Genetic algorithm based PI controller tuning for induction motor drive with ANN flux estimator

This paper presents a Genetic Algorithm (GA) based fast speed response controller for poly-phase induction motor drive. Here the proportional and integral gains of PI controller are optimized by GA to achieve quick speed response. An adaptive Recurrent Neural Network (RNN) with Real Time Recurrent Learning (RTRL) algorithm is proposed to estimate rotor flux. An online tuning scheme to update the weight of RNN is presented to overcome stator resistance variation problem. This tuning scheme requires torque estimator to calculate the torque error. Space vector modulation (SVM) technique is used to produce the motor input voltage. Simulation tests have been performed to study the dynamic performances of the drive system for both the classical PI and the genetic algorithm based PI controllers.

Siobhan Davies Replay: The Archive of Siobhan Davies Dance

The Siobhan Davies Archive project began in January 2007, with the aim of bringing together all of the materials and documentation associated with Davies' choreographies into a single collection. It is the first online dance archive in the UK and contains thousands of fully searchable digital records including moving image, still image, audio and text. Many of the objects within the archive collection have been sourced directly from Davies and her collaborators' personal collections, whilst other items have been kindly lent by institutions and private contributors. Almost all of these objects that would otherwise remain inaccessible and unavailable appear online for the first time, and in many cases represent the first time objects have been viewed by anyone since their original date of creation.

Open Page with Marion May Campbell

Interview responses regarding influences, Austrlian literary culture, publication of my experimental novella konkretion

Getting everyone on the same page : A staff focus group study for library web site redesign

Purpose - Using staff focus groups in the redevelopment of a library web site deploys their knowledge of user navigation issues and search strategies and addresses the unique needs of library staff. This paper seeks to describe the process of planning, recruiting, and conducting staff focus groups and provide a discussion of lessons learned. Design/methodology/approach - A committee of professionals and non-professionals from the University of Calgary Library conducted a series of five focus groups with library staff. The goals were to determine their content and service priorities for the redesigned library web site, and also to ensure that staff was included in the redesign process. Findings - This paper makes recommendations for library staff focus group interviewing, including planning, formulating questions, recruitment, conducting sessions, and analysis and reporting. Practical implications - Focus group interviews can be effectively conducted in-house, with careful planning and adherence to established guidelines. Focus groups are a very useful method for gathering staff input for web site redesign or any other library-planning project. Originality/value - This paper will be useful to librarians interested in assessing staff needs and priorities through focus group interviews. The paper fills a void in the library literature regarding the use of library staff as both focus group leaders and participants.

An improved Gbest guided artificial bee colony (IGGABC) algorithm for classification and prediction tasks

Artificial Neural Networks (ANN) performance depends on network topology, activation function, behaviors of data, suitable synapse's values and learning algorithms. Many existing works used different learning algorithms to train ANN for getting high performance. Artificial Bee Colony (ABC) algorithm is one of the latest successfully Swarm Intelligence based technique for training Multilayer Perceptron (MLP). Normally Gbest Guided Artificial Bee Colony (GGABC) algorithm has strong exploitation process for solving mathematical problems, however the poor exploration creates problems like slow convergence and trapping in local minima. In this paper, the Improved Gbest Guided Artificial Bee Colony (IGGABC) algorithm is proposed for finding global optima. The proposed IGGABC algorithm has strong exploitation and exploration processes. The experimental results show that IGGABC algorithm performs better than that standard GGABC, BP and ABC algorithms for Boolean data classification and time-series prediction tasks.

Development of a dynamic surface roughness monitoring system based on artificial neural networks (ANN) in milling operation

Dynamic surface roughness prediction during metal cutting operations plays an important role to enhance the productivity in manufacturing industries. Various machining parameters such as unwanted noises affect the surface roughness, whatever their effects have not been adequately quantified. In this study, a general dynamic surface roughness monitoring system in milling operations was developed. Based on the experimentally acquired data, the milling process of Al 7075 and St 52 parts was simulated. Cutting parameters (i.e., cutting speed, feed rate, and depth of cut), material type, coolant fluid, X and Z components of milling machine vibrations, and white noise were used as inputs. The original objective in the development of a dynamic monitoring system is to simulate wide ranges of machining conditions such as rough and finishing of several materials with and without cutting fluid. To achieve high accuracy of the resultant data, the full factorial design of experiment was used. To verify the accuracy of the proposed model, testing and recall/verification procedures have been carried out and results showed that the accuracy of 99.8 and 99.7 % were obtained for testing and recall processes.