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Morphotectonic Analyses of LiDAR-Derived DEMs:Insights into Tectonic Activity of the Xinhua Fault within the Three Gorges Area(Central China)

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Journal of Earth Science 2025年第2期36卷 823-829页

作者： Xiaofeng Wang Xiaohan Yin Tonghui Liu Xuan Li Hongming Wang Yaqi Zhong Gang Rao School of Computer and the Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences(Wuhan)Wuhan 430078China School of Geoscience and Technology Southwest Petroleum UniversityChengdu 610500China School of Earth Sciences China University of Geosciences(Wuhan)Wuhan 430074China Hubei Communications Planning and Design Institute Co. Ltd.Wuhan 430051China School of Economics and Management Shandong Jiaotong UniversityJinan 250357China

0 *** global availability of digital elevation model(DEM)data,such as 90-m Shuttle Radar Topography Mission(SRTM)DEM and 30-m Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model(ASTER GDEM),has been extensively utilized in morphotectonic analyses(e.g.,Wang et al.,2024;Cheng et al.,2018;Pérez-Pe?a et al.,2010;El Hamdouni et al.,2008).

关键词： xinhua fault morphotectonic analyses egwang three gorges area digital elevation model aster lidar derived dems tectonic activity morphotectonic analyses digital elevation model dem datasuch

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Reward Shaping-based Double Deep Q-networks for Unmanned Surface Vessel Navigation and Obstacle Avoidance 48

Reward Shaping-based Double Deep Q-networks for Unmanned Sur...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Gan, Zihan Zheng, Jinghong Jiang, Zhenyu Lu, Renzhi Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Wuhan430074 China Wuhan University of Science and Technology Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan430081 China

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

In this paper, a method for navigation and obstacle avoidance of unmanned surface vessel (USV) based on reinforcement learning and reward shaping is proposed. This approach uses double deep Q networks (DDQN) to make decisions based on the continuous states observed from sensors in USV. In addition, a new reward function is designed based on prior knowledge to accelerate the convergence of the algorithm and improve the performance. For training the neural networks, a simulation platform is developed, in which a 3 degree of freedom mathematical model describes USV dynamic system and two-dimension actions are required to control USV. Simulation results on the platform demonstrate the DDQN hoists USV's capabilities of navigation and obstacle avoidance, and reward shaping technique improves the speed of convergence. © 2022 IEEE.

关键词： Reinforcement learning

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Irregular IRS-aided SWIPT secure communication system with imperfect CSI

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Digital Communications and Networks 2025年第3期11卷 878-885页

作者： Chunlong He Guanhai Lin Chiya Zhang Xingquan Li Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China School of Electronic and Information Engineering Harbin Institute of Technology Shenzhen China Peng Cheng Laboratory (PCL) Shenzhen China Shenzhen Institute of Information Technology Shenzhen China

The performance of traditional regular intelligent Reflecting Surface (IRS) improves as the number of IRS elements increases, but more reflecting elements lead to higher IRS power consumption and greater overhead of channel estimation. The Irregular intelligent Reflecting Surface (IIRS) can enhance the performance of the IRS as well as boost the system performance when the number of reflecting elements is limited. However, due to the lack of radio frequency chain in IRS, it is challenging for the Base Station (BS) to gather perfect Channel State Information (CSI), especially in the presence of Eavesdroppers (Eves). Therefore, in this paper we investigate the minimum transmit power problem of IIRS-aided Simultaneous Wireless Information and Power Transfer (SWIPT) secure communication system with imperfect CSI of BS-IIRS-Eves links, which is subject to the rate outage probability constraints of the Eves, the minimum rate constraints of the Information Receivers (IRs), the energy harvesting constraints of the Energy Receivers (ERs), and the topology matrix constraints. Afterward, the formulated non-convex problem can be efficiently tackled by employing joint optimization algorithm combined with successive refinement method and adaptive topology design method. Simulation results demonstrate the effectiveness of the proposed scheme and the superiority of IIRS.

关键词： Irregular intelligent reflecting surface Simultaneous wireless information and power transfer Secure communication Topology optimization

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Ballistic Behavior of Three-Dimensional Orthotropic Woven Fabric Based on Virtual Fiber Model

SSRN

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SSRN 2024年

作者： Zhang, Jian Zhou, Yi Lu, Zhenqian Yue, Jianing Han, Jing Zhou, Kanghui Liu, Shengkai Jiang, Qian Wu, Liwei Tianjin and Ministry of Education Key Laboratory for Advanced Textile Composite Materials Tiangong University Tianjin300387 China Innovation Platform of Intelligent and Energy-Saving Textiles School of Textile Science and Engineering Tiangong University Tianjin300387 China State Key Laboratory of New Textile Materials and Advanced Processing Technologies Wuhan Textile University Wuhan China College of Textiles and Clothing Yancheng Institute of Technology Jiangsu 224051 China

This study proposes a novel virtual fiber model (VFM)1 on a fiber scale to capture the ballistic behavior of fabrics. The model effectively accounts for yarn deformation during 3D orthogonal woven fabric (3DOWF) weaving, yarn pull-out, interfiber friction, and yarn interactions during the ballistic response. The optimized VFM was used to explore the ballistic behaviors of the fabrics. The results revealed that the VFM exhibited a ballistic response consistent with high-speed photography observations and successfully captured fiber slippage and pull-out behavior under impact. Compared to the yarn model (YM), the VFM enables the observation of nonsimultaneous fiber breakage and fiber interactions. Moreover, it illustrates the role of yarn pull-out in the penetration resistance of the 3DOWF, dissipating the kinetic energy of the projectile in the form of friction. Furthermore, the VFM delineated the specific functions of each system yarn. Specifically, the warp and weft yarns primarily serve as impediments to the projectile, whereas the Z yarn binds the weft, promoting the increased involvement of the weft in dissipating kinetic energy. Building on this investigation, the impact of the clamping method on the ballistic performance of the 3DOWF was explored. The findings revealed that yarn pullout emerged as the primary failure mode under the weft sides. Notably, the warp yarns predominantly experienced pullout, which enhanced the friction energy. The Z yarn binds to weft yarns that gather with warp yarns to form a strip-like protrusion, impeding the projectile motion owing to the increased number of yarns. The VFM contributes significantly to the exploration of the impact of fabric structures on ballistic performance, offering valuable insights for designing and enhancing ballistic fabric *** study proposes a novel, fiber-scale virtual fiber model (VFM) to capture the ballistic behavior of fabrics. The model accounts for yarn deformation during 3D orthogonal

关键词： Yarn

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Human–Robot Coordination Control for Sit-to-Stand Assistance in Hemiparetic Patients With Supernumerary Robotic Leg

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IEEE Transactions on Automation Science and Engineering 2025年 22卷 16591-16602页

作者： Jie Zuo Jun Huo Xiling Xiao Yanzhao Zhang Jian Huang Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Hubei Key Laboratory of Brain-Inspired Intelligent Systems and the School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Information Engineering Wuhan University of Technology Wuhan China Department of Rehabilitation Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China

In light of global aging and prevalent stroke-related hemiplegia, this study addresses challenges in robot-assisted Sit-to-Stand (STS) movements, a daily activity prone to falls. Supernumerary Robotic Legs (SRL) serve as independent support, enhancing stability and limb movement range. Existing coordination control methods lack personalization for STS assistance, requiring solutions for human intent transmission and rapidly optimize coordination control challenges in the non-coupled human-robot system. The proposed human-SRL coordination control algorithm, grounded in personalized SRL-human coupling models, incorporates surface electromyography (sEMG) signals to design an intent-driven variable stiffness impedance control. The inclusion of incremental learning enables rapid optimization of impedance parameters, facilitating real-time adjustments in SRL assistance for adaptive coupling with users. Practical experiments involving both healthy participants and hemiparetic patients validate the algorithm’s effectiveness during STS. The results validate substantial reductions in STS time (39.54%) and muscle activity (28.01%), highlighting the efficacy of the proposed algorithm-controlled SRL support for hemiparetic individuals. Note to Practitioners—To prevent misalignment between the SRL and natural limb movements, which can impose additional strain on the body, this study combines the principles of joint bio-mimicry to develop a personalized human-robot coupling model, and also propose an sEMG-based adaptive impedance control algorithm. Our research fills a gap in the nuanced analysis of robot-assisted support effects on both the affected and unaffected sides of hemiparetic patients, while also possessing the potential for transfer or extension to other types of rehabilitation robotic platforms. Although the SRL shows promising results, it is important to note the limitations of a small sample size and the need for further exploration of the needs of diverse patient po

关键词： Robots Robot kinematics Limbs Impedance Legged locomotion Adaptation models Couplings Muscles Biological system modeling Automation

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Passivity-Based Robust Control Against Quantified False Data Injection Attacks in Cyber-Physical Systems

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IEEE/CAA Journal of Automatica Sinica 2021年第8期8卷 1440-1450页

作者： Yue Zhao Ze Chen Chunjie Zhou Yu-Chu Tian Yuanqing Qin Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China School of Computer Science Queensland University of TechnologyBrisbane QLD 4001Australia

Secure control against cyber attacks becomes increasingly significant in cyber-physical systems(CPSs).False data injection attacks are a class of cyber attacks that aim to compromise CPS functions by injecting false data such as sensor measurements and control *** quantified false data injection attacks,this paper establishes an effective defense framework from the energy conversion ***,we design an energy controller to dynamically adjust the system energy changes caused by unknown *** designed energy controller stabilizes the attacked CPSs and ensures the dynamic performance of the system by adjusting the amount of damping ***,with the disturbance attenuation technique,the burden of control system design is simplified because there is no need to design an attack *** addition,this secure control method is simple to implement because it avoids complicated mathematical *** effectiveness of our control method is demonstrated through an industrial CPS that controls a permanent magnet synchronous motor.

关键词： Cyber-physical systems energy controller energy conversion false data injection attacks L2 disturbance attenuation technology

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Active Learning with Core-Set Sampling and Scale-Sensitive Loss for 3D Object Detection

Active Learning with Core-Set Sampling and Scale-Sensitive L...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Dejun Zhang Xiaowei Lin Benxin Yi Yiqi Wu Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences Information Systems Technology and Design Pillar Singapore University of Technology and Design

Deep learning-based 3D object detectors often require large-scale labeled 3D datasets, which can be expensive to annotate. To tackle this issue, we introduce a core-set sampling strategy within an active learning framework, selecting highly informative data from a data pool to reduce reliance on such datasets. Additionally, we introduce a scale-sensitive loss function into the 3D object detector to mitigate the disparities in the influence of small and large objects on model learning, thereby enhancing the accuracy of small object detection. Our experiments on the KITTI dataset demonstrate that our method achieves comparable results to the baseline using only 20% of the data. Notably, our approach outperforms the baseline in small object detection, with an 8% accuracy improvement for pedestrians and a 4% improvement for cyclists. The source code of the proposed method is available at https://***/djzgroup/al-cs-ssl.

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Text Detection in Natural Scene Images Using Morphological Component Analysis and Laplacian Dictionary

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IEEE/CAA Journal of Automatica Sinica 2020年第1期7卷 214-222页

作者： Shuping Liu Yantuan Xian Huafeng Li Zhengtao Yu School of Information Engineering and Automation and Key Laboratory of Intelligent Information Processing Kunming University of Science and Technology

Text in natural scene images usually carries abundant semantic information. However, due to variations of text and complexity of background, detecting text in scene images becomes a critical and challenging task. In this paper, we present a novel method to detect text from scene images. Firstly, we decompose scene images into background and text components using morphological component analysis(MCA), which will reduce the adverse effects of complex backgrounds on the detection *** order to improve the performance of image decomposition,two discriminative dictionaries of background and text are learned from the training samples. Moreover, Laplacian sparse regularization is introduced into our proposed dictionary learning method which improves discrimination of dictionary. Based on the text dictionary and the sparse-representation coefficients of text, we can construct the text component. After that, the text in the query image can be detected by applying certain heuristic rules. The results of experiments show the effectiveness of the proposed method.

关键词： Dictionary learning Laplacian sparse regularization morphological component analysis(MCA) sparse representation text detection

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Improving Pathological Voice Detection: A Weakly Supervised Learning Method 9th

Improving Pathological Voice Detection: A Weakly Supervised ...

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9th Conference on Sound and Music technology, CSMT 2021

作者： Wei, Weixing Wen, Liang Qian, Jiale Shan, Yufei Wang, Jun Li, Wei School of Computer Science and Technology Fudan University Shanghai200438 China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai200433 China CETHIK Group Co. Ltd. Hangzhou311100 China

ISBN: (纸本)9789811947025

Deep learning methods are data-driven. But for pathological voice detection, it is difficult to obtain high-quality labeled data. In this work, a weakly supervised learning Method is presented to improve the quality of existing datasets by learning sample weights and fine-grained labels. First, A convolutional neural network (CNN) is devised as the basic architecture to detect the pathological voice. Then, a proposed self-training algorithm is used to iteratively run and automatically learn the sample weights and fine-grained labels. These learned sample weights and fine-grained labels are used to train the CNN model from scratch. The experiment results on the Saarbrucken Voice database show that the diagnosis accuracy improved from 75.7 to 82.5%, with a 6.8% improvement in accuracy over the CNN models trained with the original dataset. This work demonstrates that the weakly supervised learning method can significantly improve the classification performance to distinguish pathological voice and healthy voice. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks

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Temporal Transformation Network Based On Scale Sequences for Cloth-Changing Person Re-Identification in Video Datasets 9

Temporal Transformation Network Based On Scale Sequences for...

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9th International Conference on Computer and Communications, ICCC 2023

作者： Zhu, Xiaoke Zhao, Bo Dong, Zhiwei Dong, Lingyun Li, Danyang School of Computer and Information Engineering Henan University Kaifeng China Henan University Henan Engineering Research Center of Intelligent Technology and Application China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

ISBN: (纸本)9798350317251

This paper introduces the Temporal Transform Network based on Scale Sequences (TTS) for cloth-changing person re-identification in video datasets. The TTS network is designed to capture multi-scale temporal cues within video sequences. It accomplishes this by initially modeling short-term temporal cues between adjacent frames, followed by capturing long-term relationships between non-consecutive frames. In more detail, short-term temporal cues are modeled through parallel inflated convolutions with different time dilation rates, enabling the representation of pedestrian movement and appearance dynamics. Long-term relationships are effectively captured using a temporal self-attention model, mitigating challenges such as occlusion and noise within the video sequence. The TTS network outperforms existing methods across cloth-changing video ReID datasets such as CCVID. For instance, under general settings, our approach exhibits a 1.1% improvement in top-1 accuracy and a corresponding 1.1% increase in mAP compared to the baseline. In cloth-changing settings, we observe a 0.2% enhancement in top-1 accuracy and a notable 1.3% increase in mAP relative to the baseline. © 2023 IEEE.

关键词： Video recording

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