Enhancing microseismic/acoustic emission source localization accuracy with an outlier-robust kernel density estimation approach

Monitoring sensors in complex engineering environments often record abnormal data,leading to significant positioning errors.To reduce the influence of abnormal arrival times,we introduce an innovative,outlier-robust localization method that integrates kernel density estimation(KDE)with damping linear correction to enhance the precision of microseismic/acoustic emission(MS/AE)source positioning.Our approach systematically addresses abnormal arrival times through a three-step process:initial location by 4-arrival combinations,elimination of outliers based on three-dimensional KDE,and refinement using a linear correction with an adaptive damping factor.We validate our method through lead-breaking experiments,demonstrating over a 23%improvement in positioning accuracy with a maximum error of 9.12 mm(relative error of 15.80%)—outperforming 4 existing methods.Simulations under various system errors,outlier scales,and ratios substantiate our method’s superior performance.Field blasting experiments also confirm the practical applicability,with an average positioning error of 11.71 m(relative error of 7.59%),compared to 23.56,66.09,16.95,and 28.52 m for other methods.This research is significant as it enhances the robustness of MS/AE source localization when confronted with data anomalies.It also provides a practical solution for real-world engineering and safety monitoring applications.

Kernel Density Estimation of Tropical Cyclone Frequencies in the North Atlantic Basin

Previous research has identified specific areas of frequent tropical cyclone activity in the North Atlantic basin. This study examines long-term and decadal spatio-temporal patterns of Atlantic tropical cyclone frequencies from 1944 to 2009, and analyzes categorical and decadal centroid patterns using kernel density estimation (KDE) and centrographic statistics. Results corroborate previous research which has suggested that the Bermuda-Azores anticyclone plays an integral role in the direction of tropical cyclone tracks. Other teleconnections such as the North Atlantic Oscillation (NAO) may also have an impact on tropical cyclone tracks, but at a different temporal resolution. Results expand on existing knowledge of the spatial trends of tropical cyclones based on storm category and time through the use of spatial statistics. Overall, location of peak frequency varies by tropical cyclone category, with stronger storms being more concentrated in narrow regions of the southern Caribbean Sea and Gulf of Mexico, while weaker storms occur in a much larger area that encompasses much of the Caribbean Sea, Gulf of Mexico, and Atlantic Ocean off of the east coast of the United States. Additionally, the decadal centroids of tropical cyclone tracks have oscillated over a large area of the Atlantic Ocean for much of recorded history. Data collected since 1944 can be analyzed confidently to reveal these patterns.

AN EFFECTIVE IMAGE RETRIEVAL METHOD BASED ON KERNEL DENSITY ESTIMATION OF COLLAGE ERROR AND MOMENT INVARIANTS

In this paper, we propose a new method that combines collage error in fractal domain and Hu moment invariants for image retrieval with a statistical method - variable bandwidth Kernel Density Estimation (KDE). The proposed method is called CHK (KDE of Collage error and Hu moment) and it is tested on the Vistex texture database with 640 natural images. Experimental results show that the Average Retrieval Rate (ARR) can reach into 78.18%, which demonstrates that the proposed method performs better than the one with parameters respectively as well as the commonly used histogram method both on retrieval rate and retrieval time.

基于DNN-NKDE的公交车驻站时间概率区间预测

针对公交车在站台驻站时间的影响因素分析,考虑在复杂的乘车模式下引入驻站时间概率区间预测的概念。通过DNN算法构建驻站时间预测模型得到预测结果,统计预测值与真实值的误差集并划分为多个子区段,对每个子区段采用非参数核密度估计算法构建概率区间预测模型,估计重组各子区段在某置信水平下的公交车驻站时间概率区间预测结果。利用济宁市公交车到离站等信息数据,验证方法的有效性和可行性。

Spatiotemporal Patterns of Road Network and Road Development Pri-ority in Three Parallel Rivers Region in Yunnan,China:An Evaluation Based on Modified Kernel Distance Estimate

Road network is a critical component of public infrastructure,and the supporting system of social and economic development.Based on a modified kernel density estimate(KDE)algorithm,this study evaluated the road service capacity provided by a road network composed of multi-level roads(i.e.national,provincial,county and rural roads),by taking account of the differences of effect extent and intensity for roads of different levels.Summarized at town scale,the population burden and the annual rural economic income of unit road service capacity were used as the surrogates of social and economic demands for road service.This method was applied to the road network of the Three Parallel River Region,the northwestern Yunnan Province,China to evaluate the development of road network in this region.In results,the total road length of this region in 2005 was 3.70×104km,and the length ratio between national,provincial,county and rural roads was 1∶2∶8∶47.From 1989 to 2005,the regional road service capacity increased by 13.1%,of which the contributions from the national,provincial,county and rural roads were 11.1%,19.4%,22.6%,and 67.8%,respectively,revealing the effect of′All Village Accessible′policy of road development in the mountainous regions in the last decade.The spatial patterns of population burden and economic requirement of unit road service suggested that the areas farther away from the national and provincial roads have higher road development priority(RDP).Based on the modified KDE model and the framework of RDP evaluation,this study provided a useful approach for developing an optimal plan of road development at regional scale.

Data driven particle size estimation of hematite grinding process using stochastic configuration network with robust technique

As a production quality index of hematite grinding process,particle size(PS)is hard to be measured in real time.To achieve the PS estimation,this paper proposes a novel data driven model of PS using stochastic configuration network(SCN)with robust technique,namely,robust SCN(RSCN).Firstly,this paper proves the universal approximation property of RSCN with weighted least squares technique.Secondly,three robust algorithms are presented by employing M-estimation with Huber loss function,M-estimation with interquartile range(IQR)and nonparametric kernel density estimation(NKDE)function respectively to set the penalty weight.Comparison experiments are first carried out based on the UCI standard data sets to verify the effectiveness of these methods,and then the data-driven PS model based on the robust algorithms are established and verified.Experimental results show that the RSCN has an excellent performance for the PS estimation.

基于期望核密度离群因子的离群点检测算法

针对基于密度的离群点检测方法在不同分布的数据集上检测精度低的问题,提出了一种基于期望核密度离群因子的离群点检测算法。首先,引入k近邻和反向k近邻扩展邻域空间(ENS)代替传统的k邻域范围,更加全面地考虑数据对象的邻域信息;其次,在传统核密度估计(KDE)方法的基础上引入多元高斯函数,在扩展邻域空间内估计数据对象的密度,同时借鉴自适应核带宽的思想,更好地适应不同数据集的数据分布;然后,给出期望距离的概念,进一步区分局部离群点和位于低密度区域的正常点;最后,定义了期望核密度离群因子刻画数据对象离群程度。在人工数据集和真实数据集上对所提算法进行实验验证,并与部分传统算法进行对比,验证了所提算法的有效性。

非视距环境下核密度估计的全球卫星导航系统鲁棒定位方法

非视距(NLOS)传输会引起全球卫星导航系统(GNSS)接收机的伪距测量误差,最终导致定位解出现较大误差,这一问题在城市峡谷等复杂环境下尤为突出。针对该问题,该文提出核密度估计的鲁棒定位方法,其核心思想是在定位解算中引入鲁棒估计来缓解NLOS的影响。考虑到NLOS引起的伪距观测误差偏离高斯分布,所提方法首先利用核密度估计(KDE)方法估计伪距观测误差的概率密度函数,并利用该概率密度函数来构造鲁棒代价函数用于导航解算,从而缓解NLOS引起的定位误差。实验结果表明所提方法在卫星存在NLOS传输时能够较好地减小GNSS的定位误差。

基于儿童户外活动特征解析的社区活动空间更新建设策略研究

【目的】为响应国家“十四五”规划提出的建设儿童友好城市的政策要求,应探索儿童友好的、适宜儿童活动的社区空间环境更新建设策略。【方法】以成都市3个儿童友好型社区试点中的4类活动空间为例,采取社区游憩行为观察系统、核密度估算以及现状调研等方法,采集并分析儿童户外活动情况及社区活动空间环境特征,并建立相关性模型,探究影响儿童户外活动的社区空间环境因子。【结果】发现多个环境因子对儿童活动有显著影响,包括道路平整度、场地规模等,其中看护空间、游戏设施类型、休憩设施、商业设施可达性是影响儿童户外活动密度、时长、活动类型多样性的关键环境因子;据此归纳出促进儿童户外活动的社区空间环境重点建设方向包括:1)合理规划规模较大且毗邻儿童商业的平整场地;2)增设具有吸引力的、完善的配套设施;3)布置合理的看护休憩空间;4)植入新颖多样、互动性强的环境要素。【结论】为建设促进儿童户外活动的社区空间提供了科学依据,有助于在社区中为儿童营造健康友好的成长环境。

中国粮食生态效率:水平测度、时空格局演变与影响因素

为揭示中国粮食生产生态效率水平及变化特征,基于超效率SBM模型对30个省份2000—2021年粮食生态效率进行估算,通过非参数核密度分析揭示其演进特征,进而通过固定效应模型分析其影响因素。结果表明:从粮食生态效率水平来看,2000—2021年中国粮食生态效率由0.662上升至0.787,年平均增长0.827%。主产区和产销平衡区粮食生态效率的变动趋势与全国层面相近,主销区则远低于全国平均水平;从时空格局演变来看,中国粮食生态效率长期存在高-高集聚、低-低集聚的两极分化现象,且随着时间推移逐渐向较高水平一侧集聚,呈现出北方高于南方、粮食主产区高于粮食非主产区的特征;在各影响因素中,人均GDP、粮食经营规模的影响显著为正,化肥施用强度、农业机械强度和自然灾害的影响显著为负。研究表明,中国粮食生态效率总体水平仍较低且区域差距较大,多数省份未达到效率前沿面,存在较大的提升空间。

中国农业高质量发展水平评价、时间演进及空间相关性分析

本文基于新发展理念,从创新、协调、绿色、开放、共享五个维度构建农业高质量发展指标体系,依据2012—2021年宏观数据,测算我国30个省区市的农业高质量发展水平,利用非参数核密度估计及空间马尔科夫链分析农业高质量发展水平时间演进特征分布,通过莫兰指数分析各省区市之间的空间关系。得出结论:(1)我国农业高质量发展水平近年来持续上升,年增长率约为1.751%,三大地区的农业高质量发展水平存在差异,呈由东到西递减的趋势;(2)省域间农业高质量发展水平差异不断缩小,各省农业高质量发展水平一定程度上可以向邻近状态转移;(3)各省农业高质量发展水平呈显著的空间正相关关系,空间聚集性不断增强,空间聚集分化现象有向高—高集聚发展的趋势。据此提出了优化农业发展环境、加强区域交流合作、建立风险评估预警机制、落实协调发展战略等建议。

Development and application of traffic accident density estimation models using kernel density estimation

Traffic accident frequency has been decreasing in Japan in recent years. Nevertheless, many accidents still occur on residential roads. Area-wide traffic calming measures including Zone 30, which discourages traffic by setting a speed limit of 30 km/h in residential areas, have been implemented. However, no objective implementation method has been established. Development of a model for traffic accident density estimation explained by GIS data can enable the determination of dangerous areas objectively and easily, indicating where area-wide traffic calming can be implemented preferentially. This study examined the relations between traffic accidents and city characteristics, such as population, road factors, and spatial factors. A model was developed to estimate traffic accident density. Kernel density estimation （KDE） techniques were used to assess the relations efficiently. Besides, 16 models were developed by combining accident locations, accident types, and data types. By using them, the applicability of traffic accident density estimation models was examined. Results obtained using Spearman rank correlation show high coefficients between the predicted number and the actual number. The model can indicate the relative accident risk in cities. Results of this study can be used for objective determination of areas where area-wide traffic calming can be implemented preferentially, even if sufficient traffic accident data are not available.

基于LASSO回归和QRLSTM的来水预测方法研究

精准的河流断面来水流量预测对于水资源配置管理、洪水预警和防灾减灾、生态保护和水力发电工程规划有着重要意义。为了提高单一来水流量预测模型的预测精度,采用LASSO回归算法结合分位数回归长短期记忆神经网络(QRLSTM)以及核密度估计(KDE)算法,提出了一种来水流量预测方法(LASSO-QRLSTM)。首先采用LASSO回归从高维来水特征向量中提取关键的解释变量,以降低解释变量与被解释变量之间非线性关系的复杂程度;接着建立QRLSTM来水流量预测模型,以获得不同分位点下的分位数预测值;进而利用KDE拟合概率密度函数,获得未来的来水流量可能值以及相应的概率,得出最终预测结果。将提出的模型应用于广东省西江关键断面和高要水文站的来水流量预测,并与LASSO-QRNN、LASSO-GBDT、QRLSTM、QRNN、GBDT模型进行对比。结果表明:(1)结合LASSO回归的混合预测模型预测效果均好于单一的QRLSTM、QRNN、GBDT模型。(2)提出的LASSO-QRLSTM模型在对思贤滘断面流量预测中的RMSE为1 804.270 m^(3)/s,NSE值达0.973;在概率性指标方面,LASSO-QRLSTM模型的连续分级概率评分(CRPS)和弹球损失(PL)值分别为842.618和465.964,各项评价指标均为最佳,在对比模型中表现出最好的预测效果,特别是在极值处具有更好的拟合效果和更窄的概率预测区间,表现出该模型在河流来水流量预测中的独特优势。(3)在后续对高要水文站来水流量的预测中,其预测性能得到进一步验证,展现出良好的适应性和稳定性。研究成果可为精准的水文预测和水资源优化配置提供参考。

基于模糊序优化的风功率概率模型非参数核密度估计方法

风功率概率分布模型的研究对于风电场规划以及运行都具有重要意义。提出了一种基于模糊序优化的风功率概率密度模型非参数核密度估计方法。该方法利用风电运行数据样本构建风功率概率密度的非参数核密度估计模型;然后建立用于模型带宽选择的多目标优化模型;最后利用模糊序优化对带宽优化模型进行求解。实际算例结果表明,所提建模方法完全由样本数据驱动,不需要对概率密度模型进行先验主观假设,因而具有更高的建模精度和更强的适用性。

基于非参数核密度估计和改进谱多流形聚类的负荷曲线分类研究

针对高维负荷曲线的聚类问题,提出一种基于改进谱多流形聚类的负荷曲线聚类方法,该方法包括典型日负荷曲线提取、负荷曲线聚类和聚类效果评价3个步骤。首先提取用户负荷特性指标,结合非参数核密度估计方法计算提取用户典型日负荷曲线。在改进谱多流形聚类算法中,引入时间翘曲距离度量曲线相似性,并用高斯核函数计算局部相似性,基于此构建相似性矩阵并聚类。聚类后采用多种聚类有效性指标,从聚类效果、算法稳定性和运算时间3个角度对聚类结果和算法性能进行评价。采用某地区若干用户的负荷数据作为算例样本进行聚类分析,验证了所提出的典型日负荷曲线提取方法和改进谱多流形聚类算法的合理性和优越性。

长三角县域居民收入差距演变趋势——基于核密度估计的分析

以长三角74个县域单元1991—2011年的数据为研究对象,运用密度函数核估计法,对城镇居民人均可支配收入和农村居民人均纯收入进行了非参数估计,并在此基础上,分析了长三角县域居民城乡收入差距及其演变趋势。结果表明:长三角县域居民城乡收入均在逐年提高,然而,无论是城市还是农村,高低收入者之间的收入差距均不断扩大,同时,城镇居民高低收入者之间的收入差距明显大于农村居民高低收入者之间的收入差距。此外,城乡居民收入差距也在不断扩大,收入分布两极分化与城乡居民收入巨大差距将成为阻碍长三角经济发展的重要原因之一。因此,亟待采取相应措施来减缓收入分布两极分化现象,缩小城乡收入差距。

网络空间中线要素的核密度估计方法

核密度估计(KDE)方法是分析点要素或线要素空间分布模式的一种重要方法,但目前线要素核密度方法只能分析线要素在二维均质平面空间的密度分布,不能正确分析交通拥堵、交叉口排队、出租车载客等线事件在一维非均质道路网络空间中的密度分布。本文提出了一种网络空间中线要素的核密度估计方法(网络线要素KDE方法),首先确定每个线要素在网络空间上的密度分布,然后根据网络空间距离和拓扑关系确定网络空间的线要素核密度与时空分布。以出租车GPS轨迹数据中提取的"上客"线事件为例,分析出租车"上客"线事件在网络空间中的密度分布,通过与现有方法比较的试验结果表明,本文提出的方法更能准确反映路网空间中线事件的分布特征。

自适应核密度估计运动检测方法

提出了一种自适应的核密度估计(Kernel density estimation,KDE)运动检测算法.算法首先提出一种自适应前景、背景阈值的双阈值选择方法,用于像素分类.该方法用双阈值克服了单阈值分类存在的不足,阈值的选择能自适应进行,且能适应不同的场景.在此基础上,本文提出了基于概率的背景更新模型,按照像素的概率来更新背景,并利用帧间差分背景模型和KDE分类结果解决背景更新中的死锁问题,同时检测背景的突然变化.实验证明了所提出方法的适应性和可靠性.

基于核密度估计和随机滤波理论的齿轮箱剩余寿命预测方法

针对风电机组齿轮箱的剩余寿命预测过程中需要进行状态退化模型结构假设的问题,提出一种由核密度估计和随机滤波理论结合的实时剩余寿命预测方法。该方法利用从数据本身出发的核密度估计方法对齿轮箱连续退化状态的概率密度函数进行非参数估计,得到齿轮箱实时状态监测数据的退化状态概率密度函数;利用实时状态监测数据来更新随机滤波递推模型参数,从而预测齿轮箱的实时剩余寿命。通过齿轮箱的试验验证了该方法的有效性。

基于非参数核密度估计法的光储系统容量优化配置

储能技术有助于缩减光伏功率输出的预测误差,并可以提高光伏预测输出作为电力系统调度参考的可靠性。为此,提出基于光伏功率预测误差分布,提出了一种光储系统容量配置方法。利用Markov链和持续法的组合预测模型实现了光伏功率超短期预测,依靠储能系统弥补实际光伏发电功率与预测功率的预测误差。根据非参数核密度估计法建立了光伏功率预测误差分布模型,由日最大绝对能量的累计分布函数给出储能系统的总体容量配置,实现储能系统的整体容量规划。通过对某20 MW(峰值)光伏电站实测数据仿真分析可知,在满足95%储能容量的日需求下,储能配置容量为9 MWh。所提出的方法为光伏电站合理配置储能系统提供了理论依据,使光伏电站具备更好的可调可控能力。