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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Tong, Zhan Wu, Zhan Yang, Yang Mao, Weilong Wang, Shijie Li, Yinsheng Chen, Yang Southeast University Laboratory of Image Science and Technology Nanjing210096 China Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing210096 China Chinese Academy of Sciences Research Center for Medical Artificial Intelligence Shenzhen Institutes of Advanced Technology Shenzhen518055 China School of Computer Science and Engineering Key Lab. of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing The Laboratory of Image Science and Technology Nanjing210096 China

ISBN: (纸本)9798350337488

Computed Tomography (CT) is an imaging technique widely used in clinical diagnosis. However, high-attenuation metallic implants result in the obstruction of low-energy Xrays and further lead to metal artifacts in the reconstructed CT images. Deep supervised model-based metal artifact reduction(MAR) approaches are limited in clinical applications due to the difficulty in obtaining paired artifact-affected and artifactfree data. Furthermore, these model-based methods lack the consideration of data consistency in the sinogram-domain to perform exact metal trace inpainting. To address these challenges, we propose a Data-consistent unsupErVised diffusiOn model for meTal artifact rEDuction, called DEVOTED-Net. First, DEVOTED-Net leverages prior knowledge to guide the conditional diffusion model for fine-grained metal trace inpainting. Second, an unsupervised MAR framework is designed in the reverse process for the unknown metal traces restoration in the sinogram domain. Third, to further enhance the sinogram-domain data consistency, physics-based consistency constraint loss including conjugateray consistency loss and accumulation-ray consistency loss is designed. Extensive experiments are carried out to verify the performance of our algorithm on the publicly available dataset and clinical experimental dataset. This efficient, accurate, and reliable MAR approach holds great potential in clinics. © 2023 IEEE.

关键词： computed tomography deep unsupervised learning denoising diffusion probabilistic model Metal artifact reduction physics-based consistency constraint

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Topic Embedded Representation Enhanced Variational Wasserstein Autoencoder for Text Modeling 5

Topic Embedded Representation Enhanced Variational Wasserste...

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5th IEEE International Conference on Electronics Technology, ICET 2022

作者： Xiang, Zheng Liu, Xiaoming Yang, Guan Liu, Yang Zhongyuan University of Technology School of Computer Science Zhengzhou China Henan Key Laboratory on Public Opinion Intelligent Analysis Zhengzhou China Key Laboratory of text processing and image understanding Zhengzhou China Xidian University State Key Laboratory of Integrated Services Networks Xi'an China Shandong University Key Lab of Cryptologic Technology and Information Security Ministry of Education Xi'an China

ISBN: (纸本)9781665485081

Variational Autoencoder (VAE) is now popular in modeling and language generation tasks, which need to pay attention to the diversity of generation results. The existing models are insufficient in capturing the built-in relationships between topic representation and sequential words. At the same time, there is a massive contradiction between the commonly used simple Gaussian prior and the actual complex distribution of languages. To address the above problems, we introduce a hybrid Wasserstein Autoencoder (WAE) with Topic Embedded Representation (TER) for modeling. TER is obtained through an embedding-based topic model and can capture the dependencies and semantic similarities between topics and words. In this case, the learned latent variable has rich semantic knowledge with the help of TER and is easier to explain and control. Our experiments show that our method is competitive with other VAEs in modeling. © 2022 IEEE.

关键词： Semantics

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DSLO: Deep Sequence LiDAR Odometry Based on Inconsistent Spatio-temporal Propagation

DSLO: Deep Sequence LiDAR Odometry Based on Inconsistent Spa...

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

作者： Huixin Zhang Guangming Wang Xinrui Wu Chenfeng Xu Mingyu Ding Masayoshi Tomizuka Wei Zhan Hesheng Wang Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China Department of Engineering University of Cambridge Cambridge U.K. UC Berkeley Berkeley CA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This paper introduces a 3D point cloud sequence learning model based on inconsistent spatio-temporal propagation for LiDAR odometry, termed DSLO. It consists of a pyramid structure with a spatial information reuse strategy, a sequential pose initialization module, a gated hierarchical pose refinement module, and a temporal feature propagation module. First, spatial features are encoded using a point feature pyramid, with features reused in successive pose estimations to reduce computational overhead. Second, a sequential pose initialization method is introduced, leveraging the high-frequency sampling characteristic of LiDAR to initialize the LiDAR pose. Then, a gated hierarchical pose refinement mechanism refines poses from coarse to fine by selectively retaining or discarding motion information from different layers based on gate estimations. Finally, temporal feature propagation is proposed to incorporate the historical motion information from point cloud sequences, and address the spatial inconsistency issue when transmitting motion information embedded in point clouds between frames. Experimental results on the KITTI odometry dataset and Argoverse dataset demonstrate that DSLO outperforms state-of-the-art methods, achieving at least a 15.67% improvement on RTE and a 12.64% improvement on RRE, while also achieving a 34.69% reduction in runtime compared to baseline methods. Our implementation will be available at https://***/IRMVLab/DSLO.

关键词： Point cloud compression Solid modeling Laser radar Accuracy Three-dimensional displays Runtime Logic gates Robot sensing systems Real-time systems Odometry

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A Detection Method Toward Yellow Dragon Disease Spreads Using Spark and Deep Learning 3

A Detection Method Toward Yellow Dragon Disease Spreads Usin...

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3rd International Conference on High Performance Computing and Communication Engineering, HPCCE 2023

作者： Xie, Wu Su, Zhenzhao Zhang, Huimin Kang, Ping Qin, Kun Fan, Yong Zhao, Quanyou Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541214 China Key Laboratory of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China Lingchuan Science and Technology Information Institute Guilin541299 China Guilin Hivision Technology Co. Ltd. Guilin541004 China School of Mechanical and Electrical Engineering Guilin University of Electronic Technology Guilin541214 China School of Computer Science and Engineering Guilin University of Aerospace Technology Guilin541004 China

ISBN: (数字)9781510674653

ISBN: (纸本)9781510674646

The detection methods of yellow dragon disease spread via wood lice transmission networks like social networks are very important for diverse citrus trees and farmers. Although current methods have some detection accuracy or low cost, the detection processes are relatively troublesome and the detection cycles are long, making it be a difficult problem to apply them in large-scale orange farms as practical scenarios to detect citrus yellow dragon disease in a timely manner. A new method toward detecting citrus yellow dragon disease spread utilizing Spark and deep learning is proposed for this problem. By obtaining citrus field video stream data through high-definition cameras, and transferring the stream data to the Spark cluster through Kafka like intelligent agents, it is practicable to use the structured streaming component under the Spark framework via big data ecosphere to process video or image stream data transmitted via monitoring. We construct a citrus yellow dragon disease detection model via YOLOv7, and use self-made citrus yellow dragon disease images as training and testing data sets. The preliminary experimental results show that the new methods achieved an accuracy of 83.14%. To reduce the occurrence of missed and false detections, the shallow detection heads are added to the feature fusion networks for improvement, extracting and fusing shallow network information to try to improve the detection effects of yellow dragon disease. Then replace the convolution operation in the ELAN (Effective Long-range Aggregation Network) module with deep separable convolution to reduce the number of model parameters. The preliminary experimental results show that compared to the original YOLOv7 model, our improved citrus yellow dragon disease detection model with YOLOv7 has an accuracy improvement of 2.43%, maintaining a higher detection accuracy with lower time than before. © 2024 SPIE.

关键词： Deep learning

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K-BMPC: Derivative-based Koopman Bilinear Model Predictive control For Tractor-trailer Trajectory Tracking With Unknown Parameters

K-BMPC: Derivative-based Koopman Bilinear Model Predictive C...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Zehao Wang Han Zhang Jingchuan Wang Department of Automation Institute of Medical Robotics Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Nonlinear dynamics bring difficulties to controller design for control-affine systems such as tractor-trailer vehicles, especially when the parameters in the dynamics are unknown. To address this constraint, we propose a derivative-based lifting function construction method, show that the corresponding infinite dimensional Koopman bilinear model over the lifting function is equivalent to the original control-affine system. Further, we analyze the propagation and bounds of state prediction errors caused by the truncation in derivative order. The identified finite dimensional Koopman bilinear model would serve as predictive model in the next step. Koopman Bilinear Model Predictive control (K-BMPC) is proposed to solve the trajectory tracking problem. We linearize the bilinear model around the estimation of the lifted state and control input. Then the bilinear Model Predictive control problem is approximated by a quadratic programming problem. Further, the estimation is updated at each iteration until the convergence is reached. Moreover, we implement our algorithm on a tractor-trailer system, taking into account the longitudinal and side slip effects. The open-loop simulation shows the proposed Koopman bilinear model captures the dynamics with unknown parameters and has good prediction performance. Closed-loop tracking results show the proposed K-BMPC exhibits elevated tracking precision with the commendable computational efficiency. The experimental results demonstrate the feasibility of K-BMPC.

关键词： Trajectory tracking Computational modeling Estimation Predictive models control systems Data models Nonlinear dynamical systems

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Role of MaABI5-like in abscisic acid-induced cold tolerance of'Fenjiao'banana fruit

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园艺研究（英文） 2022年第1期9卷 3096-3111页

作者： Zunyang Song Xiuhua Lai Hangcong Chen Lihua Wang Xuequn Pang Yanwei Hao Wangjin Lu Weixin Chen Xiaoyang Zhu Xueping Li Guangdong Provincial Key Laboratory of Postharvest Science of Fruits and Vegetables/Engineering Research Center for Postharvest Technology of Horticultural Crops in South China Ministry of EducationCollege of HorticultureSouth China Agricultural UniversityGuangzhouGuangdong510642China Key Laboratory of Food Processing Technology and Quality Control in Shandong Province College of Food Science and EngineeringShandong Agricultural UniversityTai'an271018China Guangdong Provincial Key Laboratory of Postharvest Science of Fruits and Vegetables/Engineering Research Center for Postharvest Technology of Horticultural Crops in South China Ministry of EducationCollege of HorticultureSouth China Agricultural UniversityGuangzhouGuangdong510642China

Abscisic acid(ABA)is a phytohormone essential for plants to respond to various environmental stresses,and abscisic acid-insensitive 5(ABI5)is a basic leucine zipper transcription factor of the ABA signaling *** ABA induces cold tolerance in bananas;however,the role of MaABI5-like in ABA-induced cold tolerance remains *** present study found that exogenous ABA alleviated chilling injury of'Fenjiao'banana,induced the accumulation of endogenous ABA,unsaturated fatty acids,and flavonoid content,and reduced the saturated fatty acid ***,ABA treatment upregulated the transcription levels of MaABI5-like,fatty acid desaturation genes,and flavonoid synthesis-related genes during cold *** interestingly,MaABI5-like directly interacted with the promoter of genes related to fatty acid desaturation(MaFAD3-1,MaFAD3-4,MaFAD3-5,MaFAD6-2,MaFAD6-3)and flavonoid synthesis(MaPAL-like,MaPAL-like1,MaC4H-like3,Ma4CL-like1,Ma4CL-like10,MaCHS6-4-like,and MaFLS)and activated their ***,the transient overexpression of MaABI5-like in'Fenjiao'banana fruit and ectopic expression in tomato plants enhanced cold tolerance and upregulated fatty acid desaturation and flavonoid synthesis-related gene transcript *** reduced expression of MaABI5-like by virus-induced gene silencing in'Fenjiao'banana increased chilling injury and downregulated the expression of fatty acid desaturation and flavonoid synthesis-related ***,the study indicates that MaABI5-like regulates ABA-induced cold tolerance by increasing unsaturated fatty acid and flavonoid content.

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LHMap-loc: Cross-Modal Monocular Localization Using LiDAR Point Cloud Heat Map

arXiv

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arXiv 2024年

作者： Wu, Xinrui Xu, Jianbo Hu, Puyuan Wang, Guangming Wang, Hesheng Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom

— Localization using a monocular camera in the pre-built LiDAR point cloud map has drawn increasing attention in the field of autonomous driving and mobile robotics. However, there are still many challenges (e.g. difficulties of map storage, poor localization robustness in large scenes) in accurately and efficiently implementing cross-modal localization. To solve these problems, a novel pipeline termed LHMap-loc is proposed, which achieves accurate and efficient monocular localization in LiDAR maps. Firstly, feature encoding is carried out on the original LiDAR point cloud map by generating offline heat point clouds, by which the size of the original LiDAR map is compressed. Then, an end-to-end online pose regression network is designed based on optical flow estimation and spatial attention to achieve real-time monocular visual localization in a pre-built map. In addition, a series of experiments have been conducted to prove the effectiveness of the proposed method. Our code is available at: https://***/IRMVLab/LHMaploc. Copyright © 2024, The Authors. All rights reserved.

关键词： Optical radar

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Resilient Distributed Quadrotor UAVs Game: Addressing FDI and Physical Attacks

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IEEE Transactions on Vehicular Technology 2025年

作者： Geng, Mei-Jie Ding, Hua-Feng Yao, Xiang-Yu Lv, Maolong China University of Geosciences School of Mechanical Engineering and Electronic Information Wuhan430074 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Air Force Engineering University College of Air Traffic Control and Navigation Xi'an710038 China

Distributed quadrotor unmanned aerial vehicles (UAVs) are increasingly deployed in critical applications, including reconnaissance, disaster response, and transportation, where system resilience is paramount. However, their complex dynamics and reliance on cyber-physical systems make them highly vulnerable to adversities such as uncertain dynamics, time-varying input disturbances, false data injection (FDI), and physical attacks. This paper proposes a hierarchical framework consisting of a game layer and an execution layer to enhance the resilience of a noncooperative game involving distributed UAVs. In the game layer, a novel FDI observer mitigates communication and computational FDI attacks, facilitating convergence to infinitesimal ranges around the Nash equilibrium (NE) point. The execution layer addresses uncertain dynamics and physical attacks, ensuring that UAVs can track a narrow neighborhood of the NE even in the presence of actuator failures. Extensive simulations validate the algorithm's effectiveness and robustness against mixed attack scenarios, underscoring its potential for real-world UAV applications. © 1967-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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A Novel Brain-Inspired Hierarchical Perception-Association Circuit Based on Memristor Arrays

SSRN

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SSRN 2023年

作者： Chen, Liangyu Wang, Xiaoping Chen, Zhanfei Yang, Chao Zeng, Zhigang School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Education Ministry of China Wuhan430074 China Hubei Key Laboratory of Brain-inspired Intelligent Systems Wuhan430074 China

In the brain, primary sensory cells can efficiently perceive multimodal stimuli, and then associative memory cells perform an advanced bidirectional associative memory function with perceived information. Here, a brain-inspired hierarchical perception-association circuit based on memristor arrays is proposed. Firstly, a memristive reservoir computing circuit is designed to simulate a low-level perceptual function, which mainly comprises dynamic analog reservoirs, memristor arrays, and analog integrators, enabling efficient spatio-temporal information processing. Secondly, a spiking bidirectional associative memory memristive circuit, as the core of the whole circuit, is proposed to mimick a high-level associative function, which mainly consists of memristor arrays, current amplifiers, and leaky integrate-and-fire neuron circuits, implementing multimodal associative learning. The simulation results in LTspice show that the modified neuron circuit exhibits 66× improvement in energy efficiency, and the proposed circuit can precisely perform the letter association and vision-audio association tasks in a spiking fashion with an average firing energy consumption of 230 pJ/spike, which has a great potential to be embedded in mobile robotic platforms. © 2023, The Authors. All rights reserved.

关键词： Memristors

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A promising strategy for assessing the uricosuric effect of anti-hyperuricemia candidates: a case study of caffeoylquinic acids extract of Artemisia selengensis Turcz. leaves

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Food, Nutrition and Health 2025年第1期2卷 1-12页

作者： Xiang, Lin Mansour, Mohammed Huang, Yuting Wu, Ting Pan, Siyi Xu, Xiaoyun College of Food Science and Technology Huazhong Agricultural University Wuhan China Hubei Key Laboratory of Fruit & Vegetable Processing & Quality Control Huazhong Agricultural University Wuhan China Key Laboratory of Environment Correlative Dietology (Ministry of Education) Huazhong Agricultural University Wuhan China National Key Laboratory for Germplasm Innovation & Utilization of Horticultural Crops Huazhong Agricultural University Wuhan China

Caffeoylquinic acids (CQAs) are phenolic acids found in various plants that possess multiple biological activities including anti-oxidation, anti-inflammation, and xanthine oxidase inhibitory activity. Our previous research demonstrated that CQAs-rich Artemisia selengensis Turcz. leaves extract (ASTLE) effectively lowered uric acid levels in hyperuricemia mice. However, further evidence is needed to elucidate the mechanism of the uric acid-lowering effect of ASTLE. In this study, a sensitive but non-radioactive in vitro method based on 6-carboxyfluorescein (6-CFL) was established, validated, and further applied to assess the uricosuric potential of ASTLE. Furthermore, molecular docking, qRT-PCR, and western blot were employed to evidence the uricosuric effect of ASTLE. The results demonstrated that ASTLE exhibited the potential to inhibit uric acid reabsorption, manifested as the inhibition of 6-CFL uptake by human kidney 2 (HK-2) cells (IC50 = 533.67 ± 19.91 µg/mL), and CQAs were identified as its dominant bioactive compounds. Mechanism investigation further revealed that the uptake inhibitory effect of ASTLE was attributed to the hydrogen bonding interactions of CQAs, particularly di-CQAs, with uric acid transporters URAT1 and GLUT9, as well as to the down-regulation of mRNA and protein expression of URAT1 and GLUT9 and the up-regulation of mRNA and protein expression of ABCG2 by ASTLE. This study indicated that ASTLE has potent uricosuric effect. Moreover, this study suggested that the combination of 6-CFL uptake inhibition assessment, molecular docking, qRT-PCR, and western blot analysis could be a promising strategy for revealing the role of anti-hyperuricemia candidates in promoting uric acid excretion.

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