As the existing residual film crushing device in Xinjiang cannot directly crush membrane-impurity mixed material,by analyzing the compressive and cutting force characteristics of residual film material layer and cotto...As the existing residual film crushing device in Xinjiang cannot directly crush membrane-impurity mixed material,by analyzing the compressive and cutting force characteristics of residual film material layer and cotton stalk,the cutting conditions of mixed materials were obtained,and the method of cutting was determined.A multi-edge toothed cutters was created,and a cutting device was built.It was preliminarily determined that the number of teeth in the cutters was 8,the clearance between the teeth and between the tooth and fixed blade was 3 mm,the speed of the high-speed cutter was 800 r/min,and the speed difference between the high-and low-speed cutters was-300 r/min.Test results show that the ratio of residual film to total residual film sampling mass was(2.22±0.30)%,(19.19±2.02)%,(58.94±3.19)% and(20.65±2.05)%,respectively,when the maximum outer profile size in the range of[0,20)mm,[20,100)mm,[100,500)mm and[500,~)mm.The mass of cotton stalks with lengths of[0,50)mm,[50,100)mm and[100,~)mm accounted for(32.57±1.5)%,(27.77±1.3)%and(39.66±1.75)%,respectively,and the cutting power consumption was(85.41±15.63)kJ.The test results can provide a basis for the subsequent membrane-impurity mixed material cutting technology,as well as some guidance for the separation of it.展开更多
利用多边缘二分图代替传统的三分图,实现对低密度生成矩阵码(Low density generator matrix codes,LDGM码)的描述。基于多边缘二分图,提出多边缘置信度传播算法和滤波衰减消解方法,实现基于LDGM码的二进制信息压缩编码。仿真结果表明,...利用多边缘二分图代替传统的三分图,实现对低密度生成矩阵码(Low density generator matrix codes,LDGM码)的描述。基于多边缘二分图,提出多边缘置信度传播算法和滤波衰减消解方法,实现基于LDGM码的二进制信息压缩编码。仿真结果表明,该算法具有近香农限的压缩性能,并具有较低的复杂度。展开更多
To support the explosive growth of Information and Communications Technology(ICT),Mobile Edge Comput-ing(MEC)provides users with low latency and high bandwidth service by offloading computational tasks to the network...To support the explosive growth of Information and Communications Technology(ICT),Mobile Edge Comput-ing(MEC)provides users with low latency and high bandwidth service by offloading computational tasks to the network’s edge.However,resource-constrained mobile devices still suffer from a capacity mismatch when faced with latency-sensitive and compute-intensive emerging applications.To address the difficulty of running computationally intensive applications on resource-constrained clients,a model of the computation offloading problem in a network consisting of multiple mobile users and edge cloud servers is studied in this paper.Then a user benefit function EoU(Experience of Users)is proposed jointly considering energy consumption and time delay.The EoU maximization problem is decomposed into two steps,i.e.,resource allocation and offloading decision.The offloading decision is usually given by heuristic algorithms which are often faced with the challenge of slow convergence and poor stability.Thus,a combined offloading algorithm,i.e.,a Gini coefficient-based adaptive genetic algorithm(GCAGA),is proposed to alleviate the dilemma.The proposed algorithm optimizes the offloading decision by maximizing EoU and accelerates the convergence with the Gini coefficient.The simulation compares the proposed algorithm with the genetic algorithm(GA)and adaptive genetic algorithm(AGA).Experiment results show that the Gini coefficient and the adaptive heuristic operators can accelerate the convergence speed,and the proposed algorithm performs better in terms of convergence while obtaining higher EoU.The simulation code of the proposed algorithm is available:https://github.com/Grox888/Mobile_Edge_Computing/tree/GCAGA.展开更多
基金financially supported by the Fund for Less Developed Regions of the National Natural Science Foundation of China(Grant No.52065058)Graduate Education Innovation Project of Xinjiang Uygur Autonomous Region(Grant No.Xj2022G085)+2 种基金the Key Industry Innovation Development Support Plan of South Xinjiang(Grant No.2020DB008)the Open Fund of Jiangsu Province and Education Ministry Co-sponsored Synergistic Innovation Center of Modern Agricultural Equipment(Grant No.XTCX2006)Scientific and technological innovation team of Xinjiang Production and Construction Corps(Grant No.2020CB013).
文摘As the existing residual film crushing device in Xinjiang cannot directly crush membrane-impurity mixed material,by analyzing the compressive and cutting force characteristics of residual film material layer and cotton stalk,the cutting conditions of mixed materials were obtained,and the method of cutting was determined.A multi-edge toothed cutters was created,and a cutting device was built.It was preliminarily determined that the number of teeth in the cutters was 8,the clearance between the teeth and between the tooth and fixed blade was 3 mm,the speed of the high-speed cutter was 800 r/min,and the speed difference between the high-and low-speed cutters was-300 r/min.Test results show that the ratio of residual film to total residual film sampling mass was(2.22±0.30)%,(19.19±2.02)%,(58.94±3.19)% and(20.65±2.05)%,respectively,when the maximum outer profile size in the range of[0,20)mm,[20,100)mm,[100,500)mm and[500,~)mm.The mass of cotton stalks with lengths of[0,50)mm,[50,100)mm and[100,~)mm accounted for(32.57±1.5)%,(27.77±1.3)%and(39.66±1.75)%,respectively,and the cutting power consumption was(85.41±15.63)kJ.The test results can provide a basis for the subsequent membrane-impurity mixed material cutting technology,as well as some guidance for the separation of it.
文摘利用多边缘二分图代替传统的三分图,实现对低密度生成矩阵码(Low density generator matrix codes,LDGM码)的描述。基于多边缘二分图,提出多边缘置信度传播算法和滤波衰减消解方法,实现基于LDGM码的二进制信息压缩编码。仿真结果表明,该算法具有近香农限的压缩性能,并具有较低的复杂度。
文摘To support the explosive growth of Information and Communications Technology(ICT),Mobile Edge Comput-ing(MEC)provides users with low latency and high bandwidth service by offloading computational tasks to the network’s edge.However,resource-constrained mobile devices still suffer from a capacity mismatch when faced with latency-sensitive and compute-intensive emerging applications.To address the difficulty of running computationally intensive applications on resource-constrained clients,a model of the computation offloading problem in a network consisting of multiple mobile users and edge cloud servers is studied in this paper.Then a user benefit function EoU(Experience of Users)is proposed jointly considering energy consumption and time delay.The EoU maximization problem is decomposed into two steps,i.e.,resource allocation and offloading decision.The offloading decision is usually given by heuristic algorithms which are often faced with the challenge of slow convergence and poor stability.Thus,a combined offloading algorithm,i.e.,a Gini coefficient-based adaptive genetic algorithm(GCAGA),is proposed to alleviate the dilemma.The proposed algorithm optimizes the offloading decision by maximizing EoU and accelerates the convergence with the Gini coefficient.The simulation compares the proposed algorithm with the genetic algorithm(GA)and adaptive genetic algorithm(AGA).Experiment results show that the Gini coefficient and the adaptive heuristic operators can accelerate the convergence speed,and the proposed algorithm performs better in terms of convergence while obtaining higher EoU.The simulation code of the proposed algorithm is available:https://github.com/Grox888/Mobile_Edge_Computing/tree/GCAGA.
