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Prof.Jiang专题报告 Prof.Jiang专题报告
专题报告|AI大模型用于燃气轮机性能评估与知识库构建 清华大学蒋东翔作了题为“AI大模型用于燃气轮机性能评估与知识库构建”的主题报告。 蒋东翔，清华大学能源与动力工程系，长聘教授，博导。清华大唐绿色智慧联合研究院副院长，中
2026-01-30 大模型学习
大模型学习
工业大模型相关知识储备 工业大模型相关知识储备
京能“擎睿”大模型公众号新闻：https://mp.weixin.qq.com/s/JclVorXnHTEoD-UFl_UeOw 市发展改革委二级巡视员王志伟，京能集团副总经理申宇，清华大学AI赋能能源动力技术创新中心主任
2026-01-30 大模型学习
大模型学习
01脑电基本原理和成分 01脑电基本原理和成分
第一课时：脑电基本原理和成分一、脑电基本概念和原理EEG: Electroencephalography脑电/脑电图（EEG） 是一种记录大脑电活动的技术。它通过在头皮上放置电极来检测神经元群体的电信号。这些信号反映了大脑不同区域
2026-01-26 EEG基础
EEG基础
NeuroBridge BioInspired Self Supervised EEG to Image Decoding via Cognitive Priors and Bidirectional Semantic Alignment NeuroBridge BioInspired Self Supervised EEG to Image Decoding via Cognitive Priors and Bidirectional Semantic Alignment
No.5标签：自监督脑EEG到图像解码框架前言《NeuroBridge: Bio-Inspired Self-Supervised EEG-to-Image Decoding via Cognitive Priors and Bidirec
2026-01-13 deep learning papers
conference paper EEG to image alignment
Brain Inspired MetaLearning for Few-Shot Bearing Fault Diagnosis Brain Inspired MetaLearning for Few-Shot Bearing Fault Diagnosis
No.4标签：神经科学启发工业故障诊断前言《Brain-Inspired Meta-Learning for Few-Shot Bearing Fault Diagnosis》期刊：IEEE TRANSACTIONS ON NEURAL N
2026-01-05 deep learning papers
fault diagnosis brain-inspired learning SNN
计算机方向投稿期刊会议调研 计算机方向投稿期刊会议调研
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2026-01-03 投稿期刊
投稿期刊
Physiology-Inspired EEG Transformer for Predicting Movement Transitions in Bimanual Tasks Physiology-Inspired EEG Transformer for Predicting Movement Transitions in Bimanual Tasks
No.3标签：基于生理启发脑电Transformer模型，用于在双手任务中预测突发运动状态转换前言《Physiology-Inspired EEG Transformer for Predicting Movement Transition
2026-01-02 deep learning papers
EEG Transformer Bimanual tasks
IEEE投稿期刊调研（速度较快） IEEE投稿期刊调研（速度较快）
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2026-01-01 投稿期刊
投稿期刊
ArmBCIsys Robot Arm BCI System With Time–Frequency Network for Multiobject Grasping ArmBCIsys Robot Arm BCI System With Time–Frequency Network for Multiobject Grasping
No.2标签：融合脑电与视觉、时频网络的机器人手臂脑机接口系统前言《ArmBCIsys: Robot Arm BCI System With Time–Frequency Network for Multiobject Grasping》期
2025-12-31 deep learning papers
BCI object segmentation and tracking
Adaptive Knowledge Distillation with Attention-Based Multi-Modal Fusion for Robust Dim Object Detection Adaptive Knowledge Distillation with Attention-Based Multi-Modal Fusion for Robust Dim Object Detection
No.1标签：融合脑电与图像的多模态深度学习框架前言《Adaptive Knowledge Distillation with Attention-Based Multi-Modal Fusion for Robust Dim Object
2025-12-30 deep learning papers
EEG and image fusion
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