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Fractional Order Darwinian Pigeon-Inspired Optimization for Multi-UAV Swarm controller

Guidance, Navigation and control 2022年第2期2卷 80-98页

作者： Bingda Tong Chen Wei Yuhui Shi Science and Technology on Aircraft Control Laboratory Beihang University37 Xueyuan RoadHaidian DistrictBeijing 100191P.R.China Department of Computer Science and Engineering College of EngineeringSouthern University of Science and Technology1088 Xueyuan AvenueShenzhen 518055P.R.China

This paper presents a novel multiple unmanned aerial vehicde(UAV)swarm cotoller based on the fractional alculus *** controller i designed baed on fractional order Darwinian pigeon-inepired optimization(F 0DPI0)and PID *** comparative simulations are conducted in the *** simulation results reveal that FODPIObased muli-UAV formation controller is superior to the basic PIO and dilTerential evolution(DE)*** fractional oelfcdent in F ODPIO algorithm makes it eflective optimbation with fast convergence rate,small oversboot,and better ***,the contnoller propoeed in this paper is fessible and robust.

关键词： Pigeon-inspired optimization(PIO) fractional order Darwinian pigeon-inspired optimization(FODPIO) unmanned aerial vehicle(UAV) formation control

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Balancing objects on DOORS nodes 23

Balancing objects on DOORS nodes

引用

23rd International Conference on control Systems and computer science Technologies, CSCS 2021

作者： Palanciuc, Dorin Pop, Florin University Politehnica of Bucharest Faculty of Automatic Control and Computers Computer Science Department Romania National Institute for Research and Development in Informatics ICI Bucharest Romania

ISBN: (纸本)9781665439398

In the context of DOORS, a medium-scale distributed system, running on tens of 'normal PCs and/or embedded devices, we propose a solution for the problem of efficient allocation of execution and storage resources. We distinguish between the node-level problem of scheduling the execution of object methods and the system-level problem of optimally placing objects on nodes such that locality is maximised: inter-communicating objects are kept as close to each other as possible and the most 'active' objects are hosted on nodes with the highest processing capacity and/or the highest amount of memory available. DOORS aims for architectural simplicity and achieves this by providing more control to the solution implementer, such that the behaviour of the object balancer can be tailored to the specifics of the problem addressed by the implementation. The proposed design for the balancer provides a simple relocation service, and a set of perpetually updated set of metrics, such that the solution implementer may define, 'plug in' and enforce a balancing policy out of an unlimited number of alternatives. © 2021 IEEE.

关键词： Balancing

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Inexact Policy Iteration Methods for Large-Scale Markov Decision Processes

arXiv

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arXiv 2024年

作者： Gargiani, Matilde Sieber, Robin Balta, Efe Liao-McPherson, Dominic Lygeros, John Automatic Control Laboratory ETH Physikstrasse 3 Zurich8092 Switzerland Control and Automation Group Inspire AG Zurich Switzerland Department of Mechanical Engineering UBC 2054-6250 Applied Science Lane VancouverBC Canada

We consider inexact policy iteration (iPI) methods for large-scale infinite-horizon discounted Markov decision processes (MDPs) with finite state and action spaces, a variant of policy iteration where the policy evaluation step is implemented inexactly using an iterative solver for linear systems. In the classical dynamic programming literature, a similar principle is deployed in optimistic policy iteration, where an a-priori fixed-number of iterations of value iteration is used to inexactly solve the policy evaluation step. Inspired by the connection between policy iteration and semismooth Newton's method, we investigate a class of iPI methods that mimic the inexact variants of semismooth Newton's method by adopting a parametric stopping condition to regulate the level of inexactness of the policy evaluation step. For this class of methods we discuss local and global convergence properties and derive a practical range of values for the stopping-condition parameter that provide contraction guarantees. We also show that, when the iterative solver for policy evaluation enjoys linear contraction, the stopping criterion is verified in a finite-number of iterations. Our analysis is general and therefore encompasses a variety of iterative solvers for policy evaluation, including the standard value iteration as well as more sophisticated ones such as GMRES. As underlined by our analysis, the selection of the inner solver is of fundamental importance for the performance of the overall method. We therefore consider different iterative methods to solve the policy evaluation step and analyse their applicability and contraction properties when used for policy evaluation. In particular, we study Richardson's method, steepest descent, the minimal residual method and GMRES for policy evaluation. We show that the contraction properties of these methods tend to be enhanced by the specific structure of policy evaluation and that there is margin for substantial improvement over value

关键词： Markov processes

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Network Management devices in an SDN environment

Network Management devices in an SDN environment

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International Conference on control Systems and computer science (CSCS)

作者： Diyar Jamal Hamad Khirota Gorgees Yalda Nicolae Tapus Ibrahim Taner Okumus Faculty of Automatic Control and Computer Science Politehnica University of Bucharest Bucharest Romania Soran Technical college Erbil Polytechnic University Iraq Erbil Computer Engineering Kahramanmaras Sutcu Imam University Kahramanmaras Turkey

Network optimization and continued availability depend on a number of capabilities that are part of network management. The Maintenance, operating and also offering a safeguarded interaction network is very complicated. It calls for the network operators to grapple with low-level vendor particular arrangements to execute the high degree network policies which are complicated. A method for monitoring networks with OpenFlow controller is presented in this paper in two separate functions for the same networks. Bandwidth utilization, Meter values, charts and statistics are provided by the method to extend controller monitoring capabilities. The method architecture and implementation will be introduced in order to present the feature set. Additionally, softswitches are used as a switch and Mininet to evaluate a virtualized network. This analysis shows whether Meters or ports value is better for network management.

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Coarse-to-Fine Detection of Multiple Seams for Robotic Welding

arXiv

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arXiv 2024年

作者： Wei, Pengkun Cheng, Shuo Li, Dayou Song, Ran Zhang, Yipeng Zhang, Wei School of Control Science and Engineering Shandong University Jinan250061 China Department of Electrical and Computer Engineering University of California Los AngelesCA United States

Efficiently detecting target weld seams while ensuring sub-millimeter accuracy has always been an important challenge in autonomous welding, which has significant application in industrial practice. Previous works mostly focused on recognizing and localizing welding seams one by one, leading to inferior efficiency in modeling the workpiece. This paper proposes a novel framework capable of multiple weld seams extraction using both RGB images and 3D point clouds. The RGB image is used to obtain the region of interest by approximately localizing the weld seams, and the point cloud is used to achieve the fine-edge extraction of the weld seams within the region of interest using region growth. Our method is further accelerated by using a pre-trained deep learning model to ensure both efficiency and generalization ability. The performance of the proposed method has been comprehensively tested on various workpieces featuring both linear and curved weld seams and in physical experiment systems. The results showcase considerable potential for real-world industrial applications, emphasizing the method’s efficiency and effectiveness. Videos of the real-world experiments can be found at https://***/pq162HSP2D4. © 2024, CC BY.

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A Review of Gateway Load Balancing Methods in Connecting MANET into Internet 23

A Review of Gateway Load Balancing Methods in Connecting MAN...

引用

23rd International Conference on Advanced Communication Technology, ICACT 2021

作者： Hui, Zhang Lingli, Zhang Yonghang, Yan Linlin, Ci School of Computer Science Technology Beijing Institute of Technology Beijing China Beijing Aerospace Automatic Control Institute Beijing China School of Computer and Information Engineering Henan University Kaifeng China

ISBN: (纸本)9791188428069

Interconnection of wired Internet and Mobile Ad hoc Network (MANET), called Integrated Internet-MANET can expand the network coverages and services. In this type of networks, the gateway acts as a bridge between MANET and wired Internet. As all data packets must pass through gateway, which is likely to be congested due to uneven traffic distribution. Thus, gateway load balancing become one of the most important issues in MANET. When the gateway is transmitting a large number of packets, load Balancing is the process of improving the performance of a network through a distribution of load among different gateways. If a gateway is heavily loaded without any load balancing capability, it degrades the performance by causing congestion, delay and resource loss in network. Recently, the key issue of gateway load balancing has been addressed by researchers in various ways, but now there aren't systematic analysis and comparison among different strategies. This paper provides the complete survey on load balancing solutions and proposes a gateway load balancing strategy comparison framework. © 2021 Global IT Research Institute (GiRI).

关键词： Mobile ad hoc networks

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Measurement of unbalanced voltages in three-phase induction motors using acoustic transducers and zero-crossing-weighted energy 5

Measurement of unbalanced voltages in three-phase induction ...

引用

5th International Symposium on Instrumentation Systems, Circuits and Transducers, INSCIT 2021

作者： Lucas, Guilherme Beraldi De Castro, Bruno Albuquerque Alves, Brigite Peinado Glowacz, Adam Andreoli, Andre Luiz Sao Paulo State University Department of Electrical Engineering Bauru Brazil AGH University of Science and Technology Department of Automatic Control and Robotics Krakow Poland

ISBN: (纸本)9781728197265

In modern industries, transducers and measurement systems have been largely applied for equipment monitoring. In this context, three-phase induction motors (TIMs) are the main focus of predictive maintenance due to their relevance within the production lines. Among electrical and mechanical failures in TIMs, voltage unbalance (VU) is a critical condition that can lead the motor to total failure. It motivated the development of sensor-based solutions to diagnose this supply anomaly, including the acoustic emission (AE) technique, which stands out as a non-invasive alternative for fault diagnosis. However, none of the previous works carried out a comprehensive evaluation of VU. Therefore, to fill this research gap, this manuscript proposes a detection, phase(s) identification, and percentage classification of VU. This approach is based on AE signals acquired by piezoelectric transducers and Zero-Crossing-Weighted Energy (ZE) to accomplish the analysis. The methodology consisted of several experiments where the TIM was subjected to unbalanced voltages. The signals were processed using the ZE index to perform a 3D clustering map for phase identification and a 2D linear regression for percentage classification. The results showed that the proposed method successfully evaluated the origin and magnitude of VU faults in TIMs. © 2021 IEEE.

关键词： Failure analysis

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Feature Extraction of Motor Imagination EEG Signals for a Collaborative Exoskeleton Robot Based on PSD Analysis 42

Feature Extraction of Motor Imagination EEG Signals for a Co...

引用

42nd Chinese control Conference, CCC 2023

作者： Wu, Yunfeng Li, Junze Ren, Zichen Lu, Bo Zhang, Changzhu Qi, Peng Tongji University Department of Control Science and Engineering No. 1239 Siping Road Shanghai200092 China Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47906 United States School of Mechanicaland Electric Engineering Soochow University Robotics and Microsystems Center Suzhou215021 China

ISBN: (纸本)9789887581543

The integration of brain-machine interface and exoskeleton robot has been widespread application in gait correction, walking assistance, and numerous other scenarios. To effectively extract the electroencephalogram (EEG) signal features of motor imagery while wearing an exoskeleton, this study proposes a frequency band pre-determination method based on power spectral density (PSD) that enables common spatial patterns (CSP) to extract features from the frequency band with the highest energy in the EEG. The signal power spectral density of all channels is obtained at or near the average frequency of the maximum short interval frequency component energy. A second round of filtering is performed on the data, and the signal components are used as input for the subsequent feature extraction step. The CSP method extracts features from the spatial domain signal and generates feature maps. Finally, Support Vector Machines (SVM) are utilized to classify the EEG signals. Based on the pre-set gait of the exoskeleton robot and the motor imagery paradigm, feature extraction and classification of motor imagery EEG data during exoskeleton use were conducted, with an average accuracy rate of 77%. The experimental results demonstrate the effectiveness of this method in extracting the motor imagery EEG features during exoskeleton use. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Electroencephalography

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Ensuring Zero Trust IoT Data Privacy: Differential Privacy in Blockchain using Federated Learning

引用

IEEE Transactions on Consumer Electronics 2024年 1-1页

作者： Hussain, Altaf Akbar, Wajahat Hussain, Tariq Bashir, Ali Kashif Dabel, Maryam M. Al Ali, Farman Yang, Bailin School of Electronic and Control Engineering Chang’an University Xi’an China School of Computer Science and Technology and School of Mathematics and Statistics Zhejiang Gongshang University Hangzhou China Department of Computing and Mathematics Manchester Metropolitan University UK Department of Computer Science and Engineering College of Computer Science and Engineering University of Hafr Al Batin Saudi Arabia Department of Applied AI School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul South Korea

In the increasingly digitized world, the privacy and security of sensitive data shared via IoT devices are paramount. Traditional privacy-preserving methods like k-anonymity and ldiversity are becoming outdated due to technological advancements. In addition, data owners often worry about misuse and unauthorized access to their personal information. To address this, we propose a secure data-sharing framework that uses local differential privacy (LDP) within a permissioned blockchain, enhanced by federated learning (FL) in a zero-trust environment. To further protect sensitive data shared by IoT devices, we use the Interplanetary File System (IPFS) and cryptographic hash functions to create unique digital fingerprints for files. We mainly evaluate our system based on latency, throughput, privacy accuracy, and transaction efficiency, comparing the performance to a benchmark model. The experimental results show that the proposed system outperforms its counterpart in terms of latency, throughput, and transaction efficiency. The proposed model achieved a lower average latency of 4.0 seconds compared to the benchmark model’s 5.3 seconds. In terms of throughput, the proposed model achieved a higher throughput of 10.53 TPS (transactions per second) compared to the benchmark model’s 8 TPS. Furthermore, the proposed system achieves 85% accuracy, whereas the counterpart achieves only 49%. IEEE

关键词： Anonymity

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Concept-Level Semantic Transfer and Context-Level Distribution Modeling for Few-Shot Segmentation

引用

IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Luo, Yuxuan Chen, Jinpeng Cong, Runmin Ip, Horace Ho Shing Kwong, Sam City University of Hong Kong Department of Computer Science Hong Kong Shandong University School of Control Science and Engineering Jinan250061 China Ministry of Education Key Laboratory of Machine Intelligence and System Control Jinan250061 China Lingnan University Hong Kong

Few-shot segmentation (FSS) methods aim to segment objects using only a few pixel-level annotated samples. Current approaches either derive a generalized class representation from support samples to guide the segmentation of query samples, which often discards crucial spatial contextual information, or rely heavily on spatial affinity between support and query samples, without adequately summarizing and utilizing the core information of the target class. Consequently, the former struggles with fine detail accuracy, while the latter tends to produce errors in overall localization. To address these issues, we propose a novel FSS framework, CCFormer, which balances the transmission of core semantic concepts with the modeling of spatial context, improving both macro and micro-level segmentation accuracy. Our approach introduces three key modules: (1) the Concept Perception Generation (CPG) module, which leverages pre-trained category perception capabilities to capture high-quality core representations of the target class;(2) the Concept-Feature Integration (CFI) module, which injects the core class information into both support and query features during feature extraction;and (3) the Contextual Distribution Mining (CDM) module, which utilizes a Brownian Distance Covariance matrix to model the spatial-channel distribution between support and query samples, preserving the fine-grained integrity of the target. Experimental results on the PASCAL-5i and COCO-20i datasets demonstrate that CCFormer achieves state-of-the-art performance, with visualizations further validating its effectiveness. Our code is available at ***/lourise/ccformer. © 1991-2012 IEEE.

关键词： Semantic Segmentation

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