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Lithium-ion battery RUL prediction based on optimized VMD-SSA-PatchTST algorithm. [PDF]

Sci Rep
Tang P, Qiu Z, Yao Z, Pan J, Cheng D, Gu X, Sun C.   +6 more
europepmc   +1 more source

Artificial intelligence empowers targeted protein degradation: Core technological innovations, multi‐scenario applications, and translational prospects

Smart Molecules, EarlyView.
AI is transforming TPD by improving the design, prediction, and optimization of degraders such as PROTACs, molecular glues, and LYTACs. This review summarizes key AI‐driven advances, highlights applications across drug discovery stages, and discusses remaining challenges and future directions for accelerating the development of therapies against ...
Shuanglin Qin, Rui Peng, Guangshuai Zhang, Si Yan, Jin Xiao, Zishu Liu, Ziyu Tan, Qingqing Xie, Shangjie Li, Wei Luo, Xiaohe Xiao   +10 more
wiley   +1 more source

Mine Gas Time-Series Data Prediction and Fluctuation Monitoring Method Based on Decomposition-Enhanced Cross-Graph Forecasting and Anomaly Finding. [PDF]

Sensors (Basel)
Yuan L.
europepmc   +1 more source

Research on short-term prediction method of photovoltaic power based on HPO-VMD-BiLSTM. [PDF]

Sci Rep
Li J, Li L, Du Q, Li Y.
europepmc   +1 more source

Dual framework for rainfall prediction: a multi-seed machine and deep learning evaluation across Pakistan's climatic regimes. [PDF]

Sci Rep
Farman H, Hussain MA, Shaikh S, Hassan S, Hassan SR.   +4 more
europepmc   +1 more source

Enhanced spectro-temporal feature extraction for prosthetic control using variational mode decomposition. [PDF]

Sci Rep
Shafiq U, Waris A, Amin F, Ullah A, Ashraf H, Khan MJ, Ijaz MA, Hazzazi F, Ansari K, Gilani SO.   +9 more
europepmc   +1 more source

Carbon Market Price Forecasting Using a Bidirectional Temporal Convolution Exogenous-Enhanced Time-Series Model. [PDF]

Entropy (Basel)
Tang X, Tang M, Li N, Zhang S.
europepmc   +1 more source

Successive multivariate variational mode decomposition based on instantaneous linear mixing model

Signal Processing, 2022
Abstract In this paper, a novel Successive Multivariate Variational Mode Decomposition (SMVMD) is presented. Different from most existing multichannel signal decomposition approaches, the proposed SMVMD does not need to predefine the mode number and is able to extract the joint or common modes successively.
Shuaishuai Liu, Kaiping Yu
exaly   +2 more sources

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Successive multivariate variational mode decomposition

Multidimensional Systems and Signal Processing, 2022
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Shuaishuai Liu, Kaiping Yu
openaire   +1 more source