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arXiv 2024年

作者： Wang, Junda Hu, Minghui Li, Ning Al-Ali, Abdulaziz Suganthan, Ponnuthurai Nagaratnam The Key Laboratory of System Control and Information Processing Ministry of Education of China China The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China The School of Electrical and Electronic Nanyang Technological University Singapore The KINDI Computing Research Center College of Engineering Qatar University Qatar

Online learning of deep neural networks suffers from challenges such as hysteretic non-incremental updating, increasing memory usage, past retrospective retraining, and catastrophic forgetting. To alleviate these drawbacks and achieve progressive immediate decision-making, we propose a novel Incremental Online Learning (IOL) process of Randomized Neural Networks (Randomized NN), a framework facilitating continuous improvements to Randomized NN performance in restrictive online scenarios. Within the framework, we further introduce IOL with ridge regularization (-R) and IOL with forward regularization (-F). -R generates stepwise incremental updates without retrospective retraining and avoids catastrophic forgetting. Moreover, we substituted -R with -F as it enhanced precognition learning ability using semi-supervision and realized better online regrets to offline global experts compared to -R during IOL. The algorithms of IOL for Randomized NN with -R/-F on non-stationary batch stream were derived respectively, featuring recursive weight updates and variable learning rates. Additionally, we conducted a detailed analysis and theoretically derived relative cumulative regret bounds of the Randomized NN learners with -R/-F in IOL under adversarial assumptions using a novel methodology and presented several corollaries, from which we observed the superiority on online learning acceleration and regret bounds of employing -F in IOL. Finally, our proposed methods were rigorously examined across regression and classification tasks on diverse datasets, which distinctly validated the efficacy of IOL frameworks of Randomized NN and the advantages of forward regularization. Copyright © 2024, The Authors. All rights reserved.

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MFANet: Mixed Feature Attention Network for Port Lane Detection

MFANet: Mixed Feature Attention Network for Port Lane Detect...

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Chinese control Conference (CCC)

作者： Jinwei Zhang Jinya Su Dewei Yi Jun Yang Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education and the School of Automation Southeast University Nanjing China Department of Computing Science University of Aberdeen Aberdeen U.K.

With the advancement of industrial automation and artificial intelligence technology, unmanned port autonomy has gained increasing attention. Port automatic driving technology is a critical component of unmanned autonomous systems, enabling rubber tire gantry cranes (RTGs) to autonomously navigate by detecting port lanes and implementing control commands. In this study, a deep learning-based method is proposed for detecting port lanes in complex port scenes. The method employs the Inception module and attention mechanism to enhance lane feature extraction, and a structural loss is introduced to explicitly constrain the detection results and incorporate the prior characteristics of port lanes. A novel Mixed Feature Attention Network (MFANet) is proposed to implement the method for port lane detection. Experimental results demonstrate that MFANet effectively improves the accuracy of port lane detection while maintaining high computational efficiency. Furthermore, the performance of MFANet is evaluated in a real-world application scenario.

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Nonreciprocal Bundle Emissions of Quantum Entangled Pairs

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Physical Review Letters 2024年第4期133卷 043601-043601页

作者： Qian Bin Hui Jing Ying Wu Franco Nori Xin-You Lü School of Physics and Institute for Quantum Science and Engineering College of Physics Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha 410081 China Theoretical Quantum Physics Laboratory Cluster for Pioneering Research Center for Quantum Computing Physics Department

Realizing precise control over multiquanta emission is crucial for quantum information processing, especially when integrated with advanced techniques of manipulating quantum states. Here, by spinning the resonator to induce the Sagnac effect, we can obtain nonreciprocal photon-phonon and photon-magnon super-Rabi oscillations under conditions of optically driving resonance transitions. Opening dissipative channels for such super-Rabi oscillations enables the realization of directional bundle emissions of entangled photon-phonon pairs and photon-magnon pairs by transferring the pure multiquanta state to a bundled multiquanta outside of the system. This nonreciprocal emission is a flexible switch that can be controlled with precision, and simultaneous emissions of different entangled pairs (such as photon-phonon or photon-magnon pairs) can even emerge but in opposite directions by driving the resonator from different directions. This ability to flexibly manipulate the system allows us to achieve directional entangled multiquanta emitters, and has also potential applications for building hybrid quantum networks and on-chip quantum communications.

关键词： Cavity quantum electrodynamics Non-reciprocal propagation Photon statistics Quantum entanglement

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Wi-Wheat+: Contact-free wheat moisture sensing with commodity WiFi based on entropy

Wi-Wheat+: Contact-free wheat moisture sensing with commodit...

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作者： Yang, Weidong Shen, Erbo Wang, Xuyu Mao, Shiwen Gong, Yuehong Hu, Pengming College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China Henan Key Laboratory of Grain Photoelectric Detection and Control Zhengzhou450001 China College of International Education Kaifeng University Henan Kaifeng475004 China Knight Foundation School of Computing & Information Sciences Florida International University MiamiFL33199 United States Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

In this paper, we propose a contact-free wheat moisture monitoring system, termed Wi-Wheat+, to address the several limitations of the existing grain moisture detection technologies, such as time-consuming process, expensive equipment, low accuracy, and difficulty in real-time monitoring. The proposed system is based on Commodity WiFi and is easy to deploy. Leveraging WiFi CSI data, this paper proposes a feature extraction method based on multi-scale and multi-channel entropy. The feasibility and stability of the system are validated through experiments in both Line-Of-Sight (LOS) and Non-Line-Of-Sight (NLOS) scenarios, where ten types of wheat moisture content are tested using multi-class Support Vector Machine (SVM). Compared with the Wi-Wheat system proposed in our prior work, Wi-Wheat+ has higher efficiency, requiring only a simple training process, and can sense more wheat moisture content levels. © 2022 Chongqing University of Posts and Telecommunications

关键词： Channel state information (CSI) Multi-class support vector machine (SVM) Multi-scale entropy Radio frequency (RF) sensing WiFi

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Robust Maximum Power Point Tracking in PV Generation System: A Hybrid ANN-Backstepping Approach With PSO-GA Optimization

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IEEE Transactions on Consumer Electronics 2025年

作者： Hussan, Umair Waheed, Ahsan Bilal, Hazrat Wang, Huaizhi Hassan, Mudasser Ullah, Inam Peng, Jianchun Hosseinzadeh, Mehdi Shenzhen University College of Mechatronics and the Control Engineering Shenzhen518060 China The University of Faisalabad Department of Electrical Engineering Faisalabad38000 Pakistan University of Tasmania School of Engineering HobartTAS7005 Australia Gachon University Department of Computer Engineering Seongnam13120 Korea Republic of Macquarie University School of Computing Sydney Australia Duy Tan University School of Computer Science Da Nang Viet Nam

The growing diversity of consumer loads emphasizes the need for efficient operation of photovoltaic generation systems (PVGSs). This paper presents a novel control framework for maximum power point tracking (MPPT) in PVGSs, designed to enhance efficiency, robustness, and adaptability under dynamic environmental conditions. The proposed methodology integrates optimized Artificial Neural Network (ANN) with nonlinear backstepping controller. Hybrid particle swarm optimization (PSO) and genetic algorithm (GA) approach is used to optimize ANN, which leverages the global search capabilities of PSO and the local refinement strengths of GA to optimize the ANN’s weights and biases, ensuring accurate prediction of the PV reference voltage that is critical for MPPT. Additionally, the nonlinear backstepping controller ensures system stability and precise tracking of the predicted reference voltage by recursively constructing Lyapunov functions to guarantee asymptotic stability. The effectiveness of this control framework is evaluated through MATLAB/Simulink and validated with hardware testing. It is observed from simulation and hardware testing results, compared to existing ANN and conventional techniques, the proposed approach yielded significant improvements by achieving root mean square error of 0.103%, tracking accuracy of 99. 8%, and reduction of power loss to 0. 2%. The proposed control framework significantly enhances MPPT robustness and efficiency in dynamic environments and enables higher integration of renewable energy. This advancement supports scalable, resilient PV systems critical for sustainable energy transitions. © 1975-2011 IEEE.

关键词： Genetic algorithms

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3, 2, 1, Drones Go! A Testbed to Take off UAV Swarm Intelligence for Distributed Sensing

arXiv

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arXiv 2022年

作者： Qin, Chuhao Candan, Fethi Mihaylova, Lyudmila Pournaras, Evangelos School of Computing University of Leeds Leeds United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom

This paper introduces a testbed to study distributed sensing problems of Unmanned Aerial Vehicles (UAVs) exhibiting swarm intelligence. Several Smart City applications, such as transport and disaster response, require efficient collection of sensor data by a swarm of intelligent and cooperative UAVs. This often proves to be too complex and costly to study systematically and rigorously without compromising scale, realism and external validity. With the proposed testbed, this paper sets a stepping stone to emulate, within small laboratory spaces, large sensing areas of interest originated from empirical data and simulation models. Over this sensing map, a swarm of low-cost drones can fly allowing the study of a large spectrum of problems such as energy consumption, charging control, navigation and collision avoidance. The applicability of a decentralized multi-agent collective learning algorithm (EPOS) for UAV swarm intelligence along with the assessment of power consumption measurements provide a proof-of-concept and validate the accuracy of the proposed testbed. Copyright © 2022, The Authors. All rights reserved.

关键词： Drones

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Sparse Optical Flow-Based Line Feature Tracking

arXiv

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arXiv 2022年

作者： Fu, Qiang Yu, Hongshan Ali, Islam Zhang, Hong National Engineering Laboratory for Robot Visual Perception and Control Technology Hunan University China Department of Computing Science University of Alberta Canada

In this paper we propose a novel sparse optical flow (SOF)-based line feature tracking method for the camera pose estimation problem. This method is inspired by the point-based SOF algorithm and developed based on an observation that two adjacent images in time-varying image sequences satisfy brightness invariant. Based on this observation, we redefine the goal of line feature tracking: track two endpoints of a line feature instead of the entire line based on gray value matching instead of descriptor matching. To achieve this goal, an efficient two endpoint tracking (TET) method is presented: first, describe a given line feature with its two endpoints;next, track the two endpoints based on SOF to obtain two new tracked endpoints by minimizing a pixel-level grayscale residual function;finally, connect the two tracked endpoints to generate a new line feature. The correspondence is established between the given and the new line feature. Compared with current descriptor-based methods, our TET method needs not to compute descriptors and detect line features repeatedly. Naturally, it has an obvious advantage over computation. Experiments in several public benchmark datasets show our method yields highly competitive accuracy with an obvious advantage over speed. The source code of our method is released at: https://***/cnqiangfu/TET. Copyright © 2022, The Authors. All rights reserved.

关键词： Optical flows

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One family of 13315 stable periodic orbits of non-hierarchical unequal-mass triple systems

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Science China(Physics,Mechanics & Astronomy) 2021年第1期64卷 108-113页

作者： XiaoMing Li XiaoChen Li ShiJun Liao MOE Key Laboratory of Disaster Forecast and Control in Engineering School of Mechanics and Construction EngineeringJinan UniversityGuangzhou510632China Department of Earth Atmospheric and Planetary SciencesMassachusetts Institute of TechnologyCambridge02139USA School of Civil Engineering and Transportation South China University of TechnologyGuangzhou510641China Department of Engineering Science University of OxfordOxfordOX13PJUK Center of Advanced Computing School of Naval ArchitectureOcean and Civil EngineeringShanghai Jiao Tong UniversityShanghai200240China School of Physics and Astronomy Shanghai Jiao Tong UniversityShanghai200240China

The three-body problem can be traced back to Newton in 1687,but it is still an open question *** that only a few periodic orbits of three-body systems were found in 300 years after Newton mentioned this famous *** triple systems are common in astronomy,practically all observed periodic triple systems are hierarchical(similar to the Sun,Earth and Moon).It has traditionally been believed that non-hierarchical triple systems would be unstable and thus should disintegrate into a stable binary system and a single star,and consequently stable periodic orbits of non-hierarchical triple systems have been expected to be rather ***,we report here one family of 135445 periodic orbits of non-hierarchical triple systems with unequal masses;13315 among them are *** with the narrow mass range(only 10-5)in which stable"Figure-eight"periodic orbits of three-body systems exist,our newly found stable periodic orbits have fairly large mass *** find that many of these numerically found stable non-hierarchical periodic orbits have mass ratios close to those of hierarchical triple systems that have been measured with astronomical *** implies that these stable periodic orbits of non-hierarchical triple systems with distinctly unequal masses quite possibly can be observed in *** investigation also suggests that there should exist an infinite number of stable periodic orbits of non-hierarchical triple systems with distinctly unequal *** that our approach has general meaning:in a similar way,every known family of periodic orbits of three-body systems with two or three equal masses can be used as a starting point to generate thousands of new periodic orbits of triple systems with distinctly unequal masses.

关键词： three-body problem non-hierarchical system periodic orbits

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Drives and Impulses: Shaping Motivation and Procedural Learning for Humanoid Robots

Drives and Impulses: Shaping Motivation and Procedural Learn...

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IEEE International Conference on Development and Learning, ICDL

作者： Leonardo L. Rossi Letícia Berto Paula D. P. Costa Ricardo Gudwin Esther Colombini Alexandre Simões School of Electrical and Computer Engineering University of Campinas Campinas Brazil Hub for Artificial Intelligence and Cognitive Architectures (H.IAAC) Campinas Brazil Institute of Computing University of Campinas Campinas Brazil Department of Control and Automation Engineering São Paulo State University Sorocaba Brazil

ISBN: (数字)9798350348552

ISBN: (纸本)9798350348569

In the emerging field of robotics, the initial development of robots handling basic tasks represented a revolutionary breakthrough. With ongoing advancements in science and technology, we anticipate the integration of artificial entities into diverse environments, performing a range of tasks autonomously. This necessitates the cultivation of intelligence and adaptability in these agents. To achieve this, agents must make informed decisions, considering environmental consequences. Motivation becomes pivotal, guiding decision-making and behavior toward fulfilling the agent's needs, drawing inspiration from human adeptness driven by motivation. This research endeavors to adapt two distinct motivation theories from human literature for the learning process of artificial agents. Employing a cognitive architecture, our investigation involves the learning process of a simulated humanoid robot, incorporating two distinct motivation systems: (i) behaviors driven by enduring needs and (ii) behaviors arising from impulses linked to temporary needs. The primary aim is to enhance comprehension of behaviors under diverse motivational frameworks within artificial cognitive agents and give insights into the direction of autonomous robots.

关键词： Affordances Scalability Decision making Humanoid robots External stimuli Learning (artificial intelligence) Drives

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Experiences with Postdoctoral Training in Human Factors: Mentor and Trainee Perspectives 67th

Experiences with Postdoctoral Training in Human Factors: Men...

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67th International Annual Meeting of the Human Factors and Ergonomics Society, HFES 2023

作者： Caine, Kelly Catchpole, Ken Sasangohar, Farzan Chen, Karen B. Hegde, Sudeep Souders, Dustin McDonald, Anthony McLaurin, Elease J. Clemson University School of Computing ClemsonSC United States Medical University of South Carolina Clinical Practice and Human Factors CharlestonSC United States Texas A&M University Industrial and Systems Engineering CentralSC United States North Carolina State University Edward P. Fitts Department of Industrial and Systems Engineering RaleighNC United States Clemson University Department of Industrial Engineering CentralSC United States Clemson University Psychology Department ClemsonSC United States University of Wisconsin-Madison Department of Industrial and Systems Engineering MadisonWI United States Oak Ridge Institute for Science & Education Fellowship Program Centers for Disease Control & Prevention PhiladelphiaPA United States

Postdoctoral training positions are becoming more common in the human factors and ergonomics (HFE) discipline. However, conversations related to training in the HFE discipline have largely focused on undergraduate and graduate education. This panel assembles both postdoctoral mentors and former trainees who collectively have a diverse set of HFE-related postdoctoral experiences. By panelists discussing their experiences, observations, and recommendations related to postdoctoral training with the audience, the panel session will support HFE faculty and students in making more informed decisions about if and how a postdoctoral experience (either as a mentor or trainee) could be a part of their career development in HFE. © 2023 Human Factors and Ergonomics Society.

关键词： education human factors mentorship postdoctoral training

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