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2023 China Automation Congress, CAC 2023

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

ISBN: (纸本)9798350303759

In this article, we propose fixed-time cooperative guidance laws for achieving simultaneous attacks on a maneuvering target by multiple missiles at desired terminal angles. The 3-D guidance commands encompass radial acceleration along the line-of-sight (LOS) direction to enable coordinated attacks, as well as normal acceleration perpendicular to the LOS direction to ensure interception with terminal angle constraints. Firstly, the radial acceleration is designed from a nonlinear fixed-time cooperative protocol to achieve the fixed-time consensus of impact times for multiple missiles. Secondly, based on the fixed-time sliding manifold and fixed-time reaching law, the normal acceleration is designed for each missile to converge the LOS angle to the desired value within a fixed time. Furthermore, the unknown target acceleration is treated as a system disturbance, estimated by fixed-time observers, and compensated in the guidance commands, ensuring effective handling of uncertainties introduced by the target's acceleration. Finally, simulations are conducted to demonstrate the performance of the guidance law in the 3-D guidance scenario. © 2023 IEEE.

关键词： Acceleration

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Towards Efficient Path Finding and Moving Stability-Focused Trajectory Planning for Mobile Robots

Towards Efficient Path Finding and Moving Stability-Focused ...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Hou, Muyang Lei, Zhenya Hu, Haidong Wang, Zhong Wang, Yong Xie, Le Wang, Hesheng Shanghai Jiao Tong University Department of Automation Shanghai200240 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Beijing Institute of Control Engineering Beijing100190 China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Department of Automation Shanghai200240 China

ISBN: (纸本)9781665481090

Nowadays, mobile robots play an important role in a variety of service scenarios. They need to plan and track trajectories to accomplish tasks such as delivery or guided tours. In such tasks, there are two remaining challenges: on one hand, the time and memory consumption of present path planners are still significant;on the other hand, service robots, which have a higher center of gravity, demand greater moving stability than that of current trajectory planners provide. To address these two challenges. Firstly, we propose a safety boundary first A∗ which fully utilizes environmental obstacle information to create a safety boundary and searches in it to quickly find an initial path. Secondly, we formulate the trajectory optimization problem as a nonlinear optimization problem, where the smoothness, safety, feasibility, and moving stability of the robot are taken into account. Furthermore, to constrain the robot's lateral acceleration, we design a time allocation algorithm based on non-uniform B-spline, enhancing the quality of the resulting trajectory. Simulations and real-world experiments demonstrate that our algorithms significantly improve path search efficiency, enhance moving stability, reduce the difficulty of tracking, and improve the quality of task completion. © 2024 IEEE.

关键词： Trajectories

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Robust Ascent Trajectory Optimization for Hypersonic Vehicles Based on IGS-UMPSP

Robust Ascent Trajectory Optimization for Hypersonic Vehicl...

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Chinese intelligent Automation Conference, CIAC 2023

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

ISBN: (纸本)9789819961863

This paper proposes a new approach for solving trajectory optimization problems with uncertain system parameters, which is the generalized quasi-spectral version of unscented model predictive static programming. The proposed method combines two existing techniques, the generalized quasi-spectral model predictive static programming and the unscented optimal control, to generate control solutions that can meet multiple constraints, while minimizing the covariance of the terminal output. The technique has shown good robustness in simulations and has been demonstrated to be effective in solving ascent trajectory optimization problems of hypersonic vehicles, providing a reference trajectory for guidance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Hypersonic vehicles

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A Deep Knowledge Tracing Model Based on Multi-Head Self-Attention Mechanism

A Deep Knowledge Tracing Model Based on Multi-Head Self-Atte...

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2023 International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, AHPCAI 2023

作者： Zhou, Ya Wu, Fengzhen Huang, Guimin Lin, Jianxing Deng, Nanxiao Guo, Qingkai School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9781510671881

How to provide individualized instruction and personalized teaching for students and effectively track their knowledge status in a targeted manner is a key concern in the field of education. The target of knowledge tracing (KT) is to use the learner's behavior in historical learning activities to model the students' educational process and forcast the future performance of students. Recently, self-attention has been used several times in the KT field. Traditional models have the problem of losing information under long sequence data. Inspired by Transformer, In this paper, we present a deep knowledge tracing model (MA-DKT) based on the multi-head self-attention mechanism, which employs the LSTM model combined with the multi-head self-attention mechanism to implement a deep knowledge tracing model that is able to catch the data characteristics of the students at different temporal scales. Our model works well on publicly available datasets and achieves some improvement in AUC. © 2023 SPIE. All rights reserved.

关键词： Students

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Multi-source information fusion:Progress and future

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Chinese Journal of Aeronautics 2024年第7期37卷 24-58页

作者： Xinde LI Fir DUNKIN Jean DEZERT School of Automation Southeast UniversityNanjing 210008China Nanjing Center for Applied Mathematics Nanjing 211135China Key Laboratory of Measurement and Control of CSE Ministry of EducationNanjing 210096China Southeast University Shenzhen Research Institute Shenzhen 518063China Information Modeling and Processing Department ONERAPalaiseau 92320France

Multi-Source information Fusion(MSIF),as a comprehensive interdisciplinary field based on modern information technology,has gained significant research value and extensive application prospects in various domains,attracting high attention and interest from scholars,engineering experts,and practitioners *** achieving fruitful results in both theoretical and applied aspects over the past five decades,there remains a lack of comprehensive and systematic review articles that provide an overview of recent development in *** light of this,this paper aims to assist researchers and individuals interested in gaining a quick understanding of the relevant theoretical techniques and development trends in MSIF,which conducts a statistical analysis of academic reports and related application achievements in the field of MSIF over the past two decades,and provides a brief overview of the relevant theories,methodologies,and application domains,as well as key issues and challenges currently ***,an analysis and outlook on the future development directions of MSIF are presented.

关键词： Multi-sensor system information fusion Artificial intelligence Pattern recognition Human-machine integration

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Fractional-Order Sliding Mode Finite Time control for the Speed and Tension System of the Reversible Cold Strip Rolling Mill

Fractional-Order Sliding Mode Finite Time Control for the Sp...

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第43届中国控制会议

作者： Zhipeng Lin Le Liu Ranyang Deng Yiming Fang Miao Xu Key Laboratory of Intelligent Rehabilitation and Neromodulation of Hebei Province Yanshan University Key Lab of Intelligent Data Information Processing and Control of Hebei Province Tangshan University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

To enhance the tracking control performance of the speed and tension system of the reversible cold strip rolling mill and reduce the impact of uncertainties,a fractional-order sliding mode finite time control strategy is proposed based on adaptive sliding mode disturbance ***,novel fractional-order adaptive sliding mode disturbance observers(NFOASMDOs)are employed to dynamically observe the unmatched uncertainties in the system,thereby effectively enhancing the tracking control ***,the dynamic surface backstepping control is combined with the fractional-order sliding mode control to complete the designs of the finite time fractional-order sliding mode controllers(FTFOSMCs),which suppress the system's chattering phenomenon effectively,and avoid the "differential explosion" ***,the feasibility of the presented control method is illustrated by the simulation comparison study.

关键词： Reversible cold strip rolling mill Adaptive sliding mode disturbance observer Fractional-order sliding mode control Dynamic surface backstepping control

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Orthogonal Universal Kernel Non-Negative Matrix Factorization with Total Variation Regularization

Orthogonal Universal Kernel Non-Negative Matrix Factorizatio...

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Zhou, Rong Chen, Wen-Sheng Pan, Binbin College of Mathematics and Statistics Shenzhen University Shenzhen518060 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

ISBN: (纸本)9798350303780

Kernel-based non-negative matrix factorization (KNMF) is a promising data analysis method for nonlinear feature extraction. It has achieved better performance in pattern classification. However, KNMF algorithms suffer from problems including kernel function restriction, inaccurate pre-image learning, and sensitivity to noise. This paper proposes a novel framework of orthogonal universal KNMF with total variation regularization (OUKNMF-TV) to address these issues. The model contains three objective functions subject to TV and orthogonal constraints. The optimization problems of the OUKNMF-TV model are solved using the gradient descent method, and its updated rules are the same in the case of using different kernel functions. Convergence of the OUKNMF-TV algorithm is guaranteed using auxiliary function techniques. The experiments on non-noisy and contaminated datasets are implemented, and the results confirm the effectiveness and robustness of the proposed algorithm. © 2023 IEEE.

关键词： Face recognition

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Multi-Objective Team Personality Tourism Route Design Based on Fuzzy APACA

Multi-Objective Team Personality Tourism Route Design Based ...

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Emerging Technology (INCET), International Conference for

作者： Xiaoling Song Huanhuan Peng Key Laboratory of Intelligent Information Processing Control of Chongqing Municipal Institutions of Higher Education Chongqing Three Gorges University

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

In order to solve the challenge of multi-objective team personalized tourism route design, in view of the shortcomings of the existing divide and conquer algorithms, this study proposes an innovative personalized tourism route design method based on fuzzy APACA. This new scheme uses the principles of gradient descent theory to accurately identify and locate key influencing factors, and accordingly carries out a sensible classification of indicators to reduce possible interference. At the same time, by using the unique mechanism of fuzzy APACA, this scheme cleverly constructs the design strategy of tourism routes. The empirical results show that this scheme shows a significant improvement compared with the traditional divide and conquer algorithm in terms of key performance indicators such as the accuracy of personalized tourism route design and the processing efficiency of key factors, showing its obvious strong advantages. In the multi-objective team, the personalized tourism route design plays a crucial role, which can accurately predict and optimize the growth trend and output results of the multi-objective team's personalized tourism route design. However, in the face of complex simulation tasks, traditional divide-and-conquer algorithms show some inherent shortcomings, especially when dealing with multi-level challenges, their performance is often unsatisfactory. To overcome this, this study introduces a new idea of fuzzy APACA optimization for the design of personalized tourism routes, and accurately controls the influencing parameters through gradient descent theory, which is used as the road map for index allocation, and then uses fuzzy APACA to innovate and construct a system scheme. The test results clearly point out that in the context of the evaluation criteria, the new scheme has been significantly optimized in terms of accuracy and processing speed for a variety of challenges, showing stronger performance superiority. Therefore, in the design of multi-

关键词： Accuracy Roads key performance indicator Interference Prediction algorithms Market research Classification algorithms

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Enhanced BERT with Graph and Topic information for Short Text Classification 35

Enhanced BERT with Graph and Topic Information for Short Tex...

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35th International Conference on Software Engineering and Knowledge Engineering, SEKE 2023

作者： Zhang, Tong Tang, Ailing Yan, Rong College of Computer Science Inner Mongolia University Inner Mongolia Key Laboratory of Mongolian Information Processing Technology National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot010021 China

Short text classification is an important natural language processing task due to the prevalence of short text on the internet and social media platforms. In this paper, we propose a novel graph-based short text classification method named GBBM (Graph-BERT-BTM Model) that leverages the powerful representation ability of graph data to capture the structural features of short text. In this work, we incorporate topic information to enrich and expand the feature space for the short text and compare our proposed method on five publicly available short text datasets with five existing models. Experimental results indicate the superiority of our proposed method. © 2023 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Graphic methods

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Tomato Leaf Disease Identification and Growth Dataset Augmentation Based on UAE-GAN 13

Tomato Leaf Disease Identification and Growth Dataset Augmen...

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13th International Conference on intelligent control and information processing, ICICIP 2025

作者： Li, Xin Li, Fang Luo, Xiaonan School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9798331516147

To delve into the characterization of growth disorders in different crops, it is important to support the model with a large amount of image data that includes a variety of disease types and disease levels to capture the typical and subtle differences of various diseases on plant leaves. However, the actual process of gathering data is challenging, sample coverage is challenging to accomplish, data capture is impeded, and the quality of the data is subpar. This work aims to address the issue of data shortages by employing technical methods. In particular, we creatively investigated the UAE-GAN approach, which naturally combines CycleGAN, U-Net, Variational Autoencoder VAE, and Autoencoder to increase the data. Among these, U-Net can precisely extract the small details of disease locations in crop photos and provide a strong basis for further processing thanks to its special codec architectural benefits. The Variational Autoencoder (VAE) significantly enhances the diversity of data by mapping the image to the latent space and sampling based on a certain probability distribution, so producing new image samples that are distinct from the original image yet inherently connected. Learning the coding and decoding of the original image is the foundation of autoencoders. If a mild disruption is introduced into the coding process, it can achieve data augmentation in another dimension and create a sequence of new images with just little modifications to the original image. The aforementioned models are closely linked with CycleGAN to efficiently map and convert in a variety of picture domains and to fully leverage CycleGAN's remarkable unsupervised image conversion capabilities. The perception ability, feature capture ability, and information conversion ability of the fusion model for crop image data are significantly improved, and the key elements of each link in the data enhancement process are comprehensively considered to ensure that the generated new image data can not o

关键词： Fruits

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