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arXiv 2023年

作者： Zhan, Zhongwei Wang, Yingjie Duan, Peiyong Sai, Akshita Maradapu Vera Venkata Liu, Zhaowei Xiang, Chaocan Tong, Xiangrong Wang, Weilong Cai, Zhipeng The School of Computer and Control Engineering Yantai University Yantai264005 China The department of Electrical and Control Qilu University of Technology Shandong Academy of Sciences Jinan250000 China The department of Computer Science Georgia State University AtlantaGA30303 United States The College of Computer Science Chongqing University Chongqing400044 China The Department of Computer Science and Engineering Southeast University Nanjing211189 China

Collaborative Mobile Crowdsourcing (CMCS) allows platforms to recruit worker teams to collaboratively execute complex sensing tasks. The efficiency of such collaborations could be influenced by trust relationships among workers. To obtain the asymmetric trust values among all workers in the social network, the Trust Reinforcement Evaluation Framework (TREF) based on Graph Convolutional Neural Networks (GCNs) is proposed in this paper. The task completion effect is comprehensively calculated by considering the workers’ ability benefits, distance benefits, and trust benefits in this paper. The worker recruitment problem is modeled as an Undirected Complete Recruitment Graph (UCRG), for which a specific Tabu Search Recruitment (TSR) algorithm solution is proposed. An optimal execution team is recruited for each task by the TSR algorithm, and the collaboration team for the task is obtained under the constraint of privacy loss. To enhance the efficiency of the recruitment algorithm on a large scale and scope, the Mini-Batch K-Means clustering algorithm and edge computing technology are introduced, enabling distributed worker recruitment. Lastly, extensive experiments conducted on five real datasets validate that the recruitment algorithm proposed in this paper outperforms other baselines. Additionally, TREF proposed herein surpasses the performance of state-of-the-art trust evaluation methods in the literature. Copyright © 2023, The Authors. All rights reserved.

关键词： Tabu search

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Tight Collision Probability for UAV Motion Planning in Uncertain Environment

Tight Collision Probability for UAV Motion Planning in Uncer...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Tianyu Liu Fu Zhang Fei Gao Jia Pan Department of Mechanical Engineering The University of Hong Kong Institute of Cyber-Systems and Control Zhejiang University Department of Computer Science The University of Hong Kong

Operating unmanned aerial vehicles (UAVs) in complex environments that feature dynamic obstacles and external disturbances poses significant challenges, primarily due to the inherent uncertainty in such scenarios. Additionally, inaccurate robot localization and modeling errors further exacerbate these challenges. Recent research on UAV motion planning in static environments has been unable to cope with the rapidly changing surroundings, resulting in trajectories that may not be feasible. Moreover, previous approaches that have addressed dynamic obstacles or external disturbances in isolation are insufficient to handle the complexities of such environments. This paper proposes a reliable motion planning framework for UAVs, integrating various uncertainties into a chance constraint that characterizes the uncertainty in a probabilistic manner. The chance constraint provides a probabilistic safety certificate by calculating the collision probability between the robot's Gaussian-distributed forward reachable set and states of obstacles. To reduce the conservatism of the planned trajectory, we propose a tight upper bound of the collision probability and evaluate it both exactly and approximately. The approximated solution is used to generate motion primitives as a reference trajectory, while the exact solution is leveraged to iteratively optimize the trajectory for better results. Our method is thoroughly tested in simulation and real-world experiments, verifying its reliability and effectiveness in uncertain environments.

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Based on Linkable Ring Signature Map Review Scheme for Zero-Trust Autonomous Vehicles

Based on Linkable Ring Signature Map Review Scheme for Zero-...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Min Zhang Qing Ye Kaihuan Deng Department of Information Security Naval University of Engineering Wuhan China School of Control and Computer Engineering North China Electric Power University Beijing China

The real-time map plays a vital role in the Internet of Vehicles (IoV) navigation. Compared with the existing map review, the real-time map review method is based on crowdsensing enabling faster data updates and shortening the map period from map collection to service. However, the crowdsensing method requires frequent interaction between the vehicle and nearby devices or servers, increasing the risk of user privacy leakage. How to ensure the privacy of users is a challenge in the real-time map review. Starting from the typical architecture of IoV and combining the core idea of zero-trust “never trust, continuous verification”, IoV-ZT conceptual model architecture is designed. This paper proposes a based on linkable ring signature map review scheme for zero-trust autonomous vehicles (LRSMR). In the LRSMR, a linkable ring signature based on the SM2 digital signature algorithm is used to protect user identity privacy and ensures while ensuring signature linkability by hiding the identity of the actual signer in the ring. Through the requirement for user credit, the reliability of the review data is improved. At the same time, the distribution of rewards increases users' motivation. At last, the security analysis indicates that the LRSMR satisfies correctness, unforgeability, unconditional anonymity, linkability and nonslanderability. The simulation results illustrate that the LRSMR is efficient in terms of communication overhead and computation cost.

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Investigating Reinforcement Learning for Histopathological Image Analysis

Investigating Reinforcement Learning for Histopathological I...

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18th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Mohamad, Mohamad Ponzio, Francesco Gassier, Maxime Pote, Nicolas Ambrosetti, Damien Descombes, Xavier Université Côte d’Azur INRIA CNRS I3S INSERM IBV Sophia Antipolis France Department of Control and Computer Engineering Politecnico di Torino Turin Italy Department of Pathology Bichat Hospital Assistance Publique-Ĥopitaux de Paris Paris France Department of Pathology CHU Nice Université Côte d’Azur Nice France

In computational pathology, whole slide images represent the primary data source for AI-driven diagnostic algorithms. However, due to their high resolution and large size, these images undergo a patching phase. In this paper, we approach the diagnostic process from a pathologist’s perspective, modeling it as a Sequential decision-making problem using reinforcement learning. We build a foundational environment designed to support a range of whole slide applications. We showcase its capability by using it to construct a toy goal conditioned Navigation environment. Finally, we present an agent trained within this environment and provide results that emphasize both the promise of reinforcement learning in histopathology and the distinct challenges it faces. © 2025 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： Deep reinforcement learning

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A survey on user-centric cell-free massive MIMO systems

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Digital Communications and Networks 2022年第5期8卷 695-719页

作者： Shuaifei Chen Jiayi Zhang Jing Zhang Emil Björnson Bo Ai School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing100044China Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong UniversityBeijing100044China Department of Computer Science KTH Royal Institute of TechnologySE-16440KistaSweden Department of Electrical Engineering(ISY) Linköping UniversitySE-58183LinköpingSweden State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong UniversityBeijing100044China

The mobile data traffic has been exponentially growing during the last several *** was enabled by the densification of the network infrastructure in terms of increased cell density(i.e.,Ultra-Dense Network(UDN))and/or the increased number of active antennas per Access Point(AP)(i.e.,massive Multiple-Input Multiple-Output(mMIMO)).However,neither UDN nor mMIMO will meet the increasing demand for the data rate of the Sixth Generation(6G)wireless communications due to the inter-cell interference and large quality-of-service ***-Free(CF)mMIMO,which combines the best aspects of UDN and mMIMO,is viewed as a key solution to this *** such systems,each User Equipment(UE)is served by a preferred set of surrounding APs *** this paper,we provide a survey of the state-of-the-art literature on CF *** a starting point,the significance and the basic properties of CF mMIMO are *** then present the canonical framework to discuss the essential details(i.e.,transmission procedure and mathematical system model).Next,we provide a deep look at the resource allocation and signal processing problems related to CF mMIMO and survey the up-to-date schemes and *** that,we discuss the practical issues in implementing CF mMIMO and point out the potential future ***,we conclude this paper with a summary of the key lessons learned in this field.

关键词： 6G network User-centric cell-free network Massive multiple-input multiple-output

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Differentiable Optimization Based Time-Varying control Barrier Functions for Dynamic Obstacle Avoidance

arXiv

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arXiv 2023年

作者： Dai, Bolun Khorrambakht, Rooholla Krishnamurthy, Prashanth Khorrami, Farshad Control/Robotics Research Laboratory Electrical & Computer Engineering Department Tandon School of Engineering New York University BrooklynNY11201 United States

control barrier functions (CBFs) provide a simple yet effective way for safe control synthesis. Recently, work has been done using differentiable optimization (diffOpt) based methods to systematically construct CBFs for static obstacle avoidance tasks between geometric shapes. In this work, we extend the application of diffOpt CBFs to perform dynamic obstacle avoidance tasks. We show that by using the time-varying CBF (TVCBF) formulation, we can perform obstacle avoidance for dynamic geometric obstacles. Additionally, we show how to extend the TVCBF constraint to consider measurement noise and actuation limits. To demonstrate the efficacy of our proposed approach, we first compare its performance with a model predictive control based method and a circular CBF based method on a simulated dynamic obstacle avoidance task. Then, we demonstrate the performance of our proposed approach in experimental studies using a 7-degree-of-freedom Franka Research 3 robotic manipulator. © 2023, CC BY.

关键词： Model predictive control

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Semantic Communications Based on Adaptive Generative Models and Information Bottleneck

arXiv

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arXiv 2023年

作者： Barbarossa, Sergio Comminiello, Danilo Grassucci, Eleonora Pezone, Francesco Sardellitti, Stefania Di Lorenzo, Paolo Department of Computer Control and Management Engineering Sapienza University of Rome Italy The Dept. of Information Engineering Electronics and Telecommunications Sapienza University of Rome Italy

Semantic communications represent a significant breakthrough with respect to the current communication paradigm, as they focus on recovering the meaning behind the transmitted sequence of symbols, rather than the symbols themselves. In semantic communications, the scope of the destination is not to recover a list of symbols symbolically identical to the transmitted ones, but rather to recover a message that is semantically equivalent to the semantic message emitted by the source. This paradigm shift introduces many degrees of freedom to the encoding and decoding rules that can be exploited to make the design of communication systems much more efficient. In this paper, we present an approach to semantic communication building on three fundamental ideas: 1) represent data over a topological space as a formal way to capture semantics, as expressed through relations;2) use the information bottleneck principle as a way to identify relevant information and adapt the information bottleneck online, as a function of the wireless channel state, in order to strike an optimal trade-off between transmit power, reconstruction accuracy and delay;3) exploit probabilistic generative models as a general tool to adapt the transmission rate to the wireless channel state and make possible the regeneration of the transmitted images or run classification tasks at the receiver side. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Hybrid Routing in VANETs Using OLSR and Q-Learning for Enhanced Performance

Hybrid Routing in VANETs Using OLSR and Q-Learning for Enhan...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Wafa Akermi Nouha Alyaoui Awatef Benfradj Guiloufi Karim Chabir Computer Science and Telecommunications Department Higher Institute of Applied Science and Technology Gafsa University Gafsa Tunisia Laboratory of Modeling Analysis and Control of Systems (MACS) National Engineering School of Gabes (ENIG) Gabes University Gabes Tunisia Departement of Computer Engineering College of Computer Qassim University Buraidh Saudi Ababia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

High node mobility, rapid topology changes provide specific challenges for vehicular ad hoc networks (VANETs), which have an immediate impact on the routing protocols' performance. Traditional approaches, like the Optimized Link State Routing (OLSR) protocol, provide proactive route management but fail to fully account for critical dynamic parameters like link stability, relative vehicle speed, node distance, and bandwidth availability. This work proposes a hybrid method combining OLSR with Q-learning to facilitate real-time adaptive routing. The model leverages dynamic metrics to proactively evaluate links while employing Q-learning to optimize routing decisions based on rewards computed from performance factors like delay, packet loss rate, and link duration. Simulation results demonstrate that our approach significantly outperforms classic OLSR. The improvements include reduced packet loss rates, increased average throughput, lower average latency, and a reduction in control overhead. These findings confirm that integrating dynamic metrics and adaptive learning effectively addresses the challenges posed by VANETs.

关键词： Q-learning Simulation Vehicular ad hoc networks Packet loss Routing Throughput Routing protocols Stability analysis Topology Vehicle dynamics

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Graph-based semantic planning for adaptive human-robot-collaboration in assemble-to-order scenarios

Graph-based semantic planning for adaptive human-robot-colla...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Ruidong Ma Jingyu Chen John Oyekan Department of Automatic Control and System Engineering University of Sheffield Sheffield United Kingdom Department of Computer Science University of York York United Kingdom

Assemble-to-Order (ATO) has become a popular production strategy for the increasing demand for mass-customized manufacturing. In order to facilitate a flexible and automated Human-Robot-Collaboration system for ATO, we propose a Learning from Demonstration (LfD) framework based on 2D videos in this paper. We initially combine temporal hand motions with spatial hand-object interactions to detect assembly actions. Therefore, an assembly graph can be constructed using classified action sequences. Compared to previous studies on task planning for robots, our graph-based semantic planner can directly learn the demonstrated task structure and thus produce more detailed assistive robot actions for more effective collaboration. We validate our approach by applying it to a real-world ATO problem. The results demonstrated that our proposed system can produce actions adaptively in response to varying human action sequences, as well as guide human assembly when the robot is not involved. Our approach also shows generalizability to unseen human action sequences.

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Effects of cutting speed and milling method on cutting forces, tool wear, tool life, and surface roughness in high-speed shell milling of Inconel 718 with coated carbide insert under emulsion flood cooling strategy

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International Journal of Machining and Machinability of Materials 2021年第4期23卷 307-326页

作者： Okafor, Anthony Chukwujekwu Nwoguh, Theodore Obumselu Computer Numerical Control and Virtual Manufacturing Laboratory Department of Mechanical and Aerospace Engineering Missouri University of Science and Technology 327 Toomey Hall RollaMO65409-0050 United States

Inconel 718 use in aerospace and nuclear industries has gained wide interest due to the need to improve its machinability. This paper presents the results of experimental investigation of the effects of face milling methods and cutting speed on machinability of Inconel 718 with carbide inserts under conventional emulsion flood-cooling strategy (CEF-CS) as a benchmark for comparing alternative vegetable-oil-based MQL cooling strategy (VO-MQL-CS). The machinability parameters investigated are cutting force components, tool wear, burr formation, surface roughness, and tool life, under up and down-milling at cutting speeds of 30, 40, and 50 m/min and constant chip load. Lower cutting forces, tool wear, burr formation, surface roughness, and significant improvement in tool life and volume of material removed are achieved in down-milling over up-milling by 1,677%, 2,150% and 1,004% at 30, 40, and 50 m/min respectively. Down-milling at 40 m/min cutting speed under CEF-CS with coated carbide inserts is recommended as benchmark. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Surface roughness

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