278 research outputs found
Data Set of PLOS Computational Paper PCOMPBIOL-D-18-02181R1
No full textFigures Data of PLOS Computational paper:Modeling of the axon plasma membrane structure and its effects on protein diffusionAuthors: Yihao Zhang, Anastasios V. Tzingounis, and George LykotrafitisCorresponding Author: George Lykotrafitis, Ph.D.University of ConnecticutStorss, CT UNITED STATES</div
The state of modern Greek language as spoken in Victoria
No full textDeposited with permission of the author. © 1986 Dr. Anastasios TamisThis thesis reports a sociolinguistic study, carried out between 1981 and 1984, of the state of the Modern Greek (MG) language in Australia, as spoken by native-speaking first-generation Greek immigrants in Victoria. Particular emphasis is given to the analysis of those characteristics of the linguistic behaviour of these Greek Australians which can be attributed to the contact with English and to other environmental, social and linguistic influence. (For complete abstract open document
Special Issue “Intelligent Control in Energy Systems”
No full textThe editor of this special issue on “Intelligent Control in Energy Systems” have made an attempt to publish a book containing original technical articles addressing various elements of intelligent control in energy systems. The response to our call had 60 submissions, of which 27 were published submissions and 33 were rejections. This book contains 27 technical articles and one editorial. All have been written by authors from 15 countries (China, Netherlands, Spain, Tunisia, United States of America, Korea, Brazil, Egypt, Denmark, Indonesia, Oman, Canada, Algeria, Mexico, and Czech Republic), which elaborated several aspects of intelligent control in energy systems. It covers a broad range of topics including fuzzy PID in automotive fuel cell and MPPT tracking, neural network for fuel cell control and dynamic optimization of energy management, adaptive control on power systems, hierarchical Petri Nets in microgrid management, model predictive control for electric vehicle battery and frequency regulation in HVAC systems, deep learning for power consumption forecasting, decision tree for wind systems, risk analysis for demand side management, finite state automata for HVAC control, robust μ-synthesis for microgrid, and neuro-fuzzy systems in energy storage
Intelligent Control in Energy Systems
No full textThe editors of this Special Issue titled “Intelligent Control in Energy Systems” have attempted to create a book containing original technical articles addressing various elements of intelligent control in energy systems. In response to our call for papers, we received 60 submissions. Of those submissions, 27 were published and 33 were rejected. In this book, we offer the 27 accepted technical articles as well as one editorial. Authors from 15 countries (China, Netherlands, Spain, Tunisia, United Sates of America, Korea, Brazil, Egypt, Denmark, Indonesia, Oman, Canada, Algeria, Mexico, and the Czech Republic) elaborate on several aspects of intelligent control in energy systems. The book covers a broad range of topics including fuzzy PID in automotive fuel cell and MPPT tracking, neural networks for fuel cell control and dynamic optimization of energy management, adaptive control on power systems, hierarchical Petri Nets in microgrid management, model predictive control for electric vehicle battery and frequency regulation in HVAC systems, deep learning for power consumption forecasting, decision trees for wind systems, risk analysis for demand side management, finite state automata for HVAC control, robust ?-synthesis for microgrids, and neuro-fuzzy systems in energy storage
Fuzzy Q-Learning Agent for Online Tuning of PID Controller for DC Motor Speed Control
No full textThis paper proposes a hybrid Zeigler-Nichols (Z-N) reinforcement learning approach for online tuning of the parameters of the Proportional Integral Derivative (PID) for controlling the speed of a DC motor. The PID gains are set by the Z-N method, and are then adapted online through the fuzzy Q-Learning agent. The fuzzy Q-Learning agent is used instead of the conventional Q-Learning, in order to deal with the continuous state-action space. The fuzzy Q-Learning agent defines its state according to the value of the error. The output signal of the agent consists of three output variables, in which each one defines the percentage change of each gain. Each gain can be increased or decreased from 0% to 50% of its initial value. Through this method, the gains of the controller are adjusted online via the interaction of the environment. The knowledge of the expert is not a necessity during the setup process. The simulation results highlight the performance of the proposed control strategy. After the exploration phase, the settling time is reduced in the steady states. In the transient states, the response has less amplitude oscillations and reaches the equilibrium point faster than the conventional PID controller
New historical evidence for Anastasios Emm. Papas
No full textNo AbstractThe author’s attention has been drawn to the existence of this historicalevidence in the National Archives of Vienna, by his friend the writer EteoclesGregoriadis together with the numbers of the relevant files. Most of the documents were written in the old German script. Thus the author asked for the help of his friend and former colleague at the University of Thessaloniki and director of the Goethe Institute, Graf Kurt v. Posadowsky, for reading andstudying those documents. Without his help this study would have been impossible. This new evidence concerns the sojourn of Anastasios Papas·—son of Emmanuel Papas, leading figure of the Greek Revolution—in Austria andGermany between the 3rd January and 11th March 1822. There is informationabout his short imprisonment in Trieste, after his arival from Vienna. He then visits various towns in Germany and after negotiations with the Philhellene professor Fr. Thiersch in Munich, he purchases large quantities of ammunition to be despatched to Greece. He finally arrives in Greece early in 1824, and takes part—together with his three brothers who were already fighting—in the struggle for the liberation of the common great fartheland
Intelligent Fuzzy Models: WM, ANFIS, and Patch Learning for the Competitive Forecasting of Environmental Variables
No full textThis paper focuses on the application of fuzzy modeling methods in the field of environmental engineering. Since predicting meteorological data is considered to be a challenging task, the current work aimed to assess the performance of various fuzzy models on temperature, solar radiation, and wind speed forecasting. The models studied were taken from the fuzzy systems literature, varying from well-established to the most recent methods. Four cases were considered: a Wang–Mendel (WM)-based fuzzy predictive model, an adaptive network fuzzy inference system (ANFIS), a fuzzy system ensemble, and patch learning (PL). The prediction systems were built from input/output data without any prior information, in a model-free approach. The ability of the models to display high performance on complex real datasets, provided by the National Observatory of Athens, was demonstrated through numerical studies. Patch learning managed to not only display a similar approximation ability to that of strong machine learning models, such as support vector machines and Gaussian processes, but also outperform them on the highly demanding problem of wind speed prediction. More accurately, as far as wind speed prediction is concerned, patch learning produced a 0.9211 root mean squared error for the training data and a value of 0.9841 for the testing data. The support vector machine provided a 0.9306 training root mean squared error and a 0.9891 testing value. The Gaussian process model resulted in a 0.9343 root mean squared error for the training data and a value of 0.9861 for the testing data. Finally, as shown by the numerical experiments, the fuzzy system ensemble exhibited the highest generalisation performance among all the intelligent models
Intelligent Management of Distributed Energy Resources for Increased Resilience and Environmental Sustainability of Hospitals
No full textThere is a global trend towards zero-energy or even positive-energy buildings, including healthcare facilities. Energy efficiency activities have been investigated and applied successfully for more than 20 years in healthcare facilities in general and hospitals in particular. It is in the last decade that on-site energy production mainly from photovoltaics has been considered mainly as an extra revenue stream for healthcare facilities. Back-up systems are still diesel generator-based in most cases and only recently has there been interest in unifying the energy systems of healthcare facilities in order to integrate the operation of the main systems of the hospital with the on-site renewable energy production and the back-up systems. Hospitals play a very crucial role in our societies. There is a need to achieve the best results in terms of healthcare services but, at the same time, to reduce the cost of these services without affecting the quality level, to enhance resilience and to increase environmental sustainability. As far as energy is concerned, this is feasible and can be accomplished using energy efficiency interventions and on-site power generation and storage using renewable energy technologies. An Intelligent Energy Management System (IEMS) has to be in place in order to harvest the benefits of all the related subsystems allowing them to operate effectively and harmoniously, while at the same time ensuring the operation of the hospital under extreme conditions, e.g., after a natural disaster. The research concerning IEMSs for hospitals is at its first steps and needs to gain momentum
Machine Intelligence in Smart Buildings
No full textEnergy efficiency is a key concern in achieving sustainability in modern society [...
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