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。

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。

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.

一种基于一致性的分布式天基雷达组网空中目标高度估计与定位方法

单颗天基探测雷达对空中目标跟踪定位时,由于存在俯仰角信息缺失及量测的非线性等问题,目标高度估计误差大。多天基雷达组网为解决该问题提供了一种手段。同时,考虑系统的低计算复杂度、低通信开销、高精度、高可靠性等需求,该文提出了一种基于一致性的分布式天基雷达组网空中目标高度估计与定位方法。首先,给出了空中目标运动模型与天基雷达量测模型;然后,基于概率图模型理论,建立了多天基雷达组网融合多帧量测下目标跟踪定位问题的因子图模型;基于一致性融合,在多个局部目标运动状态之间建立耦合关系;结合粒子滤波与信度传播,建立了非参数信度传播在多天基雷达组网融合跟踪因子图上的消息表示与迭代计算规则;最后通过仿真验证了算法性能。仿真结果表明,与分布式一致扩展卡尔曼滤波算法相比,所提算法目标高度估计精度提升35.3%,有效提升了天基雷达目标定位性能。

基于天鹰优化算法的短期风电功率区间预测

为克服随机森林算法预置参数依赖经验设定和风电确定性预测难以描述其随机性的困难,提出一种基于天鹰优化算法(Aquila optimizer,AO)、随机森林(RF)和非参数核密度估计(NKDE)相结合的区间预测方法。首先将AO与RF相结合进行功率单点值预测,在此基础上,为了能够给电网调度和优化配置提供更多信息,引入NKDE进行风电功率区间预测。根据所提出的方法,对如东某风场使用WRF模式预报的风速数据进行对比实验。实验证明,AO-RF-NKDE区间预测模型能够给出综合性能更优的风电功率波动区间,对减少风电功率不确定性,弱化电网波动具有应用价值。

基于核函数最大值点跟踪的弹道实时融合方法

针对目前火箭弹道实时融合方法抗干扰性差、对初值敏感、依赖于理论弹道方程等问题,从测量误差的统计特性出发,证明了从有限样本构造的高斯核密度估计函数最大值点是产生该组样本高斯分布期望的无偏估计量这一结论,提出了一种基于高斯核密度估计函数最大值点跟踪的方法:根据各设备的测量数据构造高斯核密度估计函数,将弹道融合问题转化为求该函数最大值点的问题;由于经典的迭代求解法耗时较长,对前一帧估计结果进行平移采样,将采样结果作为当前帧最大值点的近邻进行一阶泰勒估计,最后取核函数值最大的估计位置,从而完成跟踪。对方法的有效性进行了理论分析,并与经典迭代法的时间消耗进行了对比理论计算。结合实验从抗干扰性、初值敏感度、伸缩性能、稳定性方面,与目前的选优法、样条约束法、无迹卡尔曼滤波法进行比较,证明了该文方法具有估计精度高、鲁棒性强、可扩展性强、平滑度高的优势,且满足实时性要求。

宁夏北部暖季对流风暴雷达气候学特征分析

使用2011—2016年5—9月长时间序列的银川新一代多普勒天气雷达导出产品,对宁夏北部暖季对流风暴的气候特征进行了统计分析。结果表明:77%对流风暴的生命史小于30 min,移动速度在9~13 m·s^(-1),主要向偏东方向移动,且在该方向的移动速度大于其他方向。对流风暴最大反射率因子集中在35~50 dBz,回波顶高大多在5~9 km,垂直累积液态水含量普遍小于10 kg·m^(-2)。500 hPa风为宁夏北部对流风暴的引导气流。500 hPa、700 hPa受偏北风控制时,对流风暴产生的概率较低;500 hPa、700 hPa受偏南风控制时,对流风暴产生的概率相对较高。逐月来看,每年7月对流风暴核密度达到峰值;逐时来看,每日12时、13时达到峰值。贺兰山是对流风暴的高发区,其次位于灵武中北部至以北的鄂托克前旗地区的沙地。对流风暴在贺兰山的分布并不均匀,在高山—深谷地带对流风暴发生最多。

基于图像处理的三支聚类

将数字图像处理中模糊锐化算子与三支聚类进行结合,提出了一种基于图像处理的三支聚类算法。该算法通过逆多元二次核函数将数据集的密度量化为灰度值,对数据总体采用模糊与锐化操作,提取锐化后灰度值较高的数据区域,将低密度区域从原始数据中删除。对灰度值较高的数据采用传统的聚类算法得到不同的类簇,然后对每个类簇利用图像模糊算子得到类簇的核心域,锐化算子得到类簇数据边界域,从而获得每个类簇的三支表示。试验采用不同的UCI数据集,通过比较聚类指标Adjusted Rand Index(ARI),Normalized Mutual Information(NMI)和Adjusted Mutual Information(AMI),验证了该聚类算法的有效性。

基于核密度的电力线通信网络时延估计

通过测试节点间时延构造网络拓扑是智能电网故障管理和高效运行的有效途径,而家用电器对时延测试的精度造成较大干扰。基于转移参数给出了电力线通信信道的传递函数理论模型,讨论了用电器对信道传递函数的影响;进而对测量得到的含噪声时延数据采用核密度估计法进行统计分析,有效去除了家用电器的干扰。仿真结果表明,合理设置核宽度比,采用核密度估计可使得测量误差降低到1 ns以内。文中研究结果对电力线通信网络性能优化、拓扑构造具有重要的参考价值。

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.

谷子价格波动风险的度量及防范分析——基于谷子主产区的非参数核密度估计

基于2020年6月~2023年6月的面源数据,采用非参数核密度估计法对我国谷子主产区的谷子价格波动进行风险分析。结果表明:(1)种植面积与价格风险的发生呈正相关。山西和内蒙古是谷子种植大省,供需情况与购销活力相对于其他省份更具有弹性,价格波动的风险更大。(2)各省谷子价格差异较大,偏度均不等于0,不属于正态分布。谷子价格总体均呈下降趋势,波动趋势具有一致性,但波动幅度差距较大。(3)谷子价格波动概率密度分布均不符合规则经验分布,偏度与峰度表现均较为明显,且具有“尖峰厚尾”的特征。(4)各省谷子价格下降概率均>50%,降价风险高于涨价风险。山西出现价格下降的风险最高;其次依次为辽宁、河北和内蒙古。提出建立和完善谷子市场行情监测系统,谷子种植户进一步增强风险管理意识的对策建议。

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。