Bayesian Uncertainty Quantification of Reflooding Model With PSO–Kriging and PCA Approach by Ziyue Zhang, Dong Li, Nianfeng Wang, Meng Lei

“…As reflooding is a vital stage to cool the core and prevent serious accidents and uncertainties exist in the important results of the program because of the complexity of the phenomena, IUQ is performed for reflooding models in this study based on Bayesian theory and Markov chain Monte Carlo (MCMC) algorithm. In order to solve the problem of large time costs in sampling and inefficient use of transient sample points, particle swarm optimization (PSO)–Kriging model and principal component analysis (PCA) are adopted in this paper. …”
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Enhancing Tire Condition Monitoring through Weightless Neural Networks Using MEMS-Based Vibration Signals by Siddhant Arora, Sridharan Naveen Venkatesh, Vaithiyanathan Sugumaran, Anoop Prabhakaranpillai Sreelatha, Vetri Selvi Mahamuni

“…Hyperparameter optimization of the WNN leads to improved classification accuracy and shorter computation times. …”
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Lightweight Indoor Positioning System Based on Multiple Self-Learning Features and Key Frame Classification by C. Wang, K. Bi, B. Zhao, M. Li, Y. Chen, S. Tao, J. Yang

“…However, it still has inherent defects, such as cumbersome data collection, complex algorithms, and universality. To minimize indoor information pre-collection cost, improve versatility, and enable rapid deployment in low-performance mobile devices, this paper proposes a lightweight indoor positioning system based on multiple self-learning features and key frame classification. …”
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Estimation of Optimum Dilution in the GMAW Process Using Integrated ANN-GA by P. Sreeraj, T. Kannan, Subhashis Maji

“…In this study, artificial neural network (ANN) and genetic algorithm (GA) techniques were integrated and labeled as integrated ANN-GA to estimate optimal process parameters in GMAW to get optimum dilution.…”
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High voltage direct current system-based generation and transmission expansion planning considering reactive power management of AC and DC stations by Ehsan Akbari, Ahad Faraji Naghibi, Mehdi Veisi, Sasan Pirouzi, Sheila Safaee

“…The scheme incorporates a non-linear model. The Red Panda Optimization (RPO) is utilized to solve the provided model in order to attain a dependable and optimal solution. …”
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Estimation of Wind Turbine Blade Icing Volume Based on Binocular Vision by Fangzheng Wei, Zhiyong Guo, Qiaoli Han, Wenkai Qi

“…Therefore, this paper proposes a non-contact ice volume estimation method based on binocular vision and improved image processing algorithms. The method employs a stereo matching algorithm that combines dynamic windows, multi-feature fusion, and reordering, integrating gradient, color, and other information to generate matching costs. …”
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Multi-user fine-grained task offloading scheduling strategy under cloud-edge-end collaboration by XIE Mande, HUANG Zhufang, SUN Hao

“…Then, a multi-user subtask scheduling scheme was proposed and an improved simulated annealing particle swarm algorithm was designed to minimize the total system cost to achieve the optimal offloading decision. …”
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Centralized Measurement Level Fusion of GNSS and Inertial Sensors for Robust Positioning and Navigation by Mohamed F. Elkhalea, Hossam Hendy, Ahmed Kamel, Ashraf Abosekeen, Aboelmagd Noureldin

“…The experimental results clearly illustrate the considerable improvements achieved by the recommended tightly coupled (TC) algorithm when integrated with MDDA, in contrast to the loosely coupled (LC) algorithm. …”
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Integrating personalized shape prediction, biomechanical modeling, and wearables for bone stress prediction in runners by Liangliang Xiang, Yaodong Gu, Kaili Deng, Zixiang Gao, Vickie Shim, Alan Wang, Justin Fernandez

“…The digital twin leverages a domain adaptation-based Long Short-Term Memory (LSTM) algorithm, informed by wearable sensor data, to dynamically simulate the structural behavior of foot bones under running conditions. …”
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Hybridized Deep Learning Model for Perfobond Rib Shear Strength Connector Prediction by Jamal Abdulrazzaq Khalaf, Abeer A. Majeed, Mohammed Suleman Aldlemy, Zainab Hasan Ali, Ahmed W. Al Zand, S. Adarsh, Aissa Bouaissi, Mohammed Majeed Hameed, Zaher Mundher Yaseen

“…In the second scenario, a comparable AI model hybridized with genetic algorithm (GA) as a robust bioinspired optimization approach for optimizing the related predictors for the PRSC is proposed. …”
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Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning. by Jiayi Wu, Yong-Bei Ma, Charles Congdon, Bevin Brett, Shuobing Chen, Yaofang Xu, Qi Ouyang, Youdong Mao

“…However, traditional algorithms for unsupervised classification, such as K-means clustering and maximum likelihood optimization, may classify images into wrong classes with decreasing signal-to-noise-ratio (SNR) in the image data, yet demand increased computational costs. …”
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Deep reinforcement learning applications and prospects in industrial scenarios by JING TAN, Ligang YANG, Xiaorui LI, Zhaolin YUAN, Yunduan CUI, Chao YAO, Zongjie WANG, Xiaojuan BAN

“…Central to these systems are control algorithms, which enable the automation of operations, optimization of process parameters, and reduction of operational costs. …”
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Utility-Based Joint Routing, Network Coding, and Power Control for Wireless Ad Hoc Networks by Kaiqian Ou, Yinlong Xu, Xiumin Wang, Wang Liu

“…Based on the expected utility, we explore the optimality in both unicast and multicast routing. For unicast routing, we propose an optimal algorithm. …”
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Comparison of Spatial Predictability Differences in Truck Activity Patterns: An Empirical Study Based on Truck Tracking Dataset of China by Lianghua Li, Peng Du, Guohua Jiao, Xin Fu

“…Existing research on truck location prediction focuses on direct trajectory prediction and ignores the link between activity patterns and predictability, whereas the mode of operation is an important factor in the difference between activity trajectories, and analyzing the mode of operation can help to develop the next-location prediction algorithms to improve the efficiency of matching truckloads and to reduce costs. …”
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Performance evaluation of different building envelopes integrated with phase change materials in tropical climates by Rolains Golchimard Elenga, Li Zhu, Steivan Defilla

“…To achieve this goal, a multiobjective genetic algorithm is used in conjunction with EnergyPlus building energy models to determine the optimal balance between total building energy consumption, lifecycle cost, and CO2 emissions. …”
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Pickup and delivery planning for the crowdsourced freight delivery routing problem. by Jingxian Zhang

“…To solve this combinatorial optimization problem, we propose an improved partheno genetic algorithm (IPGA) and a simulated annealing algorithm (SA). …”
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Address Translation in a Compositional Microprogram Control Unit by Alexandr Barkalov, Larysa Titarenko, Oleksandr Golovin, Oleksandr Matvienko

“…The method proposed in the article is based on the adaptation of algorithms for optimizing microprogram automata circuits to the features of CMCUs. …”
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Adaptive DBP System with Long-Term Memory for Low-Complexity and High-Robustness Fiber Nonlinearity Mitigation by Mingqing Zuo, Huitong Yang, Yi Liu, Zhengyang Xie, Dong Wang, Shan Cao, Zheng Zheng, Han Li

“…In this paper, an improved A-DBP algorithm with long-term memory (LTM) is proposed, employing root mean square propagation (RMSProp) to achieve low-complexity and high-robustness compensation performances. …”
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YOLO-SMUG: An Efficient and Lightweight Infrared Object Detection Model for Unmanned Aerial Vehicles by Xinzhe Luo, Xiaogang Zhu

“…To tackle the high computational demands and accuracy limitations in UAV-based infrared object detection, this study proposes YOLO-SMUG, a lightweight detection algorithm optimized for small object identification. …”
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Predictive modeling of hydrogen production and methane conversion from biomass-derived methane using machine learning and optimisation techniques by Adegboyega Bolu Ehinmowo, Bright Ikechukwu Nwaneri, Joseph Oluwatobi Olaide

“…In this study, the integration of various machine learning algorithms with Bayesian optimisation, firefly algorithm, Levenberg-Marquardt, and differential evolution techniques were investigated for hydrogen production via thermocatalytic methane decomposition. …”
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