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Support vector machine regression(SVR)-based nonlinear modeling of radiometric transforming relation for the coarse-resolution data-referenced relative radiometric normalization(RRN) 认领 引用 被引量：3
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作者 Jing Geng Wenxia Gan +2 位作者 Jinying Xu Ruqin Yang Shuliang Wang 《Geo-Spatial Information Science》 SCIE EI CSCD 2020年第3期237-247,I0004,共11页
Radiometric normalization,as an essential step for multi-source and multi-temporal data processing,has received critical attention.Relative Radiometric Normalization(RRN)method has been primarily used for eliminating ... Radiometric normalization,as an essential step for multi-source and multi-temporal data processing,has received critical attention.Relative Radiometric Normalization(RRN)method has been primarily used for eliminating the radiometric inconsistency.The radiometric trans-forming relation between the subject image and the reference image is an essential aspect of RRN.Aimed at accurate radiometric transforming relation modeling,the learning-based nonlinear regression method,Support Vector machine Regression(SVR)is used for fitting the complicated radiometric transforming relation for the coarse-resolution data-referenced RRN.To evaluate the effectiveness of the proposed method,a series of experiments are performed,including two synthetic data experiments and one real data experiment.And the proposed method is compared with other methods that use linear regression,Artificial Neural Network(ANN)or Random Forest(RF)for radiometric transforming relation modeling.The results show that the proposed method performs well on fitting the radiometric transforming relation and could enhance the RRN performance. 展开更多
关键词 Support Vector machine Regression(SVR) non-linear radiometric transforming relation Relative Radiometric Normalization(RRN) multi-source data
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Optical-Elevation Data Co-Registration and Classification-Based Height Normalization for Building Detection in Stereo VHR Images 认领 引用 被引量：1
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作者 Alaeldin Suliman Yun Zhang 《Advances in Remote Sensing》 2017年第2期103-119,共17页
Building detection in very high resolution (VHR) images is crucial for mapping and analysing urban environments. Since buildings are elevated objects, elevation data need to be integrated with images for reliable dete... Building detection in very high resolution (VHR) images is crucial for mapping and analysing urban environments. Since buildings are elevated objects, elevation data need to be integrated with images for reliable detection. This process requires two critical steps: optical-elevation data co-registration and aboveground elevation calculation. These two steps are still challenging to some extent. Therefore, this paper introduces optical-elevation data co-registration and normalization techniques for generating a dataset that facilitates elevation-based building detection. For achieving accurate co-registration, a dense set of stereo-based elevations is generated and co-registered to their relevant image based on their corresponding image locations. To normalize these co-registered elevations, the bare-earth elevations are detected based on classification information of some terrain-level features after achieving the image co-registration. The developed method was executed and validated. After implementation, 80% overall-quality of detection result was achieved with 94% correct detection. Together, the developed techniques successfully facilitate the incorporation of stereo-based elevations for detecting buildings in VHR remote sensing images. 展开更多
关键词 Building Detection Very High Resolution Images Optical-Elevation Data Co-Registration Classification-Based Height Normalization
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A new edge recognition technology based on the normalized vertical derivative of the total horizontal derivative for potential field data 认领 引用 被引量：108
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作者 Wang Wanyin Pan Yu Qiu Zhiyun 《Applied Geophysics》 SCIE 2009年第3期226-233,299,共8页
Edge detection and enhancement techniques are commonly used in recognizing the edge of geologic bodies using potential field data. We present a new edge recognition technology based on the normalized vertical derivati... Edge detection and enhancement techniques are commonly used in recognizing the edge of geologic bodies using potential field data. We present a new edge recognition technology based on the normalized vertical derivative of the total horizontal derivative which has the functions of both edge detection and enhancement techniques. First, we calculate the total horizontal derivative （THDR） of the potential-field data and then compute the n-order vertical derivative （VDRn） of the THDR. For the n-order vertical derivative, the peak value of total horizontal derivative （PTHDR） is obtained using a threshold value greater than 0. This PTHDR can be used for edge detection. Second, the PTHDR value is divided by the total horizontal derivative and normalized by the maximum value. Finally, we used different kinds of numerical models to verify the effectiveness and reliability of the new edge recognition technology. 展开更多
关键词 potential field data edge recognition edge enhancement total horizontal derivative normalized vertical derivative
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Similarity measurement method of high-dimensional data based on normalized net lattice subspace 认领 引用 被引量：5
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作者 李文法 Wang Gongming +1 位作者 Li Ke Huang Su 《High Technology Letters》 EI CAS 2017年第2期179-184,共6页
The performance of conventional similarity measurement methods is affected seriously by the curse of dimensionality of high-dimensional data.The reason is that data difference between sparse and noisy dimensionalities... The performance of conventional similarity measurement methods is affected seriously by the curse of dimensionality of high-dimensional data.The reason is that data difference between sparse and noisy dimensionalities occupies a large proportion of the similarity,leading to the dissimilarities between any results.A similarity measurement method of high-dimensional data based on normalized net lattice subspace is proposed.The data range of each dimension is divided into several intervals,and the components in different dimensions are mapped onto the corresponding interval.Only the component in the same or adjacent interval is used to calculate the similarity.To validate this method,three data types are used,and seven common similarity measurement methods are compared.The experimental result indicates that the relative difference of the method is increasing with the dimensionality and is approximately two or three orders of magnitude higher than the conventional method.In addition,the similarity range of this method in different dimensions is [0,1],which is fit for similarity analysis after dimensionality reduction. 展开更多
关键词 high-dimensional data the curse of dimensionality similarity normalization subspace NPsim
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Evaluation of Two Absolute Radiometric Normalization Algorithms for Pre-processing of Landsat Imagery 认领 引用 被引量：14
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作者 徐涵秋 《Journal of China University of Geosciences》 2006年第2期146-150,157,共5页
In order to evaluate radiometric normalization techniques, two image normalization algorithms for absolute radiometric correction of Landsat imagery were quantitatively compared in this paper, which are the Illuminati... In order to evaluate radiometric normalization techniques, two image normalization algorithms for absolute radiometric correction of Landsat imagery were quantitatively compared in this paper, which are the Illumination Correction Model proposed by Markham and Irish and the Illumination and Atmospheric Correction Model developed by the Remote Sensing and GIS Laboratory of the Utah State University. Relative noise, correlation coefficient and slope value were used as the criteria for the evaluation and comparison, which were derived from pseudo-invarlant features identified from multitemporal Landsat image pairs of Xiamen （厦门） and Fuzhou （福州） areas, both located in the eastern Fujian （福建） Province of China. Compared with the unnormalized image, the radiometric differences between the normalized multitemporal images were significantly reduced when the seasons of multitemporal images were different. However, there was no significant difference between the normalized and unnorrealized images with a similar seasonal condition. Furthermore, the correction results of two algorithms are similar when the images are relatively clear with a uniform atmospheric condition. Therefore, the radiometric normalization procedures should be carried out if the multitemporal images have a significant seasonal difference. 展开更多
关键词 Landsat radiometrie correction data normalization pseudo-invariant features image processing.
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Leveraging the knee point:Boosting remaining useful life prediction accuracy for lithium-ion batteries with virtual-enhanced normalizing flow 认领 引用
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作者 Bowei Zhang Mingzhe Leng +5 位作者 Changhua Hu Hong Pei Zhaoqiang Wang Chuanyang Li Li Wang Xiangming He 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2025年第11期535-547,I0013,共13页
Deep learning has emerged as a powerful tool for predicting the remaining useful life(RUL)of batteries,contingent upon access to ample data.However,the inherent limitations of data availability from traditional or acc... Deep learning has emerged as a powerful tool for predicting the remaining useful life(RUL)of batteries,contingent upon access to ample data.However,the inherent limitations of data availability from traditional or accelerated life testing pose significant challenges.To mitigate the prediction accuracy issues arising from small sample sizes in existing intelligent methods,we introduce a novel data augmentation framework for RUL prediction.This framework harnesses the inherent high coincidence of degradation patterns exhibited by lithium-ion batteries to pinpoint the knee point,a critical juncture marking a significant shift in the degradation trajectory.By focusing on this critical knee point,we leverage the power of normalizing flow models to generate virtual data,effectively augmenting the training sample size.Additionally,we integrate a Bayesian Long Short-Term Memory network,optimized with Box-Cox transformation,to address the inherent uncertainty associated with predictions based on augmented data.This integration allows for a more nuanced understanding of RUL prediction uncertainties,offering valuable confidence intervals.The efficacy and superiority of the proposed framework are validated through extensive experiments on the CS2 dataset from the University of Maryland and the CrFeMnNiCo dataset from our laboratory.The results clearly demonstrate a substantial improvement in the confidence interval of RUL predictions compared to pre-optimization,highlighting the ability of the framework to achieve high-precision RUL predictions even with limited data. 展开更多
关键词 Remaining useful life Data augmentation Knee point Normalizing flow Box-Cox transformation
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An Evolutionary Normalization Algorithm for Signed Floating-Point Multiply-Accumulate Operation 认领 引用 被引量：1
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作者 Rajkumar Sarma Cherry Bhargava Ketan Kotecha 《Computers, Materials & Continua》 SCIE EI 2022年第7期481-495,共15页
In the era of digital signal processing,like graphics and computation systems,multiplication-accumulation is one of the prime operations.A MAC unit is a vital component of a digital system,like different Fast Fourier ... In the era of digital signal processing,like graphics and computation systems,multiplication-accumulation is one of the prime operations.A MAC unit is a vital component of a digital system,like different Fast Fourier Transform(FFT)algorithms,convolution,image processing algorithms,etcetera.In the domain of digital signal processing,the use of normalization architecture is very vast.The main objective of using normalization is to performcomparison and shift operations.In this research paper,an evolutionary approach for designing an optimized normalization algorithm is proposed using basic logical blocks such as Multiplexer,Adder etc.The proposed normalization algorithm is further used in designing an 8×8 bit Signed Floating-Point Multiply-Accumulate(SFMAC)architecture.Since the SFMAC can accept an 8-bit significand and a 3-bit exponent,the input to the said architecture can be somewhere between−(7.96872)10 to+(7.96872)10.The proposed architecture is designed and implemented using the Cadence Virtuoso using 90 and 130 nm technologies(in Generic Process Design Kit(GPDK)and Taiwan Semiconductor Manufacturing Company(TSMC),respectively).To reduce the power consumption of the proposed normalization architecture,techniques such as“block enabling”and“clock gating”are used rigorously.According to the analysis done on Cadence,the proposed architecture uses the least amount of power compared to its current predecessors. 展开更多
关键词 Data normalization cadence virtuoso signed-floating-point MAC evolutionary optimized algorithm block enabling clock gating
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Issues in Statistical Data Analysis in Applied Linguistics: Data Normality, Transformations and Power Analysis 认领 引用
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作者 HE Lei 《US-China Foreign Language》 2011年第10期647-658,共12页
This paper invites researchers of applied linguistics dealing with quantitative data to the issues that have been in oblivion in statistical analysis. These issues include the evaluation of data normality and data tra... This paper invites researchers of applied linguistics dealing with quantitative data to the issues that have been in oblivion in statistical analysis. These issues include the evaluation of data normality and data transformations, as well as power analysis with the associated estimation of sample sizes and calculation of effect sizes. Methods introduced in this paper to test data normality include calculating descriptive statistics and performing the Kolmogorov-Smirnov test. Three ways to transform non-normal data are provided： the arcsine, square root and natural log transformations. A method to reflect negatively skewed data is also included. In addition, power and its related sample size and effect size analyses are introduced in the end. The applications of these methods are illustrated by examples 展开更多
关键词 data normality data transformation power sample size effect size
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单帧条纹投影深度估计的数据增强与归一化优化 认领 引用
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作者 王琳霖 厉秉桢 +2 位作者 郭万意 高骞 王传云 《光子学报》 EI CAS CSCD 北大核心 2026年第2期185-198,共14页
针对单帧条纹投影深度估计中传统数据增强与归一化方法适配性不足的问题,提出了一种结合区间裁剪幂次归一化与结构化条纹遮挡增强的数据预处理策略。区间裁剪幂次归一化通过裁剪极端亮度值、幂次变换与分段映射优化像素分布,强化暗部细... 针对单帧条纹投影深度估计中传统数据增强与归一化方法适配性不足的问题,提出了一种结合区间裁剪幂次归一化与结构化条纹遮挡增强的数据预处理策略。区间裁剪幂次归一化通过裁剪极端亮度值、幂次变换与分段映射优化像素分布,强化暗部细节表现的同时,保持整体亮度均衡与对比度稳定。结构化条纹遮挡增强则通过规则化遮挡与条纹一致性填补增加样本的多样性。通过多组实验验证,所提出的归一化与增强方法的有效性。进一步分析发现,所提出的数据增强与归一化方法在多种网络结构中均展现出稳定的性能提升,体现出良好的通用性与适配性,同时对不同结构的网络呈现出一定的差异化适应特征。该策略兼具普适性与差异化适配能力,为单帧条纹投影深度估计的性能提升提供了一种切实可行的优化思路。 展开更多
关键词 条纹投影 深度估计 数据增强 归一化 数据预处理
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基于对数归一化的随钻电阻率数据井下压缩方法 认领 引用
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作者 李新 喻越 张宗富 《石油钻探技术》 CAS CSCD 北大核心 2026年第2期183-190,共8页
针对随钻电阻率测井数据动态范围广、井下存储与传输时带宽受限的问题,提出了一种基于对数归一化的井下数据压缩方法,旨在实现高精度与高压缩效率的协同优化。该方法采用对数变换方法缩小原始数据数值范围,结合归一化处理消除量纲影响,... 针对随钻电阻率测井数据动态范围广、井下存储与传输时带宽受限的问题,提出了一种基于对数归一化的井下数据压缩方法,旨在实现高精度与高压缩效率的协同优化。该方法采用对数变换方法缩小原始数据数值范围,结合归一化处理消除量纲影响,再通过动态范围调整将数据映射至预设整数区间,利用编码技术将大范围浮点数转换为小范围整数,在保证数据幅度变化规律不变的前提下,降低了数据位数。试验结果表明,该方法可使压缩后数据位数降低50%以上,最大相对误差仅为2.85%,解压缩后的数据与原始数据高度吻合;通过引入整体误差系数与尺度映射比例的线性拟合函数,明确了映射比例对压缩效果的影响规律,可根据实际需求灵活调控精度与存储的平衡。该方法计算复杂度低,可由井下单片机独立完成实时处理,提升了井下仪器的存储效率和数据传输速率,为宽动态范围随钻测井数据的高效处理提供了可靠技术方案。 展开更多
关键词 随钻测井 数据压缩 对数变换 归一化 尺度映射
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基于相关技术指南的土壤背景数据分布检验与土壤基线确定研究——以上海市西北某区域实测数据为例 认领 引用
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作者 朱悦 刘顺 +5 位作者 韩燕 朱颜 周铭 赵俊明 张颖纯 何品晶 《环境卫生工程》 2026年第2期23-36,共14页
土壤基线值的确定是生态环境损害鉴定评估的关键环节,其准确性直接影响到污染责任认定与生态环境损害鉴定评估结果。本研究基于GB/T 39791.4—2024生态环境损害鉴定评估技术指南总纲和关键环节第4部分:土壤生态环境基线调查与确定,以上... 土壤基线值的确定是生态环境损害鉴定评估的关键环节,其准确性直接影响到污染责任认定与生态环境损害鉴定评估结果。本研究基于GB/T 39791.4—2024生态环境损害鉴定评估技术指南总纲和关键环节第4部分:土壤生态环境基线调查与确定,以上海市西北某区域土壤实测数据为例,系统开展了土壤背景数据分布检验与基线确定研究。通过对不同深度土层(表层、浅层、深层)中pH、有机质、重金属等14项指标的统计分析,综合运用Shapiro-Wilk检验、图形法(直方图、Q-Q图、P-P图)和Dixon异常值检验等方法,明确了各指标的数据分布类型,并据此计算了相应的土壤生态环境基线值。研究结果表明:砷、锌等指标在表层呈现非正态分布,反映出外源污染输入的特征;镉、汞在浅层非正态分布提示污染物可能存在垂向迁移与富集行为;深层土壤多数指标呈正态分布,代表了自然本底水平。本研究还发现pH宜采用中位数而非标准推荐的参考值上限作为基线,以避免误判。研究结果为城市建筑垃圾等污染场地的生态环境损害鉴定提供了科学依据,突出了地块特异性基线的重要性,并对GB/T 39791.4—2024的实际应用提出了优化建议。 展开更多
关键词 土壤基线值 数据分布检验 Shapiro-Wilk检验 正态分布 异常值
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基于数据挖掘算法的配电网全过程造价控制方法研究 认领 引用
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作者 陈付雷 李建青 施晓敏 《电子设计工程》 2026年第5期106-110,116,共5页
从全过程的角度分析,影响配电网造价的因素较多,且影响程度不同。为此,提出基于数据挖掘算法的配电网全过程造价控制方法。该方法分别从设计阶段、施工过程、材料设备采购三个方面,分析配电网全过程造价的影响因素构成,采用两两比较的... 从全过程的角度分析,影响配电网造价的因素较多,且影响程度不同。为此,提出基于数据挖掘算法的配电网全过程造价控制方法。该方法分别从设计阶段、施工过程、材料设备采购三个方面,分析配电网全过程造价的影响因素构成,采用两两比较的方式构建配电网全过程造价影响因素的判断矩阵,对其进行归一化处理,结合最大特征根参量为影响因素赋权。在控制阶段,采用层次分析法构建分层控制目标体系,包括总体控制目标、子控制目标、控制阶段目标与控制指标。将敏感系数趋近于0的影响因素状态作为最终控制结果,以降低其对造价的扰动。实验结果显示,设计控制方法下的单位投资成本明显低于对照组。 展开更多
关键词 数据挖掘算法 配电网全过程 造价控制 判断矩阵 归一化处理 最大特征根 敏感系数
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一种基于改进CNN的短期日前新能源出力预测方法 认领 引用
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作者 王宣元 季震 +3 位作者 孙巍 裴宇婷 孔帅皓 王泽森 《分布式能源》 2026年第3期75-82,共8页
针对光伏发电预测中存在的数据噪声干扰、特征尺度差异及多尺度气象模式建模不足等问题,提出一种动态数据预处理与门控密集多尺度卷积神经网络(gated dense multiscale convolutional neural network,GDMSCNN)的预测方法。首先,建立基... 针对光伏发电预测中存在的数据噪声干扰、特征尺度差异及多尺度气象模式建模不足等问题,提出一种动态数据预处理与门控密集多尺度卷积神经网络(gated dense multiscale convolutional neural network,GDMSCNN)的预测方法。首先,建立基于动态滑动窗口Z分数的异常检测机制,结合协方差加权多变量插值处理缺失值;其次,采用自适应分段归一化算法消除特征量纲差异,并构造云量修正因子与大气衰减因子来增强物理特征表达;最后,设计GDMS-CNN网络,通过深度可分离卷积模块优化特征提取效率,构建密集连接扩张卷积块来捕获多尺度时空关联特征,嵌入非对称门控通道注意力机制以动态校准特征权重。实验结果表明:所提方法在均方根误差(root mean square error,RMSE)上较最优基准模型遗传算法-变模态分解-回声状态网络(genetic algorithm-variational mode decomposition-echo state network,GA-VMD-ESN)降低16.4%,较传统随机森林法降低43.4%。该方法为光伏出力预测提供了新的解决方案,有效提升了电网调度的可靠性。 展开更多
关键词 光伏发电预测 动态数据预处理 门控密集多尺度卷积神经网络(GDMS-CNN) 异常值检测 自适应归一化 多变量插值
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基于二进制编码的电力企业网络海量舆情数据规格化压缩存储 认领 引用
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作者 闫嵩琦 《微型电脑应用》 2026年第5期281-285,共5页
当前舆情数据由于体量较大,对电力企业网络存储空间造成了较大的挑战,故提出基于二进制编码的电力企业网络海量舆情数据规格化压缩存储方法研究。通过均值滤波算法与最大最小值标准化法规格化舆情数据,对舆情数据进行分块处理,采用并行... 当前舆情数据由于体量较大,对电力企业网络存储空间造成了较大的挑战,故提出基于二进制编码的电力企业网络海量舆情数据规格化压缩存储方法研究。通过均值滤波算法与最大最小值标准化法规格化舆情数据,对舆情数据进行分块处理,采用并行化处理方式对舆情数据进行二进制编码,依据舆情数据块二进制编码结果的特点建立索引信息,将其共同存储于电力企业网络数据库,从而实现海量舆情数据的规格化压缩存储。由实验结果可知,应用所提出的方法获得的舆情数据平均编码效率最大值达到了420 MB/s,舆情数据压缩比最大值达到了82.12%,充分证实了所提出的方法应用性能较佳。 展开更多
关键词 海量舆情数据 电力企业网络 压缩存储 数据分块 二进制编码 数据规格化
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法向量约束的改进RANSAC点云平面提取方法 认领 引用
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作者 杨志坚 《北京测绘》 2026年第4期449-454,共6页
针对传统随机采样一致性算法(RANSAC)提取点云平面特征时存在的误识别率高、计算效率低问题,本文提出一种基于法向量约束的改进RANSAC点云平面提取方法。该方法通过在两个关键环节引入法向量夹角约束实现性能优化。在随机采样阶段,限定... 针对传统随机采样一致性算法(RANSAC)提取点云平面特征时存在的误识别率高、计算效率低问题,本文提出一种基于法向量约束的改进RANSAC点云平面提取方法。该方法通过在两个关键环节引入法向量夹角约束实现性能优化。在随机采样阶段,限定所选样本点间的法向量夹角小于设定阈值,保障初始样本集的有效性;在内点判断阶段,将点与样本点的法向量平行性作为附加约束,结合传统距离判定准则共同完成内点识别。实验结果表明,所提改进方法在保留传统RANSAC算法鲁棒性的基础上,显著提升了点云平面提取的准确性与计算效率,有效降低了特征匹配错误率,可适用于复杂场景下的点云平面处理相关任务。 展开更多
关键词 点云数据 平面点 随机采样一致性算法 法向量
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Benchmarking Analysis of scHi-C Data Imputation Methods 认领 引用
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作者 Xuyan Du Xinrui Ji +1 位作者 Li Tang Min Li 《Big Data Mining and Analytics》 EI CSCD 2026年第2期500-518,共19页
Single-cell Hi-C(scHi-C)technology is widely used to measure individual cells’three-dimensional genome structures and investigate cell-to-cell heterogeneity of multi-scale chromatin structures and cellular functions.... Single-cell Hi-C(scHi-C)technology is widely used to measure individual cells’three-dimensional genome structures and investigate cell-to-cell heterogeneity of multi-scale chromatin structures and cellular functions.It facilitates the identification of rare cell types and enhances the understanding of disease mechanisms.However,the sparsity of scHi-C data poses significant challenges for downstream analyses,such as cell clustering.Several scHi-C imputation methods have been proposed in recent years,including statistics-based and deep learning based methods.Nevertheless,these methods have not been comprehensively evaluated and analyzed in previous studies to the best of our knowledge.In this paper,seven state-of-the-art imputation methods are assessed and compared in terms of various metrics based on nine simulated datasets and one real dataset.Specifically,the performance of these methods in data recovery and cell clustering is evaluated.Experimental results show that deep learning based methods achieve better performance than statistics-based methods,but no method performs the best in all cases.Finally,we provide method recommendations for different scenarios. 展开更多
关键词 single-cell Hi-C(scHi-C) data imputation benchmarking cell clustering
Research on the Law of Garlic Price Based on Big Data 认领 引用 被引量：4
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作者 Feng Guo Pingzeng Liu +5 位作者 Chao Zhang Weijie Chen Wei Han Wanming Ren Yong Zheng Jianrui Ding 《Computers, Materials & Continua》 SCIE EI 2019年第3期795-808,共14页
In view of the frequent fluctuation of garlic price under the market economy and the current situation of garlic price,the fluctuation of garlic price in the circulation link of garlic industry chain is analyzed,and t... In view of the frequent fluctuation of garlic price under the market economy and the current situation of garlic price,the fluctuation of garlic price in the circulation link of garlic industry chain is analyzed,and the application mode of multidisciplinary in the agricultural industry is discussed.On the basis of the big data platform of garlic industry chain,this paper constructs a Garch model to analyze the fluctuation law of garlic price in the circulation link and provides the garlic industry service from the angle of price fluctuation combined with the economic analysis.The research shows that the average price rate of the price of garlic shows“agglomeration”and cyclical phenomenon,which has the characteristics of fragility,left and a non-normal distribution and the fitting value of the GARCH model is very close to the true value.Finally,it looks into the industrial service form from the perspective of garlic price fluctuation. 展开更多
关键词 Big data Big data analysis Garch normality ARCH industrial service.
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Assessment of Human Impacts on Vegetation in Built-up Areas in China Based on AVHRR,MODIS and DMSP_OLS Nighttime Light Data,1992–2010 认领 引用 被引量：7
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作者 LIU Qinping YANG Yongchun +2 位作者 TIAN Hongzhen ZHANG Bo GU Lei 《Chinese Geographical Science》 SCIE CSCD 2014年第2期231-244,共14页
Since the reform and opening-up program started in 1978,the level of urbanization has increased rapidly in China.Rapid urban expansion and restructuring have had significant impacts on the ecological environment espec... Since the reform and opening-up program started in 1978,the level of urbanization has increased rapidly in China.Rapid urban expansion and restructuring have had significant impacts on the ecological environment especially within built-up areas.In this study,ArcGIS 10,ENVI 4.5,and Visual FoxPro 6.0 were used to analyze the human impacts on vegetation in the built-up areas of 656Chinese cities from 1992 to 2010.Firstly,an existing algorithm was refined to extract the boundaries of the built-up areas based on the Defense Meteorological Satellite Program Operational Linescan System(DMSP_OLS)nighttime light data.This improved algorithm has the advantages of high accuracy and speed.Secondly,a mathematical model(Human impacts(HI))was constructed to measure the impacts of human factors on vegetation during rapid urbanization based on Advanced Very High Resolution Radiometer(AVHRR)Normalized Difference Vegetation Index(NDVI)and Moderate Resolution Imaging Spectroradiometer(MODIS)NDVI.HI values greater than zero indicate relatively beneficial effects while values less than zero indicate proportionally adverse effects.The results were analyzed from four aspects:the size of cities(metropolises,large cities,medium-sized cities,and small cities),large regions(the eastern,central,western,and northeastern China),administrative divisions of China(provinces,autonomous regions,and municipalities)and vegetation zones(humid and semi-humid forest zone,semi-arid steppe zone,and arid desert zone).Finally,we discussed how human factors impacted on vegetation changes in the built-up areas.We found that urban planning policies and developmental stages impacted on vegetation changes in the built-up areas.The negative human impacts followed an inverted′U′shape,first rising and then falling with increase of urban scales.China′s national policies,social and economic development affected vegetation changes in the built-up areas.The findings can provide a scientific basis for municipal planning departments,a decision-making reference for government,and scientific guidance for sustainable development in China. 展开更多
关键词 vegetation change human impact urbanization built-up areas nighttime light data Normalized Difference Vegetation Index（NDVI）
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ASYMPTOTIC PROPERTIES OF ESTIMATORS IN PARTIALLY LINEAR SINGLE-INDEX MODEL FOR LONGITUDINAL DATA 认领 引用 被引量：3
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作者 田萍 杨林 薛留根 《Acta Mathematica Scientia》 SCIE 2010年第3期677-687,共11页
In this article, a partially linear single-index model /or longitudinal data is investigated. The generalized penalized spline least squares estimates of the unknown parameters are suggested. All parameters can be est... In this article, a partially linear single-index model /or longitudinal data is investigated. The generalized penalized spline least squares estimates of the unknown parameters are suggested. All parameters can be estimated simultaneously by the proposed method while the feature of longitudinal data is considered. The existence, strong consistency and asymptotic normality of the estimators are proved under suitable conditions. A simulation study is conducted to investigate the finite sample performance of the proposed method. Our approach can also be used to study the pure single-index model for longitudinal data. 展开更多
关键词 Longitudinal data partially linear single-index model penalized spline strong consistency asymptotic normality
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Ensembling Neural Networks for User’s Indoor Localization Using Magnetic Field Data from Smartphones 认领 引用 被引量：2
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作者 Imran Ashraf Soojung Hur +1 位作者 Yousaf Bin Zikria Yongwan Park 《Computers, Materials & Continua》 SCIE EI 2021年第8期2597-2620,共24页
Predominantly the localization accuracy of the magnetic field-based localization approaches is severed by two limiting factors:Smartphone heterogeneity and smaller data lengths.The use of multifarioussmartphones cripp... Predominantly the localization accuracy of the magnetic field-based localization approaches is severed by two limiting factors:Smartphone heterogeneity and smaller data lengths.The use of multifarioussmartphones cripples the performance of such approaches owing to the variability of the magnetic field data.In the same vein,smaller lengths of magnetic field data decrease the localization accuracy substantially.The current study proposes the use of multiple neural networks like deep neural network(DNN),long short term memory network(LSTM),and gated recurrent unit network(GRN)to perform indoor localization based on the embedded magnetic sensor of the smartphone.A voting scheme is introduced that takes predictions from neural networks into consideration to estimate the current location of the user.Contrary to conventional magnetic field-based localization approaches that rely on the magnetic field data intensity,this study utilizes the normalized magnetic field data for this purpose.Training of neural networks is carried out using Galaxy S8 data while the testing is performed with three devices,i.e.,LG G7,Galaxy S8,and LG Q6.Experiments are performed during different times of the day to analyze the impact of time variability.Results indicate that the proposed approach minimizes the impact of smartphone variability and elevates the localization accuracy.Performance comparison with three approaches reveals that the proposed approach outperforms them in mean,50%,and 75%error even using a lesser amount of magnetic field data than those of other approaches. 展开更多
关键词 Indoor localization magnetic field data long short term memory network data normalization gated recurrent unit network deep learning
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