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Land surface modeling informed by earth observation data:toward understanding blue-green-white water fluxes in High Mountain Asia

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地球空间信息科学学报（英文版） 2024年第3期27卷 703-727页

作者： Pascal Buri Simone Fatichi Thomas E.Shaw Catriona L.Fyffe Evan S.Miles Michael J.McCarthy Marin Kneib Shaoting Ren Achille Jouberton Stefan Fugger Li Jia Jing Zhang Cong Shen Chaolei Zheng Massimo Menenti Francesca Pellicciotti Swiss Federal Institute for Forest Snow and Landscape Research WSLBirmensdorfSwitzerland Department of Civil and Environmental Engineering National University of SingaporeSingaporeSingapore Swiss Federal Institute for Forest Snow and Landscape Research WSLBirmensdorfSwitzerland Institute of Science and Technology Austria(ISTA) KlosterneuburgAustria Institute of Science and Technology Austria(ISTA) KlosterneuburgAustria Institute of Environmental Geosciences Université Grenoble AlpesGrenobleFrance Department of Atmospheric and Cryospheric Sciences University of InnsbruckInnsbruckAustria State Key Laboratory of Tibetan Plateau Earth System Environment and Resources(TPESER)Institute of Tibetan Plateau ResearchChinese Academy of SciencesBeijingChina Swiss Federal Institute for Forest Snow and Landscape Research WSLBirmensdorfSwitzerland Institute of Environmental Engineering ETH ZurichZurichSwitzerland State Key Laboratory of Remote Sensing Science Aerospace Information Research InstituteChinese Academy of SciencesBeijingChina National Space Sciences Center Chinese Academy of SciencesBeijingChina State Key Laboratory of Remote Sensing Science Aerospace Information Research InstituteChinese Academy of SciencesBeijingChina Faculty of Civil Engineering and Geosciences Delft University of TechnologyDelftThe Netherlands

Mountains are important suppliers of freshwater to downstream areas,affecting large populations in particular in High Mountain Asia(HMA).Yet,the propagation of water from HMA headwaters to downstream areas is not fully understood,as interactions in the mountain water cycle between the cryo-,hydro-and biosphere remain *** review the definition of blue and green water fluxes as liquid water that contributes to runoff at the outlet of the selected domain(blue)and water lost to the atmosphere through vapor fluxes,that is evaporation from water,ground,and interception plus transpiration(green)and propose to add the term white water to account for the(often neglected)evaporation and sublimation from snow and *** provide an assessment of models that can simulate the cryo-hydro-biosphere continuum and the interactions between spheres in high mountain catchments,going beyond disciplinary *** surface models are uniquely able to account for such complexity,since they solve the coupled fluxes of water,energy,and carbon between the land surface and *** to the mechanistic nature of such models,specific variables can be compared systematically to independent remote sensing observations-providing vital insights into model accuracy and enabling the understanding of the complex watersheds of *** discuss recent developments in spaceborne earth observation products that have the potential to support catchment modeling in high mountain *** then present a pilot study application of the mechanistic land surface model Tethys & Chloris to a glacierized watershed in the Nepalese Himalayas and discuss the use of high-resolution earth observation data to constrain the meteorological forcing uncertainty and validate model *** use these insights to highlight the remaining challenges and future opportunities that remote sensing data presents for land surface modeling in HMA.

关键词： Land surface modeling remote sensing High Mountain Asia(HMA) blue-green water cryosphere-hydrosphere-biosphere continuum snow glaciers high mountain water cycle

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Knowledge Graph Based Augmented Reality Work Instruction for Wire Harness Final Assembly on Formboard 19

Knowledge Graph Based Augmented Reality Work Instruction for...

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19th IEEE International Conference on Automation science and engineering, CASE 2023

作者： Wang, Junfeng Shi, Ting Liu, Maoding Jiang, Ke Huazhong University of Science and Technology The Department of Industrial and Manufacturing System Engineering Hubei Wuhan430074 China The Beijing Institute of Mechanical Equipment Beijing100143 China

ISBN: (纸本)9798350320695

The final assembly of wire harness on formboard is an important phase for harness development of modern mechatronic products. Process visualization based on augmented reality (AR) can superimposes the information in front of the workers' view to facilitate their work. An framework for wire harness final assembly (WHFA) based on AR visualization is proposed in this paper. The assembly process information and its visualization format are studied in this paper for wire harness final assembly. A unified description of assembly process is presented based on knowledge graph method, which provides a formal framework for consistent modeling of the wire harness data for information integration. In order to assistant the wire harness assembly tasks, different visual assets are design to deliver the required assembly information. The visual assets can help the workers to localize the object/position, understand the operating procedure and display the important digital number. A XML file which are generated from the knowledge graph is used to describe the wire harness assembly step and the corresponding AR visualization format for shop floor work instruction on formboard. An experimental AR based wire harness final assembly instruction system is developed. Examples are given to show the augmented assembly information instruction of a wire harness final assembly on formboard by using a common monitor display at a fixed workstation with a static overhead camera. © 2023 IEEE.

关键词： Augmented reality

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Using machine learning and satellite data from multiple sources to analyze mining,water management,and preservation of cultural heritage

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地球空间信息科学学报（英文版） 2024年第3期27卷 552-571页

作者： Joaquim J.Sousa Jiahui Lin Qun Wang Guang Liu Jinghui Fan Shibiao Bai Hongli Zhao Hongyu Pan Wenjing Wei Vanessa Rittlinger Peter Mayrhofer Ruth Sonnenschein Stefan Steger Luís Paulo Reis School of Science and Technology University of Trás-Os-Montes E Alto DouroVila RealPortugal Centre for Robotics in Industry and Intelligent Systems(CRIIS) INESC Technology and SciencePortoPortugal Aerospace Information Research Institute Chinese Academy of SciencesBeijingChina International Research Center of Big Data for Sustainable Development Goals BeijingChina College of Resources and Environment University of Chinese Academy of SciencesBeijingChina Application system Department Ⅰ China Siwei Surveying and Mapping Technology Co.LtdBeijingChina China Aero Geophysical Survey and Remote Sensing Center for Natural Resources BeijingChina College of Marine Science and Engineering Nanjing Normal UniversityNanjingChina Institute of Mountain Hazards and Environment Chinese Academy of SciencesChengduChina College of Marine Science and Engineering Nanjing Normal UniversityNanjingChina Institute for Earth Observation Eurac ResearchBolzanoItaly Faculty of Engineering University of PortoPortoPortugal

Remote sensing,particularly satellite-based,can play a valuable role in monitoring areas prone to *** high spatial and temporal coverage provided by satellite data can be used to reconstruct past events and continuously monitor sensitive areas for potential *** paper presents a range of techniques and methods that were applied for in-depth analysis and utilization of Earth observation data,with a particular emphasis on:(1)detecting mining sub-sidence,where a novel approach is proposed by combining an improved U-Net model and Interferometry Synthetic Aperture Radar(InSAR)*** results showed that the Efficient Channel Attention(ECA)U-Net model performed better than the U-Net(baseline)model in terms of Mean Intersection over Union(MIoU)and Intersection over Union(loU)indicators;(2)monitor-ing water conservancy and hydropower *** Xiaolangdi multipurpose dam complex was monitored using Small BAsline Subsets(SBAS)InSAR method on Sentinel-1 time series data and four small regions with high deformation rates were identified on the slope of the reservoir bank on the north *** dam body also showed obvious deformation with a velocity exceeding 60 mm/a;(3)the evaluation of the potential of InSAR results to integrate monitoring and warning systems for valuable heritage and architectural *** overall outcome of these methods showed that the use of Artificial Intelligence(AI)techniques in combination with InSAR data leads to more efficient analysis and interpretation,resulting in improved accuracy and prompt identification of potential hazards;and(4)finally,this study also presents a method for detecting landslides in mountainous regions,using optical *** new temporal landslide detection method is evaluated over a 7-year analysis period and unlike conventional bi-temporal change detection methods,this approach does not depend on any prior-knowledge and can potentially detect landslides over extended periods of time

关键词： Landslide detection InSAR time series analysis machine learning heritage preservation mining subsidence water conservancy infrastructures

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Covertvasion: Depicting threats through covert channels based novel evasive attacks in android

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International Journal of Intelligent Networks 2023年第1期4卷 337-348页

作者： Gautam, Sunil Pattani, Ketaki Zuhair, Mohd Rashid, Mamoon Ahmad, Nazir Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad Gujarat India Department of Computer Sciences and Engineering Institute of Advanced Research Gujarat Gandhinagar India School of Information Communication and Technology Bahrain Polytechnic Isa Town Bahrain Department of Information System College of Applied Sciences King Khalid University P.O. Box 61913 Muhayel63317 Saudi Arabia

Privacy and security issues concerning mobile devices have substantial consequences for individuals, groups, governments, and businesses. The Android operating system bolsters smartphone data protection by imposing restrictions on app behavior. Nevertheless, attackers conduct systematic resource analyses and divert privacy-sensitive information from plain view. They employ evasive mechanisms to evade system monitoring and create an illusion of benign and non-sensitive communication. Furthermore, covert channels amplify the impact of these malicious activities by facilitating information transfer through non-standard methods. The purpose of this research is to shed light on these novel threats targeting Android systems. The study delves into security and privacy attacks that compromise sensitive user information. The methodology leverages evasion concepts and employs sound-specific covert channel communication, particularly ultrasonic channels. This research work introduces novel evasive attacks, namely Prime-Composite Evasive Information Invasion (PCEII) and File-lock-based Evasive Information Invasion (FEII), both relying on covert channel communication. These unique variants of attacks successfully evade user data within a few milliseconds for both noisy as well as non-noisy environments and do not show any signs of detection by antivirus mechanisms like Anti-Virus Guard (AVG), 360 security, etc. and state-of-the-art tools such as TaintDroid, MockDroid and others. The paper not only assesses their impact on the privacy and security of information but also introduces avenues for their detection and mitigation. © 2023 The Authors

关键词： Android (operating system)

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A Dynamic Workload Prediction and Distribution in Cloud Computing using Deep Reinforcement Learning and LSTM

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Journal of Harbin institute of technology(New series) 2025年

作者： Nampally Vijay Kumar Satarupa Mohanty Prasant Kumar Pattnaik School of Computer Engineering KIIT Deemed to be University Department of Computer Science and Business System B V RAJU Institute of Technology

Maintaining high-quality service supply and sustainability in modern cloud computing is essential to ensuring optimal system performance and energy efficiency. A novel approach is introduced in this study to decrease a system.s overall delay and energy consumption by utilising a deep reinforcement learning(DRL)model to predict and allocate incoming workloads flexibly. The proposed methodology integrates workload prediction utilising long short-term memory(LSTM)networks with efficient load-balancing techniques led by deep Q-learning and Actor-Critic algorithms. By continuously analysing current and historical data,the model can efficiently allocate resources,prioritizing speed and energy preservation. The experimental findings demonstrate that our load balancing system,which utilises DRL,significantly reduces average response times and energy usage compared to traditional methods. This approach provides a scalable and adaptable strategy for enhancing cloud infrastructure performance. It consistently provides reliable and durable performance across a range of dynamic workloads.

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Development and Control of Quadrotors Using Constrained Model Predictive Control in a Co-Simulation Environment

Development and Control of Quadrotors Using Constrained Mode...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Long, Sian Zhao, Na Qin, Chaojun Luo, Yudong He, Zhipeng Shen, Yantao Dalian Maritime University Department of Computer Science and Technology Liaoning Dalian116026 China School of Mechatronical Engineering Beijing Institute of Technology Beijing100081 China University of Nevada Department of Electrical and Biomedical Engineering RenoNV89557 United States

ISBN: (纸本)9781665481090

Model Predictive Control (MPC) is commonly used to solve flight control problems in quadrotors due to its ability to handle multivariate and practical constraints. Considering its computational problem, which may lead to untimely control signal updates and thus affect flight stability, we adopt cascade control, where an MPC controller is used for attitude control in the inner loop, and a PD controller is used for position control in the outer loop to reduce the computational load. In the inner loop, physical constraints are considered, and an augmented MPC is constructed to reduce the steady-state error. Finally, we build a co-simulation environment based on Matlab and Coppeliasim to verify the proposed control scheme. © 2024 IEEE.

关键词： Predictive control systems

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Quantum Remote State Preparation Based on Quantum Network Coding

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computers, Materials & Continua 2022年第10期73卷 119-132页

作者： Zhen-Zhen Li Zi-Chen Li Yi-Ru Sun Haseeb Ahmad Gang Xu Xiu-Bo Chen School of Information Engineering Beijing Institute of Graphic CommunicationBeijing102600China School of Computer Science and Information Engineering Zhejiang Gongshang UniversityHangzhou310018China Information Security Center State key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China Department of Computer Science National Textile UniversityFaisalabad37610Pakistan School of Information Science and Technology North China University of TechnologyBeijing100144China Advanced Cryptography and System Security Key Laboratory of Sichuan Province China

As an innovative theory and technology,quantum network coding has become the research hotspot in quantum network *** this paper,a quantum remote state preparation scheme based on quantum network coding is *** with the general quantum remote state preparation schemes,our proposed scheme brings an arbitrary unknown quantum state finally prepared remotely through the quantum network,by designing the appropriate encoding and decoding steps for quantum network *** is worth mentioning,from the network model,this scheme is built on the quantum k-pair network which is the expansion of the typical bottleneck network—butterfly ***,it can be treated as an efficient quantum network preparation scheme due to the characteristics of network coding,and it also makes the proposed scheme more applicable to the large-scale quantum *** addition,the fact of an arbitrary unknown quantum state remotely prepared means that the senders do not need to know the desired quantum ***,the security of the proposed scheme is ***,this scheme can always achieve the success probability of 1 and 1-max flow of value ***,the communication efficiency of the proposed scheme is ***,the proposed scheme turns out to be practicable,secure and efficient,which helps to effectively enrich the theory of quantum remote state preparation.

关键词： Quantum remote state preparation quantum network coding quantum k-pair network

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A Generalized ChatGPT-Based Collaborative Multi-Objective Decision-Making Framework for Robust Vehicle Platoon Collision Avoidance

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IEEE Transactions on Vehicular technology 2025年第5期74卷 7212-7225页

作者： Zhang, Hanwen Hou, Chenfei Chen, Jicheng Zhang, Hui Wang, Fei-Yue Beihang University Hangzhou International Innovation Institute Hangzhou311115 China Beihang University School of Transportation Science and Engineering Beijing100191 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong999077 Hong Kong University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100190 China Obuda University Physiological Controls Research Center University Research and Innovation Center Budapest1034 Hungary Macau University of Science and Technology Macau Institute of Systems Engineering China

In this work, we propose a ChatGPT-based framework that collaborates with human expert instructions for safe and efficient vehicle platoon control. The collaborative framework integrates multi-objective decision-making to address challenges such as robust collision avoidance in complex traffic environments. To incorporate human-like reasoning, ChatGPT is firstly used to interpret human expert driving instructions, which often involve linguistic variables. It generates behavioral decisions and computes feasible trajectories for each vehicle to ensure driving safety. A distributed model predictive controller is then utilized for accurate, real-time path tracking. To efficiently handle ChatGPT response failures, a receding instruction utilization strategy is proposed. For critical response failures, hindsight experience replay is used to supplement ChatGPT's knowledge database and improve response accuracy in complex scenarios. Simulation tests and experimental validations demonstrate the framework's effectiveness and generalizability across various traffic conditions, highlighting its potential for broader applications in platoon control. © 1967-2012 IEEE.

关键词： Reinforcement learning

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RPViT: Vision Transformer Based on Region Proposal 2022

RPViT: Vision Transformer Based on Region Proposal

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5th International Conference on Image and Graphics Processing, ICIGP 2022

作者： Ge, Jing Wang, Qianxiang Tong, Jiahui Gao, Guangyu School of Computer Science and Technology Beijing Institute of Technology China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Beijing Electro-Mechanical Engineering Institute Beijing China

ISBN: (纸本)9781450395465

Vision Transformers constantly absorb the characteristics of convolutional neural networks to solve its shortcomings in translational invariance and scale invariance. However, dividing the image by a simple grid often destroys the position and scale features in the image at the beginning of the network. In this paper, we propose a vision transformer based on region proposal, which obtains the inductive bias in a simple way. Specifically, RPViT achieves locality and scale-invariance by extracting regions with locality using a traditional region proposal algorithm and deflating objects of different scales to the same scale by a bilinear interpolation algorithm. In addition, to enable the network to fully utilize and encode diverse candidate objects, a multi-class token approach based on orthogonalization is proposed and applied. Experiments on ImageNet demonstrate that RPViT outperforms baseline converters and related work. © 2022 ACM.

关键词： Convolutional neural networks

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Trusted Polarimetric Feature Fusion for Polsar Image Classification

Trusted Polarimetric Feature Fusion for Polsar Image Classif...

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2023 SAR in Big Data Era, BIGSARDATA 2023

作者： Han, Fangzhou Zhang, Lamei Dong, Hongwei Harbin Institute of Technology Department of Information Engineering Harbin150001 China Institute of Software Chinese Academy of Sciences Science & Technology on Integrated Information System Laboratory Beijing China

ISBN: (纸本)9798350326239

Benefiting from the rapid advancements in Deep Learning (DL), data-driven features exhibit remarkable separability in PolSAR image classification. However, data-driven features are challenging to interpret and susceptible to resolution and noise. On the contrary, polarimetric features based on the target decomposition not only have high robustness, but also has physical interpretability. Consequently, researchers have begun to explore ways to integrate both types of features to achieve enhanced performance, which typically concat the data-driven features and polarimetric features directly. However, polarimetric features are not stable in separability, which is because the inherent defects of the target decomposition algorithm. To mitigate the adverse effects of low-quality features, this paper proposes a trusted polarimetric feature fusion (TPFF). The new paradigm calculates the confidence of features and employs a novel feature fusion method. The proposed method has been tested on the EMISAR Foulum dataset, with experimental results demonstrating improved classification performance and robustness. © 2023 IEEE.

关键词： Image classification

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