Overview frequency principle/spectral bias in deep learning
Understanding deep learning is increasingly emergent as it penetrates more and more into
industry and science. In recent years, a research line from Fourier analysis sheds light on …
industry and science. In recent years, a research line from Fourier analysis sheds light on …
A temporal-aware LSTM enhanced by loss-switch mechanism for traffic flow forecasting
Short-term traffic flow forecasting at isolated points is a fundamental yet challenging task in
many intelligent transportation systems. We present a novel long short-term memory (LSTM) …
many intelligent transportation systems. We present a novel long short-term memory (LSTM) …
Spatio-temporal graph attention network for sintering temperature long-range forecasting in rotary kilns
Monitoring and forecasting of sintering temperature (ST) is vital for safe, stable, and efficient
operation of rotary kiln production process. Due to the complex coupling and time-varying …
operation of rotary kiln production process. Due to the complex coupling and time-varying …
Subspace decomposition based DNN algorithm for elliptic type multi-scale PDEs
While deep learning algorithms demonstrate a great potential in scientific computing, its
application to multi-scale problems remains to be a big challenge. This is manifested by the …
application to multi-scale problems remains to be a big challenge. This is manifested by the …
EfficientHRNet: efficient and scalable high-resolution networks for real-time multi-person 2D human pose estimation
C Neff, A Sheth, S Furgurson, J Middleton… - Journal of Real-Time …, 2021 - Springer
There is an increasing demand for lightweight multi-person pose estimation for many
emerging smart IoT applications. However, the existing algorithms tend to have large model …
emerging smart IoT applications. However, the existing algorithms tend to have large model …
Diversity-Driven Proactive Caching for Mobile Networks
Content caching in mobile networks is a highly promising technology for reducing traffic load
latency and energy consumption levels. Its fundamental goal is to satisfy the supply-and …
latency and energy consumption levels. Its fundamental goal is to satisfy the supply-and …
Infrared blind spectral deconvolution with low-rank sparse regularization for small object tracking
X Liu, KL Huang, J Zhou, T Liu, P Trtik… - Infrared Physics & …, 2023 - Elsevier
Infrared spectral signal often exists the random noise and peak overlap problems, which is
limited its widely applications. To address those issues, we proposed a novel blind spectral …
limited its widely applications. To address those issues, we proposed a novel blind spectral …
Unpaired Self-supervised Learning for Industrial Cyber-Manufacturing Spectrum Blind Deconvolution
Cyber-Manufacturing combines industrial big data with intelligent analysis to find and
understand the intangible problems in decision-making, which requires a systematic method …
understand the intangible problems in decision-making, which requires a systematic method …
Infrared spectral super-resolution model with linear canonical transforms regularization for spectral signals
P Hu, L Zhao, H Liu - Infrared Physics & Technology, 2023 - Elsevier
Infrared spectral super-resolution has achieved great success for spectral signals under the
noise-free case. However, the random noise and band overlap restricted the super …
noise-free case. However, the random noise and band overlap restricted the super …
A dual stream spectrum deconvolution neural network
With the development of spectral detection and photoelectric imaging, multiband spectrum is
always degraded by the random noise and band overlap during the acquisition of spectrum …
always degraded by the random noise and band overlap during the acquisition of spectrum …