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Tunable Metamaterial Lens for Ultra-Broadband Hydroacoustic Focusing

Journal of Applied Mechanics 2025年第5期92卷 051009页

作者： Guo, Rui Liu, Runquan Liu, Hao Zuo, Lei Zhang, Quan Zhang, Kai School of Aerospace Engineering Beijing Institute of Technology Beijing 100081 China School of Aerospace Engineering https://***/01skt4w74Beijing Institute of Technology Beijing 100081 China School of Mathematical and Statistical Sciences College of Science and Engineering https://***/03bea9k73University of Galway Galway H91 TK33 Ireland

Acoustic metamaterial plays a vital role in underwater engineering applications. Here we present a novel tunable underwater metamaterial for ultra-broadband hydroacoustic focusing. The material consists of a soft matrix embedded with cavities that are surrounded by solid shells. The proposed design enables on-demand tuning of the effective acoustic parameters of the composite material through two independent regulation strategies, namely, (i) filling the cavities with different liquids, and (ii) stretching the soft matrix. We develop a theoretical framework for calculating the effective parameters of the material and validate it through finite element simulations and experiments. Our theoretical analysis indicates that the proposed material is able to achieve a widely tunable refraction index while maintaining a high transmission coefficient. In addition, a hydroacoustic focusing functionality is demonstrated using the proposed metamaterials. These findings set the stage for the development of next-generation hydroacoustic materials for underwater communication and acoustic imaging technologies.

关键词： Metamaterials homogenization tunable metamaterials Design Acoustics underwater focusing Shells gradient index lens Lenses (Optics)

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Wide-Envelope Cooperative Guidance With Constrained Field-of-View and Varying Speed Against Maneuvering Targets

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Yi, Xin Wang, Chunyan Dong, Wei Wang, Jianan Deng, Fang Xin, Ming Beijing Institute of Technology School of Aerospace Engineering Beijing100081 China Beijing Institute of Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China University of Missouri Department of Mechanical and Aerospace Engineering ColumbiaMO65211 United States

Cooperative guidance is a practical and challenging problem involving multiple coupling constraints, especially considering missile speed variation and target maneuvers. In this paper, a wide-envelope cooperative guidance law is proposed for multiple speed-varying missiles with the field-of-view (FOV) constraint to simultaneously intercept a maneuvering target. First, the three-dimensional cooperative guidance problem is reformulated in a virtual coordinate frame to address the target maneuver. Next, a cooperative guidance law is developed based on the varying-gain proportional navigation guidance law, with an effective numerical algorithm predicting the time-to-go under time-varying speed. Then, the guidance law is modified by including a bias guidance term to satisfy the FOV constraint throughout the cooperative guidance. Since no linearized approximations are involved in the derivation, the resultant cooperative guidance law guarantees a wide launch envelope as well as high impact precision without actively adjusting missile speed. Extensive numerical simulations are performed to show the effectiveness of the suggested results. © 1965-2011 IEEE.

关键词： Guided missiles

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Coupling regulation for achieving high-efficient UOR performance of amorphous Ni-P catalyst by pulse electrodeposition

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Science China Materials 2025年第3期68卷 820-829页

作者： Chaoqun Pei Yuyang Qian Shuangqin Chen Jing Hu Shangshu Wu Mingjie Zhou Baoan Sun Tao Feng Songshan Lake Materials Laboratory Dongguan 523808China Institute of Physics Chinese Academy of SciencesBeijing 100190China Herbert Gleiter Institute of Nanoscience School of Material Science and EngineeringNanjing University of Science and TechnologyNanjing 210094China Institute of Materials Shanghai UniversityShanghai 200444China College of Light Industry Science and Engineering Beijing Technology and Business UniversityBeijing 100048China Xi’an Rare Metal Materials Institute Co. Ltd.Xi’an 710016China

The pursuit of highly efficient catalysts for the urea oxidation reaction(UOR)represents a pivotal and sustainable approach to the generation of renewable *** regulation has emerged as a particularly effective approach to achieving superior catalytic ***,in the realm of amorphous catalysts with disordered structure and remarkable catalytic potential,identifying effective regulation strategies to enhance the UOR performance remains a formidable yet critical *** this study,we present a coupling modulation strategy based on the growth mode and pulse current,utilizing pulse electrodeposition(PED)to achieve amorphous Ni-P catalysts with high-efficiency UOR *** Ni/Ni-P catalyst engineered by Stranski-Krastanov(SK)growth mode along with low pulse current exhibits unprecedented catalytic activity for UOR,as evidenced by its overpotential of 1.35 V at 10 mA/cm^(2) and 1.37 V@100 mA/cm^(2).We reveal the regulation-relationship among the growth modes,catalyst structure and UOR performance by *** also show that low-pulse current can efficiently enhance UOR performance by elevating energy states in amorphous Ni-P,and further demonstrate the broad applicability across diverse growth ***,by integrating film growth modes with a pulse current,we have established a novel method for significantly enhancing catalytic performance,setting the stage for the advancement of superior catalysts.

关键词： metallic glass catalyst pulsed electrodeposition structure regulation urea oxidation reaction

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Advancing Underwater Image Enhancement Using Hybrid Deep Learning Models

Advancing Underwater Image Enhancement Using Hybrid Deep Lea...

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2025 IEEE International Conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Jain, Upma Boob, Nandini Shirish Anandhi, R.J. Sehgal, Archana Bhavana, Munugapati Kumar, Yogendra Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India New Horizon College of Engineering Department of Information Science Engineering Bangalore India Lovely Professional University Phagwara India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India GLA University Department of Electrical Engineering Mathura India

ISBN: (纸本)9798331512965

When attempting to make underwater images better, there are a lot of obstacles to overcome, including water's inherent color distortion, inadequate visibility, and light absorption and dispersion noise. The difficulties in accurately analyzing images caused by these problems apply to marine research, underwater robots, and environmental monitoring are only a few areas which are impacted. To address the limitations of existing picture enhancement techniques, this paper proposes a new hybrid deep learning CNN + GAN model for enhancing underwater photographs. The hybrid model extracts features with GANs and noise reduces with CNNs to produce better photos with better clarity and color correction. I train and validate the model using an underwater picture dataset and it defeats the state of the art approaches on visibility, color correctness, contrast and contrast. In quantitative and qualitative assessments our hybrid deep learning method yields superior generalization in different undersea environments relative to traditional methods. A suggested approach can potentially improve the underwater photography, marine setting data collecting with more precision, as well as better underwater exploration. This work now provides confidence in research progress into autonomous underwater exploration systems and underwater image processing. © 2025 IEEE.

关键词： Convolutional neural networks

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Evaluation of the Performance of Reinforced Concrete Beams with 3D-Printed Permanent Formwork

Journal of Intelligent Construction

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Journal of Intelligent Construction 2025年第4期2卷 1-17页

作者： Jingyuan Guan Li Wang Yimiao Huang Guowei Ma Yaxin Tao School of Civil and Transportation Engineering Hebei University of Technology Tianjin China Hebei Engineering Research Center of Construction 3D Printing Hebei University of Technology Tianjin China Institute for Building Materials ETH Zurich Zurich Switzerland

Understanding the advantages of combining traditional construction methods and 3D concrete printing is the gateway to the general structural applicability of three-dimensional concrete printing (3DCP) technology. To further this ob-jective, in this study, 3D-printed concrete is used as permanent formwork for the manufacture of reinforced concrete beams. Different treatment methods for the interfaces between the printed formwork and the cast core concrete, including embedding of shear connectors, entraining of micro-cables, and inclusion of ribs in the 3D printing of the formwork, are explored to improve the integral mechanical capacities of so-called 3D-printed and cast-in-place composite beams. Specifically, flexural behaviors are studied experimentally through ob-servation of damage and failure processes via visualization with the digital image correlation (DIC) method. The mesoscale architecture at the interface between 3D-printed permanent formwork and cast-in-place concrete is investigated through computed tomography (CT) scanning to infer the cooperative bonding mechanisms of different bonding treatment methods. The stress-transfer mech-anism is thus elucidated. The applicability of 3D-printed concrete as permanent formwork is validated in view of a 14.3% increase in the bending capacity from a composite beam with bottom ribs compared to that of a completely cast counterpart. From the experiments, incorporation of shear connectors contributes most to the bonding performance of the formwork-concrete interfaces. Mean-while, interfacial bonding can be enhanced by increasing the roughness of the 3D-printed formwork, interlocking of the formwork with the inner aggregated concrete, or improving the consistency of the elastic and shear moduli of the formwork and cast concrete. In particular, the appropriate thickness of both the formwork and the aggregated concrete as a cover to yield the optimal inte-grated bending capacity of composite beams is derived from the cur

关键词： Connectors Deformation Computed tomography Bending Three-dimensional printing Concrete Structural beams Bonding Ribs Surface treatment

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A FE-S-BiLSTM and heatmap based state-of-health estimation method for lithium-ion batteries

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IEEE Sensors Journal 2025年

作者： Shao, Kui Zhai, Chao Zhang, Chaolong He, Yigang Du, Bolun Wu, Ji China University of Geosciences School of Future Technology Wuhan430074 China China University of Geosciences School of Automation Wuhan430074 China Jinling Institute of Technology College of Intelligent Science and Control Engineering Nanjing211169 China Wuhan University School of Electrical Engineering and Automation Wuhan430072 China China Electric Power Research Institute Wuhan430074 China Hefei University of Technology School of Automotive and Transportation Engineering Anhui Hefei230009 China

Accurate battery SOH estimation can improve battery reliability and ensure its safe and efficient operation. Therefore, this study proposes a novel method for battery SOH estimation. First, a thermocouple temperature sensor monitors the battery’s operating temperature and provides feedback to the thermostat for precise temperature control during experiments. The battery’s charging voltage and current are measured using voltage transmitters and Hall current sensors, respectively, and the two are fused to obtain the battery’s charging power. Next, one-dimensional charging power data is converted into two-dimensional heatmaps using image encoding techniques. The heatmap corresponding to the first cycle is selected as the reference image, and the difference between the heatmaps of subsequent cycles and the reference image is quantified using structural similarity (SSIM). The final results serve as an indicator of battery health. Additionally, this study proposes a novel battery SOH estimation model, the Feature Enhancement-Simplification-Bidirectional Long Short-Term Memory (FE-S-BiLSTM). The feature enhancement layer in the FE-S-BiLSTM model enriches global static features through enhancement learning. Based on the model’s bidirectional long short-term memory (BiLSTM) layer and simplification layer, dynamic features in the time-space domain are double-captured. Finally, this study utilizes six batteries and designed a variety of experiments to validate the effectiveness of the proposed method. The experimental design comprises three tasks: SOH estimation based on full charge data, SOH estimation based on random SOC interval charge data, and cross-battery SOH estimation. The experimental results demonstrate that the proposed SOH estimation method for batteries exhibits significant potential for practical applications. © 2001-2012 IEEE.

关键词： Lithium-ion batteries

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Estimating Battery Life from First-cycle Charging Data using HiLoSegNet model

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IEEE Transactions on Transportation Electrification 2025年

作者： Shao, Kui Zhai, Chao Zhang, Chaolong He, Yigang Du, Bolun Wu, Ji China University of Geosciences School of Future Technology Wuhan430074 China China University of Geosciences School of Automation Wuhan430074 China Jinling Institute of Technology College of Intelligent Science and Control Engineering Nanjing211169 China China Electric Power Research Institute Wuhan430074 China Wuhan University School of Electrical Engineering and Automation Wuhan430072 China Hefei University of Technology School of Automotive and Transportation Engineering Anhui Hefei230009 China

In electric vehicles, some short-life lithium-ion batteries with a comparatively higher aging rate could adversely affect the service life of the large-scale battery pack. This study proposes the use of first-cycle charging data to estimate the battery life, providing an effective indicator for battery grouping. Herein, twelve features were extracted based on the battery’s first-cycle charging data. Random forest (RF) was used to quantify the Gini impurity variation in each feature regarding the battery life, and key features were accordingly selected. Aiming at the problem of less data in the first cycle, random noise was introduced to form spurious data based on the training dataset to increase the number and diversity of training samples. Owing to relatively small differences in the charging data of different batteries in the first cycle, the high-dimensional mapping technology, regular segmentation, and local decision-making were combined to establish a model called High-Dimensional Local Segmentation Network (HiLoSegNet), which was applied to small samples. By employing high-dimensional mapping, regular segmentation mechanism, and local regression analysis, the model enhances prediction accuracy and generalization capability in the presence of sparse features. The HiLoSegNet estimation model was pseudo-trained based on pseudo data and fine-tuned based on actual features. Two sets of NCA and NCM data from Tongji University were used as research objects to verify the proposed method. Among them, the maximum error, minimum error, root mean square error (RMSE), mean absolute error (MAE), and R-Square (R2) between the estimated battery life and actual value corresponding to the NCM dataset were found to be 7.6667, 0.2334, 5.2541, 4.8333, and 0.90, respectively. The method proposed in this study, which take 1.2 s without additional battery testing, can provide battery life indicators for battery grouping. © 2015 IEEE.

关键词： Mean square error

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Multisensor-Based Fault Detection Method for Air-Exchange Device in EMU Train

IEEE Sensors Letters

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IEEE Sensors Letters 2025年第5期9卷

作者： Wei, Rongjiao Wei, Pu Pan, Chao An, Yangyang Zhu, Hao Wang, Mulan Nanjing Institute of Technology School of Communications and Information Engineering Nanjing211167 China China Railway Shanghai Group Company Ltd. Nanjing EMU Depot Nanjing210012 China Nanjing Institute of Technology Advanced Numerical Control Technology Nanjing211167 China

In this letter, we introduce and experimentally demonstrate a fault detection method for the air-exchange devices in the electric multiple unit (EMU) train, which utilizes the abnormal sound and vibration generated by the devices when the faults occur. The sound and vibration signals are fused, and the time-frequency matrix is extracted using a short-time Fourier transform (STFT). Fault recognition is performed using the trained support vector machine (SVM) classifier. A sound detection system is built for experiments, in which the package of the sound sensor is designed to shield the sound from adjacent devices. The system includes an field programmable gate array (FPGA) and an embedded system and can be used for fault detection in the future. The experiment shows that the accuracy of the fused signals is higher than the single sensor, up to 0.995. In addition, the performances of the algorithm are evaluated, and the precision, recall, accuracy, and F1-score are all up to 0.99, which meet the actual fault detection requirements. Our method effectively improves the efficiency and accuracy of fault detection for the air-exchange device and can be widely used in the EMU train. © 2017 IEEE.

关键词： Fourier transforms

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Design of a Solar-Powered Remote-Controlled Aquatic Cleaning Drone

Design of a Solar-Powered Remote-Controlled Aquatic Cleaning...

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Industrial Electronics: Developments & Applications (ICIDeA), IEEE International Conference on-

作者： Mayukh Nandi Babita Panda Chitralekha Jena Sunita Pahadsingh Ankit Kumar Soumyadip Mandal Devashish Singhania School of Electrical Engineering KIIT Deemed to be University Bhubaneswar India Department of Electrical Engineering Aryan Institute of Engineering & Technology Bhubaneswar India

ISBN: (数字)9798331533205

ISBN: (纸本)9798331533212

The Remote-Controlled Solar Lake Pool Cleaner Drone is an innovative solution designed to automate the cleaning process of large bodies of water, such as lakes and pools, using renewable solar energy. This drone combines the advantages of remote control functionality, efficient cleaning mechanisms, and environmentally friendly power sources to provide an effective and sustainable solution for maintaining water cleanliness. The drone is equipped with advanced cleaning mechanisms, including a suction system and a brush mechanism, capable of efficiently removing debris, leaves, and algae from the water surface. Its remote-control capabilities allow operators to maneuver the drone effortlessly, enabling precise navigation and targeting specific areas requiring cleaning.

关键词： Renewable energy sources Solar energy Lakes Cleaning Water pollution Safety Rivers Water resources Robots Drones

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3D Spatial Spectrum Prediction for Uav Networks Based on a Multi-Scale Temporal Model

3D Spatial Spectrum Prediction for Uav Networks Based on a M...

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IEEE Conference on Wireless Communications and Networking

作者： Sike Cheng Xuanheng Li Xiangbo Lin Haichuan Ding Yi Sun School of Information and Communication Engineering Dalian University of Technology Dalian China National Mobile Communications Research Laboratory Southeast University Nanjing China School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

An efficient 3D spatial spectrum prediction method is essential for UAV networks operating in highly heterogeneous spectrum environments, enabling UAVs to proactively navigate toward areas with abundant available spectrum and make decisions to access to idle ones in advance. This paper introduces a novel Multi-Scale Temporal model for 3D spatial spectrum prediction (MST-3DSSP) that comprehensively captures complex correlations across 3D spatial, frequency, and multi-scale temporal domains. Specifically, the proposed model incorporates a 3D Spatial-Frequency Fusion (3DS-FF) module to extract and fuse 3D spatial and frequency features, along with a MultiScale Temporal Extraction (MS-TE) module that combines BiLSTM and Transformer blocks to capture both small scale and large scale temporal dependencies. These two modules enable the model to understand the complex correlations across 3D spatial, frequency, and multi-scale temporal domains, thereby allowing for more accurate spectrum predictions. Extensive experiments on real-world spectrum datasets demonstrate that MST-3DSSP significantly outperforms existing spectrum prediction methods, achieving higher prediction accuracy and reduced errors, thus providing a robust solution for improving spectrum efficiency in UAV networks.

关键词： Solid modeling Three-dimensional displays Correlation Accuracy Navigation Frequency-domain analysis Predictive models Autonomous aerial vehicles Feature extraction Transformers

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