Search Results - "Wen, Chenglin"

Real-Time Updating High-Order Extended Kalman Filtering Method Based on Fixed-Step Life Prediction for Vehicle Lithium-Ion Batteries by Wang, Jincheng, Wen, Chenglin

“…Lithium-ion batteries have become an important power source in low-carbon transportation energy, and the safe operation and remaining useful life prediction…”
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Fault Isolation Based on k-Nearest Neighbor Rule for Industrial Processes by Zhou, Zhe, Wen, Chenglin, Yang, Chunjie

“…Recently, the well-known k-nearest neighbor (kNN) rule has been successfully applied to the fault detection of industrial processes with nonlinear, multimode,…”
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Performance Analysis of the Kalman Filter With Mismatched Noise Covariances by Ge, Quanbo, Shao, Teng, Duan, Zhansheng, Wen, Chenglin

“…The Kalman filter is a powerful state estimator and has been successfully applied in many fields. To guarantee the optimality of the Kalman filter, the noise…”
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Rotating Machinery Fault Diagnosis Method by Combining Time-Frequency Domain Features and CNN Knowledge Transfer by Ye, Lihao, Ma, Xue, Wen, Chenglin

“…Aiming at the problem of fault diagnosis when there are only a few labeled samples in the large amount of data collected during the operation of rotating…”
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Color Occlusion Face Recognition Method Based on Quaternion Non-Convex Sparse Constraint Mechanism by Wen, Chenglin, Qiu, Yiting

“…As the acquisition and application of color images become more and more extensive, color face recognition technology has also been vigorously developed,…”
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Carrier Tracking Estimation Analysis by Using the Extended Strong Tracking Filtering by Ge, Quanbo, Shao, Teng, Chen, Shaodong, Wen, Chenglin

“…When the extended strong tracking filter (ESTF) is used to improve the carrier tracking estimation instead of the extended Kalman filter (EKF), it is found to…”
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A depth iterative illumination estimation network for low-light image enhancement based on retinex theory by Chen, Yongqiang, Wen, Chenglin, Liu, Weifeng, He, Wei

“…Existing low-light image enhancement techniques face challenges in achieving high visual quality and computational efficiency, as well as in effectively…”
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Design Method of High-Order Kalman Filter for Strong Nonlinear System Based on Kronecker Product Transform by Liu, Xiaohan, Wen, Chenglin, Sun, Xiaohui

“…In this paper, a novel design idea of high-order Kalman filter based on Kronecker product transform is proposed for a class of strong nonlinear stochastic…”
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Multilevel Fine Fault Diagnosis Method for Motors Based on Feature Extraction of Fractional Fourier Transform by Wu, Hao, Ma, Xue, Wen, Chenglin

“…Motors are the main driving power for equipment operation, and they are also a major factor to promote the development of the motor and the load it drives and…”
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Design of a High-Order Kalman Filter for State and Measurement of A Class of Nonlinear Systems Based on Kronecker Product Augmented Dimension by Peng, Deyan, Wen, Chenglin, Lv, Meilei

“…A high-order Kalman filter for full-dimensional variables is proposed for a class of dynamic systems whose state model and measurement model are both…”
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A New Method of Deep Convolutional Neural Network Image Classification Based on Knowledge Transfer in Small Label Sample Environment by Kong, Yunchen, Ma, Xue, Wen, Chenglin

“…The problem of deep learning network image classification when a large number of image samples are obtained in life and with only a small amount of knowledge…”
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Design Method for a Higher Order Extended Kalman Filter Based on Maximum Correlation Entropy and a Taylor Network System by Wang, Qiupeng, Sun, Xiaohui, Wen, Chenglin

“…This paper proposes one new design method for a higher order extended Kalman filter based on combining maximum correlation entropy with a Taylor network system…”
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A Video Sequence Face Expression Recognition Method Based on Squeeze-and-Excitation and 3DPCA Network by Li, Chang, Wen, Chenglin, Qiu, Yiting

“…Expression recognition is a very important direction for computers to understand human emotions and human-computer interaction. However, for 3D data such as…”
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A High-Order Kalman Filter Method for Fusion Estimation of Motion Trajectories of Multi-Robot Formation by Wang, Miao, Liu, Weifeng, Wen, Chenglin

“…Multi-robot motion and observation generally have nonlinear characteristics; in response to the problem that the existing extended Kalman filter (EKF)…”
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High-Order Extended Kalman Filter for State Estimation of Nonlinear Systems by Ding, Linwang, Wen, Chenglin

“…In general, the extended Kalman filter (EKF) has a wide range of applications, aiming to minimize symmetric loss function (mean square error) and improve the…”
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A Differential Privacy Strategy Based on Local Features of Non-Gaussian Noise in Federated Learning by Wang, Xinyi, Wang, Jincheng, Ma, Xue, Wen, Chenglin

“…As an emerging artificial intelligence technology, federated learning plays a significant role in privacy preservation in machine learning, although its main…”
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A Novel Data Sampling Driven Kalman Filter Is Designed by Combining the Characteristic Sampling of UKF and the Random Sampling of EnKF by Cui, Tipo, Sun, Xiaohui, Wen, Chenglin

“…In order to improve the performance of the Kalman filter for nonlinear systems, this paper contains the advantages of UKF statistical sampling and EnKF random…”
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A New Method of Image Classification Based on Domain Adaptation by Zhao, Fangwen, Liu, Weifeng, Wen, Chenglin

“…Deep neural networks can learn powerful representations from massive amounts of labeled data; however, their performance is unsatisfactory in the case of large…”
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Cubature information filters with correlated noises and their applications in decentralized fusion by Ge, Quanbo, Xu, Daxing, Wen, Chenglin

“…Data fusion for nonlinear systems is one of the challenging topics in state estimation and target tracking recently. We study decentralized cubature Kalman…”
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A Multimodal Feature Fusion-Based Deep Learning Method for Online Fault Diagnosis of Rotating Machinery by Zhou, Funa, Hu, Po, Yang, Shuai, Wen, Chenglin

“…Rotating machinery usually suffers from a type of fault, where the fault feature extracted in the frequency domain is significant, while the fault feature…”
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