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IET International Radar Conference 2020, IET IRC 2020

作者： Qi, Wenwen Wu, Lin Guo, Zhengwei Huang, Dan College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng475004 China College of Environment and Planning Henan University Kaifeng475004 China Department of Laboratory and Equipment Management Henan University Kaifeng475004 China

ISBN: (纸本)9781839535635

In this study, spatial fuzzy c-means (SFCM) clustering and deep belief network (DBN) method is presented for change detection in SAR images. There are three primary steps of this approach, they are given as follows: 1) Difference Image (DI) is firstly obtained by calculating the similarity of gray levels between multitemporal SAR images. 2) SFCM clustering is used to analyze DI, which combines the spatial information factor and the fuzzy cluster membership function, thus can effectively eliminate the interference of speckle noise. 3) DBN, a powerful data processing methodology that integrates deep learning techniques to extract underlying features and structures of complicated data, is trained to classify pixels to be changed or unchanged. The effectiveness and robustness of the proposed method are verified by experimental results with the Ottawa dataset. © 2020 Institution of Engineering and technology. All rights reserved.

关键词： Membership functions

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Learning to Infer Weather States Using Partial Observations

Journal of Geophysical Research: Machine Learning and Comput...

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Journal of Geophysical Research: Machine Learning and Computation 2025年第1期2卷

作者： Jie Chao Baoxiang Pan Quanliang Chen Shangshang Yang Jingnan Wang Congyi Nai Yue Zheng Xichen Li Huiling Yuan Xi Chen Bo Lu Ziniu Xiao National Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric Physics Chinese Academy of Science Beijing China School of Atmospheric Sciences Chengdu University of Information Technology Chengdu China Key Laboratory of Mesoscale Severe Weather Ministry of Education and School of Atmospheric Sciences Nanjing University Nanjing China College of Computer National University of Defense Technology Hunan China Clustertech LTD Hong Kong China National Climate Center China Meteorological Administration Beijing China

Accurate state estimation of the high-dimensional, chaotic Earth's atmosphere marks a Sisyphean task, yet is indispensable for initiating weather forecasts and gauging climate variability. While much effort is devoted to assimilating observations and forecasts to infer weather states, the inherent low-dimensional statistical structure in atmospheric circulation, shaped by geophysical laws and geographic boundaries, is underutilized as an informative prior for state inference, or as reference for assessing representative of existing observations and planning new ones. We realize these potential by learning climatological distribution from climate reanalysis/simulation, using a deep generative model. For a case study of estimating 2 m temperature spatial patterns, the learned distribution faithfully reproduces climatology statistics. A combination of the learned climatological prior with few station observations yields strong posterior of spatial pattern estimates, which are spatially coherent, faithful and adaptive to observational constraints, and uncertainty-aware. This allows us to evaluate each observation's value in reducing pattern estimation uncertainty, and guide optimal observation network design by pinpointing the most informative sites. Our study showcases how generative models can extract and utilize information produced in the chaotic evolution of climate system. Deep generative model enables accurate spatial interpolation of weather variables from sparse observations The generative model produces spatially coherent and uncertainty-aware estimates, effectively constrained by observational data The approach provides a framework to quantify observation impact on uncertainty reduction, enabling optimization of observation networks Accurate estimation of weather conditions across a large area is crucial but challenging due to the complex and chaotic nature of the atmosphere. Traditional methods rely on combining observations with forecasts, which can be comp

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Real-time co-registered photoacoustic and ultrasonic imaging for early endometrial cancer detection driven by cylindrical diffuser

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Journal of Innovative Optical Health Sciences 2019年第2期12卷 1-12页

作者： Yongping Lin Peter Andreae Zhifang Li Jianyong Cai Hui Li College of Photonic and Electronic Engineering Fujian Normal University Fujian Provincial Key Laboratory of Photonic Technology Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application Key Laboratory of Optoelectronic Science and Technology for Medicine Ministry of Education Fuzhou 350007FujianP.R.China Xiamen University of Technology School of Optoelectronic and Communication Engineering Xiamen 361024 FujianP.R.China School of Engineering and Computer Science Victoria University of Wellington P.O.Box 600 WellingtonNew Zealand

In illuminating tissues,a cylindrical diffuser(CD)has an advantage over regular laser sources due to its ability to illuminate a larger volume of the target *** paper presents a co-registered large volume photoacoustic(PA)and ultrasonic(US)imaging for early endometrial cancer(EEC)detection using *** has the advantage that the US imaging system is outside the body and only the PA excitation device is inside the body,which makes the system more efficient and less invasive for EEC *** paper reports on two sets of *** set produced real-time PA images of blood vessel *** second set demonstrated the imaging of pig uterus ex *** results show that the system has the potential for imaging and characterizing of EEC.

关键词： Endometrial cancer cylindrical diffuser photoacoustic imaging

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Circular Economy and Sustainability of the Clothing and Textile Industry

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Materials Circular Economy 2021年第1期3卷 1-9页

作者： Chen, Xuandong Memon, Hifza A. Wang, Yuanhao Marriam, Ifra Tebyetekerwa, Mike College of Civil and Architecture Engineering Guangxi Key Laboratory of New Energy and Building Energy Saving and Guangxi Engineering Research Center for Solid Waste Resources Development and Application Guilin University of Technology Guilin People’s Republic of China Department of Textile Engineering Mehran University of Engineering and Technology Jamshoro Pakistan School of Mechanical Medical and Process Engineering Queensland University of Technology (QUT) Brisbane Australia School of Engineering College of Engineering and Computer Science The Australian National University Canberra Australia

Textiles are essential to humans in a variety of ways, especially clothing. However, the speed at which they end up in landfills is astonishing (one garbage truck per second), posing a severe risk to the environment, if the trend continues. Governments and responsible organizations are starting to make calls to different stakeholders to redesign the textile chain from linear to circular economy. In this perspective, we highlight some of the possible approaches to be undertaken including the need for the creation of renewable raw materials sources, rethinking production, maximum use and reuse of textile products, reproduction, and recycling strategies, redistribution of textiles to new and parallel markets, and improvising means to extend the textiles lifetime.

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Efficient spatialtemporal context modeling for action recognition?

arXiv

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arXiv 2021年

作者： Cao, Congqi Lu, Yue Zhang, Yifan Jiang, Dongmei Zhang, Yanning Natl. Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Lab on Speech and Image Information Processing School of Computer Science Northwestern Poly-technical University Xi'an710129 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing100190 China

Contextual information plays an important role in action recognition. Local operations have difficulty to model the relation between two elements with a long-distance interval. However, directly modeling the contextual information between any two points brings huge cost in computation and memory, especially for action recognition, where there is an additional temporal dimension. Inspired from 2D criss-cross attention used in segmentation task, we propose a recurrent 3D criss-cross attention (RCCA-3D) module to model the dense long-range spatiotemporal contextual information in video for action recognition. The global context is factorized into sparse relation maps. We model the relationship between points in the same line along the direction of horizon, vertical and depth at each time, which forms a 3D criss-cross structure, and duplicate the same operation with recurrent mechanism to transmit the relation between points in a line to a plane finally to the whole spatiotemporal space. Compared with the non-local method, the proposed RCCA-3D module reduces the number of parameters and FLOPs by 25% and 30% for video context modeling. We evaluate the performance of RCCA-3D with two latest action recognition networks on three datasets and make a thorough analysis of the architecture, obtaining the optimal way to factorize and fuse the relation maps. Comparisons with other state-of-the-art methods demonstrate the effectiveness and efficiency of our model. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Tailored sandwich structures in all-organic dielectric composite to optimize energy storage performance

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Journal of Energy Storage 2025年 130卷

作者： Yue Zhang Yue Wang Xin He Guowei Hao Zheng Gong Changhai Zhang Yongquan Zhang Tiandong Zhang Huajie Yi Qi Wang Key Laboratory of Engineering Dielectrics and Its Application Ministry of Education Harbin University of Science and Technology Harbin 150080 PR China School of Electrical and Electronic Engineering Harbin University of Science and Technology Harbin 150080 PR China School of Electronics and Computer Science University of Southampton Southampton SO17 1BJ UK Singapore Institute of Manufacturing Technology (SIMTech) Agency for Science Technology and Research (A*STAR) Singapore 636732 Singapore

Polymer-based dielectric capacitor is essential in modern electronic and power system due to the high dielectric constant, flexibility, and longevity. Yet, the progress is impeded by low energy storage density and efficiency. Here, this work introduces a novel, all-organic dielectric composite strategy that combines the high polarization strength of polyvinylidene fluoride (PVDF) with the superior insulation of linear dielectrics, specifically, polyethersulfone (PESU), polyimide (PI), and polymethylmethacrylate (PMMA). By investigating heterogeneous material compatibility and designing sandwich structure, the breakdown strength of composite is enhanced and the dielectric loss of it is minimized. Notably, a 30 wt% PMMA-PVDF composite exhibits a maximum energy storage density of 11.3 J/cm 3 at 531.5 kV/mm and a charge-discharge efficiency of 63.1 %. Moreover, sandwich-structured composites further improve performance, with a PMMA/30 wt% PMMA-PVDF/PMMA configuration achieving 11.6 J/cm 3 at 541.2 kV/mm and an efficiency of 70.1 %. This work demonstrates a promising route to overcome the intrinsic limitations of conventional dielectric materials, paving the way for advanced capacitor technologies.

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Expressivity issues in SPARQL: monotonicity and two-versus three-valued semantics

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Science China(Information Sciences) 2018年第12期61卷 187-189页

作者： Xiaowang ZHANG Chenhong MENG Lei ZOU School of Computer Science and Technology Tianjin University Tianjin Key Laboratory of Cognitive Computing and Application Tianjin University Institute of Computer Science and Technology Peking University

Dear editor,In 2008, the SPARQL query language was the official world wide web consortium (W3C) recommendation for a resource description framework(RDF) query language [1], which is a database query language that can retrieve and manipulate data stored in the RDF format. SPARQL allows a query to have triple patterns, conjunctions (AND),disjunctions (UNION), optional patterns (OPT)and built-in conditions (constraints) that can be filtered (FILTER).

关键词： Expressivity issues in SPARQL monotonicity and two-versus three-valued semantics

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Dynamic Measurement of Task Scheduling Algorithm in Multi-Processor System

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Journal of Shanghai Jiaotong university(Science) 2019年第3期24卷 372-380页

作者： XIE Ying WU Jinzhao CHEN Jianying CUI Mengtian School of Computer Science and Technology Southwest Minzu UniversityChengdu 610041China Guangxi Key Laboratory of Hybrid Computational and IC Design Analysis Guangxi University for NationalitiesNanning 530006China Chengdu Institute of Computer Application Chinese Academy of SciencesChengdu 610041China University of Chinese Academy of Sciences Beijing 100049China

It is important to evaluate function behaviors and performance features of task scheduling algorithm in the multi-processor system.A novel dynamic measurement method(DMM)was proposed to measure the task scheduling algorithm’s correctness and *** a multi-processor system,task scheduling problem is represented by a combinatorial evaluation model,interactive Markov chain(IMC),and solution space of the algorithm with time and probability metrics is described by action-based continuous stochastic logic(aCSL).DMM derives a path by logging runtime scheduling actions and corresponding *** judging whether the derived path can be received by task scheduling IMC model,DMM analyses the correctness of *** judging whether the actual values satisfy label function of the initial state,DMM analyses the dependability of *** simulation shows that DMM can effectively characterize the function behaviors and performance features of task scheduling algorithm.

关键词： multi-processor task scheduling algorithm IMC aCSL dynamic measurement

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Data-Efficient Federated Semi-Supervised Learning Framework Via Pseudo Supervision Refinement Strategy for Lung Tumor Segmentation

SSRN

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SSRN 2024年

作者： Shi, Zhenwei Li, Weixing Pan, Xipeng Zhang, Zhen Wu, Guangyao Ye, Guanchao Wee, Leonard Dekker, Andre Han, Chu Shi, Lei Liu, Zaiyi Liu, Zhenbing School of Computer and Information Security Guilin University of Electronic Technology Guilin541004 China Department of Radiology Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Southern Medical University Guangzhou510080 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou510080 China Chinese Academy of Sciences Zhejiang Hangzhou310022 China Chinese Academy of Sciences Zhejiang Hangzhou310022 China Department of Radiology Huazhong University of Science and Technology Tongji Medical College Union Hospital Hubei Wuhan430021 China Clinical Data Science Faculty of Health Medicine Life Sciences Maastricht University Maastricht6229 ET Netherlands GROW School of Oncology and Reproduction Maastricht University Medical Centre+ Maastricht6229 ET Netherlands Guangdong Provincial People's Hospital China

Background and Objective: Accurate lung tumor segmentation is crucial for early clinical diagnosis and subsequent treatment planning for patients. Conducting centralized training to enhance model performance is impractical in real-world scenarios. Although federated learning offers a promising solution to this issue, effectively and efficiently utilizing unlabeled data remains a challenge. Methods: In this work, we propose a novel federated semi-supervised learning framework (FSSL) for complex and realistic scenario, which can efficiently leverage the limited labeled data and abundant unlabeled data from multiple clinical sites. Specifically, we initially conduct federated self-supervised pre-training at completely unlabeled sites using masked image modeling strategy. Subsequently, in the fine-tuning stage, we propose a pseudo supervision refinement strategy to leverage the unlabeled data from partially labeled sites, which not only reduces pseudo label noise but also helps stabilize the training process. Moreover, we propose a dynamic model aggregation strategy to assist the server in dynamically combining all local models during communication round. Results: We conducted extensive comparative and ablation experiments to validate the effectiveness of our proposed method. When only a limited amount of labeled data is available in the partially labeled sites, even without loading the pre-trained model, our approach consistently achieves the highest average Dice scores of 0.7383, 0.4768, and 0.6629 on the internal test sets, external unseen test sets, and pre-training test sets, respectively. Upon loading the pre-trained model, the scores on the three test sets further improve to 0.8205, 0.6185, and 0.7334, respectively. Conclusion: Our proposed method is capable of collaboratively training a global model with robust segmentation performance and generalizability by efficiently utilizing unlabeled data from different sites, even when only a limited amount of labeled da

关键词： Federated learning

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Data-Efficient Federated Semi-Supervised Learning Framework Via Pseudo Supervision Refinement Strategy for Lung Tumor Segmentation

SSRN

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SSRN 2024年

作者： Shi, Zhenwei Li, Weixing Pan, Xipeng Zhang, Zhen Wu, Guangyao Ye, Guanchao Wee, Leonard Andre, Dekker Han, Chu Shi, Lei Liu, Zaiyi Liu, Zhenbing School of Computer and Information Security Guilin University of Electronic Technology Guilin541004 China Department of Radiology Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Southern Medical University Guangzhou510080 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou510080 China Chinese Academy of Sciences Zhejiang Hangzhou310022 China Chinese Academy of Sciences Zhejiang Hangzhou310022 China Department of Radiology Huazhong University of Science and Technology Tongji Medical College Union Hospital Hubei Wuhan430021 China Clinical Data Science Faculty of Health Medicine Life Sciences Maastricht University Maastricht6229 ET Netherlands GROW School of Oncology and Reproduction Maastricht University Medical Centre+ Maastricht6229 ET Netherlands Guangdong Provincial People's Hospital China

Accurate lung tumor segmentation is crucial for early clinical diagnosis and subsequent treatment planning for patients. Centralized model training faces privacy constraints, although federated learning offers a promising solution to this issue, effectively and efficiently utilizing unlabeled data remains a challenge. In this work, we propose a novel federated semi-supervised learning framework for complex and realistic scenario, which can efficiently leverage the limited labeled data and more homogeneous unlabeled data from multiple clinical sites for collaborative model training. Specifically, we initially conduct federated self-supervised pre-training at completely unlabeled sites using masked image modeling strategy. Subsequently, in the fine-tuning stage, we propose a pseudo supervision refinement strategy to leverage the unlabeled data from partially labeled sites, which not only reduces pseudo label noise but also helps stabilize the training process. Moreover, we propose a dynamic model aggregation strategy to assist the server in dynamically combining local models during each communication round to enhance the robustness and generalizability of the global model. Extensive comparative and ablation experiments confirm the efficacy of our proposed methodology, demonstrating improvement over recent state-of-the-art federated semi-supervised segmentation methods in scenarios with only a small amount of labeled data. This approach highlights the significant potential of multi-source and heterogeneous unlabeled data in facilitating the training of federated deep learning models and promotes clinical applications, assists radiologists in diagnosis and improves patient outcomes. The source code will be released on https://***/zhenweishi/FedPSR © 2024, The Authors. All rights reserved.

关键词： Federated learning

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