Time-triggered stream scheduling method combining no-wait and time-slot mapping reuse by HE Qian, GUO Yanan, ZHAO Baokang, PAN Qi, WANG Yong

“…Finally, several time-triggered stream scheduling optimization functions were given and solved based on an improved multi-objective genetic algorithm. …”
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Multiphysics Feature-Based State-of-Energy Estimation for LiFePO4 Batteries Using Bidirectional Long Short-Term Memory and Particle Swarm-Optimized Kalman Filter by Zhengpu Wu, Xu He, Haisen Chen, Lu Lv, Jiuchun Jiang, Lujun Wang

“…Therefore, this paper introduces a significantly varying mechanical force feature to tackle the flat voltage curve in the mid-SOE region. A fusion model that integrates a bidirectional long short-term memory (BiLSTM) network, particle swarm optimization (PSO), and Kalman filter (KF) algorithm is proposed for SOE estimation. …”
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Extraction of Levees from Paddy Fields Based on the SE-CBAM UNet Model and Remote Sensing Images by Hongfu Ai, Xiaomeng Zhu, Yongqi Han, Shinai Ma, Yiang Wang, Yihan Ma, Chuan Qin, Xinyi Han, Yaxin Yang, Xinle Zhang

“…We developed the SCA-UNet model by optimizing the UNet algorithm and enhancing its network architecture through the integration of the Convolutional Block Attention Module (CBAM) and Squeeze-and-Excitation Networks (SE). …”
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Dynamic load balancing in cloud computing using predictive graph networks and adaptive neural scheduling by K. Rajammal, M. Chinnadurai

“…Additionally, comparative analyses with existing optimization algorithms exhibit the proposed model ability in managing the loads in cloud computing. …”
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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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Intelligent decision-making and regulation method of gas extraction “borehole-pipe network” system by Kai WANG, Dongxu WANG, Aitao ZHOU, Junwen ZHANG, Fangzhou SONG, Chang’ang DU, Yushuang HAO, Xihui FAN, Wei ZHAO

“…Based on the improved particle swarm optimization algorithm, the intelligent optimization decision-making and regulation model of pipeline network is constructed. …”
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BAHGRF3: Human gait recognition in the indoor environment using deep learning features fusion assisted framework and posterior probability moth flame optimisation by Muhammad Abrar Ahmad Khan, Muhammad Attique Khan, Ateeq Ur Rehman, Ahmed Ibrahim Alzahrani, Nasser Alalwan, Deepak Gupta, Saima Ahmed Rahin, Yudong Zhang

“…A new framework for human gait classification in video sequences using deep learning (DL) fusion assisted and posterior probability‐based moth flames optimization (MFO) is proposed. In the first step, the video frames are resized and fine‐tuned by two pre‐trained lightweight DL models, EfficientNetB0 and MobileNetV2. …”
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STRUCTURAL SYNTHESIS OF NAVIGATION SUPPORT OF TRIAD INTEGRATED NAVIGATION SYSTEM ON THE BASIS OF INERTIAL AND SATELLITE TECHNOLOGIES by V. S. Maryukhnenko, V. V. Erokhin

“…The imitating statistical modeling of optimal filtering algorithm of the triad integrated system is carried out. …”
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Machine learning prediction and explainability analysis of high strength glass powder concrete using SHAP PDP and ICE by Muhammad Sarmad Mahmood, Tariq Ali, Inamullah Inam, Muhammad Zeeshan Qureshi, Syed Salman Ahmad Zaidi, Muwaffaq Alqurashi, Hawreen Ahmed, Muhammad Adnan, Abdul Hakim Hotak

“…To further enhance performance, XGB was optimized using Particle Swarm Optimization (PSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO). …”
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Renewable Energy Consumption Strategies for Electric Vehicle Aggregators Based on a Two-Layer Game by Xiu Ji, Mingge Li, Zheyu Yue, Haifeng Zhang, Yizhu Wang

“…For the complexity of large-scale scheduling, this paper introduces the A2C (Advantage Actor-Critic) reinforcement learning algorithm, which combines the value network and the strategy network synergistically to optimize the real-time scheduling process. …”
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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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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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Wireless Channel Prediction Using Artificial Intelligence With Imperfect Datasets by Gowhar Javanmardi, Ramiro Samano Robles

“…This stress test leads to new conclusions on channel prediction: i) how and why algorithms behave in different ways under diverse conditions (optimality region), ii) derivation of new bounds linked to channel features (coherence time, channel correlation, etc.), iii) optimum parameter settings for ML also linked to channel statistics, and iv) proposal of potential improvements. …”
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Performance evaluation of an adopted model based on big-bang big-crunch and artificial neural network for cloud applications by Pradeep Rawat, Robin Singh Bhadoria, Punit Gupta, Priti Dimri, G. P. Saroha

“…The proposed model is inspired from Big-Bang Big-Crunch algorithm in astrology. …”
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An Enhanced Framework for Assessing Pluvial Flooding Risk with Integrated Dynamic Population Vulnerability at Urban Scale by Xinyi Shu, Chenlei Ye, Zongxue Xu, Ruting Liao, Pengyue Song, Silong Zhang

“…This study uses Jinan, a typical foothill plain city in Shandong Province, as a case study to compare the performance of differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO) in calibrating the SWMM. …”
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Deep learning model of semantic direction exploration based on English V+able corpus distribution and semantic roles by Li Wang

“…In order to improve English learning efficiency, this paper constructs a deep learning model of semantic orientation exploration based on English V+able corpus distribution and semantic roles. …”
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A Pythagorean fuzzy MCDM model for evaluating career happiness in sports by selecting a suitable sport by JiaYan Zhu, Zeng Jiao

“…We present the MCDM algorithm for AHP and the derived AOs, offering solutions to practical numerical examples and identifying optimal sports options that improve career happiness. …”
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HMS-PAU-IN: A heterogeneous multi-scale spatiotemporal interaction network model for population analysis units by Xiaorui Yang, Rui Li, Jing Xia, Junhao Wang, Hongyan Li, Nixiao Zou

“…In the temporal dimension, the changes of PAUs are captured through the evolutionary relationships of nodes between different time windows. To validate the model, we developed a population prediction model that integrates the multi-scale features of PAUs and introduced Leiden-IES-PMS, a community detection method based on the Leiden algorithm, which integrates internal and external environmental semantics and adopts a proximity merging strategy. …”
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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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