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Elman neural network for predicting aero optical imaging deviation based on improved slime mould algorithm

Optoelectronics Letters 2023年第5期19卷 290-295页

作者： XU Liang WANG Luyang XUE Wei ZHAO Shiwei ZHOU Liye Tianjin Key Laboratory of Complex Control Theory and Application School of Electronic Engineering and AutomationTianjin University of TechnologyTianjin 300384China China Academy of Aerospace Science and Innovation Beijing 100048China

This research suggests a methodology to optimize Elman neural network based on improved slime mould algorithm(ISMA) to anticipate the aero optical imaging *** improved Tent chaotic sequence is added to the SMA to initialize the population to accelerate the algorithm’s speed of ***,an improved random opposition-based learning was added to further enhance the algorithm’s performance in addressing problems that the SMA has such as weak convergence ability in the late iteration and an easy tendency to fall into local optimization in the optimization process when solving the optimization ***,the algorithm model is compared to the Elman neural network and the SMA optimization Elman neural network *** three models are assessed using four evaluation indicators,and the findings demonstrate that the ISMA optimization model can anticipate the aero optical imaging deviation in an accurate way.

关键词： neural Elman network

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Real-Time Moving Horizon Estimation for Nonlinear Systems With Unknown Inputs 49

Real-Time Moving Horizon Estimation for Nonlinear Systems Wi...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Du, Songtao Wan, Yiming Zhang, Cong Zhang, Sihang School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Beijing Institute of Control Engineering Beijing China

ISBN: (纸本)9798350331820

Moving horizon estimation (MHE) is a well-known alternative to Kalman-like filtering due to its superior performance in terms of estimation accuracy, convergence speed, and robustness to poor initial state guesses. However, most MHE methods cope with unknown inputs by introducing a random walk model, which is unsuitable for fast-varying unknown inputs. To address this issue, we propose an unknown input moving horizon estimator (MHE-UI) that does not require any prior knowledge of the dynamic behavior of unknown inputs. To solve the associated nonlinear least squares problem in real time, we develop a computationally efficient Gauss-Newton iterative algorithm by exploiting the block pentadiagonal structure of the resulting Karush-Kuhn-Tucker (KKT) matrix. A numerical case study demonstrates that the proposed real-time MHE-UI algorithm achieves higher estimation accuracy than the unknown input unscented Kalman filter (UKF-UI) and significantly reduces the computational cost compared to the exact MHE-UI algorithm. © 2023 IEEE.

关键词： Nonlinear systems

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Adaptive Multi-Dimensional Taylor Network Tracking control for Stochastic Nonlinear Systems with Full-State Constraints and Input Saturation 8

Adaptive Multi-Dimensional Taylor Network Tracking Control f...

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8th International Conference on Robotics and automation engineering, ICRAE 2023

作者： Zheng, Xiao-Yi Yan, Hong-Sen Ministry of Education School of Automation Southeast University Key Laboratory of Measurement Control of Complex Systems of Engineering Nanjing210096 China

ISBN: (纸本)9798350327656

In this study, an adaptive tracking controller using multi-dimensional Taylor network (MTN) is presented for state-constrained nonlinear stochastic systems with saturated input, in which MTN is implemented to model the unknown nonlinear functions. Firstly, the barrier Lyapunov function (BLF) and backstepping technique are combined under a unified framework to eliminate the impact of full-state constraints. Then, the effect raised by saturated input is solved by introducing an appropriate auxiliary system. Furthermore, by employing the Lyapunov stability theorem, the designed adaptive controller could ensure that all closed-loop signals are bounded in probability, the output signal can track the desired signal successfully, the tracking error is bounded by the expected bound, and the system state constraints are never violated. Finally, the efficiency of the suggested control methodology is confirmed by providing an example. © 2023 IEEE.

关键词： Lyapunov functions

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Efficient Mobile Deployment of Lightweight CNN-Transformer Model for Crop Disease Diagnosis in Smart Agriculture 4

Efficient Mobile Deployment of Lightweight CNN-Transformer M...

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4th Asia Conference on Information engineering, ACIE 2024

作者： Lv, Zirong China University of Geosciences Hubei Key Lab. of Adv. Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation Wuhan China

ISBN: (纸本)9798350371925

Crop disease diagnosis is of great significance for crop yield and agricultural production. Deep learning methods have become a major research direction for solving crop disease diagnosis. However, recent studies have shown that deploying complex neural network models on mobile devices poses a significant challenge to mobile hardware performance. To address this issue, this paper proposes a lightweight network model based on the Transformer and deploys it on mobile devices. Compared to other lightweight network models, our approach significantly reduces computational complexity using Transformer modules and exhibits a particular advantage in recognition accuracy. It achieves a recognition rate of 97.76% for ten categories of tomato diseases, while the model size is only 3.86 MB. Furthermore, we deploy the model on embedded devices and apply it to tomato crop recognition, enabling farmers to monitor crop conditions in real-time and realizing smart agriculture. © 2024 IEEE.

关键词： Deep learning

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Self-supervised Depth Completion with Adaptive Online Adaptation 23

Self-supervised Depth Completion with Adaptive Online Adapta...

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7th International Conference on Machine Learning and Soft Computing, ICMLSC 2023

作者： Chen, Yang Tan, Yang Key Laboratory of Measurement and Control of Complex System of Engineering Ministry of Education School of Automation Southeast University Nanjing210096 China

ISBN: (纸本)9781450398633

Although depth completion has achieved remarkable performance relying on deep learning in recent years, these models tend to suffer a performance degradation when exposed to new environments. Online adaptation, where the model is trained in a self-supervised manner during testing, seems a promising technique to alleviate the drop. However, continuous online adaptation may cause the model to over-adapt and miss the optimal parameters, resulting in oscillation or even degradation of the model performance, in addition to wasting computational resources. Therefore, this paper proposes an adaptive online adaptation framework to make model adaptively trigger online adaptation when encountering novel environments and stop adaptation when model has adapted to the current environment. In detail, we design a trigger to detect the familiarity of model to the current scenario based on image similarity and then launch online adaptation when the scenario is novel. Besides, we elaborate a stopper to monitor the error between prediction and depth input and convert online adaptation to inference when online adaptation does not bring improvement for model. Experimental results demonstrate that our method improves the accuracy of model prediction and increases average running speed of the model on each frame in online adaptation. © 2023 ACM.

关键词： Deep learning

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CAPACITY ALLOCATION METHOD OF BATTERY ENERGY STORAGE SYSTEM CONSIDERING MULTI-SERVICE TARGET LEVEL ASSESSMENT

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International Journal of Electrical engineering 2022年第5期29卷 141-153页

作者： Ma, Suliang Wu, Yiwen Jiang, Yuan Li, Yaxin Li, Jianlin School of Electrical and Control Engineering North China University of Technology Shijingshan District Beijing China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Haidian District Beijing China

Rational planning of battery energy storage system is the key technology to solve the problem of high proportion of new energy consumption and the requirements of high performance power supply. Starting from the multiple application dimensions of power supply, power grid, load and battery energy storage system, this paper proposes a method of battery energy storage system capacity allocation based on multi-objective comprehensive evaluation. Firstly, the capacity configuration model of battery energy storage system is established from two aspects of service objectives and operation constraints;Then, under the two-level optimization framework, the capacity allocation scheme of the battery energy storage system is formed by using particle swarm optimization and multi- attribute decision-making methods;Finally, the configuration results of IEEE33 nodes with high proportion of distributed new energy access are analyzed, and the influence of model parameters and target orientation on the capacity of battery energy storage system is discussed. The simulation results show that the proposed method can flexibly and effectively configure the battery energy storage system, complete multiple different target service requirements, and promote the scientific application of the battery energy storage system. © 2022, Chinese Institute of Electrical engineering. All rights reserved.

关键词： Battery storage

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Exact controllability of rational expectations model with multiplicative noise and input delay

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Journal of automation and Intelligence 2024年第1期3卷 19-25页

作者： Wenjing Wang Juanjuan Xu Huanshui Zhang Minyue Fu School of Control Science and Engineering Shandong UniversityJinan 250061China College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhen 518055China

This paper considers the rational expectations model with multiplicative noise and input delay,where the system dynamics rely on the conditional expectations of future *** main contribution is to obtain a sufficient condition for the exact controllability of the rational expectations *** particular,we derive a sufficient Gramian matrix condition and a rank condition for the delay-free *** key is the solvability of the backward stochastic difference equations with input delay which is derived from the forward and backward stochastic system.

关键词： Rational expectations model Exact controllability Exactly null controllability Multiplicative noise Input delay

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An Adaptive Event-Triggered Secondary Regulation Strategy for Microgrids with Loss of Effectiveness Actuator Faults 49

An Adaptive Event-Triggered Secondary Regulation Strategy fo...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Qiu, Xuechao Wang, Xiangyu Niu, Dan Wang, Xiangyu School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China

ISBN: (纸本)9798350331820

In this paper, an adaptive event-triggered secondary regulation strategy is investigated for microgrids with loss of effectiveness actuator faults. In order to deal with unknown loss of effectiveness actuator faults, a distributed secondary regulation strategy is proposed, which achieves voltage and frequency regulations, as well as power sharing. Meanwhile, to save system resources and relieve the communication burden, an adaptive event-triggered mechanism is designed. Finally, some simulation results are given to validate the proposed strategy, which indicates that the proposed strategy reduces the controller updates and increases the reliability of system. © 2023 IEEE.

关键词： Actuators

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Improved frequency modeling and solution for parallel liquid-filled pipes considering both fluid-structure interaction and structural coupling

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Applied Mathematics and Mechanics(English Edition) 2022年第8期43卷 1269-1288页

作者： Xumin GUO Chunliang XIAO Hui MA Hui LI Xufang ZHANG Bangchun WEN School of Mechanical Engineering and Automation Northeastern UniversityShenyang110819China Key Laboratory of Vibration and Control of Aero-Propulsion System Ministry of Education Northeastern UniversityShenyang110819China

The dynamic characteristics of a single liquid-filled pipe have been broadly studied in the previous *** parallel liquid-filled pipe(PLFP)system is also widely used in engineering,and its structure is more complex than that of a single ***,there are few reports about the dynamic characteristics of the ***,this paper proposes improved frequency modeling and solution for the PLFPs,involving the logical alignment principle and coupled matrix *** established model incorporates both the fluid-structure interaction(FSI)and the structural coupling of the *** validity of the established model is verified by modal *** effects of some unique parameters on the dynamic characteristics of the PLFPs are *** work provides a feasible method for solving the FSI of multiple pipes in parallel and potential theoretical guidance for the dynamic analysis of the PLFPs in engineering.

关键词： parallel liquid-filled pipe(PLFP) dynamic analysis improved frequency modeling and solution fluid-structure interaction(FSI) structure coupling

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Deep learning Based Glue Line Defect Detection 18

Deep learning Based Glue Line Defect Detection

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Tong, Yingnan Qiu, Jun Lin, Hongye Wang, Yongbin Zhejiang University School of Control Sciences and Engineering Hang Zhou China NingBoTech University School of Information Science and Engineering NingBo China Jiangxi University of Science and Technology School of Electrical Engineering and Automation Ganzhou China

ISBN: (纸本)9798350312201

Most factories use automatic dispensing machines for workpiece sealing, and the processing efficiency is very high. The traditional manual detection is inefficient and costly. Besides, the rate of false detection and missed detection of defect products remains high. In view of this problem, this paper proposes a glue line defect detection model based on Yolov5 and the self-built workpiece defect data set is used for the research. Firstly, some conventional convolution modules in Backbone network are replaced with the corresponding deformable convolution modules to improve the generalization ability of the model. Then in order to improve the feature extraction ability of the model, the coordinate attention module is embedded in the Backbone network. The results show that the mean average precision of the proposed algorithm can reach 95.62%, which is greatly higher than initial Yolov5 algorithm. And the detection rate reaches 28.67 FPS, which can meet the real time detection. © 2023 IEEE.

关键词： Convolution

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