Beam shaping is required for semiconductor lasers to achieve high optical fiber coupling efficiency in many applications.But the positioning errors on optics may reduce beam shaping effects,and then lead to low optica...Beam shaping is required for semiconductor lasers to achieve high optical fiber coupling efficiency in many applications.But the positioning errors on optics may reduce beam shaping effects,and then lead to low optical fiber coupling efficiency.In this work,the positioning errors models for the single emitter laser diode beam shaping system are established.Moreover,the relationships between the errors and the beam shaping effect of each shapers are analysed.Subsequently,the relationship between the errors and the optical fiber coupling efficiency is analysed.The result shows that position errors in the Z axis direction on the fast axis collimator have the greatest influence on the shaping effect,followed by the position errors in the Z axis direction on the converging lens,which should be strictly suppressed in actual operation.Besides,the position errors have a significant influence on the optical fiber coupling efficiency and need to be avoided.展开更多
A novel mixed integer linear programming (NMILP) model for detection of gross errors is presented in this paper. Yamamura et al.(1988) designed a model for detection of gross errors and data reconciliation based on Ak...A novel mixed integer linear programming (NMILP) model for detection of gross errors is presented in this paper. Yamamura et al.(1988) designed a model for detection of gross errors and data reconciliation based on Akaike information cri- terion (AIC). But much computational cost is needed due to its combinational nature. A mixed integer linear programming (MILP) approach was performed to reduce the computational cost and enhance the robustness. But it loses the super performance of maximum likelihood estimation. To reduce the computational cost and have the merit of maximum likelihood estimation, the simultaneous data reconciliation method in an MILP framework is decomposed and replaced by an NMILP subproblem and a quadratic programming (QP) or a least squares estimation (LSE) subproblem. Simulation result of an industrial case shows the high efficiency of the method.展开更多
基金Project(51475479) supported by the National Natural Science Foundation of ChinaProject(2017YFB1104800) supported by the National Key Research and Development Program of China+2 种基金Project(2016GK2098) supported by the Key Research and Development Program of Hunan Province,ChinaProject(ZZYJKT2017-07) supported by the State Key Laboratory of High Performance Complex Manufacturing,Central South University,ChinaProject(JMTZ201804) supported by the Key Laboratory for Precision&Non-traditional Machining of Ministry of Education,Dalian University of Technology,China
文摘Beam shaping is required for semiconductor lasers to achieve high optical fiber coupling efficiency in many applications.But the positioning errors on optics may reduce beam shaping effects,and then lead to low optical fiber coupling efficiency.In this work,the positioning errors models for the single emitter laser diode beam shaping system are established.Moreover,the relationships between the errors and the beam shaping effect of each shapers are analysed.Subsequently,the relationship between the errors and the optical fiber coupling efficiency is analysed.The result shows that position errors in the Z axis direction on the fast axis collimator have the greatest influence on the shaping effect,followed by the position errors in the Z axis direction on the converging lens,which should be strictly suppressed in actual operation.Besides,the position errors have a significant influence on the optical fiber coupling efficiency and need to be avoided.
基金Project supported by the National Creative Research Groups Science Foundation of China (No. 60421002)the National "Tenth Five-Year" Science and Technology Research Program of China (No.2004BA204B08)
文摘A novel mixed integer linear programming (NMILP) model for detection of gross errors is presented in this paper. Yamamura et al.(1988) designed a model for detection of gross errors and data reconciliation based on Akaike information cri- terion (AIC). But much computational cost is needed due to its combinational nature. A mixed integer linear programming (MILP) approach was performed to reduce the computational cost and enhance the robustness. But it loses the super performance of maximum likelihood estimation. To reduce the computational cost and have the merit of maximum likelihood estimation, the simultaneous data reconciliation method in an MILP framework is decomposed and replaced by an NMILP subproblem and a quadratic programming (QP) or a least squares estimation (LSE) subproblem. Simulation result of an industrial case shows the high efficiency of the method.
