Emre Oner Tartan

Optimization of waiting and journey time in group elevator system using genetic algorithm

Efficient elevator group control is an important issue for vertical transportation in high-rise buildings. From the engineering design perspective, regulation of average waiting time and journey time while considering energy consumption is an optimization problem. Alternatively to the conventional algorithms for scheduling and dispatching cars to hall calls, intelligent systems based methods have drawn much attention in the last years. This study aims to improve the elevator group control systems performance by applying genetic algorithm based optimization algorithms considering two systems. Firstly, average passenger waiting time is optimized in the conventional elevator systems in which a hall call is submitted by indicating the travel direction. Secondly, a recent development in elevator industry is considered and it is assumed that instead of direction indicators there are destination button panels at floors that allow passengers to specify their destinations. In this case optimization of average waiting time, journey time and car trip time is investigated. Two proposed algorithms have been applied considering preload conditions in a building with 20 floors and 4 cars. The simulation results have been compared with a previous study and conventional duplex algorithm.

Square root unscented based FastSlam optimized by particle swarm optimization passive congregation

Simultaneous localization and mapping (SLAM) is known to be a problem for autonomous vehicles/robots. Different solutions have recently been proposed on this subject. The best known of these are FastSlam based approaches. In this study, two improved FastSlam based methods are proposed to solve the SLAM problem. In the first method, square root unscented (Sru) Kalman filter is used instead of extended Kalman filter in robot position prediction/update for each particle filter samples and feature updates. The second method uses Sru - Kalman filter with particle swarm optimization passive congregation (PSO-PC) for robot/feature position estimations. In the second method, particle swarm optimization passive congregation (PSO-PC) is used to optimize particle samples in case of sampling stage. The experimental results were compared with FastSlamII and unscented U-FastSlam. It is seen that proposed methods are an alternative for the solution of SLAM problem. The best results were obtained by Sru - based PSO-PC optimized FastSlam approach for the vehicle position and heading angle mean square errors.

A simple population based hybrid harmonic estimation algorithm

This paper presents a new hybrid algorithm for harmonic estimation. The algorithm combines a simple fast population based search algorithm with Least Squares Method. It is based on the structural property of the harmonic estimation problem which implies that the signal model is linear in amplitude and nonlinear in phase. The hybrid algorithm uses the search algorithm for phase estimation and LS for amplitude estimation, iteratively. Exploiting the objective function defined according to the error of single harmonics phase estimation, the proposed search algorithm distributes the population through equal intervals and simply narrows the search space sequentially in every generation. Unlike the other heuristic optimization algorithms that uses random distribution in initialization stage, the proposed method provides more robust convergence in the limits determined by the generation number. Simulation results show that the proposed hybrid algorithm not only gives accurate results but also significantly improves the computation time when compared with other heuristic optimization algorithms. Moreover this approach can be used to reduce the search duration when involved in other evolutionary optimization algorithms in a hybrid way and then can deal with frequency deviation and subharmonic estimation which are pitfalls for DFT based algorithms.

Slot Parameter Optimization for Multiband Antenna Performance Improvement Using Intelligent Systems

This paper discusses bandwidth enhancement for multiband microstrip patch antennas (MMPAs) using symmetrical rectangular/square slots etched on the patch and the substrate properties. The slot parameters on MMPA are modeled using soft computing technique of artificial neural networks (ANN). To achieve the best ANN performance, Particle Swarm Optimization (PSO) and Differential Evolution (DE) are applied with ANN’s conventional training algorithm in optimization of the modeling performance. In this study, the slot parameters are assumed as slot distance to the radiating patch edge, slot width, and length. Bandwidth enhancement is applied to a formerly designed MMPA fed by a microstrip transmission line attached to the center pin of 50 ohm SMA connecter. The simulated antennas are fabricated and measured. Measurement results are utilized for training the artificial intelligence models. The ANN provides 98% model accuracy for rectangular slots and 97% for square slots; however, ANFIS offer 90% accuracy with lack of resonance frequency tracking.