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2022 Chinese Automation Congress, CAC 2022

作者： Ma, Aoyun Li, Dewei Xi, Yugeng Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Key Laboratory of System Control and Information Processing Department of Automation Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781665465335

The hydrogen gas turbine can be the key technology for carbon-neutral in the future. The flexibility of gas turbine under various operation conditions is an important issue. A state-space model of hydrogen gas turbine with different hydrogen volume ratio mixed fuel is constructed based on Rowen model, and a full-dimensional state observer is designed to obtain the states of state-space model. The model predictive control method based on amplitude decaying aggregation strategy is presented to stabilize the rotational speed of hydrogen gas turbine under various constraints, which are to ensure the safety of operation. The number of variables contained in the optimization problem to be solved online is reduced by aggregation strategy. The simulation results confirmed that the presented method works well, and the effects of the hydrogen volume ratio for the performance of hydrogen gas turbine are discussed. © 2022 IEEE.

关键词： Model predictive control

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Ocean archaea PPI prediction with pretraining models 25

Ocean archaea PPI prediction with pretraining models

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2025 5th International Conference on Bioinformatics and Intelligent Computing, BIC 2025

作者： Zhang, Ying Liu, Yuan Pan, Xiaoyong Shen, Hongbin Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798400712203

Protein-Protein Interaction (PPI) provides important insights into the metabolic mechanisms of different biological processes. Although PPIs in some organisms have been investigated systematically, PPIs in the ocean archaea remain largely unexplored. But such species have special investigation value since their adaptation to extreme living conditions may generate unique PPIs. In this paper, we aim to characterize and predict PPIs in ocean archaea to advance understanding of their metabolic networks. First, we collect all ocean archaea PPIs with high confidence from STRING database and analyze the PPI network features, including centrality and enrichment analysis. The functional enrichment results of the largest connecting subgraph in the PPI network show most PPIs in our constructed dataset is related to the translation and transcription processes. Then, we generate an equal number of negative PPI pairs, whose members have either different subcellular locations or GO terms. We also use the generated dataset to test the performance of three pretraining methods and their ensemble methods in the binary PPI prediction task. Our results suggest the ensemble methods could be applied to further improve models' performance. Fine-tuned models trained on the ocean archaea dataset are expected to predict the other ocean archaea PPIs that are not included in the STRING database and get more understanding about the ocean archaea PPI universe. © 2025 Copyright held by the owner/author(s).

关键词： Binary PPI prediction Ocean archaea PPI network Pretraining model

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Randomly coupled trench-assisted multicore fibers with different arrangements for high tolerance of manufacturing errors

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Science China(information Sciences) 2023年第11期66卷 260-272页

作者： Yongneng JIANG Jiajing TU Shecheng GAO Weiping LIU Zhaohui LI Department of Electronic Engineering College of Information Science and Technology Jinan University School of Electronics and Information Technology Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Sun Yat-sen University Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)

Randomly coupled multicore fibers(RC-MCFs) are promising candidates for long-haul transmission. However, the degree of coupling between the homogeneous cores of an RC-MCF is weakened if manufacturing errors appear. Therefore, a low-index trench layer is introduced into the coupled cores to investigate the effects of its position and thickness on the group index to enhance the tolerance of manufacturing errors. A depressed region is designed in the center of the core to solve the problem of the effective area decreasing because of the confinement of the low-index trench. Additionally, it is important to study the effective arrangement of the coupled cores to obtain a smaller group delay spread(GDS) within the limited 125-μm cladding; we discuss the single-layer and double-layer arrangements. A moderate range is found for both the bending radius and core pitch to minimize the GDS. For the single-layer arrangement, the degree of mixing is weak, and the degeneracy state does not easily change the bending conditions; thus, it may be a good candidate for designing systemically coupled multicore fibers. For the double-layer arrangement, the bending and twisting factors can induce sufficient mixing among the different modes to reduce GDS; thus,this arrangement is more suitable for the design of RC-MCFs.

关键词： manufacturing errors randomly coupled multicore fibers trench-assisted core single-layer arrangement double-layer arrangement

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17th Chinese Intelligent systems Conference, CISC 2021

作者： Liu, Binye Wang, Jinlong Wu, Kaitian Xu, Zhezhuang College of Electrical Engineering and Automation Fuzhou University Fuzhou350000 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China

ISBN: (纸本)9789811663192

The robots have been used in the inspection of industrial wireless sensor networks (IWSNs), that includes data collection and wireless charging. The sensor nodes are always densely deployed and their appearances are similar, thus it is a non-trivial challenge to identify a specific node by the machine vision. To solve this problem, we propose to use the proximity estimation algorithm, which uses wireless signals to estimate neighbor nodes, to help the robot to identify a specific node in the network. The algorithm is deployed a patrol inspection testbed with a mobile robot and multiple sensor nodes, and the experiments are executed to verify its performance. The experimental results show that the proximity estimation algorithm is a reasonable solution to identify similar nodes in the inspection of the IWSN. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Inspection

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Online Learning Enabled Hierarchical Distributionally Robust Model Predictive control of Green-Hydrogen Microgrids under Uncertainties 5

Online Learning Enabled Hierarchical Distributionally Robust...

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5th IEEE International Electrical and Energy Conference, CIEEC 2022

作者： Ning, Chao Li, Longyan Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai China

ISBN: (数字)9781665411042

ISBN: (纸本)9781665411042

This paper proposes a novel online-learning enabled hierarchical operation framework for a green-hydrogen microgrid (MG) with a high penetration of renewable energy (RE) and hybrid energy storage. The operation framework features a bi-level multi-timescale structure which integrates a day-ahead optimal scheduling with online learning distributionally robust model predictive control (OL-DRMPC). Day-ahead scheduling optimization is employed at the high level to provide MG energy dispatch references, while the OL-DRMPC is carried out in the low level for reference-tracking under uncertainties incurred by RE and load forecast errors. To confront the imperfect knowledge of forecast-error distributions, distributionally robust conditional value-at-risk constraints are imposed on MG system states, namely the storage of battery and hydrogen, for a family of distributions called ambiguity set. By exploiting a Dirichlet process mixture model, a streaming-data-driven ambiguity set for uncertain parameters is devised over the runtime of controller and flexibly captures multimodal structure and fine-grained moment information of each mixture component. Enabled by online learning, the proposed strategy enjoys the merit of offering a reliable and intelligent MG operation. Case studies are used to demonstrate its effectiveness and superiority. © 2022 IEEE.

关键词： Energy storage

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DELFlow: Dense Efficient Learning of Scene Flow for Large-Scale Point Clouds

arXiv

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arXiv 2023年

作者： Peng, Chensheng Wang, Guangming Lo, Xian Wan Wu, Xinrui Xu, Chenfeng Tomizuka, Masayoshi Zhan, Wei Wang, Hesheng Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai Jiao Tong University China Mechanical Systems Control Laboratory University of California Berkeley United States

Point clouds are naturally sparse, while image pixels are dense. The inconsistency limits feature fusion from both modalities for point-wise scene flow estimation. Previous methods rarely predict scene flow from the entire point clouds of the scene with one-time inference due to the memory inefficiency and heavy overhead from distance calculation and sorting involved in commonly used farthest point sampling, KNN, and ball query algorithms for local feature aggregation. To mitigate these issues in scene flow learning, we regularize raw points to a dense format by storing 3D coordinates in 2D grids. Unlike the sampling operation commonly used in existing works, the dense 2D representation 1) preserves most points in the given scene, 2) brings in a significant boost of efficiency, and 3) eliminates the density gap between points and pixels, allowing us to perform effective feature fusion. We also present a novel warping projection technique to alleviate the information loss problem resulting from the fact that multiple points could be mapped into one grid during projection when computing cost volume. Sufficient experiments demonstrate the efficiency and effectiveness of our method, outperforming the prior-arts on the FlyingThings3D and KITTI dataset. Our source codes will be released on https://***/IRMVLab/DELFlow. © 2023, CC BY.

关键词： Efficiency

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Polyp Segmentation Neural Network with Simple Encoding Structure and Reverse Attention Module 13

Polyp Segmentation Neural Network with Simple Encoding Struc...

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13th International Conference on information Technology in Medicine and Education, ITME 2023

作者： Wang, Jie Li, Zuoyong Xu, Haiping Cheng, Xuesong Teng, Shenghua College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China Fujian Key Laboratory of Medical Big Data Engineering Fujian Provincial Hospital Fuzhou350001 China College of Mathematics and Data Science Minjiang University Fuzhou350121 China

ISBN: (纸本)9798350319156

Colorectal cancer is a common malignancy. In colonoscopy images, computer-assisted polyp segmentation helps doctors diagnose and treat disorders more precisely. In recent years, some methods based on deep convolutional neural networks have made significant breakthroughs in this task. However, only a few algorithms explicitly consider the effect of light intensity on segmentation performance, which results in algorithms needing to be more robust for complex samples. This paper proposes a neural network combining a simple encoding structure with a reverse attention module. Specifically, the model uses HarDNet68 as the backbone network, adds a camouflage identification module (CIM) to enhance the capture of polyp details on the underlying features, and finally utilizes adaptive feature fusion with the reverse attention module (RAM). The proposed model effectively addresses the challenge of poor segmentation quality in colonoscopy images caused by inconsistent illumination. Extensive experiments on five datasets show that the proposed model performs well in polyp segmentation. © 2023 IEEE.

关键词： Diseases

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A Compact Magnetic-Electric CP Element and Its Design in Pattern Reconfigurable Antenna with Full Coverage in Azimuth Plane and Wide Coverage in Elevation Plane

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IEEE Transactions on Antennas and Propagation 2025年

作者： Zhang, Minghui Liu, Juhua Sun Yat-sen University Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems the School of Electronics and Information Technology Guangzhou510006 China

A compact magnetic-electric circularly polarized (MECP) element is proposed and employed in the design of a pattern reconfigurable antenna that provides full coverage in the azimuth plane and wide coverage in the elevation plane. The MECP element, working in the zeroth-order resonant (ZOR) mode, inherently has a compact size of 0.17λ0 × 0.32λ0 × 0.06λ0 (λ0 denotes the free space wavelength at 920 MHz). The MECP element achieves a fan beam with wide azimuthal and elevational 3-dB axial ratio (AR) beamwidths of 192° and 110°, respectively. By integrating four such sequentially rotated MECP elements, a pattern reconfigurable antenna is designed and fabricated. The antenna has the advantage of compact size of 0.58λ0 × 0.58λ0 × 0.04λ0. By switching the excited elements through the feeding ports, the antenna provides four reconfigurable beams for full coverage in the azimuth plane (one effective beamwidth covering an angle larger than 90°) and wide coverage in the elevation plane (effective beamwidths covering angles within the range of θ ≤ 75°). © 1963-2012 IEEE.

关键词： Circular polarization

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A novel feature enhancement module for small object detection

A novel feature enhancement module for small object detectio...

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2022 IEEE International Conference on Industrial Technology, ICIT 2022

作者： Wang, Ruogu Li, Ning Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai China

ISBN: (纸本)9781728119489

Generic object detection has made great progress in recent years, yet small object detection is still facing a serious problem of missed detection. The problem is caused by two reasons: low resolution in small targets and feature loss during deep convolution downsampling. Some previous studies used feature pyramid structures and multi-scale training predictions to alleviate this problem, but still cannot efficiently utilise the feature information of small targets. In this paper, firstly we propose a novel and effective feature enhancement module to extract more features;secondly, a filter size design method is presented to extract features of matching objects and incorporate context information;finally, an adaptive fusion method is used to process outputs of our module and improve model robustness. We select YOLOv3 as base model and carry out several experiments on a special Small Object Dataset and the results show that our work can achieve an increase of about seven percentage points (35.73%) compared to 28.78% on baseline. © 2022 IEEE.

关键词： Object detection

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Image Matching Algorithm Under Adverse Conditions Based on SIFT 14

Image Matching Algorithm Under Adverse Conditions Based on S...

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14th International Conference on information Technology in Medicine and Education, ITME 2024

作者： Chen, Hao Cao, Xinrong Li, Zuoyong Lin, Lihui Fuzhou University College of Computer and Big Data Fuzhou350108 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Fuzhou350121 China Wuyi University Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry School of Mathematics and Computer Science Fujian354300 China

ISBN: (纸本)9798350356670

Image matching technology is crucial in computer vision applications. However, the traditional SIFT (Scale-Invariant Feature Transform) algorithm often faces challenges under adverse conditions, such as a high number of mismatched points and few accurately matched points. Additionally, in practical scenarios, environmental factors like lighting changes and image blurring further reduce image similarity. Therefore, this paper proposes an improved SIFT-based image matching algorithm to address these challenges. First, SIFT is used to extract image features, and a bidirectional FLANN (Fast Library for Approximate Nearest Neighbors) matching strategy is employed to initially filter out incorrect features. Then, the RANSAC (Random Sample Consensus) algorithm is used to screen out unreliable matching point pairs. Finally, the matching accuracy is evaluated based on the remaining pure matching point pairs. Experimental results demonstrate that, compared to the traditional SIFT algorithm, this improved algorithm achieves higher matching accuracy and robustness under adverse conditions, exhibiting superior performance. © 2024 IEEE.

关键词： Image matching

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