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TGAIN:Geospatial Data Recovery Algorithm Based on GAIN-LSTM

Computers, Materials & Continua 2024年第10期81卷 1471-1489页

作者： lechan yang Li Li Shouming Ma School of Soft Engineering Jinling Institute of TechnologyNanjing211169China School of Computer Jinling Institute of TechnologyNanjing211169China School of Network Security Jinling Institute of TechnologyNanjing211169China

Accurate geospatial data are essential for geographic information systems(GIS),environmental monitoring,and urban *** deep integration of the open Internet and geographic information technology has led to increasing challenges in the integrity and security of spatial *** this paper,we consider abnormal spatial data as missing data and focus on abnormal spatial data *** geospatial data recovery methods require complete datasets for training,resulting in time-consuming data recovery and lack of *** address these issues,we propose a GAIN-LSTM-based geospatial data recovery method(TGAIN),which consists of two main works:(1)it uses a long-short-term recurrent neural network(LSTM)as a generator to analyze geospatial temporal data and capture its temporal correlation;(2)it constructs a complete TGAIN network using a cue-masked fusion matrix mechanism to obtain data that matches the original distribution of the input *** experimental results on two publicly accessible datasets demonstrate that our proposed TGAIN approach surpasses four contemporary and traditional models in terms of mean absolute error(MAE),root mean square error(RMSE),mean square error(MSE),mean absolute percentage error(MAPE),coefficient of determination(R2)and average computational time across various data missing ***,TGAIN exhibits superior accuracy and robustness in data recovery compared to existing models,especially when dealing with a high rate of missing *** model is of great significance in improving the integrity of geospatial data and provides data support for practical applications such as urban traffic optimization prediction and personal mobility analysis.

关键词： Geospatial data data recovery generative adversarial networks temporal correlation

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Hybrid Gene Expression Programming-Based Sensor Data Correlation Mining

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China Communications 2017年第1期14卷 34-49页

作者： lechan yang Zhihao Qin Kun Wang Song Deng International Institute for Earth System Science Nanjing University Nanjing 210093 China Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences Beijing 100081 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing University of Posts and TelecommunicationsNanjing 210023 China Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing 210023 China

This paper deals with the reflectance estimation model issue to improve the estimation accuracy. We propose a model containing two core procedures: dimensionality reduction and model mining. First, the dimensionality reduction algorithm of hyperspectral data based on dependence degree(DRNDDD) is proposed to reduce the redundant hyperspectral band. DRND-DD solves the selection of suitable hyperspectral band via rough set theory. Furthermore, to improve the computation speed and accuracy of the model, based on DRND-DD, this paper proposes reflectance estimation model mining of leaf nitrogen concentration(LNC) for hyperspectral data by using hybrid gene expression programming(REMLNC-HGEP). Experimental results on three datasets demonstrate that the DRND-DD algorithm can obtain good results with a very short running time compared with principal component analysis(PCA), singular value decomposition(SVD), a dimensionality reduction algorithm based on the positive region(AR-PR) and a dimensionality reduction algorithm based on a discernable matrix(ARDM), and REMLNC-HGEP has low average time-consumption, high model mining success ratio and estimation accuracy. It was concluded that the REMLNC-HGEP performs better than the regression methods.

关键词： reflectance estimation dimensionality reduction gene expression programming model mining

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基于GEP和神经网络的属性约简分类算法

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计算机工程与应用 2006年第23期42卷 154-157,172页

作者：邓松元昌安赵波段磊杨乐婵饶元廖剑平广西师范学院资源与环境科学学院广西师范学院信息技术系四川大学计算机学院成都610064 四川大学计算机学院

分类(Classification)是数据挖掘(DataMining)中的一个重要研究方向,目前传统的方法有神经网络,Fisher判别法等。神经网络缺乏对分类结果的直观解释;Fisher判别对于大数据集分类准确率大大下降,且不具有属性约简能力。为此,该文做了如... 详细信息

分类(Classification)是数据挖掘(DataMining)中的一个重要研究方向,目前传统的方法有神经网络,Fisher判别法等。神经网络缺乏对分类结果的直观解释;Fisher判别对于大数据集分类准确率大大下降,且不具有属性约简能力。为此,该文做了如下工作(1)提出了自动获取最佳阈值的思想;(2)对于错分的实例,提出了运用神经网络分类器二次分类的思想;(3)提出了基于基因表达式编程和神经网络的属性约简分类算法(AttributionReductionClassificationAlgo-rithmsBasedonGEPandNeuralNetwork,ARCA-GEPNN);(4)实验表明,ARCA-GEPNN的分类精度比Fisher判别提高了约25%,比GEP提高了约21%。

关键词：分类基因表达式编程神经网络属性约简

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Microglia modulation with 1070-nm light attenuates Aβ burden and cognitive impairment in Alzheimer’s disease mouse model

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Light(Science & Applications) 2021年第10期10卷 1863-1876页

作者： lechan Tao Qi Liu Fuli Zhang Yuting Fu Xi Zhu Xiaofu Weng Hongbin Han Yong Huang Yuanzhen Suo Liang Chen Xiaoling Gao Xunbin Wei State Key Laboratory of Oncogenes and Related Genes Shanghai Cancer InstituteMed-X Research Institute and School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghai 200030China Institute of Medical Technology Peking University Health Science CenterBeijing 100191China Department of Radiology Peking University Third HospitalBeijing 100191China Key Lab of Magnetic Resonance Imaging Device and Technique Beijing 100191China Zhejiang Brainhealth Medical Technology Co. LtdHangzhou 314400China Biomedical Pioneering Innovation Center Peking UniversityBeijing 100871China School of Life Sciences Peking UniversityBeijing 100871China Department of Neurosurgery Huashan HospitalShanghai Medical CollegeFudan UniversityShanghai 200040China Tianqiao and Chrissy Chen Institute for Clinical Translational Research Huashan HospitalShanghai 200040China Department of Pharmacology and Chemical Biology State Key Laboratory of Oncogenes and Related GenesShanghai Universities Collaborative Innovation Center for Translational MedicineShanghai Jiao Tong University School of MedicineShanghai 200025China Biomedical Engineering Department Peking UniversityBeijing 100081China Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education/Beijing) Peking University Cancer Hospital&InstituteBeijing 100142China

Photobiomodulation,by utilizing low-power light in the visible and near-infrared spectra to trigger biological responses in cells and tissues,has been considered as a possible therapeutic strategy for Alzheimer’s disease(AD),while its specific mechanisms have remained ***,we demonstrate that cognitive and memory impairment in an AD mouse model can be ameliorated by 1070-nm light via reducing cerebralβ-amyloid(Aβ)burden,the hallmark of *** glial cells,including microglia and astrocytes,play important roles in Aβ*** results show that 1070-nm light pulsed at 10 Hz triggers microglia rather than astrocyte responses in AD *** 1070-nm lightinduced microglia responses with alteration in morphology and increased colocalization with Aβare sufficient to reduce Aβload in AD ***,1070-nm light pulsed at 10 Hz can reduce perivascular microglia and promote angiogenesis to further enhance Aβ*** study confirms the important roles of microglia and cerebral vessels in the use of 1070-nm light for the treatment of AD mice and provides a framework for developing a novel therapeutic approach for AD.

关键词： Aβ Alzheimer cerebral

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神经退行性疾病的光学疗法及其应用展望

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中国工程科学 2020年第3期22卷 108-116页

作者：魏勋斌周非凡陶乐婵杨思华顾波波北京大学跨学部生物医学工程系北京100191 上海交通大学生物医学工程学院Med-X研究院上海200030 深圳大学物理与光电工程学院广东深圳518061 华南师范大学生物光子学研究院暨激光生命科学教育部重点实验室广州510631

神经退行性疾病是由神经元结构或功能逐渐丧失导致认知及运动障碍的一类不可逆损伤性疾病,目前尚无安全有效的治疗方法。探索无创的物理治疗手段在神经退行性疾病中的应用潜力,对疾病缓解与有效控制具有重大的意义。光学疗法是利用光线... 详细信息

神经退行性疾病是由神经元结构或功能逐渐丧失导致认知及运动障碍的一类不可逆损伤性疾病,目前尚无安全有效的治疗方法。探索无创的物理治疗手段在神经退行性疾病中的应用潜力,对疾病缓解与有效控制具有重大的意义。光学疗法是利用光线与组织的相互作用,通过光化学或光物理反应治疗疾病和促进机体康复的方法,具有精准性和微创性的技术特点。其中,弱光治疗作为一种无创光疗类型,在促进伤口愈合、缓解疼痛、炎症消退、组织再生等方面已广泛应用于临床。临床研究也证实弱光治疗能够有效改善神经退行性疾病病患的病理症状,作为一种无创物理疗法,弱光治疗为神经退行性疾病的缓解和有效控制提供了非常具有前景的新方向。本文综述了弱光治疗在神经退行性疾病中的研究进展,并结合光电子技术的发展展望其应用前景,研究提出:需阐明弱光作用机理及量效关系,开发新型弱光治疗技术,完善临床验证体系及评价指标,尽快造福病患,服务社会。

关键词：光学疗法弱光治疗无创疗法神经退行性疾病

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