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A Deep Learning Based Personal Authentication Method Using sEMG Signal 41

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A Deep Learning Based Personal Authentication Method Using s...

第41届中国控制会议

作者： Lina Tong Mingjia Zhang Daisong Liu Hanghang Ma Yuxiang Zhang Liang Peng School of Mechanical Electronic & Information Engineering Chinese University of Mining and Technology-Beijing State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChina Academy of Science

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Personal authentication means that the relevant system confirms whether the identity of the user is real,legal and unique.A safe and convenient personal authentication method is a core for guarantee the security of our information and *** Electromyography(sEMG),which exists on the surface of the skin,is a good signal to achieve personal authentication,for it is difficult to be extracted and *** this paper,a new personal authentication method using forearm sEMG is ***,8-channel sEMG signals are recorded by Myo armband that is placed on right forearm of 14 subjects who perform the same hand open ***,two different deep learning models are presented to classify the *** multi-layers Convolution Neural Network(ML-CNN) and two stages Long Short Term Memory(2 s-LSTM) Network can achieve accuracy of 97.50% and 93.60% respectively,shows it is feasible to achieve the goal of personal *** the time cost of ML-CNN is higher than the 2 s-LSTM,both are lower than human reaction *** the architecture of ML-CNN is more suitable to achieving the goal of personal authentication.

关键词： Personal Authentication Surface Electromyography(sEMG) Myo multi-layers Convolution Neural Network(CNN) LSTM Network

ARTI: One New Adaptive Elliptical Weighting Model Combining with the Tikhonov-p-norm for Image Reconstruction 1

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12th International Conference on Broadband Communications, Networks, and systems, BROADNETS 2021

作者： Zhu, Chunhua Shi, Zhen Yang, Weidong Key Laboratory of Grain Information Processing and Control Ministry of Education Henan University of Technology Henan Zhengzhou450001 China College of Information Science and Engineering Henan University of Technology Henan Zhengzhou450001 China Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Henan Zhengzhou450001 China

ISBN: (数字)9783030934798

ISBN: (纸本)9783030934781

To reconstruct the target-induced attenuation image keeping consistent with the observed measurement data, this paper explores the use of a new horizontal distance attenuation-based elliptical weighting model in building an attenuation image, where a horizontal distance attenuation factor and a vertical distance attenuation factor are introduced, respectively, which is able to clear the difference of the voxel weightings perpendicular to the line-of-sight (LOS) direction, as well as the difference of the voxel weightings parallel to the LOS direction. Compared with the existing model, the proposed model can additively reflect the occlusion effect of the radio frequency signal when the target is close to the transceiver nodes. Besides, the Tikhonov-p-norm regularization is incorporated into the image reconstruction, which makes full use of the sparse ability of the p-norm (0 © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Image reconstruction

Co-Scheduling of Energy and Production in Discrete Manufacturing Considering Decision-Dependent Uncertainties

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arXiv 2024年

作者： Pan, Yiyuan Wang, Zhaojian The Key Laboratory of System Control and Information Processing Ministry of Education of China Department of Automation Shanghai Jiao Tong University Shanghai200240 China

Modern discrete manufacturing requires real-time energy and production co-scheduling to reduce business costs. In discrete manufacturing, production lines and equipment are complex and numerous, which introduces significant uncertainty during the production process. Among these uncertainties, decision-dependent uncertainties (DDUs) pose additional challenges in finding optimal production strategies, as the signature or the shape of the uncertainty set cannot be determined before solving the model. However, existing research does not account for decision-dependent uncertainties (DDUs) present in discrete manufacturing;moreover, current algorithms for solving models with DDUs suffer from high computational complexity, making them unsuitable for the real-time control requirements of modern industry. To this end, we proposed an energy-production co-scheduling model for discrete manufacturing, taking into account decision-dependent uncertainties (DDUs). Subsequently, we devised a method for transforming the constraints associated with DDUs into linear form ones. Finally, we designed a novel algorithm based on the column-and-constraint generation (C&CG) algorithm and undertook a theoretical analysis of its performance of convergence and algorithmic complexity. A real-world engine assembly line was used to test our model and algorithm. Simulation results demonstrate that our method significantly reduces production costs and achieves better frequency regulation performance. © 2024, CC BY.

关键词： Costs

Distributionally Robust Probabilistic Prediction for Stochastic Dynamical systems

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arXiv 2024年

作者： Xu, Tao He, Jianping The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

Probabilistic prediction of stochastic dynamical systems (SDSs) aims to accurately predict the conditional probability distributions of future states. However, accurate probabilistic predictions tightly hinge on accurate distributional information from a nominal model, which is hardly available in practice. To address this issue, we propose a novel functional-maximin-based distributionally robust probabilistic prediction (DRPP) framework. In this framework, one can design probabilistic predictors that have worst-case performance guarantees over a pre-defined ambiguity set of SDSs. Nevertheless, DRPP requires optimizing over the function space of probability distributions, which is generally intractable. We develop a methodology that equivalently transforms the original maximin in function spaces into convex-nonconcave minimax in Euclidean spaces. Although it remains intractable to seek a global optimal solution, two suboptimal solutions are derived. By relaxing the inner constraints, we obtain a suboptimal predictor called Noise-DRPP. Relaxing the outer constraints yields another suboptimal predictor, Eig-DRPP. More importantly, we establish an explicit upper bound on the optimality gap between Eig-DRPP and the global optimal predictor. Utilizing the theoretical results obtained from solving the two suboptimal predictors, we synthesize practical rules to judge whether the pre-defined ambiguity set is too large or small. Finally, we conduct elaborate numerical simulations to compare the performance of different predictors under different SDSs. © 2024, CC BY-NC-ND.

关键词： Prediction models

Stochastic Trajectory Optimization for Demonstration Imitation

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arXiv 2024年

作者： Ming, Chenlin Wang, Zitong Zhang, Boxuan Duan, Xiaoming He, Jianping The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Germany

Humans often learn new skills by imitating the experts and gradually developing their proficiency. In this work, we introduce Stochastic Trajectory Optimization for Demonstration Imitation (STODI), a trajectory optimization framework for robots to imitate the shape of demonstration trajectories with improved dynamic performance. Consistent with the human learning process, demonstration imitation serves as an initial step, while trajectory optimization aims to enhance robot motion performance. By generating random noise and constructing proper cost functions, the STODI effectively explores and exploits generated noisy trajectories while preserving the demonstration shape characteristics. We employ three metrics to measure the similarity of trajectories in both the time and frequency domains to help with demonstration imitation. Theoretical analysis reveals relationships among these metrics, emphasizing the benefits of frequency-domain analysis for specific tasks. Experiments on a 7-DOF robotic arm in the PyBullet simulator validate the efficacy of the STODI framework, showcasing the improved optimization performance and stability compared to previous methods. The source code can be found at ming-bot/STODI. Copyright © 2024, The Authors. All rights reserved.

关键词： Cost functions

Cross-Domain Buoy Placement for Underwater Target Tracking in Marine Wireless Networks

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Cross-Domain Buoy Placement for Underwater Target Tracking i...

IEEE World Forum on Internet of Things (WF-IoT)

作者： Yichen Li Wenbin Yu Liangyu Jiang Buyiyi Wang Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350311617

ISBN: (纸本)9798350311624

Underwater target tracking can continuously provide position estimates of the interested target. It is the basis for marine applications. Usually, a proper deployment topology of the available devices could guarantee that the optimal tracking performance can be obtained. This paper investigates the optimal placement problem of buoys under a cross-domain tracking framework, where the buoys are deployed with unmanned aerial vehicles (UAVs) to perform coarse tracking and provide prior information for the subsequent arriving underwater vehicles. By taking advantage of the high-speed movement of UAVs, the disadvantage of underwater tracking (difficult to quickly form an effective tracking formation when the target is first found) is made up. Specifically, the placement is formulated as a convex optimization problem based on the idea of sensor selection, and the output makes the buoys and pre-deployed anchors form a combination that optimizes the tracking accuracy while guaranteeing the detection probability. Moreover, the target mobility and deployment convenience are also considered to favor practical uses. Through various simulation results, the advantages of the proposed method are illustrated.

关键词： Performance evaluation Target tracking Wireless networks Simulation Design methodology Convex functions Underwater tracking

OIDM: An Observability-based Intelligent Distributed Edge Sensing Method for Industrial Cyber-Physical systems

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arXiv 2024年

作者： Wang, Shigeng Jin, Tiankai Ma, Yehan Chen, Cailian Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

Industrial cyber-physical systems (ICPS) integrate physical processes with computational and communication technologies in industrial settings. With the support of edge computing technology, it is feasible to schedule large-scale sensors for efficient distributed sensing. In the sensing process, observability is the key to obtaining complete system states, and stochastic scheduling is more suitable considering uncertain factors in wireless communication. However, existing works have limited research on observability in stochastic scheduling. Targeting this issue, we propose an observability-based intelligent distributed edge sensing method (OIDM). Deep reinforcement learning (DRL) methods are adopted to optimize sensing accuracy and power efficiency. Based on the system’s ability to achieve observability, we establish a bridge between observability and the number of successful sensor transmissions. Novel linear approximations of observability criteria are provided, and probabilistic bounds on observability are derived. Furthermore, these bounds guide the design of action space to achieve a probabilistic observability guarantee in stochastic scheduling. Finally, our proposed method is applied to the estimation of slab temperature in industrial hot rolling process, and simulation results validate its effectiveness. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

AMFD: Distillation via Adaptive Multimodal Fusion for Multispectral Pedestrian Detection

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arXiv 2024年

作者： Chen, Zizhao Qian, Yeqiang Yang, Xiaoxiao Wang, Chunxiang Yang, Ming Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

Multispectral pedestrian detection has been shown to be effective in improving performance within complex illumination scenarios. However, prevalent double-stream networks in multispectral detection employ two separate feature extraction branches for multi-modal data, leading to nearly double the inference time compared to single-stream networks utilizing only one feature extraction branch. This increased inference time has hindered the widespread employment of multispectral pedestrian detection in embedded devices for autonomous systems. To efficiently compress multispectral object detection networks, we propose a novel distillation method, the Adaptive Modal Fusion Distillation (AMFD) framework. Unlike traditional distillation methods, the AMFD framework fully leverages the original modal features from the teacher network, thereby significantly enhancing the performance of the student network. Specifically, a Modal Extraction Alignment (MEA) module is utilized to derive learning weights for student networks, integrating focal and global attention mechanisms. This methodology enables the student network to acquire optimal fusion strategies independent from that of teacher network without necessitating an additional feature fusion module. Furthermore, we present the SMOD dataset, a well-aligned challenging multispectral dataset for detection. Extensive experiments on the challenging KAIST, LLVIP and SMOD datasets are conducted to validate the effectiveness of AMFD. The results demonstrate that our method outperforms existing state-of-the-art methods in both reducing log-average Miss Rate and improving mean Average Precision. The code is available at https://***/bigD233/***. Copyright © 2024, The Authors. All rights reserved.

关键词： Feature extraction

Differentiator-Based Adaptive H8 Tracking control of Fully Actuated systems 3

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Differentiator-Based Adaptive H8 Tracking Control of Fully A...

3rd Conference on Fully Actuated System Theory and Applications, FASTA 2024

作者： Feng, Yuxin Li, Zhiqiang Liu, Yang He, Zhaoshui Li, Hongyi Guangdong University of Technology School of Automation Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control Guangzhou510006 China Qingdao University of Science and Technology School of Automation and Electronics Engineering Qingdao266061 China Guangdong University of Technology School of Automation Guangzhou510006 China Key Laboratory for IoT Intelligent Information Processing and System Integration of Ministry of Education Guangzhou510006 China

ISBN: (纸本)9798350373691

This article addresses an adaptive tracking control problem for uncertain high-order fully actuated (HOFA) systems with unknown parameters and disturbances. Under the framework of backstepping, the unknown parameter is estimated and the external disturbance is handled by using adaptive control and H8 technique, respectively. Moreover, the Levant differentiator is employed to reduce the computation burden. Additionally, the boundedness of signals in the closed-loop system is proven using the Lyapunov theory. Finally, the effectiveness of the proposed scheme is validated through the simulation study. © 2024 IEEE.

关键词： Backstepping