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.

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.

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.

Spatial Pattern Evolution and Influencing Factors of Cold Storage in China

Cold storage is the vital infrastructure of cold chain logistics. In this study, we analyzed the spatial pattern evolution characteristics, spatial autocorrelation and influencing factors of cold storage in China by using kernel density estimation(KDE), spatial autocorrelation analysis(SAA), and spatial error model(SEM). Results showed that: 1) the spatial distribution of cold storage in China is unbalanced, and has evolved from ‘one core’ to ‘one core and many spots’, that is, ‘one core’ refers to the Bohai Rim region mainly including Beijing, Tianjin, Hebei, Shandong and Liaoning regions, and ‘many spots’ mainly include the high-density areas such as Shanghai, Fuzhou, Guangzhou, Zhengzhou, Hefei, Wuhan, ürümqi. 2) The distribution of cold storage has significant global spatial autocorrelation and local spatial autocorrelation, and the ‘High-High’ cluster area is the most stable, mainly concentrated in the Bohai Rim;the ‘Low-Low’ cluster area is grouped in the southern China. 3) Economic development level, population density, traffic accessibility, temperature and land price, all affect the location choice of cold storage in varying degrees, while the impact of market demand on it is not explicit.

A NON-PARAMETER BAYESIAN CLASSIFIER FOR FACE RECOGNITION

A non-parameter Bayesian classifier based on Kernel Density Estimation (KDE)is presented for face recognition, which can be regarded as a weighted Nearest Neighbor (NN)classifier in formation. The class conditional density is estimated by KDE and the bandwidthof the kernel function is estimated by Expectation Maximum (EM) algorithm. Two subspaceanalysis methods-linear Principal Component Analysis (PCA) and Kernel-based PCA (KPCA)are respectively used to extract features, and the proposed method is compared with ProbabilisticReasoning Models (PRM), Nearest Center (NC) and NN classifiers which are widely used in facerecognition systems. The experiments are performed on two benchmarks and the experimentalresults show that the KDE outperforms PRM, NC and NN classifiers.

Road Centrality and Landscape Spatial Patterns in Wuhan Metropolitan Area,China

Road network is a corridor system that interacts with surrounding landscapes,and understanding their interaction helps to develop an optimal plan for sustainable transportation and land use.This study investigates the relationships between road centrality and landscape patterns in the Wuhan Metropolitan Area,China.The densities of centrality measures,including closeness,betweenness,and straightness,are calculated by kernel density estimation(KDE).The landscape patterns are characterized by four landscape metrics,including percentage of landscape(PLAND),Shannon′s diversity index(SHDI),mean patch size(MPS),and mean shape index(MSI).Spearman rank correlation analysis is then used to quantify their relationships at both landscape and class levels.The results show that the centrality measures can reflect the hierarchy of road network as they associate with road grade.Further analysis exhibit that as centrality densities increase,the whole landscape becomes more fragmented and regular.At the class level,the forest gradually decreases and becomes fragmented,while the construction land increases and turns to more compact.Therefore,these findings indicate that the ability and potential applications of centrality densities estimated by KDE in quantifying the relationships between roads and landscapes,can provide detailed information and valuable guidance for transportation and land-use planning as well as a new insight into ecological effects of roads.

Numerical simulation of hydraulic fracturing and associated microseismicity using finite-discrete element method

Hydraulic fracturing （HF） technique has been extensively used for the exploitation of unconventional oiland gas reservoirs. HF enhances the connectivity of less permeable oil and gas-bearing rock formationsby fluid injection, which creates an interconnected fracture network and increases the hydrocarbonproduction. Meanwhile, microseismic （MS） monitoring is one of the most effective approaches to evaluatesuch stimulation process. In this paper, the combined finite-discrete element method （FDEM） isadopted to numerically simulate HF and associated MS. Several post-processing tools, includingfrequency-magnitude distribution （b-value）, fractal dimension （D-value）, and seismic events clustering,are utilized to interpret numerical results. A non-parametric clustering algorithm designed specificallyfor FDEM is used to reduce the mesh dependency and extract more realistic seismic information.Simulation results indicated that at the local scale, the HF process tends to propagate following the rockmass discontinuities; while at the reservoir scale, it tends to develop in the direction parallel to themaximum in-situ stress. 2014 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting byElsevier B.V. All rights reserved.

Analysis on Potential Conflict Frequency of Intersected Air Routes in Terminal Airspace Design

In order to obtain accurate conflict risks in terminal airspace design,the concept and calculation model of potential conflict frequency for intersected routes are proposed.Conflict frequency is represented by the product of horizontal conflict frequency and vertical conflict probability.The horizontal conflict frequency is derived from the probability density distribution of conflicts in a period of time.Based on the recorded radar trajectory data,the concept and model of ROUTE distance are proposed,and the probability density function of aircraft height at a specified ROUTE distance is deduced by kernel density estimation.Furthermore,vertical conflict probability and its horizontal distribution are achieved.Examples of three intersected arrival and departure route design schemes are studied.Compared with scheme 1,the conflict frequency values of the other two improved schemes decrease to53% and 24%,respectively.The results show that the model can quantify potential conflict frequency of intersected routes.

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.

Machine learning approach for estimating the human-related VOC emissions in a university classroom

Indoor air quality becomes increasingly important,partly because the COVID-19 pandemic increases the time people spend indoors.Research into the prediction of indoor volatile organic compounds(VOCs)is traditionally confined to building materials and furniture.Relatively little research focuses on estimation of human-related VOCs,which have been shown to contribute significantly to indoor air quality,especially in densely-occupied environments.This study applies a machine learning approach to accurately estimate the human-related VOC emissions in a university classroom.The time-resolved concentrations of two typical human-related(ozone-related)VOCs in the classroom over a five-day period were analyzed,i.e.,6-methyl-5-hepten-2-one(6-MHO),4-oxopentanal(4-OPA).By comparing the results for 6-MHO concentration predicted via five machine learning approaches including the random forest regression(RFR),adaptive boosting(Adaboost),gradient boosting regression tree(GBRT),extreme gradient boosting(XGboost),and least squares support vector machine(LSSVM),we find that the LSSVM approach achieves the best performance,by using multi-feature parameters(number of occupants,ozone concentration,temperature,relative humidity)as the input.The LSSVM approach is then used to predict the 4-OPA concentration,with mean absolute percentage error(MAPE)less than 5%,indicating high accuracy.By combining the LSSVM with a kernel density estimation(KDE)method,we further establish an interval prediction model,which can provide uncertainty information and viable option for decision-makers.The machine learning approach in this study can easily incorporate the impact of various factors on VOC emission behaviors,making it especially suitable for concentration prediction and exposure assessment in realistic indoor settings.

Shading Fault Detection Method for Household Photovoltaic Power Stations Based on Inherent Characteristics of Monthly String Current Data Mapping

The poor outdoor operating conditions of household photovoltaic(PV)make the power station prone to various faults.However,the dispersion of household PV installations often increases the difficulty and cost of operation and maintenance(O&M).Although the remote monitoring and fault detection of a PV power station can be realized by the use of operation data,the particularity of a household power station also brings many problems to fault detection.In this study,we propose a shading fault detection method of household PV power based on inherent characteristics of monthly string current data mapping.The ideal current peak obtained by a new fitting method is used to normalize string current data.The current probability density function(PDF)at each time point is estimated by kernel density estimation(KDE).Through the normalized current data corresponding to the maximum probability density,the inherent characteristics of the strings are obtained,such that whether the strings have shading can be judged and the shading degree can then be evaluated.Not only are no additional sensors needed to collect environmental data,such as irradiation and temperature,but also simulating the detailed parameters of the power station is not required.The interference caused by meteorological factors can thus be eliminated,which can be easily used in old power stations and newly constructed power stations.The effectiveness and performance of the proposed shading fault detection method is verified by experimental data collected from the actual household PV power station.Index Terms-Data fitting,fault detection,household photovoltaic(PV),kernel density estimation(KDE),shading degree.

Forecasted Scenarios of Regional Wind Farms Based on Regular Vine Copulas

Owing to the uncertainty and volatility of wind energy,forecasted wind power scenarios with proper spatio-temporal correlations are needed in various decision-making problems involving power systems.In this study,forecasted scenarios are generated from an estimated multi-variate distribution of multiple regional wind farms.According to the theory of copulas,marginal distributions and the dependence structure of multi-variate distribution are modeled through the proposed distance-weighted kernel density estimation method and the regular vine(R-vine)copula,respectively.Owing to the flexibility of decomposing correlations of high dimensions into different types of pair-copulas,the R-vine copula provides more accurate results in describing the complicated dependence of wind power.In the case of 26 wind farms located in East China,highquality forecasted scenarios as well as the corresponding probabilistic forecasting and point forecasting results are obtained using the proposed method,and the results are evaluated using a comprehensive verification framework.

Determining the road traffic accident hotspots using GIS-based temporal-spatial statistical analytic techniques in Hanoi,Vietnam

This study applied GIS-based statistical analytic techniques to investigate the influence of accident Severity Index(SI)on temporal-spatial patterns of accident hotspots related to the specific time intervals of day and seasons.Road Traffic Accident(RTA)data in 3 years(2015-2017)in Hanoi,Vietnam were used to analyze and test this approach.Firstly,the RTA data were divided into four seasons in accordance with Hanoi's weather conditions and the time intervals such as the daytime,nighttime,or peak hours.Then,the Kernel Density Estimation(KDE)method was applied to analyze hotspots according to the time intervals and seasons.Finally,the results were presented by using the comap technique.This study considered both analyses with and without SI.The accident SI measures the seriousness of an accident.The approach method is to give higher weights to the more serious accidents,but not with the extremely high values calculated on a direct rate to the accident expenditures.The results showed that both analyses determined the relatively similar hotspots,but the rankings of some hotspots were quite different due to the integration of SI.It is better to take into account SI in determining RTA hotspots because the gained results are more precise and the rankings of hotspots are more accurate.From there,the traffic authorities can easily understand the causes behind each accident and provide reasonable solutions to solve the most dangerous hotspots in case of limited budget and resources appropriately.This is also the first study about this issue in Vietnam,so the contribution of the article will help the traffic authorities easily solve this problem not only in Hanoi but also in other cities.

Bayesian moving object detection in dynamic scenes using an adaptive foreground model

Accurate detection of moving objects is an important step in stable tracking or recognition. By using a nonparametric density estimation method over a joint domain-range representation of image pixels, the correlation between neighboring pixels can be used to achieve high levels of detection accuracy in the presence of dynamic background. However, color similarity between foreground and background will cause many foreground pixels to be misclassified. In this paper, an adaptive foreground model is exploited to detect moving objects in dynamic scenes. The foreground model provides an effective description of foreground by adaptively combining the temporal persistence and spatial coherence of moving objects. Building on the advantages of MAP-MRF (the maximum a posteriori in the Markov random field) decision framework, the proposed method performs well in addressing the challenging problem of missed detection caused by similarity in color between foreground and background pixels. Experimental results on real dynamic scenes show that the proposed method is robust and efficient.

Two-dimensional extreme distribution for estimating mechanism reliability under large variance

The effective estimation of the operational reliability of mechanism is a significant challenge in engineering practices,especially when the variance of uncertain factors becomes large.Addressing this challenge,a novel mechanism reliability method via a two-dimensional extreme distribution is investigated in the paper.The time-variant reliability problem for the mechanism is first transformed to the time-invariant system reliability problem by constructing the two-dimensional extreme distribution.The joint probability density functions(JPDFs),including random expansion points and extreme motion errors,are then obtained by combining the kernel density estimation(KDE)method and the copula function.Finally,a multidimensional integration is performed to calculate the system time-invariant reliability.Two cases are investigated to demonstrate the effectiveness of the presented method.