Industrial and Technological Applications of Power Electronics Systems

The Special Issue "Industrial and Technological Applications of Power Electronics Systems" focuses on: - new strategies of control for electric machines, including sensorless control and fault diagnosis; - existing and emerging industrial applications of GaN and SiC-based converters; - modern methods for electromagnetic compatibility. The book covers topics such as control systems, fault diagnosis, converters, inverters, and electromagnetic interference in power electronics systems. The Special Issue includes 19 scientific papers by industry experts and worldwide professors in the area of electrical engineering

Actuators for Intelligent Electric Vehicles

This book details the advanced actuators for IEVs and the control algorithm design. In the actuator design, the configuration four-wheel independent drive/steering electric vehicles is reviewed. An in-wheel two-speed AMT with selectable one-way clutch is designed for IEV. Considering uncertainties, the optimization design for the planetary gear train of IEV is conducted. An electric power steering system is designed for IEV. In addition, advanced control algorithms are proposed in favour of active safety improvement. A supervision mechanism is applied to the segment drift control of autonomous driving. Double super-resolution network is used to design the intelligent driving algorithm. Torque distribution control technology and four-wheel steering technology are utilized for path tracking and adaptive cruise control. To advance the control accuracy, advanced estimation algorithms are studied in this book. The tyre-road peak friction coefficient under full slip rate range is identified based on the normalized tyre model. The pressure of the electro-hydraulic brake system is estimated based on signal fusion. Besides, a multi-semantic driver behaviour recognition model of autonomous vehicles is designed using confidence fusion mechanism. Moreover, a mono-vision based lateral localization system of low-cost autonomous vehicles is proposed with deep learning curb detection. To sum up, the discussed advanced actuators, control and estimation algorithms are beneficial to the active safety improvement of IEVs

Z-Numbers-Based Approach to Hotel Service Quality Assessment

In this study, we are analyzing the possibility of using Z-numbers for measuring the service quality and decision-making for quality improvement in the hotel industry. Techniques used for these purposes are based on consumer evalu- ations - expectations and perceptions. As a rule, these evaluations are expressed in crisp numbers (Likert scale) or fuzzy estimates. However, descriptions of the respondent opinions based on crisp or fuzzy numbers formalism not in all cases are relevant. The existing methods do not take into account the degree of con- fidence of respondents in their assessments. A fuzzy approach better describes the uncertainties associated with human perceptions and expectations. Linguis- tic values are more acceptable than crisp numbers. To consider the subjective natures of both service quality estimates and confidence degree in them, the two- component Z-numbers Z = (A, B) were used. Z-numbers express more adequately the opinion of consumers. The proposed and computationally efficient approach (Z-SERVQUAL, Z-IPA) allows to determine the quality of services and iden- tify the factors that required improvement and the areas for further development. The suggested method was applied to evaluate the service quality in small and medium-sized hotels in Turkey and Azerbaijan, illustrated by the example

Generalised correlation higher order neural networks, neural network operation and Levenberg-Marquardt training on field programmable gate arrays

Higher Order Neural Networks (HONNs) were introduced in the late 80's as a solution to the increasing complexity within Neural Networks (NNs). Similar to NNs HONNs excel at performing pattern recognition, classification, optimisation particularly for non-linear systems in varied applications such as communication channel equalisation, real time intelligent control, and intrusion detection. This research introduced new HONNs called the Generalised Correlation Higher Order Neural Networks which as an extension to the ordinary first order NNs and HONNs, based on interlinked arrays of correlators with known relationships, they provide the NN with a more extensive view by introducing interactions between the data as an input to the NN model. All studies included two data sets to generalise the applicability of the findings. The research investigated the performance of HONNs in the estimation of short term returns of two financial data sets, the FTSE 100 and NASDAQ. The new models were compared against several financial models and ordinary NNs. Two new HONNs, the Correlation HONN (C-HONN) and the Horizontal HONN (Horiz-HONN) outperformed all other models tested in terms of the Akaike Information Criterion (AIC). The new work also investigated HONNs for camera calibration and image mapping. HONNs were compared against NNs and standard analytical methods in terms of mapping performance for three cases; 3D-to-2D mapping, a hybrid model combining HONNs with an analytical model, and 2D-to-3D inverse mapping. This study considered 2 types of data, planar data and co-planar (cube) data. To our knowledge this is the first study comparing HONNs against NNs and analytical models for camera calibration. HONNs were able to transform the reference grid onto the correct camera coordinate and vice versa, an aspect that the standard analytical model fails to perform with the type of data used. HONN 3D-to-2D mapping had calibration error lower than the parametric model by up to 24% for plane data and 43% for cube data. The hybrid model also had lower calibration error than the parametric model by 12% for plane data and 34% for cube data. However, the hybrid model did not outperform the fully non-parametric models. Using HONNs for inverse mapping from 2D-to-3D outperformed NNs by up to 47% in the case of cube data mapping. This thesis is also concerned with the operation and training of NNs in limited precision specifically on Field Programmable Gate Arrays (FPGAs). Our findings demonstrate the feasibility of on-line, real-time, low-latency training on limited precision electronic hardware such as Digital Signal Processors (DSPs) and FPGAs. This thesis also investigated the e�ffects of limited precision on the Back Propagation (BP) and Levenberg-Marquardt (LM) optimisation algorithms. Two new HONNs are compared against NNs for estimating the discrete XOR function and an optical waveguide sidewall roughness dataset in order to find the Minimum Precision for Lowest Error (MPLE) at which the training and operation are still possible. The new findings show that compared to NNs, HONNs require more precision to reach a similar performance level, and that the 2nd order LM algorithm requires at least 24 bits of precision. The final investigation implemented and demonstrated the LM algorithm on Field Programmable Gate Arrays (FPGAs) for the first time in our knowledge. It was used to train a Neural Network, and the estimation of camera calibration parameters. The LM algorithm approximated NN to model the XOR function in only 13 iterations from zero initial conditions with a speed-up in excess of 3 x 10^6 compared to an implementation in software. Camera calibration was also demonstrated on FPGAs; compared to the software implementation, the FPGA implementation led to an increase in the mean squared error and standard deviation of only 17.94% and 8.04% respectively, but the FPGA increased the calibration speed by a factor of 1:41 x 106

Dynamical systems : control and stability

Proceedings of the 13th Conference „Dynamical Systems - Theory and Applications" summarize 164 and the Springer Proceedings summarize 60 best papers of university teachers and students, researchers and engineers from whole the world. The papers were chosen by the International Scientific Committee from 315 papers submitted to the conference. The reader thus obtains an overview of the recent developments of dynamical systems and can study the most progressive tendencies in this field of science

Robot Manipulators

Robot manipulators are developing more in the direction of industrial robots than of human workers. Recently, the applications of robot manipulators are spreading their focus, for example Da Vinci as a medical robot, ASIMO as a humanoid robot and so on. There are many research topics within the field of robot manipulators, e.g. motion planning, cooperation with a human, and fusion with external sensors like vision, haptic and force, etc. Moreover, these include both technical problems in the industry and theoretical problems in the academic fields. This book is a collection of papers presenting the latest research issues from around the world

Data Science: Measuring Uncertainties

With the increase in data processing and storage capacity, a large amount of data is available. Data without analysis does not have much value. Thus, the demand for data analysis is increasing daily, and the consequence is the appearance of a large number of jobs and published articles. Data science has emerged as a multidisciplinary field to support data-driven activities, integrating and developing ideas, methods, and processes to extract information from data. This includes methods built from different knowledge areas: Statistics, Computer Science, Mathematics, Physics, Information Science, and Engineering. This mixture of areas has given rise to what we call Data Science. New solutions to the new problems are reproducing rapidly to generate large volumes of data. Current and future challenges require greater care in creating new solutions that satisfy the rationality for each type of problem. Labels such as Big Data, Data Science, Machine Learning, Statistical Learning, and Artificial Intelligence are demanding more sophistication in the foundations and how they are being applied. This point highlights the importance of building the foundations of Data Science. This book is dedicated to solutions and discussions of measuring uncertainties in data analysis problems

ESSE 2017. Proceedings of the International Conference on Environmental Science and Sustainable Energy

Environmental science is an interdisciplinary academic field that integrates physical-, biological-, and information sciences to study and solve environmental problems. ESSE - The International Conference on Environmental Science and Sustainable Energy provides a platform for experts, professionals, and researchers to share updated information and stimulate the communication with each other. In 2017 it was held in Suzhou, China June 23-25, 2017