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arXiv 2021年

作者： Yin, Hui-Ping Ren, Hai-Peng Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China

To retrieve the information from the serious distorted received signal is the key challenge of communication signal processing. The chaotic baseband communication promises theoretically to eliminate the inter-symbol interference (ISI), however, it needs complicated calculation, if it is not impossible. In this paper, a genetic algorithm support vector machine (GA-SVM) based symbol detection method is proposed for chaotic baseband wireless communication system (CBWCS), by this way, treating the problem from a different viewpoint, the symbol decoding process is converted to be a binary classification through GA-SVM model. A trained GA-SVM model is used to decode the symbols directly at the receiver, so as to improve the bit error rate (BER) performance of the CBWCS and simplify the symbol detection process by removing the channel identification and the threshold calculation process as compared to that using the calculated threshold to decode symbol in the traditional methods. The simulation results show that the proposed method has better BER performance in both the static and time-varying wireless channels. The experimental results, based on the wireless open-access research platform, indicate that the BER of the proposed GA-SVM based symbol detection approach is superior to the other counterparts under a practical wireless multipath channel. Copyright © 2021, The Authors. All rights reserved.

关键词： Bit error rate

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Improving the Adversarial Robustness of Quantized Neural Networks Via Exploiting the Feature Diversity

SSRN

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SSRN 2022年

作者： Chu, Tianshu Fang, Kun Yang, Jie Huang, Xiaolin Department of Automation Shanghai Jiao Tong University 800 Dongchuan Road Shanghai 200240 China Department of Automation The Institute of Medical Robotics The MOE Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University 800 Dongchuan Road Shanghai 200240 China

Quantized neural networks (QNNs) have become one of the most prevailing approaches in deep learning model compression due to their computation and storage efficiency. However, there is a lack of researches specialized in the adversarial robustness of QNNs, which is important to applications in the security-critical domains. The existing defenses focus on the conventional full-precision networks. It would result in behavioral disparities and degrade the expected performance to be transferred to the QNNs directly. A novel defensive strategy prompts the feature diversity via an orthogonal constraint, which could cooperate well with the quantization. Inspired by this intuition, an orthogonal regularization with quantization is proposed to improve the adversarial robustness of QNNs in this paper. Moreover, it is observed that the quantization is an implicit regularization and is able to alleviate the orthogonal degeneration. The proposed orthogonal regularization with quantization is validated on several typical network architectures and benchmark datasets. The results demonstrate that the proposed method could improve the adversarial robustness against both the white-box and black-box attacks notably. © 2022, The Authors. All rights reserved.

关键词： Network architecture

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Unsupervised Difference Learning for Noisy Rigid Image Alignment

arXiv

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arXiv 2022年

作者： Chen, Yu-Xuan Feng, Dagan 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 Shanghai 200240 China School of Computer Science University of Sydney Sydney2006 Australia

Rigid image alignment is a fundamental task in computer vision, while the traditional algorithms are either too sensitive to noise or time-consuming. Recent unsupervised image alignment methods developed based on spatial transformer networks show an improved performance on clean images but will not achieve satisfactory performance on noisy images due to its heavy reliance on pixel value comparations. To handle such challenging applications, we report a new unsupervised difference learning (UDL) strategy and apply it to rigid image alignment. UDL exploits the quantitative properties of regression tasks and converts the original unsupervised problem to pseudo supervised problem. Under the new UDL-based image alignment pipeline, rotation can be accurately estimated on both clean and noisy images and translations can then be easily solved. Experimental results on both nature and cryo-EM images demonstrate the efficacy of our UDL-based unsupervised rigid image alignment method. Copyright © 2022, The Authors. All rights reserved.

关键词： Image enhancement

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Room Impulse Response Reshaping-Based Blind Source Separation for an Underdetermined Reverberant Environment

SSRN

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SSRN 2024年

作者： Xie, Yuan Zou, Tao Yang, Junjie Sun, Weijun Xie, Shengli School of Mechanical and Electrical Engineering Guangzhou University Guangdong-Hong Kong-Macao Key Laboratory of Multi-scale Information Fusion and Collaborative Optimization Control of Complex Manufacturing Process Guangzhou510006 China School of Automation Guangdong University of Technology Joint International Research Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education of the P.R.C. Guangzhou510006 China School of Automation Guangdong University of Technology Guangdong Key Laboratory of IoT Information Processing Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China School of Automation Guangdong University of Technology Key Laboratory of Intelligent Detection and the IoT in Manufacturing Ministry of Education 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China

Blind source separation under an underdetermined reverberation environment is a very challenging issue. The classical method is based on the expectation maximization algorithm. However, it is limited to high reverberation environments, resulting in bad or even invalid separation performance. To eliminate this restriction, a room impulse response reshaping-based expectation maximization method is designed to solve the problem of blind signal separation in an underdetermined reverberant environment. Firstly, a room impulse response reshaping technology is designed to eliminate the influence of audible echo on the reverberant environment, improving the quality of the received signals. Then, a new mathematical model of time-frequency mixing signals is established to reduce the approximation error of model transformation caused by high reverberation. Furthermore, an improved expectation maximization method is proposed for real-time update learning rules of model parameters, and then the sources are separated using the estimators provided by the improved expectation maximization method. Experimental results based on source separation of speech and music mixtures demonstrate that the proposed algorithm achieves better separation performance while maintaining much better robustness than popular expectation maximization methods. © 2024, The Authors. All rights reserved.

关键词： Reverberation

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Building Temperature and Humidity Adaptive control for a Multi-Zone HVAC system Using Hybrid Modeling Method

Building Temperature and Humidity Adaptive Control for a Mul...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Yuliang Jiang Shanying Zhu Qimin Xu Bo Yang Xinping Guan Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (纸本)9781728119496

In this paper, the heating, ventilation and air conditioning (HVAC) system as the contributor of building climate management is studied on multi-zone climate dynamics modelling and control for occupancy comfort. The first-principles model together with data-driven model as the hybrid model is proposed for comprehensive multi-zone hygro-thermal dynamics. The unmodeled climate dynamics such as unknown nonlinearity and uncertainty is modeled in a linear form based on the compact form dynamic linearization (CFDL) method. Then model free adaptive control (MFAC) scheme including controller design and parameter estimation is proposed to realize multi-zone climate control and occupancy comfort. The dynamic response performance of the hybrid model-based adaptive control and the first-principles model-based adaptive control has been tested in a multi-zone building with four different zones. The results show that the proposed hybrid model-based adaptive control scheme can achieve obvious performance improvement in minimizing steady-state error.

关键词： Adaptation models HVAC Parameter estimation Uncertainty Atmospheric modeling Buildings Ventilation

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Opportunistic Wireless control Over State-Dependent Fading Channels*

Opportunistic Wireless Control Over State-Dependent Fading C...

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IEEE Conference on Decision and control

作者： Shuling Wang Peizhe Li Shanying Zhu Cailian Chen Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

The heterogeneous system consisting of the wireless control system (WCS) and mobile agent system (MAS) is ubiquitous in Industrial Internet of Things (IIoT) systems. Within this system, the positions of mobile agents may lead to shadow fading on the wireless channel that the WCS is controlled over and can significantly compromise the WCS’s performance. This paper focuses on the controller design for the MAS to ensure the performance of WCS in the presence of WCS and MAS coupling. Firstly, the constrained finite field network (FFN) with profile-dependent switching topology is adopted to proceed the operational control for the MAS. By virtue of the algebraic state space representation (ASSR) method, an equivalent form is obtained for the WCS and MAS coupling. A necessary and sufficient condition in terms of constrained set stabilization is then established to ensure the Lyapunov-like performance with expected decay rate. Finally, a graphical method together with the breath-first searching is provided to design state feedback controllers for the MAS. With this method, it is easy to check the constrained set stabilization of MAS and to ensure the performance requirements of WCS in the presence of WCS and MAS coupling. The study of an illustrative example shows the effectiveness of the proposed method.

关键词： Couplings Wireless communication Fading channels State feedback Mobile agents Optical wavelength conversion Switches

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Active Vehicle Re-localization Based on Non-repetitive LiDAR with Gimbal Motion Strategy

Active Vehicle Re-localization Based on Non-repetitive LiDAR...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Xin’Ao Wu Chenxi Yang Yiyang Guo Hanyang Zhuang Chunxiang Wang Ming Yang Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China Los Altos High School Los Altos CA USA University of Michigan - Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

The installation of a multi-layer 3D LiDAR atop the vehicle is a widely adopted hardware configuration for map-matching-based localization in intelligent driving. By offering a comprehensive 360° horizontal Field of View (FoV), this setup aims to achieve precise matching outcomes through the imposition of substantial geometric constraints against dynamic interferences and structural degradation. However, several factors limit its environmental adaptability, such as sparse point cloud density at distances, insufficient maximum sensing range, and notably, the restricted beam elevation angle, limiting the perception of the environment beyond obstacles. The rapid advancement of non-repetitive scanning LiDARs shows promise in mitigating such limitations. Nevertheless, their narrow FoV remains a challenge to overcome. In this study, we propose a solution by mounting such one single LiDAR on a two-axis rotating gimbal, enabling the vehicle to surpass the ranges and vertical FoV limitations of traditional setups actively. The corresponding gimbal motion strategy has been designed to automatically focus on the environment component with the most robust geometric constraints. Experimental results validate that the proposed method achieves superior robustness under high dynamic interference while delivering sufficient performance under standard conditions.

关键词： Point cloud compression Laser radar Three-dimensional displays Dynamics Interference Hardware Robustness Sensors Vehicle dynamics Standards

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Multi-type and Multi-level Feature Fusion Network for RGBD Indoor Semantic Segmentation

Multi-type and Multi-level Feature Fusion Network for RGBD I...

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Chinese control and Decision Conference, CCDC

作者： Yuwen Xia Chaochen Gu Kaijie Wu Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (纸本)9781665478977

RGBD images, which refer to the three-channel color images augmented with depth channel, have been greatly exploited to improve the performance of scene semantic segmentation. RGBD semantic segmentation task is of great significance to help the development of robot navigation and grasping. However, most existing methods addressing RGBD semantic segmentation lack effective feature fusion modules. To address the aforementioned problem, we propose a novel network with two novel modules for feature fusion, namely Channel-Attention based Complementary Feature Fusion Module (CAC-FFM) and Cross-Layer Feature Fusion Module (CL-FFM). Specifically, CAC-FFM, which is essentially based on the channel-attention mechanism, effectively utilizes the complementary information of RGB and depth to generate fusion features, and CL-FFM captures patch-wise features from low-level feature maps to assist the training of high-level features so as to further optimize the segmentation results. Experimental results on the publicly available NYUDv2 dataset validate the effectiveness and superiority of our proposed method.

关键词： Training Cross layer design Correlation Navigation Semantic segmentation Grasping Excavation

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Pyramid Pooling-based Local Profiles for Graph Classification

Pyramid Pooling-based Local Profiles for Graph Classificatio...

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IEEE International Conference on systems, Man and Cybernetics

作者： Chengpei Wu Yang Lou Junli Li College of Computer Science Sichuan Normal University Chengdu China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Graduate School of Information Science and Technology Osaka University Osaka Japan

Many natural and engineering systems can be modeled and represented in the forms of graph data, and then studied using graph theory and network analysis tools. Graph representation learning aims at generating lower-dimensional representations from higher-dimensional graph data, which is a crucial step that facilitates the follow-up tasks, such as node and graph classifications. In this paper, we present a simple but effective graph representation learning method, namely the pyramid pooling-based local profile (PPLP), which enables local nodal profiles to be transformed into a graph representation, with multi-scale features extracted. PPLP can be either embedded into a graph neural network as the readout layer, or perform independently as a graph embedding algorithm. The resultant representations of PPLP are for graph-level tasks. PPLP is experimentally tested by performing graph classification tasks on ten representative datasets, either as the readout layer of different graph neural networks, or as an independent graph embedding algorithm. Experimental results demonstrate that: 1) when embedded into graph neural networks, PPLP outperforms the widely-used global pooling-based readout methods; 2) as an independent graph embedding algorithm, PPLP performs fairly good, especially on the social network datasets. The investigation confirms PPLP as a simple but promising method for graph-level tasks.

关键词：

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Research and application of Gaussian coupled control bistable stochastic resonance system with independent parameter regulation

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Results in Physics 2025年 73卷

作者： Hou, Tiantian Jiao, Shangbin Wang, Yi Song, Nianlong Li, Jianghua Cui, Wenchuan Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an 710048 China

The theory of Stochastic Resonance (SR) has drawn significant attention due to its exceptional ability to detect faint signals. Despite this, research to date indicates that for SR systems, whether they are monostable, bistable, or multi-stable, modifications to the system parameters lead to concurrent alterations in the depth and breadth of the potential wells when analyzing engineering signals, which results in suboptimal detection outcomes. To address these issues, a two-dimensional Gaussian bistable coupled SR (GBCSR) system has been proposed that can individually adjust the potential well characteristics. This innovative system facilitates the separate adjustment of shape characteristics of potential, allowing for more precise manipulation of the system's dynamic response. The system's non-linear dynamic traits are explicated through an analysis of the steady-state probability density (SPD) function and the mean first passage time (MFPT), substantiating the effectiveness of the new model. In practical scenarios, a variety of bearing defect signals serve as the objects of detection. The structural parameters of the GBCSR system are co-optimized using the Brain Storm Optimization (BSO) algorithm. This optimization approach leverages the algorithm's ability to enhance population diversity and improve convergence accuracy, thereby optimizing the system's performance. The experimental outcome results show that the proposed system can accurately detect the frequency of bearing fault signals. When compared with traditional SR systems such as the traditional bistable stochastic SR (TBSR), the traditional Gaussian SR system (TGSR), and the cascade stochastic resonance system, the proposed coupled system demonstrates superior performance. This is achieved through the transfer of energy or information between subsystems, which enables more efficient utilization of noise energy. The system can trigger the resonance effect over a broader range of noise intensities and signi

关键词： Bearing fault detection Coupled stochastic resonance Independent parameter regulation MFPT SPD

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