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Felix Schmid and Jorge Leandro
Inundation maps that show water depths that occur in the event of a flood are essential for protection. Especially information on timings is crucial. Creating a dynamic inundation map with depth data in temporal resolution is a major challenge and is not...
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Ping Huang and Yafeng Wu
Airborne speech enhancement is always a major challenge for the security of airborne systems. Recently, multi-objective learning technology has become one of the mainstream methods of monaural speech enhancement. In this paper, we propose a novel multi-o...
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Le Hoang Anh, Gwang-Hyun Yu, Dang Thanh Vu, Hyoung-Gook Kim and Jin-Young Kim
In the face of increasing irregular temperature patterns and climate shifts, the need for accurate power consumption prediction is becoming increasingly important to ensure a steady supply of electricity. Existing deep learning models have sought to impr...
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Yong Liu, Weiwen Zhan, Yuan Li, Xingrui Li, Jingkai Guo and Xiaoling Chen
Smart grid-training systems enable trainers to achieve the high safety standards required for power operation. Effective methods for the rational segmentation of continuous fine actions can improve smart grid-training systems, which is of great significa...
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Indra Riyanto, Mia Rizkinia, Rahmat Arief and Dodi Sudiana
Flooding in urban areas is counted as a significant disaster that must be correctly mitigated due to the huge amount of affected people, material losses, hampered economic activity, and flood-related diseases. One of the technologies available for disast...
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Jiaan Zhang, Chenyu Liu and Leijiao Ge
The large fluctuations in charging loads of electric vehicles (EVs) make short-term forecasting challenging. In order to improve the short-term load forecasting performance of EV charging load, a corresponding model-based multi-channel convolutional neur...
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Qiongfang Yu, Liang Zhao and Yi Yang
For low-voltage three-phase systems, the deep fault arc features are difficult to extract, and the phase information has strong timing. This phenomenon leads to the problem of low accuracy of fault phase selection. This paper proposes a three-phase fault...
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Kewei Ouyang, Yi Hou, Shilin Zhou and Ye Zhang
Recently, some researchers adopted the convolutional neural network (CNN) for time series classification (TSC) and have achieved better performance than most hand-crafted methods in the University of California, Riverside (UCR) archive. The secret to the...
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Lanqian Yang, Jinmin Guo, Huili Tian, Min Liu, Chang Huang and Yang Cai
Accurate load forecasting is of vital importance for improving the energy utilization efficiency and economic profitability of intelligent buildings. However, load forecasting is restricted in the popularization and application of conventional load forec...
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Jiahui Zhao, Zhibin Li, Pan Liu, Mingye Zhang
Pág. 115 - 142
Demand prediction plays a critical role in traffic research. The key challenge of traffic demand prediction lies in modeling the complex spatial dependencies and temporal dynamics. However, there is no mature and widely accepted concept to support the so...
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Hwan Kim and Sungsu Lim
Non-Intrusive Load Monitoring (NILM) techniques are effective for managing energy and for addressing imbalances between the energy demand and supply. Various studies based on deep learning have reported the classification of appliances from aggregated po...
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Zhan Wu, Tong Chen, Ying Chen, Zhihao Zhang and Guangyuan Liu
Facial expression recognition (FER) under active near-infrared (NIR) illumination has the advantages of illumination invariance. In this paper, we propose a three-stream 3D convolutional neural network, named as NIRExpNet for NIR FER. The 3D structure of...
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Yepeng Cheng, Zuren Liu and Yasuhiko Morimoto
Traditional time series forecasting techniques can not extract good enough sequence data features, and their accuracies are limited. The deep learning structure SeriesNet is an advanced method, which adopts hybrid neural networks, including dilated causa...
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Petros Brimos, Areti Karamanou, Evangelos Kalampokis and Konstantinos Tarabanis
Traffic forecasting has been an important area of research for several decades, with significant implications for urban traffic planning, management, and control. In recent years, deep-learning models, such as graph neural networks (GNN), have shown grea...
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Anqi Jin and Xiangyang Zeng
Long-range underwater targets must be accurately and quickly identified for both defense and civil purposes. However, the performance of an underwater acoustic target recognition (UATR) system can be significantly affected by factors such as lack of data...
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Zhihao Zhang, Yong Han, Tongxin Peng, Zhenxin Li and Ge Chen
Accurate subway passenger flow prediction is crucial to operation management and line scheduling. It can also promote the construction of intelligent transportation systems (ITS). Due to the complex spatial features and time-varying traffic patterns of s...
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Miaomiao Hou, Xiaofeng Hu, Jitao Cai, Xinge Han and Shuaiqi Yuan
Crime issues have been attracting widespread attention from citizens and managers of cities due to their unexpected and massive consequences. As an effective technique to prevent and control urban crimes, the data-driven spatial?temporal crime prediction...
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Yue Sun, Sandor Brockhauser and Péter Hegedus
In scientific research, spectroscopy and diffraction experimental techniques are widely used and produce huge amounts of spectral data. Learning patterns from spectra is critical during these experiments. This provides immediate feedback on the actual st...
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(1) Lalasa Mukku (CHRIST (Deemed to be University), India) (2) Jyothi Thomas (CHRIST (Deemed to be University), India)
Pág. 502 - 523
Cervical cancer ranks as the fourth most prevalent malignancy among women globally. Timely identification and intervention in cases of cervical cancer hold the potential for achieving complete remission and cure. In this study, we built a deep learning m...
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Xiaolei Diao, Xiaoqiang Li and Chen Huang
The same action takes different time in different cases. This difference will affect the accuracy of action recognition to a certain extent. We propose an end-to-end deep neural network called ?Multi-Term Attention Networks? (MTANs), which solves the abo...
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