Hierarchical GraphCut Phase Unwrapping Based on Invariance of Diffeomorphisms Framework by Xiang Gao, Xinmu Wang, Zhou Zhao, Junqi Huang, Xianfeng David Gu

“…Existing methods typically involve a trade-off between speed and accuracy: Fast approaches lack precision, while accurate algorithms are too slow for real-time use. …”
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Dynamic Deformation Analysis of Super High-Rise Buildings Based on GNSS and Accelerometer Fusion by Xingxing Xiao, Houzeng Han, Jian Wang, Dong Li, Cai Chen, Lei Wang

“…To accurately capture the dynamic displacement of super-tall buildings under complex conditions, this study proposes a data fusion algorithm that integrates NRBO-FMD optimization with Adaptive Robust Kalman Filtering (ARKF). …”
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Quantum Computing and Machine Learning in Medical Decision-Making: A Comprehensive Review by James C. L. Chow

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Memory-Efficient Batching for Time Series Transformer Training: A Systematic Evaluation by Phanwadee Sinthong, Nam Nguyen, Vijay Ekambaram, Arindam Jati, Jayant Kalagnanam, Peeravit Koad

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Automatic Scheduling Method for Customs Inspection Vehicle Relocation Based on Automotive Electronic Identification and Biometric Recognition by Shengpei Zhou, Nanfeng Zhang, Qin Duan, Jinchao Xiao, Jingfeng Yang

“…This research addresses these challenges by integrating EVI and biometric systems into a comprehensive framework aimed at improving vehicle scheduling. The proposed method utilizes genetic algorithms and intelligent optimization techniques to dynamically allocate resources and prioritize vehicle movements based on real-time data. …”
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Digital Transformation in Aftersales and Warranty Management: A Review of Advanced Technologies in I4.0 by Vicente González-Prida, Carlos Parra Márquez, Pablo Viveros Gunckel, Fredy Kristjanpoller Rodríguez, Adolfo Crespo Márquez

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Q8S: Emulation of Heterogeneous Kubernetes Clusters Using QEMU by Jonathan Decker, Vincent Florens Hasse, Julian Kunkel

“…Kubernetes has emerged as the industry standard for container orchestration in cloud environments, with its scheduler dynamically placing container instances across cluster nodes based on predefined rules and algorithms. Various efforts have been made to extend and improve upon the Kubernetes scheduler. …”
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Enhanced Wind Energy Forecasting Using an Extended Long Short-Term Memory Model by Zachary Barbre, Gang Li

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Data Augmentation for Voiceprint Recognition Using Generative Adversarial Networks by Yao-San Lin, Hung-Yu Chen, Mei-Ling Huang, Tsung-Yu Hsieh

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Adaptive Cut Selection in Mixed-Integer Linear Programming by Turner, Mark, Koch, Thorsten, Serrano, Felipe, Winkler, Michael

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Corrosion rate prediction for long-distance submarine pipelines based on MWIWOA-SVM by Zhengshan LUO, Haipeng LYU, Jihao LUO

“…The extended search mode was refined by integrating the simplex method, while the optimization capability of the Whale Optimization Algorithm (WOA) was enhanced with Levy flight to improve step length. …”
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Short-Term Load Forecasting Based on EEMD-WOA-LSTM Combination Model by Lei Shao, Quanjie Guo, Chao Li, Ji Li, Huilong Yan

“…The whale bionic algorithm is used to solve the problem that the long short-term memory neural networks are easy to fall into local optimization and improve the accuracy of parameter optimization. …”
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Adaptive robust position control scheme for an electromagnetic levitation system with experimental verification. by Ziwei Wu, Kuangang Fan, Ping Yi

“…A control method combining an improved whale optimization algorithm with robust sliding mode control and adaptive linear active disturbance rejection (IWOA-SMC-ALADRC) is proposed to achieve stable control of a single point electromagnetic levitation ball. …”
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Coordinated Scheduling for Zero-Wait RGV/ASR Warehousing Systems with Finite Buffers by Wenbin Gu, Na Tang, Lei Wang, Zhenyang Guo, Yushang Cao, Minghai Yuan

“…Additionally, in 15 randomized test scenarios, AMHS demonstrated superior performance over three benchmark algorithms—Genetic Algorithm (GA), Discrete Imperialist Competitive Algorithm (DICA), and Improved Whale Optimization Algorithm (IWOA)—achieving an average makespan reduction of 12.6% relative to GA.…”
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Power Load Data Completion Method Based on Integrated Graph Convolutional Variational Transformer by YAN Li, HU Hailin, SHI Lei, WU Qinzheng, LÜ Tianguang, XU Yingdong, ZHANG Wenbin, WANG Gaozhou

“…The raw data are processed by the GCN to learn spatial features and deeply mine spatial dependencies； the hidden layer data are reconstructed by the VAE to more effectively restore data distribution characteristics； and the temporal autocorrelation information of the sequence is mined based on the Transformer model. In addition， an improved whale optimization algorithm （WOA） is introduced to optimize the network model hyperparameters and improve the completion accuracy and applicability of the model. …”
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A study on monthly sales forecasting of new energy vehicles in urban areas using the WOA-BiGRU model. by Xiangtu Li

“…The Whale Optimization Algorithm (WOA) is then employed to optimize the parameters of the Bidirectional Gated Recurrent Unit (BiGRU) model, thereby proposing a WOA-BiGRU-based model for monthly sales prediction for urban NEVs. …”
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Mix design and performance prediction of EPS lightweight structural concrete based on orthogonal experimentation by Qianhui Zhang

“…A novel dataset was established and utilized in performance prediction using XGBoost, optimized with Seagull Optimization Algorithm (SOA), Whale Optimization Algorithm (WOA), and Particle Swarm Optimization (PSO). …”
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铰链四杆刚体导引机构综合的区间逃逸粒子群算法 by 易建, 何兵, 车林仙

“…Aimed at this weakness,the improved PSO algorithm,the Escape PSO Based on the interval search （EPSObIS）,is presented to make the optimal problem for rigid-body guidance synthesis of hinged 4-bar linkages. …”
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Fault aware task scheduling in cloud using min-min and DBSCAN by S.M.F D Syed Mustapha, Punit Gupta

“…The results show 25% average improve in execution time, 6.5% improvement in number of task completed and 3.48% improvement in count of task failed as compared to ACO, PSO, BB-BC (Bib ​= ​g bang Big Crunch) and WHO(Whale optimization algorithm).…”
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Frequency Response Function-Based Finite Element Model Updating Using Extreme Learning Machine Model by Yu Zhao, Zhenrui Peng

“…Extreme learning machine (ELM) is introduced as the surrogate model of the finite element model (FEM) to construct the relationship between updating parameters and structural responses. To further improve the generalization ability, the input weights and biases of ELM are optimized by Lévy flight trajectory-based whale optimization algorithm (LWOA). …”
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