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IEEE International Conference on Intelligent Transportation Engineering (ICITE)

作者： Liangming Dong Xinxin Zhang Lijuan Liu College of Computer and Information Engineering (Xiamen University of Technology) Fujian Key Laboratory of Pattern Recognition and Image Understanding (Xiamen University of Technology) Xiamen China

As modern cities become more intelligent, traffic flow forecasting is becoming increasingly significant in intelligent transport and urban governance. Accurate traffic forecasting can help the management and the residents to travel, and how to achieve accurate and efficient traffic forecasting is a problem that has been studied by many scholars. With the growth of artificial intelligence and the generation of large-scale traffic data, deep learning can be more effective than traditional probabilistic prediction models in traffic flow forecasting, but there are two more problems to be solved. Firstly, the use of too many levels of convolution often leads to over-fitting. Secondly, the state of traffic in a city can be impacted by external factors such as meteorological conditions, temperature, and wind speed. To solve this problem, we propose an end-to-end neural network model based on the residual network, dilated convolution, and call it STDCN. Convolution and dilated convolution are used in the model to obtain local spatial dependencies and distant spatial dependencies. We designed an external feature extractor to transform the external factors into a ID tensor using the One-Hot Encoding.

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A Parallel Artificial Immune Model for Optimization

A Parallel Artificial Immune Model for Optimization

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International Conference on Computational Intelligence and Security

作者： Yutao Qi Fang Liu Licheng Jiao School of Computer Science And Technology Key Laboratory of Intelligent Perception and Imagee Understanding of Ministry of Education Xidian University Xi'an China Institute of Intelligent Information Processing and Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an China

This paper presents a parallel artificial immune model termed as tower master-slave model (TMSM) for solving optimising problems. Based on TMSM, the parallel immune memory clonal selection algorithm (PIMCSA) is also proposed. TMSM is a two level coarse-grained parallel artificial immune model with distributed immune response and distributed immune memory. In PIMCSA, vaccines are extracted and migrated between populations rather than individual migration as has been done in parallel genetic algorithms. It is a good balance between population diversity and the convergent speed. Experimental results on the numerical optimization and TSP problems show that PIMCSA achieves good performance in terms of both solution quality and computation time.

关键词： Immune system Genetic algorithms Artificial immune systems Competitive intelligence Master-slave Laboratories computer science education Poles and towers Vaccines Parallel processing

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Deep Learning Methods for Ship Classification: From Visible to Infrared images 5

Deep Learning Methods for Ship Classification: From Visible ...

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5th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2023

作者： Liu, Tianci Qin, Hengjia Zhan, Zhuo Liu, Yunpeng Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Chinese Academy of Sciences Key Laboratory of Opto-Electronic Information Processing Shenyang110016 China Key Laboratory of Image Understanding and Computer Vision Liaoning Province Shenyang110016 China The Third Military Representative Office of the Air Force Equipment Department in Shenyang Region Liaoning Province Shenyang110027 China

ISBN: (纸本)9798350357950

Deep learning methods have achieved excellent performances on visual tasks of target recognition and classification. The rapid development of autonomous seafaring vessels comes up with the requirement to recognize other maritime ships day and night. However, the recognition of ships based on the deep neural networks may not always access the results as expectation when the ships are under the nighttime environment. To this issue, we consider the ship recognition task under the deep learning framework with paired visible images and infrared images. In this article, we propose an end-to-end convolutional network based on visible images and infrared images of the autonomous seafaring vessels. To demonstrate the effectiveness of our model, we choose the V AIS dataset to test the performance of classifying the maritime ships. Experimental results show that the proposed network outperforms the state-of-the-art methods based on the V AIS database. © 2023 IEEE.

关键词： deep learning infrared images neural network Ship recognition

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A novel clinical artificial intelligence model for disease detection via retinal imaging

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The Innovation 2024年第2期5卷 11-12页

作者： Yidian Fu Liang Ma Sheng Wan Shengfang Ge Zhi Yang Department of Ophthalmology Ninth People’s HospitalShanghai JiaoTong University School of MedicineShanghai 200011China Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology Shanghai 200011China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of EducationNanjing 210000China Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and EngineeringNanjing University of Science and TechnologyNanjing 210000China

Artificial intelligence(AI)is an emerging field in which computerized systems are used to carry out complex tasks in place of *** AI algorithms have been developed for disease diagnosis and prediction and treatment recommendation across various clinical data types,e.g.,chest X-rays,electrocardiograms,and other radiological images.1 In ophthalmology,particularly,great progress has been made in AI systems over the past *** fundus photography(CFP)and optical coherence tomography(OCT),which are readily available in routine clinical practice,are both mainstream and useful retinal imaging modalities in *** September 2023,the 2023 Lasker-Debakey Clinical Medical Research Award was awarded to three scientists for their work on OCT for accurate retinal disease detection.

关键词： clinical routine diagnosis

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Nearest-neighbour Joint Probabilistic Data Association Filter Based on Random Finite Set

Nearest-neighbour Joint Probabilistic Data Association Filte...

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International Conference on Control, Automation and Information Sciences (ICCAIS)

作者： Shuang Liang Yun Zhu Hao Li Maoguo Gong Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an China Key Laboratory of Modern Teaching Technology School of Computer Science Shaanxi Normal University Xi'an China

ISBN: (数字)9781728123110

ISBN: (纸本)9781728123127

The joint probabilistic data association (JPDA) filter is effective for multitarget, but it suffers from the track coalescence problem. To solve this problem, an improved nearest-neighbour JPDA filter based on random finite set is proposed. First, the standard JPDA filter is utilized to compute the target posterior density. Then, the posterior density is optimized by reordering the target index in each global association event using a novel nearest-neighbour method. Finally, the marginalized posterior densities of targets are obtained as independent Gaussian densities. Compared to conventional data association methods, the proposed approach needs less computing time and achieves satisfactory tracking accuracy.

关键词： Target tracking Radio frequency Clutter Filtering theory Covariance matrices Automation Probabilistic logic

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A Novel Level Set Method for Segmentation of Left and Right Ventricles from Cardiac MR images

A Novel Level Set Method for Segmentation of Left and Right ...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Yu Liu Chunming Li Shuxu Guo Yihua Song Yue Zhao Center for Image Processing and Computer Vision Jilin University Center for Biomedical Image Computing and Analytics University of Pennsylvania Center for Key Laboratory of Medical Image Computing Ministry of Education Northeastern University

ISBN: (纸本)9781424479276

In this paper, we propose a novel level set method for segmentation of cardiac left and right ventricles based on the distance regularized level set evolution (DRLSE) framework [7] and the distance regularized two-layer level set (DR2LS) model [17]. First, DRLSE is applied to obtain a preliminary segmentation of left and right ventricles, which is then used to initialize the endocardial contour, which is represented by the zero level contour of the level set function in our method. Then, the epicardial contour is represented by a different level contour of the same level set function. These two level sets are optimized by an energy minimization process to best fit the true endocardium and epicardium. In order to ensure smoothly varying distance between the two level contours, we introduce a distance regularization constraint in the energy function. With the region-scalable fitting (RSF) energy [8] as the data term, our method is able to deal with intensity inhomogeneities in the images, which is a main source of difficulty in image segmentation. Our method has been tested on cardiac MR images with promising results.

关键词： Level set image segmentation Nonhomogeneous media Biomedical imaging Educational institutions Myocardium

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Research advances on structured compressive sensing

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Zidonghua Xuebao/Acta Automatica Sinica 2013年第12期39卷 1980-1995页

作者： Liu, Fang Wu, Jiao Yang, Shu-Yuan Jiao, Li-Cheng School of Computer Science and Technology Xidian University Xi'an 710071 China Key Laboratory of Intelligent Perception and Image Understanding of Education Xidian University Xi'an 710071 China College of Sciences China Jiliang University Hangzhou 310018 China

Compressive sensing (CS) is a newly developed theoretical framework for information acquisition and processing. Using the non-linear optimization methods, the signals can be recovered from fewer linear and non-adaptive measurements by taking advantage of the sparsity or compressibility inherent in real world signals. Structured compressive sensing is a new framework which can treat more general signal classes to achieve the accurate and effective reconstruction in practice by introducing the prior information matching with data acquisition hardware and complicated signal models to traditional compressive sensing. In this paper, the basic models and key techniques of structured compressive sensing are introduced in terms of the structured measurements, the structured dictionary representation and the structured signal reconstruction, which correspond to three basic aspects of compressive sensing, and the recent developments of structured compressive sensing are reviewed in detail. Finally, the current and future challenges of the structured compressive sensing are discussed. Copyright © 2013 Acta Automatica Sinica. All rights reserved.

关键词： Compressed sensing

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Collaborative Weighting for Graph Convolutional Networks

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第2期8卷 432-447页

作者： Chen, Yong Xie, Xiao-Zhu Weng, Wei Zhang, Shan-Dan Li, Tong College of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China College of Computer and Information Engineering Xiamen University of Technology Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China

Graph neural network (GNN), as a powerful method for graph representation, has attracted extensive research interest. Recently, Graph Convolutional Network (GCN) and Graph Attention Network (GAT) have shown superior performance on graph node classification and are considered as the most promising frameworks. Both GAT and GCN use a weighting mechanism in the information aggregation process. However, GCN is weighted by topological structure, while the node content is still unemployed. GAT is weighted by node content, while the topological structure remains being ignored. These weighting mechanism, which does not make full use of topology and node content, is not conducive to graph representation. In this paper, we propose a "Collaborative Weighting For Graph Convolutional Network" (CWGCN) that combines the advantages of GCN and GAT. Specifically, its weighting mechanism not only considers the topology of the graph, but also the node content. With this weighting method for representation learning, CWGCN achieves encouraging performance on a number of citation datasets in node classification. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Classification (of information)

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The Effect of a Temporary Bottleneck in Two-Lane of a Road on Traffic Flow Based on the Kerner-Klenov-Wolf Model

The Effect of a Temporary Bottleneck in Two-Lane of a Road o...

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2011 International Conference on Advanced Materials and computer Science(ICAMCS 2011)

作者： Liang Haijun Yang Hongyu Xiao Chao Sheng Peng Zhang Jianwei School of Computer Science Sichuan University Key Laboratory of Fundamental Synthetic Vision Graphics and Image Science for National Defense Sichuan University

Considering a temporary bottleneck made by special accidents,we proposed a new two-lane traffic model based on the KKW(Kerner-Klenov-Wolf) modelAccording to the difference of driver behavior and vehicle type,we present a new asymmetry lane-changing rules and the model of grabbing the entrance of a bottleneckIn our simulation,as demonstrated by various examples,the asymmetry lane-changing and the heterogeneous distribution of vehicles easily result in complex fluctuations of traffic flow and jam

关键词： asymmetry lane-changing rule KKW model and temporary bottleneck

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Fast Candidate Region Extraction for SAR Ship Target

Fast Candidate Region Extraction for SAR Ship Target

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Panpan Zhang Haibo Luo Zheng Xu Miao He Shenyang Institute of Automation Shenyang China Institutes for Robotics and Intelligent Manufacturing Shenyang China University of Chinese Academy of Sciences Beijing China Key Laboratory of Opto-Electronic Information Processing Shenyang China The Key Lab of Image Understanding and Computer Vision Shenyang China

ISBN: (纸本)9781665465373

At present, deep learning technology is widely used in ship target detection in synthetic aperture radar (SAR) images. However, high-resolution remote sensing SAR images cover a larger area and have larger image sizes. To be able to use the deep learning model for training and testing, the image needs to be cropped to the appropriate size. In a high-resolution SAR ship image, the ship target usually takes only a small part of the whole image. As a result, only part of the cropped image contains the target, and most of the rest are background regions. This will cause a lot of computational redundancy in the model inference stage. To solve this problem, a fast candidate region extraction algorithm is proposed in this paper for ship target extraction. The algorithm consists of three parts: firstly, extract the saliency map of the SAR ship image, secondly, perform median filtering on the segmented image, and thirdly, extract candidate regions. The superiority of the algorithm was demonstrated by experiments on the AIR-SARShip-1.0 dataset.

关键词： Deep learning Training Redundancy Radar polarimetry Inference algorithms Indexes Marine vehicles

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