With the continuous development of science and technology,electronic devices have begun to enter all aspects of human life,becoming increasingly closely related to human life.Users have higher quality requirements for...With the continuous development of science and technology,electronic devices have begun to enter all aspects of human life,becoming increasingly closely related to human life.Users have higher quality requirements for electronic devices.Electronic device testing has gradually become an irreplaceable engineering process in modern manufacturing enterprises to guarantee the quality of products while preventing inferior products from entering the market.Considering the large output of electronic devices,improving the testing efficiency while reducing the testing cost has become an urgent problem to be solved.This study investigates the electronic device testing machine allocation problem(EDTMAP),aiming to improve the production of electronic devices and reduce the scheduling distance among testing machines through reasonable machine allocation.First,a mathematical model was formulated for the EDTMAP to maximize both production and the scheduling distance among testing machines.Second,we developed a discrete multi-objective artificial bee colony(DMOABC)algorithm to solve EDTMAP.A crossover operator and local search operator were designed to improve the exploration and exploitation of the algorithm,respectively.Numerical experiments were conducted to evaluate the performance of the proposed algorithm.The experimental results demonstrate the superiority of the proposed algorithm compared with the non-dominated sorting genetic algorithm II(NSGA-II)and strength Pareto evolutionary algorithm 2(SPEA2).Finally,the mathematical model and DMOABC algorithm were applied to a real-world factory that tests radio-frequency modules.The results verify that our method can significantly improve production and reduce the scheduling distance among testing machines.展开更多
为区分不同种类花粉,采用扫描电子显微镜(Scanning Electron Microscope,SEM)和表面增强拉曼光谱对雪松花粉和茶花蜂花粉、荷花蜂花粉、玫瑰蜂花粉、荞麦蜂花粉、五味子蜂花粉、益母草蜂花粉、油菜蜂花粉等七种蜂花粉进行形貌观察和光...为区分不同种类花粉,采用扫描电子显微镜(Scanning Electron Microscope,SEM)和表面增强拉曼光谱对雪松花粉和茶花蜂花粉、荷花蜂花粉、玫瑰蜂花粉、荞麦蜂花粉、五味子蜂花粉、益母草蜂花粉、油菜蜂花粉等七种蜂花粉进行形貌观察和光谱表征。结果显示:花粉间的形态差异微小,扫描电子显微镜难于区分各种花粉,而表面增强拉曼光谱可以简单快速的鉴别不同种类花粉。七种蜂花粉在1800~400 cm^-1范围存在明显差异。相比蜂花粉,雪松花粉的拉曼信号更清晰可辨,其主要特征谱带出现在1701、1657、1572、1522、1397、1293、1208、810和565 cm^-1附近。展开更多
为了考察东北黑蜂蜂胶乙醇提取物(Ethanol extracts from the Chinese northeast black bee propolis,EENBP)的理化性质,采用扫描电镜、紫外光谱、红外光谱、GC-MS和LC-MS等技术,分别研究了EENBP的化学组成和特征图谱。扫描电镜分析结...为了考察东北黑蜂蜂胶乙醇提取物(Ethanol extracts from the Chinese northeast black bee propolis,EENBP)的理化性质,采用扫描电镜、紫外光谱、红外光谱、GC-MS和LC-MS等技术,分别研究了EENBP的化学组成和特征图谱。扫描电镜分析结果表明超声辅助提取制得的EENBP混合更加均匀;紫外和红外光谱分析结果表明EENBP中的的化学组分主要包括黄酮类、萜烯类、酯类、酚酸类和醇类等。GC-MS结果表明超声辅助提取制得的EENBP中挥发性成分主要是萜烯类化合物,分别是大根香叶烯、杜松烯、愈创木醇和芹子烯。LC-MS从超声辅助提取制得的EENBP中鉴定出苯甲醛、咖啡酸、香豆酸和阿魏酸,蜂胶中还含有柯因、异鼠李素和山奈素等成分。上述结果说明超声辅助提取制得的EENBP活性成分丰富,可以进一步进行相应功能的健康食品、药品的开发。展开更多
基金National Key R&D Program of China(Grant No.2019YFB1704600)National Natural Science Foundation of China(Grant Nos.51825502,51775216)Program for HUST Academic Frontier Youth Team of China(Grant No.2017QYTD04).
文摘With the continuous development of science and technology,electronic devices have begun to enter all aspects of human life,becoming increasingly closely related to human life.Users have higher quality requirements for electronic devices.Electronic device testing has gradually become an irreplaceable engineering process in modern manufacturing enterprises to guarantee the quality of products while preventing inferior products from entering the market.Considering the large output of electronic devices,improving the testing efficiency while reducing the testing cost has become an urgent problem to be solved.This study investigates the electronic device testing machine allocation problem(EDTMAP),aiming to improve the production of electronic devices and reduce the scheduling distance among testing machines through reasonable machine allocation.First,a mathematical model was formulated for the EDTMAP to maximize both production and the scheduling distance among testing machines.Second,we developed a discrete multi-objective artificial bee colony(DMOABC)algorithm to solve EDTMAP.A crossover operator and local search operator were designed to improve the exploration and exploitation of the algorithm,respectively.Numerical experiments were conducted to evaluate the performance of the proposed algorithm.The experimental results demonstrate the superiority of the proposed algorithm compared with the non-dominated sorting genetic algorithm II(NSGA-II)and strength Pareto evolutionary algorithm 2(SPEA2).Finally,the mathematical model and DMOABC algorithm were applied to a real-world factory that tests radio-frequency modules.The results verify that our method can significantly improve production and reduce the scheduling distance among testing machines.
文摘为区分不同种类花粉,采用扫描电子显微镜(Scanning Electron Microscope,SEM)和表面增强拉曼光谱对雪松花粉和茶花蜂花粉、荷花蜂花粉、玫瑰蜂花粉、荞麦蜂花粉、五味子蜂花粉、益母草蜂花粉、油菜蜂花粉等七种蜂花粉进行形貌观察和光谱表征。结果显示:花粉间的形态差异微小,扫描电子显微镜难于区分各种花粉,而表面增强拉曼光谱可以简单快速的鉴别不同种类花粉。七种蜂花粉在1800~400 cm^-1范围存在明显差异。相比蜂花粉,雪松花粉的拉曼信号更清晰可辨,其主要特征谱带出现在1701、1657、1572、1522、1397、1293、1208、810和565 cm^-1附近。
文摘为了考察东北黑蜂蜂胶乙醇提取物(Ethanol extracts from the Chinese northeast black bee propolis,EENBP)的理化性质,采用扫描电镜、紫外光谱、红外光谱、GC-MS和LC-MS等技术,分别研究了EENBP的化学组成和特征图谱。扫描电镜分析结果表明超声辅助提取制得的EENBP混合更加均匀;紫外和红外光谱分析结果表明EENBP中的的化学组分主要包括黄酮类、萜烯类、酯类、酚酸类和醇类等。GC-MS结果表明超声辅助提取制得的EENBP中挥发性成分主要是萜烯类化合物,分别是大根香叶烯、杜松烯、愈创木醇和芹子烯。LC-MS从超声辅助提取制得的EENBP中鉴定出苯甲醛、咖啡酸、香豆酸和阿魏酸,蜂胶中还含有柯因、异鼠李素和山奈素等成分。上述结果说明超声辅助提取制得的EENBP活性成分丰富,可以进一步进行相应功能的健康食品、药品的开发。
