Relationship analysis between deterioration of switch support and railway passenger comfort using machine learning by Sresakoolchai Jessada, Cheputeh Ni-Asri

“…Switches in the railway system have the function of guiding rolling stocks or trains to the preferred directions and tracks. …”
Get full text

Using Deep Learning to Predict Complex Systems: A Case Study in Wind Farm Generation by J. M. Torres, R. M. Aguilar

“…An analysis of our findings shows that the most accurate and robust estimators are those based on feedforward neural networks with a SELU activation function and convolutional neural networks.…”
Get full text

Backstepping Command Filter Control for Electromechanical Servo Systems with Unknown Dynamics Based on Reinforcement Learning by Chenchen Xu, Jian Hu, Jiong Wang, Wenxiang Deng, Jianyong Yao, Xiaoli Zhao

“…The weights of the networks are updated using an online gradient descent algorithm, eliminating the need for an offline learning phase. In addition, the Lyapunov function analysis method is used to demonstrate the stability of the closed-loop system. …”
Get full text

A novel deep learning technique for multi classify Alzheimer disease: hyperparameter optimization technique by A. S. Elmotelb, Fayroz F. Sherif, A. S. Abohamama, A. S. Abohamama, Mahmoud Fakhr, Amr M. Abdelatif

“…A progressive brain disease that affects memory and cognitive function is Alzheimer’s disease (AD). To put therapies in place that potentially slow the progression of AD, early diagnosis and detection are essential. …”
Get full text

Effective 3C Resource Utilization and Fair Allocation Strategy for Multi-Task Federated Learning by Chaofeng Zhang, Mianxiong Dong, Kaoru Ota

“…Firstly, a utility function based on convergence efficiency is proposed to reflect the physical benefits of processing AI tasks. …”
Get full text

An Innovative Study for Tool Wear Prediction Based on Stacked Sparse Autoencoder and Ensemble Learning Strategy by Zhaopeng He, Tielin Shi, Xu Chen

“…SSAE models with different activation function configurations of hidden layers were utilized to construct deep neural network models with different prediction performance, which were taken as primary learners of integrated deep learning model. …”
Get full text

Neural Learning Control of Flexible Joint Manipulator with Predefined Tracking Performance and Application to Baxter Robot by Min Wang, Huiping Ye, Zhiguang Chen

“…This paper focuses on neural learning from adaptive neural control (ANC) for a class of flexible joint manipulator under the output tracking constraint. …”
Get full text

An Adaptive Variable-Weight Combination Forecasting Method for Energy Product Sales Based on Meta-Learning by Meitian Wang

“…Secondly, Lasso and LSTM models are constructed to deal with overlapping linear and nonlinear features in sales data, respectively. Finally, the meta-learning method is used to establish a relationship function between model combination weights and external features, which realizes adaptive variable model combination for different moments and energy products. …”
Get full text

From Halos to Galaxies. VI. Improved Halo Mass Estimation for SDSS Groups and Measurement of the Halo Mass Function by Dingyi Zhao, Yingjie Peng, Yipeng Jing, Xiaohu Yang, Luis C. Ho, Alvio Renzini, Anna R. Gallazzi, Cheqiu Lyu, Roberto Maiolino, Jing Dou, Zeyu Gao, Qiusheng Gu, Filippo Mannucci, Houjun Mo, Bitao Wang, Enci Wang, Kai Wang, Yu-Chen Wang, Bingxiao Xu, Feng Yuan, Xingye Zhu

“…To improve this, we have developed a new machine learning (ML) algorithm that accounts for these effects and is trained on simulations. …”
Get full text

Tailoring industrial enzymes for thermostability and activity evolution by the machine learning-based iCASE strategy by Nan Zheng, Yongchao Cai, Zehua Zhang, Huimin Zhou, Yu Deng, Shuang Du, Mai Tu, Wei Fang, Xiaole Xia

“…Furthermore, this dynamic response predictive model using structure-based supervised machine learning is established to predict enzyme function and fitness, demonstrating robust performance across different datasets and reliable prediction for epistasis. …”
Get full text

A Deep Learning Strategy for the Retrieval of Sea Wave Spectra from Marine Radar Data by Giovanni Ludeno, Giuseppe Esposito, Claudio Lugni, Francesco Soldovieri, Gianluca Gennarelli

“…A performance analysis of the proposed deep-learning estimation strategy is carried out, along with a comparison to the classical modulation transfer function approach. …”
Get full text

Effect analysis of adding selective experiments in power electronics course to encourage students’ active learning by Guopeng Zhao

“…The final examination scores of the students who have performed the selective experiments are higher than the average score of the class, which shows that the students who have performed the selective experiments are good students with a strong need for active learning. The curriculum of this study meets their need for active learning. …”
Get full text

Short-term traffic flow prediction at isolated intersections based on parallel multi-task learning by Bao-Lin Ye, Shiwei Zhu, Lingxi Li, Weimin Wu

“…In the fusion strategy, we develop a parallel multi-task prediction framework whereby a new loss function is defined to improve the prediction accuracy. …”
Get full text

Development of Advanced Machine Learning Models for Predicting CO₂ Solubility in Brine by Xuejia Du, Ganesh C. Thakur

“…This study explores the application of advanced machine learning (ML) models to predict CO₂ solubility in NaCl brine, a critical parameter for effective carbon capture, utilization, and storage (CCUS). …”
Get full text

Unlocking the black box beyond Bayesian global optimization for materials design using reinforcement learning by Yuehui Xian, Xiangdong Ding, Xue Jiang, Yumei Zhou, Jun Sun, Dezhen Xue, Turab Lookman

“…This approach demonstrates better performance in high-dimensional spaces (D ≥ 6) compared to Bayesian optimization (BO) with the Expected Improvement (EI) acquisition function through more dispersed sampling patterns and better landscape learning capabilities. …”
Get full text

Modeling of settlement of shallow-founded rocking structures using explainable physics-guided machine learning by Sivapalan Gajan, Christopher Kantor

“…An approximate physics-based model (PBM) is derived for rocking-induced total settlement as a function of critical contact area ratio and cumulative rotation of the foundation. …”
Get full text

Coordinated Optimization of Active and Reactive Power of Active Distribution Network Based on Safety Reinforcement Learning by Hao JIAO, Yanyan YIN, Chen WU, Jian LIU, Chunlei XU, Xian XU, Guoqiang SUN

“…A safe reinforcement learning method based on offline strategies is proposed. …”
Get full text

Comparative Analysis of Reinforcement Learning Approaches for Multi-Objective Optimization in Residential Hybrid Energy Systems by Yang Xu, Yanxue Li, Weijun Gao

“…In pursuit of this objective, we also devise a new reward function for multi-objective optimization and an interactive environment grounded in expert experience. …”
Get full text

A Mechanism-aid Deep Learning Method for Li-ion Battery State-of-charge Estimation by Zhanghua Xiao, Cheng Ji, Fangyuan Ma, Jingde Wang, Wei Sun, Chi Zhai

“…For this purpose, a mechanism-aid deep learning method is proposed, in which the loss function of the LSTM neural network is modified by the equivalent circuit model. …”
Get full text

Transformer With Regularized Dual Modal Meta Metric Learning for Attribute-Image Person Re-Identification by Xianri Xu, Rongxian Xu

“…In this paper, we propose a regularized dual modal meta metric learning (RDM3L) method for AIPR, which employs meta-learning training methods to enhance the transformer’s capacity to acquire latent knowledge. …”
Get full text