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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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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Prediction of Landslide Displacement Based on EMD-TAR Combined Model by CHEN Xi, GAO Yaping, TU Rui

“…This study aims to more accurately predict the displacement changes of landslides with nonlinear volatility development.The empirical mode decomposition is first employed to process the time series of monitoring surface displacement of a landslide,and then the irregularly changing displacement series is converted into modal components with regular changes,which generates displacement components at different frequencies.Each component is predicted separately so that the mutual influence of errors can be avoided.The comprehensive prediction of the changing trend of displacement series is based on the prediction of the changing trends of all components.The improved threshold autoregressive model able to well describe non-stationary harmonics is used to predict the landslide displacement components.Finally,the modal superposition yields the final predicted displacement.In this way,a combined prediction model based on empirical mode decomposition and threshold autoregressive model is established,and its prediction accuracy is verified with Baishuihe landslide data.Compared with a BP neural network model and a long short-term memory network model,the proposed model has a high prediction accuracy,which provides a new method for the prediction of landslide displacement.…”
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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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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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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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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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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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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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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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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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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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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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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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A Three-Component Polarimetric Target Decomposition Algorithm for Grasslands by Baokun Liu, Xiaoqi Lv, Xiujuan Li, Xiangli Yang, Pingping Huang, Weixian Tan, Yongguang Zhai, Yuejuan Chen, Kunpeng Xu

“…Previous polarimetric target decomposition methods have been widely used in forests and buildings and have achieved good results. …”
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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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Hierarchical-Variational Mode Decomposition for Baseline Correction in Electroencephalogram Signals by Shireen Fathima, Maaz Ahmed

“…To address this issue, this article deals with developing a novel scheme of hierarchically decomposing a signal using variational mode decomposition (VMD) in a tree-based model for a given level of the tree for accurate and effective analysis of the EEG signal and research in brain&#x2013;computer interface (BCI). …”
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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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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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