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Chinese control and Decision Conference, CCDC

作者： Gengen Li Chunxi Yang Xiufeng Zhang Jin Zhang Jing Na Guanbin Gao Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Intelligent Control and Application Kunming China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

The particle degeneration is a significant factor effecting filter performance in traditional particle filter, which poses a challenge for its application in multi-sensor fusion. The resampling is a valid scheme to counter this issue, such as roughening, a prior editing procedure, and regularisation, but these measures are prone to generate the sample impoverishment. Different from these methods, this paper proposes a multi-sensor particle filter based on improved grey wolf optimizer (IGWO) from the perspective of intelligent optimal sampling. Firstly, the grey wolf optimizer is modified to acquire better exploitation and exploration abilities. Then, the IGWO is used to optimize the posteriori distribution of particles through guiding them to move towards the high likelihood region, which not only avoids the particle degeneration, but also holds the diversity of particles. Finally, a numerical example that an indoor localization for mobile robot with multi-camera visual system is provided to illustrate the effectiveness of the proposed algorithm.

关键词： Location awareness Accuracy Noise Filtering algorithms Visual systems Approximation algorithms Particle filters

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Consensus Tracking Data-Driven control of Multi-Agent Systems based on Matrix S-Lemma Under Noisy Data

Consensus Tracking Data-Driven Control of Multi-Agent System...

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Data Driven control and Learning Systems (DDCLS)

作者： Shuli Tan Xiufeng Zhang Chunxi Yang Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Intelligent Control and Application Kunming Yunnan China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In this paper, the problem of consensus tracking for multi-agent systems in the presence of noise inter-ference is investigated. Unlike the traditional model-based approach, this paper assumes that the dynamics of all agents are unknown and only a portion of the input-state data is available. The offline data contaminated with noise interference is utilized. By introducing the matrix S-lemma, a necessary and sufficient conditions for directly designing consensus tracking controllers from bounded noisy input-state data is given. This method can be applied to large datasets and effectively addresses computational challenges associated with extensive data. Simulation result illustrates the effectiveness of the theoretical analysis.

关键词： Learning systems Sufficient conditions Simulation Noise Interference control systems Data models

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Adaptive Depth Estimation of Homography-Based Visual Servo with Finite-time Convergence

Adaptive Depth Estimation of Homography-Based Visual Servo w...

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Data Driven control and Learning Systems (DDCLS)

作者： Qiwen Fang Xian Wang Yingbo Huangl Haoran He Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Intelligent Control and Application Kunming University of Science and Technology Kunming China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Although the homography-based visual servo (HBVS) has been studied for many decades, how to obtain the depth information with fast and accurate convergence performance has not been fully studied. In this paper, a finite-time (FT) adaptive depth estimation method is suggested for the manipulator regulation of the eye-in-hand (EIH) visual servo system. To realize this objective, a linearized camera model including depth information is first established. To obtain the depth, a set of auxiliary variables are constructed to extract the depth estimation error with a series of simple algebraic calculation. Then, inspired by the sliding mode control, a FT adaptive depth estimation algorithm is developed, in which the predictor and/or observer error are not required, but the depth estimation error is employed to drive the estimation toward to its true value within finite time. With the estimated depth, the HBVS controller of the EIH system can regulate the robot arm to the desired position with fast response. Finally, comparative simulation results are given to show the efficiency of the proposed method.

关键词： Visualization Estimation error Adaptive systems Simulation Observers Manipulators Cameras

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Informer-LSTM-EWMA for Panax Notoginseng with Prediction of Early Intervention Time

Informer-LSTM-EWMA for Panax Notoginseng with Prediction of ...

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Data Driven control and Learning Systems (DDCLS)

作者： Jiahui Ye Chunxi Yang Xiufeng Zhang Jin Zhang Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming China Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Panax notoginseng is one of economic crop with significant medicinal value in southwest China. However, traditional extensive fertilization and irrigation method usually damages the soil, making it difficult to achieve sustainable cultivation of Panax notoginseng. In order to assess the growth status of Panax notoginseng and take timely action, this paper proposed an Informer-LSTM-EWMA model to predict the growth trend of Panax notoginseng in cultivation fields. In the experimental greenhouse, we set up two experimental fields A and B to verify the feasibility of the proposed method. Firstly, wavelet denoising is utilized on the date collected by the sensor. Then, Informer and Long short-term Memory (LSTM) are used to train the denoised data to obtain data prediction. Further process the predicted data through Exponential Weighted Moving Average (EWMA) for trend prediction. Finally, the early intervention time is obtained by searching for the maximum value. The results indicate that the model can predict early intervention time and its predictive performance is far better than that of a single model.

关键词： Learning systems Economics Irrigation Noise reduction Crops Predictive models Soil

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An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

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Computers, Materials & Continua 2023年第2期74卷 2905-2925页

作者： Walid El-Shafai Noha A.El-Hag Ahmed Sedik Ghada Elbanby Fathi E.Abd El-Samie Naglaa F.Soliman Hussah Nasser AlEisa Mohammed E.Abdel Samea Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Higher Institute of Commercial Science Management Information SystemsEl-Mahala El-KobraEgypt Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr El-SheikhEgypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Medical Imaging and Interventional Radiology National Liver InstituteMenoufia UniversityEgypt

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and *** learning provides a high performance for several medical image analysis *** paper proposes a deep learning model for the medical image fusion *** model depends on Convolutional Neural Network(CNN).The basic idea of the proposed model is to extract features from both CT and MR ***,an additional process is executed on the extracted *** that,the fused feature map is reconstructed to obtain the resulting fused ***,the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching(HM),Histogram Equalization(HE),fuzzy technique,fuzzy type,and Contrast Limited Histogram Equalization(CLAHE).The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement *** realistic datasets of different modalities and diseases are tested and ***,real datasets are tested in the simulation analysis.

关键词： Image fusion CNN deep learning feature extraction evaluation metrics medical diagnosis

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Distributed Fixed-Time Algorithms for Time-Varying Constrained Optimization Problems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： He, Xing Li, Yue Zhang, Meng Huang, Tingwen Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Chongqing400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

In this paper, the distributed form of the zeroing neural network for solving time-varying optimal problems is put forward. Compared with traditional centralized algorithms, distributed algorithms possess better privacy and scalability. This paper initially proposes a centralized time-varying optimisation algorithm with fixed-time convergence and certain robustness, which is based on the integration-enhanced zeroing neural network. Subsequently, the algorithm is enhanced, and two distributed algorithms are designed separately. Both of these two algorithms have a fixed convergence time and certain robustness. Additionally, this paper utilizes the penalty function approach to handle time-varying optimization problems with inequality constraints, thereby making the algorithm more widely applicable. The effectiveness of the algorithm is verified through several numerical examples, and the applicability of the algorithm is demonstrated by solving the package-level state-of-charge balancing problem. © 2020 IEEE.

关键词： Optimization algorithms

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Adaptive Finite-Time Parameter Estimation for a Reaction Force-Sensing Series Elastic Actuator 19th

Adaptive Finite-Time Parameter Estimation for a Reaction F...

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19th Chinese intelligent Systems Conference, CISC 2023

作者： Li, Qi Xing, Yashan Gao, Guanbin Na, Jing Lu, Sheng Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming650500 China Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment Kunming University of Science and Technology Kunming650500 China Department of Orthopedics First People’s Hospital of Yunnan Province Kunming650032 China

ISBN: (纸本)9789819968855

In this paper, we propose an alternatively adaptive finite-time (FT) parameter estimation method for a developed reaction force-sensing series elastic actuator (RFSEA). Specifically, a compact RFSEA is first designed for exoskeletons, where the spring and drive components are configured in parallel. In contrast to classic estimation approaches depending on the predictor errors to derive the adaptive law, the proposed method is developed directly based on the parameter estimation errors, which is extracted by applying a low-pass filter operation. Moreover, a feasible way to achieve the PE condition verification online is provided. Furthermore, the homogeneous correction term is used for designing the adaptive law such that the FT convergence can be ensured. Finally, the effectiveness validation of the proposed method is provided by comparative simulations for the developed RFSEA. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Low pass filters

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A Data Transmission Approach Based on Ant Colony Optimization and Threshold Proxy Re-encryption in WSNs

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Journal of Artificial Intelligence and Technology 2022年第1期2卷 23-31页

作者： Jing Liu Zenghui Liu Chenyu Sun Junxi Zhuang College of Computer Science Faculty of Information TechnologyBeijing University of TechnologyBeijing 100124China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of EducationBeijing 100124China College of Automation Engineering Beijing PolytechnicBeijing 100176China

Wireless sensor networks(WSNs)have become increasingly popular due to the rapid growth of the Internet of *** open wireless transmission media are easy to attack,security is one of the primary design concerns for *** solutions consider routing and data encryption as two isolated issues,providing incomplete ***,in this paper,we divide the WSN communication process into a data path selection phase and a data encryption *** propose an improved transmission method based on ant colony optimization(ACO)and threshold proxy re-encryption for WSNs,and we named it as *** method resists internal and external attacks and ensures safe and efficient data *** the data path selection stage,the ACO algorithm is used for network *** improvement of the pheromone concentration is *** order to resist attacks from external attackers,proxy re-encryption is extended to WSN in the data encryption *** threshold secret sharing algorithm is introduced to generate a set of re-encryption key fragments composed of random numbers at the source *** confirm the performance of our model via simulation studies.

关键词： wireless sensors network ant colony optimization pheromone proxy re-encryption threshold

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Human-Mechanical Biomechanical Analysis of a Novel Knee Exoskeleton Robot for Rehabilitation Training

Human-Mechanical Biomechanical Analysis of a Novel Knee Exos...

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International Conference on Applied Nonlinear Dynamics, Vibration, and control, ICANDVC 2023

作者： Yan, Mengmeng Gao, Guanbin Chen, Xin Xing, Yashan Lu, Sheng Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming650500 China Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment Kunming650500 China The First People’s Hospital of Yunnan Province Kunming650034 China Yunnan Key Laboratory of Digital Orthopedics Kunming650034 China

ISBN: (纸本)9789819705535

Exoskeleton robots are a new kind of wearable assistive and rehabilitation training equipment, with user adaptability and assistive effectiveness being important performance indicators. To improve the comfort of wearers and the effectiveness of rehabilitation training, this paper designs a knee rehabilitation training exoskeleton robot and analyzes its performance. Firstly, the exoskeleton robot is designed to reduce the lower limb movement load by placing the power source and motors at the wearer’s waist, and transmitting power to the knee through Bowden cables. A three-dimensional model of the exoskeleton robot is established. Secondly, a human-exoskeleton robot biomechanical simulation environment is constructed, including scenarios of the human body alone, the human body wearing a passive exoskeleton, and the human body wearing an active exoskeleton. Biomechanical simulations of the human-exoskeleton interactions are conducted in these three scenarios. The results show that the designed exoskeleton does not significantly impede human movement. When wearing the exoskeleton, the force and torque of the knees during squatting exercises are significantly reduced, and the muscle force and muscle activation of the quadriceps and gluteus maximus are significantly decreased, while the impact on the calf muscles is minimal. This verifies the good user adaptability and assistive effectiveness of the designed exoskeleton. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cables

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Multi-Evader Dynamic Pursuit Strategy Based on Improved Aapc and Auction Algorithm Amidst Static and Dynamic Obstacles

SSRN

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SSRN 2024年

作者： Chen, Maoxing Zhang, Xiufeng Li, Gengen Lai, Wendixin Yang, Chunxi Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming650504 China Yunnan Key Laboratory of Intelligent Control and Application Kunming University of Science and Technology Kunming650500 China

To address the multi-agent pursuit-evasion problem in complex environments, a multi-evader dynamic capture strategy is proposed. The strategy efficiently completes the pursuit task in complex environments by reasonably allocating pursuers. The strategy comprises three components: pursuer allocation, target point allocation, and interactive reallocation during the pursuit process. In the pursuer allocation phase, an improved AAPC algorithm is designed to allocate pursuers to each evader appropriately. For target point allocation, an auction algorithm is used to establish a benefit function between target points and pursuers, assigning target points to pursuers in a manner that maximizes overall benefits. During the pursuit process, an interactive reallocation strategy based on the positions of both pursuers and evaders is proposed to enhance the efficiency and flexibility of the pursuit strategy. To improve collision avoidance with static/dynamic irregular obstacles, an artificial potential field method incorporating discretized obstacle boundaries is employed, making the algorithm suitable for complex environments. Simulation results demonstrate that the strategy can effectively capture multiple evaders even in complex environments © 2024, The Authors. All rights reserved.

关键词： Collision avoidance

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