Deep Learning Method for Bearing Fault Diagnosis by LIU Xiu, MA Shan-tao, XIE Yi-ning, HE Yong-jun

“…In recent years, deep learning technology has shown great potential in bearing fault diagnosis based on vibration signals.However, in the fault diagnosis method based on deep learning, the traditional single network topology feature extraction has weak discrimination and low noise robustness, and the accuracy of fault diagnosis is not high.In addition, most of the current research methods have a low fault recognition rate in a variable load environment.In response to the above problems, this paper proposes an improved neural network end-to-end fault diagnosis model.The model combines convolutional neural networks (CNN) and the attention long short-term memory (ALSTM) based on the attention mechanism, and uses ALSTM to capture long-distance correlations in time series data , Effectively suppress the high frequency noise in the input signal.At the same time, a multi-scale and attention mechanism is introduced to broaden the range of the convolution kernel to capture high and low frequency features, and highlight the key features of the fault. …”
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Recommendation model based on separated embedding interaction networks by FENG Shulei, JIANG Zhongyun

“…Aiming at the problems that the existing feature interaction methods in deep learning recommendation models cannot fully utilize the embedding vector information and thus have the insufficient accuracy, we propose a deep learning recommendation model based on separated embedding interaction networks (SEIN). …”
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Optimization of deep learning architecture based on multi-path convolutional neural network algorithm by Chuan Zhou, Yan Liu, Xinghan An, Xiyao Xu, Hao Wang

“…At present, there are some problems in multi-path architecture, such as isolated information among paths, low efficiency of feature fusion mechanism, and high computational complexity. …”
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Symbolic Regression-Based Modeling for Aerodynamic Ground-to-Flight Deviation Laws of Aerospace Vehicles by Di Ding, Qing Wang, Qin Chen, Lei He

“…This paper proposes methods for modelling this correlation that combine feature extraction and symbolic regression. The neighborhood component analysis (NCA) method is utilized to extract features from the high-dimensional state space and then symbolic regression (SR) is applied to find the concise optimal expression. …”
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BINARY HONEY BADGER ALGORITHM ENHANCED WITH TIME-VARYING SIGMOID TRANSFER FUNCTION AND CROSSOVER STRATEGY by Emine Baş, Gülnur Yıldızdan

“…The results showed that the proposed BinHBA for binary optimization problems is effective and preferable.…”
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A Novel Reconstruction Method for Irregularly Sampled Observation Sequences for Digital Twin by Haonan Jiang, Yanbo Zhao, Qiao Zhu, Yuanli Cai

“…Second, to improve the accuracy of sequence reconstruction under large noise levels, a PRN is established to obtain reference features, which are weighted and fused with the features of observed data. …”
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Smart Farming: Enhancing Urban Agriculture Through Predictive Analytics and Resource Optimization by Eman Ali Aldhahri, Abdulwahab Ali Almazroi, Monagi Hassan Alkinani, Nasir Ayub, Elham Abdullah Alghamdi, Nourah Fahad Janbi

“…This model uses HBA’s Dynamic Exploration-Exploitation Balance-fine-tuned Dynamic Feature Recalibration and adaptive convolutions. Imputation Weight Crop Labels (WICL) to accurately fill in missing data, Localised Feature Scaling (LFS) and Adaptive Feature Discretization (AFD) to standardize and categorize features, and the Environmental Stress Factor (ESF) to evaluate crop stress address data problems ASRFS and Crop-Specific Environmental Impact Weighting increase model performance. …”
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PIPNet: A Deep Convolutional Neural Network for Multibaseline InSAR Phase Unwrapping Based on Pure Integer Programming by Hui Liu, Ke Zheng, Changwei Miao, Xuemei Liu, Xianlin Liu, Lin Li, Yongguang Zhang, Longhai Xiong

“…This article transforms the MB PU problem into pure integer programming (PIP) problem and innovates a deep convolutional neural network named PIPNet to solve PU problem. …”
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STATE PREDICTION OF WIND TURBINE GENERATOR BASED ON K-CNN AND N-GRU (MT) by CHAI Tong, YUAN YiPing, MA JunYan, FAN PanPan

“…Secondly, to solve the problem of ignoring shallow features in traditional feature extraction, CNN was used to extract the features of one-dimensional fusion parameters in layers, and KPCA was used to reduce the feature extraction results of different layers to one dimension. …”
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Remote sensing satellite big data high-recision integration processing technology by Xiaolan QIU, Yuxin HU, Songtao SHANGGUAN, Kun FU

“…With the rapid development of China’s aerospace information acquisition technology, aerospace data presents the characteristics of large data volume, large number of types, rapid growth and relatively low value density.Remote sensing satellite data is an important part of aerospace big data.How to make use of the scale effect and the complementary advantages of the data of different satellites, so as to improve the processing accuracy and efficiency, is the key problem to be solved in the remote sensing satellite big data processing system.The development history of our country’s remote sensing satellite ground data processing system was reviewed briefly.The core difficulties faced by the ground processing system were pointed out.A high-precision processing technology for remote sensing satellite big data based on stability feature mining was proposed.And a preliminary implementation was given.The method provides a useful reference for the development of our country’s aerospace big data processing system.…”
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Pedestrian trajectory prediction model based on self-supervised spatiotemporal graph network by Shiji Yang, Xuezhong Xiao

“…Firstly, in the process of spatiotemporal graph modeling, this model introduces hop interaction instead of node interaction to update node features, which greatly reduces the times of graph convolution operations, alleviates the problem of feature smoothing, and greatly improves the accuracy of prediction. …”
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Fine-grained image classification using the MogaNet network and a multi-level gating mechanism by Dahai Li, Su Chen

“…A feature extraction network based on MogaNet is constructed, and multi-scale feature fusion is combined to fully mine image information. …”
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Grading evaluation method for inter-turn short circuit of permanent magnet traction motor based on deep Gaussian processes by DAI Jisheng, HU Dean, XU Hailong, ZHU Wenlong, LIAO Qishu

“…Therefore, this paper proposed a grading evaluation method for inter-turn short circuit in permanent magnet traction motors based on multi-feature fusion of deep Gaussian processes. Firstly, by establishing a fault model for inter-turn short circuit in permanent magnet traction motors, features such as current unbalance, third harmonic currents and second harmonic feature of <italic>dq</italic> currents were extracted. …”
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AI-Based Classification of Pediatric Breath Sounds: Toward a Tool for Early Respiratory Screening by Lichuan Liu, Wei Li, Beth Moxley

“…Auscultation, the act of listening to breath sounds, is a crucial diagnostic method for respiratory system diseases. Problem: Parents and caregivers often lack the necessary knowledge and experience to identify subtle differences in children’s breath sounds. …”
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A Novel Deep Hybrid Model for Automatic Femoral Stem Classification in Hip Arthroplasty From Radiographs: MSFT-Net With CBAM and Transformer Modules by Emre Gogus, Atinc Yilmaz, Meric Enercan

“…In cases where prior implant data are unavailable, manual identification is often required, posing significant challenges due to its time-consuming and error-prone nature. To solve this problem, a novel hybrid deep learning architecture that includes a convolutional block attention module and a swin transformer with multi-scale feature fusion from pre-trained architectures DenseNet201, VGG19, and InceptionV3 under the transfer learning paradigm was proposed in this study. …”
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Transformer attention fusion for fine grained medical image classification by Danyal Badar, Junaid Abbas, Raed Alsini, Tahir Abbas, Wang ChengLiang, Ali Daud

“…Through its algorithm, the model solves problems with imbalanced datasets and inconsistent image quality without needing data augmentation because it learns stable features. …”
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The Narrative of the Northern Territories in the Socio-Political Discourse of Contemporary Japan by D. V. Streltsov

“…It is concluded that, despite all the efforts of the government, Japanese public opinion in reality turns out to be relatively poorly informed about the problem of the Northern Territories. At the same time, as generations change, the interest in this problem is gradually decreasing, especially among young people. …”
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External barycentric coordinates for arbitrary polygons and an approximate method for calculating them by Ivan S. Polyansky

“…In the article, the concept of external barycentric coordinates is introduced to generalize the applicability of the barycentric method in solving external boundary value and initial boundary value problems of mathematical physics. Aim of the work is to form a simple analytical relation that allows calculating barycentric coordinates external to a given arbitrary polygonal area with a given accuracy. …”
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Optimizing visual data retrieval using deep learning driven CBIR for improved human machine interaction by Arulmozhi P, Gopi R

“…Robust performance across varied datasets is challenging for traditional CBIR methods due to their reliance on hand-crafted features and inflexible structures. This study presents a deep adaptive attention network (DAAN) for CBIR that combines multi-scale feature extraction and hybrid neural architectures to solve these problems and improve the speed and accuracy of visual retrieval. …”
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Machine learning based method for forecasting short-term passenger flow in urban rail stations by HU Mingwei, SHI Xiaolong, WU Wenlin, HE Guoqing

“…It also plays a prominent part in solving urban traffic problems. In order to improve the operation efficiency of the urban rail transit system and achieve the goal of smart operations, this paper applies machine learning algorithms and completes the feature engineering of urban rail transit passenger flow data in terms of time, space and external factors. …”
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