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2023 International Conference on Remote Sensing, Mapping, and Geographic Systems, RSMG 2023

作者： Liu, Mingyue Shu, Gaofeng Li, Ning School of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Kaifeng475004 China Henan Province Engineering Research Center of Spatial Information Processing Kaifeng475004 China

ISBN: (纸本)9781510668898

Synthetic aperture radar (SAR), as an active microwave remote sensor with high-resolution earth observation, is easily affected by radio frequency interference (RFI). Pulse radio frequency interference (PRFI), as a typical form of RFI, pollutes SAR raw data with bright stripes, which increases the difficulty of SAR image interpretation. However, the conventional eigen subspace projection (ESP) method decomposes the whole pulse into the interferences and the useful signals, resulting in the loss of useful signals in the non-interference positions with the pulse. To solve this problem, this paper proposes an improved SAR PRFI suppression method based on ESP. Firstly, the receiving pulses containing PRFI are detected and the specific position of PRFI in the pulse are determined by eigenvalue decomposition and energy accumulation in time-frequency domain, respectively. Secondly, the PRFI signal is separated from the useful signal by applying the ESP method only to the data where the PRFI is located. Finally, the PRFI signal projected by ESP is eliminated from the raw data. Simulation results shown that the proposed method can better protected useful signals and improve SAR image quality compared with conventional methods. © 2023 SPIE.

关键词： Synthetic aperture radar

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BcMatch: Semi-supervised Medical Image Segmentation with Bias Correction

BcMatch: Semi-supervised Medical Image Segmentation with Bia...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Xiang, Jinhai Zhang, Yan Yu, JiaKun He, Xinwei Huazhong Agricultural University College of Informatics Wuhan430070 China Huazhong Agricultural University Agricultural Bioinformatics Key Laboratory of Hubei Province Wuhan430070 China Huazhong Agricultural University Key Laboratory of Smart Farming for Agricultural Animals Ministry of Agriculture Hubei Engineering Technology Research Center of Agricultural Big Data Wuhan430070 China

ISBN: (纸本)9798350386226

Semi-supervised semantic segmentation (SSS), which allows for learning a better model with a small fraction of labeled samples and a large amount of unlabeled ones, is valuable yet challenging in medical image analysis. Recent works (e.g., UniMatch) have found that weak-to-strong consistency via augmentation is especially conducive to SSS training. However, they inadvertently introduce cognitive biases for unlabeled images, making it difficult to segment accurately near the target's edge regions. In this paper we present a lightweight bias-correct module to self-correct these mistakes between the strong perturbations. Based on it, we design a new framework named BcMatch by plugging it into UniMatch to reduce such cognitive biases stemming from incorrect pseudo-labels for unlabeled images. Moreover, we also introduce a bias correction loss, which works in tandem with the consistency loss to guide the model learning, focusing more on the edge regions of the targets. Experiments on the representative semi-supervised segmentation dataset, ACDC, demonstrate our BcMatch surpasses UniMatch by a large margin, attaining new state-of-the-art performance. The code is at https://***/zhangyan498/BcMatch. © 2024 IEEE.

关键词： Semantic Segmentation

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Research and Design of Disaster Monitoring and Early Warning System Based on big data 5

Research and Design of Disaster Monitoring and Early Warning...

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5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Wang, Pengfei Liu, Yongqiang Fang, Yudong Xu, Cheng Sun, Jiwu Lu, Lu Big Data Centre Ministry of Emergency Management Beijing100013 China Beijing Union University Beijing Key Laboratory of Information Service Engineering Beijing100101 China Institute of Urban Safety and Emergency Management Beijing Union University Beijing100101 China

ISBN: (纸本)9798350385557

Disaster prevention, mitigation and relief have always been one of the core businesses of governance. However, there are a series of problems in disaster prevention, mitigation and relief business support, especially in the prevention and control of the risk of compound chain-generated natural disasters, such as insufficient sensing capability, low accuracy of monitoring and warning, and lagging assessment models. In order to cope with these challenges, the work of using tower big data for disaster analysis in complex environments is proposed. Currently, the number of communication towers in the country is huge and widely distributed, and they are affected by various abnormalities in natural disasters. Therefore, through the construction of the tower disaster big data analysis system, big data technology and tower data are used to enhance the effectiveness of disaster monitoring and early warning, and provide brand new support for the disaster prevention, mitigation and relief operations. © 2024 IEEE.

关键词： big data

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Optimization of cloud computing task scheduling based on whale optimization algorithm with quasi-opposition-based and nonlinear factor

Optimization of cloud computing task scheduling based on wha...

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Internet of Things, Automation and Artificial Intelligence (IoTAAI), International Conference on

作者： Xiong Xuan Shaobo Li Liya Yu Fengbin Wu Xingxing Zhang Xue An State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China State Key Laboratory of Public Big Data Guizhou University Guiyang China College of Mechanical Engineering Guizhou University Guiyang China

ISBN: (数字)9798350386974

ISBN: (纸本)9798350386981

Task scheduling has a significant impact on the resource availability and operation cost of the system in cloud computing. In order to improve the efficiency of task execution in the cloud for a given computational resource, the improved whale optimization algorithm is applied to the multi-objective optimization model for cloud task scheduling. Specifically, the quasi-opposition-based learning strategy is added in the initiation phase to increase the diversity of the population. Meanwhile, a novel nonlinear convergence factor was introduced to augment the local search capability. Experimental results indicate that the improved whale optimization algorithm converges faster and searches with higher precision in finding the optimal task scheduling solution compared to existing meta-heuristic algorithms. In addition, the modified algorithm achieves superior system resource usage when dealing with both small and large tasks.

关键词： Cloud computing Costs Processor scheduling System performance Optimization models Metaheuristics Scheduling Whale optimization algorithms Internet of Things Convergence

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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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Image-Guided Point Cloud Completion with Multi-modal Fusion Transformers

Image-Guided Point Cloud Completion with Multi-modal Fusion ...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Li, Zhaowen Lin, Shujin Zhou, Fan School of Computer Science and Engineering National Engineering Research Center of Digital Life China Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion China School of Communication and Design Sun Yat-sen University Guangzhou China

ISBN: (纸本)9798350337020

The task of image-guided point cloud completion aims to leverage information from images to address uncertainty issues in the completion inference of point clouds. The key challenge in this setting lies in how to effectively combine features extracted from both modalities. Due to the large domain discrepancy between the image and point cloud, existing methods that use cross-modal attention to directly fuse features have increased attention on redundant information and noise from different modalities, resulting in poor feature fusion performance. Hence, by introducing multi-modal fusion transformers that use bottleneck tokens, we enabled point cloud feature to learn image feature through information bridges, leading to improved point cloud completion performance. Our method can not only benefit from RGB images, but also from sketches with less feature information but more emphasis on edge information. Extensive experiments demonstrate that our proposed method enhances the quality of point cloud completion and outperforms other state-of-the-art methods. © 2023 IEEE.

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LESaET: Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER

LESaET: Low-Dimensional Embedding Method for Link Prediction...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Ding, Lichao Zhao, Jing Lu, Kai Hao, Zenghao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781665410205

knowledge graphs (KGs) provide a structured representation of the real world through entity-relation triples. However, current KGs are often incomplete, typically containing only a small fraction of all possible facts. This involves inferring missing content from existing information, a task known as link prediction. Existing methods in the field of link prediction struggle with controlling the dimensionality of embedding vectors or suffer from overly complex models. In order to tackle these challenges, we introduce a method in this paper, named Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER (LESaET). LESaET leverages both self-attention mechanisms and tensor factorization to learn expressive contextual-enhanced representations of KGs. Specifically, LESaET employs the multi-head self-attention mechanism of Transformer as an encoder to capture the mutual information between entities and relationships, and utilizes Enhanced-TuckER as a decoder, ultimately achieving expressive low-dimensional embeddings for link prediction tasks. LESaET demonstrates competitive performance compared to advanced methods on standard datasets. © 2024 IEEE.

关键词： Prediction models

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data-driven evacuation and rescue traffic optimization with rescue contraflow control

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Journal of Safety Science and Resilience 2024年第1期5卷 1-12页

作者： Zheng Liu Jialin Liu Xuecheng Shang Xingang Li School of Management Engineering Zhengzhou University of AeronauticsZhengzhou 450046China Collaborative Innovation Center for Aviation Economy Development of Henan Province Zhengzhou University of AeronauticsZhengzhou 450046China School of Systems science Beijing Jiaotong UniversityBeijing 100044China Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong UniversityBeijing 100044China School of Transportation Engineering Shandong Jianzhu UniversityJinan 250101China

In response to local sudden disasters,e.g.,high-rise office or residential building fire disasters,road occupation can cause conflicts,and traffic directions may be opposite between evacuation vehicles and rescue vehicles;moreover,lane contraflow can be adopted to meet these surge traffic ***,lane contraflow that provides more roads for rescue vehicles reduces the traffic supply in the evacuation *** is unclear how to control the number of contraflow roads used by rescue vehicles to coordinate evacuation and rescue traffic ***,we adjust the critical rescue traffic volume of reversing the normal road traffic direction to control rescue ***,we propose a multiobjective mixed integer linear programming formulation for evacuation and rescue traffic ***,considering that the upper limit of the critical rescue traffic volume is unknown and that the proposed formulation includes multiple objectives and multi-priority vehicle classes,a three-stage solving algorithm is ***,a large-scale evacuation and rescue traffic optimization result dataset is obtained for the Nguyen–Dupuis road network,and the impact of different rescue contraflow control plans on evacuation and rescue traffic is studied based on data-driven sta-tistical *** results show that by adjusting the optimal rescue traffic route,the critical rescue traffic volume for reversing the normal road traffic direction can reduce the interference of rescue traffic to evacuation traffic operation performance without reducing rescue traffic operation performance,and can be used to coor-dinate evacuation and rescue traffic operation under rescue contraflow.

关键词： Evacuation and rescue traffic optimization Rescue contraflow control Multiobjective mixed integer linear programming Three-stage solving algorithm data-driven statistical analysis

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Regional Winter Wheat Yield Estimation Based on CASA Model and Improved Harvest Index Using Remote Sensing Images

Regional Winter Wheat Yield Estimation Based on CASA Model a...

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2023 International Conference on Remote Sensing, Mapping, and Geographic Systems, RSMG 2023

作者： Li, Qian Zhao, Jianhui Yang, Huijin Li, Ning School of Computer and Information Engineering Henan University Kaifeng475004 China Henan Province Engineering Research Center of Spatial Information Processing Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Kaifeng475004 China

ISBN: (纸本)9781510668898

Accurately and timely grasping agricultural information at the regional scale helps to solve food security issues and formulate agricultural policies. Remote sensing images have the advantages of wide monitoring range and the ability to eliminate human interference. In recent years, they have been increasingly valued in crop yield estimation. The CASA model is used to estimate the net primary productivity (NPP) of crops and then combine it with the harvest index (HI) to estimate crop yields. However, most studies use a fixed HI for yield estimation, which can lead to low accuracy. In this context, the HI is studied and improved for winter wheat yield prediction in this research. The main contents are as follows: Firstly, time-series Sentinel-2 optical data and meteorological data were used as inputs of the CASA model to calculate the NPP of winter wheat. Then, HI was calculated using the fieldwork data and a method was proposed to improve it due to its geographic location-related characteristics. Finally, the calculated NPP and improved HI were used to estimate the winter wheat yield. The experimental results prove that the proposed method has higher accuracy than original methods, with determination coefficient(R2) of 0.595, root mean square error(RMSE) of 793.4 kg/ha, and mean absolute error(MAE) of 659.53 kg/ha. © 2023 SPIE.

关键词： Crops

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Few-Shot Object Detection Based on the Transformer and High-Resolution Network

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Computers, Materials & Continua 2023年第2期74卷 3439-3454页

作者： Dengyong Zhang Huaijian Pu Feng Li Xiangling Ding Victor S.Sheng Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan411004China Department of Computer Science Texas Tech UniversityLubbock79409TXUSA

Now object detection based on deep learning tries different *** uses fewer data training networks to achieve the effect of large dataset ***,the existing methods usually do not achieve the balance between network parameters and training *** makes the information provided by a small amount of picture data insufficient to optimize model parameters,resulting in unsatisfactory detection *** improve the accuracy of few shot object detection,this paper proposes a network based on the transformer and high-resolution feature extraction(THR).High-resolution feature extractionmaintains the resolution representation of the *** and spatial attention are used to make the network focus on features that are more useful to the *** addition,the recently popular transformer is used to fuse the features of the existing *** compensates for the previous network failure by making full use of existing object *** on the Pascal VOC and MS-COCO datasets prove that the THR network has achieved better results than previous mainstream few shot object detection.

关键词： Object detection few shot object detection transformer high-resolution

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