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Cooperative guidance strategy for multiple hypersonic gliding vehicles system

Chinese Journal of Aeronautics 2020年第3期33卷 990-1005页

作者： Jianglong YU Xiwang DONG Qingdong LI Zhang REN Jinhu LV School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control LaboratoryBeihang UniversityBeijing 100083China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing 100083China Shananxi Key Laboratory of Integrated and Intelligent Navigation Xi’an 710068China

Cooperative guidance strategy for multiple hypersonic gliding vehicles system with flight constraints and cooperative constraints is *** paper mainly cares about the coordination of the entry glide flight phase and driving-down *** from the existing results,both the attack time and the attack angle constraints are considered ***, for the entry glide flight phase, a two-stage method is proposed to achieve the rapid cooperative trajectories planning, where the control signal corridors are designed based on the quasi-equilibrium gliding *** the first stage, the bank angle curve is optimized to achieve the attack angle *** the second stage, the angle of attack curve is optimized to achieve the attack time *** optimized parameters can be obtained by the secant ***, for the driving-down phase, the cooperative terminal guidance law is designed where the terminal attack time and attack angle are *** guidance law is then transformed into the bank angle and angle of attack *** cooperative guidance strategy is summarized as an ***, a numerical simulation example with three hypersonic gliding vehicles is provided for revealing the effectiveness of the acquired strategy and algorithm.

关键词： Attack angle constraints Attack time constraints Cooperative guidance Cooperative path planning Entry guidance Hypersonic vehicles

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A Constraints Fusion-induced Symmetric Nonnegative Matrix Factorization Approach for Community Detection

arXiv

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arXiv 2023年

作者： Liu, Zhigang Luo, Xin College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China College of Computer and Information Science Southwest University Chongqing400715 China

Community is a fundamental and critical characteristic of an undirected social network, making community detection be a vital yet thorny issue in network representation learning. A symmetric and non-negative matrix factorization (SNMF) model is frequently adopted to address this issue owing to its great interpretability and scalability. However, it adopts a single latent factor matrix to represent an undirected network for precisely representing its symmetry, which leads to loss of representation learning ability due to the reduced latent space. Motivated by this discovery, this paper proposes a novel Constraints Fusion-induced Symmetric Nonnegative Matrix Factorization (CFS) model that adopts three-fold ideas: a) Representing a target undirected network with multiple latent factor matrices, thus preserving its representation learning capacity;b) Incorporating a symmetry-regularizer that preserves the symmetry of the learnt low-rank approximation to the adjacency matrix into the loss function, thus making the resultant detector well-aware of the target network's symmetry;and c) Introducing a graph-regularizer that preserves local invariance of the network's intrinsic geometry, thus making the achieved detector well-aware of community structure within the target network. Extensively empirical studies on eight real-world social networks from industrial applications demonstrate that the proposed CFS model significantly outperforms state-of-the-art models in achieving highly-accurate community detection results. Copyright © 2023, The Authors. All rights reserved.

关键词： Matrix algebra

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DAMPER: A Dual-Stage Medical Report Generation Framework with Coarse-Grained MeSH Alignment and Fine-Grained Hypergraph Matching

arXiv

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arXiv 2024年

作者： Huang, Xiaofei Chen, Wenting Liu, Jie Lu, Qisheng Luo, Xiaoling Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Medical report generation is crucial for clinical diagnosis and patient management, summarizing diagnoses and recommendations based on medical imaging. However, existing work often overlook the clinical pipeline involved in report writing, where physicians typically conduct an initial quick review followed by a detailed examination. Moreover, current alignment methods may lead to misaligned relationships. To address these issues, we propose DAMPER, a dual-stage framework for medical report generation that mimics the clinical pipeline of report writing in two stages. In the first stage, a MeSH-Guided Coarse-Grained Alignment (MCG) stage that aligns chest X-ray (CXR) image features with medical subject headings (MeSH) features to generate a rough keyphrase representation of the overall impression. In the second stage, a Hypergraph-Enhanced Fine-Grained Alignment (HFG) stage that constructs hypergraphs for image patches and report annotations, modeling high-order relationships within each modality and performing hypergraph matching to capture semantic correlations between image regions and textual phrases. Finally,the coarse-grained visual features, generated MeSH representations, and visual hypergraph features are fed into a report decoder to produce the final medical report. Extensive experiments on public datasets demonstrate the effectiveness of DAMPER in generating comprehensive and accurate medical reports, outperforming state-of-the-art methods across various evaluation metrics. Copyright © 2024, The Authors. All rights reserved.

关键词： Coarse-grained modeling

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ChartKG: A Knowledge-Graph-Based Representation for Chart Images

arXiv

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arXiv 2024年

作者： Zhou, Zhiguang Wang, Haoxuan Zhao, Zhengqing Zheng, Fengling Wang, Yongheng Chen, Wei Wang, Yong School of Media and Design Hangzhou Dianzi University CO Hangzhou310018 China School of Media and Design Hangzhou Dianzi University China Big Data Visualization and Human Computer Collaborative Intelligent Laboratory Hangzhou Dianzi University China Research Center of Big Data Intelligence Zhejiang Lab Hangzhou China State Key Lab of CAD&CG Zhejiang University China College of Computing and Data Science Nanyang Technological University Singapore

Chart images, such as bar charts, pie charts, and line charts, are explosively produced due to the wide usage of data visualizations. Accordingly, knowledge mining from chart images is becoming increasingly important, which can benefit downstream tasks like chart retrieval and knowledge graph completion. However, existing methods for chart knowledge mining mainly focus on converting chart images into raw data and often ignore their visual encodings and semantic meanings, which can result in information loss for many downstream tasks. In this paper, we propose ChartKG, a novel knowledge graph (KG) based representation for chart images, which can model the visual elements in a chart image and semantic relations among them including visual encodings and visual insights in a unified ***, we develop a general framework to convert chart images to the proposed KG-based representation. It integrates a series of image processing techniques to identify visual elements and relations, e.g., CNNs to classify charts, yolov5 and optical character recognition to parse charts, and rule-based methods to construct graphs. We present four cases to illustrate how our knowledge-graph-based representation can model the detailed visual elements and semantic relations in charts, and further demonstrate how our approach can benefit downstream applications such as semantic-aware chart retrieval and chart question answering. We also conduct quantitative evaluations to assess the two fundamental building blocks of our chart-to-KG framework, i.e., object recognition and optical character recognition. The results provide support for the usefulness and effectiveness of ChartKG. © 2024, CC BY.

关键词： Knowledge graph

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Learning a Typhoon Bayesian Network Structure from Natural Language Reports 5th

Learning a Typhoon Bayesian Network Structure from Natural...

引用

5th IFIP TC 12 International Conference on Intelligence Science, ICIS 2022

作者： Yao, Zhangrui Chen, Junhan Pan, Yinghui Zeng, Yifeng Ma, Biyang Ming, Zhong Department of Automation and Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision-Making Xiamen University Xiamen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer and Information Sciences Northumbria University Newcastle upon Tyne United Kingdom School of Computer Science and Engineering Minnan Normal University Zhangzhou China

ISBN: (纸本)9783031149023

Given the huge toll caused by natural disasters, it is critically important to develop an effective disaster management and emergency response technique. In this article, we investigate relationships between typhoon-related variables and emergency response from natural language (NL) reports. A major challenge is to exploit typhoon state information for typhoon contingency plan generation, especially from unstructured text data based on NL input. To tackle this issue, we propose a novel framework for learning typhoon Bayesian network structures (FLTB), which can extract typhoon state information from unstructured NL, mine inter-information causal relationships and then generate Bayesian networks. We first extract information about typhoon states through NL processing (NLP) techniques, and then analyze typhoon reports by designing heuristic rules to identify causal relationships between states. We leverage these features to improve the learned structures and provide user-interaction mechanisms to finalize Bayesian networks. We evaluate the performance of our framework on real-world typhoon datasets and develop the Bayesian networks based typhoon emergency response systems. © 2022, IFIP International Federation for Information Processing.

关键词： Bayesian networks

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Multi User Offloading Strategy for Edge computing in Charging Load Forecasting Scenario

Multi User Offloading Strategy for Edge Computing in Chargin...

引用

Chinese automation Congress (CAC)

作者： Hongyu Long Zhaocheng Huang Chao Liu Daolin Xu Tao Li Fan Ye Key Laboratory of Big Data Intelligent Computing School of Automation/School of Industrial Internet Chongqing University of Posts and Telecommunications Chongqing China School of Automation/School of Industrial Internet Chongqing University of Posts and Telecommunications Chongqing China Economic and Technical Research Institute of State Grid Chongqing Electric Power Company Chongqing China

In order to mitigate the impact of large-scale new energy vehicle charging access to the grid, edge computing technology is widely used in smart charging perception scenarios. By deploying various edge terminal devices or application software at the perception sites, it can collect and predict the power load in various regions, so as to effectively guide and control the charging amount of new energy vehicles. However, with the increase of computing demand brought by the increase of edge device nodes, the edge server may face the problem of insufficient computing resources, which will affect the low latency characteristics. To alleviate this problem, this paper abstracts each edge terminal device as an independent user with task computing needs, establishes a multi-user edge computing system, and proposes a multi-level resource allocation strategy for edge computing, Decompose and allocate communication channel resources and server resources in multiple rounds. This method can reduce task latency by optimizing the task offloading ratio and resource allocation method of users while meeting their basic needs. The simulation results show that this method can effectively reduce the latency of task computation in multi user scenarios.

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Electric Vehicle Load Forecasting in Mountainous Cities Considering Traffic Behavior

Electric Vehicle Load Forecasting in Mountainous Cities Cons...

引用

Chinese automation Congress (CAC)

作者： Hongyu Long Xiao Yin Chao Liu Yuansen Xu Tian Tian Fan Ye Key Laboratory of Big Data Intelligent Computing School of Automation/School of Industrial Internet Chongqing University of Posts and Telecommunications Chongqing China School of Automation/School of Industrial Internet Chongqing University of Posts and Telecommunications Chongqing China Economic and Technical Research Institute of State Grid Chongqing Electric Power Company Chongqing China

In response to the unique topographical challenges posed by mountainous environments, this paper investigates a probabilistic model of electric vehicle charging load, taking into account the unique traffic behavior and regional characteristics of mountainous cities. Regarding the traffic behavior, first, a traffic road network model for mountainous cities is proposed. Next, the travel chain theory is combined with the road chain to consider the functional partition of the city, which can better describe the travel patterns and charging behavior of users. Based on the analysis of traffic behavior, by exploring the influences of ambient temperature, road grade, and the undulating nature of mountainous roads, an improved Floyd shortest path algorithm is used for path decision-making, leading to the development of an EV power consumption model specifically tailored for mountainous cities. Based on this model, the prediction of EV load in mountainous cities is subsequently conducted. Overall, this study proposes a more realistic EV load prediction model by integrating traffic behavior, regional characteristics and user travel patterns, and the simulation results demonstrate the adaptability as well as the effectiveness of the method for application in mountainous cities.

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Ascl: Accelerating Semi-Supervised Learning Via Contrastive Learning

SSRN

引用

SSRN 2024年

作者： Liu, Haixiong Li, Zuoyong Wu, Jiawei Zeng, Kun Hu, Rong Zeng, Wei Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Data Science Minjiang University Fuzhou350121 China School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University Guangdong Shenzhen518107 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan354300 China School of Physics and Mechanical and Electrical Engineering Longyan University Longyan364012 China

SSL(Semi-supervised learning) is widely used in machine learning, which leverages labeled and unlabeled data to improve model performance. SSL aims to optimize class mutual information, but noisy pseudo-labels introduce false class information due to the scarcity of labels. Therefore, these algorithms often need significant training time to refine pseudo-labels for performance improvement iteratively. To tackle this challenge, we propose a novel plug-and-play method named ASCL(Accelerating Semi-Supervised Learning via Contrastive Learning). This method combines contrastive learning with uncertainty-based selection for performance improvement and accelerates the convergence of SSL algorithms. Contrastive learning initially emphasizes the mutual information between samples as a means to decrease dependence on pseudo-labels. Subsequently, it gradually turns to maximizing the mutual information between classes, aligning with the objective of semi-supervised learning. Uncertainty-based selection provides a robust mechanism for acquiring pseudo-labels. The combination of the contrastive learning module and the uncertainty-based selection module forms a virtuous cycle to improve the performance of the proposed model. Extensive experiments demonstrate that ASCL outperforms state-of-the-art methods in terms of both convergence efficiency and performance. In the experimental scenario where only one label is assigned per class in the CIFAR-10 dataset, the application of ASCL to Pseudo-label, UDA (Unsupervised data Augmentation for Consistency Training), and Fixmatch benefits substantial improvements in classification accuracy. Specifically, the results demonstrate notable improvements in respect of 16.32%, 6.9%, and 24.43% when compared to the original outcomes. Moreover, the required training time is reduced by almost 50%. © 2024, The Authors. All rights reserved.

关键词： Image classification

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Self-supervised fast reconstruction method for diffusion MRI based on multi-directional assistance

Self-supervised fast reconstruction method for diffusion MRI...

引用

International Conference on Signal Processing Proceedings (ICSP)

作者： Yuxin Wu Jian Zhang Lihui Wang Ying Cao Linhai Wu Yunjia Wu Chen Ye Yuemin Zhu State Key Laboratory of Public Big Data College of Computer Science and Technology Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Guizhou University Guiyang China INSA Lyon CNRS Inserm CREATIS UMR 5220 U1206 Univ Lyon France

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Diffusion MRI (dMRI) is widely used for the detection and diagnosis of various pathologies due to its non-invasive nature. However, its signal acquisition process is relatively slow and susceptible to motions. In order to accelerate reconstruction process, many deep learning-based methods have been developed in recent years to achieve promising results, while most of them are supervised based methods and require fully sampled data which are difficult to obtain in practical applications to train the network. Self-supervised based methods were proposed to deal with this problem to a certain extent, however, they are devoted only to conventional MRI data, ignoring the correlation between the directions of dMRI data. To address this issue, we propose a self-supervised fast reconstruction method for dMRI based on multi-directional assistance. The method better utilizes the information between dMRI data directions by Partition-Replace operation, cleverly constructing data pairs and designing effective self-supervised strategy for bi-directional prediction, which can overcome the problem of data incompleteness and guide the model to reconstruct unsampled signals in spite of the difficulty of acquiring fully sampled data. Experimental results show that our method achieves better reconstruction in the absence of fully sampled data, even with an 8-fold acceleration factor.

关键词： Learning systems Pathology Correlation Magnetic resonance imaging Bidirectional control Reconstruction algorithms Signal processing Predictive models data models

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Penta-Encoder with Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation

Penta-Encoder with Medical Transformer for Incomplete Multim...

引用

International Conference on Signal Processing Proceedings (ICSP)

作者： Guohui Yu Lihui Wang Qijian Chen Li Wang Kun Tang Xinyu Cheng Yuemin Zhu State Key Laboratory of Public Big Data College of Computer Science and Technology Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Guizhou University Guiyang China INSA Lyon CNRS Inserm CREATIS UMR 5220 U1206 Univ Lyon Lyon France

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Accurate brain tumor segmentation of magnetic resonance imaging (MRI) images requires the integration of multimodal data in effective collaborative learning. However, clinical scenarios often face missing modalities, significantly impacting segmentation performance. In this paper, we propose the penta-encoder with medical transformer (Penta-MedTrans) for incomplete multimodal learning, which consists of four main components: a modality-specific encoder (MSE), a modality-dynamic encoder (MDE), a multimodal fusion module (MMFM) and a convolutional decoder. The MSE extracts unique features for each modality, while the MDE captures inter-modal semantics to address missing modalities. The MMFM establishes and aligns long-range correlations between modal-invariant features and global semantics of tumor regions. The convolutional decoder generates robust segmentation results by progressively upsampling and fusing modality-invariant features. We extensively validated our method on the BraTS 2020 and BraTS 2021 datasets for brain tumor segmentation. Results showed that our method outperforms SOTA for incomplete multimodal brain tumor segmentation across most subsets.

关键词： Image segmentation Correlation Convolution Magnetic resonance imaging Semantics Brain tumors Transformers Feature extraction Decoding Biomedical imaging

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