Kemal Leblebicioglu

Optimum geometry for torque ripple minimization of switched reluctance motors

For switched reluctance motors, one of the major problems is torque ripple which causes increased undesirable acoustic noise and possibly speed ripple. This paper describes an approach to determine optimum magnetic circuit parameters to minimize low speed torque ripple for such motors. The prediction of torque ripple is based on a set of normalized permeance and force data obtained from numerical field solution for doubly-salient geometries. For that purpose a neural net is trained to extract the data needed to predict the torque produced by a given geometry and excitation at any position of teeth. Hence the static torque curve can be constructed and torque ripple can be found. The accuracy of the approach developed is illustrated by comparing measured and predicted torque for a switched reluctance motor. The optimum parameters for minimum torque ripple conditions are sought using the augmented Lagrangian method. The paper presents the optimization results, and then proceeds to determine the range of geometric parameters which keep the torque ripple within /spl plusmn/10 of the optimum value.

An automated differential blood count system

While the early diagnosis of hematopoietic system disorders is very important in hematology, it is a highly complex and time consuming task. The early diagnosis requires a lot of patients to be followed-up by experts which, in general is unfeasible because of the required number of experts. The differential blood counter (DBC) system that we have developed is an attempt to automate the task performed manually by experts in routine. In our system, the cells are segmented using active contour models (snakes and balloons), which are initialized using morphological operators. Shape based and texture based features are utilized for the classification task. Different classifiers such as k-nearest neighbors, learning vector quantization, multi-layer perceptron and support vector machine are employed.

Torque Distribution in a Six-Legged Robot

In this paper, distribution of required forces and moments to the supporting legs of a six-legged robot is handled as a torque-distribution problem. This approach is comparatively contrasted to the conventional approach of tip-point force distribution. The formulation of dynamics is performed by using the joint torques as the primary variables. The sum of the squares of the joint torques on the supporting legs is considered to be proportional to the dissipated power. The objective function is constructed as this sum, and the problem is formulated as to minimize this quadratic objective function with respect to linear equality and inequality constraints. It is demonstrated that the torque-distribution scheme results in a much more efficient distribution compared with the conventional scheme of force distribution. In contrast to the force distribution, the torque-distribution scheme makes good use of interaction forces and friction in order to minimize the required joint torques

Free gait generation with reinforcement learning for a six-legged robot

In this paper the problem of free gait generation and adaptability with reinforcement learning are addressed for a six-legged robot. Using the developed free gait generation algorithm the robot maintains to generate stable gaits according to the commanded velocity. The reinforcement learning scheme incorporated into the free gait generation makes the robot choose more stable states and develop a continuous walking pattern with a larger average stability margin. While walking in normal conditions with no external effects causing unstability, the robot is guaranteed to have stable walk, and the reinforcement learning only improves the stability. The adaptability of the learning scheme is tested also for the abnormal case of deficiency in one of the rear-legs. The robot gets a negative reinforcement when it falls, and a positive reinforcement when a stable transition is achieved. In this way the robot learns to achieve a continuous pattern of stable walk with five legs. The developed free gait generation with reinforcement learning is applied in real-time on the actual robot both for normal walking with different speeds and learning of five-legged walking in the abnormal case.

Feature extraction and classification of blood cells for an automated differential blood count system

The differential blood counter system we developed is an attempt to automate the task performed manually by experts in routine. Feature extraction and classification are two important components of our automated system. In this paper, classification of blood cells using various approaches including neural network based classifiers and support vector machine are presented together with the features used in the classification.

Sleep spindles detection using short time Fourier transform and neural networks

Sleep spindles are 2 hallmark of the stage 2 sleep. Their distribution over the non-REM sleep is clinically important. In this paper, a method that detects the sleep spindles in sleep EEG is proposed. Short time Fourier transform is used for feature extraction. Both multilayer perceptron and Support Vector Machine are utilized in detection of the spindles in sleep EEG for comparison. The classification performance of MLP is found to be 88.7% and that of SVM as 95.4%. It should be noted that there might be differences also in visual scoring by experts, so the results obtained are quite satisfactory.

Path Planning for UAVs for Maximum Information Collection

Path planning considers the problem of designing the path a vehicle is supposed to follow. Along the designed path the objectives are to maximize the collected information (CI) from desired regions (DR), while avoiding flying over forbidden regions (FR) and reaching the destination. The path planning problem for a single unmanned air vehicle (UAV) is studied with the proposal of novel evolutionary operators: pull-to-desired-region (PTDR), push-from-forbidden-region (PFFR), and pull-to-final-point (PTFP). In addition to these newly proposed operators, standard mutation and crossover operators are used. The initial population seed-path is obtained by both utilizing the pattern search method and solving the traveling salesman problem (TSP). Using this seed-path the initial population of paths is generated by randomly selected heading angles. It should be emphasized that all of the paths in population in any generation of the genetic algorithm (GA) are constructed using the dynamical mathematical model of a UAV equipped with the autopilot and guidance algorithms. Simulations are realized in the MATLAB/Simulink environment. The path planning algorithm is tested with different scenarios, and the results are presented in Section VI. Although there are previous studies in this field, the focus here is on maximizing the CI instead of minimizing the total mission time. In addition it is observed that the proposed operators generate better paths than classical evolutionary operators.

Multi-Agent System-Based Fuzzy Controller Design with Genetic Tuning for a Mobile Manipulator Robot in the Hand Over Task

This paper presents an application of the multi-agent system approach to a service mobile manipulator robot that interacts with a human during an object delivery and hand-over task in two dimensions. The base, elbow and shoulder of the robot are identified as three different agents, and are controlled using fuzzy control. The control variables of the controllers are linear velocity of the base, angular velocity of the elbow, and angular velocity of the shoulder. Main inputs to the system are the horizontal and vertical distances between the human and robot hands. These are input to all three agents. In developing the fuzzy control rules, effective delivery and avoidance of contact with humans, not to cause physical damage, are considered. The membership functions of the fuzzy controllers are tuned by using genetic algorithms. In tuning, the performance is calculated considering the distance deviation from the direct path, time spent to reach the human hand and energy consumed by the actuators. The proposed multi-agent system structure based on fuzzy control for the object delivery task succeeded in both effective and safe delivery.

A new neural network approach to the target tracking problem with smart structure

The algorithm presented in this paper, namely the modified neural multiple source tracking algorithm (MN-MUST) is the modified form of the recently published work, a NN algorithm, the neural multiple-source tracking (N-MUST) algorithm, was presented for locating and tracking angles of arrival from multiple sources. MN-MUST algorithm consists of three stages that are classified as the detection, filtering and DoA estimation stages. In the first stage a number of radial basis function neural networks (RBFNN) are trained for detection of the angular sectors which have source or sources. A spatial filter stage applied individually to the every angular sector which is classified in the first stage as having source or sources. Each individual spatial filter is designed to filter out the signals coming from all the other angular sectors outside the particular source detected angular sector. This stage considerably improves the performance of the algorithm in the case where more than one angular sector have source or sources at the same time. Insertion of this spatial filtering stage is the main contribution of this paper. The third stage consists of a neural network trained for DoA estimation. In all three stages neural networks size and the training data are considerably reduced as compared to the previous approach, without loss of accuracy

Application of Genetic Algorithms to Geometry Optimization of Microclusters: A Comparative Study of Empirical Potential Energy Functions for Silicon

Abstract Evolutionary computation techniques (in particular, genetic algorithms) have been applied to optimize the structure of microclusters. Various empirical potential energy functions have been used to describe the interactions among the atoms in the clusters. A comparative study of silicon microclusters has been performed.