Deep Learning in Defect Detection of Wind Turbine Blades: A Review by Katleho Masita, Ali N. Hasan, Thokozani Shongwe, Hasan Abu Hilal

“…Key advancements are highlighted, including the integration of Convolutional Neural Networks (CNNs), Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs) for image-based detection and anomaly identification. …”
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Complying with the EU AI Act: Innovations in explainable and user-centric hand gesture recognition by Sarah Seifi, Tobias Sukianto, Cecilia Carbonelli, Lorenzo Servadei, Robert Wille

“…Leveraging this real-world dataset, we enhance XentricAI’s capabilities by integrating a variational autoencoder module for improved gesture anomaly detection, incorporating user-specific dynamic thresholding. …”
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Information Bottleneck-Based Domain Adaptation for Hybrid Deep Learning in Scalable Network Slicing by Tianlun Hu, Qi Liao, Qiang Liu, Georg Carle

“…Then, we introduce a variational information bottleneck (VIB)-assisted domain adaptation (DA) approach to enhance integrated deep learning and Lagrangian method (IDLA)’s adaptability across diverse network environments and conditions. …”
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Modeling Energy Communities: A Case Study of Quantum Approximate Optimization on a Superconducting Processor by Mateo Alonso, Guillermo Rubinos Rodriguez, Pablo Diez-Valle, Ana Garbayo, Xela Garcia-Santiago, Gonzalo Blazquez Gil

“…This requires integrating various energy sources, storage solutions, and the ability to respond to variations in demand within energy systems, while maintaining the capacity to adapt to the variability of renewable energy sources. …”
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A Hybrid Model for Carbon Price Forecasting Based on Secondary Decomposition and Weight Optimization by Yongfa Chen, Yingjie Zhu, Jie Wang, Meng Li

“…The empirical results of the carbon markets in Hubei and Guangdong indicate that the proposed method outperforms the benchmark model in terms of prediction accuracy and robustness, and the method has been tested by Diebold Mariano (DM). …”
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Chaotic and quasi-periodic dynamics in fractional-order nonlinear wave systems within dispersive-dissipative media by Wael W Mohammed, Fakhr Gassem, Rabeb Sidaoui, Aleksander Vokhmintsev, Mostafa M. A. Khater

“…Through the complementary application of the Khater III method and an enhanced Kudryashov technique, we derive closed-form solutions and rigorously validate them via numerical implementation of He’s variational iteration approach. …”
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Electric load forecasting based on kernel extreme learning machine optimized by improved sparrow search algorithm by Diming Zhang, Yuchen Xu, Yuanjiang Li

“…These subsequences are then integrated with the Kernel Extreme Learning Machine (KELM) to develop a forecasting model named WHFSSA-KELM. …”
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CA-ViT: Contour-Guided and Augmented Vision Transformers to Enhance Glaucoma Classification Using Fundus Images by Tewodros Gizaw Tohye, Zhiguang Qin, Mugahed A. Al-antari, Chiagoziem C. Ukwuoma, Zenebe Markos Lonseko, Yeong Hyeon Gu

“…Therefore, the integration of augmentation with the CVGAN and contour guidance can effectively enhance glaucoma classification tasks.…”
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Transformations of Optimal Control Problems by A. Tsirlin

“…The paper considers such transformations of variational and optimal control problems (by a change of phase variables) that help to get the solution. …”
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A new numerical technique to seek properties of the nonlinear oscillators by Jing-Yan Niu, Guang-Qing Feng

“…This new transformation coupled with the variational iteration method is highly effective for various nonlinear problems and offers a new window for wide applications. …”
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Robust Adaptive Multiple Backtracking VBKF for In-Motion Alignment of Low-Cost SINS/GNSS by Weiwei Lyu, Yingli Wang, Shuanggen Jin, Haocai Huang, Xiaojuan Tian, Jinling Wang

“…To address the issue that low-cost SINS/GNSS cannot effectively achieve rapid and high-accuracy alignment in complex environments that contain noise and external interference, an adaptive multiple backtracking robust alignment method is proposed. The sliding window that constructs observation and reference vectors is established, which effectively avoids the accumulation of sensor errors during the full integration process. …”
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STAGE framework: A stock dynamic anomaly detection and trend prediction model based on graph attention network and sparse spatiotemporal convolutional network. by Ming Shi, Roznim Mohamad Rasli, Shir Li Wang

“…This paper proposes the STAGE framework, which integrates the Graph Attention Network (GAT), Variational Autoencoder (VAE), and Sparse Spatiotemporal Convolutional Network (STCN), to enhance the accuracy of stock prediction and the robustness of anomaly data detection. …”
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Cold-start visualization recommendation driven by large language models for ocean data analysis by Xin Li, Jixiu Liao, Wen Liu, Yu Miao, Leyu Wang, Shuqing Sun

“…Moreover, although existing interaction methods are diverse, their precise control fails to effectively convey users' ambiguous intentions.MethodsTo address these issues, we introduce a novel cold-start visualization recommendation system that integrates a Large Language Model (LLM) and a Grammar Variational Autoencoder (GVAE). …”
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Simultaneous Vibration and Nonlinearity Compensation for One-Period Triangular FMCW Ladar Signal Based on MSST by Wei Li, Ruihua Shi, Qinghai Dong, Juanying Zhao, Bingnan Wang, Maosheng Xiang

“…Firstly, variational mode decomposition (VMD) is applied to the optical prism signal to eliminate low-frequency noise and harmonic peaks. …”
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A data-driven hybrid framework for voltage transformer ratio error prediction: Addressing challenges in complex power systems by Jiuxi Cui, Zhenhua Li, Heping Lu, Feng Zhou, Haoyu Chen, Zhenxing Li

“…First, an energy entropy-optimized adaptive variational mode decomposition method is developed, which introduces convergence constraints for parameter selection, reducing randomness during the decomposition process and is validated by power spectrum analysis. …”
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Measuring. Monitoring. Management. Control by F.G. Agayev, H.H. Asadov, M.E. Omarov, S.S. Aliyeva

“…The variational method was used to reach the formulated goal. …”

Smart furniture using radar technology for cardiac health monitoring by Ali Gharamohammadi, Mohammad Omid Bagheri, Serene Abu-Sardanah, Michael M. Y. R. Riad, Hajar Abedi, Ahmad Ansariyan, Kang Wang, Ashish Saragadam, Dmytro Chumachenko, Shahabeddin Abhari, Plinio Pelegrini Morita, Amir Khajepour, George Shaker

“…Experiment results demonstrate a maximum interbeat intervals (IBI) error of 30 milliseconds and an average relative error of 4.8% in heart rate estimation, showcasing the efficacy of the proposed method utilizing variational mode decomposition and a multi-bin approach.…”
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Existence of Solutions to Fractional Differential Equations with Mixed Caputo–Riemann Derivative by Mahir Almatarneh, Sonuc Zorlu, Nazim I. Mahmudov

“…The study of fractional differential equations is gaining increasing significance due to their wide-ranging applications across various fields. Different methods, including fixed-point theory, variational approaches, and the lower and upper solutions method, are employed to analyze the existence and uniqueness of solutions to fractional differential equations. …”
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A Multi-Scale Unsupervised Feature Extraction Network with Structured Layer-Wise Decomposition by Yusuf Şevki Günaydın, Baha Şen

“…This study introduces a novel unsupervised multi-scale feature extraction framework based on a multi-branch auto-encoder architecture. The proposed method decomposes input images into smooth, detailed and residual components, using variational loss functions to ensure that each branch captures distinct and non-overlapping representations. …”
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Bearing remaining useful life prediction based on optimized VMD and BiLSTM-CBAM. by Wei Liu, Sen Liu

“…To address the issue of low accuracy in existing remaining useful life (RUL) prediction algorithms for rolling bearings, this paper proposes a novel RUL prediction method based on the Beluga Whale Optimization (BWO) algorithm, Variational Mode Decomposition (VMD), an improved Convolutional Block Attention Module (CBAM*), and a Bidirectional Long Short-Term Memory (BiLSTM) network. …”
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