IEEE Std C37.13.1-2016 (Revision of IEEE Std C37.13.1-2006) : IEEE Standard for Definite-Purpose Switching Devices for Use in Metal-Enclosed Low-Voltage (600 V AC and Below) Power Circuit Breaker Switchgear / / IEEE

• Record Nr.: UNISA996280197003316
• Pubbl/distr/stampa: New York : , : IEEE, , 2017
• ISBN: 1-5044-2276-7
• Descrizione fisica: 1 online resource
• Disciplina: 621.317
• Soggetti: Electric circuit-breakers
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Sommario/riassunto: Definite-purpose switching devices for use in metal-enclosed low-voltage power circuit breaker switchgear are covered in this standard. The switching devices are fused, drawout type, three-pole construction, with one or more rated maximum ac voltages of 600 V, 508 V, and 254 V for application on systems having nominal ac voltages of 600 V, 480 V, and 240 V. The switching devices are power operated with integral or separately mounted overcurrent protective devices. Service conditions, ratings, functional components, temperature limitations and classifications of insulating materials, insulation (dielectric) withstand voltage requirements, test procedures, and application are addressed in this standard. The switching devices are normally used in applications that require frequency of operation greater than normal operations expected of low-voltage power circuit breakers.

Advances in Computational Intelligence : 18th International Work-Conference on Artificial Neural Networks, IWANN 2025, A Coruña, Spain, June 16–18, 2025, Proceedings, Part II / / edited by Ignacio Rojas, Gonzalo Joya, Andreu Catala

• Record Nr.: UNISA996691670203316
• Autore: Rojas Ignacio
• Pubbl/distr/stampa: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2026
• ISBN: 3-032-02728-4
• Edizione: [1st ed. 2026.]
• Descrizione fisica: 1 online resource (999 pages)
• Collana: Lecture Notes in Computer Science, , 1611-3349 ; ; 16009
• Altri autori (Persone): JoyaGonzalo; CatalaAndreu
• Disciplina: 006.3
• Soggetti: Artificial intelligence; Artificial Intelligence
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: -- Explainable and Interpretable Machine Learning (xAI) with a Focus on Applications. -- Understanding of Latent spaces in a battery aging prediction model through eXplainable AI. -- Exploring brain lateralization using Tensor decomposition of EEG phase-amplitude coupling. -- Ethical Considerations in Artificial Intelligence and Machine Learning. -- Kolmogorov-Arnold Networks for the Development of Intrusion Detection Systems. -- General Applications of AI. -- Machine Learning based Screening for Psychological Distress using a Perceived Control Mobile App. -- Tobacco and Weed Segmentation from Remote Images Using Artificial Intelligence. -- A Hybrid ResNet50-LSTM Architecture for Video Sentiment Analysis. -- Towards a Framework that facilitates the Construction of Image Segmentation Models. -- TASER-Net: Transformer Based Speech Emotion Recognition. -- Experimental Analysis and Modeling of Electrochemical Oxygen Pump Cell ECOpump. -- Empowering Scalable Fraud Detection Using Graph Neural Networks and Incremental Learning. -- Transfer Learning approach for prediction of maximum wave height in two locations of the Bay of Biscay: Bilbao and Cabo de Pe˜nas. -- Classifier fusion for the detection of defects from active thermography. -- Multimodal analysis of neuropsychological tests ; from EEG and fMRI data. -- Solid-waste Classification Using Deep Learning Fusion Model. -- Improving PV power prediction based on GRU and meteorological factors. -- Poisson Hamiltonian Neural Networks: Structure-Preserving Learning of Dynamical Systems. -- SEF-Net: A Hybrid Deep Learning Architecture for Multi-Step Forecasting in Sustainable Energy Markets. -- A new approach to detecting occupational diseases using time series. -- Comparative Analysis of Spiking Neurons Mathematical Models Training using Surrogate Gradients Techniques. -- ITOMAD – Intelligent Techniques for Optimization, Modeling, and Anomaly Detection. -- Design and Capture of a 5G SA Traffic Dataset Under Jamming Conditions. -- Predicting TiO2 and FeO Concentrations in Lunar Regolith Using Machine Learning Models: A Spectral Reflectance Approach. -- Optimal malware mitigation in IoT networks: A comparative study of Neural ODEs and Pontryagin’s maximum principle. -- Study on the Impact of Low-Cost Sensor Alternatives for Photovoltaic Panel Modelling in Smart Grid Applications. -- A Short Analysis of Hybrid Frameworks Based on Self-Organizing Maps to Improve Traditional Systems. -- Comparative Performance of Convolutional Neural Networks and Vision Transformers for Quality Assurance of a Welding Process. -- A Novel Indicator for Nitrogen Prediction in Wastewater Treatment Plants. Implementation of Intelligent Agent-Based. -- Power Prediction System for Photovoltaic Panels Using Artificial Intelligence. -- Towards safer hydrogen infrastructure: anomaly detection in synthetic hydrogen dispensing data. -- Machine Learning for 4.0 Industry Solutions. -- Physics Informed Machine Learning for Power Flow Analysis: Injecting Knowledge via Pre-, In-, and Post-Processing. -- Dimensionality Reduction and Outlier Analysis for the NF-ToN-IoT Cybersecurity Dataset. -- Data-Driven All-Optical Magnetometry: A Comparative Evaluation of Regression Models Using NV Center Fluorescence Lifetimes. -- Smart Incident Prediction from NOC Alert Events in Digital TV Broadcasting Networks. -- Machine Learning for Photovoltaic System Optimization and Control in Modern Energy Grids. -- Symmetrical Current Flow Reconstruction for Sector-shaped Multi-Wire Cables using Machine Learning. -- Comparison of Multiclass Classification on Impedance Spectra to Estimate the State of Charge of Zinc-Air Batteries. -- Edge Machine Learning for All-Optical Fluorescence Lifetime-Based Sensing With NV Centers. -- Evaluating LSTM Model Performance for Solar Energy Prediction Using Real vs. Forecasted Exogenous Weather Data. -- Computational Approaches for Resolving the Low-Field Ambiguity in All-Optical Magnetic Field Sensing With NV Centers. -- Improved Post Processing Model for Photovoltaic Power Forecasting based on Clustering. -- New and future advances in BCI-based Spellers. -- An event-related potential BCI speller using a wearable, single-channel EEG headset with electrodes on the forehead. -- A Framework for Controlling NV Centers with OPX+: Design, Implementation, and Applications. -- Exploring Code-Modulated Visual Evoked Potentials Spellers in Realistic Scenarios. -- Towards Secure Transaction Authentication Using a cVEP-Based BCI. -- Evaluating Color Heterogeneity in RSVP-Based ERP-BCIs. -- Graph-Attentive CNN for cVEP-BCI with Insights into Electrode Significance. -- BCI with Intuitive Object Control based on Code-Modulated Visual Evoked Potentials. -- Exploring the integration of c-VEP-based BCI spellers in mixed reality: a pilot study. -- Social and Ethical aspects of AI. -- Quantitative and qualitative evaluation on local explainability models for anomaly detection algorithms. -- Bias and Fairness in NLP: Addressing Social and Cultural Biases. -- Trustworthy AI Benchmark for Responsible Smart Grid as Critical Infrastructure. -- TextNet: End-; to-End Deep Learning Framework for Dynamic and Contextually Aware Text Clustering. -- Implications of Human+Machine Systems as Critical Infrastructures under Sustainable Development Goals.
• Sommario/riassunto: The two-volume set LNCS 16008 & 16009 constitutes the refereed conference proceedings of the 18th International Work-Conference on Advances in Computational Intelligence, IWANN 2025, held in A Coruña, Spain, during June 16–18, 2025. The 103 revised full papers presented in these proceedings were carefully reviewed and selected from 144 submissions. The papers are organized in the following topical sections: Part I: Advanced Topics in Computational Intelligence; AI:Bioinformatics and Biomedical Applications; ANN HW-Accelerators; Bio-Inspired Systems and Neuro-Engineering; Recent Advances in Deep Learning; Deep Learning Applied to Computer Vision, Healthcare and Robotics; and Emerging Methodologies in Time Series Forecasting. Part II: Explainable and Interpretable Machine Learning (xAI) with a Focus on Applications; General Applications of AI; ITOMAD – Intelligent Techniques for Optimization, Modeling, and Anomaly Detection; Machine Learning for 4.0 Industry Solutions; Machine Learning for Photovoltaic System Optimization and Control in Modern Energy Grids; New and future advances in BCI-based Spellers; and Social and Ethical aspects of AI.