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43rd Chinese Control Conference, CCC 2024

作者： Yang, Jiahao Zhi, Yongran Xue, Chenlong Liu, Lei Fan, Huijin Wang, Bo Huazhong University of Science and Technology National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Wuhan430074 China

ISBN: (纸本)9789887581581

In this paper, a novel fixed-time formation control algorithm is proposed for multiple unmanned aerial vehicles (UAVs) in environments where global navigation satellite system(GNSS) is denied, where the absolute positions of UAVs are unmeasurable, and only relative positions are accessible. To address this challenge, firstly, a composite observers is designed with one estimating relative positions between leader and followers and the other being a distributed fixed-time velocity observer. The integration of these observers enables each follower to obtain relative position and velocity of the leader within a fixed time. Subsequently, based on the composite observers and the desired formation matrix, a fixed-time controller is formulated aiming to achieve the formation task within a fixed time. Furthermore, Lyapunov stability theory demonstrates that the designed observers and controller can successfully accomplish the formation task within a fixed time. Finally, numerical simulations validate the effectiveness of the analytical results. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Unmanned aerial vehicles (UAV)

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ResPocket: A Multi-Scale Feature Fusion Method for Improving Protein Binding Site Detection

ResPocket: A Multi-Scale Feature Fusion Method for Improving...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lin, Xiaoli Yang, Wenli Chen, Yaoyu Liao, Xiongwei Wu, Zimo Zhang, Xiaolong Wuhan University of Science and Technology Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial System School of Computer Science and Technology China

ISBN: (纸本)9798350386226

Accurate detection of protein binding sites is critical for facilitating drug design. Most existing models rely on features at a single scale, leading to the loss of crucial global or local structural information. Therefore, this paper proposes a novel 3D model (ResPocket), to fully capture the multi-scale features of proteins. First, Residual Block (ResB) based on U-Net is used to capture features from different scales, including global and local structural information. Then, an Enhanced Attention Module (EAM) is designed to encode semantic dependencies by effectively capturing global context information. In addition, ResPocket focuses on learning local information about protein pockets with Self-Guided Mask Block (SGMB), to increase the robustness of the overall model and reduce its dependence on external mask guidance. Experimental results show that ResPocket improves by an average of 4.89% on DCA Top-n, by an average of 1.48% and 2.25% on DCC and DVO metrics, respectively. © 2024 IEEE.

关键词： Binding sites

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Joint AP Clustering and Beamforming Design for RIS-Aided Cell-Free Networks

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IEEE Transactions on Vehicular technology 2025年第5期74卷 8315-8320页

作者： Xu, Chunmei Jia, Yuanqi Chen, Youjia Huang, Wei GuildfordGU2 7XH United Kingdom Southeast University School of Information Science and Engineering Nanjing210096 China Fuzhou University Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou350108 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China

Cell-free networks and reconfigurable intelligent surfaces (RIS) are two promising techniques for future wireless communications. The integration of RIS into cell-free networks, termed RIS-aided cell-free networks, offers the potential to significantly enhance network performance. However, the realization of this potential is constrained by the limited capacities of the fronthaul links. To address this challenge, we investigate the joint design of access point (AP) clustering, transmit and passive beamforming in RIS-aided cell-free networks. The objective is to maximize the weighted sum-rate performance while minimizing the number of clustered APs to alleviate the fronthaul overhead. The problem is formulated in a group sparse manner, employing a mixed zero-norm/two-norm term to represent the number of the clustered APs. To solve this problem, we first approximate the mixed zero-norm/two-norm term by the mixed one-norm/two-norm term and provide its equivalent formulation by introducing receive beamforming vectors and weight parameters. Then, an iterative method is proposed based on the block coordinate descent (BCD) technique, which is guaranteed to converge to a stationary point. Simulation results demonstrate the effectiveness of the proposed method. © 1967-2012 IEEE.

关键词： Iterative methods

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Observer-based Hierarchical Control of Heterogeneous Multiagent Systems under Attacks and Disturbances 43

Observer-based Hierarchical Control of Heterogeneous Multiag...

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43rd Chinese Control Conference, CCC 2024

作者： Zhi, Yongran Fan, Huijin Liu, Lei Wang, Bo Huazhong University of Science and Technology The National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Wuhan430074 China

ISBN: (纸本)9789887581581

The resilient cooperative output regulation of heterogeneous leader-follower multiagent systems (MASs) under asynchronous DoS attacks and unknown external disturbances is investigated in this paper. An observer-based hierarchical control frame is constructed, which contains communication layer and controller layer. In the communication layer, based on an improved dynamic event-triggered mechanism, a novel fully distributed observer is proposed to estimate the leader's state under the asynchronous DoS attack. In the controller layer, an anti-disturbance state observer is firstly designed to estimate the follower's state in the existence of unknown external disturbances, then an output feedback controller is constructed to track the estimate leader's state. The proposed two observers has increased the tolerance to DoS attacks and external disturbances. Finally, simulation results have demonstrated the effectiveness of the proposed hierarchical control frame. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Feedback control

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Turbulent drag reduction by sector-shaped counter-flow dielectric barrier discharge plasma actuator

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Chinese Physics B 2025年第2期34卷 362-370页

作者： Borui Zheng Shaojie Qi Minghao Yu Jianbo Zhang Linwu Wang Dongliang Bian School of Automation and Information Engineering Xi’an University of TechnologyXi’an 710048China School of Mechanical and Precision Instrument Engineering Xi’an University of TechnologyXi’an 710048China Science and Technology on Plasma Dynamics Laboratory Air Force Engineering UniversityXi’an 710038China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an 710048China Shaanxi University Key Laboratory of Photonic Power Devices and Discharge Regulation Xi’an 710048China

The primary objective in aircraft transportation is to minimize turbulent drag, thereby conserving energy and reducing emissions. We propose a sector-shaped counter-flow dielectric barrier discharge plasma actuator, which leverages jet synthesis for drag reduction. A drag control experiment was conducted in a low-speed wind tunnel with a controlled flow velocity of 9.6 m/s(Re = 1.445 × 10^(4)). This study investigated the effects of varying pulse frequencies and actuation voltages on the turbulent boundary layer. Using a hot-wire measurement system, we analyzed the pulsating and time-averaged velocity distributions within the boundary layer to evaluate the streamwise turbulent drag reduction. The results show that the local TDR decreases as the pulse frequency increases, reaching a maximum reduction of approximately 20.97% at a pulse frequency of 50 Hz. In addition, as the actuation voltage increases, the friction coefficient decreases, increasing the drag reduction rate. The maximum drag reduction of approximately 33.34% is achieved at an actuation voltage of 10 kV.

关键词： plasma flow control turbulent boundary layer turbulent drag reduction

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Improved Kalman Filtering Algorithm Based Multi-Sensor Integration for Silicon Single Crystal Growth Process 7

Improved Kalman Filtering Algorithm Based Multi-Sensor Integ...

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7th International Symposium on Autonomous Systems, ISAS 2024

作者： Xie, Ruidong Zhu, Pei Ma, Wenle Zhang, Zheqi Yi, Yingmin Guo, Zheng Xi'an University of Technology Key Lab of Manufacturing Equipment of Shaanxi Province Xi'an710048 China Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an710048 China

ISBN: (纸本)9798350363173

The study primarily delves into addressing the data filtering challenge inherent in the silicon single crystal growth process by proposing a multi-sensor integrated detection approach. This approach is designed to alleviate the diminished filtering accuracy resulting from the reliance on a single sensor and the conventional Kalman filtering algorithm. By utilizing an enhanced adaptive Kalman filtering algorithm and the maximum likelihood estimation criterion to incorporate the innovation variance directly into the gain calculation of the Kalman filter, dynamic adjustment of the estimation model is achieved while interference from system noise and measurement noise is reduced. Finally, simulation-based comparative experiments between the adaptive and conventional Kalman filtering algorithms are performed. The results show that the performance of the adaptive Kalman filtering algorithm is superior to that of the conventional Kalman filtering algorithm in terms of accuracy and stability when detecting signals from multiple sensors. The research findings furnish dependable data support for refining the decision-making process in crystal growth technology, augmenting the precision and scientific integrity of crystal safety production scheduling and decision-making endeavors. © 2024 IEEE.

关键词： Kalman filters

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An Edge-Guided Network for Building Footprint Extraction from Optical Remote Sensing Images 12

An Edge-Guided Network for Building Footprint Extraction fro...

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SPIE 12th International Symposium on Multispectral Image processing and Pattern Recognition, MIPPR 2023

作者： Zhou, Yuhang Tan, Yihua National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

Accurate building footprint extraction from optical remote sensing images remains challenging due to the diverse appearance and complex scenarios. Although recent deep learning-based methods have been shown to greatly improve the accuracy of building footprint extraction, vanilla deep networks still suffer from ambiguous predictions of edge pixels. The building edge contains abundant location and shape information, which is important for downstream applications such as building positioning and area measurement. Therefore, the problem of inaccurate edge prediction needs to be resolved urgently. To this end, we propose a novel edge-guided network (EGNet) that makes ample use of the edge prior in an end-to-end manner. First, an edge extraction module (EEM) is proposed to extract the building edge map. Then, an edge-guidance module (EGM) is designed to utilize the edge map to guide each encoder block in extracting edge-related features. Furthermore, a multi-scale context aggregation module (MCAM) is built to enhance the feature representation by aggregating contextual semantics with different receptive fields. EGNet can effectively mine edge semantics and guide the representation learning of boundaries, achieving 75.21% and 91.16% IoU on the Massachusetts and WHU datasets, respectively. Experimental results demonstrate that the proposed EGNet has a certain superiority in both accuracy and efficiency compared with current state-of-the-art (SOTA) methods. © 2024 SPIE.

关键词： Extraction

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Unpaired Thick Cloud Removal with Multiscale SAR-optical Data Fusion 5

Unpaired Thick Cloud Removal with Multiscale SAR-optical Dat...

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5th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2024

作者： Xiang, Xuanyu Tan, Yihua Huazhong University of Science and Technology National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Wuhan430074 China

ISBN: (纸本)9798350373820

Cloud removal is vital for the analysis of optical satellite images. To alleviate the impact of thick clouds, recent advances integrate deep learning withmultimodal data. To relax the requirements of paired training samples, existing methods utilized cycle-consistent architecture to learn the relation between cloudy and cloud-free images based on unpaired training samples. When considering thick cloud removal based on unpaired training data, the relevant studies are insufficient and they face two challenges: 1) The information in non-cloudy areas is not preserved well after cloud removal;2) The recovered information lacks consistency with the global textures and structures. Based on optical-SAR fusion and cycle-consistent training, this paper proposes Multiscale Cycle-consistent Fusion (MCF) model. MCF designs preservation loss to overcome the first challenge, and proposes global-local discriminator combined with Parallel Dilated Channel-weighted Module (PDCM) for the second challenge. MCF is evaluated both on a simulated dataset and a real dataset, which demonstrates its effectiveness. © 2024 IEEE.

关键词： Image fusion

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Study of multilevel feature weighted fusion based image classification method for silicon single crystal growth process

Study of multilevel feature weighted fusion based image clas...

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2024 International Conference on Image processing and Artificial Intelligence, ICIPAl 2024

作者： Xie, Ruidong Zhang, Zheqi Zhu, Pei Ma, Wenle Yi, Yingmin Key Lab of Manufacturing Equipment of Shaanxi Province Xi’an University of Technology Xi’an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an710048 China

ISBN: (纸本)9781510681514

A multi-level feature weighted fusion based image classification method for silicon single crystal growth process has been proposed to address the issue of the long silicon single crystal pulling times at industrial sites, which prevents timely understanding of the crystal growth process. A multi-level feature weighted fusion method was employed to identify and classify the growth process of silicon single crystals. A Czochralski single crystal silicon growth process recognition system was then designed. A comparison of the experimental results obtained from this design with the traditional single image classification method clearly showed that using different levels of silicon single crystal image information simultaneously effectively improved the accuracy of silicon single crystal growth image classification. © 2024 SPIE.

关键词： Image classification

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A shape and texture feature blending network for bone marrow cells classification using auxiliary learning 12

A shape and texture feature blending network for bone marrow...

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SPIE 12th International Symposium on Multispectral Image processing and Pattern Recognition, MIPPR 2023

作者： Guo, Zhengyang Yang, Zengbiao Xiang, Xuanyu Tan, Yihua National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (数字)9781510674981

ISBN: (纸本)9781510674974

Morphological examination of bone marrow cells is crucial for diagnosing blood diseases. However, manual classification of bone marrow cells is time-consuming and subjective. Therefore, it is necessary to develop an autoclassification method for bone marrow cells. Although deep learning methods are commonly used for cell classification like Resnet50, they don't take advantage of the features of bone marrow cells such as the shape features of cells. However, the shape of cell and nucleus plays a significant role in distinguishing between different cell types. In this paper, we proposed a Shape and Texture Feature Blending Network (STFB-Net) for bone marrow cells classification based on auxiliary learning. We used ResNext50 as the backbone network for STFB-Net due to its exceptional ability to extract texture features from cells. In addition, we proposed the Shape Feature Extraction Module (SFEM) to enhance the backbone network's ability for capturing shape features. SFEM shares a part of parameters with the backbone network. SFEM extracts features at multiple scales and up-samples them, then fuses multi-scale features to predict the shape of cells. We performed experiments on two bone marrow cell datasets. The results show that the proposed STFB-Net can effectively extract texture and shape features, which brings better performance than other cell classification methods. By using the Grad-CAM method to visualize the features extracted by STFB-Net, we proved the reliability and the effectiveness of the STFB-Net in extracting cell shape features. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Feature extraction

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