Hurricane damage assessment for residential construction considering the non-stationarity in hurricane intensity and frequency

Natural hazards such as hurricanes may cause extensive economic losses and social disruption for civil structures and infrastructures in coastal areas, implying the importance of understanding the construction performance subjected to hurricanes and assessing the hurricane damages properly. The intensity and frequency of hurricanes have been reported to change with time due to the potential impact of climate change.In this paper, a probability-based model of hurricane damage assessment for coastal constructions is proposed taking into account the non-stationarity in hurricane intensity and frequency. The nonhomogeneous Poisson process is employed to model the non-stationarity in hurricane occurrence while the non-stationarity in hurricane intensity is reflected by the time-variant statistical parameters(e.g., mean value and/or standard deviation), with which the mean value and variation of the cumulative hurricane damage are evaluated explicitly. The Miami-Dade County, Florida, USA, is chosen to illustrate the hurricane damage assessment method proposed in this paper. The role of non-stationarity in hurricane intensity and occurrence rate due to climate change in hurricane damage is investigated using some representative changing patterns of hurricane parameters.

Method of non-stationary random vibration reliability of hydro-turbine generator unit

The hydraulic excitation acting on a hydro-turbine generator unit exhibits obvious non-stationary characteristics.In order to account for these characteristics,this study focuses on the non-stationary random vibration reliability of the hydro-turbine generator unit.Firstly,the non-stationary characteristics of the hydraulic excitation are analyzed,and a mathematical ex-pression is constructed using the virtual excitation method.Secondly,a dynamic model of the unit is established to demonstrate the non-stationary random vibration characteristics under hydraulic excitation.Thirdly,an active learning non-stationary vibration reliability analysis method AK-MCS-T-H is proposed combining the Kriging model,the Monte Carlo simulation(MCS)method,and the information entropy learning function H.This method reveals the influence of the non-stationary hydraulic excitation on the random vibration reliability of the hydro-turbine generator unit.Finally,an example is presented to analyze the random vibration reliability.The study shows that the AK-MCS-T-H proposed in this paper can solve the problem of non-stationary random vibration reliability of the Francis hydro-turbine generator unit more effectively.

Efficient simulation of spatially correlated non-stationary ground motions by wavelet-packet algorithm and spectral representation method

Although the classical spectral representation method(SRM)has been widely used in the generation of spatially varying ground motions,there are still challenges in efficient simulation of the non-stationary stochastic vector process in practice.The first problem is the inherent limitation and inflexibility of the deterministic time/frequency modulation function.Another difficulty is the estimation of evolutionary power spectral density(EPSD)with quite a few samples.To tackle these problems,the wavelet packet transform(WPT)algorithm is utilized to build a time-varying spectrum of seed recording which describes the energy distribution in the time-frequency domain.The time-varying spectrum is proven to preserve the time and frequency marginal property as theoretical EPSD will do for the stationary process.For the simulation of spatially varying ground motions,the auto-EPSD for all locations is directly estimated using the time-varying spectrum of seed recording rather than matching predefined EPSD models.Then the constructed spectral matrix is incorporated in SRM to simulate spatially varying non-stationary ground motions using efficient Cholesky decomposition techniques.In addition to a good match with the target coherency model,two numerical examples indicate that the generated time histories retain the physical properties of the prescribed seed recording,including waveform,temporal/spectral non-stationarity,normalized energy buildup,and significant duration.

A Non-Stationary Beam-Enabled Stochastic Channel Model and Characterization over Non-Reciprocal Beam Patterns

The multiple-input multiple-output(MIMO)-enabled beamforming technology offers great data rate and channel quality for next-generation communication.In this paper,we propose a beam channel model and enable it with time-varying simulation capability by adopting the stochastic geometry theory.First,clusters are generated located within transceivers'beam ranges based on the Mate?rn hardcore Poisson cluster process.The line-of-sight,singlebounce,and double-bounce components are calculated when generating the complex channel impulse response.Furthermore,we elaborate on the expressions of channel links based on the propagation-graph theory.A birth-death process consisting of the effects of beams and cluster velocities is also formulated.Numerical simulation results prove that the proposed model can capture the channel non-stationarity.Besides,the non-reciprocal beam patterns yield severe channel dispersion compared to the reciprocal patterns.

A measure for non-stationarity with applications to gearbox noise

From the evolutionary vector deacription of slowly sime-varying noise process, a measure for non-stationarity is developed. It includes both the non-stationarities of power and of spectrum shape. As a single parameter, it is a comparable quantity for different processes. Application to the analysis of precise gearbox is presented.

Towards Near-Field Communications for 6G:Challenges and Opportunities

Extremely large-scale multiple-input multiple-output(XL-MIMO)and terahertz(THz)communications are pivotal candidate technologies for supporting the development of 6G mobile networks.However,these techniques invalidate the common assumptions of far-field plane waves and introduce many new properties.To accurately understand the performance of these new techniques,spherical wave modeling of near-field communications needs to be applied for future research.Hence,the investigation of near-field communication holds significant importance for the advancement of 6G,which brings many new and open research challenges in contrast to conventional far-field communication.In this paper,we first formulate a general model of the near-field channel and discuss the influence of spatial nonstationary properties on the near-field channel modeling.Subsequently,we discuss the challenges encountered in the near field in terms of beam training,localization,and transmission scheme design,respectively.Finally,we point out some promising research directions for near-field communications.

A Statistical Model for Phase Difference Spectrum of Ground-Motion and Its Application in Generating Non-Stationary Seismic Waves

The intensity non-stationarity is one of the most important features of earthquake records.Modeling of this feature is significant to the generation of artificial earthquake waves.Based on the theory of phase difference spectrum,an intensity non-stationary envelope function with log-normal form is proposed.Through a tremendous amount of earthquake records downloaded on Kik-net,a parameter fitting procedure using the genetic algorithm is conducted to obtain the value of model parameters under different magnitudes,epicenter distances and site conditions.A numerical example is presented to describe the procedure of generating fully non-stationary ground motions via spectral representation,and the mean EPSD(evolutionary power spectral density)of simulated waves is proved to agree well with the target EPSD.The results show that the proposed model is capable of describing the intensity non-stationary features of ground motions,and it can be used in structural anti-seismic analysis and ground motion simulation.

An Improved Granulated Convolutional Neural Network Data Analysis Model for COVID-19 Prediction

As COVID-19 poses a major threat to people’s health and economy,there is an urgent need for forecasting methodologies that can anticipate its trajectory efficiently.In non-stationary time series forecasting jobs,there is frequently a hysteresis in the anticipated values relative to the real values.The multilayer deep-time convolutional network and a feature fusion network are combined in this paper’s proposal of an enhanced Multilayer Deep Time Convolutional Neural Network(MDTCNet)for COVID-19 prediction to address this problem.In particular,it is possible to record the deep features and temporal dependencies in uncertain time series,and the features may then be combined using a feature fusion network and a multilayer perceptron.Last but not least,the experimental verification is conducted on the prediction task of COVID-19 real daily confirmed cases in the world and the United States with uncertainty,realizing the short-term and long-term prediction of COVID-19 daily confirmed cases,and verifying the effectiveness and accuracy of the suggested prediction method,as well as reducing the hysteresis of the prediction results.

MmWave extra-large-scale MIMO based active user detection and channel estimation for high-speed railway communications

The current High-Speed Railway(HSR)communications increasingly fail to satisfy the massive access services of numerous user equipment brought by the increasing number of people traveling by HSRs.To this end,this paper investigates millimeter-Wave(mmWave)extra-large scale(XL)-MIMO-based massive Internet-of-Things(loT)access in near-field HSR communications,and proposes a block simultaneous orthogonal matching pursuit(B-SOMP)-based Active User Detection(AUD)and Channel Estimation(CE)scheme by exploiting the spatial block sparsity of the XLMIMO-based massive access channels.Specifically,we first model the uplink mmWave XL-MIMO channels,which exhibit the near-field propagation characteristics of electromagnetic signals and the spatial non-stationarity of mmWave XL-MIMO arrays.By exploiting the spatial block sparsity and common frequency-domain sparsity pattern of massive access channels,the joint AUD and CE problem can be then formulated as a Multiple Measurement Vectors Compressive Sensing(MIMV-CS)problem.Based on the designed sensing matrix,a B-SOMP algorithm is proposed to achieve joint AUD and CE.Finally,simulation results show that the proposed solution can obtain a better AUD and CE performance than the conventional CS-based scheme for massive IoT access in near-field HSR communications.

A 3-D Hybrid Dynamic Channel Model for Indoor THz Communications

To meet the demands for the explosive growth of mobile data rates and scarcity of spectrum resources in the near future,the terahertz(THz)band has widely been regarded as a key enabler for the upcoming beyond fifth-generation(B5G)wireless communications.An accurate THz channel model is crucial for the design and deployment of the THz wireless communication systems.In this paper,a three-dimensional(3-D)dynamic indoor THz channel model is proposed by means of combining deterministic and stochastic modeling approaches.Clusters are randomly distributed in the indoor environment and each ray is characterized with consideration of molecular absorption and diffuse scattering.Moreover,we present the dynamic generation procedure of the channel impulse responses(CIRs).Statistical properties are investigated to indicate the non-stationarity and feasibility of the proposed model.Finally,by comparing with delay spread and K-factor results from the measurements,the utility of the proposed channel model is verified.

Bayesian Estimation for GEV-B-Spline Model

The stationarity hypothesis is essential in hydrological frequency analysis and statistical inference. This assumption is often not fulfilled for large observed datasets, especially in the case of hydro-climatic variables. The Generalized Extreme Value distribution with covariates allows to model data in the presence of non-stationarity and/or dependence on covariates. Linear and non-linear dependence structures have been proposed with the corresponding fitting approach. The objective of the present study is to develop the GEV model with B-Spline in a Bayesian framework. A Markov Chain Monte Carlo (MCMC) algorithm has been developed to estimate quantiles and their posterior distributions. The methods are tested and illustrated using simulated data and applied to meteorological data. Results indicate the better performance of the proposed Bayesian method for rainfall quantile estimation according to BIAS and RMSE criteria especially for high return period events.

Application Study on Synthesis Method of Earthquake Motion

For seismic design of structure and machinery, it is important to reproduce input earthquake motions that are likely to occur at a target site. Among the various methods used for generating artificial earthquake motions, the Synthesis Method of Trigonometric Function is used widely. In this method, artificial waves are reproduced by superimposing sine waves and then adding information about amplitude and phase in the frequency domain. In the Japanese architectural design code, the amplitude is standardized as the target response spectrum, and the phase can be defined by random numbers or by the phase of one observed wave. However, a random phase is distinctly different from the phase of an actual earthquake. Further, the phase of one observed wave is confined to the phase characteristic of the artificial wave of only one specific earthquake motion. In this paper, the authors introduce a new convenient method to reproduce artificial waves that not only satisfy the standardized spectrum property but also have the time-frequency characteristics of multiple observed waves. The authors show the feature of the artificial waves, discuss the merits of the method by comparing with existing methods, and report the tendencies of the non-liuear response by using simple model.

Original Research Confounding associations between green space and outdoor artificial light at night: Systematic investigations and implications for urban health

Excessive urbanization leads to considerable nature deficiency and abundant artificial infrastructure in urban areas,which triggered intensive discussions on people's exposure to green space and outdoor artificial light at night(ALAN).Recent academic progress highlights that people's exposure to green space and outdoor ALAN may be confounders of each other but lacks systematic investigations.This study investigates the associations between people's exposure to green space and outdoor ALAN by adopting the three most used research paradigms:population-level residence-based,individual-level residencebased,and individual-level mobility-oriented paradigms.We employed the green space and outdoor ALAN data of 291 Tertiary Planning Units in Hong Kong for population-level analysis.We also used data from 940 participants in six representative communities for individual-level analyses.Hong Kong green space and outdoor ALAN were derived from high-resolution remote sensing data.The total exposures were derived using the spatiotemporally weighted approaches.Our results confirm that the negative associations between people's exposure to green space and outdoor ALAN are universal across different research paradigms,spatially non-stationary,and consistent among different socio-demographic groups.We also observed that mobility-oriented measures may lead to stronger negative associations than residence-based measures by mitigating the contextual errors of residence-based measures.Our results highlight the potential confounding associations between people's exposure to green space and outdoor ALAN,and we strongly recommend relevant studies to consider both of them in modeling people's health outcomes,especially for those health outcomes impacted by the co-exposure to them.

Pattern Recognition of Non-Stationary Time Series with Finite Length

Statistical learning and recognition methods were used to extract the characteristics of size series measurements of cocoon filaments that are non-stationary in terms of mean and auto-covariance, by using the time varying parameter auto-regressive （TVPAR） model. After the system was taught to recognize the size data, the system correctly recognized the size of series of cocoon filaments as much as 96.95% of the time for a single series and 98.72% of the time for the mean of two series. The correct recognition rate was higher after suitable filtering. The theory and method can be used to analyze other types of non-stationary finite length time series.

Locally varying geostatistical machine learning for spatial prediction

Machine learning methods dealing with the spatial auto-correlation of the response variable have garnered significant attention in the context of spatial prediction.Nonetheless,under these methods,the relationship between the response variable and explanatory variables is assumed to be homogeneous throughout the entire study area.This assumption,known as spatial stationarity,is very questionable in real-world situations due to the influence of contextual factors.Therefore,allowing the relationship between the target variable and predictor variables to vary spatially within the study region is more reasonable.However,existing machine learning techniques accounting for the spatially varying relationship between the dependent variable and the predictor variables do not capture the spatial auto-correlation of the dependent variable itself.Moreover,under these techniques,local machine learning models are effectively built using only fewer observations,which can lead to well-known issues such as over-fitting and the curse of dimensionality.This paper introduces a novel geostatistical machine learning approach where both the spatial auto-correlation of the response variable and the spatial non-stationarity of the regression relationship between the response and predictor variables are explicitly considered.The basic idea consists of relying on the local stationarity assumption to build a collection of local machine learning models while leveraging on the local spatial auto-correlation of the response variable to locally augment the training dataset.The proposed method’s effectiveness is showcased via experiments conducted on synthetic spatial data with known characteristics as well as real-world spatial data.In the synthetic(resp.real)case study,the proposed method’s predictive accuracy,as indicated by the Root Mean Square Error(RMSE)on the test set,is 17%(resp.7%)better than that of popular machine learning methods dealing with the response variable’s spatial auto-correlation.Additionally,this method is not only valuable for spatial prediction but also offers a deeper understanding of how the relationship between the target and predictor variables varies across space,and it can even be used to investigate the local significance of predictor variables.

Performance analysis and low complexity receiver design for extra-large scale MIMO systems with residual hardware impairments

The research purpose of this paper is focused on investigating the performance of extra-large scale massive multiple-input multiple-output(XL-MIMO)systems with residual hardware impairments.The closed-form expression of the achievable rate under the match filter(MF)receiving strategy was derived and the influence of spatial non-stationarity and residual hardware impairments on the system performance was investigated.In order to maximize the signal-to-interference-plus-noise ratio(SINR)of the systems in the presence of hardware impairments,a hardware impairments-aware minimum mean squared error(HIA-MMSE)receiver was proposed.Furthermore,the stair Neumann series approximation was used to reduce the computational complexity of the HIA-MMSE receiver,which can avoid matrix inversion.Simulation results demonstrate the tightness of the derived analytical expressions and the effectiveness of the low complexity HIA-MMSE(LC-HIA-MMSE)receiver.

Physical random function model of ground motions for engineering purposes

A physical random function model of ground motions for engineering purposes is presented with verification of sample level. Firstly,we derive the Fourier spectral transfer form of the solution to the definition problem,which describes the one-dimensional seismic wave field. Then based on the special models of the source,path and local site,the physical random function model of ground motions is obtained whose physical parameters are random variables. The superposition method of narrow-band harmonic wave groups is improved to synthesize ground motion samples. Finally,an application of this model to simulate ground motion records in 1995 Kobe earthquake is described. The resulting accelerograms have the frequencydomain and non-stationary characteristics that are in full agreement with the realistic ground motion records.

High-performance solutions of geographically weighted regression in R

As an established spatial analytical tool,Geographically Weighted Regression(GWR)has been applied across a variety of disciplines.However,its usage can be challenging for large datasets,which are increasingly prevalent in today’s digital world.In this study,we propose two high-performance R solutions for GWR via Multi-core Parallel(MP)and Compute Unified Device Architecture(CUDA)techniques,respectively GWR-MP and GWR-CUDA.We compared GWR-MP and GWR-CUDA with three existing solutions available in Geographically Weighted Models(GWmodel),Multi-scale GWR(MGWR)and Fast GWR(FastGWR).Results showed that all five solutions perform differently across varying sample sizes,with no single solution a clear winner in terms of computational efficiency.Specifically,solutions given in GWmodel and MGWR provided acceptable computational costs for GWR studies with a relatively small sample size.For a large sample size,GWR-MP and FastGWR provided coherent solutions on a Personal Computer(PC)with a common multi-core configuration,GWR-MP provided more efficient computing capacity for each core or thread than FastGWR.For cases when the sample size was very large,and for these cases only,GWR-CUDA provided the most efficient solution,but should note its I/O cost with small samples.In summary,GWR-MP and GWR-CUDA provided complementary high-performance R solutions to existing ones,where for certain data-rich GWR studies,they should be preferred.

Correlation analysis of Normalized Different Vegetation Index(NDVI)difference series and climate variables in the Xilingole steppe,China from 1983 to 1999

There is a crucial need in the study of global change to understand how terrestrial ecosystems respond to the climate system.It has been demonstrated by many researches that Normalized Different Vegetation Index(NDVI)time series from remotely sensed data,which provide effective information of vegetation conditions on a large scale with highly temporal resolution,have a good relation with meteorological factors.However,few of these studies have taken the cumulative property of NDVI time series into account.In this study,NDVI difference series were proposed to replace the original NDVI time series with NDVI difference series to reappraise the relationship between NDVI and meteorological factors.As a proxy of the vegetation growing process,NDVI difference represents net primary productivity of vegetation at a certain time interval under an environment controlled by certain climatic conditions and other factors.This data replacement is helpful to eliminate the cumulative effect that exist in original NDVI time series,and thus is more appropriate to understand how climate system affects vegetation growth in a short time scale.By using the correlation analysis method,we studied the relationship between NOAA/AVHRR ten-day NDVI difference series and corresponding meteorological data from 1983 to 1999 from 11 meteorological stations located in the Xilingole steppe in Inner Mongolia.The results show that:(1)meteorological factors are found to be more significantly correlation with NDVI difference at the biomass-rising phase than that at the falling phase;(2)the relationship between NDVI difference and climate variables varies with vegetation types and vegetation communities.In a typical steppe dominated by Leymus chinensis,temperature has higher correlation with NDVI difference than precipitation does,and in a typical steppe dominated by Stipa krylovii,the correlation between temperature and NDVI difference is lower than that between precipitation and NDVI difference.In a typical steppe dominated by Stipa grandis,there is no significant difference between the two correlations.Precipitation is the key factor influencing vegetation growth in a desert steppe,and temperature has poor correlation with NDVI dif-ference;(3)the response of NDVI difference to precipitation is fast and almost simultaneous both in a typical steppe and desert steppe,however,mean temperature exhibits a time-lag effect especially in the desert steppe and some typical steppe dominated by Stipa krylovii;(4)the relationship between NDVI difference and temperature is becoming stronger with global warming.

STATISTICAL INFERENCES FOR VARYING-COEFFICINT MODELS BASED ON LOCALLY WEIGHTED REGRESSION TECHNIQUE

Some fundamental issues on statistical inferences relating to varying-coefficient regression models are addressed and studied. An exact testing procedure is proposed for checking the goodness of fit of a varying-coefficient model fited by the locally weighted regression technique versus an ordinary linear regression model. Also, an appropriate statistic for testing variation of model parameters over the locations where the observations are collected is constructed and a formal testing approach which is essential to exploring spatial non-stationarity in geography science is suggested.