检索结果-内蒙古大学图书馆

63rd IEEE Conference on Decision and control, CDC 2024

作者： Zhu, Shiyong Wang, Miaomiao Chen, Jie Rantzer, Anders City University of Hong Kong Department of Electrical Engineering Hong Kong Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Lund University Department of Automatic Control Lund Sweden

ISBN: (纸本)9798350316339

Cybersecurity has in recent years emerged as a paramount concern in the design and operation of industrial systems and civil infrastructures, due mainly to their susceptibility to malicious cyber attacks which take advantage of the vulnerability of communication networks and IT devices. In this paper, we investigate such an attack and counter attack scenario by considering multiagent systems, a somewhat basic prototype of cyberphysical systems. We study false data injection attacks launched on the agent sensors, and possible defense of such attacks at the agent actuators. The primary issue under consideration is the stealthiness of the attacks, while steering a multiagent system away from its consensual state. We propose a metric to quantify the stealthiness, and formulate the stealthiness problem as one of zero-sum games. We solve the problem explicitly, which gives rise to a fundamental bound on the stealthiness achievable, and as well optimal attack and defense strategies that achieve the optimal stealthiness, both of which can be obtained in terms of certain augmented controllability Gramians associated with the agents. The stealthiness bound is seen to depend on agent dynamics and network characteristics including a measure of connectivity. © 2024 IEEE.

关键词： Cyber attacks

来源：评论

学校读者我要写书评

暂无评论

Estimating the AGV load and a battery lifetime based on the current measurement and random forest application

Estimating the AGV load and a battery lifetime based on the ...

引用

2023 IEEE International Conference on Big Data, BigData 2023

作者： Hanzel, Krzysztof Grzechca, Damian Ziebinski, Adam Chruszczyk, Lukas Janus, Artur Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Telecommunications and Teleinformatics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Electronics Electrical Engineering and Microelectronics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Distributed Systems and Informatics Devices Gliwice Poland Silesian University of Technology Student of Electrical EngineeringFaculty of Electrical Engineering Gliwice Poland

ISBN: (纸本)9798350324457

The paper describes the energy consumption from the battery based on the current measurements for various cases, i.e., speed (PWL adjustment) and loads. The main purpose of the research is to have additional and reliable information about power consumption and battery life estimation for autonomous guided vehicles (AGV). The authors propose a two-step algorithm. In the first step, a linear classifier was proposed. Then, the KNN classifier was tested;however, it did not give satisfactory results, so it was finally decided to use the random forest to estimate the load and PWL. The time domain current measurement is evaluated, and the beforementioned algorithms process the selected statistical measures. It has been proven that a two-step algorithm allows for achieving high accuracy. Based on the current observation, the paper is a good starting point for further investigation of the AGV because it is usually implemented in the AGV - so it does not require additional hardware. Moreover, it can lead to better energy management and increase battery lifetime. © 2023 IEEE.

关键词： AGV load estimation machine learning Prediction of energy consumption random forest

来源：评论

学校读者我要写书评

暂无评论

ORB-Net: End-to-end Planning Using Feature-based Imitation Learning for Autonomous Drone Racing 56

ORB-Net: End-to-end Planning Using Feature-based Imitation L...

引用

56th International Symposium on Robotics, ISR Europe 2023

作者： Pham, Huy Xuan Heiß, Micha Tran, Dung Nguyen, Minh Anh Nguyen, Anh Quang Kayacan, Erdal Department of Electrical and Computer Engineering Aarhus University Aarhus C8000 Denmark Hanoi Viet Nam Automatic Control Group Department of Electrical Engineering and Information Technology Paderborn University Paderborn Germany

ISBN: (纸本)9783800761418

Deep learning-based methods have enhanced the performance of many robot applications thanks to their superior ability to robustly extract rich high-dimensional features. However, it comes with a high computational cost that often increases the latency of the overall system. On the other hand, traditional feature extraction methods, such as oriented FAST and rotated BRIEF (ORB), can be computed efficiently and remains the backbone of some critical robot algorithms, e.g. ORB-SLAM for robot state estimation. In this work, the usefulness of the aforementioned features for a robot navigation task is investigated. The features are experimentally incorporated with a deep-learning method, called ORB-Net1, which allows an agile aerial robot to learn a motion policy to complete a racing track in an autonomous drone racing context. The experimental studies demonstrate that it can be beneficial to reuse these computed features for end-to-end motion planning for the agile quadrotor, as the proposed method using combined input of ORB feature position and RGB images outperforms the baseline methods which use only RGB images. © VDE VERLAG GMBH Berlin Offenbach.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An iterative algorithm for phase hologram generation with overlap constraints 8

An iterative algorithm for phase hologram generation with ov...

引用

8th International Conference on Speckle Metrology, Speckle 2023

作者： Lin, Angyi Chen, Rui Lin, Huixiang You, Jiangfeng Zhang, Fucai The Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China School of Control and Computer Engineering North China Electric Power University Baoding071003 China

ISBN: (数字)9781510674592

ISBN: (纸本)9781510674585

Currently, the dominant encoding method for computer-generated holograms is phase-only holography. The Gerchberg-Saxton (GS) algorithm is a commonly used approach for hologram generation. However, it suffers from issues such as poor convergence and low efficiency. In this paper, we introduce an iterative hologram algorithm that addresses these challenges. Our proposed algorithm divides the target object into segments with a certain degree of data redundancy. It computes the hologram iteratively, giving extra weight to each iteration. We also incorporate overlap constraints to enhance hologram quality and accelerate convergence by leveraging data redundancy. What sets our algorithm apart from others is that it varies the order in which segments are processed during iterations, resulting in different final holograms. By randomly selecting the order in each iteration, we can rapidly generate multiple diverse holograms. Importantly, objects reconstructed from these holograms exhibit consistency, allowing for the quick generation of multiple phase-only holograms that help mitigate the speckle effect. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Holograms

来源：评论

学校读者我要写书评

暂无评论

Enhancing Fault Detection in Induction Motors using LSTM-Attention Neural Networks 9

Enhancing Fault Detection in Induction Motors using LSTM-Att...

引用

9th International Conference on control, Instrumentation and Automation, ICCIA 2023

作者： Khaniki, Mohammad Ali Labbaf Mirzaeibonehkhater, Marzieh Manthouri, Mohammad K.N. Toosi University of Technology Department of Control Engineering Tehran Iran Indiana University-Purdue University Department of Electrical and Computer Engineering United States Shahed University Department of Electrical and Electronic Engineering Tehran Iran

ISBN: (纸本)9798350366013

Induction motors carry notable importance in modern machinery and industrial equipment. Hence, the imperative to establish an early fault detection system for discerning operational states and potential faults in these motors is evident. This paper introduces a pioneering methodology for fault detection in induction motors by leveraging the power of Long Short-Term Memory (LSTM) networks and attention mechanisms within a neural network framework. By combining the temporal sequence analysis capabilities of LSTMs with the selective focus feature of attention mechanisms, the proposed architecture enhances the accuracy and timeliness of fault detection, paving the way for improved machinery health monitoring and predictive maintenance. The proposed approach is rigorously evaluated alongside established methods, including simple Artificial Neural Networks (ANN) and LSTM networks to showcase the strengths of the LSTM-Attention approach. This comprehensive analysis reveals the additional advantages of attention mechanisms and positions the LSTM-Attention architecture as a robust solution for detecting faults in induction motors. © 2023 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Enhancing Pneumonia Detection using Vision Transformer with Dynamic Mapping Re-Attention Mechanism 13

Enhancing Pneumonia Detection using Vision Transformer with ...

引用

13th International Conference on computer and Knowledge engineering, ICCKE 2023

作者： Labbaf Khaniki, Mohammad Ali Mirzaeibonehkhater, Marzieh Manthouri, Mohammad K.N. Toosi University of Technology Department of Control Engineering Tehran Iran Indiana University-Purdue University Department of Electrical and Computer Engineering United States Shahed University Department of Electrical and Electronic Engineering Tehran Iran

ISBN: (纸本)9798350330151

Pneumonia is a prevalent respiratory infection with potentially life-threatening consequences. In this research, we propose a novel deep learning approach to enhance pneumonia detection using the Vision Transformer (ViT) architecture with a dynamic mapping re-attention mechanism. The re-attention mechanism in the ViT architecture facilitates the flow of information between patches of the input image, enabling contextual understanding through self-attention. This mechanism captures long-range dependencies and enhances the ViT's ability to effectively recognize and interpret images. The dynamic mapping attention mechanism allows the model to dynamically assign attention weights to different regions of the input data based on their relevance to pneumonia detection. By adaptively adjusting the attention weights, the model can capture both local details and global dependencies, enabling more accurate diagnosis. The ViT utilizes the dynamic mapping re-attention mechanism with attention dropout. Here the performance of this ViT compared to the conventional ViT and the ViT incorporating the re-attention mechanism. This comparative analysis offers valuable insights into the effectiveness of the proposed dynamic mapping re-attention mechanism. Additionally, it highlights the added benefits of attention dropout in enhancing the overall performance of the model. © 2023 IEEE.

关键词： Image enhancement

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 35

Microservice Deployment for Satellite Edge AI Inference via ...

引用

35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Yu, Zhiyong Cheng, Hei Victor Yang, Zhanpeng Liu, Xin Jiang, Yuning Zhou, Yong Shi, Yuanming ShanghaiTech University School of Information Science and Technology Shanghai201210 China Aarhus University Department of Electrical and Computer Engineering Denmark Automatic Control Laboratory Switzerland

ISBN: (纸本)9798350362244

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. © 2024 IEEE.

关键词： Markov processes

来源：评论

学校读者我要写书评

暂无评论

Adaptive Active Fault-Tolerant Attitude control for Spacecraft with Adaptive Prescribed Performance 6

Adaptive Active Fault-Tolerant Attitude Control for Spacecra...

引用

6th International Symposium on Autonomous Systems, ISAS 2023

作者： Yang, Ze Yang, Baoqing Ji, Ruihang Ma, Jie Harbin Institute of Technology Control and Simulation Center Harbin China National University of Singapore Department of Electrical and Computer Engineering Singapore

ISBN: (纸本)9798350316155

For spacecraft attitude control affected by environmental disturbance, parameter uncertainty and actuator fault, a novel composite active fault-tolerant scheme, combining a strong tracking Cubature Kalman filter (STCKF) with adaptive prescribed performance control (APPC), is investigated in this paper. The proposed STCKF is capable of estimating lumped fault rapidly but accurately, and it is robust to model uncertainty. An adaptive finite-time prescribed performance function (APPF) whose boundaries can be flexibly adjusted in the case of actuator faults is proposed. Then an active fault tolerant controller is designed using nonsingular terminal sliding mode control (NTSMC) in conjunction with APPF. Simulation experiments and comparisons show that the proposed strategy exhibits better fault tolerance, lower conservatism and better steady-state performance. © 2023 IEEE.

关键词： Attitude control

来源：评论

学校读者我要写书评

暂无评论

Data-Enabled Finite control-Set Model Predictive control of a Grid-Tied Inverter

Data-Enabled Finite Control-Set Model Predictive Control of ...

引用

2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Wu, Wenjie Qiu, Lin Li, Hui Liu, Xing Du, Chao Fang, Youtong Zhejiang University College of Electrical Engineering Hangzhou China Florida State University Department of Electrical and Computer Engineering TallahasseeFL United States Shaanxi University of Science & Technology College of Electrical and Control Engineering Xian China

ISBN: (纸本)9798350316445

This paper presents a novel predictive control architecture for power converters that addresses the challenges of model mismatch and parameter sensitivity in the finite control-set model predictive control (FCS-MPC) framework. The proposed method employs a dynamic-linearization-based approach, which replaces the detailed model used in the FCS-MPC controller with a virtual equivalent data model, creating a data-enabled FCS-MPC architecture. By relying solely on input-output data, the proposed method exhibits considerable robustness against parameter perturbations while retaining the attractive features of the conventional FCS-MPC method. The effectiveness of the proposed design is demonstrated through comparative simulations results on a two-level grid-tied inverter. © 2023 IEEE.

关键词： Grid-tied inverter Model predictive control (MPC) Robustness

来源：评论

学校读者我要写书评

暂无评论

Multi-Objective Complementary control

引用

IEEE Transactions on Automatic control 2024年第6期70卷 3556-3570页

作者： Xu, Jiapeng Chen, Xiang Tan, Ying Zhou, Kemin University of Windsor Department of Electrical and Computer Engineering 401 Sunset Avenue WindsorONN9B 3P4 Canada The University of Melbourne Department of Mechanical Engineering MelbourneVIC3010 Australia Nanjing University Center for Advanced Control and Smart Operations Jiangsu Suzhou215163 China

This paper proposes a novel multi-objective control framework for linear time-invariant systems in which performance and robustness can be achieved in a complementary way instead of a trade-off. In particular, a state-space solution is first established for a new stabilizing control structure consisting of two independently designed controllers coordinated with a Youla-type operator Q. It is then shown by performance analysis that these two independently designed controllers operate in a naturally complementary way for a tracking control system, due to the coordination function of Q driven by the residual signal of a Luenberger observer. Moreover, it is pointed out that Q could be further optimized with an additional gain factor to achieve improved performance, through a data-driven methodology for a measured cost function. © 1963-2012 IEEE.

关键词： Cost functions

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：