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Target Detection for Reconfigurable Intelligent Surface Assisted MIMO Radar IEEE Trans. Veh. Technol. (IF 6.1) Pub Date : 2025-02-10 Huajun Zou, Liang Wu, Zaichen Zhang
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Two-stage Reconstruction for Co-array 2D DOA Estimation of Mixed Circular and Noncircular Signals IEEE Trans. Veh. Technol. (IF 6.1) Pub Date : 2025-02-10 Yaxing Yue, Hang Zheng, Zhiguo Shi, Guisheng Liao
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Partitioned Edge Learning Over Fast Fading Channels IEEE Trans. Veh. Technol. (IF 6.1) Pub Date : 2025-02-10 Zhihui Jiang, Dingzhu Wen, Shengli Liu, Guangxu Zhu, Guanding Yu
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Nested U-shape Network for Robust Direction-of-Arrival Estimation IEEE Trans. Veh. Technol. (IF 6.1) Pub Date : 2025-02-10 Bingzheng Chen, Zhaohui Du, Xuchen Wang, Yinan Zhu
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A Secured Energy Saving with Federated Assisted Modified Actor-Critic Framework for 6G Networks IEEE Trans. Veh. Technol. (IF 6.1) Pub Date : 2025-02-10 Attai Ibrahim Abubakar, Michael S. Mollel, Metin Ozturk, Naeem Ramzan
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Unwinding-Free Attitude Control Based on Fully Actuated Systems: Transformation, Design, and Analysis IEEE Trans. Aerosp. Electron. Sys. (IF 5.1) Pub Date : 2025-02-10 Fu-Zheng Xiao, Li-Qun Chen
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Three-Dimensional Resilient Cooperative Guidance Under Varying Speed and Field-of-View Constraint IEEE Trans. Aerosp. Electron. Sys. (IF 5.1) Pub Date : 2025-02-10 Xiangjun Ding, Wei Dong, Jianan Wang, Junhui Liu, Jiayuan Shan
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IEEE Aerospace and Electronic Systems Society Information IEEE Trans. Aerosp. Electron. Sys. (IF 5.1) Pub Date : 2025-02-10
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Experience Replay Enhances Excitation Condition of Neural-Network Adaptive Control Learning J. Guid. Control Dyn. (IF 2.3) Pub Date : 2025-02-07 Chaoran Qu, Lin Cheng, Shengping Gong, Xu Huang
Journal of Guidance, Control, and Dynamics, Ahead of Print.
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Controlling Hamiltonian Integral Invariants of Spacecraft Phase Space Distributions J. Guid. Control Dyn. (IF 2.3) Pub Date : 2025-02-07 Oliver H. Boodram, Daniel J. Scheeres
Journal of Guidance, Control, and Dynamics, Ahead of Print.
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Repetitive transient impact detection and its application in cross-machine fault detection of rolling bearings Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2025-02-08 Xin Kang, Junsheng Cheng, Yu Yang, Feng Liu
This paper aims to develop a rolling bearing fault detection method that can be directly applied to unseen domains, addressing the limitations of current transfer learning methods, include the dependency on target domain data for domain adaptation methods and the lack of interpretability for domain generalization methods. Inspired by the intrinsic indicator of rolling bearing fault, i.e, repetitive
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Multi-channel adjoint least mean square algorithm with momentum factor applied on active noise control Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2025-02-08 Xiaoyi Shen, Yu Guo, Junwei Ji, Dongyuan Shi, Woon-Seng Gan
Active noise control (ANC) is extensively utilized to attenuate unwanted environmental noise, creating a more conducive environment for work and daily activities. Traditional approaches face challenges when scaled to larger areas using multi-channel ANC (McANC) due to escalating computational burden and slower convergence speeds as channel numbers increase. To overcome these limitations, we introduce
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Phononic crystals with non-quantized Zak phases for controlling interface state frequencies Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2025-02-08 Seongmin Park, Wonju Jeon
Topological interface and edge states have been extensively studied in phononic and photonic crystals with Zak phases quantized at 0 or π. In this work, we design phononic crystals with non-quantized Zak phases ranging from ?0.45π to 0.45π by deliberately breaking the symmetry of the unit cell. Through adjustments of the non-quantized Zak phase, we control the phases of waves reflected from the phononic
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Rapid Trajectory Design for Low-Thrust Many-Revolution Rendezvous Using Analytical Derivations J. Guid. Control Dyn. (IF 2.3) Pub Date : 2025-02-07 Jincheng Hu, Hongwei Yang, Shuang Li, Guoliang Liang
Journal of Guidance, Control, and Dynamics, Ahead of Print.
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Remaining useful life prediction for stochastic deteriorating Devices: A direct approach via inverse degradation modeling Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2025-02-07 Tianmei Li, Zhenyu Cai, Zhaoju Zeng, Zhengxin Zhang, Xiaosheng Si
Remaining useful life (RUL) prediction has been extensively recognized for its fundamental and significant value in enhancing safety, improving reliability, and reducing cost for industrial devices. The advancement of condition monitoring (CM) for degrading devices stimulates the development and prosperity of data-driven prognosis approach for RUL prediction, among which the stochastic-data-driven
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Performance evaluation of elevators using a novel hierarchical softmax regression model Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2025-02-07 Dapeng Niu, Minghao Yang, Mingxing Jia, Hongli Jin, Gang Luo
Elevator performance evaluation plays a guiding role in improving passenger experience and directing the maintenance work. Traditional elevator performance evaluation studies mainly depend on defect data or pertinent norms while ignoring the operation performance. In order to solve these issues, a new method for performance evaluation using hierarchical softmax regression (HSR) based on the operating
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A Lock-In thermography based post-processing scheme for the detection of sub-surface rivet-related defects in aircraft structures Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2025-02-07 Haris Ali Khan, Shahab Uddin, Sharjeel Salik, Ali Javaid, Talha Ali Khan, Zia Ul Islam
This research is focused on developing a novel Lock-In Thermography-based scanning system to detect subsurface defects in aircraft rivets. The proposed system included a customized thermographic device consisting of a thermal camera, heating source, synchronized control circuitry, and post-processing software utilizing the Discrete Wavelet Transform technique, augmented by spatial gradient and mean
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Study on developing predicted system model of cutting-edge trajectory for micro-milling process based on tool runout error, chip thickness and force signal Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2025-02-07 Yao Sun, Yirong Sun, Yiming Huang, Siqian Gong, Mingsheng Sun, Ming Liu
The cutting-edge trajectory of micro end mills directly affects machining stability, surface quality and tool wear involved in micro milling process. However, the size effect and geometric characteristics of micro end mills make its cutting-edge trace diverge markedly from the traditional milling cutter paths. The cutting-edge trajectory prediction system model is specifically developed for a two-edged
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Fault detection and monitoring using a data-driven information-based strategy: Method, theory, and application Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2025-02-07 Camilo Ramírez, Jorge F. Silva, Ferhat Tamssaouet, Tomás Rojas, Marcos E. Orchard
The ability to detect when a system undergoes an incipient fault is of paramount importance in preventing a critical failure. Classic methods for fault detection – including model-based and data-driven approaches – rely on thresholding error statistics or simple input-residual dependencies but face difficulties with non-linear or non-Gaussian systems. Behavioral methods – e.g., those relying on digital
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Task Offloading and Scheduling under Hard Deadlines in Vehicular Edge Computing Systems IEEE Trans. Veh. Technol. (IF 6.1) Pub Date : 2025-02-07 Kangyu Gao, Jaeyoung Song, Changjun Zhou, Zhonglong Zheng, Sang-Woon Jeon
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A Composite Observer-Based Optimal Attitude Tracking Control for FWEPAUV via Reinforcement Learning IEEE Trans. Veh. Technol. (IF 6.1) Pub Date : 2025-02-07 Ning Pang, Botao Dong, Longyang Huang, Zhihuan Hu, Hongtian Chen, Weidong Zhang
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Carrier Phase-based Sensor Relative Localization IEEE Trans. Veh. Technol. (IF 6.1) Pub Date : 2025-02-07 Yudong Sun, Zheng Yao, Mingquan Lu
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Integrated Sensing and Communications for UAV Assisted Internet of Things Based on Deep Reinforcement Learning IEEE Trans. Veh. Technol. (IF 6.1) Pub Date : 2025-02-07 Xin Liu, Jiahua Wu, Chang Zhao, Zechen Liu
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Directivity-Aware Degrees of Freedom Analysis for Extremely Large-Scale MIMO IEEE Trans. Veh. Technol. (IF 6.1) Pub Date : 2025-02-07 Shaohua Yue, Liang Liu, Boya Di
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Probabilistic Assessment of Vehicle-to-Grid Power of Electric Vehicle Parking Lots: A New Comprehensive Approach IEEE Trans. Veh. Technol. (IF 6.1) Pub Date : 2025-02-07 Meghdad Tourandaz Kenari, Aydogan Ozdemir
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FRFT-Based Interference Suppression for Automotive FMCW Radars IEEE Trans. Veh. Technol. (IF 6.1) Pub Date : 2025-02-07 Youlong Weng, Guangzhi Chen, Jingxuan Chen, Ziang Zhang, Zhiyu Jia, Shunchuan Yang, Donglin Su
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Improving Maritime Data: A Machine Learning-based Model for Missing Vessel Trajectories Reconstruction IEEE Trans. Veh. Technol. (IF 6.1) Pub Date : 2025-02-07 Jin Chen, Maohan Liang, Chang Peng, Jixin Zhang, Shengxu Huo
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SAR Frequency Shift Jamming Suppression Method by Shaping the Cross Ambiguity Function IEEE Trans. Aerosp. Electron. Sys. (IF 5.1) Pub Date : 2025-02-07 Kai Zhou, Zhenyu Hou, Zhongguo Wu, Kun Li, Dongdong Chen, Huiwei Yao
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Cognitive Jammer Time Resource Scheduling With Imperfect Information Via Fuzzy Q-Learning IEEE Trans. Aerosp. Electron. Sys. (IF 5.1) Pub Date : 2025-02-07 Linchuan Gan, Kui Xiong, Maosen Liao, Xianxiang Yu, Guolong Cui
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Implicit Neural Representation with Imaging Geometry for SAR Target Recognition IEEE Trans. Aerosp. Electron. Sys. (IF 5.1) Pub Date : 2025-02-07 Ziheng Cheng, Yucheng Ding, Chunhui Qu, Bo Chen
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Enhancing the Performance of Aerial Relay System through Rate-Splitting Multiple Access and Reconfigurable Intelligent Surface IEEE Trans. Aerosp. Electron. Sys. (IF 5.1) Pub Date : 2025-02-07 Thi Thanh Huyen Le, Tran Manh Hoang, Xuan Nam Tran, Pham Thanh Hiep, Ba Cao Nguyen
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Physics-based deep learning framework for Terahertz thickness measurement of thermal barrier coatings with variable refractive index Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2025-02-06 Fengshan Sun, Binghua Cao, Mengbao Fan, Lin Liu
Accurate terahertz (THz) thickness measurement of topcoat in thermal barrier coatings remains a challenge due to the change of refractive index from uneven microstructures and temperature variations. Here, a novel physics-based deep learning framework with original sparse features is proposed to measure the topcoat thickness in an accurate and low-cost manner. Firstly, the pores in the topcoat causes
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Zero-shot pipeline fault detection using percussion method and multi-attribute learning model Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2025-02-06 Longguang Peng, Wenjie Huang, Jicheng Zhang, Guofeng Du
In recent years, the machine learning (ML)-based percussion method has gained considerable attention as a cost-effective and user-friendly non-destructive testing (NDT) technique. However, traditional ML classification methods fail to identify previously unseen fault levels that are not included in the training dataset, thereby limiting their practical applicability. This paper proposes a zero-shot
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Mixed time-frequency-domain method for nonlinear hybrid floating breakwater-WEC Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2025-02-06 Pengcheng Li, Haiheng Zhang, Xin Zhao, Huaqing Jin, Jun Ding, Daolin Xu
Ocean waves represent a vast and renewable resource that is prevalent across the globe. However, the relentless erosion of marine equipment and coastal structures poses an ongoing challenge to safety. The integration of a floating breakwater with a wave energy converter (FB-WEC) offers a dual solution that addresses both wave protection and energy harnessing. The attenuation of low-frequency ocean
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On the Benefits of Torque Vectoring for Automated Collision Avoidance At the Limits of Handling IEEE Trans. Veh. Technol. (IF 6.1) Pub Date : 2025-02-06 Alberto Bertipaglia, Davide Tavernini, Umberto Montanaro, Mohsen Alirezaei, Riender Happee, Aldo Sorniotti, Barys Shyrokau
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Optimizing Hybrid RIS-Aided ISAC Systems in V2X Networks: A Deep Reinforcement Learning Method for Anti-eavesdropping Techniques IEEE Trans. Veh. Technol. (IF 6.1) Pub Date : 2025-02-06 Yu Yao, Zhixing Zhu, Pu Miao, Xu Cheng, Feng Shu, Jiangzhou Wang
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Outage Analysis of RSMA Enabled Integrated Satellite-Terrestrial Networks IEEE Trans. Veh. Technol. (IF 6.1) Pub Date : 2025-02-06 Chenbo Hu, Hongjuan Yang, Zhiquan Zhou, Bo Li, Xu Jiang, Nan Zhao, Arumugam Nallanathan
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An Anytime Trajectory Optimizer for Accurately Parking an Autonomous Vehicle in Tiny Spaces IEEE Trans. Veh. Technol. (IF 6.1) Pub Date : 2025-02-06 Xiaoming Chen, Yueshuo Sun, Tantan Zhang, Xinwei Wang, Shengjian Xiong, Kai Cao
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Sensor Fusion and Resource Management in MIMO-OFDM Joint Sensing and Communication IEEE Trans. Veh. Technol. (IF 6.1) Pub Date : 2025-02-06 Elia Favarelli, Elisabetta Matricardi, Lorenzo Pucci, Wen Xu, Enrico Paolini, Andrea Giorgetti
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High-level Service Type Analysis and MORL-based Network Slice Configuration for Cell-Free-based 6G Networks IEEE Trans. Veh. Technol. (IF 6.1) Pub Date : 2025-02-06 Navideh Ghafouri, John S. Vardakas, Adlen Ksentini, Christos Verikoukis
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Efficient Likelihood Function Learning Method for Time-Varying MIMO Systems Using One-Bit ADCs IEEE Trans. Veh. Technol. (IF 6.1) Pub Date : 2025-02-06 Jaemin Kim, Yo-Seb Jeon, Tae-Kyoung Kim
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End-to-End Scheduling of Space-Air-Ground Integrated Networks for High-Speed Railways IEEE Trans. Veh. Technol. (IF 6.1) Pub Date : 2025-02-06 Wenqing Li, Bo Ai, Yong Niu, Zhu Han, Ning Wang, Lei Xiong
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A Game Theory-Reinforcement Learning Approach to Cooperation for UAVs IEEE Trans. Veh. Technol. (IF 6.1) Pub Date : 2025-02-06 Changbing Tang, Linchao Pan, Jie Chen, Yang Liu, Jingang Lai
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Data-Driven Tracking Control for Non-Affine Yaw Channel of Helicopter via Off-Policy Reinforcement Learning IEEE Trans. Aerosp. Electron. Sys. (IF 5.1) Pub Date : 2025-02-06 Kun Zhang, Shijie Luo, Huai-Ning Wu, Rong Su
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Non-Singular Fixed-Time Attitude Tracking Control for Rigid Spacecraft IEEE Trans. Aerosp. Electron. Sys. (IF 5.1) Pub Date : 2025-02-06 Rui-Qi Dong, Ai-Guo Wu, Wen-Nian Qi
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Synthetic Generator for Ionospheric Amplitude Scintillation Fading Channels Using Generative Adversarial Networks IEEE Trans. Aerosp. Electron. Sys. (IF 5.1) Pub Date : 2025-02-06 Moisés J. S. Freitas, Alison O. Moraes, Jonas Sousasantos, Marcos R. O. A. Máximo
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Efficient machine learning method for supercritical combustion: Predicting real-fluid properties and chemical ODEs Aerosp. Sci. Technol. (IF 5.0) Pub Date : 2025-02-06 Yuqing Cai, Ruixin Yang, Han Li, Jiayang Xu, Ke Xiao, Zhi X. Chen, Hu Wang
In the context of supercritical reactive flow simulations, the real-time direct integration of chemical ordinary differential equations (ODE) and multi-component real fluid thermal-physical properties (Therm) are the primary demands on computational resources. As the number of components requiring a solution increases, the computational load also rises, particularly for combustion models involving
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A nonlinear transmissibility function-based diagnosis approach for multi-disks rub-impact faults in rotor systems with nonlinear supports Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2025-02-05 Quankun Li, Heyu Hu, Mingfu Liao, Xingjian Jing
For diagnosing rub-impact faults in rotor systems, numerous advanced methods leveraging nonlinear vibration features such as Frequency Response Function (FRF), Output Frequency Response (OFR), and Transmissibility Function (TF) have been developed and implemented. Addressing limitations in existing methods, such as the need for reference data from healthy rotors, neglect of nonlinear supports, and
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Fast assessment of non-Gaussian inputs in structural dynamics exploiting modal solutions Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2025-02-05 Arvid Trapp, Peter Wolfsteiner
In various technical applications, assessing the impact of non-Gaussian and non-stationary processes on responses of dynamic systems is crucial. While simulating time-domain realizations offers an effective solution for linear dynamic systems, this method proves time-consuming for finite element (FE) models, which may contain thousands to millions of degrees-of-freedom (DOF). Given the central role
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Maximum Likelihood Direct Position Determination of Multiple Sources With Channel State Information IEEE Trans. Veh. Technol. (IF 6.1) Pub Date : 2025-02-05 Ziqiang Wang, Bo Tan, Elena Simona Lohan, Mikko Valkama, Qun Wan
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Joint Task Migration and Resource Allocation in Vehicular Edge Computing: A Deep Reinforcement Learning-Based Approach IEEE Trans. Veh. Technol. (IF 6.1) Pub Date : 2025-02-05 Quyuan Luo, Jiyun Zhang, Shihong Hu, Tom H. Luan, Pingzhi Fan
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Joint Task Offloading and Resource Allocation for Streaming Applications in Cooperative Mobile Edge Computing IEEE Trans. Veh. Technol. (IF 6.1) Pub Date : 2025-02-05 Xiang Li, Rongfei Fan, Han Hu, Xiangming Li, Shimin Gong, Jian Yang
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Dual-Band Dual-Polarized Omnidirectional MIMO Antenna with Ultra-Low Profile for Vehicular Communications IEEE Trans. Veh. Technol. (IF 6.1) Pub Date : 2025-02-05 Yan-Hui Ke, Jian Zhou, Jian-Xin Chen
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Trajectory Optimization in User-Centric Distributed Massive MIMO Systems Enabled by UAV Swarms IEEE Trans. Veh. Technol. (IF 6.1) Pub Date : 2025-02-05 Daynara D. Souza, Marx M. M. Freitas, André L. P. Fernandes, Pedro H. J. Nardelli, Daniel Benevides da Costa, André Mendes Cavalcante, Jo?o C. Weyl Albuquerque Costa
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Resilient Distributed Kalman Filtering Against Malicious Cyber Attacks IEEE Trans. Aerosp. Electron. Sys. (IF 5.1) Pub Date : 2025-02-05 Wei Xia, Mengqing Zhou
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Experimental and numerical research on combined design of corner separation in compressor Aerosp. Sci. Technol. (IF 5.0) Pub Date : 2025-02-05 Tongtong Meng, Xin Li, Xinyu Ren, Ling Zhou, Lucheng Ji
In this manuscript, to simultaneously inhibit multiple causes of corner separation and therefore improve the flow around endwall as much as possible, the combined control by both Full Blended Blade and Endwall (Full-BBEW) and endwall Vortex Generator (VG) are studied. Firstly, a Full-BBEW design is built for a linear cascade and then an combined control is carefully designed by placing a VG in the
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Handling Qualities sizing for aerial vehicles based on control moment polytopes Aerosp. Sci. Technol. (IF 5.0) Pub Date : 2025-02-05 Aristeidis Antonakis
The study of Handling Qualities (HQ) constitutes an integral part of the air vehicle design process, ensuring safety and flyability. Nevertheless, traditional HQ sizing methods derived for conventional aircraft configurations provide decreasing insight on modern, unconventional, flight-control-augmented vehicles, mainly due to the lack of related operational experience. In this article, a new HQ sizing
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Suppression of flow separation of a high-lift wing with active flow control Aerosp. Sci. Technol. (IF 5.0) Pub Date : 2025-02-05 Qiangqiang Sun, Faycal Bahri, Mark Jabbal, Wit Stryczniewicz, Richard Jefferson-Loveday, Bruno Stefes, Alexander Büscher
Flow separation caused by the integration of a leading edge slat cut-out to accommodate an ultra-high bypass ratio engine reduces the maximum lift coefficient. In this study, an active flow control approach including 88 pulsed jet nozzles near the leading edge is used to control flow separation over a multi-element high-lift aerofoil. A hybrid large-eddy simulation (LES) and stress-blended eddy simulation
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Mechanical properties measurement of chromium coatings based on laser ultrasonic technology in high-temperature conditions Mech. Syst. Signal Process. (IF 7.9) Pub Date : 2025-02-04 Jiajian Meng, Xianke Li, Junrong Li, Haomiao Fang, Zhiyuan Zhu, Zerui Zhao, Enpei Zhao, LiLi Cheng, Jianhai Zhang, Hongwei Zhao
The rapid development of the laser ultrasonic technology has promoted non-contact in situ testing of the mechanical properties of materials in high-temperature environments, pursuing enhanced efficiency, precision, and applicability. This paper proposes a laser ultrasound-based technique for characterizing the mechanical properties of materials at high temperatures, and it was applied to determine