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Aero-optic imaging deviation prediction based on ISSA-ELM

Optoelectronics Letters 2023年第7期19卷 425-431页

作者： XU Liang ZHAO Shiwei WANG Tao Tianjin Key Laboratory for Control Theory&Applications in Complicated Systems School of Electrical Engineering and AutomationTianjin University of TechnologyTianjin 300384China School of Intelligent Engineering Sun Yat-sen UniversityGuangzhou 510275China

When the aircraft is moving at high speed in the atmosphere,aero-optical imaging deviation will appear due to the influence of aero-optical *** order to achieve real-time compensation during the flight of the aircraft,it is necessary to analyze and predict the obtained imaging deviation *** order to improve the search speed and accuracy of the prediction algorithm and the ability to jump out of local optimum,in this paper,an improved sparrow search algorithm optimized extreme learning machine(ISSA-ELM) neural network model is proposed to predict the aero-optical imagine ***,the performance of ISSA-ELM,ELM neural network and SSA-ELM neural network was *** results showed that compared with ELM and SSA-ELM algorithms,the convergence speed of ISSA-ELM was significantly enhanced,and the accuracy of data prediction was also significantly improved.

关键词： effect. deviation extreme

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Enhancing Agricultural Productivity: A Sensor Fusion, Spectroscopic Near Infrared, and AI-Based Framework for Monitoring Fruit Ripeness 21

Enhancing Agricultural Productivity: A Sensor Fusion, Spectr...

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21st International Bhurban Conference on Applied Sciences and Technology, IBCAST 2024

作者： Ambreen, Hira Imran, Muhammad Diyin, Tang Zhang, Chongsheng Sir Syed CASE Institute of Technology Islamabad Pakistan School of Automation Science and Electrical Engineering Beihang University Aeronautics and Astronautics China Henan Univerity Department of EE Kaifeng475001 China School of Automation Science and Electrical Engineering Beihang University Aeronautics and Astronautics Department of Detection and Automation Engineering China School of Computer & Information Engineering Henan University Kaifeng475001 China

ISBN: (纸本)9798331516680

Fruit ripeness evaluation is essential for harvest optimization and post-harvest loss prevention. Traditional visual inspection is inefficient. This study employs sensor fusion and AI algorithms to evaluate fruit maturity in real-time using gas sensors (i.e., MQ-3, MQ-6, MQ-8, MQ-135) and Near-Infrared spectral sensors (i.e., GY-AS7263). Primarily, data pro-cessing-sensor fusion, data preparation, and AI model application-is crucial to the proposed framework. Subsequently, sensor fusion approaches improve data accuracy by combining signals from various sensors. Secondly, data pretreatment techniques including noise reduction, normalization, and feature extraction prepare data for analysis. Thirdly, we use powerful machine learning algorithms such as Random Forest to judge maturity more accurately and reliably than conventional techniques. Lastly, integrating NIR spectroscopy with gas sensor data resolves cost and model transferability issues, enhancing accuracy and acces-sibility for agricultural use. Static image processing techniques provide challenges for real-time applications, including expensive computer costs and lower reliability. The proposed framework based on multi-sensor fusion system enhances agricultural applications' versatility, scalability, and reactivity, surpassing existing methodologies. © 2024 IEEE.

关键词： Fruits

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Cascading Delays for the High-Speed Rail Network Under Different Emergencies:A Double Layer Network Approach

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IEEE/CAA Journal of Automatica Sinica 2023年第10期10卷 2014-2025页

作者： Xingtang Wu Mingkun Yang Wenbo Lian Min Zhou Hongwei Wang Hairong Dong School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China School of Control and Computer Engineering North China Electric Power UniversityBeijing 100096China State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong UniversityBeijing 100044China National Research Center of Railway Safety Assessment Beijing Jiaotong UniversityBeijing 100044China

High-speed rail(HSR) has formed a networked operational scale in China. Any internal or external disturbance may deviate trains’ operation from the planned schedules, resulting in primary delays or even cascading delays on a network scale. Studying the delay propagation mechanism could help to improve the timetable resilience in the planning stage and realize cooperative rescheduling for dispatchers. To quickly and effectively predict the spatial-temporal range of cascading delays, this paper proposes a max-plus algebra based delay propagation model considering trains’ operation strategy and the systems’ constraints. A double-layer network based breadth-first search algorithm based on the constraint network and the timetable network is further proposed to solve the delay propagation process for different kinds of emergencies. The proposed model could deal with the delay propagation problem when emergencies occur in sections or stations and is suitable for static emergencies and dynamic emergencies. Case studies show that the proposed algorithm can significantly improve the computational efficiency of the large-scale HSR network. Moreover, the real operational data of China HSR is adopted to verify the proposed model, and the results show that the cascading delays can be timely and accurately inferred, and the delay propagation characteristics under three kinds of emergencies are unfolded.

关键词： Delay propagation double layer network high speed rail network max-plus algebra

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Switching control for Identification Deception in Cyber-Physical Systems 63

Switching Control for Identification Deception in Cyber-Phys...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Mavridis, Christos Kanellopoulos, Aris Sandberg, Henrik Johansson, Karl Henrik Kth Royal Institute of Technology Division of Decision and Control School of Electrical Engineering and Computer Science Stockholm Sweden

ISBN: (纸本)9798350316339

We investigate the problem of deceiving a malicious agent employing an identification method to estimate the closed-loop dynamics of a cyber-physical system. In particular, we propose a moving target defense mechanism that utilizes stochastic switching between linear closed-loop dynamics to drive a linear system identification process of a potential adversary to sub-optimal solutions with non-vanishing error. We provide a statistical analysis of the induced identification error and show that it is not possible for any linear system identification method to reconstruct the average dynamics of a stochastic switched linear system. Finally, we utilize the theory of Markov jump linear systems to guarantee asymptotic stability of the switching system, and formulate the switching control problem as an optimization problem that guarantees stability while taking into account the trade-off between security and switching effort. Simulation results showcase the efficacy of the proposed approach in inducing identification error for the adversary using minimal switching. © 2024 IEEE.

关键词： Markov processes

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A new appearance-based gaze estimation via multi-modal fusion 3

A new appearance-based gaze estimation via multi-modal fusio...

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3rd International Conference on Neural Networks, Information and Communication engineering, NNICE 2023

作者： Yang, Huijie Yang, Zuoyun Liu, Jiahui Chi, Jiannan School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing China

ISBN: (纸本)9798350335972

Appearance-based gaze estimation has gained more and more attention because of its generality, robustness, and subject independence. Deep learning, which has made a great deal of success in computer vision, has also greatly improved the accuracy of appearance-based gaze estimation. To further reduce the error in gaze estimation, we focus on extracting better feature information from eye and face images. In this paper, we propose a novel multimodal fusion gaze estimation model based on ConvNext and dilated convolution. In this model, the eye image and face image are used as input, and the ConvNext network is used to extract the features of the face image and the eye features are extracted by a dilated convolution-based network, and the feature map of the two images are fused using the fully connected layer to perform gaze estimation. In the experimental part, the designed model is verified on the public dataset MPIIGaze, and compared the proposed model with other gaze estimation models. The experimental results show that our proposed method has greatly improved the accuracy of gaze estimation on the MPIIGaze dataset compared to other related works. Our proposed multimodal fusion gaze estimation model achieves state-of-the-art result on the MPIIGaze dataset. © 2023 IEEE.

关键词： Convolution

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Multi-Feature Fusion Transformer for Natural Language Inference 5

Multi-Feature Fusion Transformer for Natural Language Infere...

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5th International Conference on Robotics, Intelligent control and Artificial Intelligence, RICAI 2023

作者： Sun, Lei Li, Xiangyang Yan, Hengxin School of Electrical Engineering and Automation Tianjin University of Technology Tianj in Key Laboratory for Control Theory and Complicated Industry Systems Tianjin China School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China

ISBN: (纸本)9798350357950

Natural Language Inference (NLI) is a branch of Natural Language Processing (NLP) whose main task is to determine the relationship between two sentences. Such tasks essentially use pre-trained models to ensure accuracy, and many applications are found in human-computer dialog and question-answering systems. However, in some cases where small devices are deployed offline, lightweight model implementations are often required to save computational resources. To address this problem and ensure more efficient inference capabilities, this paper describes how to improve the preexisting Transformer and proposes a Multi-Feature Fusion Transformer Network for NLI processing. The model integrates sequence features and introduces a priori information to enhance local features. It also achieves a comprehensive fusion of sequence information, local features, and non-local features, thus compensating for many potential shortcomings of Transformer. At the same time, it maintains the lightweight feature of the model and facilitates the implementation of downstream tasks. We verified the above point by experiments, our model has better inference performance compared to previous models used for the same task. The accuracy in the SNLI dataset reached 89.0%, while the matched and mismatched versions of MultiNLI reached 79.3% and 78.7%. © 2023 IEEE.

关键词： Feature Fusion Natural language inference Natural language processing Transformer

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Resilient observer-based event-triggered control for cyber-physical systems under asynchronous denial-of-service attacks

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Science China(Information Sciences) 2022年第4期65卷 182-196页

作者： Yifang ZHANG Zheng-Guang WU Zongze WU Deyuan MENG State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang University School of Automation Guangdong University of Technology Seventh Research Division Beihang University (BUAA) School of Automation Science and Electrical Engineering Beihang University (BUAA)

We develop the resilient observer-based event-triggered control update strategy in this *** is utilized to achieve the input-to-state stability（ISS） of cyber-physical systems（CPSs） when there is an asynchronous denial-of-service（DoS） attack, which is launched by malicious adversaries both on measurement channel and control channel in a random attack strategy. To estimate the unmeasurable states while saving the limited networked bandwidth, an H∞observer-based event-triggered control scheme is first designed to guarantee the ISS of CPSs with economic communication. Moreover, the occurrence of Zeno behavior can be eliminated by providing the existence of a lower positive bound of any two inter-event times. Then, a recursive model of asynchronous DoS attacks is introduced. A resilient control update strategy is proposed and further analyzed to derive the stability criterion of CPSs. At last, a numerical simulation example is given to demonstrate the effectiveness of the introduced control update policy.

关键词： cyber-physical systems input-to-state stability asynchronous DoS attacks $H_\infty~$ observer event-triggered control

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Dimensionality Reduction for Classification Using Divide-and-Conquer Based Genetic Programming 13

Dimensionality Reduction for Classification Using Divide-and...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Wang, Peng Xue, Bing Liang, Jing Zhang, Mengjie School of Electrical and Information Engineering Zhengzhou University Zhengzhou China Center for Data Science and Artificial Intelligence School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand School of Electrical Engineering and Automation Henan Institute of Technology Xinxiang China

ISBN: (纸本)9798350308365

Dimensionality reduction (DR) is to obtain meaningful low-dimensional representation concealed within high-dimensional data. Genetic programming (GP) has been used to achieve DR for classification because of the flexible representations and promising performance. However, one drawback of many current GP-based DR methods is the high computational demands, potentially limiting their utility in high-dimensional scenarios. To tackle this issue, this work develops a new GP algorithm to achieve DR for classification. A divide-and-conquer strategy based on feature clustering is proposed to split the original feature set into multiple small feature groups. Each tree in the multi-tree GP is used to learn one high-level feature from each feature group. The performance of the proposed method is examined on 10 classification datasets with varying difficulty. The results indicate that the new GP method achieves significantly better classification performance than commonly used feature construction and feature selection methods. More importantly, the proposed GP method uses fewer nodes but achieves better or comparable classification accuracy than the baseline multi-tree GP method. © 2024 IEEE.

关键词： Dimensionality reduction

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Optimizing Hydrogen Highway Planning Model Considering Hydrogen Cost and Vehicle Flow Effects

Optimizing Hydrogen Highway Planning Model Considering Hydro...

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2024 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2024

作者： Zhang, Yan Lv, Yong Wan, Yanming Sun, Yuxin Zhang, Xian Wang, Guibin Liu, Chang China Hydrogen Alliance Research Institute Beijing China Shenzhen University School of Mechanical Electrical and Control Engineering Shenzhen China University of Liverpool Department of Electrical and Electronic Engineering Liverpool United Kingdom School of Mechanical Engineering and Automation Shenzhen China

ISBN: (纸本)9798350352290

In recent years, global governments have been actively advocating for the adoption of hydrogen fuel cell vehicles (HFCVs) as a means to foster a more sustainable future in transportation. However, the widespread integration of these vehicles necessitates robust hydrogenation infrastructure support. This paper presents a novel hydrogen highway optimization planning model tailored for the strategic placement of hydrogen refueling stations (HRSs) along highways. Based on the traditional HRS site selection models, this approach innovatively integrates factors encompassing hydrogen procurement, transportation, and utilization. The model optimizes the location, scale of the HRSs and quantity of hydrogen dispensers within each HRS, utilizing the minimum unit cost of hydrogen as the objective function. To verify the effectiveness, the proposed model is applied in the Beijing-Tianjin-Hebei (BTH) and Chongqing regions, conducting HRS location planning across both demonstration and promotion stages and the specific planning results are obtained. This study offers valuable insights into hydrogen infrastructure planning along highways, serving as a reference for future endeavors in this domain. © 2024 IEEE.

关键词： Hydrogen fuels

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Design of De-tumbling Device for Improving the De-tumbling Performance of Uncooperative Space Target

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Space(Science & Technology) 2024年第1期4卷 10-19页

作者： Lei Du Zhen Chen Hengzai Hu Xiangdong Liu Youguang Guo School of Automation Beijing Institute of TechnologyBeijingChina Key Laboratory of Drive and Control of Servo Motion System Ministry of Industry and Information TechnologyBeijingChina School of Electrical and Data Engineering University of Technology SydneySydneyAustralia

This article presents the design of an optimal coil structure for 2 de-tumbling devices, each is carried by a de-tumbling robot. The design is based on electromagnetic eddy current method and aims to reduce the angular velocity of uncooperative space targets. It proposes an optimization framework with the advantages of safety and high performance. The magnetic field analytical model is established by the designed coil’s structure parameters, and the optimal structure parameters of the coil are determined. To further ensure the maximum magnetic field at the target, the electromagnetic characteristics under different current directions in the 2 coils are analyzed based on magnetic field analytical model, and their accuracy is verified using finite element method (FEM). Additionally, an improved Maxwell’s stress tensor method is proposed to calculate the de-tumbling torque, and its accuracy is assessed using traditional Maxwell’s stress tensor and virtual displacement method. The proposed optimal coil structure and its optimization framework can de-tumble over 1 million targets of various sizes, demonstrating universality.

关键词： uncooperative space target optimal structure parameters detumbling optimization framework electromagnetic eddy current reduce angular velocity electromagnetic eddy current method coil structure

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