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Evaluating the Effects of Educational Multimedia Design Principles on Cognitive Load Using EEG Signal Analysis
Educational multimedia has proven to be an effective and efficient way of learning. Designers strive to produce multimedia that convey concepts most...
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MGP: a monitoring-based VR interactive mode to support guided practice
Currently, VR training systems are primarily designed according to the non-directive mode supporting independent practice without a real teacher....
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Conv-NILM-Net, a Causal and Multi-appliance Model for Energy Source Separation
Non-Intrusive Load Monitoring (NILM) seeks to save energy by estimating individual appliance power usage from a single aggregate measurement. Deep...
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On performance evaluation and machine learning approaches in non-intrusive load monitoring
Non-Intrusive Load Monitoring (NILM) is a set of techniques to gain deep insights into workflows inside buildings based on data provided by smart...
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Enhancing neural non-intrusive load monitoring with generative adversarial networks
The application of Deep Learning methodologies to Non-Intrusive Load Monitoring (NILM) gave rise to a new family of Neural NILM approaches which...
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Approaching Cyber Situational Awareness Through Digital Services Availability Monitoring and Threat Intelligence: The MonSys Platform Experience
The security community has long identified cyber situational awareness as a critical component of effective cyber defense on a national, sectoral,...
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The Impact of Allostatic Load on Machine Learning Models
Stress is a social problem affecting society in different ways. Obtaining an accurate diagnosis of stress is complex because the symptoms of stress...
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Non-intrusive Load Monitoring Algorithms for Privacy Mining in Smart Grid
Non-intrusive load monitoring (NILM) method is essentially artificial intelligence algorithms for energy conservation and privacy mining. It obtains...
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Enhancing Home Security with Pressure Mat Sensors: A Multi-modal IoT Approach
Home security is a major concern worldwide, and there are various solutions available, but with limitations. This paper proposes a novel security...
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Multivariate Time-Series Methods with Uncertainty Estimation for Correcting Physics-Based Model: Comparisons and Generalization for Industrial Drilling Process
This study employs a hybrid methodology, integrating data-driven and physics-based models to refine the latter’s predictions. Referred to as Hybrid...
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Neural Network-Based Load Identification for Residential Electrical Installations. A Review and an Online Experimental Application
This study presents the implementation of a feed-forward neural network (FNN) for the classification of household appliances, specifically...
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Conditioned Fully Convolutional Denoising Autoencoder for Energy Disaggregation
Energy management increasingly requires tools to support decisions for improving consumption. This is achieved not only obtaining feedback from...
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A non-intrusive load decomposition algorithm for residents
In view of the large amount of data involved in the existing decomposition algorithm, which leads to low decomposition efficiency and high hardware...
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Gridchain: an investigation of privacy for the future local distribution grid
As part of building the smart grid, there is a massive deployment of so-called smart meters that aggregate information and communicate with the...
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Hierarchical Classification for Symmetrized VI Trajectory Based on Lightweight Swin Transformer
Non-intrusive Load Monitoring (NILM) is an important means to realize household energy management, and appliance identification is a significant...
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Privacy Issues in Smart Grid Data: From Energy Disaggregation to Disclosure Risk
The advancement in artificial intelligence (AI) techniques has given rise to the success rate recorded in the field of Non-Intrusive Load Monitoring...
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MapReduce with Deep Learning Framework for Student Health Monitoring System using IoT Technology for Big Data
The efficient well-being and health interventions of students are ensured by better knowledge of student’s health and fitness factors. Effective...
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Development of an AI-based FSA for real-time condition monitoring for industrial machine
Automated continuous condition monitoring of industrial electrical machines to identify internal faults has become one of the critical research areas...
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Future Flight Safety Monitoring: Comparison of Different Computational Methods for Predicting Pilot Performance Under Time Series During Descent by Flight Data and Eye-Tracking Data
Introduction. Effective and real-time analysis of pilot performance is important for improving flight safety and enabling remote flight safety...
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Research on Fault Monitoring Device of Highway Bridge Expansion Joint Based on Internet of Things Technology
Among various large-scale engineering structures, highway bridge structures are characterized by a large number, long service period, and huge...