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 …

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) …

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 …

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 …

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 …

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 …

Cyber-Manufacturing combines industrial big data with intelligent analysis to find and
understand the intangible problems in decision-making, which requires a systematic method …

Infrared spectral data often exhibit band overlap and random noise when it is applied to
recognize the unknown chemical materials. To address these issues, a novel regularization …

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 …

Accurate identification of weed species is the premise for controlling weeds in field. But it is a
challenging task due to the complexity and high-dimensional nonlinearity of the weed …