Results 71 to 80 of about 11,734,216 (296)
Numbers of layers and hidden sizes for LSTM, LSTM-ATT and Transformer for all provinces.
, 2022 LSTM = long short-term memory. LSTM-ATT = attention mechanism-enhanced LSTM. (DOCX)
James Mulhall (12867579) +18 more
core +1 more source
Advanced Science, EarlyView.A flexible pressure sensor with triple‐gradient design of conductivity, modulus, and dimension in binary micro‐dome pixels is proposed. Based on precisely‐designed CNT/PDMS matrix, the device exhibits a linear sensitivity of 974.1 kPa−1 across range up to 1.8 MPa (R2 > 0.99), offering an effective strategy for potential applications in healthcare ...
Yifan Liu +9 more
wiley +1 more source
A Hybrid Soft Sensor Model for Measuring the Oxygen Content in Boiler Flue Gas
SensorsAs an indispensable component of coal-fired power plants, boilers play a crucial role in converting water into high-pressure steam. The oxygen content in the flue gas is a crucial indicator, which indicates the state of combustion within the boiler.
Yonggang Wang, Zhida Li, Nannan Zhang
doaj +1 more source
Understanding LSTM -- a tutorial into Long Short-Term Memory Recurrent Neural Networks
CoRR, 2019 Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN) are one of the most powerful dynamic classifiers publicly known. The network itself and the related learning algorithms are reasonably well documented to get an idea how it works. This paper will shed more light into understanding how LSTM-RNNs evolved and why they work impressively well ...
Ralf C. Staudemeyer +1 more
openaire +2 more sources
Electricity is one of the most important elements for economic growth and development of societies in today’s modern societies. The research of electricity generation, knowing the size of the electricity supply, and the methods developed to meet this ...
İnayet Özge Aksu, Tuğçe Demirdelen
core +1 more source
Advanced Science, EarlyView.Large language models serve as representation generators for gradient‐free scientific optimization. Two complementary approaches produce executable battery‐charging policies either directly or through LLM‐designed neural representations coupled to Bayesian optimization.
Ge Lei +3 more
wiley +1 more source
Detecting the Insider Threat with Long Short Term Memory (LSTM) Neural Networks
CoRR, 2020 Information systems enable many organizational processes in every industry. The efficiencies and effectiveness in the use of information technologies create an unintended byproduct: misuse by existing users or somebody impersonating them - an insider threat. Detecting the insider threat may be possible if thorough analysis of electronic logs, capturing
Eduardo Lopez, Kamran Sartipi
openaire +2 more sources
A Combined Model Based on Secondary Decomposition and Long Short-Term Memory Networks for Enhancing Wind Power Forecast [PDF]
Accurately predicting the potential wind power generation is of paramount importance in advancing the contribution of wind energy within the overall energy production landscape.
Mehmet Balcı +5 more
core +1 more source
, 2022 The whole decision process of Long Short Term Memory Recurrent Neural Network and Hidden Markov Model (LSTM-HMM) with quarterly univariate-input that introduces LSTM to forecast CPI fluctuation states.
Jiaqi Wen (6838376) +2 more
core +1 more source
An Integrated Artificial Intelligence Framework for Nanomedicine Engineering
Advanced Science, EarlyView.This review outlines a closed‐loop AI ecosystem for nanomedicine engineering. By synergizing multimodal data harmonization, objective‐driven AI architectures, and automated high‐throughput validation, this “Design‐Make‐Test‐Analyze” (DMTA) workflow transforms traditional heuristic formulation into a predictive, self‐optimizing paradigm, accelerating ...
Meichen Dong +10 more
wiley +1 more source