In cloud computing Resource allocation is a very complex task.Handling the customer demand makes the challenges of on-demand resource allocation.Many challenges are faced by conventional methods for resource allocatio...In cloud computing Resource allocation is a very complex task.Handling the customer demand makes the challenges of on-demand resource allocation.Many challenges are faced by conventional methods for resource allocation in order tomeet the Quality of Service(QoS)requirements of users.For solving the about said problems a new method was implemented with the utility of machine learning framework of resource allocation by utilizing the cloud computing technique was taken in to an account in this research work.The accuracy in the machine learning algorithm can be improved by introducing Bat Algorithm with feature selection(BFS)in the proposed work,this further reduces the inappropriate features from the data.The similarities that were hidden can be demoralized by the Support Vector Machine(SVM)classifier which is also determine the subspace vector and then a new feature vector can be predicted by using SVM.For an unexpected circumstance SVM model can make a resource allocation decision.The efficiency of proposed SVM classifier of resource allocation can be highlighted by using a singlecell multiuser massive Multiple-Input Multiple Output(MIMO)system,with beam allocation problem as an example.The proposed resource allocation based on SVM performs efficiently than the existing conventional methods;this has been proven by analysing its results.展开更多
The present study aims to give a detailed characterization of blast furnace sludge (BFS) by using different techniques, in order to determine the most effective recycling method to recover valuable metals from this wa...The present study aims to give a detailed characterization of blast furnace sludge (BFS) by using different techniques, in order to determine the most effective recycling method to recover valuable metals from this waste. BFS is composed mainly of hematite, as its iron-bearing phase, and carbon, in addition to fractions of silicate and carbonate materials. The studied BFS shows relatively high contents of iron (Fe) (390 g.kg-1), and carbon (C) (290 g.kg-1), due to abundance of hematite and coke, while the concentration of zinc (Zn) (2.5 g.kg-1) is low. The XRD analyses indicated that, hematite is more concentrated in the fine fraction (<20 μm), while the coarser fraction (90 - 250 μm) is dominated by calcite, quartz and X-ray amorphous coke. SEM-EDX analyses confirmed that particles rich in iron and zinc were detected in the fine fraction (<20 μm) of the sludge. Due to high Fe and C content in BFS, it can be utilized as self-reducing material and briquetting represent a potential method for recycling of blast furnace sludge.展开更多
文摘In cloud computing Resource allocation is a very complex task.Handling the customer demand makes the challenges of on-demand resource allocation.Many challenges are faced by conventional methods for resource allocation in order tomeet the Quality of Service(QoS)requirements of users.For solving the about said problems a new method was implemented with the utility of machine learning framework of resource allocation by utilizing the cloud computing technique was taken in to an account in this research work.The accuracy in the machine learning algorithm can be improved by introducing Bat Algorithm with feature selection(BFS)in the proposed work,this further reduces the inappropriate features from the data.The similarities that were hidden can be demoralized by the Support Vector Machine(SVM)classifier which is also determine the subspace vector and then a new feature vector can be predicted by using SVM.For an unexpected circumstance SVM model can make a resource allocation decision.The efficiency of proposed SVM classifier of resource allocation can be highlighted by using a singlecell multiuser massive Multiple-Input Multiple Output(MIMO)system,with beam allocation problem as an example.The proposed resource allocation based on SVM performs efficiently than the existing conventional methods;this has been proven by analysing its results.
文摘The present study aims to give a detailed characterization of blast furnace sludge (BFS) by using different techniques, in order to determine the most effective recycling method to recover valuable metals from this waste. BFS is composed mainly of hematite, as its iron-bearing phase, and carbon, in addition to fractions of silicate and carbonate materials. The studied BFS shows relatively high contents of iron (Fe) (390 g.kg-1), and carbon (C) (290 g.kg-1), due to abundance of hematite and coke, while the concentration of zinc (Zn) (2.5 g.kg-1) is low. The XRD analyses indicated that, hematite is more concentrated in the fine fraction (<20 μm), while the coarser fraction (90 - 250 μm) is dominated by calcite, quartz and X-ray amorphous coke. SEM-EDX analyses confirmed that particles rich in iron and zinc were detected in the fine fraction (<20 μm) of the sludge. Due to high Fe and C content in BFS, it can be utilized as self-reducing material and briquetting represent a potential method for recycling of blast furnace sludge.
