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2021 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2021

作者： Jiang, Junhao Zuo, Yi Li, Tieshan Ma, He Navigation College Dalian Maritime University Dalian China Collaborative Innovation Center of Maritime Big Data and Shipping Artificial General Intelligence Dalian China School of Automation Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665443227

With the development of inland river transportation, the navigable conditions of inland waterways are becoming more and more complicated. Therefore, navigation with the aid of navigator alone is not enough. It is also necessary to make reasonable decisions on the navigation of the ship based on the future navigation behavior of the ship. Traditional trajectory prediction methods are difficult to accurately train models with the small amount of Automatic Identification System (AIS) data. In order to solve the problem of the data and the original models, this paper uses the Hermite interpolation method to expand the data. On this basis, we propose a model ship trajectory based on Gated Recurrent Unit (GRU) neural network. In the experiment, we select the AIS data of ships on the Huai'an section of the Beijing-Hangzhou Canal. Finally, Back Propagation Neural Network (BPNN) model and Support Vector Regression (SVR) model are conducted to a comparative experiments. The results show that the GRU neural network trajectory prediction model has high prediction accuracy, feasibility and effectiveness. © 2021 IEEE.

关键词： Ships

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An infection-microenvironment-targeted and responsive peptide-drug nanosystem for sepsis emergency by suppressing infection and inflammation

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Asian Journal of Pharmaceutical Sciences 2023年第6期18卷 126-143页

作者： Wei He Daan Fu Yongkang Gai Xingxin Liu Chang Yang Zhilan Ye Xu Chen Jia Liu Bingcheng Chang The Second Clinical College The Second Affiliated HospitalGuizhou University of Traditional Chinese MedicineGuiyang 550003China Research Center for Tissue Engineering and Regenerative Medicine Tongji Medical CollegeUnion HospitalHuazhong University of Science and TechnologyWuhan 430022China Department of Anesthesiology Tongji Medical CollegeUnion HospitalHuazhong University of Science and TechnologyWuhan 430022China Department of Laboratory Medicine West China Second University HospitalSichuan UniversityChengdu 610065China Department of Nuclear Medicine Hubei Province Key Laboratory of Molecular ImagingTongji Medical CollegeUnion HospitalHuazhong University of Science and TechnologyWuhan 430022China Guizhou Provincial Key Laboratory of Pharmaceutics Guizhou Medical UniversityGuiyang 550004China Department of Geriatrics Tongji Medical CollegeUnion HospitalHuazhong University of Science and TechnologyWuhan 430022China

Sepsis is a life-threatening emergency that causes millions of deaths every year due to severe infection and ***,current therapeutic regimens are inadequate to promptly address the vast diversity of potential ***,an antimicrobial peptide,has shown promise for neutralizing endotoxins and eliminating diverse ***,its clinical application is hindered by safety and stability ***,we present a nanoscale drug delivery system(Omi-hyd-Dex@HA NPs)that selectively targets infectious microenvironments(IMEs)and responds to specific stimuli for efficient intervention in *** system consists of omiganan-dexamethasone conjugates linked by hydrazone bonds which self-assemble into nanoparticles coated with a hyaluronic acid(HA).The HA coating not only facilitates IMEs-targeting through interaction with intercellular-adhesion-molecule-1 on inflamed endotheliocytes,but also improves the biosafety of the nanosystem and enhances drug accumulation in primary infection sites triggered by *** nanoparticles release dual drugs in IMEs through pH-sensitive cleavage of hydrazone bonds to eradicate pathogens and suppress *** multiple tissue infection and sepsis animal models,Omi-hyd-Dex@HA NPs exhibited rapid source control and comprehensive inflammation reduction,thereby preventing subsequent fatal complications and significantly improving survival *** bio-responsive and self-delivering nanosystem offers a promising strategy for systemic sepsis treatment in emergencies.

关键词： Infectious microenvironments Sepsis Nanoscale drug delivery systems Pathogens Omiganan

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DynPL-SVO: A Robust Stereo Visual Odometry for Dynamic Scenes

arXiv

引用

arXiv 2022年

作者： Zhang, Baosheng Ma, Xiaoguang Ma, Hong-Jun Luo, Chunbo The College of Information Science and Engineering Northeastern University Shenyang110819 China The School of Automation Science and Engineering South China University of Technology Guangzhou510641 China The School of Information School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China

Most feature-based stereo visual odometry (SVO) approaches estimate the motion of mobile robots by matching and tracking point features along a sequence of stereo images. However, in dynamic scenes mainly comprising moving pedestrians, vehicles, etc., there are insufficient robust static point features to enable accurate motion estimation, causing failures when reconstructing robotic motion. In this paper, we proposed DynPL-SVO, a complete dynamic SVO method that integrated united cost functions containing information between matched point features and re-projection errors perpendicular and parallel to the direction of the line features. Additionally, we introduced a dynamic grid algorithm to enhance its performance in dynamic scenes. The stereo camera motion was estimated through Levenberg-Marquard minimization of the re-projection errors of both point and line features. Comprehensive experimental results on KITTI and EuRoC MAV datasets showed that accuracy of the DynPL-SVO was improved by over 20% on average compared to other state-of-the-art SVO systems, especially in dynamic scenes. Copyright © 2022, The Authors. All rights reserved.

关键词： Motion estimation

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Deep Learning-based Image and Video Inpainting: A Survey

arXiv

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arXiv 2024年

作者： Quan, Weize Chen, Jiaxi Liu, Yanli Yan, Dong-Ming Wonka, Peter MAIS & NLPR Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China College of Computer Science Sichuan University Chengdu China Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia

Image and video inpainting is a classic problem in computer vision and computer graphics, aiming to fill in the plausible and realistic content in the missing areas of images and videos. With the advance of deep learning, this problem has achieved significant progress recently. The goal of this paper is to comprehensively review the deep learning-based methods for image and video inpainting. Specifically, we sort existing methods into different categories from the perspective of their high-level inpainting pipeline, present different deep learning architectures, including CNN, VAE, GAN, diffusion models, etc., and summarize techniques for module design. We review the training objectives and the common benchmark datasets. We present evaluation metrics for low-level pixel and high-level perceptional similarity, conduct a performance evaluation, and discuss the strengths and weaknesses of representative inpainting methods. We also discuss related real-world applications. Finally, we discuss open challenges and suggest potential future research directions. © 2024, CC BY-NC-SA.

关键词： Computer graphics

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Analyzing Characteristic and Modeling of High-Temperature Proton Exchange Membrane Fuel Cells with Co Poisoning Effect

SSRN

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SSRN 2023年

作者： Lei, Gang Zheng, Hualin Zhang, Jun Chin, Cheng Siong Xu, Xinhai Zhou, Weijiang Zhang, Caizhi School of Mechatronic Engineering Southwest Petroleum University Chengdu610500 China College of Mechanical and Vehicle Engineering The State Key Laboratory of Mechanical Transmissions Chongqing Automotive Collaborative Innovation Centre Chongqing University Chongqing400044 China School of Mechanical Engineering Zhejiang University Hangzhou310027 China Faculty of Science Agriculture and Engineering Newcastle University in Singapore 599493 Singapore School of Mechanical Engineering and Automation Harbin Institute of Technology Guangdong Shenzhen China Sinocat Environmental Technology Co Ltd Chengdu Hi-tech Industrial Development Zone Chengdu611731 China

High-temperature proton exchange membrane fuel cells (HT-PEMFC) have strong resistance to CO poisoning. However, the published CO poisoning models for HT-PEMFC are based on the finite element analysis method, which are difficult to use for the performance prediction and development of system control strategy due to a large amount of calculation. In the beginning, a semi-empirical model of HT-PEMFC is deduced based on the analysis of the CO poisoning characteristics. Then the key parameters of ohmic impedance and concentration polarization advance correction factor are obtained by fitting the polarization curves, dissociation and adsorption activation energy of H2 and CO are obtained by the calculation, respectively. The slope of the straight line segment in the ohmic polarization interval of the polarization curve is linearly related to ln[H2/CO]. The change of limiting current density is the main factor for the advance of concentration polarization. The fitted ohmic impedance varies with temperature and CO concentration, but changes little above 175 oC. CO coverage depends primarily on dissociation and adsorption activation energy of CO. Subsequently, the proposed model is validated with experimental data and shows high similarity. The model can be used for studying the systematic control strategy development and performance monitoring of HT-PEMFC. © 2023, The Authors. All rights reserved.

关键词： Dissociation

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An improved SNIP denoising algorithm applied in EDXRF

An improved SNIP denoising algorithm applied in EDXRF

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2019 International Conference on applied Machine Learning and Data Science, ICAMLDS 2019

作者： Ge, Liangquan Li, Hui Tang, Chuanfeng Li, Fei Zhu, Kai Yang, Jin Applied Nuclear Technology in Geosciences Key Laboratory Sichuan Province China College of Nuclear Technology and Automation Engineering Chengdu University of Technology China

Based on the phenomenon of peak height reduction in existing denoising algorithms, this paper modifies the traditional SNIP algorithm, proposes an improved SNIP algorithm and applies it to EDXRF denoising. Two evaluation parameters RMSE and PCC are used to compare the improved SNIP algorithm with the traditional SNIP algorithm. The experimental results show that the improved SNIP algorithm has great advantages both in similarity and relevance, and the results of the improved SNIP algorithm are more consistent with the characteristics of the original data peak than the original SNIP algorithm, which is beneficial to the qualitative and quantitative analysis of the energy dispersive X-ray fluorescence spectrum. © Published under licence by IOP Publishing Ltd.

关键词： Energy dispersive spectroscopy

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Game-theoretical approach for task allocation problems with constraints

arXiv

引用

arXiv 2023年

作者： Liu, Chunxia Lu, Kaihong Chen, Xiaojie Szolnoki, Attila School of Mathematical Sciences University of Electronic Science and Technology of China Chengdu611731 China College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao266590 China Institute of Technical Physics and Materials Science Centre for Energy Research Budapest P.O. Box 49 H-1525 Hungary

The distributed task allocation problem, as one of the most interesting distributed optimization challenges, has received considerable research attention recently. Previous works mainly focused on the task allocation problem in a population of individuals, where there are no constraints for affording task amounts. The latter condition, however, cannot always be hold. In this paper, we study the task allocation problem with constraints of task allocation in a game-theoretical framework. We assume that each individual can afford different amounts of task and the cost function is convex. To investigate the problem in the framework of population games, we construct a potential game and calculate the fitness function for each individual. We prove that when the Nash equilibrium point in the potential game is in the feasible solutions for the limited task allocation problem, the Nash equilibrium point is the unique globally optimal solution. Otherwise, we also derive analytically the unique globally optimal solution. In addition, in order to confirm our theoretical results, we consider the exponential and quadratic forms of cost function for each agent. Two algorithms with the mentioned representative cost functions are proposed to numerically seek the optimal solution to the limited task problems. We further perform Monte Carlo simulations which provide agreeing results with our analytical calculations. Copyright © 2023, The Authors. All rights reserved.

关键词： Monte Carlo methods

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Research on gamma spectrum semi-quantitative analysis based on convolutional neural network

Research on gamma spectrum semi-quantitative analysis based ...

引用

2019 International Conference on applied Machine Learning and Data Science, ICAMLDS 2019

作者： Li, Fei Wang, Jing Ge, Liangquan Hu, Fangyan Cheng, Feng Sun, Kun Applied Nuclear Technology in Geosciences Key Laboratory Sichuan Province China College of Nuclear Technology and Automation Engineering Chengdu University of Technology China

In the field of practical application of nuclear technology, one of the vital steps is the interpretation of gamma energy spectrum, which can obtain the type and content of radionuclides and achieve further analyzation. Convolutional neural network is one the most basic and effective algorithm structures in deep learning because it has local receptive field, weight sharing and down sampling structure, it solves the problem of parameter expansion caused by full connection and reduces the number of weights. That can be used to study the resolution of gamma-ray spectra of convolutional neural networks. This paper introduces the spectral forming principle of gamma spectra and the convolutional neural network, and USES the convolutional neural network to study the spectral decomposition of gamma spectra. In this paper, a multi-layer convolution neural network model is built based on C# software. The convolution neural network is applied to gamma-ray spectrum decomposition, and the U, Th, K nuclides are identified and semi-quantitatively calculated. By identifying and analysing different energy spectrum data, it is shown that the modified model structure can be applied to the spectral decomposition of gamma spectrum. © Published under licence by IOP Publishing Ltd.

关键词： Convolution

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Application of small sample BP neural network in quantitative analysis of EDXRF

Application of small sample BP neural network in quantitativ...

引用

2019 International Conference on applied Machine Learning and Data Science, ICAMLDS 2019

作者： Li, Fei Tang, Zhuoyao Ge, Liangquan Wu, Nanxing Cheng, Feng Sun, Kun Applied Nuclear Technology in Geosciences Key Laboratory Sichuan Province China College of Nuclear Technology and Automation Engineering Chengdu University of Technology China

Quantitative analysis of EDXRF is affected by matrix effect, randomness of nuclear radiation, interaction of elements, statistical fluctuation of radiation detection process, etc. Its algorithm needs to consider many factors, and the established functional relationship is often a complex non-linear function. Therefore, effective quantitative analysis method has always been the key research direction of spectral resolution technology. In this paper, we use MATLAB software to study the effect of BP neural network in quantitative analysis of EDXRF by establishing the non-linear relationship between the counting rate of each element and the content of a single element in a small sample. The experimental results show that the small sample neural network can establish a stable structure and be applied to quantitative analysis of EDXRF, but the number of samples restricts the accuracy of prediction results, most of which can only guarantee 20% to 30% relative error. © Published under licence by IOP Publishing Ltd.

关键词： MATLAB

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Real-time and effective pan-sharpening for remote sensing using multi-scale fusion network

Real-time and effective pan-sharpening for remote sensing us...

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作者： Lai, Zhibing Chen, Lihui Jeon, Gwanggil Liu, Zitao Zhong, Rui Yang, Xiaomin College of Electronics and Information Engineering Sichuan University ChengduSichuan610064 China School of Electronic Engineering Xidian University Xi’an710071 China Department of Embedded Systems Engineering Incheon National University Incheon22012 Korea Republic of TAL Education Group Beijing China Institute of Nuclear Science and Technology Sichuan University Chengdu610064 China School of Aeronautics & Astronautics Sichuan University Chengdu610064 China

Real-time monitoring and surveillance play an important role in the field of remote sensing, where multi-spectral (MS) images with high spatial resolution are widely desired for better analysis. However, high-resolution MS images cannot be directly obtained due to the limitations of sensors and bandwidth. As an essential way to alleviate this problem, pan-sharpening aims at fusing the complementary information of a low-resolution MS image and a high-resolution panchromatic (PAN) image to reconstruct a high-resolution MS image. Most previous deep-learning based methods can meet the real-time requirements with the help of graphics processing unit (GPU). However, they don’t fully exploit the favorable hierarchical information, sparing huge room for performance improvement. In this paper, to meet the requirement of real-time implementation and achieve more effective performance simultaneously, we propose a multi-scale fusion network (MSFN) to make full use of hierarchical complementary features of PAN and MS images. Specifically, we introduce an encoder–decoder structure and coarse-to-fine strategy to effectively extract multi-scale features of PAN and MS images, separately. Meanwhile, an information pool is adopted to preserve primitive information. Then a multi-scale feature fusion module is applied to fuse multi-scale features from the decoder and information pool. Finally, the fused features are utilized to reconstruct the high-resolution MS image. Extensive experiments demonstrate that our proposed method achieves favorable performance against other methods in terms of quantitative metrics and visual quality. Besides, the results on running time indicate that our method can achieve real-time performance. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.

关键词： Graphics processing unit

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