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2nd International Conference on Computer Technologies, ICCTech 2023

作者： Zhou, Haoshang Gao, Zhaoling Wang, Zhongfu Wang, Xiaopeng State Key Laboratory for Traction and Control System of EMU and Locomotive CRRC Dalian Electric Traction R&D Center Co. Ltd China School of Intelligence and Electronic Engineering Dalian Neusoft University of Information China

ISBN: (纸本)9781665455824

At present, in the communication between Electric Multiple Units (EMU) and locomotive network control system, in order to ensure the stability of vehicle control and the accuracy and reliability of data recording, its key components - Central control Unit (CCU) and Event Record Machine (ERM) adopt an embedded real-time operation system. In order to facilitate debugging, the system needs to start Microsoft's DOS system before being called to start. Based on the feedback of the embedded real-time operating system startup failure from each locomotive and rolling stock depot, this paper will elaborate on its startup process and propose a solution that can automatically repair the startup problem, which has been tested in the laboratory to prove that this method can effectively avoid the system startup failure. © 2023 IEEE.

关键词： Program debugging

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H∞ control of fractional-order singularly perturbed systems: The 1 < ν < 2 case

H∞ control of fractional-order singularly perturbed systems...

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Chinese control and Decision Conference, CCDC

作者： Long-Jun Zhu Jun-Guo Lu Department of Automation Shanghai Jiao Tong University School of Artificial Intelligence Shanghai Normal University Tianhua College Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

This paper addresses the problems of the H ∞ analysis and control of fractional-order singularly perturbed systems with the commensurate order 1 < ν < 2. Based on the bounded real lemma for fractional-order systems and singular fractional-order systems, the sufficient conditions for the H ∞ performance analysis of fractional-order singularly perturbed systems are firstly presented in terms of -independent linear matrix inequalities (LMIs). Then, the state feedback controllers are designed, and the sufficient -independent stabilization conditions are established via bilinear matrix inequalities(BMIs). Moreover, by the provided iterative algorithm, these BMI-based conditions can be solved based on LMIs. Finally, two numerical examples are exploited to verify the effectiveness of the proposed results.

关键词： Robust control State feedback Sufficient conditions Upper bound Perturbation methods Transfer functions Mathematical models

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Towards Supervisory control Theory in Tactical Environments: A Stackelberg Game Approach

Towards Supervisory Control Theory in Tactical Environments:...

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IEEE Conference on Decision and control

作者： Bohan Cui Alessandro Giua Xiang Yin Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China Department of Electrical and Electronic Engineering University of Cagliari Cagliari Italy

In this paper, we propose a new framework for supervisory control of discrete-event systems in tactical environments. In contrast to the standard supervisory control theory, where the environments are considered fully adversarial, we consider the possibility of the presence of attackers who have their own objectives that may not necessarily be in opposition to the specification of the supervisor. We formulate this scenario as a Stakelberg game in the leader-follower setting, where the designer proposes a supervisor, and the attacker takes a best response to the supervisor. We characterize the solution to the Stakelberg supervisory control problem as having both cooperative and antagonistic solutions. Moreover, we provide an effective algorithm for synthesizing a cooperative supervisor that enables both players to achieve their objectives. Our work makes an initial step forward from the traditional zero-sum setting of supervisory control theory to the non-zero-sum setting. Examples are provided to illustrate our results.

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Online Monitoring of Dynamic systems for Signal Temporal Logic Specifications with Model information

arXiv

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arXiv 2022年

作者： Yu, Xinyi Dong, Weijie Yin, Xiang Li, Shaoyuan Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai 200240 China

Online monitoring aims to evaluate or to predict, at runtime, whether or not the behaviors of a system satisfy some desired specification. It plays a key role in safety-critical cyber-physical systems. In this work, we propose a new model-based approach for online monitoring for specifications described by Signal Temporal Logic (STL) formulae. Specifically, we assume that the observed state traces are generated by an underlying dynamic system whose model is known. The main idea is to consider the dynamic of the system when evaluating the satisfaction of the STL formulae. To this end, effective approaches for the computation of feasible sets for STL formulae are provided. We show that, by explicitly utilizing the model information of the dynamic system, the proposed online monitoring algorithm can falsify or certify of the specification in advance compared with existing algorithms, where no model information is used. We also demonstrate the proposed monitoring algorithm by case studies. Copyright © 2022, The Authors. All rights reserved.

关键词： Specifications

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Reduced-Order Consensus Protocols with Pure Relative Output: Design, Existence, and Dual Results

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IEEE Transactions on Automatic control 2025年

作者： Ma, Yuwen Li, Xianwei Li, Shaoyuan Lin, Zongli Shanghai Jiao Tong University Department of Automation Shanghai200240 China State Key Laboratory of Submarine Geoscience China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China University of Virginia Charles L. Brown Department of Electrical and Computer Engineering CharlottesvilleVA22904 United States

This paper studies reduced-order dynamic consensus protocols for homogeneous linear multi-agent systems using pure relative output information. By applying H∞ control theory, a separation principle-like method with an additional small-gain constraint is proposed for designing an (nx-nu)th order protocol, where nx and nu are the numbers of states and inputs of each agent, respectively. Existence conditions for the protocol are then systematically discussed from the graph, low gain and high gain perspectives, respectively. It is revealed that the protocol always exists and can be found by the proposed design method for three cases: 1) directed acyclic networks, 2) agents with all eigenvalues in the closed left-half complex plane and 3) agents of minimum phase. Another reduced-order protocol of (nx - ny)th order is further discussed through duality. Finally, a numerical example is provided to illustrate the effectiveness of the proposed method. © 1963-2012 IEEE.

关键词： Robust control

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Content-Aware Deep Feature Matching 4th

Content-Aware Deep Feature Matching

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Wang, Weice Li, Zuoyong Zheng, Xiangpan Lai, Taotao Keikhosrokiani, Pantea College of Computer and Information Sciences Fujian Agriculture and Forestry University Fuzhou350002 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China College of Physics and Electronic Information Engineering Minjiang University Fuzhou350121 China School of Computer Sciences Universiti Sains Malaysia Penang Gelugor11800 Malaysia

ISBN: (纸本)9783031200984

Feature matching is a fundamental and critical step for many tasks of computer vision. In recent years, many methods for feature matching have been proposed. However, the image alignment algorithm used in the state-of-the-art matching method is not robust enough, and the number of the features extracted from the images are insufficient, thus the performance obtained by the state-of-the-art matching method is unsatisfactory. To address these issues, firstly, we use a more robust geometric estimation algorithm to align image pairs. Secondly, based on aligned image pairs, we design a content-aware block to more focus on effective features by using a feature extractor and a mask predictor. Experimental results show that our method achieves more superior results, and outperforms several classical and deep learning methods on the HPatches dataset. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Research on the logic monitoring method for the cloud computing based safety computer

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Transportation Safety and Environment 2023年第2期5卷 79-87页

作者： Yaran Yang Lianchuan Ma Tao Tang Huan Ding Zhen Liu School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong UniversityBeijing 100044China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong UniversityBeijing 100044China CRSC Research&Design Institute Group Co.Ltd. Beijing 100070China

With the development of railway construction in China,the computing demand of the train control system is increasing day by *** application of cloud computing technology on the rail transit signal system has become a research hotspot in recent *** to improve the safety and availability of the safety computer platform in the cloud computing environment is the key problem when applying cloud computing to the train operation control *** the cloud platform is in an open network environment,fac-ing many security vulnerabilities and malicious network attacks,it is necessary to monitor the operation of computer programmes through edge safety ***,this paper encrypts the logical monitoring method,and then proposes a secure computer de fence model based on the dynamic heterogeneous redundancy *** the continuous time Markov chain(CTMC)is used to quantitatively solve the stable probability of the system,and the influence of different logical monitoring methods on the anti-attack performance of the system is ***,the experiment proves that the dynamic heterogeneous redundancy structure composed of encryption logic monitoring can guarantee the safe and stable operation of the safety computer more effectively.

关键词： safety computer logical monitoring method continuous time Markov modelling(CTMC) cloud computing anti-aggression analysis

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Semantic Transformation-Based Data Augmentation for Few-Shot Learning

SSRN

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SSRN 2023年

作者： Pan, Mei-Hong Xin, Hong-Yi Shen, Hong-Bin Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Jiaotong University Shanghai200240 China

Few-shot learning (FSL) as a data-scarce method, aims to recognize instances of unseen classes solely based on very few examples. However, the model can easily become overfitted due to the biased distribution formed with extremely limited training data. This paper presents a task specific data augmentation approach by transferring samples from base dataset to the novel tasks in an encoder-decoder paradigm, which guarantees generating semantically meaningful features. Specifically, the feature transfer process is carried out in semantic space. We further impose a compactness constraint to the generated features with the prototypes working as the reference points, which ensures the generated features distribute around the class centers. Moreover, we incorporate the cluster centers of the query set with the prototypes of the support set to reduce the bias of the class centers. With the supervision of the compactness loss, the model is encouraged to generate discriminative features with high inter-class dispersion and intra-class compactness. Extensive experiments show that our method outperforms the state-of-the-arts on four benchmarks, namely MiniImageNet, TieredImageNet, CUB and CIFAR-FS. © 2023, The Authors. All rights reserved.

关键词： Motion planning

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Causal Attention Transformer for Video Text Retrieval

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IET Image processing 2025年第1期19卷

作者： Lan, Hua Lv, Chaohui School of Information and Communication Engineering Communication University of China Beijing China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Ministry of Culture and Tourism Beijing China

In the metaverse, video text retrieval is an urgent and challenging need for users in social entertainment. The current attention-based video text retrieval models have not fully explored the interaction between video and text, and only brute force feature embedding. Moreover, Due to the unsupervised nature of attention weight training, existing models have weak generalization performance for dataset bias. Essentially, the model learns that false relevant information in the data is caused by confounding factors. Therefore, this article proposes a video text retrieval method based on causal attention transformer. Assuming that the confounding factors affecting the performance of video text retrieval all come from the dataset, a structural causal model that conforms to the video text retrieval task is constructed, and the impact of confounding effects during data training is reduced by adjusting the front door. In addition, we use causal attention transformer to construct a causal inference network to extract causal features between video text pairs, and replace the similarity statistical probability with causal probability in the video text retrieval framework. Experiments are conducted on the MSR-VTT, MSVD, and LSMDC datasets, which proves the effectiveness of the retrieval model proposed in this paper. © 2025 The Author(s). IET Image processing published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： Interactive computer graphics

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Attn-VAE-GAN:Text-Driven High-Fidelity Image Generation Model with Deep Fusion of Self-Attention and Variational Autoencoder

Attn-VAE-GAN:Text-Driven High-Fidelity Image Generation Mode...

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International Joint Conference on Neural Networks (IJCNN)

作者： Chen Yang Key Laboratory of Intelligent Information Processing and Control Chongqing Three Gorges University School of Computer Science and Engineering Chongqing Three Gorges University

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In the current realm of research on text-to-image (T2I) transformation, the daunting task of translating natural language descriptions into visually realistic images is apparent. This intricate undertaking requires models to deeply comprehend cross-modal information and achieve precise semantic mapping. Despite the commendable progress made by GANs in image synthesis—crafting high-resolution, lifelike visual effects from random noise—persistent challenges like training instability, mode collapse, and fidelity issues persist, prompting the need for innovative *** study introduces an avant-garde text-driven high-fidelity image generation model, labeled as Attn-VAE-GAN. The initial integration of a generator module featuring a fused Self-Attention mechanism empowers the model to thoroughly grasp and accurately capture intricate semantic information from the input text. Moreover, the assimilation of a Variational Autoencoder (VAE) module taps into its latent representation learning prowess to optimize both image quality and diversity. To amplify the realism and detail expression of the generated images, a meticulously designed comprehensive loss function combines intrinsic VAE loss and hinge *** results underscore the substantial enhancement achieved by the Attn-VAE model in both the quality and diversity of the generated images across diverse publicly available benchmark datasets.

关键词： Training Representation learning Measurement Technological innovation Image synthesis Semantics Neural networks

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