检索结果-内蒙古大学图书馆

A peer-to-peer blockchain-based architecture for trusted and reliable agricultural product traceability

学校读者我要写书评

暂无评论

Decision Analytics Journal 2023年 9卷

作者： Hasan, A.S.M. Touhidul Sabah, Shabnam Daria, Apubra Haque, Rakib Ul Division of Computing Analytics and Mathematics School of Science and Engineering University of Missouri-Kansas City Kansas City 64110 MO United States Institute of Automation Research and Engineering Dhaka 1205 Bangladesh Department of Electrical and Computer Engineering University of Texas at San Antonio One UTSA Circle San Antonio 78249 TX United States

The lack of new and advanced technologies, such as the Internet of Things (IoT) and Fog computing, makes it challenging to trace the origin of agricultural products in supply chains. In addition, the system's traditional centralized architecture cannot provide a trusted traceability service for farming goods in these supply chains. This paper proposes a peer-to-peer Blockchain-based architecture integrating self-sovereign identity (SSI) and a decentralized key management system (DKMS) for a trusted and reliable traceability service for agricultural food products in the supply chain networks. The public Blockchain network Fantom will provide faster and cheaper transactions. In contrast, SSI will ensure the identity of each entity by adopting DKMS technology on top of Blockchain to facilitate faster authenticity, integrity, and confidentiality service of the supply chain's transaction and verification process. The system is deployed on the different public networks, i.e., Fantom and Ethereum, and shows that the proposed Fantom-based Blockchain network outperforms in terms of transaction and verification timing. © 2023 The Author(s)

关键词： Blockchain Fantom network Self-sovereign identity Supply chain Traceability

Quantum-Inspired Machine Learning for Molecular Docking

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Shu, Runqiu Liu, Bowen Xiong, Zhaoping Cui, Xiaopeng Li, Yunting Cui, Wei Yung, Man-Hong Qiao, Nan School of Automation Science and Engineering South China University of Technology Guangzhou510641 China Central Research Institute 2012 Labs Huawei Technologies China Laboratory of AI for Science Huawei Cloud Computing Technologies Co. Ltd Guizhou550025 China State Key Laboratory of Surface Physics Department of Physics Fudan University Shanghai200433 China Shenzhen Institute for Quantum Science and Engineering China

Molecular docking is an important tool for structure-based drug design, accelerating the efficiency of drug development. Complex and dynamic binding processes between proteins and small molecules require searching and sampling over a wide spatial range. Traditional docking by searching for possible binding sites and conformations is computationally complex and results poorly under blind docking. Quantum-inspired algorithms combining quantum properties and annealing show great advantages in solving combinatorial optimization problems. Inspired by this, we achieve an improved in blind docking by using quantum-inspired combined with gradients learned by deep learning in the encoded molecular space. Numerical simulation shows that our method outperforms traditional docking algorithms and deep learning-based algorithms over 10%. Compared to the current state-of-the-art deep learning-based docking algorithm DiffDock, the success rate of Top-1 (RMSD Copyright © 2024, The Authors. All rights reserved.

关键词： Binding sites

Multiobjective Sequential Transfer Optimization: Benchmark Problems and Preliminary Results

学校读者我要写书评

暂无评论

Multiobjective Sequential Transfer Optimization: Benchmark P...

Congress on Evolutionary Computation

作者： Xiaoming Xue Liang Feng Cuie Yang Songbai Liu Linqi Song Kay Chen Tan Department of Computer Science City University of Hong Kong Hong Kong SAR China College of Computer Science Chongqing University Chongqing China State Key Lab of Synthetic Automation for Process Industries Northeastern University Shenyang China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computing The Hong Kong Polytechnic University Hong Kong SAR China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

In cases of frequent problem-solving of multiobjective optimization tasks from a domain due to changing conditions or problem features, a growing number of individual tasks will be solved and stored in a database, providing an opportunity for a target task at hand to achieve better optimization performance through knowledge transfer from the previously-solved tasks, which is also known as sequential transfer optimization. Despite a variety of transfer algorithms that have been developed over the years, the research on the design of benchmark problems for evaluating such algorithms received far less attention. Oftentimes, the source and target tasks in existing test problems are manually assembled or extended from specific practical problems, limiting their ability to represent the diverse yet complex source-target similarity relationships in real-world problems. In light of this, we propose design methods to generate multiobjective sequential transfer optimization problems (MSTOPs) systematically in this work, wherein the Pareto manifolds of individual tasks and the manifold-based similarity between the tasks can be customized with ease, enabling a broad spectrum of representation of the diverse similarity relationships between the source-target Pareto manifolds of MSTOPs. Lastly, a benchmark suite with 12 test problems is developed using the proposed methods, which would serve as an arena for electing superior multiobjective sequential transfer optimization algorithms. The source code is available at https://***/XmingHsueh/MSTOP.

关键词： Manifolds Uncertainty Limiting Shape Design methodology Source coding Benchmark testing

Analysis of Artifactual Components Rejection Threshold towards Enhanced Characterization of Neural Activity in Post-Stroke Survivor

学校读者我要写书评

暂无评论

Analysis of Artifactual Components Rejection Threshold towar...

Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Mojisola Grace Asogbon Yaping Huai Oluwarotimi Williams Samuel Zhengxiang Jing Yixin Ma Jingyu Liu Yibo Jiang Yunfa Fu Guanglin Li Yongcheng Li CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology (SIAT) Chinese Academy of Sciences (CAS) Shenzhen Guangdong China Department of Rehabilitation Medicine Shenzhen Longhua District Central Hospital Shenzhen Guangdong China School of Computing and Engineering University of Derby Derby United Kingdom Department of Information Engineering and Automation Kunming University of Science and Technology Yunnan China

Research advancement has spurred the usage of electroencephalography (EEG)-based neural oscillatory rhythms as a biomarker to complement clinical rehabilitation strategies for the recovery of motor functions in stroke survivors. However, the inevitable contamination of EEG signals with artifacts from various sources limits its utilization and effectiveness. Thus, the integration of Independent Component Analysis (ICA) and Independent Component Label (ICLabel) has been widely employed to separate neural activity from artifacts. A crucial step in the ICLabel preprocessing pipeline is the artifactual ICs rejection threshold (TH) parameter, which determines the overall signal's quality. For instance, selecting a high TH will cause many ICs to be rejected, thereby leading to signal over-cleaning, and choosing a low TH may result in under-cleaning of the signal. Toward determining the optimal TH parameter, this study investigates the effect of six different TH groups (NO-TH and TH1-TH6) on EEG signals recorded from post-stroke patients who performed four distinct motor imagery (MI) tasks including wrist and grasping movements. Utilizing the EEG-beta band signal at the brain's sensorimotor cortex, the performance of the TH groups was evaluated using three notable EEG quantifiers. Overall, the obtained result shows that the considered THs will significantly alter neural oscillatory patterns. Comparing the performance of the TH-groups, TH-3 with a confidence level of 60% showed consistently stronger signal desynchronization and lateralization. The correlation result shows that most of the electrode pairs with high correlation values are replicable across all the MI tasks. It also revealed that brain activity correlates linearly with distance, and a strong correlation between electrode pairs is independent of the different brain cortices. The study outcome may facilitate adequate therapeutic intervention for stroke *** Relevance: This study indicated that optimal s

关键词：

Towards Energy-Efficiency: Integrating MATD3 Reinforcement Learning Method for Computational Offloading in RIS-Aided UAV-MEC Environments

学校读者我要写书评

暂无评论

IEEE Internet of Things Journal 2025年

作者： Wu, Liangshun Zhang, Cong Zhang, Bin Du, Jianbo Qu, Junsuo Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai201804 China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai201210 China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China Xi'an Key Laboratory of Advanced Control and Intelligent Process School of Automation Xi'an University of Posts & Telecommunications Xi'an710061 China Xinjiang University of Political Science and Law Tumushuke832003 China School of Remote Sensing and Information Engineering Wuhan University Wuhan430079 China Department of Computer City University of Hong Kong 999077 Hong Kong School of Communications and Information Engineering Xi'an University of Posts & Telecommunications Xi'an710061 China

With the proliferation of IoT devices, there is an escalating demand for enhanced computing and communication capabilities. Mobile Edge computing (MEC) addresses this need by relocating computing resources to the network edge, thereby delivering swifter and more efficient services. This paper introduces a computation offloading and energy consumption optimization framework that leverages Reconfigurable Intelligent Surfaces (RIS), Unmanned Aerial Vehicles (UAVs), and MEC. The scheme aims to maximize energy efficiency through the optimization of task allocation, RIS phase shifts, and UAV trajectories. By employing the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) reinforcement learning algorithm, the paper further refines UAV trajectories and RIS configurations. The simulation results indicate that the proposed method surpasses the traditional Concave-Convex Procedure (CCCP) algorithm in both UAV trajectory control and RIS configuration, demonstrating quicker convergence and enhanced stability. The method proves to be adaptable to diverse environments and tasks, showcasing notable benefits in RIS-assisted interference suppression, particularly with large RIS, thereby enhancing UAV data reception rates. Additionally, MATD3 exhibits faster and smoother convergence for extended task durations and smaller RIS scenarios. Simulation results reveal that UAVs tend to move closer to RIS, with energy efficiency falling as IoT tasks increase, affirming the proposed algorithm's high energy efficiency and effectiveness. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

Wireless Hallucination in Generative AI-enabled Communications: Concepts, Issues, and Solutions

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Wang, Xudong Wang, Jiacheng Feng, Lei Niyato, Dusit Zhang, Ruichen Kang, Jiawen Xiong, Zehui Du, Hongyang Mao, Shiwen State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China College of Computing and Data Science Nanyang Technological University Singapore School of Automation Guangdong University of Technology Guangzhou510006 China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong Department of Electrical and Computer Engineering Auburn University Auburn United States

Generative AI (GenAI) is driving the intelligence of wireless communications. Due to data limitations, random generation, and dynamic environments, GenAI may generate channel information or optimization strategies that violate physical laws or deviate from actual real-world requirements. We refer to this phenomenon as wireless hallucination, which results in invalid channel information, spectrum wastage, and low communication reliability but remains underexplored. To address this gap, this article provides a comprehensive concept of wireless hallucinations in GenAI-driven communications, focusing on hallucination mitigation. Specifically, we first introduce the fundamental, analyze its causes based on the GenAI workflow, and propose mitigation solutions at the data, model, and post-generation levels. Then, we systematically examines representative hallucination scenarios in GenAI-enabled communications and their corresponding solutions. Finally, we propose a novel integrated mitigation solution for GenAI-based channel estimation. At the data level, we establish a channel estimation hallucination dataset and employ generative adversarial networks (GANs)-based data augmentation. Additionally, we incorporate attention mechanisms and large language models (LLMs) to enhance both training and inference performance. Experimental results demonstrate that the proposed hybrid solutions reduce the normalized mean square error (NMSE) by 0.19, effectively reducing wireless hallucinations. Copyright © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks

Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Sun, Geng Xiao, Jian Li, Jiahui Wang, Jiacheng Kang, Jiawen Niyato, Dusit Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China affiliated with the College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Automation Guangdong University of Technology Guangzhou510641 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

Unmanned aerial vehicles (UAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications. However, the limited onboard energy and antenna power of a UAV restrict its communication range and transmission capability. To address these limitations, this work employs collaborative beamforming through a UAV-enabled virtual antenna array to improve transmission performance from the UAV to terrestrial mobile users, under interference from non-associated BSs and dynamic channel conditions. Specifically, we introduce a memory-based random walk model to more accurately depict the mobility patterns of terrestrial mobile users. Following this, we formulate a multi-objective optimization problem (MOP) focused on maximizing the transmission rate while minimizing the flight energy consumption of the UAV swarm. Given the NP-hard nature of the formulated MOP and the highly dynamic environment, we transform this problem into a multi-objective Markov decision process and propose an improved evolutionary multi-objective reinforcement learning algorithm. Specifically, this algorithm introduces an evolutionary learning approach to obtain the approximate Pareto set for the formulated MOP. Moreover, the algorithm incorporates a long short-term memory network and hyper-sphere-based task selection method to discern the movement patterns of terrestrial mobile users and improve the diversity of the obtained Pareto set. Simulation results demonstrate that the proposed method effectively generates a diverse range of non-dominated policies and outperforms existing methods. Additional simulations demonstrate the scalability and robustness of the proposed CB-based method under different system parameters and various unexpected circumstances. © 2025, CC0.

关键词： Unmanned aerial vehicles (UAV)

An Optimized Federation Model for Park-Level Integrated Energy Systems in Industrial Internet of Things

学校读者我要写书评

暂无评论

IEEE Internet of Things Journal 2024年

作者： Bai, Xingzhen Yang, Junxiang Yuan, Xiyao Zhou, Weiwei Liu, Lu Howlader, S M Ruhul Kabir Shandong University of Science and Echnology School of Electrical and Automation Engineering Qingdao266590 China State Grid Intelligence Technology Co. Ltd Jinan China Shandong Yingcai University School of Business Jinan250104 China University of Exeter Department of Computer Science ExeterEX4 4RN United Kingdom University of Leicester School of Computing and Mathematical Sciences Leicester United Kingdom

In order to achieve energy consumption optimization and decarbonization in the Industrial Internet of Things, this paper establishes a novel framework for optimal scheduling of park-level integrated energy systems. This framework incorporates carbon capture technologies alongside a multi-energy joint supply subsystem model featuring hydrogen storage, complemented by a structured stepped carbon trading mechanism. Furthermore, a novel optimized federation model is developed to balance low carbon emissions and economic performance by federating the carbon capture system into the tiered carbon trading mechanism. The primary objective of this model in the context of the Industrial Internet of Things is to minimize energy procurement costs, wind abandonment costs, and carbon trading expenses while maximizing revenues from carbon dioxide sales. The CPLEX optimization tool is utilized to address this complex challenge. Finally, several scenarios are simulated to demonstrate the effectiveness of the optimized park-level integrated energy systems in reducing the operational costs and minimizing carbon footprint. © 2014 IEEE.

关键词： Carbon capture and utilization

Real-time Holistic Robot Pose Estimation with Unknown States

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Ban, Shikun Fan, Juling Ma, Xiaoxuan Zhu, Wentao Qiao, Yu Wang, Yizhou Center on Frontiers of Computing Studies School of Computer Science Peking University China Inst. for Artificial Intelligence Peking University China Nat’l Eng. Research Center of Visual Technology Nat’l Key Lab of General Artificial Intelligence Department of Automation School of Electronics Information and Electrical Engineering Shanghai Jiao Tong University China

Estimating robot pose from RGB images is a crucial problem in computer vision and robotics. While previous methods have achieved promising performance, most of them presume full knowledge of robot internal states, e.g. ground-truth robot joint angles. However, this assumption is not always valid in practical situations. In real-world applications such as multi-robot collaboration or human-robot interaction, the robot joint states might not be shared or could be unreliable. On the other hand, existing approaches that estimate robot pose without joint state priors suffer from heavy computation burdens and thus cannot support real-time applications. This work introduces an efficient framework for real-time robot pose estimation from RGB images without requiring known robot states. Our method estimates camera-to-robot rotation, robot state parameters, keypoint locations, and root depth, employing a neural network module for each task to facilitate learning and sim-to-real transfer. Notably, it achieves inference in a single feed-forward pass without iterative optimization. Our approach offers a 12× speed increase with state-of-the-art accuracy, enabling real-time holistic robot pose estimation for the first time. Code and models are available at https: //***/Oliverbansk/Holistic-Robot-Pose-Estimation. Copyright © 2024, The Authors. All rights reserved.

关键词：