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

作者： Kai Li Kun Qian Rui Liu Fang Fang Hai Yu Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing China State Grid Corporation of China Global Energy Interc onnection Research Institute Company Ltd Nanjing China

ISBN: (数字)9781728172934

ISBN: (纸本)9781728172941

Semantic information of objects and environment is a basis for robots to effectively complete complicated tasks. However, scene recognition methods based on image descriptors or convolutional neural networks often have misclassifications in actual environment. One reason is that highly complex environment has inconspicuous physical boundaries. In this paper, we propose a regional semantic learning method based on convolutional neural networks (CNNs) and conditional random fields (CRFs). The method combines global information obtained by scene classification network and local object information obtained by object detection network to train a CRF scene recognition model. Then the model can be used to infer the semantics of the region. After that, the regional semantic information is applied to build a sparse semantic map based on ORB-SLAM2. The proposed method was tested on a self-built environment dataset which contains four regional categories. Experimental results have demonstrated that the proposed method is effective and can obtain better classification results.

关键词： Feature extraction Semantics Erbium Training Three-dimensional displays Robots Real-time systems

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Nonsingular Terminal Sliding Mode control for Fuel Quantity Actuator System Using High-order Sliding Mode Observer

Nonsingular Terminal Sliding Mode Control for Fuel Quantity ...

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第三十八届中国控制会议

作者： Jianpei Hu Hao Sun Shihua Li School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education

Fuel quantity actuator can directly control fuel injection quantity of the diesel engine, so it is the main component of the electronic distribution pump. In this paper, a position tracking problem for fuel quantity actuator system under the influence of disturbances and system uncertainties is studied via a high-order sliding mode observer(HOSMO) based nonsingular terminal sliding mode control(NTSMC) approach. The proposed finite-time NTSMC method integrates the lumped disturbance estimation information estimated by the HOSMO into the designed control law, and realizes the finite time tracking performance under the influence of disturbances and uncertainties. The simulation results show the effectiveness of the proposed method.

关键词： Lumped Disturbances Fuel Quantity Actuator Non-singular Terminal Sliding Mode control(NTSMC) High-order Sliding Mode Observer(HOSMO)

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Smoky Vehicle Detection Algorithm Based On Improved Transfer Learning

Smoky Vehicle Detection Algorithm Based On Improved Transfer...

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International Conference on systems and Informatics (ICSAI)

作者： Guangming Zhang Dexiong Zhang Xiaobo Lu Yichao Cao Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China

ISBN: (数字)9781728152561

ISBN: (纸本)9781728152578

With the demand for pollution prevention has increased. Establishing a smoky vehicle inspection and evidence collection system will help to supervise the problem of excessive emissions of vehicles. Because the sample in the training data set is insufficient and unbalanced, the existing methods cannot obtain sufficient classification accuracy in the classification task of the smoky vehicle. This paper uses the transfer learning method to compare the effects of smoky car classification tasks on the five basic networks of DenseNet, NasNet, Inception, VGG19, MobileNet. In addition, for the classification task of the smoke vehicle, it is necessary to consider both the global information of the tail of the vehicle and the detailed information of the smoke area. In this paper, the global average pooling layer is changed to the spatial pyramid pooling layer, and it has achieved better results. Finally, the classification Precision nearly 0.1, Recall 0.3 are obtained, which is much better than the traditional classification method and the shallow non-transfer deep learning method.

关键词： Informatics

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Identity Recognition Based on Face Image 4

Identity Recognition Based on Face Image

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2019 4th Annual International Conference on Information System and Artificial Intelligence, ISAI 2019

作者： Qin, Chen Lu, Xiaobo Zhang, Pengguo Xie, Huibin Zeng, Wenbin School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China Zhejiang Uniview Technologies Co. Ltd Zhejiang310018 China

This article is aimed at designing a human identity recognition algorithm based on face image, which will be used in indoor environment. As the working environment is set as indoor environment, the camera will not be affected much by illumination variation. The key is how to detect human face and handle the variation of facial pose. This article divides the whole recognition process into 4 parts: image pre-processing, face detection, face alignment, feature extraction and comparison. Face detection and feature extraction are the core functions and both realized by deep learning. The process of the whole algorithm can be described as following: images after pre-processing are fed to face detection network to get the locations of face and face landmarks. Then face alignment will be conducted. Finally, deep features of face will be extracted and compared. The unique features of this algorithm are its good performance of handling the variation of facial pose and its clear framework which allows the whole method can be easily adjusted and upgraded. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Face recognition

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Adaptive Multiscale Illumination-Invariant Feature Representation for Undersampled Face Recognition

arXiv

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arXiv 2020年

作者： Zhang, Yang Hu, Changhui Lu, Xiaobo School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China School of Automation Nanjing University of Posts and Telecommunications Nanjing210023 China

This paper presents an novel illumination-invariant feature representation approach used to eliminate the varying illumination affection in undersampled face recognition. Firstly, a new illumination level classification technique based on Singular Value Decomposition (SVD) is proposed to judge the illumination level of input image. Secondly, we construct the logarithm edgemaps feature (LEF) based on lambertian model and local near neighbor feature of the face image, applying to local region within multiple scales. Then, the illumination level is referenced to construct the high performance LEF as well realize adaptive fusion for multiple scales LEFs for the face image, performing JLEF-feature. In addition, the constrain operation is used to remove the useless high-frequency interference, disentangling useful facial feature edges and constructing AJLEF-face. Finally, the effects of the our methods and other state-of-the-art algorithms including deep learning methods are tested on Extended Yale B, CMU PIE, AR as well as our Self-build Driver database (SDB). The experimental results demonstrate that the JLEF-feature and AJLEF-face outperform other related approaches for undersampled face recognition under varying illumination. keywords: Undersampled face recognition, Singular value decomposition, Multiple scales edgemaps, Illumination-invariant feature extraction Copyright © 2020, The Authors. All rights reserved.

关键词： Face recognition

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Double Deep Q-learning for Anti-saturation Attack Problem of Warship Group

Double Deep Q-learning for Anti-saturation Attack Problem of...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Wei Li Gang Xiong Hao Tao Yunjun Han Fang Wang Xisong Dong Zhen Shen State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences China China Ship Development and Design Center Wuhan China School of Control Science and Engineering (CSE) Shandong University Jinan China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China

Anti-saturation attack (ASA) strategy is vital for the survival of a warship group, and attracts the focus of many researchers. In this paper, the dynamics of ASA is formulated as a Markov Decision Process (MDP) with an enhanced states space since those characters are involved, such as the formation and detection and interception areas of warship group. A reinforcement learning method (Double Deep Q-leaning, DDQN) is developed to solve the problem and deal with the curse of dimensionality whereby the cost-to-go value is calculated by a marine engagement simulation system. A heuristic defense algorithm guided by field expert knowledge is designed for comparison. The experimental results show that the DDQN method performs better in anti-saturation attack scenarios.

关键词： Industries Heuristic algorithms Digital twin Conferences Knowledge based systems Reinforcement learning Markov processes

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Sensing With Random Communication Signals

arXiv

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arXiv 2025年

作者： Lu, Shihang Liu, Fan Xiong, Yifeng Du, Zhen Cui, Yuanhao Li, Shuangyang Yuan, Weijie Yang, Jie Jin, Shi National Mobile Communications Research Laboratory Southeast University Nanjing210096 China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China School of Electronic and Information Engineering Nanjing University of Information Science and Technology Nanjing210044 China Faculty of Electrical Engineering and Computer Science Technical University of Berlin Berlin10587 Germany Southern University of Science and Technology Shenzhen518055 China Frontiers Science Center for Mobile Information Communication and Security and also The Key Laboratory of Measurement and Control of Complex Systems of Engineering Southeast University Nanjing211189 China

Communication-centric Integrated Sensing and Communication (ISAC) has been recognized as a promising methodology to implement wireless sensing functionality over existing network architectures, due to its cost-effectiveness and backward compatibility to legacy cellular systems. However, the inherent randomness of the communication signal may incur huge fluctuations in sensing capabilities, leading to unfavorable detection and estimation performance. To address this issue, we elaborate on random ISAC signal processing methods in this article, aiming at improving the sensing performance without unduly deteriorating the communication functionality. Specifically, we commence by discussing the fundamentals of sensing with random communication signals, including the performance metrics and optimal ranging waveforms. Building on these concepts, we then present a general framework for random ISAC signal transmission, followed by an in-depth exploration of time-domain pulse shaping, frequency-domain constellation shaping, and spatial-domain precoding methods. We provide a comprehensive overview of each of these topics, including models, results, and design guidelines. Finally, we conclude this article by identifying several promising research directions for random ISAC signal transmission. © 2025, CC BY-SA.

关键词： Signaling

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Fire Detection based on Convolutional Neural Networks with Channel Attention

Fire Detection based on Convolutional Neural Networks with C...

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Chinese Automation Congress (CAC)

作者： Xiaobo Zhang Kun Qian Kaihe Jing Jianwei Yang Hai Yu School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education of China Future Science & Technology Park Beijing China Global Energy Interconnection Research Institute State Grid Nanjing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

The existing research on fire detection is basically based on a two-stage method, resulting in slower detection speed, and the positioning accuracy is limited by the first-stage candidate region extraction algorithm. In order to solve the problem of high-precision real-time fire detection, this paper proposes a Yolo detection network combined with attention mechanism. The attention module is serially added to the final three convolutional networks of different scales of Yolo v3. The channel attention module updates the feature map by weighting and summing all channels, which captures the semantic dependencies between channels in the deep layers of the network, and improves the generalization ability of the model. Experiments show that the method proposed in this paper improves the accuracy of fire detection without reducing the detection speed.

关键词： Fires Semantics Feature extraction Annotations Image color analysis Deep learning Cameras

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A Driver Fatigue Recognition Algorithm Based on Spatio-Temporal Feature Sequence 12

A Driver Fatigue Recognition Algorithm Based on Spatio-Tempo...

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12th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2019

作者： Zhang, Chen Lu, Xiaobo Huang, Zhiliang Xia, Shudong Fu, Changqing School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China Beijing PKU TransInfo Technology Co. Ltd Beijing100193 China

ISBN: (纸本)9781728148526

Researches show that fatigue driving is one of the important causes of road traffic accidents, so it is of great significance to study the driver fatigue recognition algorithm to improve road traffic safety. In recent years, with the development of deep learning, the field of pattern recognition has made great development. This paper designs a real-time fatigue state recognition algorithm based on spatio-temporal feature sequence, which can be mainly applied to the scene of fatigue driving recognition. The algorithm is divided into three task networks: face detection network, facial landmark detection and head pose estimation network, fatigue recognition network. Experiments show that the algorithm has the advantages of small volume, high speed and high accuracy. © 2019 IEEE.

关键词： Face recognition

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Spherical formation flight of UAVs with bidirectional communication delays

Spherical formation flight of UAVs with bidirectional commun...

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第三十八届中国控制会议

作者： Xiang Ai Yang-Yang Chen School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University

This paper studies three-dimensional(3 D) formation flight problem of networked UAVs in non-holonomic kinematics under the bidirectional communication links with time delays, which makes an identity distinct from the literatures without communication delay. A novel Lyapunov function is built base on neighbors' delay information, which results in the algorithms for the surge velocity, pitch velocity and yaw velocity associated with neighbors' delay information. The system stability is proven by the Barbalat lemma. The numerical simulation result has been presented to support the theoretical results.

关键词： Sphere landing orbit tracking lateral formation forming communication delay

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