Technical institutes
Researchers at Northwestern Polytechnical University have designed a new intrusion detection system called KronNet, which is capable of operating within the stringent resource constraints of Internet of Things (IoT) devices. KronNet leverages a lightweight feed-forward neural network enhanced with Kronecker product operations to detect various types of attacks in real-time.
Technical institutes
Research conducted by the Beijing Institute of Technology has resulted in a novel combination of deep learning algorithms with large-scale real-world charging data, providing practical solutions for promoting electric vehicle adoption and achieving low-carbon transportation. The study aimed to analyze current charging behaviors and evaluate the impact of key variables
Technical institutes
Research conducted at Anhui Polytechnic University in Wuhu, People's Republic of China, has resulted in the development of a novel neural network-based spectral optimization model for enhancing the performance and detection accuracy of gas sensors using tunable diode laser absorption spectroscopy (TDLAS) technology. The model, which combines a
Technical institutes
Researchers at Omsk State Technical University have been investigating the application of artificial neural networks for saturation correction in current and voltage transformers. Their study found that artificial neural networks can increase accuracy in signal processing, improving the reliability and safety of electric power systems. Under saturation conditions, transformers can
Artificial neural networks
Researchers from the University Hospital Center Vaudois have conducted a pilot study to investigate the resilience of neural networks in patients who have suffered from severe COVID-19. The study, published in Brain Sciences, assessed the activation patterns of neural networks in patients who had been hospitalized in intensive care units
Artificial neural networks
Researchers at Western Carolina University have proposed a new technique using Convolutional Neural Networks (CNN) to improve fault detection and classification in wind farms. The technique, which leverages Low Voltage Ride Through (LVRT) code, integrates renewable energy sources and power system stability, enabling faster responses to faults. This innovative approach
Artificial neural networks
Researchers from China, collaborating with international teams, have made a significant breakthrough in brain mapping, publishing ten seminal studies on mesoscale brain atlases in leading journals. The studies, published in Cell, Neuron, and other Cell Press journals, demonstrate China's growing expertise and international leadership in mesoscale brain mapping
Artificial neural networks
Researchers from Tulane University have made significant strides in multimodal neuroimaging data modeling, developing a novel approach that integrates functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and structural MRI (sMRI) for joint analysis. This breakthrough has led to a deeper understanding of the intricate relationships between the brain&
Artificial neural networks
Researchers from Tongji University in Shanghai, China, have developed a new approach to predict the degradation trend of proton exchange membrane fuel cells, a crucial technology for clean energy applications. The study, published in the journal Energies, utilizes a data-driven method to construct a degradation prediction model, incorporating a neural
Artificial neural networks
Researchers from Aswan University in Egypt have developed a robust deep neural network-based internet of things (IoT) system for diagnosing power transformer faults. The system, which utilizes a deep learning model hybridized with an IoT platform, has achieved high accuracy in diagnosing faults under imbalanced data and uncertainties. The proposed
Artificial neural networks
Researchers from the University of Naples Federico II in Naples, Italy, have made a breakthrough in the field of big data and cognitive computing, developing an automated method for bone segmentation in magnetic resonance imaging (MRI) scans. The method uses a 3D U-Net convolutional neural network to segment the femur,
Artificial neural networks
Researchers from Central South University have developed an artificial neural network (ANN) model using magnetic resonance imaging (MRI) radiomics data to accurately classify gliomas into four pathological grades. The model demonstrates a high degree of diagnostic accuracy, with overall ratings of 91.28% and 87.04% for the training and
