检索结果-内蒙古大学图书馆

European control Conference (ECC)

作者： Xingkang He Ehsan Hashemi Karl H. Johansson Ericsson AB Sweden Department of Mechanical Engineering University of Alberta Canada Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

This paper proposes a method to navigate a mobile robot by estimating its state over a number of distributed sensor networks (DSNs) such that it can successively accomplish a sequence of tasks, i.e., its state enters each targeted set and stays inside no less than the desired time, under a resource-aware, time-efficient, and computation-and communication-constrained setting. We propose a new robot state estimation and navigation architecture, which integrates an event-triggered task-switching feedback controller for the robot and a two-time-scale distributed state estimator for each sensor. With the controller, the robot is able to accomplish a task by following a reference trajectory and switch to the next task when an event-triggered condition is fulfilled. With the estimator, each active sensor is able to estimate the robot state. We provide conditions to ensure that the state estimation error and the trajectory tracking deviation are upper bounded by two time-varying sequences, respectively. Furthermore, we find a sufficient condition for accomplishing a task and provide an upper bound of running time for the task. Numerical simulations of an indoor robot’s localization and navigation are provided to validate the proposed architecture

关键词：

来源：评论

学校读者我要写书评

暂无评论

Comparative study for different types of mppt algorithms using direct control method 1

引用

2nd International Conference on electrical and Electronics engineering, ICEEE 2021

作者： Shams, Immad Mohammed, Karam Khairullah Mekhilef, Saad Tey, Kok Soon Power Electronics and Renewable Energy Research Laboratory Department of Electrical Engineering Faculty of Engineering Kuala Lumpur50603 Malaysia Control and Mechatronics Engineering Department School of Electrical Engineering Universiti Teknologi Malaysia Johor Bahru81310 Malaysia School of Software and Electrical Engineering Faculty of Science Engineering and Technology Swinburne University of Technology MelbourneVIC3122 Australia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia

ISBN: (数字)9789811607493

ISBN: (纸本)9789811607486

Maximum power point tracking (MPPT) controllers achieve higher importance due to the high expansion rate of photovoltaic systems. The probability of partial shading conditions (PSCs) is also increased which drastically reduced the maximum power extraction capability from PV systems. In this paper, different types of MPPT algorithms using direct control method have been examined under the same test conditions. The current research trend on MPPT controllers has also been highlighted. This paper gives a brief description to engineers and practitioners working in the area of MPPT controllers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： controllers

来源：评论

学校读者我要写书评

暂无评论

Microservice Deployment for Satellite Edge AI Inference via Deep Reinforcement Learning

Microservice Deployment for Satellite Edge AI Inference via ...

引用

IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Zhiyong Yu Hei Victor Cheng Zhanpeng Yang Xin Liu Yuning Jiang Yong Zhou Yuanming Shi School of Information Science and Technology ShanghaiTech University Shanghai China Department of Electrical and Computer Engineering Aarhus University Denmark Automatic Control Laboratory École Polytechnique Fédérale de Lausanne (EPFL) Switzerland

ISBN: (数字)9798350362244

ISBN: (纸本)9798350362251

Artificial intelligence (AI) is critical in evolving 5G and developing 6G networks, running on edge devices, and solving resource management challenges. The burgeoning number of edge devices draws attention to the potential of low-earth orbit (LEO) satellite networks with their onboard computing capabilities for edge inference. This paper explores LEO scenarios where multiple remote sensing edge AI inference tasks concurrently process data from a single source. However, due to there being parts with the same functions between different AI applications, traditional monolithic edge AI architecture must be deployed repeatedly and falls short in efficiently harnessing the heterogeneous resources of LEO satellite networks. To solve this problem, we utilize the microservice architecture to decouple a single AI application into several independent microservices to reuse these same functions. However, due to the high latency caused by multiple microservices’ communication, we need to design a deployment strategy to fully utilize resources to reduce the service latency. We present a microservice deployment model to minimize the total service latency across all AI applications and meet resource constraints with the constraints of hardware, energy, and memory limitations. This latency optimization problem is rewritten as a Markov decision process (MDP) to effectively deal with the challenge posed by the time-varying transmission rate caused by satellite mobility. To increase the training data utilization, we employ a Proximal Policy Optimization (PPO) based reinforcement learning algorithm to meet the dynamic environment challenge. Finally, we obtain a sub-optimal solution with minimal accuracy loss and an acceptable solution time.

关键词： Satellites Accuracy Heuristic algorithms Satellite broadcasting Microservice architectures Low earth orbit satellites Edge AI computer architecture Resource management Optimization

来源：评论

学校读者我要写书评

暂无评论

AWGUNET: ATTENTION-AIDED WAVELET GUIDED U-NET FOR NUCLEI SEGMENTATION IN HISTOPATHOLOGY IMAGES

arXiv

引用

arXiv 2024年

作者： Roy, Ayush Pramanik, Payel Kaplun, Dmitrii Antonov, Sergei Sarkar, Ram Department of Electrical Engineering Jadavpur University Kolkata India Department of Computer Science and Engineering Jadavpur University Kolkata India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes Saint Petersburg Electrotechnical University"LETI" Saint Petersburg Russia

Accurate nuclei segmentation in histopathological images is crucial for cancer diagnosis. Automating this process offers valuable support to clinical experts, as manual annotation is time-consuming and prone to human errors. However, automating nuclei segmentation presents challenges due to uncertain cell boundaries, intricate staining, and diverse structures. In this paper, we present a segmentation approach that combines the U-Net architecture with a DenseNet-121 backbone, harnessing the strengths of both to capture comprehensive contextual and spatial information. Our model introduces the Wavelet-guided channel attention module to enhance cell boundary delineation, along with a learnable weighted global attention module for channel-specific attention. The decoder module, composed of an upsample block and convolution block, further refines segmentation in handling staining patterns. The experimental results conducted on two publicly accessible histopathology datasets, namely Monuseg and TNBC, underscore the superiority of our proposed model, demonstrating its potential to advance histopathological image analysis and cancer diagnosis. The code is made available at: https://***/AyushRoy2001/AWGUNET © 2024, CC BY.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation

引用

IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5520-5537页

作者： Jian Liu Wei Sun Hui Yang Pengchao Deng Chongpei Liu Nicu Sebe Hossein Rahmani Ajmal Mian National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering School of Robotics and the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China Department of Information Engineering and Computer Science University of Trento Trento Italy School of Computing and Communications Lancaster University Lancaster U.K. Department of Computer Science The University of Western Australia Crawley WA Australia

Nine-degrees-of-freedom (9-DoF) object pose and size estimation is crucial for enabling augmented reality and robotic manipulation. Category-level methods have received extensive research attention due to their potential for generalization to intra-class unknown objects. However, these methods require manual collection and labeling of large-scale real-world training data. To address this problem, we introduce a diffusion-based paradigm for domain-generalized category-level 9-DoF object pose estimation. Our motivation is to leverage the latent generalization ability of the diffusion model to address the domain generalization challenge in object pose estimation. This entails training the model exclusively on rendered synthetic data to achieve generalization to real-world scenes. We propose an effective diffusion model to redefine 9-DoF object pose estimation from a generative perspective. Our model does not require any 3D shape priors during training or inference. By employing the Denoising Diffusion Implicit Model, we demonstrate that the reverse diffusion process can be executed in as few as 3 steps, achieving near real-time performance. Finally, we design a robotic grasping system comprising both hardware and software components. Through comprehensive experiments on two benchmark datasets and the real-world robotic system, we show that our method achieves state-of-the-art domain generalization performance.

关键词： Shape Solid modeling Three-dimensional displays Pose estimation Training Robots Grasping Diffusion models Real-time systems Point cloud compression

来源：评论

学校读者我要写书评

暂无评论

Enhancing Worker Recruitment in Collaborative Mobile Crowdsourcing: A Graph Neural Network Trust Evaluation Approach

arXiv

引用

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

来源：评论

学校读者我要写书评

暂无评论

Non-Convex Potential Games for Finding Global Solutions to Sensor Network Localization

Non-Convex Potential Games for Finding Global Solutions to S...

引用

European control Conference (ECC)

作者： Gehui Xu Guanpu Chen Yiguang Hong Baris Fidan Thomas Parisini Karl H. Johansson Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Control Science and Engineering Tongji University Shanghai China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai China Department of Mechanical and Mechatronics Engineering University of Waterloo Waterloo ON Canada Department of Electrical and Electronic Engineering Imperial College London London UK Department of Engineering and Architecture University of Trieste Trieste Italy

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Sensor network localization (SNL) problems require determining the physical coordinates of all sensors in a network. This process relies on the global coordinates of anchors and the available measurements between non-anchor and anchor nodes. Attributed to the intrinsic non-convexity, obtaining a globally optimal solution to SNL is challenging, as well as implementing corresponding algorithms. In this paper, we formulate a non-convex multi-player potential game for a generic SNL problem to investigate the identification condition of the global Nash equilibrium (NE) therein, where the global NE represents the global solution of SNL. We employ canonical duality theory to transform the non-convex game into a complementary dual problem. Then we develop a conjugation-based algorithm to compute the stationary points of the complementary dual problem. On this basis, we show an identification condition of the global NE: the stationary point of the proposed algorithm satisfies a duality relation. Finally, simulation results are provided to validate the effectiveness of the theoretical results.

关键词： Location awareness Coordinate measuring machines Simulation Current measurement Noise Europe Games

来源：评论

学校读者我要写书评

暂无评论

Partially Occluded Face Expression Recognition with CBAM-Based Residual Network for Teaching Scene

Partially Occluded Face Expression Recognition with CBAM-Bas...

引用

2023 China Automation Congress, CAC 2023

作者： Bai, Yanxing Chen, Luefeng Li, Min Wu, Min Pedrycz, Witold Hirota, Kaoru School of Automation China University of Geosciences Wuhan430074 China School of Automation Ministry of Education China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Department of Computer Engineering Sariyer Istanbul34396 Turkey Tokyo Institute of Technology Yokohama226-8502 Japan

ISBN: (纸本)9798350303759

In this paper, a deep residual network based on convolutional block attention module (CBAM) is proposed, which is utilized for feature extraction of partially occluded face expression data. The proposed method overcomes the problem of localized occlusion face feature extraction by focusing on the regions and channels containing important information in the occluded face data through CBAM. Multi-task cascaded convolutional networks (MTCNN) are firstly utilized to localize the key regions of face emotion, and then deep emotion features are extracted by CBAM-ResNet network. The final emotion labels are generated. The effectiveness of this paper's method is verified on the RAF-DB dataset and the occluded CK+ dataset. The experimental accuracy in the RAF-DB dataset is 76.3%, which is 3.74% and 1.64% higher than the accuracy produced by the method of RGBT, and the WLS-RF, respectively. Application experiments are carried out in the real teaching scenario, which verifies the applicability of the algorithm in the real teaching scene. © 2023 IEEE.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Awgunet: Attention-Aided Wavelet Guided U-Net for Nuclei Segmentation in Histopathology Images

Awgunet: Attention-Aided Wavelet Guided U-Net for Nuclei Seg...

引用

IEEE International Symposium on Biomedical Imaging

作者： Ayush Roy Payel Pramanik Dmitrii Kaplun Sergei Antonov Ram Sarkar Department of Electrical Engineering Jadavpur University Kolkata India Department of Computer Science and Engineering Jadavpur University Kolkata India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

关键词： Image segmentation Convolution Histopathology Microprocessors Noise computer architecture Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Reinforcement Learning-Based Sensitive Semantic Location Privacy Protection for VANETs

引用

China Communications 2021年第6期18卷 244-260页

作者： Minghui Min Weihang Wang Liang Xiao Yilin Xiao Zhu Han Department of Information and Communication Engineering Xiamen UniversityXiamen 361005China School of Information and Control Engineering ChinaUniversity of Mining and TechnologyXuzhou 221116China The Affiliated Hospital of China University of Mining and Technology Xuzhou 221116China Department of Electrical and Computer Engineering University of HoustonHouston TX 77004USA Beijing Key Laboratory of Mobile Computing and Pervasive Device No.6 Kexueyuan South RoadBeijing 100190China

Location-based services(LBS)in vehicular ad hoc networks(VANETs)must protect users’privacy and address the threat of the exposure of sensitive locations during LBS *** release not only their geographical but also semantic information of the visited places(e.g.,hospital).This sensitive information enables the inference attacker to exploit the users’preferences and life *** this paper we propose a reinforcement learning(RL)based sensitive semantic location privacy protection *** scheme uses the idea of differential privacy to randomize the released vehicle locations and adaptively selects the perturbation policy based on the sensitivity of the semantic location and the attack *** scheme enables a vehicle to optimize the perturbation policy in terms of the privacy and the quality of service(QoS)loss without being aware of the current inference attack model in a dynamic privacy protection *** solve the location protection problem with high-dimensional and continuous-valued perturbation policy variables,a deep deterministic policy gradientbased semantic location perturbation scheme(DSLP)is *** actor part is used to generate continuous privacy budget and perturbation angle,and the critic part is used to estimate the performance of the *** demonstrate the DSLP-based scheme outperforms the benchmark schemes,which increases the privacy,reduces the QoS loss,and increases the utility of the vehicle.

关键词： semantic location sensitivity locationbased services VANET differential privacy reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：