AN ERROR ANALYSIS ON THE SIMULATED DATA OF TOVS HIRS BY RTTOV5 MODEL

The sounding data of meteorological satellites provide not only the real time weather information about the distribution of both cloud and rainfall,but also some others about the movement and state of atmosphere.They are important variables and parameters for NWP model used to simulate and predict atmospheric state.In order to introduce remote sensing information from satellites into NWP model,there is an efficient way of establishing an RT model by use of the atmosphere radiation sounding data of meteorological satellites to get the variables and parameters valuable to NWP model.In this paper,we set up profiles of air temperature and water vapor from the surface to upper (0.1 hPa) using the radiosounding data and the surface data from May to August 1998 atmosphere East Asia.A TOVS RT model (RTTOV5) is provided to compute the value of radiation value of HIRS channels in NOAA14.Then the radiation values of 19 HIRS channels are gotten.After matching these data computed by the RT model and the corresponding values coming from satellite sounding in time,the statistic distribution of bias between tile model output and the satellite sounding at each sounding channel can be gotten.At the same time.the distribution of RMS to every TOVS HIRS channel,the standard biases to different scanning angle to each channel are also obtained.

Study of M-estimator Variational Retrieval Using Simulated Feng Yun-3A Data

This paper adopts satellite channel brightness temperature simulation to study M-estimator variational retrieval. This approach combines both the advantages of classical variational inversion and robust M-estimators. Classical variational inversion depends on prior quality control to elim- inate outliers, and its errors follow a Gaussian distribution. We coupled the M-estimators to the framework of classical variational inversion to obtain a M-estimator variational inversion. The cost function contains the M-estimator to guarantee the robustness to outliers and improve the retrieval re- sults. The experimental evaluation adopts Feng Yun-3A （FY-3A） simulated data to add to the Gaussian and Non-Gaussian error. The variational in- version is used to obtain the inversion brightness temperature, and temperature and humidity data are used for validation. The preliminary results demonstrate the potential of M-estimator variational retrieval.

Efficient Strip-Mode SAR Raw-Data Simulator of Extended Scenes Included Moving Targets Based on Reversion of Series

The Synthetic Aperture Radar(SAR)raw data generator is required to the evaluation of focusing algorithms,moving target analysis,and hardware design.The time-domain SAR simulator can generate the accurate raw data but it needs much time.The frequency-domain simulator not only increases the efficiency but also considers the trajectory deviations of the radar.In addition,the raw signal of the extended scene included static and moving targets can be generated by some frequency-domain simulators.However,the existing simulators concentrate on the raw signal simulation of the static extended scene and moving targets at uniform speed mostly.As for the issue,the two-dimensional signal spectrum of moving targets with constant acceleration can be derived accurately based on the geometric model of a side-looking SAR and reversion of series.And a frequency-domain algorithm for SAR echo signal simulation is presented based on the two-dimensional signal spectrum.The raw data generated with proposed method is verified by several simulation experiments.In addition to reveal the efficiency of the presented frequency-domain SAR scene simulator,the computational complexity of the proposed method is compared with the time-domain approach using the complex multiplication.Numerical results demonstrate that the present method can reduce the computational time significantly without accuracy loss while simulating SAR raw data.

ANALYSIS OF THE IMPACT OF SAMPLE SIZE, ATTRIBUTE VARIANCE AND WITHIN-SAMPLE CHOICE DISTRIBUTION ON THE ESTIMATION ACCURACY OF MULTINOMIAL LOGIT MODELS USING SIMULATED DATA

Literature review indicates that sample size, attribute variance and within-sample choice distribution of alternatives are important considerations in the estimation of multinomial logit （MNL） models, but their impacts on the estimation accuracy have not been systematically studied. Therefore, the objective of this paper is to provide an empirical examination to the above issues through a set of simulated discrete choice preference and rank ordered preference datasets. In this paper, the utility coefficients, alternative specific constants （ASCs）, and the mean and standard deviation of the four attributes for a set of seven hypothetical alternatives are specified as a priori. Then, synthetic datasets, with varying sample size, attribute variance and within-sample choice distribution are simulated. Based on these datasets, the utility coefficients and ASCs of the specified MNLs are re-estimated and compared with the original values specified as the priori. It is found that （1） the estimation accuracy of utility parameters increases as the sample size increases; （2） the utility coefficients can be re-estimated with reasonable accuracy, but the estimates of the ASCs are confronted with much larger errors; （3） as the variances of the alternative attributes increase, the estimation accuracy improves significantly; and （4） as the distribution of chosen choices becomes more balanced across alternatives within sample datasets, the hit-ratio decreases. The results indicate that （a） under a similar setting presented in this paper, a large sample consisting of a few thousand observations （3000 - 4000） may be needed in order to provide reasonable estimates for utility coefficients, particularly for ASCs; （b） a larger, but realistic attribute space is preferred in the stated preference survey design; and （c） choice datasets with unbalanced ＂chosen＂ choice frequency distribution is preferred, in order to better capture the elasticity between the ＂perceived utility＂ associated with alternative＇s attributes.

Performance Anomaly Detection in Web Services: An RNN-Based Approach Using Dynamic Quality of Service Features

Performance anomaly detection is the process of identifying occurrences that do not conform to expected behavior or correlate with other incidents or events in time series data.Anomaly detection has been applied to areas such as fraud detection,intrusion detection systems,and network systems.In this paper,we propose an anomaly detection framework that uses dynamic features of quality of service that are collected in a simulated setup.Three variants of recurrent neural networks-SimpleRNN,long short term memory,and gated recurrent unit are evaluated.The results reveal that the proposed method effectively detects anomalies in web services with high accuracy.The performance of the proposed anomaly detection framework is superior to that of existing approaches using maximum accuracy and detection rate metrics.

Sensitivity Study of the RegCM4’s Surface Schemes in the Simulations of West Africa Climate

Two simulations of five years (2003-2007) were conducted with the Regional Climate models RegCM4, one coupled with Land surface models BATS and the other with CLM4.5 over West Africa, where simulated air temperature and precipitation were analyzed. The purpose of this study is to assess the performance of RegCM4 coupled with the new CLM4.5 Land surface scheme and the standard one named BATS in order to find the best configuration of RegCM4 over West African. This study could improve our understanding of the sensitivity of land surface model in West Africa climate simulation, and provide relevant information to RegCM4 users. The results show fairly realistic restitution of West Africa’s climatology and indicate correlations of 0.60 to 0.82 between the simulated fields (BATS and CLM4.5) for precipitation. The substitution of BATS surface scheme by CLM4.5 in the model configuration, leads mainly to an improvement of precipitation over the Atlantic Ocean, however, the impact is not sufficiently noticeable over the continent. While the CLM4.5 experiment restores the seasonal cycles and spatial distribution, the biases increase for precipitation and temperature. Positive biases already existing with BATS are amplified over some sub-regions. This study concludes that temporal localization (seasonal effect), spatial distribution (grid points) and magnitude of precipitation and temperature (bias) are not simultaneously improved by CLM4.5. The introduction of the new land surface scheme CLM4.5, therefore, leads to a performance of the same order as that of BATS, albeit with a more detailed formulation.

EFFICIENT STRIP-MODE SAR RAW SIGNAL SIMULATION OF MIXED TARGETS BASED ON ACCURATE 2-D SPECTRUM

An accurate and efficient Synthetic Aperture Radar(SAR)raw data generator is of considerable value for testing system parameters and verifying imaging algorithms.Nevertheless,the existing simulator cannot exactly handle the case of the fast moving targets in high squint geometry.As for the issue,the analytical expression for the two Dimensional(2-D)signal spectrum of moving targets is derived and a fast raw echo simulation method is proposed in this study.The proposed simulator can accommodate the moving targets in the high squint geometry,whose processing steps of the simulation are given in detail and its computational complexity is analyzed.The simulation data for static and moving targets are processed and analyzed,and the results are given to validate the effectiveness of the proposed approach.

Research on Credit Regulation Mechanism of E-commerce Platform Based on Evolutionary Game Theory

In the growing e-commerce industry,the problem of malicious business operations has become increasingly prominent,exposing many problems such as weak supervision of e-commerce platforms and no way for consumers to complain.In order to solve the problems of counterfeit and shoddy products on e-commerce platforms and promote the sustainable development of the e-commerce industry in China,this paper constructs a three-party evolutionary game model of e-commerce platforms,merchants,and consumers,investigates the influence of each influencing factor on each party’s strategy choice,and provides targeted suggestions to e-commerce platforms based on relevant factors.Finally,the impact of several important parameters on the equilibrium solution is discussed through sensitivity analysis.The results show that:1)the smaller the cost difference between active and negative regulation,the more the e-commerce platform tends to active regulation strategy,but increasing fines for dishonest merchants and consumer complaints have little impact on the e-commerce platform;2)increasing consumer compensation,creating an honest business environment,and reducing the cost of honest business all help companies tend to operate in good faith;3)the only factor that affects the tendency of consumer complaint strategies is the cost of complaints.The loss suffered by silence and the compensation given to consumers have little effect on consumers’tendency to complain strategy.The results can provide theoretical guidance for participants to make useful strategic decisions in the e-commerce market.

Evaluation of the applicability of climate forecast system reanalysis weather data for hydrologic simulation： A case study in the Bahe River Basin of the Qinling Mountains, China

In recent years, global reanalysis weather data has been widely used in hydrological modeling around the world, but the results of simulations vary greatly. To consider the applicability of Climate Forecast System Reanalysis（CFSR） data in the hydrologic simulation of watersheds, the Bahe River Basin was used as a case study. Two types of weather data（conventional weather data and CFSR weather data） were considered to establish a Soil and Water Assessment Tool（SWAT） model, which was used to simulate runoff from 2001 to 2012 in the basin at annual and monthly scales. The effect of both datasets on the simulation was assessed using regression analysis, Nash-Sutcliffe Efficiency（NSE）, and Percent Bias（PBIAS）. A CFSR weather data correction method was proposed. The main results were as follows.（1） The CFSR climate data was applicable for hydrologic simulation in the Bahe River Basin（R^2 of the simulated results above 0.50, NSE above 0.33, and ｜PBIAS｜ below 14.8. Although the quality of the CFSR weather data is not perfect, it achieved a satisfactory hydrological simulation after rainfall data correction.（2） The simulated streamflow using the CFSR data was higher than the observed streamflow, which was likely because the estimation of daily rainfall data by CFSR weather data resulted in more rainy days and stronger rainfall intensity than was actually observed. Therefore, the data simulated a higher base flow and flood peak discharge in terms of the water balance, except for some individual years.（3） The relation between the CFSR rainfall data（x） and the observed rainfall data（y） could berepresented by a power exponent equation： y=1.4789x0.8875（R2=0.98,P〈0.001）. There was a slight variation between the fitted equations for each station. The equation provides a theoretical basis for the correction of CFSR rainfall data.

Development of Spectral Features for Monitoring Rice Bacterial Leaf Blight Disease Using Broad-Band Remote Sensing Systems

As an important rice disease, rice bacterial leaf blight (RBLB, caused by the bacterium Xanthomonas oryzae pv.oryzae), has become widespread in east China in recent years. Significant losses in rice yield occurred as a result ofthe disease’s epidemic, making it imperative to monitor RBLB at a large scale. With the development of remotesensing technology, the broad-band sensors equipped with red-edge channels over multiple spatial resolutionsoffer numerous available data for large-scale monitoring of rice diseases. However, RBLB is characterized by rapiddispersal under suitable conditions, making it difficult to track the disease at a regional scale with a single sensorin practice. Therefore, it is necessary to identify or construct features that are effective across different sensors formonitoring RBLB. To achieve this goal, the spectral response of RBLB was first analyzed based on the canopyhyperspectral data. Using the relative spectral response (RSR) functions of four representative satellite or UAVsensors (i.e., Sentinel-2, GF-6, Planet, and Rededge-M) and the hyperspectral data, the corresponding broad-bandspectral data was simulated. According to a thorough band combination and sensitivity analysis, two novel spectralindices for monitoring RBLB that can be effective across multiple sensors (i.e., RBBRI and RBBDI) weredeveloped. An optimal feature set that includes the two novel indices and a classical vegetation index was formed.The capability of such a feature set in monitoring RBLB was assessed via FLDA and SVM algorithms. The resultdemonstrated that both constructed novel indices exhibited high sensitivity to the disease across multiple sensors.Meanwhile, the feature set yielded an overall accuracy above 90% for all sensors, which indicates its cross-sensorgenerality in monitoring RBLB. The outcome of this research permits disease monitoring with different remotesensing data over a large scale.

A Survey of Multi-Space Techniques in Spatio-Temporal Simulation Data Visualization

The widespread use of numerical simulations in different scientific domains provides a variety of research opportunities.They often output a great deal of spatio-temporal simulation data,which are traditionally characterized as single-run,multi-run,multi-variate,multi-modal and multi-dimensional.From the perspective of data exploration and analysis,we noticed that many works focusing on spatiotemporal simulation data often share similar exploration techniques,for example,the exploration schemes designed in simulation space,parameter space,feature space and combinations of them.However,it lacks a survey to have a systematic overview of the essential commonalities shared by those works.In this survey,we take a novel multi-space perspective to categorize the state-ofthe-art works into three major categories.Specifically,the works are characterized as using similar techniques such as visual designs in simulation space(e.g,visual mapping,boxplot-based visual summarization,etc.),parameter space analysis(e.g,visual steering,parameter space projection,etc.)and data processing in feature space(e.g,feature definition and extraction,sampling,reduction and clustering of simulation data,etc.).

Temporal-spatial risk assessment of COVID-19 under the influence of urban spatial environmental parameters:The case of Shenyang city

Respiratory infection is the main route for the transmission of coronavirus pneumonia,and the results have shown that the urban spatial environment significantly influences the risk of infection.Based on the Wells-Riley model of respiratory infection probability,the study determined the human respiratory-related parameters and the effective influence range;extracted urban morphological parameters,assessed the ventilation effects of different spatial environments,and,combined with population flow monitoring data,constructed a method for assessing the risk of Covid-19 respiratory infection in urban-scale grid cells.In the empirical study in Shenyang city,a severe cold region,urban morphological parameters,population size,background wind speed,and individual behavior patterns were used to calculate the distribution characteristics of temporal and spatial concomitant risks in urban areas grids under different scenarios.The results showed that the correlation between the risk of respiratory infection in urban public spaces and the above variables was significant.The exposure time had the greatest degree of influence on the probability of respiratory infection risk among the variables.At the same time,the change in human body spacing beyond 1 m had a minor influence on the risk of infection.Among the urban morphological parameters,building height had the highest correlation with the risk of infection,while building density had the lowest correlation.The actual point distribution of the epidemic in Shenyang from March to April 2022 was used to verify the evaluation results.The overlap rate between medium or higher risk areas and actual cases was 78.55%.The planning strategies for epidemic prevention and control were proposed for the spatial differentiation characteristics of different risk elements.The research results can accurately classify the risk level of urban space and provide a scientific basis for the planning response of epidemic prevention and control and the safety of public activities.

An integrated virtual geographic environmental simulation framework:a case study of flood disaster simulation

Dynamic flood disaster simulation is an emerging and promising technology significantly useful in urban planning,risk assessment,and integrated decision support systems.It is still an important issue to integrate the large assets such as dynamic observational data,numerical flood simulation models,geographic information technologies,and computing resources into a unified framework.For the intended end user,it is also a holistic solution to create computer interpretable representations and gain insightful understanding of the dynamic disaster processes,the complex impacts,and interactions of disaster factors.In particular,it is still difficult to access and join harmonized data,processing algorithms,and models that are provided by different environmental information infrastructures.In this paper,we demonstrate a virtual geographic environments-based integrated environmental simulation framework for flood disaster management based on the notion of interlinked resources,which is capable of automated accumulating and manipulating of sensor data,creating dynamic geo-analysis and three-dimensional visualizations of ongoing geo-process,and updating the contents of simulation models representing the real environment.The prototype system is evaluated by applying it as a proof of concept to integrate in situ weather observations,numerical weather and flood disaster simulation models,visualization,and analysis of the real time flood event.Case applications indicate that the developed framework can be adopted for use by decision-makers for short-term planning and control since the resulting simulation and visualization are completely based on the latest status of environment.

Experimental-computational study of fibrous particle transport and deposition in a bifurcating lung model

Experiments carried out using a lung model with a single horizontal bifurcation under different steady inhalation conditions explored the orientation of depositing carbon fibers, and particle deposition frac- tions. The orientations of deposited fibers were obtained from micrographs. Specifically, the effects of the sedimentation parameter （γ）, fiber length, and flow rate on orientations were analyzed. Our results indicate that gravitational effect on deposition cannot be neglected for 0.0228 〈 γ 〈 0.247. The absolute orientation angle of depositing fibers decreased linearly with increasing y for values 0.0228 〈 γ 〈 0.15. Correspondence between Stokes numbers and y suggests these characteristics can be used to estimate fiber deposition in the lower airways. Computer simulations with sphere-equivalent diameter models for the fibers explored deposition efficiency vs. Stokes number. Using the volume-equivalent diameter model, our experimental data for the horizontal bifurcation were replicated. Results for particle deposition using a lung model with a vertical bifurcation indicate that body position also affects deposition.

WaveLines:towards effective visualization and analysis of stability in power grid simulation

Closely related to the safety and stability of power grids,stability analysis has long been a core topic in the electric industry.Conventional approaches employ computational simulation to make the quantitative judgement of the grid stability under distinctive conditions.The lack of in-depth data analysis tools has led to the difficulty in analytical tasks such as situation-aware analysis,instability reasoning and pattern recognition.To facilitate visual exploration and reasoning on the simulation data,we introduce WaveLines,a visual analysis approach which supports the supervisory control of multivariate simulation time series of power grids.We design and implement an interactive system that supports a set of analytical tasks proposed by domain experts and experienced operators.Experiments have been conducted with domain experts to illustrate the usability and effectiveness of WaveLines.

Generative Adversarial Network Based Status Generation Simulation Approach

The generative adversarial network based methods have beenapplied in many fields for simulation data generation. For power equipment,due to the combined influences of multiple factors, how to generatereasonable simulation data that meets specific requirements has becomea challenge. This paper proposes a power equipment status generationapproach based on generative confrontation network. This approach considersthe changing factors of power equipment and takes it as the conditionaldistribution of simulation data during training. The proposedapproach is applied to the status generation of power equipment, andthe rationality and effectiveness of the approach are verified throughexperiments.