The MW5.5 earthquake on August 6,2023,in Pingyuan,Shandong,China:A rupture on a buried fault

On August 6,2023,a magnitude MW5.5 earthquake struck Pingyuan County,Dezhou City,Shandong Province,China.This event was significant as no large earthquakes had been recorded in the region for over a century,and no active fault had been previously identified.This study collects 1309 P-wave arrival times and 866 S-wave arrival times from 74 seismic stations less than 200 km to the epicenter to constrain the spatial distribution of the mainshock and its 125 early aftershocks by the double difference earthquake relocation method,and selects 864 P-waveforms from 288 stations located within 800 km of the epicenter to constrain the focal mechanism solution of the mainshock through centroid moment tensor inversion.The relocation and the inversion indicate,the Pingyuan MW5.5 earthquake was caused by a rupture on a buried fault,likely an extensive segment of the Gaotang fault.This buried fault exhibited a dip of approximately 75°to the northwest,with a strike of 222°,similar to the Gaotang fault.The rupture initiated at the depth of 18.6 km and propagated upward and northeastward.However,the ground surface was not broken.The total duration of the rupture was~6.0 s,releasing the scalar moment of 2.5895×1017 N·m,equivalent to MW5.54.The moment rate reached the maximum only 1.4 seconds after the rupture initiation,and the 90%scalar moment was released in the first 4.6 s.In the first 1.4 seconds of the rupture process,the rupture velocity was estimated to be 2.6 km/s,slower than the local S-wave velocity.As the rupture neared its end,the rupture velocity decreased significantly.This study provides valuable insights into the seismic characteristics of the Pingyuan MW5.5 earthquake,shedding light on the previously unidentified buried fault responsible for the seismic activity in the region.Understanding the behavior of such faults is crucial for assessing seismic hazards and enhancing earthquake preparedness in the future.

Boosting Adversarial Training with Learnable Distribution

In recent years,various adversarial defense methods have been proposed to improve the robustness of deep neural networks.Adversarial training is one of the most potent methods to defend against adversarial attacks.However,the difference in the feature space between natural and adversarial examples hinders the accuracy and robustness of the model in adversarial training.This paper proposes a learnable distribution adversarial training method,aiming to construct the same distribution for training data utilizing the Gaussian mixture model.The distribution centroid is built to classify samples and constrain the distribution of the sample features.The natural and adversarial examples are pushed to the same distribution centroid to improve the accuracy and robustness of the model.The proposed method generates adversarial examples to close the distribution gap between the natural and adversarial examples through an attack algorithm explicitly designed for adversarial training.This algorithm gradually increases the accuracy and robustness of the model by scaling perturbation.Finally,the proposed method outputs the predicted labels and the distance between the sample and the distribution centroid.The distribution characteristics of the samples can be utilized to detect adversarial cases that can potentially evade the model defense.The effectiveness of the proposed method is demonstrated through comprehensive experiments.

A typhoon-induced storm surge numerical model with GPU acceleration based on an unstructured spherical centroidal Voronoi tessellation grid

Storm surge is often the marine disaster that poses the greatest threat to life and property in coastal areas.Accurate and timely issuance of storm surge warnings to take appropriate countermeasures is an important means to reduce storm surge-related losses.Storm surge numerical models are important for storm surge forecasting.To further improve the performance of the storm surge forecast models,we developed a numerical storm surge forecast model based on an unstructured spherical centroidal Voronoi tessellation(SCVT)grid.The model is based on shallow water equations in vector-invariant form,and is discretized by Arakawa C grid.The SCVT grid can not only better describe the coastline information but also avoid rigid transitions,and it has a better global consistency by generating high-resolution grids in the key areas through transition refinement.In addition,the simulation speed of the model is accelerated by using the openACC-based GPU acceleration technology to meet the timeliness requirements of operational ensemble forecast.It only takes 37 s to simulate a day in the coastal waters of China.The newly developed storm surge model was applied to simulate typhoon-induced storm surges in the coastal waters of China.The hindcast experiments on the selected representative typhoon-induced storm surge processes indicate that the model can reasonably simulate the distribution characteristics of storm surges.The simulated maximum storm surges and their occurrence times are consistent with the observed data at the representative tide gauge stations,and the mean absolute errors are 3.5 cm and 0.6 h respectively,showing high accuracy and application prospects.

Analyzing the Effect of the Intra-Pixel Position of Small PSFs for Optimizing the PL of Optical Subpixel Localization

Subpixel localization techniques for estimating the positions of point-like images captured by pixelated image sensors have been widely used in diverse optical measurement fields.With unavoidable imaging noise,there is a precision limit(PL)when estimating the target positions on image sensors,which depends on the detected photon count,noise,point spread function(PSF)radius,and PSF’s intra-pixel position.Previous studies have clearly reported the effects of the first three parameters on the PL but have neglected the intra-pixel position information.Here,we develop a localization PL analysis framework for revealing the effect of the intra-pixel position of small PSFs.To accurately estimate the PL in practical applications,we provide effective PSF(e PSF)modeling approaches and apply the Cramér–Rao lower bound.Based on the characteristics of small PSFs,we first derive simplified equations for finding the best PL and the best intra-pixel region for an arbitrary small PSF;we then verify these equations on real PSFs.Next,we use the typical Gaussian PSF to perform a further analysis and find that the final optimum of the PL is achieved at the pixel boundaries when the Gaussian radius is as small as possible,indicating that the optimum is ultimately limited by light diffraction.Finally,we apply the maximum likelihood method.Its combination with e PSF modeling allows us to successfully reach the PL in experiments,making the above theoretical analysis effective.This work provides a new perspective on combining image sensor position control with PSF engineering to make full use of information theory,thereby paving the way for thoroughly understanding and achieving the final optimum of the PL in optical localization.

Potential global distribution of the guava root-knot nematode Meloidogyne enterolobii under different climate change scenarios using MaxEnt ecological niche modeling

In recent years,Meloidogyne enterolobii has emerged as a major parasitic nematode infesting many plants in tropical or subtropical areas.However,the regions of potential distribution and the main contributing environmental variables for this nematode are unclear.Under the current climate scenario,we predicted the potential geographic distributions of M.enterolobii worldwide and in China using a Maximum Entropy(MaxEnt)model with the occurrence data of this species.Furthermore,the potential distributions of M.enterolobii were projected under three future climate scenarios(BCC-CSM2-MR,CanESM5 and CNRM-CM6-1)for the periods 2050s and 2090s.Changes in the potential distribution were also predicted under different climate conditions.The results showed that highly suitable regions for M.enterolobii were concentrated in Africa,South America,Asia,and North America between latitudes 30°S to 30°N.Bio16(precipitation of the wettest quarter),bio10(mean temperature of the warmest quarter),and bio11(mean temperature of the coldest quarter)were the variables contributing most in predicting potential distributions of M.enterolobii.In addition,the potential suitable areas for M.enterolobii will shift toward higher latitudes under future climate scenarios.This study provides a theoretical basis for controlling and managing this nematode.

一类特殊幂零李代数的Centroid

本文研究了一类特殊的幂零李代数。详细的计算了最简线状李代数的Centroid的表达式,并对其结构进行了研究。

扩张Schrdinger-Virasoro李代数的Centroid（英文）

利用李代数的分次以及李括号,确定了扩张Schrdinger-Virasoro及其泛中心扩张的centroid,为进一步解决这些李代数的Poisson结构做准备.

A New Fire Detection Method Using a Multi-Expert System Based on Color Dispersion, Similarity and Centroid Motion in Indoor Environment

In this paper, a video fire detection method is proposed, which demonstrated good performance in indoor environment. Three main novel ideas have been introduced. Firstly, a flame color model in RGB and HIS color space is used to extract pre-detected regions instead of traditional motion differential method, as it’s more suitable for fire detection in indoor environment. Secondly, according to the flicker characteristic of the flame, similarity and two main values of centroid motion are proposed. At the same time, a simple but effective method for tracking the same regions in consecutive frames is established. Thirdly,a multi-expert system consisting of color component dispersion,similarity and centroid motion is established to identify flames.The proposed method has been tested on a very large dataset of fire videos acquired both in real indoor environment tests and from the Internet. The experimental results show that the proposed approach achieved a balance between the false positive rate and the false negative rate, and demonstrated a better performance in terms of overall accuracy and F standard with respect to other similar fire detection methods in indoor environment.

Synoptic Characteristics Related to Warm-Sector Torrential Rainfall Events in South China During the Annually First Rainy Season

Warm-sector torrential rainfall(WSTR)events that occur in the annually first rainy season in south China are characterized by high rainfall intensity and low radar echo centroids.To understand the synoptic characteristics related to these features,16 WSTR events that occurred in 2013-2017 were examined with another 16 squall line(SL)events occurred during the same period as references.Composite analysis derived from ERA-Interim reanalysis data indicated the importance of the deep layer of warm and moist air for WSTR events.The most significant difference between WSTR and SL events lies in their low-level convergence and lifting;for WSTR events,the low-level convergence and lifting is much shallower with comparable or stronger intensity.The trumpet-shaped topography to the north of the WSTR centers is favorable for the development of such shallow convergences in WSTR events.Results in this study will provide references for future studies to improve the predictability of WSTR.

Reach Centroid Localization Algorithm

As much as accurate or precise position estimation is always desirable, coarse accuracy due to sensor node localization is often sufficient. For such level of accuracy, Range-free localization techniques are being explored as low cost alternatives to range based localization techniques. To manage cost, few location aware nodes, called anchors are deployed in the wireless sensor environment. It is from these anchors that all other free nodes are expected to estimate their own positions. This paper therefore, takes a look at some of the foremost Range-free localization algorithms, detailing their limitations, with a view to proposing a modified form of Centroid Localization Algorithm called Reach Centroid Localization Algorithm. The algorithm employs a form of anchor nodes position validation mechanism by looking at the consistency in the quality of Received Signal Strength. Each anchor within the vicinity of a free node seeks to validate the actual position or proximity of other anchors within its vicinity using received signal strength. This process mitigates multipath effects of radio waves, particularly in an enclosed environment, and consequently limits localization estimation errors and uncertainties. Centroid Localization Algorithm is then used to estimate the location of a node using the anchors selected through the validation mechanism. Our approach to localization becomes more significant, particularly in indoor environments, where radio signal signatures are inconsistent or outrightly unreliable. Simulated results show a significant improvement in localization accuracy when compared with the original Centroid Localization Algorithm, Approximate Point in Triangulation and DV-Hop.

Impact of ionospheric irregularity on SBAS integrity:spatial threat modeling and improvement

The ionosphere, as the largest and least predictable error source, its behavior cannot be observed at all places simultaneously. The confidence bound, called the grid ionospheric vertical error(GIVE), can only be determined with the aid of a threat model which is used to restrict the expected ionospheric behavior. However, the spatial threat model at present widespread used, which is based on fit radius and relative centroid metric(RCM), is too conservative or the resulting GIVEs will be too large and will reduce the availability of satellite-based augmentation system(SBAS). In this paper, layered two-dimensional parameters, the vertical direction double RCMs, are introduced based on the spatial variability of the ionosphere. Comparing with the traditional threat model, the experimental results show that the user ionospheric vertical error(UIVE) average reduction rate reaches 16%. And the 95% protection level of conterminous United States(CONUS) is 28%, even under disturbed days, which reaches about 5% reduction rates.The results show that the system service performance has been improved better.

An improved global-direction stencil based on the face-area-weighted centroid for the gradient reconstruction of unstructured finite volume methods

The accuracy of unstructured finite volume methods is greatly influenced by the gradient reconstruction, for which the stencil selection plays a critical role. Compared with the commonly used face-neighbor and vertex-neighbor stencils, the global-direction stencil is independent of the mesh topology, and characteristics of the flow field can be well reflected by this novel stencil. However, for a high-aspect-ratio triangular grid, the grid skewness is evident, which is one of the most important grid-quality measures known to affect the accuracy and stability of finite volume solvers. On this basis and inspired by an approach of using face-area-weighted centroid to reduce the grid skewness, we explore a method by combining the global-direction stencil and face-area-weighted centroid on high-aspect-ratio triangular grids, so as to improve the computational accuracy. Four representative numerical cases are simulated on high-aspect-ratio triangular grids to examine the validity of the improved global-direction stencil. Results illustrate that errors of this improved methods are the lowest among all methods we tested, and in high-mach-number flow, with the increase of cell aspect ratio, the improved global-direction stencil always has a better stability than commonly used face-neighbor and vertex-neighbor stencils. Therefore, the computational accuracy as well as stability is greatly improved, and superiorities of this novel method are verified.

An Improved DV-Hop Localization Algorithm Based on Selected Anchors

Wireless Sensor Network(WSN)based applications has been extraordinarily helpful in monitoring interested area.Only information of surrounding environment with meaningful geometric information is useful.How to design the localization algorithm that can effectively extract unknown node position has been a challenge in WSN.Among all localization technologies,the Distance Vector-Hop(DV-Hop)algorithm has been most popular because it simply utilizes the hop counts as connectivity measurements.This paper proposes an improved DV-Hop based algorithm,a centroid DV-hop localization with selected anchors and inverse distance weighting schemes(SIC-DV-Hop).We adopt an inverse distance weighting method for average distance amelioration to improve accuracy.Also in this paper,we propose an inclusive checking rule to select proper anchors to avoid the inconsistency existing in centroid localization schemes.Finally,an improved multilateration centroid method is presented for the localization.Simulations are conducted on two different network topologies and experiments results show that compared with existing DV-Hop based algorithms,our algorithm can significantly improve the performance meanwhile cost less network resource.

Analysis of Five Typical Localization Algorithms for Wireless Sensor Networks

In this paper, the self-localization problem is studied. It is one of the key technologies in wireless sensor networks (WSNs). And five localization algorithms: Centroid algorithm, Amorphous algorithm, DV-hop algorithm, APIT algorithm and Bounding Box algorithm are discussed. Simulation of those five localization algorithms is done by MATLAB. The simulation results show that the positioning error of Amorphous algorithm is the minimum. Considering economy and localization accuracy, the Amorphous algorithm can achieve the best localization performance under certain conditions.

Convex decomposition of concave clouds for the ultra-short-term power prediction of distributed photovoltaic system

Concave clouds will cause miscalculation by the power prediction model based on cloud features for distributed photovoltaic( PV) plant. The algorithm for decomposing concave cloud into convex images is proposed. Adopting minimum polygonal approximation( MPP) to demonstrate the contour of concave cloud,cloud features are described and the subdivision lines of convex decomposition for the concave clouds are determined by the centroid point scattering model and centroid angle function,which realizes the convex decomposition of concave cloud. The result of MATLAB simulation indicates that the proposed algorithm can accurately detect cloud contour corners and recognize the concave points. The proposed decomposition algorithm has advantages of less time complexity and decomposition part numbers compared to traditional algorithms. So the established model can make the convex decomposition of complex concave clouds completely and quickly,which is available for the existing prediction algorithm for the ultra-short-term power output of distributed PV system based on the cloud features.

The Measurement Accuracy of Ball Bearing Center in Portal Images Using an Intensity-Weighted Centroid Method

Medical linac based imaging modalities such as portal imaging can be utilized for highly accurate measurements. An intensity-weighted centroid method for determining object center is proposed that can detect the position of small object at subpixel accuracy. The principles and algorithms of the intensity-weighted centroid method are presented. Analytical results are derived for positional accuracy of a rod and a sphere in digital images, and the theoretical accuracy limits are calculated. The method was experimentally examined using phantoms with embedded ball bearings (BBs). Images of the phantoms were taken by the MV portal imager of a medical linac. The image pixel size was 0.26 mm when projected at the linac isocenter plane. The BB coordinates were calculated by applying the intensity-weighted centroid method after removing the background. The reproducibility of BB position detection was measured with 3 monitor unit (MU) exposures at various dose rates. A stationary BB, of 0.25 image contrast, showed position reproducibility in the range of 0.004 - 0.013 mm. When the method was used to measure the displacement of a moving BB, the difference between the measured and expected BB position had a standard deviation of 0.006 mm. The effect of image noise on the BB detection accuracy was measured using a phantom with multiple BBs. The overall detection accuracy, represented by standard deviation, steadily improved from 0.13 mm at 0.03 MU to 0.008 mm at 5.0 MU, and showed an inverse correlation with contrast-to-noise ratio. We demonstrated that intensity-weighted centroid method can achieve subpixel accuracy in position detection. With a linac based imaging system, precise mechanical measurement with accuracy of microns could be achieved.

Fuzzy Inventory Model for Deteriorating Items with Time Dependent Demand and Partial Backlogging

In this paper we developed a fuzzy inventory model for deteriorating items with time dependent demand rate. Shortages are allowed and completely backlogged. The backlogging rate of unsatisfied demand is assumed to be a decreasing exponential function of waiting time. The demand rate, deterioration rate and backlogging rate are assumed as a triangular fuzzy numbers. The purpose of our study is to defuzzify the total profit function by signed distance method and centroid method. Further a numerical example is also given to demonstrate the developed crisp and fuzzy models. A sensitivity analysis is also given to show the effect of change of the parameters.

Balance control of a 12-DOF mobile manipulator based on two-wheel inverted pendulum robot

Humanoid mobile manipulator which is based on two-wheel inverted pendulum robot has been studied.Balance control is a key problem for this kind of centroid-variable robot.Due to the principle of two wheel inverted pendulum,a timely angle compensation is necessary to make the system keep balance when the centroid changes.In this paper,a method based on coordinate transformation is introduced to get the compensatory angle and a 12-DOF mobile manipulator is also used to check the method.Simulation and experimental results show the effectiveness of the method.

一类无限维李代数的Centroid

李代数的Centroid在一定程度上刻画了李代数的结构.利用李括号进行计算,确定了一类无限维李代数的Centroid,这为进一步确定这类李代数上的Poisson结构奠定基础.

Characteristics Analysis of Urban Space Expansion in Luoyang, China

The characteristics of urban space expansion reflect the changes of urban space layout and space structure, as well as the urban developing direction and urban orientation in the future. Based on the remote sensing image data of 1990, 2000, 2010 and 2020, this paper analyzes the urban space expansion characteristics of Luoyang, by using the urban land compaction index, expansion intensity index and centroid distribution in 4 time sections and 3 periods. The results show that: 1) the urban land area of Luoyang has expanded rapidly, the urban planning policy has successfully guided the urban expansion. 2) In the first period, the urban expansion belongs to the rapid expansion type, mainly filling the built-up area and natural expansion to outward;the second period is of high-speed expansion type, the urban expansion mainly enlarges the urban framework, then fills the expanded district, and the influence of urban natural growth factor is very small;the third period belongs to the high-speed expansion type, and the expansion rate is significantly lower than that in the second period, the urban expansion is mainly to fill in the urban framework enlarged in the previous period and continue to enlarge the urban framework, and the influence of urban natural growth factor is very little. 3) The urban land centroid gradually migrates to the southeast with the azimuth of 151.47°, the annual migration distance is of 138.29 m. The migration rate in the second period is the fastest, which is about 7.43 times that of the first period, and 2.70 times that of the third period. In the first period, the urban land is mainly expanded to southward, with the main azimuth of 201.13°. Compared with the urban land centroid in 2000, the urban land in the second period mainly expands to east-southeast and southward along the azimuths of 141.92° and 154.17° respectively. Compared with the urban centroid in 2010, the urban land in the third period mainly expands in the orientations of southeast and eastward along the azimuth of 96.24° and 133.45°.