EDT-MCFEF: a multi-channel feature fusion model for emergency department triage of medical texts by Tao Lin, Shiming Yi

“…The model employs a hybrid masking approach and RoBERTa (Robustly Optimized BERT Approach) to facilitate feature enhancement and word vector processing of text. …”
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Construction of a sugar and acid content estimation model for Miliang-1 kiwifruit during storage by LIU Li, YANG Tianyi, DONG Congying, SHI Caiyun, SI Peng, WEI Zhifeng, GAO Dengtao

“…The regression models utilized a combination of Support Vector Regression (SVR) and Backpropagation Neural Network (BP) algorithms to determine the optimal predictive performance for each quality indicator. …”
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A novel lightweight YOLOv8-PSS model for obstacle detection on the path of unmanned agricultural vehicles by Zhijian Chen, Yijun Fang, Jianjun Yin, Shiyu Lv, Farhan Sheikh Muhammad, Lu Liu

“…When compared with other algorithms, such as Faster RCNN, SSD, YOLOv3-tiny, and YOLOv5, the improved model strikes an optimal balance between parameter count, computational efficiency, detection speed, and accuracy, yielding superior results. …”
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A Data Reconciliation-Based Method for Performance Estimation of Entrained-Flow Pulverized Coal Gasification by Yan Zhang, Kai Yue, Chang Yuan, Jiahao Xiang

“…The thermodynamic equilibrium model is employed as constraints of the gasification and quench processes, and the Particle Swarm Optimization (PSO) algorithm is applied for parameter estimation. …”
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ARMC-RL: Adaptive Caching With Reinforcement Learning for Efficient 360° Video Streaming in Edge Networks by Minji Choi, Somin Park, Jin-Hyun Ahn, Dong Ho Kim, Cheolwoo You

“…In particular, ARMC improved the hit rate by up to 29% compared to Least Frequency Used algorithm under constrained cache conditions of 6% or less of the total data. …”
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Extended State Observer-Based Robust Model Predictive Velocity Control for Permanent Magnet Synchronous Motor by Eunji Lee, Yonghao Gui, Sesun You, Jun Moon, Wonhee Kim

“…Thus, the proposed method is robust against external disturbances and parameter uncertainties owing to feedback linearization, state feedback, and ESO-based MPC using the acceleration PMSM model. The proposed control algorithm was experimentally verified and it showed improved velocity tracking performance compared with ESO-based MPC using the conventional PMSM model.…”
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Prediction of Urban Rail Transit Train Door Faults Based on FOA-BP Neural Network Model by WEN Kaiyue, QIU Weibin, DING Xianze, OU Hongxiang

“…[Method] Taking the abnormal current signal of URT train door before failure as the research object, a FIR (finite impulse response) filter is designed to filter and dimensionally normalize the collected URT train door current signal data; the FOA (fruit fly optimization algorithm)-BP (back propagation) neural network model is used to train the closed state learning sample data of different doors after dimensional normalization, and output test results; FOA-BP results and output results after BP neural network models training are compared and analyzed. …”
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Automatic Synthesis of Recurrent Neurons for Imitation Learning From CNC Machine Operators by Hoa Thi Nguyen, Roland Olsson, Oystein Haugen

“…Analyzing time series data in industrial settings demands domain knowledge and computer science expertise to develop effective algorithms. AutoML approaches aim to automate this process, reducing human bias and improving accuracy and cost-effectiveness. …”
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Integrating artificial intelligence for sustainable waste management: Insights from machine learning and deep learning by Son V.T. Dao, Tuan M. Le, Hieu M. Tran, Hung V. Pham, Minh T. Vu, Tuan Chu

“…Moreover, in the proposed framework, the data augmentation approach has been utilized to improve the model’s performance by increasing the amount of samples. …”
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Research on Channel Modeling and Communication Coverage of Wireless Sensor Networks in Barrier Area of Nuclear Power Plants by Zhi-Guang Deng, Qian Wu, Xin Lv, Bi-Wei Zhu, Mei-Qiong Xiang, Xue-Mei Wang, Jia-Liang Zhu

“…Based on the channel modeling, this paper optimizes the coverage of the network in the obstacle area by using the improved teaching and learning group intelligent algorithm. …”
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Open-Loop Control System for High Precision Extrusion-Based Bioprinting Through Machine Learning Modeling by Javier Arduengo, Nicolas Hascoet, Francisco Chinesta, Jean-Yves Hascoet

“…This study introduces an open-loop control system designed to improve the accuracy of extrusion-based bioprinting techniques, which is composed of a specific experimental setup and a series of algorithms and models. …”
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Learning-Based Model Predictive Control for Legged Robots with Battery–Supercapacitor Hybrid Energy Storage System by Boyu Shu, Zhiwu Huang, Wanwan Ren, Yue Wu, Heng Li

“…Firstly, the mathematical model of the legged robot is established, and a dual-layer long short-term memory network is constructed to predict the load power demand, providing the model and measurable disturbance for the MPC. …”
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EEG-Based Sleep Stage Classification via Neural Architecture Search by Gangwei Kong, Chang Li, Hu Peng, Zhihui Han, Heyuan Qiao

“…The proposed NAS architecture mainly performs the architectural search through a bilevel optimization approximation, and the model is optimized by search space approximation and search space regularization with parameters shared among cells. …”
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Consider the master-slave game model of integrated energy system with step carbon trading and demand response by Yi Zhang, Zhezhou Zhang

“…Finally, the model is solved using the differential evolution algorithm and the Cplex tool on the Matlab platform, resulting in an equilibrium solution. …”
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Dynamic Adaptive Environmental Flows (DAE‐Flows) to Reconcile Long‐Term Ecosystem Demands With Hydropower Objectives by Ana Paula Dalcin, Guilherme Fernandes Marques, Amaury Tilmant, Marcelo Olivares

“…The methodology framework combines a fish‐flow model with a multi‐objective evolutionary algorithm to construct multiple environmental water demand curves and capture the opportunity cost of different levels of ecosystem preservation. …”
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Real-Time Multi-Vehicle Scheduling in Tasks With Dependency Relationships Using Multi-Agent Reinforcement Learning by Shupei Zhang, Huapeng Shi, Wei Zhang, Ying Pang, Pengju Sun

“…Moreover, traditional optimization algorithms make it difficult to achieve timeliness in real-time changing traffic conditions. …”
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Multi-Agent Coordinated Dispatch of Power Grid and Pumped Hydro Storage with Embedded Market Game Model by LOU Wei, HU Rong, YU Jinming, ZHANG Xipeng, FAN Feilong, LIU Songyuan

“…To formulate the proposed scheduling strategy, a bi-level optimization problem with an embedded game model is solved: the decision-making problem of the pumped storage power station participating in the electric energy spot market, and the optimization with the embedded marketing game model of capacity allocation and power scheduling strategy about pumped storage. …”
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Research on Location Planning of Battery Swap Stations for Operating Electric Vehicles by Pengcheng Ma, Shuai Zhang, Bin Zhou, Wenqi Shao, Haowen Li, Tengfei Ma, Dong Guo

“…The results show that the MLP-NSGA-II algorithm improves the convergence efficiency by about 30.23%, and the service coverage of the optimal solution reaches 94.30%; the service utilization rate is 85.35%, which is 6.25% and 19.69% higher than that of the GA and DPC, respectively. …”
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Predictive modeling for the adsorptive and photocatalytic removal of phenolic contaminants from water using artificial neural networks by Shahzar Hafeez, Ayesha Ishaq, Azeem Intisar, Tariq Mahmood, Muhammad Imran Din, Ejaz Ahmed, Muhammad Rizwan Tariq, Muhammad Amin Abid

“…To overcome these limitations, the modeling and optimization of water treatment methods is required. …”
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Anomaly Detection Using Machine Learning in Hydrochemical Data From Hot Springs: Implications for Earthquake Prediction by Ruijie Zhu, Fengtian Yang, Xiaocheng Zhou, Jiao Tian, Yongxian Zhang, Miao He, Jingchao Li, Jinyuan Dong, Ying Li

“…Additionally, including sampling time in the data sets significantly improves the model's predictive performance. However, it is important to note that the model's predictive performance varies across different hot spring and indicators type, highlighting the importance of identifying optimal indicators for specific scenarios. …”
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