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International Joint Conference on Neural Networks (IJCNN)

作者： Xiaolu Lin Pinya Lu Jie Pan Hongqin Yang Xuemei Ding Fujian Provincial Engineering Research Center for Public Service Big Data Mining and Application Fujian Provincial University Engineering Research Center for Big Data Analysis and Application Fujian Normal University Fuzhou China Xiamen Jingyi Zhikang Technology Co. Ltd Xiamen China Fujian Provincial Key Laboratory for Photonics Technology Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education Fujian Normal University Fuzhou China School of Computing Engineering and Intelligent Systems Ulster University Derry-Londonderry NI UK

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Alzheimer's disease (AD) is a neurodegenerative disease and mild cognitive impairment (MCI) is the early stage of AD. Previous studies have predominantly focused on binary classification using 3 dimensional - convolutional neural network (3D-CNN) for AD diagnosis, with limited progress in multi-classification. Moreover, the current 3D-CNNs often adopt a single-scale architecture with massive parameters growth. Additionally, obtaining precise location information of brain imaging data is crucial for improving the classification accuracy with 3D-CNN. Hence, we propose a multi-scale 3D-CNN based on coordinate attention mechanism to marvelously capture and integrate 3D features with fewer parameters, improving the accuracy of AD diagnosis. A total of 447 cognitively normal (CN), 512 MCI, and 358 AD sMRI images from the Alzheimer's Disease Neuroimaging Initiative datasets are used for multi-class classification task, yielding a classification accuracy of 92.8%. The model merely involves 2.41 M parameters and achieves the best classification results with the least number of parameters when compared to other representative CNN architectures including ResNet 18, ResNet 34, ConvNeXt tiny, and VGG 11. Through the ablation experiment, the addition of attention mechanism and the multi-scale classification enhances the classification performance by 4.5% and 1.5%, respectively. Furthermore, our model outperforms the other six existing studies in terms of accuracy for classifying AD vs. MCI vs. CN. Overall, this study underscores the efficacy of our approach for AD diagnosis, showcasing its utility in diagnosing AD patients and providing novel insights for diagnosing other neurological disorder diseases.

关键词： Neuroimaging Accuracy Attention mechanisms Three-dimensional displays Resonance Convolutional neural networks Alzheimer's disease

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Innovative remote sensing methodologies and applications in coastal and marine environments

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地球空间信息科学学报（英文版） 2024年第3期27卷 836-853页

作者： Qing Zhao Antonio Pepe Virginia Zamparelli Pietro Mastro Francesco Falabella Saygin Abdikan Caglar Bayik Fusun Balik Sanli Mustafa Ustuner Nevin Betul Avşar Jingjing Wang Peng Chen Zhengjie Li Adam T.Devlin Fabiana Calò Key Laboratory of Geographical Information Science Ministry of EducationEast China Normal UniversityShanghaiChina School of Geographic Sciences East China Normal UniversityShanghaiChina Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities Ministry of Natural ResourcesShanghaiChina Institute for Electromagnetic Sensing of the Environment(IREA) Italian National Research CouncilNapoliItaly School of Engineering University of BasilicataPotenzaItaly Institute for Electromagnetic Sensing of the Environment(IREA) Italian National Research CouncilNapoliItaly Institute for Electromagnetic Sensing of the Environment(IREA) Italian National Research CouncilNapoliItaly Institute of Methodologies for Environmental Analysis(IMAA) Italian National Research CouncilTito ScaloItaly Department of Geomatics Engineering Hacettepe UniversityAnkaraTurkey Department of Geomatics Engineering Zonguldak Bulent Ecevit UniversityZonguldakTurkey Department of Geomatic Engineering Yildiz Technical UniversityIstanbulTurkey Department of Geomatic Engineering Artvin Çoruh UniversityArtvinTurkey Department of Geomatics Engineering Izmir Katip Celebi UniversityİzmirTurkey School of Ocean and Earth Science and Technology University of Hawaii at MānoaHonoluluHIUSA

Remote sensing(RS)technologies are extensively exploited by scientists and a vast audience of local authorities,urban managers,and city *** regions,geohazard-prone areas,and highly populated cities represent natural laboratories to apply RS technologies and test new *** the last decades,many efforts have been spent on improving Earth's surface monitoring,including intensifying Earth Observation(EO)operations by the major national space *** oversee to plan and make operational constellations of satellite sensors providing the scientific community with extensive research and development oppor-tunities in the geoscience *** instance,within this framework,the European Space Agency(ESA)and the Ministry of Science and Technology of China(MOST)have sponsored,since the early 2000s,the DRAGON initiative jointly carried out by the European and Chinese RS scientific *** manuscript aims to provide a synthetic overview of some research activities and new methods recently designed and applied and trace the route for further *** main findings are related to ⅰ)the analysis of flood risk in China,ⅱ)the potential of new methods for the estimation and removal of ground displacement biases in small-baseline oriented interferometric Synthetic Aperture Radar(SAR)methods,ⅲ)the analy-sis of the inundation risk in low-lying regions using coherent and incoherent SAR methods;andⅳ)the use of SAR-based technologies for marine applications.

关键词： Disaster risk management Remote Sensing(RS) Earth Observation(EO) Synthetic Aperture Radar(SAR) flooding subsidence coastal/marine environments

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Identification of soybean in Argentina using Sentinel-2 composite images

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International Journal of Agricultural and Biological Engineering 2024年第5期17卷 266-274页

作者： Linsheng Huang Yue Chen Yuhao Pan Zihang Lou Shijun Zheng Xiaoyang Zhang Le Yu Shengwei Liu Dailiang Peng National Engineering Research Center for Agro-Ecological Big Data Analysis&Application Anhui UniversityHefei 230601China Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China Research Area of Ecology and Biodiversity School of Biological SciencesThe University of Hong KongPokfulamHong KongChina College of Environmental and Resource Sciences Zhejiang UniversityHangzhou 310058China Department of Earth System Science Ministry of Education Key Laboratory for Earth System ModelingInstitute for Global Change StudiesTsinghua UniversityBeijing 100084China Geospatial Sciences Center of Excellence Department of Geography&Geospatial SciencesSouth Dakota State UniversityBrookingsSD 57007USA Ministry of Education Ecological Field Station for East Asian Migratory Birds Beijing 100084China Tsinghua University(Department of Earth System Science)-Xi’an Institute of Surveying and Mapping Joint Research Center for Next-Generation Smart Mapping Beijing 100084China School of Surveying and Land Information Engineering Henan Polytechnic UniversityJiaozuo 454000HenanChina

Soybean is one of the most important oil crops, and Argentina is the third-largest soybean producer in the world, accounting for 17% of the global soybean yield. Timely and accurate information on soybean spatial distribution is critical for ensuring global food security. Sentinel-2 multispectral data and machine learning classification models are used to investigate the potential of soybean identification in the early stage of the growing season in Argentina, with the help of Google Earth Engine (GEE). The earliest time window and optimal feature set for soybean identification are explored. Results are as follows: 1) the random forest (RF) classification model demonstrated the highest level of classification accuracy compared to the backpropagation neural network (BPNN), support vector machine (SVM), and naive Bayes (NB) models;2) Soybean can be accurately identified as early as the end of February (filling stage), which is approximately one month before harvest;3) The optimal feature-subset can reduce the amount of input data by 80% while maintaining high classification accuracy. The overall accuracy (OA) of the RF classification model is 85.87%, and the relative error between the estimated soybean planting area and the agricultural statistics is 3.45%. This study provided a high-precision method for early-season identification of soybeans over large scales. The results can provide a data support for early futures trading and agricultural insurance, as well as a reference for policy-making to ensure global soybean food security.

关键词： soybean machine learning time window feature selection Sentinel-2 Google Earth Engine

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WiViPose: A Video-aided Wi-Fi Framework for Environment-Independent 3D Human Pose Estimation

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IEEE Transactions on Multimedia 2025年

作者： Zhang, Lei Ning, Haoran Tang, Jiaxin Chen, Zhenxiang Zhong, Yaping Han, Yahong Tianjin University College of Intelligence and Computing the Tianjin Key Laboratory of Advanced Network Technology and Application Tianjin300050 China Key Laboratory of Computing Power Network and Information Security Ministry of Education China University of Jinan Shandong Provincial Key Laboratory of Ubiquitous Intelligent Computing the School of Information Science and Engineering Jinan250022 China Wuhan Sports University Sports Big-data Research Center Wuhan430079 China Tianjin University College of Intelligence and Computing the Tianjin Key Laboratory of Machine Learning Tianjin300350 China

The inherent complexity of Wi-Fi signals makes video-aided Wi-Fi 3D pose estimation difficult. The challenges include the limited generalizability of the task across diverse environments, its significant signal heterogeneity, and its inadequate ability to analyze local and geometric information. To overcome these challenges, we introduce WiViPose, a video-aided Wi-Fi framework for 3D pose estimation, which attains enhanced cross-environment generalization through cross-layer optimization. Bilinear temporal-spectral fusion (BTSF) is initially used to fuse the time-domain and frequency-domain features derived from Wi-Fi. Video features are derived from a multiresolution convolutional pose machine and enhanced by local self-attention. Cross-modality data fusion is facilitated through an attention-based transformer, with the process further refined under a supervisory mechanism. WiViPose demonstrates effectiveness by achieving an average percentage of correct keypoints (PCK)@50 of 91.01% across three typical indoor environments. © 2025 IEEE.

关键词： Frequency domain analysis

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ConsistencyDet: A Robust Object Detector with a Denoising Paradigm of Consistency Model

arXiv

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arXiv 2024年

作者： Jiang, Lifan Wang, Zhihui Wang, Changmiao Li, Ming Leng, Jiaxu Wu, Xindong the College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266510 China Shenzhen Research Institute of Big Data Shenzhen518172 China Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua321004 China the Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing400065 China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing210094 China the Key Laboratory of Knowledge Engineering with Big Data the Ministry of Education of China China School of Computer Science and Information Technology Hefei University of Technology Hefei230009 China

Object detection, a quintessential task in the realm of perceptual computing, can be tackled using a generative methodology. In the present study, we introduce a novel framework designed to articulate object detection as a denoising diffusion process, which operates on the perturbed bounding boxes of annotated entities. This framework, termed ConsistencyDet, leverages an innovative denoising concept known as the Consistency Model. The hallmark of this model is its self-consistency feature, which empowers the model to map distorted information from any temporal stage back to its pristine state, thereby realizing a "one-step denoising" mechanism. Such an attribute markedly elevates the operational efficiency of the model, setting it apart from the conventional Diffusion Model. Throughout the training phase, ConsistencyDet initiates the diffusion sequence with noise-infused boxes derived from the ground-truth annotations and conditions the model to perform the denoising task. Subsequently, in the inference stage, the model employs a denoising sampling strategy that commences with bounding boxes randomly sampled from a normal distribution. Through iterative refinement, the model transforms an assortment of arbitrarily generated boxes into definitive detections. Comprehensive evaluations employing standard benchmarks, such as MS-COCO and LVIS, corroborate that ConsistencyDet surpasses other leading-edge detectors in performance metrics. Our code is available at https://***/Tankowa/ConsistencyDet. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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ICSDA: a multi-modal deep learning model to predict breast cancer recurrence and metastasis risk by integrating pathological, clinical and gene expression data

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BRIEFINGS IN BIOINFORMATICS 2022年第6期23卷 bbac448-bbac448页

作者： Yao, Yuhua Lv, Yaping Tong, Ling Liang, Yuebin Xi, Shuxue Ji, Binbin Zhang, Guanglu Li, Ling Tian, Geng Tang, Min Hu, Xiyue Li, Shijun Yang, Jialiang School of Mathematics and Statistics Hainan Normal University Haikou 570100 China Key Laboratory of Data Science and Intelligence Education Ministry of Education Hainan Normal University Haikou China Key Laboratory of Computational Science and Application of Hainan Province Hainan Normal University Haikou China Genies Beijing Co. Ltd. Beijing 100102 China Chifeng Municipal Hospital Chifeng Inner Mongolia 024000 China Qingdao Geneis Institute of Big Data Mining and Precision Medicine Qingdao 266000 China Basic Courses Department Zhejiang Shuren University Hangzhou 310000 China School of Life Sciences Jiangsu University Zhenjiang 212013 China Dept. of Colorectal Surgery National Cancer Center/ Cancer Hospital Chinese Academy of Medical Science 17 Panjiayuan Nanli Chaoyang District Beijing China 100021

Breast cancer patients often have recurrence and metastasis after surgery. Predicting the risk of recurrence and metastasis for a breast cancer patient is essential for the development of precision treatment. In this study, we proposed a novel multi-modal deep learning prediction model by integrating hematoxylin & eosin (H&E)-stained histopathological images, clinical information and gene expression data. Specifically, we segmented tumor regions in H&E into image blocks (256 x 256 pixels) and encoded each image block into a 1D feature vector using a deep neural network. Then, the attention module scored each area of the H&E-stained images and combined image features with clinical and gene expression data to predict the risk of recurrence and metastasis for each patient. To test the model, we downloaded all 196 breast cancer samples from the Cancer Genome Atlas with clinical, gene expression and H&E information simultaneously available. The samples were then divided into the training and testing sets with a ratio of 7: 3, in which the distributions of the samples were kept between the two datasets by hierarchical sampling. The multi-modal model achieved an area-under-the-curve value of 0.75 on the testing set better than those based solely on H&E image, sequencing data and clinical data, respectively. This study might have clinical significance in identifying high-risk breast cancer patients, who may benefit from postoperative adjuvant treatment.

关键词： breast cancer recurrence and metastasis multi-modality residual network attention module

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Unsupervised learning for lake underwater vegetation classification: Constructing high-precision, large-scale aquatic ecological datasets

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Science of the Total Environment 2025年 958卷 177895页

作者： Liu, Lei Bao, Zhengsen Liang, Ying Deng, Huanxi Zhang, Xiaolin Cao, Te Zhou, Chichun Zhang, Zhenyu School of Engineering Dali University Yunnan 671003 China National Observation and Research Station of Erhai Lake Ecosystem in Yunnan Dali671006 China Air-Space-Ground Integrated Intelligence and Big Data Application Engineering Research Center of Yunnan Provincial Department of Education Yunnan 671003 China State Key Laboratory of Freshwater Ecology and Biotechnology Institute of Hydrobiology Chinese Academy of Sciences Wuhan430072 China

Monitoring underwater vegetation is vital for evaluating lake ecosystem health. Automated data collection and analysis play key roles in achieving large-scale, high-precision, and high-frequency monitoring. While technologies such as unmanned vessels have made data collection more efficient, challenges persist in the analysis process, particularly in addressing the varied needs of different lake environments. Supervised AI methods can automatically identify underwater vegetation but are heavily reliant on labeled datasets. In practice, models trained on public datasets often struggle with generalization due to differences in vegetation types, collection environments, and equipment, resulting in discrepancies between training and testing datasets. Moreover, traditional dataset construction methods that rely on manual annotation are time-consuming and costly, limiting their scalability and application. This study aims to overcome these challenges by proposing an unsupervised method for automatically classifying underwater vegetation data, aiming to reduce manual annotation efforts and construct unbiased datasets at lower costs with greater efficiency. Compared with existing unsupervised, self-supervised, and unsupervised domain adaptation methods, this method introduces two key innovations: 1) a two-step dimensionality reduction method that combines pre-trained model and manifold learning to extract key features and 2) a multialgorithm voting mechanism to increase classification confidence. These features enable high-accuracy classification without prior data annotation. Experiments show 97.32 % accuracy on public dataset and 92.43 % and 96.15 % accuracy on private datasets from Erhai Lake and Wuhan East Lake, respectively, surpassing supervised methods and matching manual classification. Additionally, it drastically reduces the annotation effort, requiring only approximately 20 labeled images to classify thousands of points. By integrating unmanned vessel technology,

关键词： Lake ecological monitoring Species classification Unbiased datasets Underwater vegetation Unsupervised methods

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Mass Transfer during Hematitization and Implications for Uranium Mineralization in the Zoujiashan Deposit,Xiangshan Volcanic Basin

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Journal of Earth Science 2022年第2期33卷 422-434页

作者： Teng Deng Guoxiang Chi Xiongjie Zhang Zenghua Li Deru Xu Shengmiao Li Pengfei Du Pei Shang Shaohao Zou Wanpeng Zhou Ke Xu Hai Yan MaWen Zhengpeng Ding State Key Laboratory of Nuclear Resources and Environment East China University of TechnologyNanchang 330013China Key Laboratory of Uranium Geology China Geological SurveyTianjin 300170China School of Earth Sciences East China University of TechnologyNanchang 330013China Department of Geology University of ReginaRegina S4S 0A2Canada Engineering Research Center of Nuclear Technology Application Ministry of EducationEast China University of TechnologyNanchang 330013China Jiangxi Engineering Laboratory on Radioactive Geoscience and Big Data Technology East China University of TechnologyNanchang330013China Geological Bureau of Hunan Province Changsha 410014China Geologic Party No.208 China National Nuclear CorporationBaotou 410014China

The Zoujiashan uranium deposit in the Xiangshan ore field is the largest volcanic-related uranium deposit in ***-and fluorite-type ores are the predominant mineralization *** in the Xiangshan ore field is closely associated with uranium mineralization,mainly occurring as hematitized rocks enclosing fluorite-type vein ores developed in pre-ore illitized porphyritic *** petrographic and mass balance calculation studies were conducted to evaluate the mechanisms for uranium precipitation and mass transfer during *** observations suggest that in the hematitized rocks,orthoclase is more altered than plagioclase,and quartz dissolution is common,whereas in the illitized rocks,pyrite commonly occurs within the altered biotite grains,and chlorite grains are locally *** balance calculations indicate that Na2O and U were gained,K2O,Ca O and Si O2were lost,whereas Fe2O3-t remained more or less constant during *** observations suggest that the hydrothermal fluids were Na-and U-rich and Ca-K-poor,and the Fe2+used for hematitization was locally derived,most likely from biotite,pyrite and chlorite in the host *** Fe2+is inferred to have played the role of reductant to precipitate uranium,and calculation indicates that oxidation of Fe2+provided by host rocks is sufficient to form ores of economic ***,the hematite-type ore is interpreted to be generated by the reaction between oxidized ore fluids and reduced components in host *** development of calcite and pyrite in the fluorite ores suggests that perhaps mixing between the U-rich fluid and another fluid carrying reduced sulfur and carbon may have also contributed to uranium mineralization,in addition to temperature and pressure drop associated with the veining.

关键词： Xiangshan uranium ore field mass transfer uranium precipitation hematitization redox reaction ore deposit geology

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A Lane Usage Strategy for General Traffic Access on Bus Lanes under Mixed Traffic Environment

arXiv

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arXiv 2024年

作者： Li, Haoran Yuan, Zhenzhou Yue, Rui Yang, Guangchuan Zhu, Chuang Chen, Siyuan the Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong University Beijing100044 China the Institute of Transportation Research and Education North Carolina State University RaleighNC27606 United States the Zhejiang Communications Investment Group Co. Ltd. Zhejiang Hangzhou310000 China

The strategy of permitting general traffic to use the bus lane for improved utilization while ensuring bus priority has gained increasingly attention, particularly with the support of vehicle-to-everything technology. In this study, we propose a novel lane usage strategy called Dynamic Spatial-Temporal Priority (DSTP) to ensure bus priority and optimize bus lane usage in a mixed traffic environment. DSTP leverages dynamic methods to identify available spatial-temporal resources in the lane, utilizing signal timing, road information, and vehicle data. A Right-of-Way assignment optimization model is then developed based on these resources to determine which vehicles can enter the bus lane. The model is dynamically enacted using a rolling horizon scheme to accommodate time-varying traffic conditions. Numerical studies have validated the advantages of DSTP, showing maintained bus priority, improved traffic efficiency, reduced fuel consumption, and lower CO2 emissions, especially during periods of high traffic demand and concentrated bus arrivals. Copyright © 2024, The Authors. All rights reserved.

关键词： Vehicles

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A CPK-Based Identity Authentication Scheme for IoT

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Computer Systems Science & Engineering 2022年第3期40卷 1217-1231页

作者： Mingming Zhang Jiaming Mao Yuanyuan Ma Liangjie Xu Chuanjun Wang Ran Zhao Zhihao Li Lu Chen Wenbing Zhao State Grid Jiangsu Electric Power Co. Ltd.Information&Telecommunication BranchNanjing210008China State Grid Key Laboratory of Information&Network Security Institute of Information and CommunicationGlobal Energy Interconnection Research InstituteNanjing210003China Anhui Jiyuan Software Co.Ltd SGITGHefei230088China Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province Research and Development Center of Post Industry Technology of the State Posts Bureau(Internet of Things Technology)Engineering Research Center of Broadband Wireless Communication Technology of the Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing210003China Department of Electrical Engineering and Computer Science Cleveland State UniversityCleveland44115USA

As the power Internet of Things(IoT)enters the security construction stage,the massive use of perception layer devices urgently requires an identity authentication scheme that considers both security and *** existing public key infrastructure(PKI)-based security authentication scheme is currently difficult to apply in many terminals in *** key distribution and management costs are high,which hinders the development of power IoT security *** Public key(CPK)technology uses a small number of seeds to generate unlimited public *** is very suitable for identity authentication in the power Internet of *** this paper,we propose a novel identity authentication scheme for power *** scheme combines the physical unclonable function(PUF)with improved CPK technology to achieve mutual identity authentication between power IoT terminals and *** proposed scheme does not require third-party authentication and improves the security of identity authentication for power ***,the scheme reduces the resource consumption of power IoT *** improved CPK algorithm solves the key collision problem,and the third party only needs to save the private key and the public key *** results show that the amount of storage resources occupied in our scheme is *** proposed scheme is more suitable for the power IoT.

关键词： Power Internet of Things terminal equipment identity authentication CPK PUF

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