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International Conference on systems and Informatics (ICSAI)

作者： Minghao Mu Wei Wang Xiaobo Lu Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing China

ISBN: (数字)9798331510138

ISBN: (纸本)9798331510145

Joint face super-resolution and frontalization of non-frontal low-resolution faces is of significant importance for many face analysis applications. However, at the same time, it is a challenging task. In this paper, a novel Guided Transformative Generative Adversarial Network (GT-GAN) is proposed to address the challenge of generating frontal high-resolution face images from non-frontal low-resolution face images. Existing methods usually treat super-resolution and frontalization as two separate tasks, leading to unsatisfactory processing results. GTG AN innovatively achieves both goals within a unified frame-work. Specifically, we propose Profile Attention Guidance (PAG) to generate landmark heatmaps that provide a priori pose information to the generative network and direct its attention to critical facial regions. In addition, an Attention Fusion Transformation Module (AFTM) is proposed to significantly improve the hallucination performance of the network by utilizing the attention mechanism and STNs, combined with sequential feature enhancement strategies. The experimental results show that GT-GAN exhibits superior performance in the joint face super-resolution and frontalization task on the public face dataset Multi-PIE, which provides new ideas and references for this challenging topic.

关键词： Heating systems Attention mechanisms Superresolution Generative adversarial networks Informatics Faces

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A Image steganography Technique based on Denoising Diffusion Probabilistic Models

A Image steganography Technique based on Denoising Diffusion...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Enzhi Xu Yang Cao Le Hu Chenxing Wang School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

Image steganography is the process of hiding secret information into an image while still keeping its original appearance intact. It plays a crucial role in secure communication and data storage. While deep learning-based techniques have shown notable progress in the field of image steganography, they still face challenges in extraction accuracy and robust security. This paper introduces an innovative image steganography method utilizing the diffusion model, which is the most powerful technique of image generation. Our method utilizes the sampling process of the diffusion model to hide the secret image in the generated image, and the receiver uses the secret key to extract the secret data accurately. The results of our experiments validate the effectiveness of our approach, outperforming existing methods in security, capacity, and extraction precision, which shows the potential for practical applications.

关键词： Resistance Steganography Visualization Accuracy Noise reduction Receivers Diffusion models

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Parallel Sensing in Metaverses: Virtual-Real Interactive Smart systems for “6S” Sensing

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IEEE/CAA Journal of Automatica Sinica 2022年第12期9卷 2047-2054页

作者： Yu Shen Yuhang Liu Yonglin Tian Xiaoxiang Na School of Artificial Intelligence University of Chinese Academy of ScienceBeijing 100190China IEEE The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China Department of Engineering University of CambridgeTrumpington StreetCB21PZUnited Kingdom

In the construction of Metaverses,sensors that are referred to as the“bridge of information transmission”,play a key *** functionality and efficiency of today’s sensors,which operate in a manner similar to physical sensing,are frequently constrained by their hardware and *** this research,we proposed the Parallel Sensing framework,which includes background,concept,basic methods and typical application of parallel *** our formulation,sensors are redefined as the integration of real physical sensors and virtual software-defined sensors based on parallel intelligence,in order to boost the performance of the *** sensor will have a parallel counterpart in the virtual world within the framework of parallel *** sensors serve as the brain of sensors and maintain the same properties as physical *** sensing allows physical sensors to operate in discrete time periods to conserve energy,while cloud-based descriptive,predictive,and prescriptive sensors operate continuously to offer compensation data and serve as *** better illustrate parallel sensing concept,we show some example applications of parallel sensing such as parallel vision,parallel point cloud and parallel light fields,both of which are designed by construct virtual sensors to extend small real data to virtual big data and then boost the performance of perception *** results demonstrate the effective of parallel sensing *** interaction between the real and virtual worlds enables sensors to operate actively,allowing them to intelligently adapt to various scenarios and ultimately attain the goal of“Cognitive,Parallel,Crypto,Federated,Social and Ecologic”6S sensing.

关键词： Parallel Sensing Parallel Intelligence CPSS Metaverses Virtual and Real Interaction “6S”Sensing.

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Animatable Human Neural Radiance Field from Sparse Images Captured by a Single Camera 43

Animatable Human Neural Radiance Field from Sparse Images Ca...

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43rd Chinese control Conference, CCC 2024

作者： Yang, Qingru Xu, Chi China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9789887581581

Recently, the use of neural radiance fields (NeRF) for three-dimensional reconstruction of humans has achieved high-quality view rendering. However, existing methods often rely on multiple or single videos as input, requiring dense images, and the rendering quality tends to be poor with sparse image inputs. To address this issue, this paper proposes a novel method that solely utilizes sparse images captured by a single camera to create an animatable human neural radiance field, enabling the rendering of high-fidelity human images under arbitrary novel viewpoints and poses. Specifically, our method utilizes body poses and multi-view feature to build a deformable neural radiance field, and employs frequency-segmented positional encoding to mitigate overfitting during training process. Extensive quantitative and qualitative experiments demonstrate that our proposed method outperforms prior works. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Image reconstruction

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Surface deformation-dependent mechanical properties of bending nanowires:an ab initio core-shell model

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Applied Mathematics and Mechanics(English Edition) 2022年第2期43卷 219-232页

作者： Ye XIAO J.SHANG L.Z.KOU Chun LI School of Mechanics Civil Engineering and ArchitectureNorthwestern Polytechnical UniversityXi’an710072China MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical UniversityXi’an710072China School of Mechanical Medical and Process EngineeringQueensland University of TechnologyBrisbaneQLD4000Australia

An ab initio core-shell model is proposed to evaluate the surface effect in bending nanowires,in which the elastic modulus depends on the surface relaxation and deformation induced by external *** using first-principles calculations based on the density functional theory(DFT),the surface and bulk properties are calculated for Ag,Pb,and Si *** obtained theoretical predictions of the effective Young’s modulus of nanowires agree well with the experimental data,which shows that the fixed-fixed nanowire is stiffened and the cantilevered nanowire is softened as the characteristic size of the cross section ***,the contrastive analysis on the two kinds of nanowires demonstrates that increasing the nanowire aspect ratio would enhance the surface *** present results could be helpful for understanding the size effect in nanowires and designing nanobeam-based devices in nanoelectromechanical systems(NEMSs).

关键词： surface effect density functional theory(DFT) surface relaxation surface stress nanowire

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Joint Local Contrast and First-Order Directional Derivative Methods for Detecting Infrared Small Targets in complex Backgrounds

IEEE Journal on Miniaturization for Air and Space Systems

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IEEE Journal on Miniaturization for Air and Space systems 2025年

作者： Zheng, Hanjia Zong, Xiaofeng China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

Infrared target detection technology is one of the most key technologies of current detection systems, which is not only applied to automatic navigation, security protection and surveillance devices in daily life, but also widely used in the military. However, due to the influence of complex background, infrared detection system is often difficult to achieve accurate and fast detection of small targets, so the current small target detection methods still need to be improved. In this paper, an effective algorithm is proposed by complementally combining local contrast and first-order directional derivative algorithms, incorporating human visual characteristics for enhancement. Then according to the different characteristics of the background and small targets in infrared images, for the problem of excessive redundant information in infrared weak small target images, the high real-time and low leakage rate of LCM is used to find out the information-rich suspicious blocks, and then the low false alarm rate of FODD is used to find out the targets in the suspicious area. Through a lot number of experiments, and comparing with a variety of other algorithms. The results show that the detection algorithm proposed in this paper is significantly better than other algorithms in terms of low false alarm rate and can maintain good real-time performance. © 2019 IEEE.

关键词： Infrared imaging

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Detection of Termite Nests in Dams Using Ground Penetrating Radar Based on Deep Learning 20

Detection of Termite Nests in Dams Using Ground Penetrating ...

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20th International Conference on Ground Penetrating Radar, GPR, 2024

作者： Peng, Jiao Xue, Wei Wang, Wenkai Song, Junlei Zhou, Feng Huang, Pengchun Zhang, Zhixuan Zhang, Feng Zhang, Xu Chen, Longjia Sun, Li School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China Dam Safety Monitoring and Termite Control Center of Hubei Provincial Department of Water Resources China Key Laboratory of Termite Control Ministry of Water Resources China School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan China

Termites have always been one of the major hidden dangers to the operational safety of dams. Traditional detection methods such as manual inspection and termite baiting have limitations in terms of efficiency and accuracy. To solve the problems, paper proposes a termite nest detection method based on ground penetrating radar (GPR) and deep learning. First, the simulated GPR data are generated using gprMax software, and the real data are collected using a GPR at several dams. Subsequently, the YOLOv8 deep neural network is employed for termite nest recognition in GPR images. The experimental results show that the proposed model can achieve the mean average precision (mAP) of 0.96 and a detection speed of 53.76 frames per second (FPS). © Published under licence by IOP Publishing Ltd.

关键词： Deep neural networks

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Distributed Event-Triggered Formation control for a Multi-robotic Fish System

Distributed Event-Triggered Formation Control for a Multi-ro...

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International Conference on Guidance, Navigation and control, ICGNC 2022

作者： Dai, Shijie Wu, Zhengxing Tan, Min Yu, Junzhi School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China State Key Laboratory for Turbulence and Complex System Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Peking University Beijing100871 China

ISBN: (纸本)9789811966125

This paper investigates the formation control problem for a multi-robotic fish system and a distributed event-triggered-based formation control framework is proposed. The framework consists of a communication topology, a distributed formation control law, and a dynamic event-triggered communication mechanism. In particular, a position-based formation control law is presented to drive the multi-robotic fish system to an anticipated configuration based on local measurements while keeping pace with a moving target. Meanwhile, a dynamic event-triggered mechanism is utilized to measure importance of the interactive information and decide the communication timing. The proposed formation control method shows its merits to greatly reduce communication consumption with a limited loss of formation control performance. Finally, simulations with quantitative comparisons are presented to verify the effectiveness of the proposed framework. This formation control framework provides a solid foundation for future marine cooperative control of the multi-robotic fish system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multi agent systems

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Finite-Time Observer Based Integral Terminal SMC for Converter-Driven Motor systems with Mismatched Disturbances

Finite-Time Observer Based Integral Terminal SMC for Convert...

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2023 China Automation Congress, CAC 2023

作者： Zhang, Zhongding Guo, Zeyu Wang, Zuo Li, Shihua School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Shenzhen Research Institute Southeast University Shenzhen518063 China

ISBN: (纸本)9798350303759

Converter-driven motor systems play crucial roles in contemporary industrial applications owning to the advantages of smooth starting and stepless speed regulation. For such fourth-order systems with both immeasurable states and mismatched disturbances, a finite-time control solution is proposed to address the speed tracking problem in this paper. Firstly, a finite-time observer is constructed to precisely estimate the immeasurable states and mismatched disturbances. Then, based on integral terminal sliding mode control (ITSMC), a continuous finite-time controller is designed for the systems to achieve both rapid response and superior speed tracking performance. Furthermore, a rigorous global finite-time stability analysis for the closed-loop system is presented. Finally, simulation results validate the effectiveness of the proposed control method. As compared with existing methods, the composite ITSMC algorithm design demonstrates reliable finite-time speed tracking performance and exceptional capability in disturbance rejection. © 2023 IEEE.

关键词： Disturbance rejection

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Single Camera Based Gait Analysis Method with Scaled and Image Coordinate key-points

Single Camera Based Gait Analysis Method with Scaled and Ima...

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2022 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2022

作者： Xiang, Tian-Yu Zhou, Xiao-Hu Xie, Xiao-Liang Liu, Shi-Qi Wang, Chen Gui, Mei-Jiang Li, Hao Hou, Zeng-Guang University of Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of Automation School of Artificial Intelligence Beijing100049 China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

Clinical experiments and scientific evidence show the relations between gait and cognition. Neurological diseases (like stroke and cerebral palsy) distract patients' attention and restrict their executive functions. Furthermore, these diseases impair patients' movement and limit their social participation. Quantitative motion assessment is significant to medical decision-making for neurological disorders but is possible only with expensive motion capture systems and professional medical workers. Here, an end-To-end video-based quantitative gait analysis algorithm is proposed. Unlike previous works, which only focus on image coordinate key-points, our algorithm also utilizes scaled key-points that can depict the size of patients. These two groups of features are sent to other neural networks to extract temporal and spatial characteristics, and then the deep features are fused by the attention mechanism. Finally, a statistic model is used to process the proposed outputs to get a final result. Experiments on the public patients' dataset show the effectiveness and accuracy of our model. Furthermore, the ablation study proves that every step of the proposed method contributes to the whole system's performance. © 2022 IEEE.

关键词： Gait analysis

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