. In proposed controller, the PID parameters are adjusted by Mamdani fuzzy rules. Combined adaptive methods and dual PID controllers can improve solving of the uncertainty challenge in the PID parameters and AUV modeling uncertainty. The simulation results indicate that developed control system is stable, competent, and efficient enough to control the AUV in tracking the two channels of heading and depth with stabilized speed. Obtained results show that the proposed controller is not only robust, but also gives excellent dynamic, stunning steady-state characteristics and robust stability compared with a classically tuned PID controller.