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arXiv 2023年

作者： Hu, Bin-Bin Zhang, Hai-Tao Liu, Bin Ding, Jianing Xu, Yifan Luo, Chuanshang Cao, Haosen The School of Artificial Intelligence and Automation The Engineering Research Center of Autonomous Intelligent Unmanned Systems The Key Laboratory of Image Processing and Intelligent Control The State Key Lab of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China Dongguan University of Technology Dongguan523106 China

This paper proposes a distributed guiding-vector-field (DGVF) controller for cross-domain unmanned systems (CDUSs) consisting of heterogeneous unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs), to achieve coordinated navigation whereas maneuvering along their prescribed paths. In particular, the DGVF controller provides a hierarchical architecture of an upper-level heterogeneous guidance velocity controller and a lower-level signal tracking regulator. Therein, the upper-level controller is to govern multiple heterogeneous USVs and UAVs to approach and maneuver along the prescribed paths and coordinate the formation simultaneously, whereas the low-level regulator is to track the corresponding desired guidance signals provided by the upper-level module. Significantly, the heterogeneous coordination among neighboring UAVs and USVs is achieved merely by the lightweight communication of a scalar (i.e., the additional virtual coordinate), which substantially decreases the communication and computational costs. Sufficient conditions assuring asymptotical convergence of the closed-loop system are derived in presence of the exponentially vanishing tracking errors. Finally, real-lake experiments are conducted on a self-established cross-domain heterogeneous platform consisting of three M-100 UAVs, two HUSTER-16 USVs, a HUSTER-12C USV, and a WiFi 5G wireless communication station to verify the effectiveness of the present DGVF controller. Copyright © 2023, The Authors. All rights reserved.

关键词： Air navigation

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Visual Semantic Enhancement for Multi-Modal Named Entity Recognition

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SSRN 2024年

作者： Wang, Tong Wang, Hairong Xu, Xi Chen, Fangping School of Computer Science and Engineering North Minzu University Yinchuan750021 China Key Laboratory of Image and Graphics Intelligent Processing State Ethnic Affairs Commission North Minzu University Yinchuan750021 China

In recent years, a series of methods have been proposed to use image semantics to assist in extracting named entities. However, in these multi-modal named entity recognition methods, there are problems of visual semantic missing and weak semantic constraints in multi-modal representation. Therefore, a visual semantic enhancement for multi-modal named entity recognition (EVS-MNER) method is proposed. In this method, multiple visual features are used for collaborative representation to obtain more complete visual semantics. The semantic information in multiple visual features is integrated through the multi-modal features fusion module to generate visual semantic features, and the semantics are passed to the text features to obtain semantically enhanced multi-modal text representation. The visual entity classifier is used to decode visual semantic features, and the semantic consistency constraint of visual features is realized. The multi-task label decoder is called to mine the fine-grained semantics of the multi-modal text representation and text features, and the problem of semantic bias is solved by joint decoding, to further improve the accuracy of named entity recognition. On two multimodal entity recognition datasets of Twitter-2015 and Twitter-2017, the proposed method is compared with 10 methods such as PCEN and M3S. The experimental results show that the proposed method is effective. © 2024, The Authors. All rights reserved.

关键词： Semantics

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A Multimodal Fault Diagnosis Model Utilizing GAF Transformation for controllable Pitch Propeller Hydraulic System

A Multimodal Fault Diagnosis Model Utilizing GAF Transformat...

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International Conference on control, Decision and Information Technologies (CoDIT)

作者： Huihuang Cai Wei Chen Huanglong Fu Hua Wang Jingcheng Wang Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China Shanghai Marine Equipment Research Institute Shanghai China Department of Automation the Key Laboratory of System Control and Information Processing Ministry of Education of China and the Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China Autonomous Systems and Intelligent Control International Joint Research Center Xi'an Technological University Xi'an China

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

The hydraulic system of the controllable pitch propeller (CPP) serves as the power source for adjusting the blade pitch. Due to its structural complexity and harsh operating conditions, any malfunction of this system could significantly impair the vessel's navigation. Apart from the pitch command and the pitch feedback of CPP itself, the number and types of sensors added for fault diagnosis are not fixed. Therefore, this study establishes a multimodal fault diagnosis model based on Gramian angular field(GAF). By considering the deviation values of pitch feedback and pitch command as key variables, this model transforms them into a two-dimensional image using GAF, thereby obtaining more effective feature information. image features are extracted using ResNet, while the temporal features of additional sensors are extracted using GRU. Finally, a multi-head attention mechanism is employed for feature fusion. This method achieves a classification accuracy of 100% in the classification of four types of hydraulic system faults simulated on the experimental platform.

关键词： Fault diagnosis Propellers Navigation Hydraulic systems Transforms Sensor phenomena and characterization Feature extraction Sensor systems Marine vehicles Load modeling

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Clglf: Confidence Learning Guides Label Fusion for Multimodal Named Entity Recognition Method

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SSRN 2024年

作者： Wang, Hairong Wang, Tong Wang, Yiyan Chen, Fangping School of Computer Science and Engineering North Minzu University Yinchuan750021 China Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission North Minzu University Yinchuan750021 China

A B S T R A C TTo solve the visual semantic understanding bias and multimodal semantic bias in multimodal named entity recognition, the Confidence Learning Guides Label Fusion for Multimodal Named Entity Recognition (CLGLF) method is proposed. This method invokes the BLIP-2 pre-trained model to generate image captions, concatenates them with the input texts, and performs joint coding to achieve multimodal feature fusion. The candidate labels and text labels are obtained after decoding the multimodal representations and text representations. Based on using the KL divergence loss function to align the two groups of labels, the confidence score is calculated to evaluate the quality of the multimodal representation, and a confidence threshold is set to help screen out the biased candidate labels, the text labels in the corresponding positions are used to replace the biased candidate labels, to achieve the label fusion, and finally complete the multimodal named entity recognition. In order to verify the proposed method, experiments are carried out on the Twitter-2015 and Twitter-2017 multimodal datasets, and the experimental results are compared with 7 mainstream methods, such as MSB and UMT. The experimental results show the effectiveness of the CLGLF method. © 2024, The Authors. All rights reserved.

关键词： Semantics

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Data selection strategy in data-driven model predictive control of uncertain systems

Data selection strategy in data-driven model predictive cont...

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第34届中国过程控制会议

作者： Lingyi Yang dewei Li Department of Automation Shanghai Jiao Tong UniversityKey Laboratory of System Control and Information ProcessingMinistry of Education of ChinaShanghai Engineering Research Center of Intelligent Control and Management

In this paper,a novel data-driven methodology for MPC with data selection strategies is *** novel selection strategy involves two ***,we check the similarity between the current data and the past data items already existing in the data *** enables the controller to pick a set of informative data items which contain sufficient information on each direction of the signal ***,for the data items within the data oracle,we integrate multiple similar data vectors into a single vector to cut down amount of *** simulation examples displays the upgraded performance and smaller amount of calculation of selection strategies.

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A Real-time Local Planner Design for Smart Wheelchairs Based on Koopman Bilinear Model Predictive control

A Real-time Local Planner Design for Smart Wheelchairs Based...

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Guanfeng Yu Han Zhang Zehao Wang Jingchuan Wang Department of Automation Institute of Medical Robotics Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9798350372601

ISBN: (纸本)9798350372618

The paper introduces a Koopman bilinear model predictive control (KBMPC) as a local planner for smart wheelchair systems. This approach leverages the Koopman Operator to streamline the nonlinear terms in MPC, aiming to achieve superior real-time performance. In particular, we employ the Extended Dynamic Mode Decomposition (EDMD) method to approximate the infinite dimensional Koopman operator. To tackle the challenge of designing lifting functions, we systematically utilize the derivatives of known nonlinear structures to methodically derive lifting functions. This systematic approach facilitates the identification of a precise bilinear model. Next, we formulate the corresponding Koopman bilinear MPC problem. Finally, an iterative algorithm is employed to efficiently solve the KBMPC problem. This method linearizes the bilinear model around estimations and iteratively solves the resulting quadratic programming problem to refine estimations. This allows KBMPC to function as a real-time local planner for smart wheelchairs. The prediction of nonlinear terms shows that the identified Koopman bilinear model increases the prediction accuracy. Furthermore, experiments demonstrate the advantages of the designed KBMPC local planner, highlighting its superior real-time performance and predictive capabilities.

关键词： Pedestrians Accuracy Systematics Wheelchairs Estimation Predictive models Real-time systems

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K-BMPC: Derivative-based Koopman Bilinear Model Predictive control For Tractor-trailer Trajectory Tracking With Unknown Parameters

K-BMPC: Derivative-based Koopman Bilinear Model Predictive C...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Zehao Wang Han Zhang Jingchuan Wang Department of Automation Institute of Medical Robotics Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Nonlinear dynamics bring difficulties to controller design for control-affine systems such as tractor-trailer vehicles, especially when the parameters in the dynamics are unknown. To address this constraint, we propose a derivative-based lifting function construction method, show that the corresponding infinite dimensional Koopman bilinear model over the lifting function is equivalent to the original control-affine system. Further, we analyze the propagation and bounds of state prediction errors caused by the truncation in derivative order. The identified finite dimensional Koopman bilinear model would serve as predictive model in the next step. Koopman Bilinear Model Predictive control (K-BMPC) is proposed to solve the trajectory tracking problem. We linearize the bilinear model around the estimation of the lifted state and control input. Then the bilinear Model Predictive control problem is approximated by a quadratic programming problem. Further, the estimation is updated at each iteration until the convergence is reached. Moreover, we implement our algorithm on a tractor-trailer system, taking into account the longitudinal and side slip effects. The open-loop simulation shows the proposed Koopman bilinear model captures the dynamics with unknown parameters and has good prediction performance. Closed-loop tracking results show the proposed K-BMPC exhibits elevated tracking precision with the commendable computational efficiency. The experimental results demonstrate the feasibility of K-BMPC.

关键词： Trajectory tracking Computational modeling Estimation Predictive models control systems Data models Nonlinear dynamical systems

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An OpenPLC-based Active Real-time Anomaly Detection Framework for Industrial control Systems

An OpenPLC-based Active Real-time Anomaly Detection Framewor...

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2022 Chinese Automation Congress, CAC 2022

作者： Zheng, Chengxu Wang, Xiaopeng Luo, Xiaoyu Fang, Chongrong He, Jianping Shanghai Jiao Tong University The Key Laboratory of System Control and Information Processing Ministry of Education of China And Shanghai Engineering Research Center of Intelligent Control and Management The Dept. of Automation Shanghai China NSFOCUS Technologies Group Co. Ltd Beijing China

ISBN: (纸本)9781665465335

In recent years, the design of anomaly detectors has attracted a tremendous surge of interest due to security issues in industrial control systems (ICS). Restricted by hardware resources, most anomaly detectors can only be deployed at the remote monitoring ends, far away from the control sites, which brings potential threats to anomaly detection. In this paper, we propose an active real-time anomaly detection framework deployed in the controller of OpenPLC, which is a standardized open-source PLC and has high scalability. Specifically, we add adaptive active noises to control signals, and then identify a linear dynamic system model of the plant offline and implement it in the controller. Finally, we design two filters to process the estimated residuals based on the obtained model and use χ2 detector for anomaly detection. Extensive experiments are conducted on an industrial control virtual platform to show the effectiveness of the proposed detection framework. © 2022 IEEE.

关键词： Anomaly detection

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Research on Waxiang Dialect Recognition Based on Wav2vec2.0 Pre-Training and Transfer Learning

Research on Waxiang Dialect Recognition Based on Wav2vec2.0 ...

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Computing, Robotics and System Sciences (ICRSS), International Conference on

作者： Mixuan Deng Pengju Tang Yanxia Gao Huixuan Li Guodong Zhen Kaifeng Fu Xiaotian Wu College of Computer and Artificial Intelligence(Software College) Huaihua University Huaihua China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua China

ISBN: (数字)9798331511296

ISBN: (纸本)9798331511302

As a unique local dialect, Waxiang dialect carries rich regional culture and historical information. However, with the acceleration of modernization and the increase of population mobility, the inheritance and protection of Waxiang dialect are facing severe challenges. By constructing the ‘Xuewatong’ APP- Waxiang dialect digital protection platform, based on cloud storage technology, digital resource display, multimedia integration and other technologies, and providing learning resources through big data analysis of user needs, it can effectively promote the digital recording and preservation of Waxiang dialect language resources, and significantly improve the dissemination and educational effect of Waxiang dialect culture, showing the great potential of digital technology in dialect protection.

关键词： Technological innovation Cloud computing Transfer learning Media Information age Digital recording Global communication Cultural differences Protection Robots

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A Point Cloud Segmentation Method Based on Ground Point Cloud Removal and Multi-Scale Twin Range image

A Point Cloud Segmentation Method Based on Ground Point Clou...

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Chinese control Conference (CCC)

作者： Yicheng Zhou Gang Peng Hangqi Duan Zhangang Wu Xukang Zhu School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan China Shantui Construction Machinery Co. Ltd. Jining China

Aiming at the shortcomings of traditional point cloud segmentation algorithms, this paper proposes a 3D lidar point cloud segmentation method combining ground filtering and multi-scale twin range image construction. This method fully considers the geometric space distribution characteristics of the point cloud and the randomness of the positions between objects. Based on the assumption of multi plane model, the point cloud is divided into sectors. The ground points are removed through region ground plane fitting and growth, and then the multi-scale twin range image is constructed according to non-ground points. On the basis of the twin range image, the Breadth First Search algorithm is improved, the depth-breadth joint search strategy is proposed, all the points to be clustered are traversed, and the adaptive clustering threshold is used for point cloud clustering, which realizes a point cloud segmentation method with higher accuracy and robustness. Through the experimental analysis of KITTI dataset and collected campus outdoor scene dataset, compared with the traditional point cloud segmentation algorithm, the method proposed in this paper can effectively improve the point cloud segmentation accuracy and robustness of mobile robots in complex environments.

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