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Asian control Conference

作者： Yanan Bie Jiajing Tang Kaibiao Sun School of Control and Engineering Dalian Univerity of Technology Dalian China School of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

This paper considers the problem of target search in unknown environments and proposes a Unmanned Aerial Vehicle (UAV) cooperative target search based on Behavior Expression Tree (BET). Using behavior expression tree as the structure of UAV motion control strategies helps handle unknown and complex environments, effectively solving problems such as target search, group response and obstacle avoidance. A Heuristic Optimization Algorithm based on Behavior Expression Tree (HOA-BET) is proposed to train and generate an effective behavior expression tree. Multiple tree update operators are designed to help the tree population update and evolve. The relationship between target search efficiency and UAV trajectory rationality is balanced through a mixed approach of multiple indicators. The effectiveness and reliability of the algorithm and control strategy have been demonstrated through comparison with various methods. Experiments in various scenarios have shown the adaptability of the control strategy generated by the algorithm to the environment. Additionally, it has good results when applied to large-scale target search.

关键词： Measurement Heuristic algorithms Autonomous aerial vehicles Search problems Trajectory Reliability Collision avoidance

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Event-Triggered-Based Privacy-Preserving Finite-Time Flocking control for Networked Multi-Agent Systems 63

Event-Triggered-Based Privacy-Preserving Finite-Time Flockin...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Yue, Jiangfeng Qin, Kaiyu Shao, Jinliang Shi, Mengji Li, Weihao Zheng, Wei Xing University of Electronic Science and Technology of China School of Aeronautics and Astronautics Chengdu611731 China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu611731 China University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing100089 China Western Sydney University School of Computer Data and Mathematical Sciences SydneyNSW2751 Australia

ISBN: (纸本)9798350316339

Drawing inspiration from the dynamics of biological groups, flocking behavior has captivated interest due to its adaptive, self-organizing, and resilient characteristics. However, the presence of numerous agents in an open network environment raises concerns regarding the potential for information leakage during flocking movements. To address this issue, we present a decentralized output mapping function, executed independently by each individual agent, to ensure the maintenance of flocking behavior, including velocity alignment, cohesion and collision avoidance. Additionally, to enhance resource efficiency and convergence speed, an event-triggered mechanism is employed to mitigate the excessive consumption of bandwidth and computation resources in traditional flocking control algorithms. Moreover, the designed finite-time controller facilitates rapid convergence of flocking behavior. Finally, the efficacy of our proposed approach is corroborated through numerical simulation experiments. © 2024 IEEE.

关键词： Multi agent systems

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Tailored Output Layers of Neural Networks for Satisfaction of State Constraints in Nonlinear control Systems

Tailored Output Layers of Neural Networks for Satisfaction o...

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American control Conference (ACC)

作者： Lukas Markolf Olaf Stursberg Dept. of Electrical Engineering and Computer Science Inst. of Control and System Theory University of Kassel Germany

This work considers the synthesis of state feedback controllers established as deep artificial feed-forward neural networks for the control of discrete-time nonlinear but input-affine systems. The idea is to design output layers of particular structure to guarantee the satisfaction of state constraints in form of control-invariant ellipsoids. Since an analytical expression can be derived for the resulting neural network controller, the latter can be stored and evaluated efficiently. Moreover, the proposed output layer guarantees the satisfaction of the considered state constraints for each specification of the parameter vector. Numerical examples are provided for illustration and evaluation of the approach, in which the approximation of a nonlinear model predictive control law is considered as application.

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Periodic-Enhanced Informer Model for Short-Term Wind Power Forecasting Using SCADA Data

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IEEE Transactions on Sustainable Energy 2025年

作者： Liu, Zhao-Hua Li, Long-Wei Wei, Hua-Liang Li, Ming Lv, Ming-Yang Zhang, Ying-Jie Hunan University of Science and Technology School of Information and Electrical Engineering Xiangtan411201 China University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

Supervisory control and Data Acquisition (SCADA) systems can collect abundant information about wind farm operation and environment. To better make use of SCADA data, a periodic-enhanced informer model for short-term wind power forecasting using scada data is proposed. Firstly, to effectively filter out noise in SCADA data, a v-p curve-based method is adopted by incorporating a quartile approach to remove sparse outliers;a density-based spatial clustering of applications with noise (DBSCAN) algorithm is then employed to eliminate stacked outliers from the power curve. Secondly, a multi-feature input set selection method based on Maximization Information Coefficient is introduced to make better use of the SCADA system data by reducing the number of features. Thirdly, a Temporal Convolutional Network (TCN) is designed to extract the scalar projection of the input set, followed by fusing the local time stamp and global time stamp to build the periodic information enhanced prediction model embedding layer. Subsequently, the enhanced input set is fed into an informer model to predict future wind power. Finally, considering the multiple temporal scales structure characteristics present in the dataset, a multi-scale deep fusion module is established in the informer model to deeply integrate the features of different scales. It can simultaneously avoid the resource waste and overfitting problems caused by increasing the network depth. The experimental results, which are obtained from several deep learning methods on real SCADA data, demonstrate that the suggested approach has good predictive capability. © 2010-2012 IEEE.

关键词： Wind forecasting

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AdvDiffuser: Generating Adversarial Safety-Critical Driving Scenarios via Guided Diffusion

AdvDiffuser: Generating Adversarial Safety-Critical Driving ...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Yuting Xie Xianda Guo Cong Wang Kunhua Liu Long Chen School of Computer Science and Engineering Sun Yat-Sen University School of Computer Science Wuhan University State Key Laboratory for Management and Control of Complex Systems at the Institute of Automation Chinese Academy of Sciences School of Mechanical and Automotive Engineering Qingdao University of Technology

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Safety-critical scenarios are infrequent in natural driving environments but hold significant importance for the training and testing of autonomous driving systems. The prevailing approach involves generating safety-critical scenarios automatically in simulation by introducing adversarial adjustments to natural environments. These adjustments are often tailored to specific tested systems, thereby disregarding their transferability across different systems. In this paper, we propose AdvDiffuser, an adversarial framework for generating safety-critical driving scenarios through guided diffusion. By incorporating a diffusion model to capture plausible collective behaviors of background vehicles and a lightweight guide model to effectively handle adversarial scenarios, AdvDiffuser facilitates transferability. Experimental results on the nuScenes dataset demonstrate that AdvDiffuser, trained on offline driving logs, can be applied to various tested systems with minimal warm-up episode data and outperform other existing methods in terms of realism, diversity, and adversarial performance.

关键词： Training Diffusion models Intelligent robots Autonomous vehicles Testing

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Spatial adaptivity with boundary refinement for smoothed particle hydrodynamics fluid simulation

Spatial adaptivity with boundary refinement for smoothed par...

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作者： Xu, Yanrui Song, Chongming Wang, Xiaokun Ban, Xiaojuan Wang, Jiamin Zhang, Yalan Chang, Jian School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China Beijing Key Laboratory of Knowledge Engineering for Materials Science University of Science and Technology Beijing Beijing China National Centre for Computer Animation Bournemouth University Poole United Kingdom Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing Beijing China Key Laboratory of Perception and Control of Intelligent Bionic Unmanned Systems Ministry of Education University of Science and Technology Beijing Beijing China

Fluid simulation is well-known for being visually stunning while computationally expensive. Spatial adaptivity can effectively ease the computational cost by discretizing the simulation space with varying resolutions. Adaptive methods nowadays mainly focus on the mechanism of refining the fluid surfaces to obtain more vivid splashes and wave effects. But such techniques hinder further performance gain under the condition where most of the vast fluid surface is tranquil. Moreover, energetic flow beneath the surface cannot be adequately captured with the interior of the fluid still being simulated under coarse discretization. This article proposes a novel boundary-distance based adaptive method for smoothed particle hydrodynamics fluid simulation. The signed-distance field constructed with respect to the coupling boundary is introduced to determine particle resolution in different spatial positions. The resolution is maximal within a specific distance to the boundary and decreases smoothly as the distance increases until a threshold is reached. The sizes of the particles are then adjusted towards the resolution via splitting and merging. Additionally, a wake flow preservation mechanism is introduced to keep the particle resolution at a high level for a period of time after a particle flows through the boundary object to prevent the loss of flow details. Experiments show that our method can refine fluid–solid coupling details more efficiently and effectively capture dynamic effects beneath the surface. © 2023 The Authors. computer Animation and Virtual Worlds published by John Wiley & Sons Ltd.

关键词： Hydrodynamics

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Dynamical Analysis of Discrete Monochamus Alternatus-Dastarcus Helophoroides Model with Artificially Released and Spatial Diffusion

SSRN

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SSRN 2024年

作者： Liu, Xiaoxiao Zhang, Chunrui College of Computer and Control Engineering Northeast Forestry University Harbin 150040 China School of Science Northeast Forestry University Harbin 150040 China

In this paper, we consider a discrete-time Monochamus alternates-Dastarcus helophoroides model with artificially released and spatial diffusion. When model without diffusion, we prove that Neimark-Sacker bifurcation and Flip bifurcation occur. The eradication of Monochamus alternatus can be achieved by incorporating sufficient quantities of artificially released Dastarcus helophoroides. And we obtain the normal form of Neimark-Sacker bifurcation. When considering spatial diffusion, taking the parameter regulating the growth of Dastarcus helophoroides c as a bifurcation parameter, we prove that Neimark-Sacker bifurcation, 1:2 Resonance and Turing instability occur. Monochamus alternatus and Dastarcus helophoroides populations generally exhibit complex periodic oscillatory behaviour in two-dimensional space. © 2024, The Authors. All rights reserved.

关键词： Diffusion

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GAN-DIRNet:A Novel Deformable Image Registration Approach for Multimodal Histological Images

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computers, Materials & Continua 2024年第7期80卷 487-506页

作者： Haiyue Li Jing Xie Jing Ke Ye Yuan Xiaoyong Pan Hongyi Xin Hongbin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of ChinaShanghai200240China Department of Pathology Ruijin HospitalShanghai Jiao Tong UniversitySchool of MedicineShanghai200025China Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai200240China Global Institute of Future Technology Shanghai Jiao Tong UniversityShanghai200240China

Multi-modal histological image registration tasks pose significant challenges due to tissue staining operations causing partial loss and folding of *** neural network(CNN)and generative adversarial network(GAN)are pivotal inmedical image ***,existing methods often struggle with severe interference and deformation,as seen in histological images of conditions like Cushing’s *** argue that the failure of current approaches lies in underutilizing the feature extraction capability of the discriminator *** this study,we propose a novel multi-modal registration approach GAN-DIRNet based on GAN for deformable histological image *** begin with,the discriminators of two GANs are embedded as a new dual parallel feature extraction module into the unsupervised registration networks,characterized by implicitly extracting feature descriptors of specific ***,modal feature description layers and registration layers collaborate in unsupervised optimization,facilitating faster convergence and more precise ***,experiments and evaluations were conducted on the registration of the Mixed National Institute of Standards and Technology database(MNIST),eight publicly available datasets of histological sections and the Clustering-Registration-Classification-Segmentation(CRCS)dataset on the Cushing’s *** results demonstrate that our proposed GAN-DIRNet method surpasses existing approaches like DIRNet in terms of both registration accuracy and time efficiency,while also exhibiting robustness across different image types.

关键词： Histological images registration deformable registration generative adversarial network cushing’s disease machine learning computer vision

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An Investigation into Improved YOLOv8-based Target Detection Algorithms for UAV Aerial Imagery

An Investigation into Improved YOLOv8-based Target Detection...

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Image Processing, computer Vision and Machine Learning (ICICML), International Conference on

作者： Rui Ming Xinyu Liu Yuxuan Lin Qian Gong College of Computer and Control Engineering Minjiang University Fuzhou China College of Computer and Data Science/College of Software Fuzhou University Fuzhou China

In UAV imagery, the intricate backgrounds combined with the high quantity and compact distribution of minute targets have consistently made target detection a formidable challenge in the realm of computer vision. This study introduces an enhancement over the YOLOv8 algorithm, wherein a sophisticated multi-scale convolutional layer, integrating depth-separable convolution, attention mechanisms, and multi-scale processing techniques, replaces the original model's convolution. Moreover, we introduce an attention mechanism for a Bi-Level Routing within the core component of the base model, and adjustments are made to the original model's loss function. Lastly, to confirm the viability of the enhanced model proposed in this paper, we conducted a validation of the metrics using publicly accessible datasets. The findings illustrate that the improved model outlined in this research substantially enhances target recognition accuracy in UAV images. Furthermore, the model exhibits superior performance in mitigating issues of duplicate detection and target omission.

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Predicting Off-Design Performance of Axial Compressors Using Multilayer Neural Network Model for Surge control

SSRN

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SSRN 2024年

作者： Elmelegy, Mohamed Ali Elhosseini, Mostafa A. Ali, Hesham Arafat Haikal, Amira Y. DLNG Damietta LNG Egypt College of Computer Science and Engineering Taibah University Yanbu Saudi Arabia Department of Computers and Control Systems Engineering Faculty of Engineering Mansoura University Mansoura Egypt

Incidence of surge within axial compressors profoundly influences efficacy, reliability of aero-engines. Conventional methodologies in engine design have predominantly concentrated on precise ,efficient forecasting critical characteristics during such occurrences. paper presents novel approach for predicting surge phenomenon in axial compressors. Surge is a major issue in operation axial compressors, causing reduced performance, increased wear, tear, complete machine failure. proposed approach utilizes NN model based on compressor's design data to forecast behavior of compressor's working map under off-design conditions. model is verified using real data collected over five years during compressor operation, including opening angle (IGV), important parameter often ignored or assumed to be fixed in previous studies. results show NN-based approach effectively predicts compressor's behavior under different operating conditions, providing valuable insights into onset of surge , offering real-time surge prediction ,control ,ability to predict compressor's working map enables optimizing its performance , controlling instability. model was validated by comparing its predictions with real operating conditions observed over five years across various compressor operating modes. As result, predictive model enables safe operation of axial compressors by mitigating instability risks during operation. study's findings highlight model's effectiveness, as evidenced by minimal error observed between predicted and actual data collected across various compressor operating modes. model exhibits strong performance, characterized by high regression factor of 0.92667 , low mean squared error of 3.04465 x 10^-3. numerical values underscore reliability , precision proposed approach in predicting operation of axial compressor under diverse conditions, ultimately contributing to enhanced stability , safety during compressor operations. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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