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multisensor Remote Sensing and AI-Driven Analysis for Coastal and Urban Resilience Classification

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 9166-9180页

作者： Ren, Sumei Ghaffar, Bushra Mubbin, Muhammad Haseeb, Muhammad Tahir, Zainab Hassan, Sher Shah Kucher, Dmitry E. Kucher, Olga D. Abdullah-Al-Wadud, M. Leshan Normal Univ Dept Fine Arts & Design Leshan 614000 Peoples R China Int Islamic Univ Fac Basic & Appl Sci Dept Environm Sci Islamabad 44000 Pakistan Univ Witwatersrand Sch Geog & Environm Studies ZA-2017 Johannesburg South Africa Univ Punjab Inst Space Sci Lahore 54590 Pakistan Global Climate Change Impact Studies Ctr Islamabad 44000 Pakistan RUDN Univ Inst Environm Engn Dept Environm Management Moscow 117198 Russia King Saud Univ Coll Comp & Informat Sci Dept Software Engn Riyadh 11543 Saudi Arabia

Urban resilience is essential for cities to endure and adjust to environmental and socioeconomic upheavals. The static indicators and rule-based spatial frameworks that are the mainstays of traditional resilience assessment models frequently fall short of capturing the dynamic character of coastal and urban resilience. This article suggests a deep learning-based categorization framework for identifying resilience levels in urban and coastal settings by combining long short-term memory (LSTM) networks with multisensor remote sensing data. The Copernicus Marine data Service's spatiotemporal ocean physics data, namely the eastward (uo) and northward (vo) seawater velocity, are used in the model to increase the precision of resilience evaluations. The methodology includes a multistep deep learning pipeline, incorporating data preprocessing, feature extraction, class balancing with SMOTE, and LSTM-based classification. The proposed LSTM model is optimized to enhance performance with dropout regularization (0.3), an Adam optimizer (learning rate = 0.0003), and class weighting strategies. The model is evaluated using accuracy, F1-score, confusion matrices, and loss curves, ensuring reliable classification across different resilience categories. Results indicate that the framework achieves high classification accuracy (91.5%), demonstrating superior performance compared to traditional machine learning approaches. Regarding multisensor fusion and deep learning, this study provides a scalable, adaptive, and data-driven solution for resilience classification, supporting climate adaptation strategies, disaster risk management, and sustainable urban development. The proposed methodology offers a robust tool for policymakers and urban planners, enabling more effective resilience monitoring and decision-making in rapidly evolving urban and coastal environments.

关键词： Resilience Sea measurements Urban areas Deep learning Long short term memory data integration Remote sensing Monitoring Meteorology Forecasting Coastal resilience disaster preparedness GIS long short-term memory (LSTM) machine learning (ML) multisensor data fusion remote sensing resilient city sustainable urban development

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MCFPred: A Novel Multichannel Signal Adaptive fusion Framework for Fault Diagnosis in Hydraulic Systems

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Jiang, Ruosong Yuan, Zhaohui Wang, Honghui Fan, Zeming Zhang, Yufan Liang, Na Yu, Xiaojun Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China

Fault diagnosis plays a critical role in industrial equipment health monitoring, while effective multichannel sensor data processing is a key to efficient and accurate fault diagnosis. This article proposes a novel multichannel signal adaptive fusion framework, namely MCFPred, for fault diagnosis in hydraulic systems. Specifically, MCFPred adopts a data acquisition module to capture multidimensional equipment operational signals via multichannel sensors first, and then employs an adaptive weighted nonsymmetric projection for a time series fusion (AWNPTF) module to adaptively fuse multichannel time series data to generate high-quality 2-D feature maps. Finally, a deep residual network (ResNet) [attention-integrated residual network (AIRNet)] is devised and utilized for feature extraction and classification. In MCFPred, AWNPTF helps realize adaptive weighted multichannel time series data fusion with improved computational efficiency, while AIRNet incorporates channel attention mechanism (CAM) and spatial attention mechanism (SAM) to improve fault feature extraction and diagnostic accuracy. Extensive experiments with publicly available hydraulic system datasets are conducted to verify the effectiveness of MCFPred. Results convincingly demonstrate that MCFPred outperforms those existing methods in different cases with an average fault diagnosis accuracy of over 99% been achieved. Both stability and generalization of MCFPred are further validated with another bearing fault dataset.

关键词： Fault diagnosis Feature extraction Hydraulic systems Time series analysis Accuracy data mining Convolutional neural networks Adaptive systems Deep learning Circuit faults Convolutional neural network (CNN) deep learning fault diagnosis hydraulic system multisensor data fusion

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Multifocus Image fusion With Complex Sparse Representation

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IEEE SENSORS JOURNAL 2024年第21期24卷 34744-34755页

作者： Chen, Yuhang Liu, Yu Ward, Rabab K. Chen, Xun Univ Sci & Technol China Sch Informat Sci & Technol Hefei 230027 Peoples R China Hefei Univ Technol Dept Biomed Engn Hefei 230009 Peoples R China Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada

Multifocus image fusion aims to merge source images with distinct focused areas into a single, fully focused fused image. Sparse representation (SR) stands out as a robust signal modeling technique that has achieved remarkable success in multifocus image fusion. Numerous SR-based fusion methods have been proposed over the years, underscoring the importance of SR in enhancing fusion quality. However, a fundamental problem appearing in most existing SR models is the absence of directionality. This deficiency restricts their capacity to extract intricate details. To address this issue, we propose the complex SR (CSR) model for image fusion. This model utilizes the properties of hypercomplex signals to extract directional information from real-valued signals through complex extension. Subsequently, the directional components of the input signal are decomposed into sparse coefficients over corresponding directional dictionaries. The key advantage of our design over conventional SR models is the ability to capture the geometrical image structures effectively, since CSR coefficients can provide precise measurements of detailed information along specific directions. Experimental results conducted on three widely used multifocus image fusion datasets substantiate the superiority of our method over 17 representative multifocus image fusion methods in terms of both visual quality and objective evaluation.

关键词： Complex sparse representation (CSR) Hilbert transform hypercomplex signals image fusion multisensor data fusion sparse representation (SR)

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Research on Enhanced Gait Phase Segmentation Based on Multimodal Spatiotemporal Information fusion

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IEEE INTERNET OF THINGS JOURNAL 2025年第7期12卷 8773-8788页

作者： Zhang, Hao Liu, Xiaofeng Li, Jie Pan, Jia Loo, Chu Kiong Cangelosi, Angelo Hohai Univ Coll Artificial Intelligence & Automat Changzhou 213022 Peoples R China Nanjing Med Univ Engn Res Ctr Intelligent Theranost Technol & Instr Minist Educ Nanjing 211166 Peoples R China Nanjing Med Univ Sch Biomed Engn & Informat Nanjing 211166 Peoples R China Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Univ Malaya Fac Comp Sci & Informat Technol Dept Artificial Intelligence Kuala Lumpur 50603 Malaysia Univ Manchester Machine Learning & Optimizat Manchester M13 9RL England

Gait phase segmentation, pivotal for understanding lower limb motion, finds applications in diverse fields like medicine and sports. While existing method often struggle with accuracy and adaptability in real-world settings, this study presents a novel methodology employing particle filters for precise lower limb motion capture (MoCap) utilizing inertial sensors, which can be used in more everyday environments and in a wider range of applications over a longer period of time. The innovative approach adeptly tracks walking movements, labeling six gait phases via skeleton reconstruction facilitated by the MoCap algorithm. Subsequently, we propose a neural network architecture amalgamating temporal convolutional network (TCN), graph convolutional network (GCN), and long short-term memory (LSTM). This architecture integrates raw data from inertial sensors with joint angles derived from reconstructed motion, achieving accurate segmentation of the six gait phases. Experimental validation compares the MoCap algorithm against an optical motion capture system, and the neural network's performance against state-of-the-art methods. Results demonstrate our method's superior accuracy of 96.94%, highlighting its efficacy in addressing gait phase segmentation challenges and propelling advancements in gait analysis.

关键词： Sensors Motion segmentation Accuracy Wearable sensors Tracking Skeleton Legged locomotion Optical sensors Estimation Biomedical optical imaging Gait phase segmentation graph convolutional network (GCN) inertial sensor motion capture (MoCap) multisensor data fusion

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Smart Swimming Training: Wearable Body Sensor Networks Empower Technical Evaluation of Competitive Swimming

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IEEE INTERNET OF THINGS JOURNAL 2025年第4期12卷 4448-4465页

作者： Li, Jie Wang, Jiaxin Qiu, Sen Liu, Xiaofeng Li, Jianqing Xiang, Wentao Liu, Bin Zhu, Songsheng Kiong, Loo Chu Cangelosi, Angelo Fortino, Giancarlo Nanjing Med Univ Sch Biomed Engn & Informat Nanjing 211166 Peoples R China Hebei Univ Technol Sch Mech Engn Hebei Peoples R China Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China Hohai Univ Coll Artificial Intelligence & Automat Changzhou 213200 Peoples R China Univ Malaya Fac Comp Sci & Informat Technol Dept Artificial Intelligence Kuala Lumpur 50603 Malaysia Univ Manchester Sch Comp Sci Manchester M13 9RL England Univ Calabria Dept Informat Modeling Elect & Syst I-87036 Arcavacata Di Rende Italy

The combination of wearable sensors and competitive sports provides quantitative information for scientific training, effectively assisting athletes in improving their athletic performance. This study presents a technical framework for athletic sports assessment in competitive swimming based on body-area sensor networks. In our approach, wearable inertial sensor nodes are placed on specific body parts of the athletes to capture motion data during different competitive swimming strokes. Multiwearable inertial sensor nodes are worn on specific body parts of athletes for real-time monitoring motion data during training sessions. A motion intensity detection-based error-state-Kalman-filter algorithm is proposed for multisensor data fusion. Additionally, through kinematic statistical analysis, the characteristics of joint motion during training are clearly explained. Furthermore, a deep learning network that fuses sensor time series and human skeleton graphs is proposed for different stroke phase segmentation, enabling quantitative measurement of motion phases, and several baseline classifiers are chosen for comparison to validate the robustness of our phase segmentation method. We also investigate the sensor combination selection issue during the phase segmentation process to determine the optimal sensor configuration. Our approach provides a scientific solution for the integration of wearable sensors and competitive sports, contributing to the high-quality development of the next generation of smart sports.

关键词： Body sensor network (BSN) competitive swimming motion capture motion capture multisensor data fusion multisensor data fusion phase segmentation phase segmentation phase segmentation

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Adaptive multisensor fusion for Remote Sensing Change Detection Using USASE

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IEEE SENSORS JOURNAL 2025年第7期25卷 12265-12277页

作者： Shi, Guangyi Changchun Inst Technol Changchun 130021 Peoples R China

Binary change detection (BCD) in remote sensing has advanced, yet challenges remain in reducing feature redundancy and effectively utilizing difference information between dual-time images, which affects precision in identifying change areas. In addition, the effective fusion of multisensor data types limits adaptability and accuracy in change detection (CD) models. This article presents the ultralightweight semantic-aware spatial exchange (USASE) network, a three-encoder-three-decoder architecture designed for improved adaptability in multisensor data fusion. USASE integrates a micro convolutional unit (MCU) for reduced feature redundancy through pointwise and depthwise separable convolutions, while a temporal-aware feature aggregation module (TAFAM) captures global semantic relationships to enhance detection precision across sensor types. An adaptive weighting mechanism further optimizes dual-time image accuracy in multisource data fusion. Tested on the SYSU-CD, LEVIR-CD, and DSIFN datasets, USASE achieves the ${F}1$ -scores of 83.12%, 90.72%, and 81.34%, respectively, outperforming several baselines in accuracy, efficiency, and computational cost. This study highlights USASEs potential as a robust, real-time solution for dynamic and complex remote sensing applications.

关键词： Feature extraction Semantics Remote sensing Sensors Adaptation models Computer architecture Computational modeling Accuracy Decoding Robustness Adaptive weighting bitemporal remote sensing imagery multisensor data fusion temporal-aware feature aggregation

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Spatio-Temporal data fusion-Based Method for Working Cycles Identification of Hydraulic Excavators

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Cui, Jian Zhang, Dailin Zhu, Guoli Wang, Yuexing Zou, Kaiduan Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China Zhejiang Univ Sch Mech Engn Hangzhou 310007 Peoples R China Jiangsu Adv Construct Machinery Innovat Ctr Ltd Xuzhou 221000 Peoples R China

Accurately describing and identifying the operational modes of excavators is fundamental to achieving efficient and energy-saving control of excavators. Existing methods predominantly rely on hydraulic power system parameters, utilizing machine learning models to recognize the operational states of excavators. However, the limited operational data from a single or a few sensors leads to inputs that inadequately reflect the diverse action states of the equipment and overlook the spatial correlations between actions of different actuators, failing to effectively learn the distinctive features between operational conditions. To address these issues, this article introduces a spatio-temporal fusion method for condition recognition based on multidimensional sensor data, encapsulating comprehensive operational information of the entire machine. This study developed an attention mechanism linked to an operation-related attention module (OAM), integrated with a designed dual-channel feature extraction network for time and space, to enhance and capture the spatio-temporal correlations between adjacent operational states. Furthermore, a classifier based on imbalanced interclass distance metrics was designed to adaptively learn the distances between category samples, thereby mitigating the learning challenges posed by imbalanced condition samples. Experiments conducted under various conditions on single and multiple working cycles achieved recognition accuracies of 99.17% and 99.24%, respectively, demonstrating the effectiveness and robustness of our proposed method and significantly improving the accuracy of condition recognition.

关键词： Excavation Sensors Feature extraction Hydraulic systems data models Accuracy Sensor phenomena and characterization Correlation Deep learning Valves Class imbalance hydraulic excavator multisensor data fusion operated-related attention mechanism spatio-temporal feature learning

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A novel multi-sensor data fusion enabled health indicator construction and remaining useful life prediction of aero-engine

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2025年

作者： Su, Yu Lei, Zihao Wen, Guangrui Chen, Xuefeng Xi An Jiao Tong Univ Natl Key Lab Aerosp Power Syst & Plasma Technol Xian Peoples R China Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian Peoples R China Xi An Jiao Tong Univ Sch Mech Engn 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China

Remaining useful life (RUL) prediction is vital to formulate a suitable maintenance strategy in manufacturing systems health management. multisensor data fusion of complex engineering systems has attracted substantial attention due to the fact that a single sensor can only collect partial information. Health indicator (HI) construction plays a crucial role in multisensor data fusion and machinery prognostic, mainly because it attempts to quantify a history and ongoing degradation process by fusing the advantages of multiple sensors. However, large numbers of coefficients are involved for most of the existing HIs. Additionally, simplifications during modeling may inhibit the wide application of the constructed HI. To address these two challenges, a new multisensor data fusion method is proposed in this paper by constructing a HI for the characterization of the degradation process. Firstly, the sensors that collect invalid data or conflicting data are removed through a correlation coefficient operation. Then, principal component analysis (PCA) is adopted to reduce the number of coefficient before constructing the HI. Furthermore, the objective function is constructed under the comprehensive consideration of the three factors of the HI, that is, monotonicity, trendability, and fitting errors. The effectiveness of the proposed method is verified using the C-MAPSS dataset. Multiple comparison results show that the HI possesses excellent performance in both degradation characterization and remaining useful life prediction.

关键词： multisensor data fusion health indicator condition monitoring degradation characterization remaining useful life prediction

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multisensor fusion for 3-D Reconstruction of Urban Pipeline Interior Using the Enhanced R³LIVE Algorithm

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IEEE SENSORS JOURNAL 2025年第9期25卷 15830-15841页

作者： Chen, Xinbao Zhang, Yaohui Zhu, Xiaodong Liu, Chang Wang, Shan Deng, Xinping Hunan Univ Sci & Technol Sanya Res Inst Sanya 572025 Hainan Peoples R China Hunan Univ Sci & Technol Sch Earth Sci & Space Informat Engn Xiangtan 411201 Peoples R China Hunan Prov Inst Geol Hazard Invest & Monitoring Changsha 410001 Peoples R China Engn & Mine Geol Survey & Monitor Inst Hunan Prov Changsha 410019 Peoples R China

Accurately obtaining the 3-D geometry and texture of urban pipeline interiors is crucial for pipeline inspection, urban planning, and digital management. The interior environment of urban pipelines is characterized by insufficient illumination and a lack of textural features, which poses significant challenges for real-time 3-D reconstruction. In this study, we propose a novel multisensor fusion system based on the enhanced (RLIVE)-L-3 algorithm, which integrates LiDAR, cameras, and inertial measurement unit (IMU). Our approach addresses the performance degradation of LiDAR and vision systems within pipelines by employing a hybrid optical-flow method based on the convolutional neural networks (CNNs), which significantly enhances the accuracy and efficiency of tracking feature points on the inner wall of pipeline. Quantitative experimental results obtained in a real urban pipeline environment demonstrate that the accuracy and robustness of the 3-D reconstruction are substantially improved, achieving a relative rotation error (RRE) of 1.75 degrees and a relative translation error (RTE) of 2.41% over a range of 150 m. Moreover, the algorithm significantly outperforms the FAST-LIVO, FAST-LIO2, and the original (RLIVE)-L-3 conventional simultaneous localization and mapping (SLAM) methods at longer ranges. These results underscore the practical value and superiority of our multisensor fusion method for the 3-D reconstruction of urban pipelines.

关键词： Pipelines Three-dimensional displays Simultaneous localization and mapping Accuracy Laser radar Cameras Sensors Real-time systems Lighting Optical flow 3-D reconstruction multisensor data fusion the enhanced (RLIVE)-L-3 algorithm urban pipeline

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In-situ process monitoring and adaptive quality enhancement in laser additive manufacturing: A critical review

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JOURNAL OF MANUFACTURING SYSTEMS 2024年 74卷 527-574页

作者： Chen, Lequn Bi, Guijun Yao, Xiling Su, Jinlong Tan, Chaolin Feng, Wenhe Benakis, Michalis Chew, Youxiang Moon, Seung Ki ASTAR Adv Remfg & Technol Ctr ARTC 3 CleanTech Loop01-01 CleanTech Two Singapore 637143 Singapore Nanyang Technol Univ Sch Mech & Aerosp Engn Singapore 639798 Singapore Guangdong Acad Sci Inst Intelligent Mfg Guangzhou 510070 Peoples R China ASTAR Singapore Inst Mfg Technol SIMTech 5 CleanTech LoopCleanTech Two Block B Singapore 636732 Singapore

Laser Additive Manufacturing (LAM) presents unparalleled opportunities for fabricating complex, highperformance structures and components with unique material properties. Despite these advancements, achieving consistent part quality and process repeatability remains challenging. This paper provides a comprehensive review of various state-of-the-art in-situ process monitoring techniques, including optical-based monitoring, acoustic-based sensing, laser line scanning, and operando X-ray monitoring. These techniques are evaluated for their capabilities and limitations in detecting defects within Laser Powder Bed fusion (LPBF) and Laser Directed Energy Deposition (LDED) processes. Furthermore, the review discusses emerging multisensor monitoring and machine learning (ML)-assisted defect detection methods, benchmarking ML models tailored for in-situ defect detection. The paper also discusses in-situ adaptive defect remediation strategies that advance LAM towards zero-defect autonomous operations, focusing on real-time closed-loop feedback control and defect correction methods. Research gaps such as the need for standardization, improved reliability and sensitivity, and decision-making strategies beyond early stopping are highlighted. Future directions are proposed, with an emphasis on multimodal sensor fusion for multiscale defect prediction and fault diagnosis, ultimately enabling self-adaptation in LAM processes. This paper aims to equip researchers and industry professionals with a holistic understanding of the current capabilities, limitations, and future directions in in-situ process monitoring and adaptive quality enhancement in LAM.

关键词： Additive manufacturing Laser Directed Energy Deposition Laser Powder Bed fusion In-situ monitoring multisensor data fusion Defect detection Closed-loop control Digital twin Machine learning

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