A Temporal Convolutional Network–Bidirectional Long Short-Term Memory (TCN-BiLSTM) Prediction Model for Temporal Faults in Industrial Equipment by Jinyin Bai, Wei Zhu, Shuhong Liu, Chenhao Ye, Peng Zheng, Xiangchen Wang

“…Traditional algorithms and single predictive models often face challenges such as limited prediction accuracy and insufficient modeling capabilities for complex time-series data in fault prediction tasks. …”
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Optimal Linear Precoding Under Realistic Satellite Communications Scenarios by Geoffrey Eappen, Jorge Luis Gonzalez, Vibhum Singh, Rakesh Palisetty, Alireza Haqiqtnejad, Liz Martinez Marrero, Jevgenij Krivochiza, Jorge Querol, Nicola Maturo, Juan Carlos Merlano Duncan, Eva Lagunas, Stefano Andrenacci, Symeon Chatzinotas

“…The optimal linear precoders are derived considering beamformer update and power control with an iterative per-antenna power optimization algorithm with a limited required number of iterations. …”
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A modified particle swarm optimization-based adaptive maximum power point tracking approach for proton exchange membrane fuel cells by Bhukya Laxman, Ramesh Gugulothu, Surender Reddy Salkuti

“…In this paper, an MPSO algorithm-based MPPT tracking approach without a PID controller is proposed to achieve the maximum power point (MPP) of a PEMFC. …”
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METHOD FOR PLANNING THE OPTIMAL TRAJECTORY OF A THREE-LINK MANIPULATOR IN TRIDIMENSIONAL SPACE WITH AN OBSTACLE by V. O. Antonov, M. M. Gurchinsky, V. I. Petrenko, F. B. Tebueva

“…A literature review on the research problem indicates that universal methods for planning the trajectory of the manipulator's movement are faced with problems of operational low accuracy or the algorithm's large computational complexity. …”
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Reinforcement learning-based assimilation of the WOFOST crop model by Haochong Chen, Xiangning Yuan, Jian Kang, Danni Yang, Tianyi Yang, Xiang Ao, Sien Li

“…The Proximal Policy Optimization (PPO) algorithm was used to train these environments for 100,000 iterations. …”
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Replay-Based Incremental Learning Framework for Gesture Recognition Overcoming the Time-Varying Characteristics of sEMG Signals by Xingguo Zhang, Tengfei Li, Maoxun Sun, Lei Zhang, Cheng Zhang, Yue Zhang

“…Gesture recognition techniques based on surface electromyography (sEMG) signals face instability problems caused by electrode displacement and the time-varying characteristics of the signals in cross-time applications. …”
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Implementing Remote Radiotherapy Planning to Increase Patient Flow at a Johannesburg Academic Hospital, South Africa: Protocol for a Prospective Feasibility Study by Duvern Ramiah, Sonwabile Ngcezu, Oluwatosin Ayeni, Okechinyere Achilonu, Mariam Adeleke, Theo Nair, Joseph Otten, Daniel Mmereki

“…Initial findings show successful MLC transmission and dosimetric leaf gap optimization through iterative testing. Gamma pass rates exceeded 90% on both clinical and test servers, demonstrating initial accuracy. …”
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Rekayasa Perangkat Lunak Aplikasi Presensi Mobile Menggunakan Metode Deep Learning by Ragil Setiawan, Nurcahya Pradana Taufik Prakisya, Rosihan Ariyuana

“…This paper aims to determine the value of the memorization and generalization algorithms model of CNN MobileFaceNet  on the application.  …”
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An Enhanced Deep Neural Network Approach for WiFi Fingerprinting-Based Multi-Floor Indoor Localization by Shehu Lukman Ayinla, Azrina Abd Aziz, Micheal Drieberg, Misfa Susanto, Afidalina Tumian, Mazlaini Yahya

“…While these techniques have shown promising results, they face two significant challenges. First, their complex multilayer architecture requires extensive training iterations. …”
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Fourier Analysis of CMFD Method in Cylindrical Geometry by WEN Yuchen, HAO Chen, WANG Yizhen

“…The coarse mesh finite difference (CMFD) method has been widely adopted as an acceleration algorithm for neutron transport calculations, effectively reducing computational costs and improving convergence. …”

A Study on Panoramic Camera System of ROV Platform by GUO Xu, TU Shaoping, XU Lei

“…Unlike imaging in air, underwater panoramic imaging faces great challenges arising from water pressure and diminished light, necessitating special lens packaging and focused underwater illumination. …”
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Efficient and secure multi-party computation protocol supporting deep learning by Shancheng Zhang, Gang Qu, Zongyang Zhang, Minzhe Huang, Haochun Jin, Liqun Yang

“…For Softmax computation, we extend existing two-party protocols to a multi-party Shamir setting, developing the nQSMax algorithm. This algorithm achieves exceptional accuracy exceeding 99% within seconds, requiring only a few iterations.…”
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Research on Parallel Computing Method for Neutron Transport on Tetrahedral Unstructured Meshes Based on COME by ZHONG Yangyu, ZHANG Tiancheng, ZHOU Xiafeng

“…Owing to the inhomogeneous distribution of materials in complex geometric reactor cores, traditional source iteration methods face significant challenges in solving neutron transport equations efficiently. …”

A Model Predictive Control to Improve Grid Resilience by Joseph Young, David G. Wilson, Wayne Weaver, Rush D. Robinett

“…To accomplish this, the following article includes a description of a reduced order model (ROM) of an electric power grid based on a circuit model, an optimization formulation that describes the MPC, a collocation method for solving linear time-dependent differential algebraic equations (DAEs) that result from the ROM, and an overall strategy for iteratively refining the behavior of the MPC. Next, the algorithm is validated using two separate numerical experiments. …”
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FedDBO: A Novel Federated Learning Approach for Communication Cost and Data Heterogeneity Using Dung Beetle Optimizer by Dongyan Wang, Limin Chen, Xiaotong Lu, Yidi Wang, Yue Shen, Jingjing Xu

“…As an emerging distributed machine learning technology, federated learning has gained widespread attention due to its critical privacy protection mechanism. However, it also faces challenges such as high communication costs and heterogeneous client data.In order to address the above issues. …”
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Research on Hyperspectral Image Reconstruction Based on GISMT Compressed Sensing and Interspectral Prediction by Sheng Cang, Achuan Wang

“…According to the prediction algorithm, the corresponding difference vector is obtained and the predicted measured value is iteratively updated by the difference vector until the hyperspectral reconstructed image of the relevant general band is finally reconstructed. …”
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Prediction of Transformer Residual Flux Based on J-A Hysteresis Theory by Qi Long, Xu Yang, Keru Jiang, Changhong Zhang, Mingchun Hou, Yu Xin, Dehua Xiong, Xiongying Duan

“…For the prediction of residual flux, the voltage integration method faces the difficult problem of determining the integration upper limit, while the Jiles- Atherton (J-A) model has the advantages of clear physical meaning of parameters, accurate calculation, and the ability to iteratively solve residual magnetism. …”
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A temporal fusion method for modeling the rate of penetration during deep geological drilling by Yang ZHOU, Chengda LU, Min WU, Xin CHEN, Ningping YAO, Haitao SONG, Youzhen ZHANG

“…To solve the non-convex optimization problem encountered in model parameter design, a modified dung beetle optimizer (MDBO) algorithm was designed through weight fusion, modified echolocation, modified iterated local search, and the re-updating strategy of the optimal solution. …”
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Optimization strategies for public health education based on ISSA and information system technology by Zhanyu Ye, Yifei Li, Yan Zhang

“…IntroductionAgainst the backdrop of rapid development of information technology, public health education is facing challenges such as uneven resource allocation and lagging content.MethodsTo propose an optimization strategy that can effectively improve the level of public health education, this study improves the sparrow search algorithm by introducing the theory of best point sets to optimize the allocation of public health education resources. …”
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Damage Identification in Large-Scale Structures Using Time Series Analysis and Improved Sparse Regularization by Huihui Chen, Xiaojing Yuan

“…Compared to the moth-flame optimization (MFO) algorithm and traditional regularization methods, for both noise-free and noise polluted data, the iteration curves illustrate that the proposed method can achieve convergence within about 200 iterations, while the MFO algorithm is always trapped into local optima; meanwhile, the traditional regularization method needs more iterations or even cannot meet the preset tolerance. …”
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