Query：
学者姓名：周宇
Refining：
Year
Type
Indexed by
Source
Complex
Former Name
Co-
Language
Clean All
Abstract ：
Underwater robots are critical observation platforms for diverse ocean environments. However, existing robotic designs often lack long-range and deep-sea observation capabilities and overlook the effects of environmental uncertainties on robotic operations. This article presents a novel long-range underwater robot for extreme ocean environments, featuring a low-power dual-circuit buoyancy adjustment system, an efficient mass-based attitude adjustment system, flying wings, and an open sensor cabin. After that, an extended environment perception strategy with incremental updating is proposed to understand and predict full hydrological dynamics based on sparse observations. On this basis, a real-time dynamic modeling approach integrates multibody dynamics, perceived hydrological dynamics, and environment-robot interactions to provide accurate dynamics predictions and enhance motion efficiency. Extensive simulations and field experiments covering 600 km validated the reliability and autonomy of the robot in long-range ocean observations, highlighting the accuracy of the extended perception and real-time dynamics modeling methods.
Keyword ：
Accuracy Accuracy Adaptation models Adaptation models Autonomous underwater vehicles Autonomous underwater vehicles Buoyancy Buoyancy Dynamics Dynamics environment monitoring and management environment monitoring and management marine robotics marine robotics mechanism design mechanism design Oceans Oceans Ocean temperature Ocean temperature Real-time systems Real-time systems Robots Robots Robot sensing systems Robot sensing systems
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Lei, Lei , Zhou, Yu , Zhang, Jianxing . From Extended Environment Perception Toward Real-Time Dynamic Modeling for Long-Range Underwater Robot [J]. | IEEE TRANSACTIONS ON ROBOTICS , 2025 , 41 : 3423-3441 . |
| MLA | Lei, Lei 等. "From Extended Environment Perception Toward Real-Time Dynamic Modeling for Long-Range Underwater Robot" . | IEEE TRANSACTIONS ON ROBOTICS 41 (2025) : 3423-3441 . |
| APA | Lei, Lei , Zhou, Yu , Zhang, Jianxing . From Extended Environment Perception Toward Real-Time Dynamic Modeling for Long-Range Underwater Robot . | IEEE TRANSACTIONS ON ROBOTICS , 2025 , 41 , 3423-3441 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Sensors play a key role in monitoring distributed parameter systems (DPSs), such as thermal and chemical diffusion-reaction processes. However, sensor faults can result in data distortion, degraded system performance, and even catastrophic consequences. This article proposes a model-based sensor fault diagnosis framework for DPSs, which can effectively detect the fault time and estimate the fault intensity. The proposed method only requires the limited measured output without any state information. Besides, noise will cause perturbations in the sampling data. Correspondingly, a linear matrix inequality (LMI) considering disturbance is designed. Through theoretical analysis, the convergence of fault estimation error is ensured. The effectiveness of the proposed method is verified on a heat transfer rod and a chemical diffusion-reaction system with static and time-varying sensor faults under disturbances. The root mean square errors of all sensor fault estimates are within 0.1078.
Keyword ：
Chemicals Chemicals Detection Detection distributed parameter system (DPS) distributed parameter system (DPS) disturbance disturbance Electronic mail Electronic mail estimation estimation Estimation Estimation Fault detection Fault detection Fault tolerant systems Fault tolerant systems Interference Interference Mathematical models Mathematical models Noise Noise Observers Observers sensor fault sensor fault Training Training
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Wang, Kui , Zhou, Jinhui , Shen, Wenjing et al. Robust Sensor Fault Detection and Estimation for Parabolic Distributed Parameter Systems [J]. | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2025 , 74 . |
| MLA | Wang, Kui et al. "Robust Sensor Fault Detection and Estimation for Parabolic Distributed Parameter Systems" . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 74 (2025) . |
| APA | Wang, Kui , Zhou, Jinhui , Shen, Wenjing , Zhou, Yu , Wei, Peng , Mou, Xiaolin et al. Robust Sensor Fault Detection and Estimation for Parabolic Distributed Parameter Systems . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2025 , 74 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Predicting subsurface temperatures is critical for comprehending ocean dynamics and climate shifts. This study presents an incremental stable/dynamic (SD) disentanglement learning framework merging data-driven methods with physics-based insights. It separates stable and dynamic temperature modes to untangle intricate spatiotemporal interactions. To accommodate ongoing data influx, we introduce a recursive evolution approach for updating stable representations, employing orthogonal-triangular decomposition (QR) to capture incremental information. Moreover, a retrospective learning algorithm, guided by temporal changes and ocean temperature correlations, is employed to track the dynamic behavior adaptively. The subsurface temperature fields can be efficiently reconstructed and predicted after model convergence. Extensive experiments validate the model across various depths (-2.5 to -800 m) and times (from May 1964 to December 2021), achieving robust performance metrics: root mean square error (RMSE) of 0.1362, mean absolute error (MAE) of 0.0901, accuracy (ACC) of 0.9911, and coefficient of determination ( R-2 ) between predictions and observations of 0.9998. Comparative analysis underscores the proposed method's interpretability, adaptability, and overall performance superiority. Temperature anomaly analysis accurately identifies subsurface decadal oscillations in the low- and mid-latitude Pacific regions.
Keyword ：
Adaptation models Adaptation models Data models Data models Global warming Global warming incremental adaptation incremental adaptation Incremental learning Incremental learning Meteorology Meteorology Oceans Oceans Ocean temperature Ocean temperature Pacific Ocean Pacific Ocean Real-time systems Real-time systems remote sensing remote sensing Spatiotemporal phenomena Spatiotemporal phenomena Temperature distribution Temperature distribution temperature prediction temperature prediction Temperature sensors Temperature sensors
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Lei, Lei , Zhou, Yu . Incremental Stable/Dynamic Disentanglement Learning for Ocean Subsurface Temperature Prediction [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2025 , 63 . |
| MLA | Lei, Lei et al. "Incremental Stable/Dynamic Disentanglement Learning for Ocean Subsurface Temperature Prediction" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 63 (2025) . |
| APA | Lei, Lei , Zhou, Yu . Incremental Stable/Dynamic Disentanglement Learning for Ocean Subsurface Temperature Prediction . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2025 , 63 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Achieving synchronization in a designated time for multilayered networks (MLNs) is particularly challenging when state information is unavailable. This study provides theoretical solutions to the fixed-time (FIM) predetermined-time (PIM) synchronization problems in MLNs. Firstly, a high-resolution model of established with both intra-layer and inter-layer coupling. Additionally, a practical PIM stability and high-precision settling time estimation are formulated. Furthermore, two edge-based event-triggered intermittent control (EAIC) strategies are developed for MLNs using various auxiliary functions. synchronization issues are addressed while eliminating Zeno behaviors throughout, except during the time. Ultimately, the feasibility of the proposed theory is demonstrated through the design of a synchronization circuit within the complex system.
Keyword ：
Event-triggered control Event-triggered control Fixed-time synchronization Fixed-time synchronization Intermittent control Intermittent control Multilayered networks Multilayered networks Preassigned-time synchronization Preassigned-time synchronization
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Zhao, Tingting , Zhou, Yu , Qi, Yiwen et al. Complete synchronization in predetermined/fixed-time for multilayered networks under edge-based discontinuous control [J]. | CHAOS SOLITONS & FRACTALS , 2025 , 195 . |
| MLA | Zhao, Tingting et al. "Complete synchronization in predetermined/fixed-time for multilayered networks under edge-based discontinuous control" . | CHAOS SOLITONS & FRACTALS 195 (2025) . |
| APA | Zhao, Tingting , Zhou, Yu , Qi, Yiwen , Huang, Jie . Complete synchronization in predetermined/fixed-time for multilayered networks under edge-based discontinuous control . | CHAOS SOLITONS & FRACTALS , 2025 , 195 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Advanced wind power prediction technique plays an essential role in the stable operation of the grid with largescale grid integration of wind power. Most research focuses on distance-based static classification where the subjective nature of initial center selection increases the uncertainty of the prediction. And the data classification on a daily basis neglects the potentially significant climate changes at smaller time scales. To address these issues, the improved snake optimization-long short-term memory (ISO-LSTM) model with Gaussian mixture model (GMM) clustering is proposed to forecast wind power from an adaptive perspective. By exploiting the merits of the probabilistic classification, the K-means optimized GMM clustering enables an appropriate feature modelling for substantial climate changes at smaller time scales. Then the ISO algorithm exhibits higher search accuracy and is better suited for finding hyperparameter combinations for LSTM neural networks. The data from the National Aeronautics and Space Administration (NASA) of the US is used to validate the effectiveness of the proposed method. Compared to the traditional K-means clustering, the K-means optimized GMM clustering has increased accuracy by 2.63 %. Simultaneously, with the adoption of the enhanced ISO algorithm, the accuracy further increases by 7.27 %. Different existing models have also been tested; it shows that the proposed model demonstrates higher prediction accuracy.
Keyword ：
Gaussian mixture model Gaussian mixture model Improved snake optimization Improved snake optimization K -means algorithm K -means algorithm Long short-term memory network Long short-term memory network Probabilistic classification Probabilistic classification
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Zhou, Yu , Huang, Ruochen , Lin, Qiongbin et al. Probabilistic optimization based adaptive neural network for short-term wind power forecasting with climate uncertainty [J]. | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS , 2024 , 157 . |
| MLA | Zhou, Yu et al. "Probabilistic optimization based adaptive neural network for short-term wind power forecasting with climate uncertainty" . | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 157 (2024) . |
| APA | Zhou, Yu , Huang, Ruochen , Lin, Qiongbin , Chai, Qinqin , Wang, Wu . Probabilistic optimization based adaptive neural network for short-term wind power forecasting with climate uncertainty . | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS , 2024 , 157 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Uneven temperature distributions can significantly impact battery performance and cycle life. Industrial applications, in particular, where unknown disturbances and limited sensors exist, pose significant challenges for accurately estimating the battery temperature field. This work proposes a multi -source information fusion framework for capturing the spatiotemporal temperature dynamics of pouch -type Lithium -ion batteries. First, we develop a system model of the battery thermal process based on physical insights, considering unknown parameter deviations and unmodeled dynamics. Next, we use the Galerkin-spectral method to expand the spatiotemporal variable and reduce the original infinite -dimensional system to a low -order model containing the most important system modes. The unknown component of the model is then entirely stripped and integrated into a nonlinear term. To close the reality gap of the physics -based model, we subsequently develop an error compensation model that learns the dynamic behavior from sparse observations. Ultimately, our framework design yields a fusion -driven model for reliable temperature field prediction. Simulations and experimental data validate the superior performance and generalization capability of the proposed method.
Keyword ：
Information fusion Information fusion Lithium-ion battery Lithium-ion battery Sparse observation Sparse observation Temperature field Temperature field
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Zhou, Yu , Chen, Liqun , Lei, Lei . Modeling spatiotemporal temperature dynamics of large-format power batteries: A multi-source information fusion approach [J]. | ADVANCED ENGINEERING INFORMATICS , 2024 , 62 . |
| MLA | Zhou, Yu et al. "Modeling spatiotemporal temperature dynamics of large-format power batteries: A multi-source information fusion approach" . | ADVANCED ENGINEERING INFORMATICS 62 (2024) . |
| APA | Zhou, Yu , Chen, Liqun , Lei, Lei . Modeling spatiotemporal temperature dynamics of large-format power batteries: A multi-source information fusion approach . | ADVANCED ENGINEERING INFORMATICS , 2024 , 62 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Accurate ocean environment perception is crucial for weather and climate prediction. Environmental limitations and deployment costs constrain satellite and buoy real-time observation, leading to sparse data availability. This paper proposes a novel approach, multimodal fusion -based spatiotemporal incremental learning, enhancing the ocean environment perception under sparse observations. This method uses sparse real-time observations to comprehend, reconstruct, and predict the full environment. First, spatiotemporal disentanglement decouples intrinsic features by integrating physical principles and data learning. Subsequently, incremental extension captures the dynamic environment through stable representation updating and dynamic behavior learning. Then, multimodal information fusion synergizes multisource intrinsic features, enabling the full perception of the ocean environment. Finally, the methodology is supported by convergence analysis and error boundary evaluation. Validation with global sea surface temperature and western Pacific Ocean highdimensional temperature datasets demonstrates its potential for advancing ocean research and applications using sparse real-time observation.
Keyword ：
Incremental learning Incremental learning Information fusion Information fusion Ocean environment Ocean environment Sparse observation Sparse observation Spatiotemporal modeling Spatiotemporal modeling
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Lei, Lei , Huang, Jie , Zhou, Yu . Multimodal fusion-based spatiotemporal incremental learning for ocean environment perception under sparse observation [J]. | INFORMATION FUSION , 2024 , 108 . |
| MLA | Lei, Lei et al. "Multimodal fusion-based spatiotemporal incremental learning for ocean environment perception under sparse observation" . | INFORMATION FUSION 108 (2024) . |
| APA | Lei, Lei , Huang, Jie , Zhou, Yu . Multimodal fusion-based spatiotemporal incremental learning for ocean environment perception under sparse observation . | INFORMATION FUSION , 2024 , 108 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Thermal monitoring is critical to the safety of electric vehicles. Due to the uneven surface temperature of large-format lithium-ion (Li-ion) batteries, traditional lump-based thermal monitoring methods cannot capture two-dimensional (2-D) spatiotemporal thermodynamics. This article proposes a distributed thermal monitoring framework to solve this problem. It contains two parts. In the modeling part, the spectral method is used to establish the globally distributed temperature field of the pouch cell using only four sensors. In the fault detection part, there are two stages: 1) in the offline training stage, the 2-D battery thermal process is first decomposed into basis functions (BFs) and time coefficients using the spectral method. The time coefficients are further decomposed by independent component analysis (ICA). Then, the dominant temporal components are formed as the monitoring statistic, which is used to derive the confidence bound through kernel density estimation (KDE) and 2) in the online detection stage, the thermal fault can be detected in real-time by comparing the updated monitoring statistic with the confidence bound. Simulations and experiments on a pouch-type Li-ion battery are conducted to verify the effectiveness of the proposed method.
Keyword ：
Batteries Batteries distributed parameter systems (DPSs) distributed parameter systems (DPSs) Fault detection Fault detection fault diagnosis fault diagnosis Lithium-ion batteries Lithium-ion batteries modeling modeling Monitoring Monitoring real-time systems real-time systems Real-time systems Real-time systems Safety Safety Temperature sensors Temperature sensors thermal variables' measurement thermal variables' measurement
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Zhou, Jinhui , Chen, Liqun , Zhang, Shupeng et al. Distributed Thermal Monitoring for Large-Format Li-Ion Battery Under Limited Sensing [J]. | IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION , 2024 , 10 (2) : 3206-3217 . |
| MLA | Zhou, Jinhui et al. "Distributed Thermal Monitoring for Large-Format Li-Ion Battery Under Limited Sensing" . | IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 10 . 2 (2024) : 3206-3217 . |
| APA | Zhou, Jinhui , Chen, Liqun , Zhang, Shupeng , Zhou, Yu , Wang, Shuqiang , Shen, Wenjing . Distributed Thermal Monitoring for Large-Format Li-Ion Battery Under Limited Sensing . | IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION , 2024 , 10 (2) , 3206-3217 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Distributed thermal modeling of Lithium-ion batteries (LIBs) is critical for the safety of electric vehicles. Due to the installation and cost constraints, only limited sensors are allowed for practical applications. In this paper, a learning-based framework is proposed for online spatiotemporal modeling of distributed thermal processes in pouch-type LIBs under sparse sensing. It consists of two stages. In the offline learning stage under full sensing, the Karhunen-Loeve (KL) decomposition is used to extract the full spatial basis functions (BFs). In the subsequent online modeling stage under sparse sensing, a spatial mapping filter is first designed to recover the missing spatial information using the initial full BFs, which are then dynamically updated by the incremental KL technique as the thermal process evolves. By iteratively repeating these two steps, the streaming sparse spatiotemporal output can be accurately completed. Finally, the typical KL-based time-space separation method can be used for online temperature prediction. The simulation results of the distributed thermal processes on a pouch-type cell and a LIB pack demonstrate the effectiveness of the proposed method.
Keyword ：
Distributed thermal process Distributed thermal process Lithium-ion battery Lithium-ion battery Sparse spatiotemporal modeling Sparse spatiotemporal modeling Time-space separation Time-space separation
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Chen, Liqun , Shen, Wenjing , Zhou, Yu et al. Learning-based sparse spatiotemporal modeling for distributed thermal processes of Lithium-ion batteries [J]. | JOURNAL OF ENERGY STORAGE , 2023 , 69 . |
| MLA | Chen, Liqun et al. "Learning-based sparse spatiotemporal modeling for distributed thermal processes of Lithium-ion batteries" . | JOURNAL OF ENERGY STORAGE 69 (2023) . |
| APA | Chen, Liqun , Shen, Wenjing , Zhou, Yu , Mou, Xiaolin , Lei, Lei . Learning-based sparse spatiotemporal modeling for distributed thermal processes of Lithium-ion batteries . | JOURNAL OF ENERGY STORAGE , 2023 , 69 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
The irregular ocean environment brings difficulties to observation and affects the movement state of under-water vehicles. Multisource information fusion can provide more reliable results on information-rich datasets. This paper proposes a multisource information fusion-based environment perception and dynamics model for underwater vehicles in irregular ocean environments. First, the multisource observation data is decomposed by the latent feature disentanglement learning model into the latent stable and dynamical features. The dynamical feature is predicted by the retrospect regression method and then integrated with the latent stable feature to predict the ocean field. After that, the environment-vehicle coupling effect is explored by thermodynamics and statics simulation. Then, a real-time dynamic model of the underwater vehicle is constructed by combining multisource information from physical principles, environmental perception, and sensor observations. Finally, extensive experiments are performed on a novel underwater glider and a publicly available South China Sea dataset to verify the proposed method.
Keyword ：
Dynamic model Dynamic model Feature disentanglement Feature disentanglement Information fusion Information fusion Ocean environment Ocean environment Underwater vehicle Underwater vehicle
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Lei, Lei , Zhou, Yu , Yang, Gang . Multisource information fusion-based environment perception and dynamic model of underwater vehicle in irregular ocean environment [J]. | INFORMATION FUSION , 2023 , 94 : 257-271 . |
| MLA | Lei, Lei et al. "Multisource information fusion-based environment perception and dynamic model of underwater vehicle in irregular ocean environment" . | INFORMATION FUSION 94 (2023) : 257-271 . |
| APA | Lei, Lei , Zhou, Yu , Yang, Gang . Multisource information fusion-based environment perception and dynamic model of underwater vehicle in irregular ocean environment . | INFORMATION FUSION , 2023 , 94 , 257-271 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Export
| Results： |
Selected to |
| Format： |
