Published: Dec. 20, 2024
Language: Английский
Published: Dec. 20, 2024
Language: Английский
Molecular & cellular biomechanics, Journal Year: 2025, Volume and Issue: 22(1), P. 996 - 996
Published: Jan. 6, 2025
This paper introduced biomechanical theory into ideological education management, analyzed the impact of educational intervention and optimized strategy by establishing a dynamic prediction model behavior, aiming to improve effect, reduce resource investment, realize personalized precise management. It constructed thought behavior based on LSTM (Long Short-Term Memory), used core concepts biomechanics analogize key variables in Thought tendencies can be analogized state variables, interventions regarded as external forces, inertia corresponded internal resistance transformation, thereby revealing law change providing quantitative basis. In terms optimization, GA (Genetic Algorithm) is optimize strategy, fitness function comprehensively evaluate degree transformation costs achieve multi-objective balance. The experimental results show that proposed shows high accuracy tendency scores, with an average RMSE (Root Mean Square Error) 0.12 MAE (Mean Absolute 0.08. superior traditional strategies improving class participation rate, learning management system login frequency, reducing costs. perspective provide accurate predictions states efficient use resources optimizing design, which verifies theoretical innovation practical application value this method.
Language: Английский
Citations
0Electronics, Journal Year: 2025, Volume and Issue: 14(3), P. 403 - 403
Published: Jan. 21, 2025
As power demand increases and the scale of grids expands, accurately predicting transmission line temperatures is becoming essential for ensuring stability security systems. Traditional physical statistical models struggle with complex multivariate time series, often failing to balance short-term fluctuations long-term dependencies, their prediction accuracy adaptability remain limited. To address these challenges, this paper proposes a deep learning model architecture based on Dynamic Adaptive Artificial Hummingbird Algorithm (DA-AHA), named DA-AHA-CNN-LSTM-TPA (DA-AHA-CLT). The integrates convolutional neural networks (CNNs) local feature extraction, long memory (LSTM) temporal modeling, pattern attention mechanisms (TPA) dynamic weighting, while DA-AHA optimizes hyperparameters enhance stability. traditional artificial hummingbird algorithm (AHA) further improved by introducing step-size adjustment, greedy search, grouped parallel search global exploration exploitation. Our experimental results demonstrate that DA-AHA-CLT achieves coefficient determination (R2) 0.987, root-mean-square error (RMSE) 0.023, mean absolute (MAE) 0.018, median (MedAE) 0.011, outperforming such as CNN-LSTM LSTM-TPA. These findings confirm effectively captures characteristics temperatures, offering superior performance robustness in full-time-step tasks, highlight its potential solving challenging time-series forecasting problems
Language: Английский
Citations
0Energies, Journal Year: 2025, Volume and Issue: 18(4), P. 936 - 936
Published: Feb. 15, 2025
In the realm of predictive maintenance for energy-intensive machinery, effective anomaly detection is crucial minimizing downtime and optimizing operational efficiency. This paper introduces a novel approach that integrates federated learning (FL) with Neural Circuit Policies (NCPs) to enhance in compressors utilized leather tanning operations. Unlike traditional Long Short-Term Memory (LSTM) networks, which rely heavily on historical data patterns often struggle generalization, NCPs incorporate physical constraints system dynamics, resulting superior performance. Our comparative analysis reveals significantly outperform LSTMs accuracy interpretability within framework. innovative combination not only addresses pressing privacy concerns but also facilitates collaborative across decentralized sources. By showcasing effectiveness FL NCPs, this research paves way advanced strategies prioritize both performance integrity industries.
Language: Английский
Citations
0Applied Mathematics and Nonlinear Sciences, Journal Year: 2025, Volume and Issue: 10(1)
Published: Jan. 1, 2025
Abstract The study identifies the relevant semantic dependencies after establishing a corpus system database, and then searches for degree of differentiation utterances through English sentence similarity algorithm, which adopts vector space modeling criterion uses computed similarities as elements. Meanwhile, two simple efficient labeling transformation algorithms, namely, label algorithm graph-to-graph linear are proposed way to improve performance language learning in cross-cultural communication. Based on above, develops an AMR intelligent analysis using stack-LSTM analyzes its role enhancing intercultural communication skills during acquisition. accuracy annotation can be verified by applying automatic syntax Chinese languages, utterance is recognized with different components, results show that highly accurate, 92% recognition precision rate good effect. Finally, regression conducted according effect acquisition groups people, it found use has significant improvement students’ ability.
Language: Английский
Citations
0Published: April 21, 2025
Summary Stuck pipe is one of the most serious drilling challenges, resulting in significant non-productive time (NPT), higher costs, and even well abandonment. Thus, developing a reliable real-time stuck prediction system critical. This study develops an LSTM-based model to predict pipes real during operations. Unlike previous research, which relied on non-real-time accessible features such as mud rheology survey data, this work uses Mud Logging readily available online majority wells, ensuring practical application. Furthermore, many existing studies use inappropriate metrics assess performance, accuracy, F1-score, recall, can lead misleading results highly imbalanced datasets. To address this, current employs Area Under ROC Curve (AUC) its primary evaluation metric, yielding more assessment. The systematically investigates how key parameters, temporal window size resampling techniques, affect performance. dataset was collected from that includes four pipes, with data sampled at 0.5 Hz. Expert recommendations led selection hook load, torque, standpipe pressure, surface RPM, weight bit sensor readings, all have strong correlation pipes. show LSTM 10-second undersampling strategy performs best, AUC 0.868. value than those obtained using conventional models support vector machines (SVM) or random forests (RF) techniques. proposed approach effectively captures dependencies allowing for detection early warning patterns associated incidents. broadens scope beyond operations by including examples tripping reaming processes. A case real-world field validates model's ability provide warnings, lowering operational risks downtime costs. Level 2 triggered 33 minutes before event, followed 3 alert just prior incident. These findings demonstrate LSTM's potential prediction, improving safety efficiency.
Language: Английский
Citations
0Technologies, Journal Year: 2024, Volume and Issue: 12(11), P. 227 - 227
Published: Nov. 11, 2024
There is a need for further, in-depth research that explores the synthesis of newly developed materials created using advanced technologies [...]
Language: Английский
Citations
0Published: Dec. 20, 2024
Language: Английский
Citations
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