|
|
|
|
|
Jose Luis Vieira Sobrinho, Flavio Henrique Teles Vieira and Alisson Assis Cardoso
The high dimensionality of real-life datasets is one of the biggest challenges in the machine learning field. Due to the increased need for computational resources, the higher the dimension of the input data is, the more difficult the learning task will ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Oleg Gaidai, Jingxiang Xu, Vladimir Yakimov and Fang Wang
Wind turbines and their associated parts are subjected to cyclical loads, such as bending, torque, longitudinal stresses, and twisting moments. The novel spatiotemporal reliability technique described in this research is especially useful for high-dimens...
ver más
|
|
|
|
|
|
|
|
|
|
|
Zhenwen He, Chi Zhang and Yunhui Cheng
Time series data typically exhibit high dimensionality and complexity, necessitating the use of specific approximation methods to perform computations on the data. The currently employed compression methods suffer from varying degrees of feature loss, le...
ver más
|
|
|
|
|
|
|
|
|
|
|
Rubén E. Nogales and Marco E. Benalcázar
Gesture recognition is widely used to express emotions or to communicate with other people or machines. Hand gesture recognition is a problem of great interest to researchers because it is a high-dimensional pattern recognition problem. The high dimensio...
ver más
|
|
|
|
|
|
|
|
|
|
|
Mengran Zhou, Xiaokang Yao, Ziwei Zhu and Feng Hu
A prerequisite for refined load management, crucial for intelligent energy management, is the precise classification of electric loads. However, the high dimensionality of electric load samples and poor identification accuracy of industrial scenarios mak...
ver más
|
|
|
|
|
|
|
|
|
|
|
Duo Sun, Lei Zhang, Kai Jin, Jiasheng Ling and Xiaoyuan Zheng
Aiming at the imbalance of industrial control system data and the poor detection effect of industrial control intrusion detection systems on network attack traffic problems, we propose an ETM-TBD model based on hybrid machine learning and neural network ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Justice J. Mason, Christine Allen-Blanchette, Nicholas Zolman, Elizabeth Davison and Naomi Ehrich Leonard
In many real-world settings, image observations of freely rotating 3D rigid bodies may be available when low-dimensional measurements are not. However, the high-dimensionality of image data precludes the use of classical estimation techniques to learn th...
ver más
|
|
|
|
|
|
|
|
|
|
|
Lexin Zhang, Ruihan Wang, Zhuoyuan Li, Jiaxun Li, Yichen Ge, Shiyun Wa, Sirui Huang and Chunli Lv
This research introduces a novel high-accuracy time-series forecasting method, namely the Time Neural Network (TNN), which is based on a kernel filter and time attention mechanism. Taking into account the complex characteristics of time-series data, such...
ver más
|
|
|
|
|
|
|
|
|
|
|
Ming Chen, Xinhu Zhang, Kechun Shen and Guang Pan
The geometrical dimensions and mechanical properties of composite materials exhibit inherent variation and uncertainty in practical engineering. Uncertainties in geometrical dimensions and mechanical properties propagate to the structural performance of ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Evandro S. Ortigossa, Fábio Felix Dias and Diego Carvalho do Nascimento
The exploration and analysis of multidimensional data can be pretty complex tasks, requiring sophisticated tools able to transform large amounts of data bearing multiple parameters into helpful information. Multidimensional projection techniques figure a...
ver más
|
|
|
|
|
|