Acrylic acid was grafted onto the surface of cotton fabric after being short time treated by corona-discharge inair in the presence of initiator.The means of gas-phaseSO<sub>2</sub> derivatization was used...Acrylic acid was grafted onto the surface of cotton fabric after being short time treated by corona-discharge inair in the presence of initiator.The means of gas-phaseSO<sub>2</sub> derivatization was used along with ESCA to deter-mine corona-discharge-induced-hydroperoxidegroups on the surface.The content of hydroperoxideshows a maxmium value at 15 sec.of corona-dischargetime.Effect of corona treatment time and various con-centration initlator on graft yield was studied.The addit-ion of initiator increases the graft yield.Acceleratedgraft with an increase in the concentration of Mohr’s saltshows that peroxide groups on the corona treated cottonfabric initiate graft copolymerization.展开更多
Multiuser detection can be described as a quadratic optimization problem with binary constraint. Many techniques are available to find approximate solution to this problem. These tech- niques can be characterized in t...Multiuser detection can be described as a quadratic optimization problem with binary constraint. Many techniques are available to find approximate solution to this problem. These tech- niques can be characterized in terms of complexity and detection performance. The ＂efficient frontier＂ of known techniques include the decision-feedback, branch-and-bound and probabilistic data association detectors. The presented iterative multiuser detection technique is based on joint deregularized and box-constrained so- lution to quadratic optimization with iterations similar to that used in the nonstationary Tikhonov iterated algorithm. The deregulari- zation maximizes the energy of the solution, this is opposite to the Tikhonov regularization where the energy is minimized. However, combined with box-constraints, the deregularization forces the solution to be close to the binary set. We further exploit the box- constrained dichotomous coordinate descent （DCD） algorithm and adapt it to the nonstationary iterative Tikhonov regularization to present an efficient detector. As a result, the worst-case and aver- age complexity are reduced down to K28 and K2~ floating point operation per second, respectively. The development improves the ＂efficient frontier＂ in multiuser detection, which is illustrated by simulation results. Finally, a field programmable gate array （FPGA） design of the detector is presented. The detection performance obtained from the fixed-point FPGA implementation shows a good match to the floating-point implementation.展开更多
This paper presents a trust region algorithm for nonlinear optimization with linear inequality constraints. The global convergence of the algorithm is proved. Local quadratic convergence is obtained for a strong local...This paper presents a trust region algorithm for nonlinear optimization with linear inequality constraints. The global convergence of the algorithm is proved. Local quadratic convergence is obtained for a strong local minimizer.展开更多
随着工业互联网的蓬勃发展,工业生产需要满足用户的个性化需求.由于个性化产品规格多样种类繁多,一个高效的智能排产方法对企业的生产制造尤为重要.从部署模式来看,现有的智能排产系统可分为企业本地部署和云排产服务两类.本地排产系统...随着工业互联网的蓬勃发展,工业生产需要满足用户的个性化需求.由于个性化产品规格多样种类繁多,一个高效的智能排产方法对企业的生产制造尤为重要.从部署模式来看,现有的智能排产系统可分为企业本地部署和云排产服务两类.本地排产系统的计算与存储资源相对有限,难以满足精确排产算法的需求;而云排产系统需要大量工业核心排产数据的支撑并按需计费,计算存储与网络传输的开销使排产服务成本较高.此外,工业核心数据上传至云可能存在数据泄露的风险.针对以上问题,本文以钢铁热轧生产为例,将边缘计算技术引入智能排产,提出了一种云边协作的工业互联网排产框架(Production Scheduling based on Edge-Cloud-Collaboration,PSECC),本框架在边缘端预处理原始工业数据,保证核心生产数据保留在企业端;在云端进行算法求解,通过部署通用型求解算法又为框架赋予了可扩展性.本文基于PSECC框架设计实现了针对钢铁热轧排产任务的云边分解方法,实验证明本文提出的云边协作排产方法与常规求解器的性能相似,但是可以避免工业核心数据上云,且云端求解器的选择更加灵活.在性能方面,云排产的总排产时间是PSECC的1.4~3.7倍,其中网络传输时间是10~15倍.展开更多
文摘Acrylic acid was grafted onto the surface of cotton fabric after being short time treated by corona-discharge inair in the presence of initiator.The means of gas-phaseSO<sub>2</sub> derivatization was used along with ESCA to deter-mine corona-discharge-induced-hydroperoxidegroups on the surface.The content of hydroperoxideshows a maxmium value at 15 sec.of corona-dischargetime.Effect of corona treatment time and various con-centration initlator on graft yield was studied.The addit-ion of initiator increases the graft yield.Acceleratedgraft with an increase in the concentration of Mohr’s saltshows that peroxide groups on the corona treated cottonfabric initiate graft copolymerization.
基金supported by the National Council for Technological and Scientific Development of Brazil (RN82/2008)
文摘Multiuser detection can be described as a quadratic optimization problem with binary constraint. Many techniques are available to find approximate solution to this problem. These tech- niques can be characterized in terms of complexity and detection performance. The ＂efficient frontier＂ of known techniques include the decision-feedback, branch-and-bound and probabilistic data association detectors. The presented iterative multiuser detection technique is based on joint deregularized and box-constrained so- lution to quadratic optimization with iterations similar to that used in the nonstationary Tikhonov iterated algorithm. The deregulari- zation maximizes the energy of the solution, this is opposite to the Tikhonov regularization where the energy is minimized. However, combined with box-constraints, the deregularization forces the solution to be close to the binary set. We further exploit the box- constrained dichotomous coordinate descent （DCD） algorithm and adapt it to the nonstationary iterative Tikhonov regularization to present an efficient detector. As a result, the worst-case and aver- age complexity are reduced down to K28 and K2~ floating point operation per second, respectively. The development improves the ＂efficient frontier＂ in multiuser detection, which is illustrated by simulation results. Finally, a field programmable gate array （FPGA） design of the detector is presented. The detection performance obtained from the fixed-point FPGA implementation shows a good match to the floating-point implementation.
文摘This paper presents a trust region algorithm for nonlinear optimization with linear inequality constraints. The global convergence of the algorithm is proved. Local quadratic convergence is obtained for a strong local minimizer.
文摘随着工业互联网的蓬勃发展,工业生产需要满足用户的个性化需求.由于个性化产品规格多样种类繁多,一个高效的智能排产方法对企业的生产制造尤为重要.从部署模式来看,现有的智能排产系统可分为企业本地部署和云排产服务两类.本地排产系统的计算与存储资源相对有限,难以满足精确排产算法的需求;而云排产系统需要大量工业核心排产数据的支撑并按需计费,计算存储与网络传输的开销使排产服务成本较高.此外,工业核心数据上传至云可能存在数据泄露的风险.针对以上问题,本文以钢铁热轧生产为例,将边缘计算技术引入智能排产,提出了一种云边协作的工业互联网排产框架(Production Scheduling based on Edge-Cloud-Collaboration,PSECC),本框架在边缘端预处理原始工业数据,保证核心生产数据保留在企业端;在云端进行算法求解,通过部署通用型求解算法又为框架赋予了可扩展性.本文基于PSECC框架设计实现了针对钢铁热轧排产任务的云边分解方法,实验证明本文提出的云边协作排产方法与常规求解器的性能相似,但是可以避免工业核心数据上云,且云端求解器的选择更加灵活.在性能方面,云排产的总排产时间是PSECC的1.4~3.7倍,其中网络传输时间是10~15倍.
