Control of hybrid electromagnetic bearing and elastic foil gas bearing under deep learning. by Xiangxi Du, Yanhua Sun

“…The hybrid electromagnetic and elastic foil gas bearing is explored based on the radial basis function (RBF) neural network in this study so as to improve its stabilization in work. …”
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An Interpretable Deep Learning-Based Feature Reduction in Video-Based Human Activity Recognition by Micheal Dutt, Morten Goodwin, Christian W. Omlin

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Multi-AUV Hunting Strategy Based on Regularized Competitor Model in Deep Reinforcement Learning by Yancheng Sui, Zhuo Wang, Guiqiang Bai, Hao Lu

“…We introduce an optimality operator and redefine the objective function of multi-agent reinforcement learning (MARL), transforming the uncertain states of other agents into solvable inference problems, namely the Regularized Competitor Model (RCM). …”
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Evaluation of deep learning approaches for classification of drought stages using satellite imagery for Tharparker by Muhammad Owais Raza, Tarique Ahmed Khuhro, Sania Bhatti, Mohsin Memon

“…DenseNet, ResNet, InceptionV3, Xception, and VGG19 deep learning architectures were utilized for training the models. …”
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Application of a Supervised Learning Machine for Accurate Prognostication of Hydrogen Contents of Bio-Oil by Binghui Xu, Tzu-Chia Chen, Danial Ahangari, S. M. Alizadeh, Marischa Elveny, Jeren Makhdoumi

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Convolutional neural network based on transfer learning for discriminating the fermentation degree of black tea by Xuesong Zhu, Zezhong Ding, Mei Wang, Shanjian Liu, Yulong Chen, Chunwang Dong

“…Next, we modified the loss function with PolyLoss and optimizer with AdamW for the student model. …”
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Embedding-based pair generation for contrastive representation learning in audio-visual surveillance data by Wei-Cheng Wang, Sander De Coninck, Sam Leroux, Pieter Simoens

“…As data annotation is expensive, self-supervised methods such as contrastive learning are used to learn audio-visual representations for downstream tasks. …”
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Learning the non-proportional multiaxial elastic–plastic response of an aluminium alloy with neural networks by Burcu Tasdemir, Antonio Pellegrino, Xinyu Su, Vito L. Tagarielli

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Traffic concealed data detection method based on contrastive learning and pre-trained Transformer by HE Shuai, ZHANG Jingchao, XU Di, JIANG Shuai, GUO Xiaowei, FU Cai

“…By transforming the problem of concealed data detection into a similarity analysis problem, a diversity-sensitive Transformer architecture was developed leveraging contrastive learning, which enhanced the model’s sensitivity to traffic differences through the use of positive and negative sample pairs, and using information noise contrastive estimation (Info NCE) as the loss function for fine-tuning downstream tasks of encrypted traffic. …”
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PhyloFunc: phylogeny-informed functional distance as a new ecological metric for metaproteomic data analysis by Luman Wang, Caitlin M. A. Simopoulos, Joeselle M. Serrana, Zhibin Ning, Yutong Li, Boyan Sun, Jinhui Yuan, Daniel Figeys, Leyuan Li

“…Abstract Background Beta-diversity is a fundamental ecological metric for exploring dissimilarities between microbial communities. On the functional dimension, metaproteomics data can be used to quantify beta-diversity to understand how microbial community functional profiles vary under different environmental conditions. …”
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Deep learning prioritizes cancer mutations that alter protein nucleocytoplasmic shuttling to drive tumorigenesis by Yongqiang Zheng, Kai Yu, Jin-Fei Lin, Zhuoran Liang, Qingfeng Zhang, Junteng Li, Qi-Nian Wu, Cai-Yun He, Mei Lin, Qi Zhao, Zhi-Xiang Zuo, Huai-Qiang Ju, Rui-Hua Xu, Ze-Xian Liu

“…Abstract Genetic variants can affect protein function by driving aberrant subcellular localization. …”
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Mathematical Modeling for Ceramic Shape 3D Image Based on Deep Learning Algorithm by Lijian Zhang, Guangfu Liu

“…Each module in the compression framework is optimized by a rate-distortion loss function. The experimental results show that the proposed 3D image modeling method has significant advantages in compression performance compared with the optimal 2D 3D image modeling method based on deep learning, and the experimental results show that the performance of the proposed method is superior to JP3D and HEVC methods, especially at low bit rate points.…”
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Improved QT-Opt Algorithm for Robotic Arm Grasping Based on Offline Reinforcement Learning by Haojun Zhang, Sheng Zeng, Yaokun Hou, Haojie Huang, Zhezhuang Xu

“…Furthermore, a regularization term is proposed during the value iteration process to facilitate the learning of a conservative Q-function, enabling precise estimation of the robotic arm’s action values. …”
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Bank data protection and fraud identification based on improved adaptive federated learning and WGAN by Yunjiao Zheng

“…The experiment outcomes show that the adaptive data protection architecture designed in this study notably raises the convergence performance of the loss function after introducing adaptive gradient pruning and noise differentiation strategies, with a minimum loss value of 0.09 and excellent learning ability. …”
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Intelligent Counterforce Allocation Method Using Multi-Agent Reinforcement Learning for Ground Operations by Kiwoong Park, Sangheun Shim

“…This study proposes the Intelligent Counterforce Allocation for Ground Operations (ICAGO) method, which leverages multi-agent reinforcement learning (MARL) to support command-level decision-making in ground warfare. …”
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An Inverse Modeling Multi-Objective Optimization Technique Based on Incremental Learning and Fuzzy Clustering by Gadallah Mohamed Abd Elaziz, Yasmine Abouelseoud, Sara H. Kamel

“…Extensive simulations on various benchmark problems show that the proposed algorithm drastically reduces the number of function evaluations required to reach an optimal solution compared to existing methods. …”
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A Bluetooth Indoor Positioning System Based on Deep Learning with RSSI and AoA by Yongjie Yang, Hao Yang, Fandi Meng

“…In the deep learning network architecture, we propose a convolutional neural network (CNN)–multi-head attention (MHA) model. …”
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On Conditional Independence Graph Learning From Multi-Attribute Gaussian Dependent Time Series by Jitendra K. Tugnait

“…In this paper we provide a unified theoretical analysis of multi-attribute graph learning for dependent time series using a penalized log-likelihood objective function formulated in the frequency domain using the discrete Fourier transform of the time-domain data. …”
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Exercise Improves Spatial Learning and Memory Performance through the Central GLP-1 Receptors by Majid Taati, Peyman Esmaeili Fard Barzegar, Abbas Raisi

“…The central GLP-1R’s role in cognitive functioning is well known. On the contrary, it has been shown that exercise has positive effects on brain function. …”
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Deep learning-driven likelihood-free parameter inference for 21-cm forest observations by Tian-Yang Sun, Yue Shao, Yichao Li, Yidong Xu, He Wang, Xin Zhang

“…However, conventional methods are hindered by computationally expensive simulations and a non-Gaussian likelihood function. To overcome these challenges, we propose a deep learning approach that combines generative normalizing flows for data augmentation and inference normalizing flows for parameter estimation, enabling accurate results from minimally simulated datasets. …”
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