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Your search keyword 'LN cat08778a' showing total 5 results
5 results on 'LN cat08778a'

1. AI Applications to Power Systems.

Author

Tjing Lie, Tek and Tjing Lie, Tek

Subjects
Technology: general issues, History of engineering & technology, self-healing grid, machine-learning, feature extraction, event detection, optimization techniques, manta ray foraging optimization algorithm, multi-objective function, radial networks, optimal power flow, automatic P2P energy trading, Markov decision process, deep reinforcement learning, deep Q-network, long short-term delayed reward, inter-area oscillations, modal analysis, reduced order modeling, dynamic mode decomposition, machine learning, artificial neural networks, steady-state security assessment, situation awareness, cellular computational networks, load flow prediction, contingency, fuzzy system, change detection, data analytics, data mining, filtering, optimization, power quality, signal processing, total variation smoothing, n/a
Abstract

Summary: Today, the flow of electricity is bidirectional, and not all electricity is centrally produced in large power plants. With the growing emergence of prosumers and microgrids, the amount of electricity produced by sources other than large, traditional power plants is ever-increasing. These alternative sources include photovoltaic (PV), wind turbine (WT), geothermal, and biomass renewable generation plants. Some renewable energy resources (solar PV and wind turbine generation) are highly dependent on natural processes and parameters (wind speed, wind direction, temperature, solar irradiation, humidity, etc.). Thus, the outputs are so stochastic in nature. New data-science-inspired real-time solutions are needed in order to co-develop digital twins of large intermittent renewable plants whose services can be globally delivered.

2. Alternative Sources of Energy Modeling, Automation, Optimal Planning and Operation.

Author

Stavrakakis, George S. and Stavrakakis, George S.

Subjects
Technology: general issues, History of engineering & technology, entrained flow coal gasification, ash melting point, operation temperature, Markov process, stochastic optimization model, genetic algorithm, gallium nitride, magnetic-free converters, module-level converters, parallel architecture, partial shading, photovoltaic systems, switched capacitor converters, hybrid energy storage system, supercapacitor, lead-acid battery, energy management system, battery degradation, depth of discharge, techno-economic analysis, hybrid power station, green island, energy storage, remote community, reserves, k-means, probabilistic dimensioning, dynamic dimensioning, balancing, wave energy converters, deep neural networks, renewable energy sources, spatial planning, sentinel satellite imagery, permanent magnet synchronous machines, generators, fault detection, demagnetization, artificial intelligence, data mining, machine learning, advanced deep learning, windspeed forecasting, solar irradiation forecasting, increased RES penetration, smart grid, scalability, replicability, FLEXITRANSTORE, Angolan economy, diversification, strategic alternative, biofuels
Abstract

Summary: An economic development model analyzes the adoption of alternative strategy capable of leveraging the economy, based essentially on RES. The combination of wind turbine, PV installation with new technology battery energy storage, DSM network and RES forecasting algorithms maximizes RES integration in isolated islands. An innovative model of power system (PS) imbalances is presented, which aims to capture various features of the stochastic behavior of imbalances and to reduce in average reserve requirements and PS risk. Deep learning techniques for medium-term wind speed and solar irradiance forecasting are presented, using for first time a specific cloud index. Scalability-replicability of the FLEXITRANSTORE technology innovations integrates hardware-software solutions in all areas of the transmission system and the wholesale markets, promoting increased RES. A deep learning and GIS approach are combined for the optimal positioning of wave energy converters. An innovative methodology to hybridize battery-based energy storage using supercapacitors for smoother power profile, a new control scheme and battery degradation mechanism and their economic viability are presented. An innovative module-level photovoltaic (PV) architecture in parallel configuration is introduced maximizing power extraction under partial shading. A new method for detecting demagnetization faults in axial flux permanent magnet synchronous wind generators is presented. The stochastic operating temperature (OT) optimization integrated with Markov Chain simulation ascertains a more accurate OT for guiding the coal gasification practice.

3. Bioinformatics Applications Based On Machine Learning.

Author

Chamoso, Pablo, Rodriguez, Sara, Mohamad, Mohd, González-Briones, Alfonso, and Chamoso, Pablo

Subjects
Technology: general issues, machine learning, metagenomics, bioinformatics, CTX-M, data mining, cluster, clinical implications, diabetes, epidemiology, forecast, PART, Decision table, Weka, real-life patients, regression, ear detection, computer vision, convolutional neural network, image recognition, video analysis, gene clustering, swarm intelligence, biological functions detection, informative genes, fuel cell, hydrogen energy, intelligent systems, hybrid systems, Artificial Neural Networks, power management, Machine Learning, personality assessment, gradient boosting, Affective Computing, transposable elements, metrics, deep learning, detection, classification, mitochondrial protein, bi-directional LSTM, plasmodium falciparum, Particle Swarm Optimization, Harmony Search, parameter estimation, Arabidopsis thaliana, clinical data, feature selection, genetic programming, evolutionary computation, dynamic models, evolutionary computing, derivative-free optimization, metabolism, glycolysis, yeast
Abstract

Summary: The great advances in information technology (IT) have implications for many sectors, such as bioinformatics, and has considerably increased their possibilities. This book presents a collection of 11 original research papers, all of them related to the application of IT-related techniques within the bioinformatics sector: from new applications created from the adaptation and application of existing techniques to the creation of new methodologies to solve existing problems.

4. Computation in Complex Networks.

Author

Pizzuti, Clara, Socievole, Annalisa, and Pizzuti, Clara

Subjects
Technology: general issues, city interaction network, evolution model, preferential attachment, WeChat, maximum likelihood, chimera states, coupled map lattice, nilpotent matrix, community detection, membrane algorithm, self-organizing map network, complex networks, optimization, structural balance, minimum memory based sign adjustment, social networks, NW network, convergence, complex system simulation, cloud computing architecture, service-oriented modeling, semantic search framework, QoS-based service selection, cascading failures, network topology, null models, SciSci, knowledge evolution, machine learning, bridging centrality, disjoint nodes, disjunct nodes, node similarity, overlapping nodes, Bayesian networks, entropy, socio-ecological system, complex network, chaotic time series, Gaussian mixture model, maximum mean discrepancy, angiogenesis, network properties, variational inference, graph neural network, variational autoencoder, network embedding, online social networks, social media, information spreading, information diffusion, cross-entropy, cross-domain recommendation, sentiment analysis, latent sentiment review feature, non-linear mapping, dissimilarity spaces, support vector machines, kernel methods, computational biology, systems biology, protein contact networks, data mining, overlapping communities, modularity, literary works, genre classification, stylistic attributes, lemmatization, renormalisation process, network growth, inverse preferential attachment, language networks, language development, multilayer complex networks, stability, spreading control, graph neural networks, node classification, active learning, graph representation learning, n/a
Abstract

Summary: Complex networks are one of the most challenging research focuses of disciplines, including physics, mathematics, biology, medicine, engineering, and computer science, among others. The interest in complex networks is increasingly growing, due to their ability to model several daily life systems, such as technology networks, the Internet, and communication, chemical, neural, social, political and financial networks. The Special Issue "Computation in Complex Networks" of Entropy offers a multidisciplinary view on how some complex systems behave, providing a collection of original and high-quality papers within the research fields of: • Community detection • Complex network modelling • Complex network analysis • Node classification • Information spreading and control • Network robustness • Social networks • Network medicine

5. Machine Learning in Tribology.

Author

Tremmel, Stephan, Marian, Max, and Tremmel, Stephan

Subjects
Technology: general issues, History of engineering & technology, artificial intelligence, machine learning, artificial neural networks, tribology, condition monitoring, semi-supervised learning, random forest classifier, self-lubricating journal bearings, reduced order modelling, dynamic friction, rubber seal applications, tensor decomposition, laser surface texturing, texturing during moulding, digital twin, PINN, reynolds equation, triboinformatics, databases, data mining, meta-modeling, monitoring, analysis, prediction, optimization, fault data generation, Convolutional Neural Network (CNN), Generative Adversarial Network (GAN), bearing fault diagnosis, unbalanced datasets, tribo-testing, tribo-informatics, natural language processing, tribAIn, BERT, amorphous carbon coatings, UHWMPE, total knee replacement, Gaussian processes, rolling bearing dynamics, cage instability, regression, neural networks, random forest, gradient boosting, evolutionary algorithms, rolling bearings, remaining useful life, feature engineering, structure-borne sound, n/a
Abstract

Summary: Tribology has been and continues to be one of the most relevant fields, being present in almost all aspects of our lives. The understanding of tribology provides us with solutions for future technical challenges. At the root of all advances made so far are multitudes of precise experiments and an increasing number of advanced computer simulations across different scales and multiple physical disciplines. Based upon this sound and data-rich foundation, advanced data handling, analysis and learning methods can be developed and employed to expand existing knowledge. Therefore, modern machine learning (ML) or artificial intelligence (AI) methods provide opportunities to explore the complex processes in tribological systems and to classify or quantify their behavior in an efficient or even real-time way. Thus, their potential also goes beyond purely academic aspects into actual industrial applications. To help pave the way, this article collection aimed to present the latest research on ML or AI approaches for solving tribology-related issues generating true added value beyond just buzzwords. In this sense, this Special Issue can support researchers in identifying initial selections and best practice solutions for ML in tribology.

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