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Sensitivity Analysis of Petrophysical Parameters for Estimating Hydrate Saturation in the Shenhu Area

Applied Geophysics 2020年第5期17卷 649-659,899,900页

作者： Liu Xue-Qin Liu Huai-Shan Xing Lei Qin Zhi-Liang Ma Ben-Jun Wang Min Acoustic Science and Technology Laboratory Harbin Engineering UniversityHarbin 150001China Key Laboratory of Marine Information Acquisition and Security(Harbin Engineering University) Ministry of Industry and Information TechnologyHarbin 150001China College of Underwater Acoustic Engineering Harbin Engineering UniversityHarbin 150001China Key Lab of Submarine Geosciences and Prospecting Techniques MOE and College of Marine GeosciencesOcean University of ChinaQingdao 266100P.R China Evaluation and Detection Technology Laboratory of Marine Mineral Resources Qingdao National Laboratory for Marine Science and TechnologyQingdao 266061China Tianjin Sino-German University of Applied Sciences School of Intelligent ManufacturingTianjin300350

Acoustic and electrical methods are commonly used to evaluate hydrate saturation based on P-wave velocity(Vp)and resistivity,*** evaluate hydrate saturation using petrophysical parameters directly related to the presence of *** petrophysical parameters sensitive to hydrate saturation were first analyzed using the equivalent medium rock physical model,logging intersection plots,and petrophysical parameter *** simulated annealing global optimization method was then used to estimate the hydrate saturation profile in the Shenhu Area,*** petrophysical parameters Vp,λρ,andλμ,which are associated with the rock elastic and shear moduli,are highly sensitive to hydrate saturation for an estimated saturation range of *** range is consistent with that obtained from the original well diameter ***,the parameters Vs andμρ,which are only related to the rock shear modulus,yield high hydrate saturation estimates of 0.22-0.43 and exhibit some deviations from the real-time *** to its sensitivity,the Poisson’s ratio is least desired for hydrate evaluation among the studied *** sensitivity of hydrate saturation depends on the petrophysical model used for studying hydrate physical properties and storage analysis.

关键词： natural gas hydrate saturation sensitivity parameters petrophysical properties

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Service chain caching and task offloading in two-tier UAV-assisted vehicular edge computing networks: an Attention-DRL method

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Digital Communications and Networks 2024年

作者： Fengyan Wu Chao Yang Yanqun Tang Zhen Li Shengli Xie School of Automation Guangdong University of Technology and Guangdong Key Laboratory of IoT Information Technology Guangzhou 510006 China The Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing and the Joint International Research Laboratory of Intelligent Information Processing and System Integration of IoT Guangzhou China School of Electronics and Communications Sun Yat-Sen University Shenzhen 518107 China Department of Mathematics and Information Technology The Education University of Hong Kong HongKong China

Unmanned Aerial Vehicle (UAV)-assisted Vehicular Edge Computing Networks (VECNs) have emerged as a promising solution to enhance service quality for ground vehicle users. However, the growing demands from users and the limited computing and storage resources of UAVs present significant challenges in designing an efficient edge service caching scheme to minimize latency. Moreover, the integration of service caching and task offloading complicates the support of complex tasks by a single UAV. To address these challenges, this paper proposes a novel two-tier UAV-assisted VECNs framework. In this framework, multi-rotor UAVs function as hovering nodes for computational offloading, while a fixed-wing UAV serves as a mobile auxiliary cloud platform, forming a cohesive UAV group. User tasks are structured into a task chain based on the available UAVs. We integrate a joint service chain caching and task offloading scheme that considers UAV computing and storage capacities, duplicate caching, and dynamic transmission latency. To optimize task chain completion latency, we propose an Attention-based Multi-Agent Deep Q-Network (A-MADQN) algorithm. This algorithm incorporates an attention mechanism to narrow the UAV selection space, enabling the selected UAVs to collaboratively make caching and task offloading decisions. Numerical results demonstrate that the proposed algorithm significantly enhances system processing efficiency and reduces task completion latency compared to the benchmark approaches.

关键词： Service caching Task chain Multi-UAV Attention mechanism Multi-agent DQN

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Corrigendum to “MEC-Driven Fast Deformation Monitoring Based on GNSS Signal”

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Wireless Communications and Mobile Computing 2021年第1期2021卷

作者： Bo Li Shangwei Chen Yi Liu Kan Xie Shengli Xie School of Automation Guangdong University of Technology Guangzhou *** Guangdong Key Laboratory of IoT Information Technology (GDUT) Guangzhou China Key Laboratory of Intelligent Detection and the Internet of Things in Manufacturing Ministry of Education (GDUT) Guangzhou *** Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing (GDUT) Guangzhou China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology (GDUT) Guangzhou China

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IR2Net: Information Restriction and Information Recovery for Accurate Binary Neural Networks

arXiv

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arXiv 2022年

作者： Xue, Ping Lu, Yang Chang, Jingfei Wei, Xing Wei, Zhen The School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China The School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China The Anhui Mine IOT and Security Monitoring Technology Key Laboratory Hefei230088 China The Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of Education Hefei University of Technology Hefei230009 China The Intelligent Manufacturing Institute of HeFei University of Technology Hefei230009 China

Weight and activation binarization can efficiently compress deep neural networks and accelerate model inference, but cause severe accuracy degradation. Existing optimization methods for binary neural networks (BNNs) focus on fitting full-precision networks to reduce quantization errors, and suffer from the trade-off between accuracy and computational complexity. In contrast, considering the limited learning ability and information loss caused by the limited representational capability of BNNs, we propose IR2Net to stimulate the potential of BNNs and improve the network accuracy by restricting the input information and recovering the feature information, including: 1) information restriction: for a BNN, by evaluating the learning ability on the input information, discarding some of the information it cannot focus on, and limiting the amount of input information to match its learning ability;2) information recovery: due to the information loss in forward propagation, the output feature information of the network is not enough to support accurate classification. By selecting some shallow feature maps with richer information, and fusing them with the final feature maps to recover the feature information. In addition, the computational cost is reduced by streamlining the information recovery method to strike a better trade-off between accuracy and efficiency. Experimental results demonstrate that our approach still achieves comparable accuracy even with ∼10x floating-point operations (FLOPs) reduction for ResNet-18. The models and code are available at https://***/pingxue-hfut/IR2Net. Copyright © 2022, The Authors. All rights reserved.

关键词： Recovery

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Diffusion models demand contrastive guidance for adversarial purification to advance 24

Diffusion models demand contrastive guidance for adversarial...

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Proceedings of the 41st International Conference on Machine Learning

作者： Mingyuan Bai Wei Huang Tenghui Li Andong Wang Junbin Gao Cesare F Caiafa Qibin Zhao Tensor Learning Team Center of Advanced Intelligence Project RIKEN Tokyo JAPAN Deep Learning Theory Team Center of Advanced Intelligence Project RIKEN Tokyo JAPAN School of Automation Guangdong University of Technology Guangzhou CHINA and Tensor Learning Team Center of Advanced Intelligence Project RIKEN Tokyo JAPAN and Key Laboratory of Intelligent Detection and the Internet of Things in Manufacturing Ministry of Education Guangzhou CHINA Discipline of Business Analytics The University of Sydney Business School The University of Sydney Darlington NSW AUSTRALIA Tensor Learning Team Center of Advanced Intelligence Project RIKEN Tokyo JAPAN and Instituto Argentino de Radioastronomìa CONICET CCT La Plata/CIC-PBA/UNLP V. Elisa ARGENTINA

In adversarial defense, adversarial purification can be viewed as a special generation task with the purpose to remove adversarial attacks and diffusion models excel in adversarial purification for their strong generative power. With different predetermined generation requirements, various types of guidance have been proposed, but few of them focuses on adversarial purification. In this work, we propose to guide diffusion models for adversarial purification using contrastive guidance. We theoretically derive the proper noise level added in the forward process diffusion models for adversarial purification from a feature learning perspective. For the reverse process, it is implied that the role of contrastive loss guidance is to facilitate the evolution towards the signal direction. From the theoretical findings and implications, we design the forward process with the proper amount of Gaussian noise added and the reverse process with the gradient of contrastive loss as the guidance of diffusion models for adversarial purification. Empirically, extensive experiments on CIFAR-10, CIFAR-100, the German Traffic Sign Recognition Benchmark and ImageNet datasets with ResNet and WideResNet classifiers show that our method outperforms most of current adversarial training and adversarial purification methods by a large improvement.

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Integration of Mixture of Experts and Multimodal Generative AI in Internet of Vehicles: A Survey

arXiv

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arXiv 2024年

作者： Xu, Minrui Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Fang, Yuguang Kim, Dong In Shen, Xuemin The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia Department of Computer Science City University of Hong Kong Kowloon Hong Kong The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Generative AI (GAI) can enhance the cognitive, reasoning, and planning capabilities of intelligent modules in the Internet of Vehicles (IoV) by synthesizing augmented datasets, completing sensor data, and making sequential decisions. In addition, the mixture of experts (MoE) can enable the distributed and collaborative execution of AI models without performance degradation between connected vehicles. In this survey, we explore the integration of MoE and GAI to enable Artificial General Intelligence in IoV, which can enable the realization of full autonomy for IoV with minimal human supervision and applicability in a wide range of mobility scenarios, including environment monitoring, traffic management, and autonomous driving. In particular, we present the fundamentals of GAI, MoE, and their interplay applications in IoV. Furthermore, we discuss the potential integration of MoE and GAI in IoV, including distributed perception and monitoring, collaborative decision-making and planning, and generative modeling and simulation. Finally, we present several potential research directions for facilitating the integration. © 2024, CC BY.

关键词： Integration

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Effect of the tilt on the three-dimensional quantum Hall effect in tilted Weyl semimetals

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Physica E: Low-dimensional Systems and Nanostructures 2025年 173卷

作者： Mingqi Chang Rong Ma Li Sheng School of Intelligent Manufacturing Nanjing University of Science and Technology Nanjing Jiangsu 210094 China Jiangsu Key Laboratory for Optoelectronic Detection of Atmosphere and Ocean Nanjing University of Information Science and Technology Nanjing 210044 China National Laboratory of Solid State Microstructures and Department of Physics Nanjing University Nanjing 210093 China Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing 210093 China

Three-dimensional (3D) quantum Hall effect (QHE) in Weyl semimetal has attracted intense interests recent years. In this paper, we theoretically investigate the 3D QHE in tilted Weyl semimetals from bulk states for magnetic field parallel to the separation of Weyl nodes. According to the correspondence between bulk Chern number and Hall conductivity, we analytically calculate the Hall conductivity as a function of Fermi energy and the tilted terms in a thin film of a tilted Weyl semimetal using periodic boundary condition. The width of the Hall plateaus as a function of Fermi energy is influenced by the tilted terms and the energy gap between the n = − 1 and n = 1 Landau levels (LLs). When the two Weyl nodes tilt to the same direction and tilt away from each others, the plateau width decreases as a quadratic function and linear function respectively as the tilt increases. When the two Weyl nodes tilt towards to each other, the plateau width increases as a linear function. However, the width is limited by the energy gap between the n = − 1 and n = 1 LLs which decreases as the tilt increases. We also numerically calculate the Hall conductivity using open boundary condition and the numerical results show well consistent with the analytical results. Our study can be significant for understanding the effects of tilt on the 3D QHE in tilted Weyl semimetals.

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Structural Design and Simulation Studies of ADRC for High-order Nonlinear Systems

Structural Design and Simulation Studies of ADRC for High-or...

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第三十八届中国控制会议

作者： Yachao Liu Jian Gao Lanyu Zhang Xiaoliang Wang State Key Laboratory of Precision Electronic Manufacturing Technology & Equipment Guangdong University of Technology Key Laboratory of Intelligent Inspection and Manufacturing IoT of Ministry of Education Guangdong University of Technology

In the controller design of high-order nonlinear systems, it is beneficial to possess high-order differential information of input and output signals. In this paper, an N-order differentiator(NOD) method is proposed and designed to obtain the differential information of each order of the reference signal, which is based on the idea of a control loop and only needs to adjust one parameter. Then two high-order active disturbance rejection control(ADRC) structures are designed for the control of high-order nonlinear systems. One is ADRC with high-order observer(NOD-HOADRC) that directly designs the high-order extended state observer(HOESO), and the other is ADRC with cascade observes(NOD-SOADRC) that designs several extended state observers in series(SOESO). Numerical simulations verify the effectiveness of the two control structures. The simulation results show that the NOD-HOADRC can achieve no static difference control, and the dynamic performance is obviously stronger than the PID controller. The NOD-SOADRC has extremely strong tracking performance and anti-interference ability by widening the observer bandwidth.

关键词： ADRC ESO N-order differentiator High-order system

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PA²Net: Period-Aware Attention Network for Robust Fetal ECG detection

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IEEE Transactions on Instrumentation and Measurement 2022年 71卷

作者： Xu Wang Zhaoshui He Zhijie Lin Yang Han Taiheng Liu Jun Lu Shengli Xie School of Automation and the Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangdong University of Technology Guangzhou China School of Automation and the Key Laboratory for IoT Intelligent Information Processing and System Integration of Ministry of Education Guangdong University of Technology Guangzhou China School of Automation and the Guangdong Key Laboratory of IoT Information Technology Guangdong University of Technology Guangzhou China Department of Obstetrics The First Affiliated Hospital Sun Yat-sen University Guangzhou China School of Automation Guangdong University of Technology Guangzhou China

The noninvasive fetal ECG (FECG) is used to monitor fetal well-being at prenatal and intrapartum. However, it is challenging to detect the FECG signal from the abdominal electrocardiogram (ECG) due to the following problems: 1) the FECG signal is weak and often masked by noise and 2) the FECG signal is mixed or overlapped with the maternal ECG (MECG) signal. To solve such problems, a period-aware attention network (PA 2 Net) is proposed for FECG detection by designing three modules, where an FECG period-aware attention module (FPAM), which is designed to suppress the noise interference by modeling the periods and features of signals, is first employed to detect FECG signals masked in noise; next, an MECG period-aware attention module, which is generated from the FPAM by the KL-divergence-based weight sharing module, can collaborate with the FPAM to search for mixed ECG signals of FECG and MECG. Finally, an antialiasing signal separation module is developed to estimate FECG signals from mixed ECG signals. Experiments conducted on the benchmarks show that the proposed PA 2 Net can achieve excellent performance with the PPV of 98.83%, 99.74%, 99.67%, and 98.47% on PCDB, ADFECGDB, NIFECGDB, and LHDB databases, respectively, allowing obstetricians to use PA 2 Net to monitor fetal health and diagnose diseases.

关键词： Electrocardiography Feature extraction Indexes Monitoring Compounds Biomedical monitoring Wavelet transforms

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The Age of Generative AI and AI-Generated Everything

arXiv

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arXiv 2023年

作者： Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Zhang, Ping Cui, Shuguang Shen, Xuemin Mao, Shiwen Han, Zhu Jamalipour, Abbas Vincent Poor, H. Kim, Dong In School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Generative AI (GAI) has emerged as a significant advancement in artificial intelligence, renowned for its language and image generation capabilities. This paper presents "AIGenerated Everything" (AIGX), a concept that extends GAI beyond mere content creation to real-time adaptation and control across diverse technological domains. In networking, AIGX collaborates closely with physical, data link, network, and application layers to enhance real-time network management that responds to various system and service settings as well as application and user requirements. Networks, in return, serve as crucial components in further AIGX capability optimization through the AIGX lifecycle, i.e., data collection, distributed pre-training, and rapid decision-making, thereby establishing a mutually enhancing interplay. Moreover, we offer an in-depth case study focused on power allocation to illustrate the interdependence between AIGX and networking systems. Through this exploration, the article analyzes the significant role of GAI for networking, clarifies the ways networks augment AIGX functionalities, and underscores the virtuous interactive cycle they form. This article paves the way for subsequent future research aimed at fully unlocking the potential of GAI and networks. © 2023, CC BY.

关键词： Decision making

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