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Inverse Compensation Feedforward Control Strategy for a Vertical Pneumatic Artificial Muscle System

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Inverse Compensation Feedforward Control Strategy for a Vert...

IEEE International Conference on Control Science and systems Engineering (CCSSE)

作者： Huai Xiao Qingxin Meng Xuzhi Lai Ze Yan Shiying Zhao Min Wu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan Hubei China

This paper proposes an inverse compensation feed-forward control strategy for a vertical pneumatic artificial muscle (PAM) system. Firstly, we conduct a preliminary experiment on the vertical PAM system, and on this basis, we analyze the motion characteristics of the system. Moreover, we clarify the control objective of this paper. Then, we establish a model that can describe the hysteresis characteristics of the system, and we further construct an inverse compensation feedforward controller by inversing this established model. Finally, we carry out some tracking control experiments based on the vertical PAM experimental platform to verify the effectiveness and superiority of the proposed control strategy,

关键词： Artificial muscles Tracking Control systems Real-time systems Modeling Feedforward systems Hysteresis

DATA SMOOTHING FILLING METHOD BASED ON SCRNA-SEQ DATA ZERO-VALUE IDENTIFICATION

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arXiv 2024年

作者： Jiang, Linfeng Zhu, Yuan School of Automation China University of Geosciences Hongshan District No. 388 Lumo Road Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Hongshan District No. 388 Lumo Road Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Hongshan District No. 388 Lumo Road Wuhan430074 China

Single-cell RNA sequencing (scRNA-seq) determines RNA expression at single-cell resolution. It provides a powerful tool for studying immunity, regulation, and other life activities of cells. However, due to the limitations of the sequencing technique, the scRNA-seq data are represented with sparsity, which contains missing gene values, i.e., zero values, called dropout. Therefore, it is necessary to impute missing values before analyzing scRNA-seq data. However, existing imputation computation methods often only focus on the identification of technical zeros or imputing all zeros based on cell similarity. This study proposes a new method (SFAG) to reconstruct the gene expression relationship matrix by using graph regularization technology to preserve the high-dimensional manifold information of the data, and to mine the relationship between genes and cells in the data, and then uses a method of averaging the clustering results to fill in the identified technical zeros. Experimental results show that SFAG can help improve downstream analysis and reconstruct cell trajectory. © 2024, CC BY.

关键词： RNA

Recursive Filtering for Time-Varying Nonlinear Delayed systems with Stochastic Parameter Matrices and Censored Measurements

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Recursive Filtering for Time-Varying Nonlinear Delayed Syste...

Chinese Control Conference (CCC)

作者： Shuo Yang Jun Hu Peixia Gao Ruijie Dong Zhihui Wu Department of Applied Mathematics Harbin University of Science and Technology Harbin China Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems Harbin University of Science and Technology Harbin China School of Automation Harbin University of Science and Technology Harbin China

This paper discusses the design problem of recursive filtering method for time-varying nonlinear delayed systems (NDSs) with stochastic parameter matrices (SPMs) and censored measurements. In particular, the Tobit Type I model provides a description of the censored measurements. The main objective of this paper is to construct a recursive filter for NDSs with both SPMs and censored measurements. The upper bound of the filtering error covariance is first calculated via mathematical induction, and the upper bound is then minimized by choosing proper filter gain. Besides, a sufficient condition is provided to guarantee that the filtering error is uniformly bounded in the mean-square sense. Finally, the viability and applicability of the proposed filtering method are demonstrated using a numerical simulation.

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Research on Inventory Model of Non-instantaneous Deteriorating Items under Shortage Conditions 32

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Research on Inventory Model of Non-instantaneous Deteriorati...

第32届中国控制与决策会议

作者： Zheng Jiming Zhu Hongyu Chongqing University of Posts and Telecommunication Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunication School of Science

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

This paper studies the inventory control and pricing strategies of non-instantaneous deteriorating items in an order period under shortage conditions for retailers with online sales channels and store sales channels.A new inventory model is established with the goal of maximizing the average profit of retailers in the period,and an effective algorithm for solving the optimal price is designed based on the iterative ***,the optimal solution of the retailer’s average profit function is obtained by MATLAB simulation in numerical examples,namely,the optimal sales price,the optimal stock-out point and the optimal order *** sensitivity of the main parameters such as initial inventory and sales price is analyzed,and the corresponding inventory management strategy is summarized.

关键词： Inventory model Dual-channel Non-instantaneous deteriorating items Shortage

Discretizing Continuous Action Space with Unimodal Probability Distributions for On-Policy Reinforcement Learning

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arXiv 2024年

作者： Zhu, Yuanyang Wang, Zhi Zhu, Yuanheng Chen, Chunlin Zhao, Dongbin The Department of Control Science and Intelligent Engineering School of Management and Engineering Nanjing University Nanjing210093 China The Research Center for Novel Technology of Intelligent Equipment Nanjing University Nanjing210093 China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

For on-policy reinforcement learning, discretizing action space for continuous control can easily express multiple modes and is straightforward to optimize. However, without considering the inherent ordering between the discrete atomic actions, the explosion in the number of discrete actions can possess undesired properties and induce a higher variance for the policy gradient estimator. In this paper, we introduce a straightforward architecture that addresses this issue by constraining the discrete policy to be unimodal using Poisson probability distributions. This unimodal architecture can better leverage the continuity in the underlying continuous action space using explicit unimodal probability distributions. We conduct extensive experiments to show that the discrete policy with the unimodal probability distribution provides significantly faster convergence and higher performance for on-policy reinforcement learning algorithms in challenging control tasks, especially in highly complex tasks such as Humanoid. We provide theoretical analysis on the variance of the policy gradient estimator, which suggests that our attentively designed unimodal discrete policy can retain a lower variance and yield a stable learning process. © 2024, CC BY.

关键词： Contrastive Learning

Robust Control of Feeding Speed in Coal Seam Drilling Process Considering Borehole Friction

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Robust Control of Feeding Speed in Coal Seam Drilling Proces...

Chinese Automation Congress (CAC)

作者： Xiao Liu Luefeng Chen Chengda Lu Haipeng Fan Min Wu Witold Pedrycz School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation China University of Geosciences Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Department of Computer Engineering Istinye University Istanbul Turkey

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

During the coal seam drilling process, the drill string is subject to compressive deformation, compounded by unpredictable variations in formation hardness and borehole wall friction, leading to challenges in maintaining a stable feeding speed. This paper presents a novel approach by introducing uncertain parameters to describe the effects of formation hardness and borehole wall friction. Drill string axial movement model is modeled as a polyhedral system based on a lumped parameter representation. To meet industrial performance requirements, we design a robust $H_{\infty}$ controller to achieve consistent feeding speed control. Our simulation results demonstrate the controller's effectiveness in ensuring system stability despite fluctuations in formation hardness and drill string friction.

关键词： Drilling Robust control Friction Simulation Geology Velocity control Stability criteria Process control Coal Output feedback

Multi-Agent Reinforcement Learning for Traffic Signal Control through Universal Communication Method

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arXiv 2022年

作者： Jiang, Qize Qin, Minhao Shi, Shengmin Sun, Weiwei Zheng, Baihua School of Computer Science Fudan University China Shanghai Key Laboratory of Data Science Fudan University China Shanghai Institute of Intelligent Electronics & Systems China School of Computing and Information Systems Singapore Management University Singapore

How to coordinate the communication among intersections effectively in real complex traffic scenarios with multi-intersection is challenging. Existing approaches only enable the communication in a heuristic manner without considering the content/importance of information to be shared. In this paper, we propose a universal communication form UniComm between intersections. UniComm embeds massive observations collected at one agent into crucial predictions of their impact on its neighbors, which improves the communication efficiency and is universal across existing methods. We also propose a concise network UniLight to make full use of communications enabled by UniComm. Experimental results on real datasets demonstrate that UniComm universally improves the performance of existing state-of-the-art methods, and UniLight significantly outperforms existing methods on a wide range of traffic situations. Source codes are available at https://***/zyr17/UniLight. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

ESVAE: An Efficient Spiking Variational Autoencoder with Reparameterizable Poisson Spiking Sampling

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arXiv 2023年

作者： Zhan, Qiugang Tao, Ran Xie, Xiurui Liu, Guisong Zhang, Malu Tang, Huajin Yang, Yang The Complex Laboratory of New Finance and Economics School of Computing and Artificial Intelligence Southwestern University of Finance and Economics Chengdu611130 China The Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of China Chengdu611731 China The School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China The College of Computer Science The State Key Laboratory of Brain-Machine Intelligence Zhejiang University Hangzhou310027 China

In recent years, studies on image generation models of spiking neural networks (SNNs) have gained the attention of many researchers. Variational autoencoders (VAEs), as one of the most popular image generation models, have attracted a lot of work exploring their SNN implementation. Due to the constrained binary representation in SNNs, existing SNN VAE methods implicitly construct the latent space by an elaborated autoregressive network and use the network outputs as the sampling variables. However, this unspecified implicit representation of the latent space will increase the difficulty of generating high-quality images and introduce additional network parameters. In this paper, we propose an efficient spiking variational autoencoder (ESVAE) that constructs an interpretable latent space distribution and designs a reparameterizable spiking sampling method. Specifically, we construct the prior and posterior of the latent space as a Poisson distribution using the firing rate of the spiking neurons. Subsequently, we propose a reparameterizable Poisson spiking sampling method, which is free from the additional network. Comprehensive experiments have been conducted, and the experimental results show that the proposed ESVAE outperforms previous SNN VAE methods in reconstructed & generated image quality. In addition, experiments demonstrate that the encoder of ESVAE can retain the original image information more efficiently and is more robust. The source code is available at https://***/QgZhan/ESVAE. Copyright © 2023, The Authors. All rights reserved.

关键词： Poisson distribution

Few-shot learning based histopathological image classification of colorectal cancer

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intelligent Medicine 2024年第4期4卷 256-267页

作者： Rui Li Xiaoyan Li Hongzan Sun Jinzhu Yang Md Rahaman Marcin Grzegozek Tao Jiang Xinyu Huang Chen Li Key Laboratory of Intelligent Computing in Medical Image Ministry of EducationNortheastern UniversityShenyangLiaoning 110167China Cancer Hospital China Medical UniversityShenyangLiaoning 110122China Shengjing Hospital China Medical UniversityShenyang.Liaoning 110000China Institute for Medical Informatics University of LuebeckGermany Department of Knowledge Engineering University of Economics in KatowicePoland School of Intelligent Medicine Chengdu University of Traditional Chinese MedicineChengduSichuan 610075China International Joint Institute of Robotics and Intelligent Systems Chengdu University of Information TechnologyChengduSichuan 610225China School of Computer Science and Engineering University of New South WalesSydneyNSW 2052Australia

Background Colorectal cancer is a prevalent and deadly disease worldwide,posing significant diagnostic *** histopathologic image classification is often inefficient and *** some histopathologists use computer-aided diagnosis to improve efficiency,these methods depend heavily on exten-sive data and specific annotations,limiting their *** address these challenges,this paper proposes a method based on few-shot *** This study introduced a few-shot learning approach that combines transfer learning and contrastive learning to classify colorectal cancer histopathology images into benign and malignant *** model comprises modules for feature extraction,dimensionality reduction,and classification,trained using a combi-nation of contrast loss and cross-entropy *** this paper,we detailed the setup of hyperparameters:n-way,κ-shot,β,and the creation of support,query,and test *** Our method achieved over 98% accuracy on a query dataset with 35 samples per category using only 10 training samples per *** documented the model’s loss,accuracy,and the confusion matrix of the ***,we employed the t-SNE algorithm to analyze and assess the model’s classification *** The proposed model may demonstrate significant advantages in accuracy and minimal data depen-dency,performing robustly across all tested n-way,κ-shot *** consistently achieved over 93% accuracy on comprehensive test datasets,including 1916 samples,confirming its high classification accuracy and strong generalization *** research could advance the use of few-shot learning in medical diagnostics and also lays the groundwork for extending it to deal with rare,difficult-to-diagnose cases.

关键词： Colorectal cancer Few-shot learning Transfer learning Contrastive learning Histopathological images Benign and malignant categories