Efficient Rate Control Algorithm for Multi-view Video Coding

Rate control is one of the key factors influencing the multi-view video transmission.However,there is not a rate control algorithm in the existing Joint Multi-view Video Coding Model.In this paper,an efficient rate control algorithm and a bit allocation strategy for multi-view video coding are proposed.In order to obtain the consistent view quality,a bit allocation model based on the Lagrange optimum algorithm is firstly proposed.Secondly,considering the encoding statistical characteristics of different view types,a view weighting factor is introduced,and it will help improve the precision of bit allocation among views.Compared with the fixed QP control strategy,experiment results show that the proposed algorithm can efficiently control the bit rate and obtain more consistent views,with video visual quality improved.

Machine Learning for heat radiation modeling of bi-and polydisperse particle systems including walls

We investigated the ability of four popular Machine Learning methods i.e.,Deep Neural Networks(DNNs),Random Forest-based regressors(RFRs),Extreme Gradient Boosting-based regressors(XGBs),and stacked ensembles of DNNs,to model the radiative heat transfer based on view factors in bi-and polydisperse particle beds including walls.Before training and analyzing the predictive capability of each method,an adjustment of markers used in monodisperse systems,as well as an evaluation of new markers was performed.On the basis of our dataset that considers a wide range of particle radii ratios,system sizes,particle volume fractions,as well as different particle-species volume fractions,we found that(i)the addition of particle size information allows the transition from monodisperse to bi-and polydisperse beds,and(ii)the addition of particle volume fraction information as the fourth marker leads to very accurate predictions.In terms of the overall performance,DNNs and RFRs should be preferred compared to the other two options.For particle-particle view factors,DNN and RFR are on par,while for particle-wall the RFR is superior.We demonstrate that DNNs and RFRs can be built to meet or even exceed the prediction quality standards achieved in a monodisperse system.

Numerical Evaluation on the Thermal Performance of the Solar External Cylinder Receiver using Monte Carlo Ray-Tracing Algorithm

The heat receiver is an essential part of the Concentrating Solar Power plant,directly affecting its operation and safety.In this paper,the Monte Carlo ray-tracing algorithm was introduced to evaluate a 50 MW(e)external cylindrical receiver’s thermal performance.The radiation heat flux concentrated from the heliostats field and the view factors between grids divided from the tubes were both calculated using Monte Carlo ray-tracing algorithm.Besides,an in-house code was developed and verified,including three modules of the view-factor calculation,thermal performance calculation,and thermal stress calculation.It was also employed to investigate the 50 MW(e)receiver,and the detailed 3D profiles of temperature and thermal stress in the receiver were analyzed.It was found that the molten salt was heated from 298℃to 565℃and the tube at the 50 MW(e)receiver’s outlet had a high temperature,while the high thermal stress came out at the receiver’s entrance.Finally,the over-temperature of the receiver was discussed,and an optimization algorithm was introduced.The tube wall temperature and film temperature at the overheated area matched the safety criteria,and the outlet molten salt temperature still reached 563℃after the optimization process,with only 2℃dropped.

Geohazard Recognition and Inventory Mapping Using Airborne LiDAR Data in Complex Mountainous Areas

Geohazard recognition and inventory mapping are absolutely the keys to the establishment of reliable susceptibility and hazard maps. However, it has been challenging to implement geohazards recognition and inventory mapping in mountainous areas with complex topography and vegetation cover. Progress in the light detection and ranging(Li DAR) technology provides a new possibility for geohazard recognition in such areas. Specifically, this study aims to evaluate the performances of the Li DAR technology in recognizing geohazard in the mountainous areas of Southwest China through visually analyzing airborne Li DAR DEM derivatives. Quasi-3 D relief image maps are generated based on the sky-view factor(SVF), which makes it feasible to interpret precisely the features of geohazard. A total of 146 geohazards are remotely mapped in the entire 135 km^(2) study area in Danba County, Southwest China, and classified as landslide, rock fall, debris flow based on morphologic characteristics interpreted from SVF visualization maps. Field validation indicate the success rate of Li DAR-derived DEM in recognition and mapping geohazard with higher precision and accuracy. These mapped geohazards lie along both sides of the river, and their spatial distributions are related highly to human engineering activities, such as road excavation and slope cutting. The minimum geohazard that can be recognized in the 0.5 m resolution DEM is about 900 m^(2). Meanwhile, the SVF visualization method is demonstrated to be a great alternative to the classical hillshaded DEM method when it comes to the determination of geomorphological properties of geohazard. Results of this study highlight the importance of Li DAR data for creating complete and accurate geohazard inventories, which can then be used for the production of reliable susceptibility and hazard maps and thus contribute to a better understanding of the movement processes and reducing related losses.

Impacts of the Urban Spatial Landscape in Beijing on Surface and Canopy Urban Heat Islands

How does the urban spatial landscape(USL)pattern affect the land surface urban heat islands(SUHIs)and canopy urban heat islands(CUHIs)?Based on satellite and meteorological observations,this case study compares the impacts of the USL pattern on SUHI and CUHI in the central urban area(CUA)of Beijing using the satellite land-surface-temperature product and hourly temperature data from automatic meteorological stations from 2009 to 2018.Eleven USL metrics—building height(BH),building density(BD),standard deviation of building height(BSD),floor area ratio(FAR),frontal area index(FAI),roughness length(RL),sky view factor(SVF),urban fractal dimension(FD),vegetation coverage(VC),impervious coverage(IC),and albedo(AB)—with a 500-m spatial resolution in the CUA are extracted for comparative analysis.The results show that SUHI is higher than CUHI at night,and SUHI is only consistent with CUHI at spatial-temporal scales at night,particularly in winter.Spatially,all 11 metrics are strongly correlated with both the SUHI and CUHI at night,with stronger correlation between most metrics and SUHI.VC,AB,and SVF have the greatest impact on both the SUHI and CUHI.High SUHI and CUHI values tend to appear in areas with BD≥0.26,VC≤0.09,AB≤0.09,and SVF≤0.67.In summer,most metrics have a greater impact on the SUHI than CUHI;the opposite is observed in winter.SUHI variation is affected primarily by VC in summer and by VC and AB in winter,which is different for the CUHI variation.The collective contribution of all 11metrics to SUHI spatial variation in summer(61.8%)is higher than that to CUHI;however,the opposite holds in winter and for the entire year,where the cumulative contribution of the factors accounts for 66.6%and 49.6%,respectively,of the SUHI variation.

An efficient method of evaluating large scale urban residential skylight environment and an empirical study of Beijing main area

A good skylight environment in urban residential areas is an important component of a healthy city,and has always been highly valued.With the rapid development of new-type urbanization,the density of buildings continues to increase,and megacities have entered the stage of stock transformation.An effective method for evaluating the skylight environment of large-scale urban residential areas is urgently needed.However,there is still a lack of empirical research methods and cases of large-scale residential skylight environment.In this regard,this article takes the megacity Beijing as the research object,and proposes an efficient analysis method of residential skylight environment that integrates multiple real-world data at city scale.In terms of data,it collects and integrates 3D data of urban-scale building space and residential boundary data;in terms of algorithm,Sky View Factor(SVF)is used as the evaluation index of residential skylight environment,and an efficient analysis method of urban-scale skylight environment based on cloud parallel simulation is realized.Through analysis,it is found that:(1)the average SVF value of Beijing residential area is 61%,which means that its skylight quality is in general level;(2)the skylight environment of Beijing residential area is distributed in a circle,and there are 4 types of skylight environment quality residential areas;(3)The skylight environment of Beijing residential area is relatively weakly related to the distance from the residential area to the city center and the average height of the residential buildings,and is closely related to the plot volume ratio,the residential building density and the shading from surrounding buildings.The highlight of this study lies in the empirical research on the skylight environment of mega-city residential areas that incorporates multiple real data for the first time,which can promote the study of skylight environment on a city scale and provide a reference for the updating of Beijing’s residential daylight environment.

Numerical Prediction of Mean Radiant Temperature in Radiant Cooling Indoor Environments

Mean radiant temperature(MRT)is an indispensable physical parameter of indoor thermal environments.Especially in indoor environments controlled by radiant systems,MRT plays an important role in thermal comfort.In order to determine MRT of indoor environments controlled by radiant cooling systems quickly and inexpensively,a numerical program is developed in this study.Based on the finite element method(FEM),view factors and radiant temperature fields are numerically calculated.The singular solution problem generated by FEM is corrected using the Monte Carlo method.The numerical program is validated against the results of an experiment performed in a radiant cooling laboratory and the reported data from previous studies.Then radiant temperature fields of different shaped surfaces in a radiant cooling indoor environment are predicted,and thermal comfort level is preliminarily evaluated.