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6th International Conference on Geoscience and Remote Sensing Mapping, GRSM 2024

作者： Hou, Yixuan Ge, Wenyi Fan, Shurui Lang, Liying School of Artificial Intelligence Hebei University of Technology Tianjin300401 China School of Computer Science Chengdu University of Information Technology Chengdu610225 China School of Electronic Information Engineering Hebei University of Technology Tianjin300401 China Center for Advanced Laser Technology Hebei University of Technology Tianjin300401 China Innovation and Research Institute of Hebei University of Technology in Shijiazhuang Shijiazhuang050299 China

ISBN: (纸本)9781510688018

Recently, 3D Gaussian Splatting (3DGS) has made remarkable progress in real-time rendering. It has significantly advanced realistic 3D scene rendering in various academic and industrial applications. However, 3DGS requires a substantial number of consecutive views for scene modeling and faces challenges in synthesis quality due to inputs such as sparse view input. Overfitting of the training views by 3DGS can result in artifacts and floaters in the rendered images, particularly when the number of training views is limited. To address this challenge, we introduce a novel method for training 3D models from limited data. Our approach involves continuously tracking Gaussian variations to guide the synthesis of novel view synthesis, thereby reducing local geometric overfitting and improving rendering quality without sacrificing speed. Additionally, we propose an annealing strategy to prevent the emergence of ambiguous scene representations while considering both global structure and fine texture details. Experimental results demonstrate that our method can achieve superior results in novel view synthesis with limited data. In the single-view training results compared to the baseline on the Nerf-LLFF dataset, our method exhibited an improvement of 5.40% in PSNR, 9.36% in SSIM, 8.54% in LPIPS without additional inference costs. © 2025 SPIE.

关键词： Rendering (computer graphics)

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research on Optimal Samples Selection Method for Digital Camera-Based Spectral Reflectance Estimation 11th

Research on Optimal Samples Selection Method for Digital Cam...

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11th China Academic Conference on Printing and Packaging, CACPP 2020

作者： Liang, Jinxing Chen, Jia Hu, Xinrong College of Mathematics and Computer Science Wuhan Textile University Hubei China Engineering Research Center of Hubei Province for Clothing Information Hubei China

ISBN: (纸本)9789811605024

Considering the spectral redundancy and the inconvenience of using large number of training sample in digital camera-based spectral reflectance estimation application, we proposed an optimal samples selection method. A representative simulation spectral estimation system is firstly constructed, and based on the constructed representative simulation system, the optimal sample subset is selected from the total sample set based on the minimization of spectral reflectance estimation error. The proposed optimal samples selection method is tested and compared with existing commonly used methods, result shows that the spectral reflectance estimation error tends to converge with the increasing of selected samples, and it is also outperforms the existing commonly used methods. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Digital cameras

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Distributed Active Disturbance Rejection Control of Multiple Mobile Robots for Online Workload Partition

Distributed Active Disturbance Rejection Control of Multiple...

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2023 China Automation Congress, CAC 2023

作者： Li, Xin Zhai, Chao Zhang, Hehong China University of Geosciences School of Automation Wuhan Campus Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (纸本)9798350303759

Considering the actual multi-agent coverage process, the motion trajectories are seriously affected by disturbances and noise. In this paper, a cooperative control method based on active disturbance rejection controller (ADRC) for multiple mecanum-wheeled robots (MWMR) is proposed to improve its robustness. In particular, to suppress the total disturbances during the running of each robot and reduce large computation and observation cost of high order ADRC, a reduced-order cascaded ADRC (RC-ADRC) is proposed. Moreover, distributed controller is proposed to track the expected trajectories of multiple mecanum-wheeled robots under disturbances. Compared with the proposed RC-ADRC based cooperative control scheme and PID based cooperative control one, the favorable motion trajectory tracking performance is obtained. Simulation results verify the superiority of the method. © 2023 IEEE.

关键词： Controllers

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Intrinsic ion migration-induced susceptible two-dimensional phase-transition memristor with ultralow power consumption

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science Bulletin 2025年

作者： Qin, Lanhao Yu, Yimeng Fang, Cheng Liu, Yujie Zhu, Kaichen Ouyang, Decai Liu, Shenghong Song, Bailing Zhou, Ruochen Lanza, Mario Hu, Wenhua Wu, Jinsong Li, Yuan Zhai, Tianyou State Key Laboratory of Materials Processing and Die & Mould Technology School of Materials Science and Engineering Huazhong University of Science and Technology Wuhan430074 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of Technology Wuhan430070 China Center of Hydrogen Science State Key Laboratory of Metal Matrix Composites School of Materials Science and Engineering Innovation Center for Future Materials Shanghai Jiao Tong University Shanghai200240 China Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430070 China

Two-dimensional (2D) phase-transition memristors have demonstrated transformative potential for neuromorphic computing, yet challenges like high power consumption, limited endurance, and crystal damage from external ion intercalation persist. Here, we introduce a novel 2D phase-transition memristor leveraging a paradigm-shifting mechanism by exploiting the ultrafast intrinsic Cu+ ion migration within Cu2S. This approach eliminates the need for external ion insertion, significantly reducing crystal damage and enabling exceptional cycling stability with over 400 DC cycles and 500 pulse cycles. The susceptible monoclinic-tetragonal phase-transition induced by intrinsic Cu+ migration achieves an unprecedented SET power consumption of 1 μW at 100 mV, significantly lower in currently reported phase-transition memristors. To further demonstrate the potential of intrinsic ion migration-induced (IIM) memristor, we simulated an IIM memristor crossbar array for image preprocessing in gesture recognition with a high SSIM value of 0.94, showcasing its potential for scalable neuromorphic hardware. This work establishes a new paradigm in low-power, high-performance phase-transition memristors, advancing their practical application in next-generation computing. © 2025 science China Press

关键词： Memristor Neuromorphic device Two-dimensional materials

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Efficient Lane Detection based on Feature Aggregation for advanced Driver Assistance Systems

Efficient Lane Detection based on Feature Aggregation for Ad...

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International Conference on advanced Robotics and Intelligent Systems (ARIS)

作者： Sin-Ye Jhong Ching-Hsiang Ko Yi-Feng Su Kai-Lung Hua Yung-Yao Chen Dept. of Engineering Science National Cheng Kung University Tainan Taiwan Dept. of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan Research and Development Division Automotive Research and Testing Center Changhua Taiwan Dept. of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan

As we enter the era of smart cars and electric vehicles, the significance of autonomous driving systems, especially crucial components like lane detection technology, increases dramatically. However, advanced solutions often face daunting challenges, especially under adverse environmental and lighting conditions, yielding varying detection results. This variability prompts automakers to enforce numerous restrictions on auxiliary functions, often deterring potential customers from upgrading relevant services. This study addresses these limitations and introduces a sophisticated deep learning-based lane detection model to supersede existing technologies. The model fuses an efficient feature extraction network with a Recurrent Feature-Shift Aggregator (RESA) and a Bilateral Up-Sampling Decoder (BUSD). These elements work harmoniously, enabling enhanced global information extraction and fortifying detection capability under challenging conditions. The methodology of this study is segregated into three parts: Efficient Backbone and Neck for feature extraction, Global Feature Enhancement Decoder for maximizing feature map details, and the actual implementation of a Lane Assist System. These modules collectively help us calculate high-accuracy lane coordinates, thereby overcoming the limitations of previous lane detection methods. Following the successful creation and validation of the model, the study extends the implementation to the Hexagon vehicle control platform, where the Lane Departure and Lane Keeping assistance systems are deployed. The proposed technology is validated through rigorous experiments and analyses, utilizing the highly recognized TuSimple dataset, custom-collected Taiwanese scene data, and actual road tests. Results illustrate high and consistent detection performance under diverse environmental conditions, validating the technology’s effectiveness and feasibility.

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Nacre-liked material with tough and post-tunable mechanical properties

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Journal of Materials science & Technology 2022年第19期114卷 172-179页

作者： Zhengyi Mao Mengke Huo Fucong Lyu Yongsen Zhou Yu Bu Lei Wan Lulu Pan Jie Pan Hui Liu Jian Lu Department of Mechanical Engineering City University of Hong KongTat Chee AvenueKowloonHong KongChina CityU-Shenzhen Futian Research Institute No.3Binglang RoadFutian DistrictShenzhen 518000China Department of Material Science Engineering City University of Hong KongTat Chee AvenueKowloonHong KongChina Hong Kong Branch of National Precious Metals Material Engineering Research Center City University of Hong KongHong KongChina Centre for Advanced Structural Materials City University of Hong Kong Shenzhen Research InstituteGreater Bay Joint DivisionShenyang National Laboratory for Materials ScienceShenzhen 518057China Laboratory of Nanomaterials&Nanomechanics City University of Hong KongHong KongChina

Biomaterials,often imparted time-dependent mechanical properties,which are promising in fields rang-ing from sensors to ***,a facile method was proposed to fabricate post-tunable mechanical properties composites based on hydrogels and ceramic *** wide tunable range of Young’s modulus(27.3 kPa to 3.5 GPa)and ultimate stress(173 kPa to 102 MPa)can be achieved by combining solvent absorption and evaporation process with platelets reinforcement ***,a large fracture toughness(∼32,000 J m^(-2))is obtained as a result of the nacre-liked“brick and mortar”structure introduced by shear force during *** superior flexibility and designability of this material were demonstrated via actuators,portable structure,and *** all,this study provides a new thought to fabricate tough materials with post-tunable mechanical properties.

关键词： Mechanical property Post-tunable Hydrogels Fracture toughness

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The Evolution of the Peridynamics Co-Authorship Network

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Journal of Peridynamics and Nonlocal Modeling 2023年第3期5卷 311-355页

作者： Dahal, Biraj Seleson, Pablo Trageser, Jeremy School of Mathematics Georgia Institute of Technology North Ave NW Atlanta 30332 GA United States Computer Science and Mathematics Division Oak Ridge National Laboratory P.O. Box 2008 MS-6013 Oak Ridge 37831-6013 TN United States Center for Computing Research Sandia National Laboratories P.O. Box 5800 MS-1322 Albuquerque 87185-1322 NM United States

Peridynamics is a relatively new field in continuum mechanics that has developed over the past 20 years. This paper studies the evolution of collaborations in the field of peridynamics since its inception using social network analysis. For this purpose, we construct a network for each year from 2000 to 2019 describing co-authorship between scientists in peridynamics. In these networks, each node represents a scientist and each link connects two co-authoring scientists with a link weight representing the frequency and strength of their collaboration;each network as a whole can be thought of as a graph representation of the peridynamics community for the given year. By constructing a network for each year, we are able to analyze the evolution of the network in time and discuss the implications of this evolution for the peridynamics community. Our study demonstrates that the peridynamics community has been growing exponentially in size in recent years. Centrality metrics are also used to identify the most collaborative scientists in the community. Moreover, we compute link recommendations based on both elevating a scientist’s position in the network with respect to certain centrality metrics or closing structural holes in the network identified with persistent homology. We further extend the analysis to higher-order networks whose nodes represent groups of scientists in the community and whose links connect collaborating groups. In some sense, our work studies the past, present, and future of the peridynamics community. © 2022, Springer Nature Switzerland AG.

关键词： Co-authorship Network science Peridynamics Persistent homology

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Banana Disease Recognition Using Deep Learning Based Method Combined With Attention Mechanism

Banana Disease Recognition Using Deep Learning Based Method ...

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International Conference on computer and Information Technology (ICCIT)

作者： Tahmina Hoque Norli Anida Abdullah Muhammad Asif Hasan Najiah Mohd Sadali School of Science Engineering and Technology East Delta University Chattogram Bangladesh Mathematics Division Center for Foundation Studies in Science University of Malaya Kuala Lumpur Malaysia Centre for Research in Biotechnology for Agriculture (CEBAR) Universiti Malaya Kuala Lumpur Malaysia

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

In numerous Asian countries, bananas are a key component of international trade and a significant source of foreign exchange earnings. However, the banana agriculture sector faces a substantial challenge due to disease affecting banana leaves. These diseases significantly affect the sector by reducing both the quality and quantity of the produce. To address this problem, this study proposes a convolutional neural network (CNN) model, enhanced with a spatial attention mechanism as an augmented module for identifying diseases in banana leaves. We compiled a dataset of 5,583 images, from real-life instances and the Mendeley database, divided into 13 classes representing different banana leaf diseases. During preprocessing, a deep convolutional generative adversarial network (DCGAN) was utilized to augment the image samples, thereby addressing the problem of uneven data distribution. In our comparative analysis, we assessed several well-known pre-trained models employing transfer learning and fine-tuning techniques, alongside a vision transformer model VIT32. The experimental results were promising: the proposed model achieved an accuracy of 96.37% indicating its efficacy in classifying various types of banana leaf diseases on this multiclass dataset.

关键词： Deep learning Training Attention mechanisms Accuracy Computational modeling Transfer learning Graph neural networks Data models Convolutional neural networks Diseases

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A Reinforcement Learning Based Dynamic Multi-Objective Constrained Evolutionary Algorithm for Open-Pit Mine Truck Scheduling

A Reinforcement Learning Based Dynamic Multi-Objective Const...

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2023 China Automation Congress, CAC 2023

作者： Qiu, Junxiang Li, Changhe Yang, Shengxiang School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo- Exploration Ministry of Education Wuhan430074 China School of Computer Science and Informatics De Montfort University Leicester United Kingdom

ISBN: (纸本)9798350303759

Aiming at the truck scheduling problem in the open-pit mine scenario, a truck scheduling model based on real-time ore blending is established, and an adaptive evolution algorithm for truck scheduling based on DCNSGA-III is proposed. In the established scheduling model, the real-time grade variance of the crushing plant is minimized as one of the optimization objectives, and the Q-learning algorithm is introduced to adaptively select one of the most effective operators during the search process. Experiments show that the proposed method can effectively control the grade fluctuation of the ore flow and better scheduling schemes are obtained in comparison with algorithms equipped with the traditional search operator selection methods. © 2023 IEEE.

关键词： Evolutionary algorithms

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Bayesian hypergame approach to equilibrium stability and robustness in moving target defense

Bayesian hypergame approach to equilibrium stability and rob...

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IEEE Conference on Decision and Control

作者： Hanzheng Zhang Zhaoyang Cheng Guanpu Chen Karl Henrik Johansson Shanghai Research Institute for Autonomous Intelligent Systems Tongji University Shanghai China Academy of Mathematics and System Science Chinese Academy of Science University of Chinese Academy of Science Beijing China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology and the Digital Futures Stockholm Sweden

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We investigate the equilibrium stability and robustness in a class of moving target defense problems, in which players have both incomplete information and asymmetric cognition. We first establish a Bayesian Stackelberg game model for incomplete information and then employ a hypergame reformulation to address asymmetric cognition. With the core concept of the hyper Bayesian Nash equilibrium (HBNE), a condition for achieving both the strategic and cognitive stability in equilibria can be realized by solving linear equations. Moreover, to deal with players’ underlying perturbed knowledge, we study the equilibrium robustness by presenting a condition of robust HBNE under the given configuration. Experiments evaluate our theoretical results.

关键词： Games Nash equilibrium Stability analysis Robustness Mathematical models Cognition Bayes methods

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