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Robust Load Prediction of Power Network Clusters Based on Cloud-Model-Improved Transformer

Autor: Jiang, Cheng, Lu, Gang, Ma, Xue, Wu, Di

Load data from power network clusters indicates economic development in each area, crucial for predicting regional trends and guiding power enterprise decisions. The Transformer model, a leading method for load prediction, faces challenges modeling h

Externí odkaz: http://arxiv.org/abs/2407.20817

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Beyond MOT: Semantic Multi-Object Tracking

Autor: Li, Yunhao, Li, Qin, Wang, Hao, Ma, Xue, Yao, Jiali, Dong, Shaohua, Fan, Heng, Zhang, Libo

Current multi-object tracking (MOT) aims to predict trajectories of targets (i.e., ''where'') in videos. Yet, knowing merely ''where'' is insufficient in many crucial applications. In comparison, semantic understanding such as fine-grained behaviors,

Externí odkaz: http://arxiv.org/abs/2403.05021

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TSViT: A Time Series Vision Transformer for Fault Diagnosis

Autor: Zhang, Shouhua, Zhou, Jiehan, Ma, Xue, Pirttikangas, Susanna, Yang, Chunsheng

Traditional fault diagnosis methods using Convolutional Neural Networks (CNNs) often struggle with capturing the temporal dynamics of vibration signals. To overcome this, the application of Transformer-based Vision Transformer (ViT) methods to fault

Externí odkaz: http://arxiv.org/abs/2311.06916

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