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Jingxiong Lei, Xuzhi Liu, Haolang Yang, Zeyu Zeng and Jun Feng
High-resolution remote sensing images (HRRSI) have important theoretical and practical value in urban planning. However, current segmentation methods often struggle with issues like blurred edges and loss of detailed information due to the intricate back...
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Jin Zheng, Tong Wang, Zhi Zhang and Hongwei Wang
The objects in remote sensing images have large-scale variations, arbitrary directions, and are usually densely arranged, and small objects are easily submerged by background noises. They all hinder accurate object detection. To address the above problem...
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Kun Qin, Qixin Wang, Binbin Lu, Huabo Sun and Ping Shu
In the civil aviation industry, security risk management has shifted from post-accident investigations and analyses to pre-accident warnings in an attempt to reduce flight risks by identifying currently untracked flight events and their trends and effect...
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Rui Zhang, Zhendong Yin, Zhilu Wu and Siyang Zhou
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Gorsev Argin, Burak Pak and Handan Turkoglu
The extensive use of smartphones in our everyday lives has created new modes of appropriation and behavior in public spaces. Recognition of these are essential for urban design and planning practices which help us to improve the relationship between huma...
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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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Long Wu, Ta Li, Li Wang and Yonghong Yan
As demonstrated in hybrid connectionist temporal classification (CTC)/Attention architecture, joint training with a CTC objective is very effective to solve the misalignment problem existing in the attention-based end-to-end automatic speech recognition ...
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Qingliang Xiong, Mingping Liu, Yuqin Li, Chaodan Zheng and Suhui Deng
Due to difficulties with electric energy storage, balancing the supply and demand of the power grid is crucial for the stable operation of power systems. Short-term load forecasting can provide an early warning of excessive power consumption for utilitie...
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Shuai Ma, Yafeng Wu, Hua Zheng and Linfeng Gou
Aiming at engine health management, a novel hybrid prediction method is proposed for exhaust gas temperature (EGT) prediction of gas turbine engines. This hybrid model combines a nonlinear autoregressive with exogenous input (NARX) model and a moving ave...
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Kun Xiang and Akihiro Fujii
Climate change (CC) has become a central global topic within the multiple branches of social disciplines. Natural Language Processing (NLP) plays a superior role since it has achieved marvelous accomplishments in various application scenarios. However, C...
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Yong Han, Tongxin Peng, Cheng Wang, Zhihao Zhang and Ge Chen
Accurate prediction of citywide short-term metro passenger flow is essential to urban management and transport scheduling. Recently, an increasing number of researchers have applied deep learning models to passenger flow prediction. Nevertheless, the tas...
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Yijie Jiao, Xiaohua Wang, Wenjie Wang and Shuang Li
Deep learning has been widely used in various fields because of its accuracy and efficiency. At present, the improvement of image semantic segmentation accuracy has become the area of most concern. In terms of increasing accuracy, improved semantic segme...
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Qingtian Ke and Peng Zhang
Existing optical remote sensing image change detection (CD) methods aim to learn an appropriate discriminate decision by analyzing the feature information of bitemporal images obtained at the same place. However, the complex scenes in high-resolution (HR...
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Mingxin Zou, Yanqing Zhou, Xinhua Jiang, Julin Gao, Xiaofang Yu and Xuelei Ma
Field manual labor behavior recognition is an important task that applies deep learning algorithms to industrial equipment for capturing and analyzing people?s behavior during field labor. In this study, we propose a field manual labor behavior recogniti...
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Tao Tang, Yuting Cui, Rui Feng and Deliang Xiang
With the development of deep learning in the field of computer vision, convolutional neural network models and attention mechanisms have been widely applied in SAR image target recognition. The improvement of convolutional neural network attention in exi...
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Yundong Li, Xiaokun Wei and Hanlu Fan
Monocular depth estimation (MDE), as one of the fundamental tasks of computer vision, plays important roles in downstream applications such as virtual reality, 3D reconstruction, and robotic navigation. Convolutional neural networks (CNN)-based methods g...
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Xiaoli Liu and Xiaorong Cheng
To address the problem of a low accuracy and blurred boundaries in segmenting multimodal brain tumor images using the TransBTS network, a 3D BCS_T model incorporating a channel space attention mechanism is proposed. Firstly, the TransBTS model hierarchy ...
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Xiaonan Si, Lei Wang, Wenchang Xu, Biao Wang and Wenbo Cheng
Gout is one of the most painful diseases in the world. Accurate classification of gout is crucial for diagnosis and treatment which can potentially save lives. However, the current methods for classifying gout periods have demonstrated poor performance a...
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Mohamed Assaf, Mohamed Hussein, Sherif Abdelkhalek and Tarek Zayed
Off-site construction (OSC) is an innovative construction method that transfers most of the site-based work to a more controlled environment. Construction waste minimization, speedy schedules, higher sustainability, and better quality are some of the per...
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Nafisa Anjum, Khaleda Akhter Sathi, Md. Azad Hossain and M. Ali Akber Dewan
By using computer-aided arrhythmia diagnosis tools, electrocardiogram (ECG) signal plays a vital role in lowering the fatality rate associated with cardiovascular diseases (CVDs) and providing information about the patient?s cardiac health to the special...
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