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Chinese control Conference (CCC)

作者： Xutao Ma Chao Ning Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai China

Toward carbon peaking and carbon neutrality goals in China, a transition from traditional fossil power to renewable energy becomes an imperative need. This paper aims to address the planning problem of a highly renewable-energy penetrated energy hub system, coupled with carbon capture facilities and downstream production processes of hydrogen and methanol, under the uncertainty of renewable power output. To this end, we propose a two-stage distributionally robust optimal planning model, where investment decisions are the first-stage variables and operation variables are determined at the second stage after the realization of uncertainty. In the proposed model, we utilize historical data samples to construct a strengthened ambiguity set that leverages both Wasserstein distance and moment information for a better characterization of renewable generation uncertainty. Moreover, to efficiently solve the proposed planning problem, we develop a simplified affine decision rule method in a general setting with both moment and Wasserstein ambiguity, and then apply it to reformulate the distributionally robust planning problem. The derived reformulation possesses the favorable property that its scale is free from the sample size, thus greatly reducing the computational complexity. Computational results validate the effectiveness of the proposed planning model and the computational efficiency of the solution methodology.

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3D reconstruction of moving object by double sampling based on phase shifting profilometry 9

3D reconstruction of moving object by double sampling based ...

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9th Symposium on Novel Photoelectronic Detection Technology and Applications

作者： Zhang, Qinghui Li, Hao Lu, Lei Pan, Wei Su, Zhilong Zhang, Mengya Lv, Pengtao Key Laboratory of Grain Information Processing and Control of Ministry of Education Henan University of Technology Ministry of Education Zhengzhou450001 China College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China Department of R & D OPT Machine Vision Tech Co. Ltd Dongguan523860 China Shanghai Institute of Applied Mathematics and Mechanics School of Mechanics and Engineering Science Shanghai University Shanghai200444 China

ISBN: (纸本)9781510664432

When using traditional phase-shift profilometry for 3D measurement, it is necessary to keep the measured object static during the shooting process. When the measured object is moving, errors will occur if the projection and capture of the fringe image is not fast enough. This paper proposes a new method to reconstruct the moving object by double sampling. A trigger control device is applied to the camera and projector, which ensures that after each projection, two consecutive images are captured before the next projection. Then, the phase information is retrieved by analyzing the relationship between the motion and fringe patterns. Finally, the moving object is retrieved successfully. The proposed method increased the frame rate of the moving object reconstruction. © 2023 SPIE.

关键词： Profilometry

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An image enhancement algorithm based on multi-scale Retinex theory to improve the images quality of sensors 12

An image enhancement algorithm based on multi-scale Retinex ...

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SPIE 12th International Symposium on Multispectral Image processing and Pattern Recognition, MIPPR 2023

作者： Li, Yiyao Chen, Zhong Sun, Wenyuan School of Journalism and Communication Central China Normal University Luoyu Road 152 Wuhan China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Luoyu Road 1037 Wuhan China National Key Laboratory of Science and Technology on Multi-spectral Information Processing Luoyu Road 1037 Wuhan China Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Luoyu Road 1037 Wuhan China Central China Normal University Library Luoyu Road 152 Wuhan China

ISBN: (数字)9781510674967

ISBN: (纸本)9781510674950

Nowadays, image recognition plays a pivotal role in acquiring data via sensors. However, the adaptability of traditional algorithms is hindered by the unpredictable nature of open environments, varying sensor quality, and image dimensions. Challenges arise in adverse conditions like inclement weather, low light, and optical distortions. Retinex-based methods have emerged as a viable solution, effectively enhancing images plagued by shadows or poor lighting. Yet, issues surface when images possess saturated colors;the conventional multi-scale Retinex with color restoration risks color inversion. Moreover, during gain compensation, extreme histogram values occupy significant gray level space, obscuring vital image details. This study delves into these challenges and proposes an enhanced multi-scale Retinex algorithm. Our approach substitutes logarithmic functions with tansig functions, eliminating color inversion risks. Additionally, a novel gain compensation method, integrating histogram stretching with Gamma correction, refines image clarity. The algorithm's robustness is evidenced in diverse scenarios, including adverse weather, low light, underwater imaging, and non-uniform lighting. Experimental results validate our method's superiority, surpassing other Retinex-based techniques both qualitatively and quantitatively. This research contributes valuable insights into image enhancement methodologies, fostering advancements in sensor-based data gathering in Smart Spaces. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Image enhancement

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Depth-Aware Fusion Method Based on Image and 4D Radar Spectrum for 3D Object Detection

Depth-Aware Fusion Method Based on Image and 4D Radar Spectr...

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IEEE International Conference on Robotics and Biomimetics

作者： Yue Sun Yeqiang Qian Chunxiang Wang Ming Yang The Global Institute of Future Technology Shanghai Jiao Tong University Shanghai China The Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Safety and reliability are crucial for the public acceptance of autonomous driving. To ensure accurate and reliable environmental perception, intelligent vehicles must exhibit accuracy and robustness in various environments. Millimeter-wave radar, known for its high penetration capability, can operate effectively in adverse weather conditions such as rain, snow, and fog. Traditional 3D millimeter-wave radars can only provide range, Doppler, and azimuth information for objects. Although the recent emergence of 4D millimeter-wave radars has added elevation resolution, the radar point clouds remain sparse due to Constant False Alarm Rate (CFAR) operations. In contrast, cameras offer rich semantic details but are sensitive to lighting and weather conditions. Hence, this paper leverages these two highly complementary and cost-effective sensors, 4D millimeter-wave radar and camera. By integrating 4D radar spectra with depth-aware camera images and employing attention mech-anisms, we fuse texture-rich images with depth-rich radar data in the Bird's Eye View (BEV) perspective, enhancing 3D object detection. Additionally, we propose using GAN-based networks to generate depth images from radar spectra in the absence of depth sensors, further improving detection accuracy.

关键词： Three-dimensional displays Accuracy Snow Radar detection Object detection Radar imaging Millimeter wave radar Cameras Sensors Meteorology

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High Performance Distributed control Law for Large-Scale Linear systems: A Partitioned Distributed Observer Approach

arXiv

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arXiv 2024年

作者： Xu, Haotian Liu, Shuai Shi, Ling Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

In recent years, the distributed-observer-based distributed control law has shown powerful ability to arbitrarily approximate the centralized control performance. However, the traditional distributed observer requires each local observer to reconstruct the state information of the whole system, which is unrealistic for large-scale scenarios. To fill this gap, this paper develops a greedy-idea-based large-scale system partition algorithm, which can significantly reduce the dimension of local observers. Then, the partitioned distributed observer for large-scale systems is proposed to overcome the problem that the system dynamics are difficult to estimate due to the coupling between partitions. Furthermore, the two-layer Lyapunov analysis method is adopted and the dynamic transformation lemma of compact errors is proved, which solves the problem of analyzing stability of the error dynamic of the partitioned distributed observer. Finally, it is proved that the distributed control law based on the partitioned distributed observer can also arbitrarily approximate the control performance of the centralized control law, and the dimension of the local observer is greatly reduced compared with the traditional method. The simulation results show that when the similarity between the physical network and the communication network is about 80%, the local observer dimension is greatly reduced by 90% and the relative error between the performance of the distributed control law and that of the centralized control law is less than 1%. © 2024, CC BY.

关键词： Continuous time systems

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Cross-Modal Monocular Localization in Prior LiDAR Maps Utilizing Semantic Consistency

Cross-Modal Monocular Localization in Prior LiDAR Maps Utili...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Chi Zhang Hengwang Zhao Chunxiang Wang Xuanlai Tang Ming Yang Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China KEENON Robotics Co. Ltd. China

Visual localization for mobile robots and intelligent vehicles in prior LiDAR maps can achieve high accuracy and low cost. However, algorithms for finding the cross-modal correspondences between images and LiDAR map points are not yet stable. In this paper, we propose a monocular visual localization system in prior LiDAR maps, which is based on the cross-modal registration to optimize the camera pose. To align the point clouds from vision and LiDAR map, a point-to-plane Iterative Closest Point algorithm utilizing semantic consistency is designed, and a decoupling optimization strategy is proposed to compute the affine transformation for the monocular scale ambiguity. Experiments on KITTI dataset show that utilizing the semantic consistency and geometric information of the map makes our system competitive with other methods. On the self-collected dataset, experiments on different light intensities demonstrate the robustness of the system in long-term localization tasks, and the ablation study demonstrates the effectiveness of the proposed algorithms.

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Diagnosis of Occupational Pneumoconiosis Based on Adaptive Feature Fusion and Multi-Class Loss Function

Diagnosis of Occupational Pneumoconiosis Based on Adaptive F...

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Chinese Automation Congress (CAC)

作者： Qian Zhao Yinan Guo Dunwei Gong China University of Mining and Technology Xuzhou China China University of Mining and Technology Beijing China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Qingdao University of Science and Technology Qingdao China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Pneumoconiosis, as the most prevalent and severe occupational disease in China, is categorized into four stages based on the area of small shadows in lungs. Previous diagnostic methods have limited accuracy for identifying different stages due to complex and diverse lesions of pneumoconiosis, as well as the difficulty of collecting rich X-ray images of patients. To address the issues, a novel diagnostic method based on adaptive feature fusion and multi-class loss function is proposed. First, the prototype network is introduced to establish a diagnostic model under the insufficient images, with the purpose of distinguishing the disease stage by the distance between the features of various samples. Following that, the multi-channel and multi-level feature fusion modules are proposed, respectively. These modules incorporate a gate-based adaptive selection mechanism to efficiently integrate the most important features from different channels and levels. Finally, the multi-class loss function is designed to train the diagnostic model of pneumoconiosis. The experimental results on a self-built dataset of chest X-ray radiographs demonstrate that the proposed method outperforms ten comparative methods in terms of comprehensive performance.

关键词： Training Adaptation models Accuracy Lungs Prototypes Logic gates Lesions X-ray imaging Diseases Biomedical imaging

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HOPE: A Reinforcement Learning-based Hybrid Policy Path Planner for Diverse Parking Scenarios

arXiv

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arXiv 2024年

作者： Jiang, Mingyang Li, Yueyuan Zhang, Songan Chen, Siyuan Wang, Chunxiang Yang, Ming Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Global Institute of Future Technology Shanghai Jiao Tong University Shanghai200240 China

Automated parking stands as a highly anticipated application of autonomous driving technology. However, existing path planning methodologies fall short of addressing this need due to their incapability to handle the diverse and complex parking scenarios in reality. While non-learning methods provide reliable planning results, they are vulnerable to intricate occasions, whereas learning-based ones are good at exploration but unstable in converging to feasible solutions. To leverage the strengths of both approaches, we introduce Hybrid pOlicy Path plannEr (HOPE). This novel solution integrates a reinforcement learning agent with Reeds-Shepp curves, enabling effective planning across diverse scenarios. HOPE guides the exploration of the reinforcement learning agent by applying an action mask mechanism and employs a transformer to integrate the perceived environmental information with the mask. To facilitate the training and evaluation of the proposed planner, we propose a criterion for categorizing the difficulty level of parking scenarios based on space and obstacle distribution. Experimental results demonstrate that our approach outperforms typical rule-based algorithms and traditional reinforcement learning methods, showing higher planning success rates and generalization across various scenarios. We also conduct real-world experiments to verify the practicability of HOPE. The code for our solution is openly available on https://***/jiamiya/HOPE. Copyright © 2024, The Authors. All rights reserved.

关键词： Parking

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Entity Representation Learning with Multimodal Neighbors for Link Prediction in Knowledge Graph 7

Entity Representation Learning with Multimodal Neighbors for...

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7th International Conference on Computer and Communications, ICCC 2021

作者： Liu, Wenxuan Duan, Hao Li, Zeng Liu, Jingdong Huo, Hong Fang, Tao Shanghai Jiao Tong University Department of Automation China Key Laboratory of System Control and Information Processing Ministry of Education of China China Shanghai Engineering Research Center of Intelligent Control and Management China

ISBN: (纸本)9781665409506

Entity representation learning is both fundamental and crucial for link prediction in knowledge graph. Existing entity representation learning approaches mainly focus on learning from knowledge triples with single modal text data which could only describe the things or concepts in words. However, multimodal data commonly exists in the real worlds and may describe things or concepts from different aspects. Therefore, multimodal entities have emerged in knowledge graph in recent years, which greatly enrich the knowledge graph by different information. This paper focuses on how to learn more robust and comprehensive representations of multimodal entities for better link prediction. A new representation learning method for multimodal entities is proposed, called Graph Attention with Bi-pooling Representation Learning (GBRL), where multimodal fusion and knowledge embedding are combined for learning representations of an entity with consideration of its multimodal neighbors. Firstly, feature vectors are obtained by pre-trained models for each modality. Then, the bilinear pooling is utilized for combining feature vectors of multimodal entities into multimodal feature vectors. A two-layer graph attention module is followed to generate multimodal representations of an entity from its multimodal neighbors. Finally, the inception decoder uses multimodal knowledge embedding to calculate scores for link prediction. The experimental results have shown that the proposed method performs well on link prediction by consideration of multimodal entities comparing with traditional methods that using only single modal text data. © 2021 IEEE.

关键词： Knowledge graph

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Distributed Fuzzy Model Predictive control of Nonlinear systems: An Interval Predictor Approach

Distributed Fuzzy Model Predictive Control of Nonlinear Syst...

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Asian control Conference

作者： Hua Zheng Yuanyuan Zou Shaoyuan Li SJTU Sanya Yazhou Bay Institute of Deepsea Science and Technology Sanya P. R. China Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Ministry of Education Shanghai

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

In this paper, distributed fuzzy predictive control (DFMPC) for nonlinear systems with a coupled objective function is investigated. Firstly, the original nonlinear subsystem is reformulated as Takagi-Sugeno fuzzy models, thereby enabling the application of certain linear system properties, such as the design of a terminal set and corresponding local controllers within the optimization framework. Subsequently, an interval predictor is devised by taking positive and negative mapping of the fuzzy system matrices. Based on the interval predictor, the DFMPC optimization problem is reconstructed to eliminate non- convexity resulting from membership functions (MFs) and their associated premise variables. This approach allows for solving the DFMPC optimization problem independently of the specific shapes of the MFs and premise variables. Finally, the effectiveness of the proposed Algorithm is verified through its implementation in a two-cart-damp-spring simulation example.

关键词： Linear systems Shape Predictive models Prediction algorithms Linear programming Trajectory Takagi-Sugeno model

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