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第33届中国控制与决策会议

作者： Hanru Tang Junguo Lu Dong Yang Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China China Academy of Space Technology

This paper investigates the problems of robust stability and stabilization for fractional-order systems with polytopic *** is assumed that the fractional-order α is a known constant and belongs to 0 <α <***,based on the H bounded real lemma for commensurate fractional-order control systems,a sufficient condition for the above stability problem is established in terms of linear matrix inequalities（LMIs）.Secondly,on the foundation of this condition,sufficient LMI methods for the design of stabilizing controller are obtained for two cases where the polytopic coefficients are known and *** the case of unknown polytopic coefficients,by introducing the additional matrices,the state matrix and the positive-definite Hermitian matrices are *** the case of known polytopic coefficients,by introducing parameter-dependent matrices,a less-conservative robust H stabilization condition is ***,two different numerical examples are provided to compare the conservatism between the existing results and the results in this paper.

关键词： fractional-order systems robust stability robust stabilization robust H_∞ control convex polytopic uncertainties

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Priv-Code: Preserving privacy against traffic analysis through network coding for multihop wireless networks

Priv-Code: Preserving privacy against traffic analysis throu...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Zhiguo Wan Kai Xing Yunhao Liu MOE Key Laboratory for Information System Security School of Software Tsinghua National Laboratory for Information Science and Technology Tsinghua University China School of Computer Science and Technology University of Science and Technology China Department of Computer Science and Engineering HKUST Hong Kong China

ISBN: (纸本)9781467307734

Traffic analysis presents a serious threat to wireless network privacy due to the open nature of wireless medium. Traditional solutions are mainly based on the mix mechanism proposed by David Chaum, but the main drawback is its low network performance due to mixing and cryptographic operations. We propose a novel privacy preserving scheme based on network coding called Priv-Code to counter against traffic analysis attacks for wireless communications. Priv-Code is able to provide strong privacy protection for wireless networks as the mix system because of its intrinsic mixing feature, and moreover, it can achieve better network performance owing to the advantage of network coding. We first construct a hypergraph-based network coding model for wireless networks, under which we formalize an optimization problem whose objective function is to make each node have identical transmission rate. Then we provide a decentralized algorithm for this optimization problem. After that we develop an information theoretic metric for privacy measurement using entropy, and based on this metric we demonstrate that Priv-Code achieves stronger privacy protection than the mix system while achieving better network performance.

关键词： Optimization

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CPR-Net: A Novel Joint Learning Network for Pulmonary Nodule Evaluation

CPR-Net: A Novel Joint Learning Network for Pulmonary Nodule...

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IEEE International Conference on systems, Man and Cybernetics

作者： Gong Chen Jun Liu Chen-Qian Li Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

Lung cancer is the most common malignant tumor worldwide, with high mortality rates. Pulmonary nodule is a common manifestation of lung cancer. Accurate segmentation and detection of pulmonary nodules from CT scans are essential for proper assessment of patient prognosis. However, this task remains challenging due to various factors, such as class imbalance and the need for detailed characterization of lung nodule segmentation. To address these issues, we propose a novel network, CPR-Net (Convolutional Pyramid Residuals-Net), that combines segmentation and benign-malignant classification for CT images of lung nodules. Our approach utilizes a PBMM module to expand the perception field and enhance the representation capability of the model, allowing it to learn more detailed information. We evaluate the classification performance using accuracy, precision, recall, F1 score, AUC, and the segmentation performance using the Dice coefficient. Experiments on both publicly available datasets and self-constructed datasets demonstrate that our proposed method in this paper outperforms other methods in terms of classification and segmentation performance under limited labeled data conditions. Moreover, Our results suggest that this model has great potential for improving lung nodule diagnosis in radiology for heterogeneous intranodal images.

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ILNet: Low-level Matters for Salient Infrared Small Target Detection

arXiv

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arXiv 2023年

作者： Li, Haoqing Yang, Jinfu Wang, Runshi Xu, Yifei The Faculty of Information Beijing University of Technology Beijing100124 China The Faculty of Information Technology Beijing University of Technology Beijing100124 China The Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing100124 China

Infrared small target detection is a technique for finding small targets from infrared clutter background. Due to the dearth of high-level semantic information, small infrared target features are weakened in the deep layers of the CNN, which underachieves the CNN’s representation ability. To address the above problem, in this paper, we propose an infrared low-level network (ILNet) that considers infrared small targets as salient areas with little semantic information. Unlike other SOTA methods, ILNet pays greater attention to low-level information instead of treating them equally. A new lightweight feature fusion module, named Interactive Polarized Orthogonal Fusion module (IPOF), is proposed, which integrates more important low-level features from the shallow layers into the deep layers. A Dynamic One-Dimensional Aggregation layers (DODA) are inserted into the IPOF, to dynamically adjust the aggregation of low dimensional information according to the number of input channels. In addition, the idea of ensemble learning is used to design a Representative Block (RB) to dynamically allocate weights for shallow and deep layers. Experimental results on the challenging NUAA-SIRST (78.22%nIoU and 1.33×10−6Fa) and IRSTD-1K (68.91%nIoU and 3.23×10−6Fa) dataset demonstrate that the proposed ILNet can get better performances than other SOTA methods. Moreover, ILNet can obtain a greater improvement with the increasement of data volume. Training code are available at https://***/Li-Haoqing/ILNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Multiobjective genetic algorithm for optimized DNA sequences for DNA self-assembly

Communications in Computer and Information Science

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Communications in Computer and information Science 2014年 472卷 591-597页

作者： Zhang, Kai Yi, Jiaren Liu, Jun Hu, Wei School of Computer Science Wuhan University of Science and Technology Wuhan Hubei430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China

ISBN: (纸本)9783662450482

DNA sequence design is a very important task for DNA selfassembly technologies, including DNA computing, complex 3D nanostructures and nano-devices design. These experimental DNA molecules must satisfy several combinatorial, thermodynamic and secondary structure criteria, which aim to avoid undesired hybridizations and make the molecular experiment more re-liable and stable. In this paper, the DNA sequence design problem is formulated as a multi-objective optimization problem and solving it using multi-objective genetic algorithm. Moreover, the performance of our algorithm is compared with the other sequence design methods, and the comparison result shows that our algorithm is able to generate higher quality DNA sequences than previously published studies which consider same criteria. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Multiobjective optimization

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Optimization of Extreme Learning Machine Based on Improved Beetle Antennae Search for Slot Die Coating Prediction

Optimization of Extreme Learning Machine Based on Improved B...

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Chinese Control and Decision Conference, CCDC

作者： Haonan Yang Ding Liu Jun-Chao Ren Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China

In the actual production of slot die coating, the minimum coating thickness and the maximum substrate moving speed could only be judged by production experience, and there was no accurate prediction model due to the nonlinear characteristics of fluid motion. Therefore, building a reasonable and efficient prediction model for slot die coating is now an urgent and challenging task. In this paper, an optimized extreme learning machine (ELM) based on improved beetle antennae search (IBAS) algorithm is proposed for slot die coating prediction. The optimized ELM model can well learn the nonlinear characteristics of the system and make accurate predictions, thus solving the traditional inaccurate empirical judgment. As the prediction accuracy of ELM depends on the selection of weights and biases, the IBAS optimization algorithm is used to quickly search for the optimal value of weights and biases in the ELM network. IBAS algorithm improves the generation mechanism of antennae on the basis of the original algorithm, so that the algorithm can converge quickly. At the same time, the search strategy of the algorithm is improved to avoid falling into the local optimal solution. By predicting the production data of slit coating, the feasibility and effectiveness of IBAS-ELM model are proved.

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Quadruped Robot Get Bionic Learning Method Based on Intelligent Memory Soft Actor-Critic

Quadruped Robot Get Bionic Learning Method Based on Intellig...

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Chinese Control and Decision Conference, CCDC

作者： Chunyang Li Xiaoqing Zhu Xiaogang Ruan Borui Nan Lanyue Bi Xiaoyu Zhu Department of Information Beijing University of Technology Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124China Beijing China

Quadruped robot gait control is a widely studied topic, with the advancements in artificial intelligence, reinforcement learning-based approaches provide a promising solution for bionic gait learning of quadruped robots. In this study, we propose an Intelligent Memory Soft Actor-Critic (IM-SAC) algorithm that uses the Soft Actor-Critic algorithm as the basic framework and incorporates Long Short-Term Memory network (LSTM) and Gated Recurrent Unit (GRU) to extract time-sequence-related sequence information of the quadruped robot motion. The IM-SAC algorithm aims to maximize the motion reward by controlling the degree of memory and forgetting of sample information, giving priority to learning samples with high reward values, and achieving faster cumulative rewards and optimization models. We design a reward function using the quadruped robot's own speed, swing angle, and other information to train the robot, and use the 12 motor angles as output values to control the movement of the quadruped robot. We conducted experiments on the Pybullet platform to test the algorithm's performance in gait learning tasks of the quadruped robot. The results show that our study provides a promising solution for gait control of quadruped robots by integrating reinforcement learning and intelligent memory mechanisms.

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Mobile SAM-based Motion Target Detection

Mobile SAM-based Motion Target Detection

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第43届中国控制会议

作者： Enkui Zhou Mingming Wang Yuan Ping Chong Wu Juntong Qi Hengzhuang Zhang Wenyue Wu Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information EngineeringTianjin University School of Artificial Intelligence Shanghai University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The study introduces a motion target detection algorithm that aims to effectively detect unconventional targets such as irregularly shaped unmanned aerial vehicles(UAVs),unmanned ground vehicles(UGVs),and specialized ***,Mobile SAM(Mobile Segment Anything Model) is utilized for object segmentation,extracting mask information for all entities within a ***,the centroid coordinates extracted from the mask data are used as input for the K-Nearest Neighbors(KNN) *** integrating trajectory prediction and data fusion technologies,the objective of motion target detection is *** make use of publicly available datasets for UAV detection and tracking in order to design and validate the *** results demonstrate a 6.73% improvement in detection accuracy,an 8.63% improvement in recall rate,and a 7.66% improvement in F1 score compared to the modified three-frame differential motion target detection *** research makes significant contributions by addressing the limitations of traditional motion target detection,reducing the algorithm's dependence on datasets and sensitivity to lighting changes,and enhancing the precision of motion target *** findings of this research provide an effective technical solution for monitoring and defending against potential threats,particularly in countering the encroachments of irregularly shaped UAVs,UGVs,and specialized robots.

关键词： Mobile SAM KNN Motion Target Detection

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Resilient Average Consensus: A Detection and Compensation Approach

arXiv

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arXiv 2022年

作者： Zheng, Wenzhe He, Zhiyu He, Jianping Zhao, Chengcheng Fang, Chongrong Department of Automation Shanghai Jiao Tong University China Key Laboratory of System Control and Information Processing Ministry of Education of China China State Key Laboratory of Industrial Control Technology Zhejiang University China

We study the problem of resilient average consensus for multi-agent systems with misbehaving nodes. To protect consensus value from being influenced by misbehaving nodes, we address this problem by detecting misbehaviors, mitigating the corresponding adverse impact and achieving the resilient average consensus. In this paper, general types of misbehaviors are considered, including deception attacks, accidental faults and link failures. We characterize the adverse impact of misbehaving nodes in a distributed manner via two-hop communication information and develop a deterministic detection-compensation-based consensus (D-DCC) algorithm with a decaying fault-tolerant error bound. Considering scenarios where information sets are intermittently available due to link failures, a stochastic extension named stochastic detection-compensation-based consensus (S-DCC) algorithm is proposed. We prove that D-DCC and S-DCC allow nodes to asymptotically achieve resilient average consensus exactly and in expectation, respectively. Then, the Wasserstein distance is introduced to analyze the accuracy of S-DCC. Finally, extensive simulations are conducted to verify the effectiveness of the proposed algorithms. © 2022, CC BY.

关键词： Stochastic systems

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A Novel Approach to MI-EEG Classification via 3D Interpolation and 3DCNN

A Novel Approach to MI-EEG Classification via 3D Interpolati...

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Chinese Control and Decision Conference, CCDC

作者： Ming-Ai Li Jing Zhang Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education Beijing China Faculty of Information Technology Beijing University of Technology Beijing China

Electroencephalography (EEG) contains rich information about brain activity. Classification based on motor imagery EEG (MI-EEG) is essential for active intelligent rehabilitation using brain-computer interface technology. However, most of the current MI-EEG decoding methods are dedicated to the study of efficient time-frequency feature extraction from the raw EEG, ignoring the spatial features associated with electrode distribution. Therefore, this paper proposed an MI-EEG classification method using a combination of three dimensional (3D) spatial interpolation and 3D convolutional neural network (3DCNN) to fully utilize the 3D spatial features of electrodes. First, the frequency transformation was applied to the EEG signal at each electrode using the fast Fourier transform and the energy value was obtained. Then, the 3D coordinates of the scalp electrodes were projected into the 3D space and the energy values were interpolated using the 3D interpolation method to generate a 3D feature image containing the 3D real spatial location information of the electrodes. Finally, a 3DCNN was designed to match the 3D feature image for feature extraction and recognition. The proposed method obtained 77.19% accuracy in the BCI Competition IV 2a dataset, which is higher than the existing decoding methods. Results from experiments validate the effectiveness of the proposed MI-EEG classification method.

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