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A novel swarm budorcas taxicolor optimization-based multi-support vector method for transformer fault diagnosis.
Neural Netw
January 2025
School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, China; Key Laboratory of Autonomous Systems and Network Control, Ministry of Education, South China University of Technology, Guangzhou, 510640, China; Institute for Super Robotics (Huangpu), Guangzhou, 510555, China; Nanchang University, Nanchang, 330031, China; College of Computer Science and Engineering, Jishou University, Jishou, 416000, China; Guangdong Artificial Intelligence and Digital Economy Laboratory (Pazhou Lab), Guangzhou, 510335, China; School of Electronical Engineering, Shaanxi University of Technology, Hanzhong, 723001, China; School of Information Science and Engineering, Changsha Normal University, Changsha, 410100, China; Institute of Artificial Intelligence and Automation, Guangdong University of Petrochemical Technology, Maoming, 525000, China. Electronic address:
To address the challenge of low recognition accuracy in transformer fault detection, a novel method called swarm budorcas taxicolor optimization-based multi-support vector (SBTO-MSV) is proposed. Firstly, a multi-support vector (MSV) model is proposed to realize multi-classification of transformer faults based on dissolved gas data. Then, a swarm budorcas taxicolor optimization (SBTO) algorithm is proposed to iteratively search the optimal model parameters during MSV model training, so as to obtain the most effective transformer fault diagnosis model.
View Article and Find Full Text PDFA Fine-grained Hemispheric Asymmetry Network for accurate and interpretable EEG-based emotion classification.
Neural Netw
January 2025
Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region; Department of Computing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region. Electronic address:
In this work, we propose a Fine-grained Hemispheric Asymmetry Network (FG-HANet), an end-to-end deep learning model that leverages hemispheric asymmetry features within 2-Hz narrow frequency bands for accurate and interpretable emotion classification over raw EEG data. In particular, the FG-HANet extracts features not only from original inputs but also from their mirrored versions, and applies Finite Impulse Response (FIR) filters at a granularity as fine as 2-Hz to acquire fine-grained spectral information. Furthermore, to guarantee sufficient attention to hemispheric asymmetry features, we tailor a three-stage training pipeline for the FG-HANet to further boost its performance.
View Article and Find Full Text PDFUltrasensitive Quantification of Neonicotinoid Thiamethoxam in Environment using MOF-derived CuCoO/3D rGO Based Electrochemical Sensor Integrated with Optimized Neural Network.
Environ Res
January 2025
Department of Chemistry, University college in Al-Jamoum, Umm Al-Qura University, 21955, Makkah, Saudi Arabia.
Accurate quantification of neonicotinoid insecticides is pivotal to ensure environmental safety by examining and mitigating their potential harmful effects on pollinators and aquatic ecosystems. In this scenario, detection of neonicotinoid insecticide, thiamethoxam (TMX), is significant for safeguarding ecological balance and human health. Hence, we developed a highly sensitive electrochemical sensor for detection of TMX in environmental samples, utilizing a novel nanocomposite with superior electrocatalytic properties and integrating an optimized neural network for accurate data analysis.
View Article and Find Full Text PDFContrastive Graph Representation Learning with Adversarial Cross-View Reconstruction and Information Bottleneck.
Neural Netw
January 2025
School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China; Ministry of Education Key Laboratory for Intelligent Networks and Network Security, Xi'an Jiaotong University, Xi'an, 710049, China. Electronic address:
Graph Neural Networks (GNNs) have received extensive research attention due to their powerful information aggregation capabilities. Despite the success of GNNs, most of them suffer from the popularity bias issue in a graph caused by a small number of popular categories. Additionally, real graph datasets always contain incorrect node labels, which hinders GNNs from learning effective node representations.
View Article and Find Full Text PDFVPT: Video portraits transformer for realistic talking face generation.
Neural Netw
January 2025
School of Automation Science and Engineering, South China University of Technology, China. Electronic address:
Talking face generation is a promising approach within various domains, such as digital assistants, video editing, and virtual video conferences. Previous works with audio-driven talking faces focused primarily on the synchronization between audio and video. However, existing methods still have certain limitations in synthesizing photo-realistic video with high identity preservation, audiovisual synchronization, and facial details like blink movements.
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