Sequential image deep learning-based Wi-Fi human activity recognition method by Qizhen ZHOU, Jianchun XING, Qiliang YANG, Deshuai HAN

“…For the problems existing in most of the researches,such as weak anti-noise ability,incompatible signal size and insufficient feature extraction of deep-learning-based Wi-Fi human activity recognition,a kind of sequential image deep learning-based recognition method was proposed.Based on the idea of sequential image deep learning,a series of image frames were reconstructed from time-varied Wi-Fi signal to ensure the consistency of input size.In addition,a low-rank decomposition method was innovatively designed to separate low-rank activity information merged in noises.Finally,a deep model combining temporal stream and spatial stream was proposed to automatically capture the spatiotemporal features from length-varied image sequences.The proposed method was extensively tested in WiAR dataset and self collected dataset.The experimental results show the proposed method could achieve the accuracy of 0.94 and 0.96,which indicate its high-accuracy performance and robustness in pervasive environments.…”
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Using Event Logs for Local Correction of Process Models by Alexey A. Mitsyuk, Irina A. Lomazova, Wil M.P. van der Aalst

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Displacement Patterns and Predictive Modeling of Slopes in the Bayan Obo Open-Pit Iron Mine by Penghai Zhang, Yang Li, Xin Dong, Tianhong Yang, Honglei Liu

“…The reconstructed total displacement achieved an R² of 0.9973, verifying the proposed multi-scale decomposition and hybrid modeling framework’s high accuracy and robustness in slope deformation modeling and early warning.…”
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A Novel Method for 3-D Building Structure Determination in Through-the-Wall Radar by Qichang Guo, Xingdong Liang, Yanlei Li

“…Moreover, in order to keep the global correlation of the image in the case of the errors, the tensor Tucker decomposition is adopted. The performance of this method is discussed in the simulation and real radar data results. …”
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Motor Fault Diagnosis Under Strong Background Noise Based on Parameter-Optimized Feature Mode Decomposition and Spatial–Temporal Features Fusion by Jingcan Wang, Yiping Yuan, Fangqi Shen, Caifeng Chen

“…To address this issue, this study introduces a high-performance fault diagnosis approach for mining motors operating under strong background noise by integrating parameter-optimized feature mode decomposition (WOA-FMD) with the RepLKNet-BiGRU-Attention dual-channel model. …”
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Optimized dynamic mode decomposition for reconstruction and forecasting of atmospheric chemistry data by M. Velagar, C. Keller, C. Keller, J. N. Kutz

“…<p>We introduce the optimized dynamic mode decomposition (DMD) algorithm for constructing an adaptive and computationally efficient reduced-order model and forecasting tool for global atmospheric chemistry dynamics. …”
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Deep Learning-Based Rapid Flow Field Reconstruction Model with Limited Monitoring Point Information by Ping Wang, Guangzhong Hu, Wenli Hu, Xiangdong Xue, Jing Tao, Huabin Wen

“…Conventional CFD simulation methods require several hours, whereas the reconstruction model proposed here can rapidly reconstruct the flow field within 1 min after training is completed, significantly reducing reconstruction time. …”
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A Novel Optimized Hybrid VMD-PCA-XGBoost Model for Forecasting Precipitation: Exemplified by the Beijing-Tianjin-Hebei Study Region in China by Qiaoli Kong, Qian Li, Qi Bai, Xiaolong Mi, Joseph Awange, Shi Wang, Yi Yang, Guoli Bo

“…In this study, we propose a novel hybrid model, variational mode decomposition-principal component analysis-extreme gradient boosting (VMD-PCA-XGBoost), which integrates VMD for effective signal processing, PCA for dimensionality reduction, and XGBoost for enhancing predictive accuracy. …”
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Calculating the true level of predictors significance when carrying out the procedure of regression equation specification by Nikita A. Moiseev

Subjects: “…regression models…”
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Data-driven non-intrusive reduced order modelling of selective laser melting additive manufacturing process using proper orthogonal decomposition and convolutional autoencoder by Shubham Chaudhry, Azzedine Abdedou, Azzeddine Soulaïmani

“…The POD-ANN model utilizes proper orthogonal decomposition to create a reduced-order model, which is then combined with an artificial neural network to establish a surrogate model linking the snapshot matrix to the input parameters. …”
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A hybrid model for short-term offshore wind power prediction combining Kepler optimization algorithm with variational mode decomposition and stochastic configuration networks by Bingbing Yu, Yonggang Wang, Jun Wang, Yuanchu Ma, Wenpeng Li, Weigang Zheng

“…Compared with the basic VMD model, the data decomposition efficiency of the optimized VMD model has been improved by 28.86%. …”
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Wrist Torque Estimation by Combining Motor Unit Discharges With Musculoskeletal Model by Chen Chen, Jiamin Zhao, Yang Yu, Xinjun Sheng, Xiangyang Zhu

“…As neuromusculoskeletal modeling provides a promising alternative, this study proposes a decomposition-based musculoskeletal model for simultaneous and proportional myoelectric control. …”
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Research on Bearing Fault Diagnosis Method Based on MESO-TCN by Ruibin Gao, Jing Zhu, Yifan Wu, Kaiwen Xiao, Yang Shen

“…To address the issues of information redundancy, limited feature representation, and empirically set parameters in rolling bearing fault diagnosis, this paper proposes a Multi-Entropy Screening and Optimization Temporal Convolutional Network (MESO-TCN). The method integrates feature filtering, network modeling, and parameter optimization into a unified diagnostic framework. …”
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Acceleration of Gas Reservoir Simulation Using Proper Orthogonal Decomposition by Yi Wang, Bo Yu, Ye Wang

“…Proper orthogonal decomposition (POD) is introduced to accelerate the reservoir simulation of gas flow in single-continuum porous media via establishing a reduced-order model by POD combined with Galerkin projection. …”
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Gear Fault Diagnosis based on Variational Mode Decomposition and ANFIS by Zheng Xiaoxia, Jia Wenhui, Zhou Guowang, Li Jia

“…Then the extracted feature vectors are input into the adaptive neuro-fuzzy inference system to establish the fault diagnosis model. Finally,the model is validated by the vibration signal data of the gears and compared with the support vector machine（ SVM） method. …”
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Statistics release and privacy protection method of location big data based on deep learning by Yan YAN, Yiming CONG, Mahmood Adnan, Quanzheng SHENG

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Thermo-kinetics of Forest Waste Using Model-Free Methods by Alok Dhaundiyal, Abdulrahman Th Mohammad, Toth Laszlo

“…Kinetic parameters of thermal decomposition reactions of pine needles are obtained through the model-free schemes. …”
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Wavelet Decomposition Prediction for Digital Predistortion of Wideband Power Amplifiers by Shaocheng Peng, Jing You

“…Once the nonlinear model is trained, it is frozen to preserve its learned features. …”
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Order assignment mechanism based on hierarchical decomposition and element matching by ZHANG Xinyue, LYU Weimin, WEI Dingjiang, YANG Dongsheng

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Decomposition Mechanisms of Lignin-Related Aromatic Monomers in Solution Plasma by Takaki Miyamoto, Jeanielle Amurao, Eiji Minami, Haruo Kawamoto

“…These results provide fundamental insights into lignin decomposition and demonstrate that aqueous solution plasma is a promising method for producing dicarboxylic acids from lignin under mild conditions without catalysts.…”
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