Solid fuel use(SFU)is common in most developing countries and would release many hazardous air pollutants posing high risks on human health.The Global Burden of Disease(GBD)study highlighted risks associated with hous...Solid fuel use(SFU)is common in most developing countries and would release many hazardous air pollutants posing high risks on human health.The Global Burden of Disease(GBD)study highlighted risks associated with household SFU in Pakistan,however,high uncertainties prevail because of scanty data on SFU and unaccounted energy stacking.This study conducted a field campaign aiming at collecting first-hand data on household energy mix in Pakistan.The first survey was in Punjab and Azad Kashmir,and revealed that stacked energy use was pervasive,especially for cooking.The stacking was found to be much more obvious in SFU households(defined as those using SFU dominantly)compared to those non-SFU.There were significantly substantial differences between Azad Kashmir and Punjab because of distinct resources available and economic conditions.Woody materials comprised up to nearly 70% in Azad Kashmir,but in Punjab,gas was frequently used for cooking.Only investigating primary household energy would probably overestimate main energy types that being used for a longer time but underestimated other supplements,suggesting the preference of multiple-energy surveys in household energy studies.展开更多
Specific features of tile access patterns can be applied in a cache replacement strategy to a limited distributed high-speed cache for the cloud-based networked geographic information services(NGISs),aiming to adapt t...Specific features of tile access patterns can be applied in a cache replacement strategy to a limited distributed high-speed cache for the cloud-based networked geographic information services(NGISs),aiming to adapt to changes in the access distribution of hotspots.By taking advantage of the spatiotemporal locality,the sequential features in tile access patterns,and the cache reading performance in the burst mode,this article proposes a tile sequence replacement method,which involves structuring a Least Recently Used(LRU)stack into three portions for the different functions in cache replacement and deriving an expression for the temporal locality and popularity of the relevant tile to facilitate the replacement process.Based on the spatial characteristics of both the tiles and the cache burst mode with regard to reading data,the proposed method generates multiple tile sequences to reflect spatiotemporal locality in tile access patterns.Then,we measure the caching value by a technique based on a weighted-based method.This technique draws on the recent access popularity and low caching costs of tile sequences,with the aim of balancing the temporal and spatial localities in tile access.It ranks tile sequences in a replacement queue to adapt to the changes in accessed hotspots while reducing the replacement frequency.Experimental results show that the proposed method effectively improves the hit rate and utilization rate for a limited distributed cache while achieving satisfactory response performance and high throughput for users in an NGIS.Therefore,it can be adapted to handle numerous data access requests in NGISs in a cloud-based environment.展开更多
Guaranteeing the safety of equipment is extremely important in industry.To improve reliability and availability of equipment,various methods for prognostics and health management(PHM)have been proposed.Predicting rema...Guaranteeing the safety of equipment is extremely important in industry.To improve reliability and availability of equipment,various methods for prognostics and health management(PHM)have been proposed.Predicting remaining useful life(RUL)of industrial equipment is a key aspect of PHM and it is always one of the most challenging issues.With the rapid development of industrial equipment and sensing technology,an increasing amount of data on the health level of equipment can be obtained for RUL prediction.This paper proposes a hybrid data-driven approach based on stacked denoising autoencode(SDAE)and similarity theory for estimating remaining useful life of industrial equipment,which is named RULESS.Our work is making the most of stacked SDAE and similarity theory to improve the accuracy of RUL prediction.The effectiveness of the proposed approach was evaluated by using aircraft engine health data simulated by commercial modular Aero-Propulsion system simulation(C-MAPSS).展开更多
基金supported by the National Natural Science Foundation of China(Nos.41922057,41830641 and 42077328)the Ministry of Science and Technology(No.2019QZKK0605)the undergraduate student research training program of the Ministry of Education(No.B111).
文摘Solid fuel use(SFU)is common in most developing countries and would release many hazardous air pollutants posing high risks on human health.The Global Burden of Disease(GBD)study highlighted risks associated with household SFU in Pakistan,however,high uncertainties prevail because of scanty data on SFU and unaccounted energy stacking.This study conducted a field campaign aiming at collecting first-hand data on household energy mix in Pakistan.The first survey was in Punjab and Azad Kashmir,and revealed that stacked energy use was pervasive,especially for cooking.The stacking was found to be much more obvious in SFU households(defined as those using SFU dominantly)compared to those non-SFU.There were significantly substantial differences between Azad Kashmir and Punjab because of distinct resources available and economic conditions.Woody materials comprised up to nearly 70% in Azad Kashmir,but in Punjab,gas was frequently used for cooking.Only investigating primary household energy would probably overestimate main energy types that being used for a longer time but underestimated other supplements,suggesting the preference of multiple-energy surveys in household energy studies.
基金This work was supported by the National Natural Science Foundation of China[grant number 41371370]the National Basic Research Program of China[grant number 2012CB719906].
文摘Specific features of tile access patterns can be applied in a cache replacement strategy to a limited distributed high-speed cache for the cloud-based networked geographic information services(NGISs),aiming to adapt to changes in the access distribution of hotspots.By taking advantage of the spatiotemporal locality,the sequential features in tile access patterns,and the cache reading performance in the burst mode,this article proposes a tile sequence replacement method,which involves structuring a Least Recently Used(LRU)stack into three portions for the different functions in cache replacement and deriving an expression for the temporal locality and popularity of the relevant tile to facilitate the replacement process.Based on the spatial characteristics of both the tiles and the cache burst mode with regard to reading data,the proposed method generates multiple tile sequences to reflect spatiotemporal locality in tile access patterns.Then,we measure the caching value by a technique based on a weighted-based method.This technique draws on the recent access popularity and low caching costs of tile sequences,with the aim of balancing the temporal and spatial localities in tile access.It ranks tile sequences in a replacement queue to adapt to the changes in accessed hotspots while reducing the replacement frequency.Experimental results show that the proposed method effectively improves the hit rate and utilization rate for a limited distributed cache while achieving satisfactory response performance and high throughput for users in an NGIS.Therefore,it can be adapted to handle numerous data access requests in NGISs in a cloud-based environment.
基金the National Key Research and DevelopmentProjectof China (No. 2018YFB1702600, 2018YFB1702602)National Natural Science Foundationof China (No. 61402167, 61772193, 61872139)+1 种基金Hunan Provincial Natural ScienceFoundation of China (No. 2017JJ4036, 2018JJ2139)Research Foundation of HunanProvincial Education Department of China (No.17K033, 19A174).
文摘Guaranteeing the safety of equipment is extremely important in industry.To improve reliability and availability of equipment,various methods for prognostics and health management(PHM)have been proposed.Predicting remaining useful life(RUL)of industrial equipment is a key aspect of PHM and it is always one of the most challenging issues.With the rapid development of industrial equipment and sensing technology,an increasing amount of data on the health level of equipment can be obtained for RUL prediction.This paper proposes a hybrid data-driven approach based on stacked denoising autoencode(SDAE)and similarity theory for estimating remaining useful life of industrial equipment,which is named RULESS.Our work is making the most of stacked SDAE and similarity theory to improve the accuracy of RUL prediction.The effectiveness of the proposed approach was evaluated by using aircraft engine health data simulated by commercial modular Aero-Propulsion system simulation(C-MAPSS).
