Gelatincarbonyl iron composite particle was prepared by micro emulsion method. The analysis of scanning electron microscope(SEM) shows that the ultrafine particles are spheroids coated by gelatin, and the average size...Gelatincarbonyl iron composite particle was prepared by micro emulsion method. The analysis of scanning electron microscope(SEM) shows that the ultrafine particles are spheroids coated by gelatin, and the average sizes of particles are 310μm. The specific saturation magnetization σ_s is 130.9A·m2/kg, coercivity H_c is 0.823A/m, and residual magnetism r is 4.98Am2/kg for the composite particles. It is shown that the particles possess properties of soft magnetic. The yield stress of magnetorheological fluid(MRF) with composite particle reaches 70kPa at 0.5T magnetic induction. Magnetorheological effects are superior in lower magnetic field intensity and the subsidence stability of the MRF is excellent compared with pure carbonyl iron powder.展开更多
This paper addresses the problem of service composition in military organization cloud cooperation（MOCC）. Military service providers（MSP） cooperate together to provide military resources for military service users...This paper addresses the problem of service composition in military organization cloud cooperation（MOCC）. Military service providers（MSP） cooperate together to provide military resources for military service users（MSU）. A group of atom services, each of which has its level of quality of service（QoS）, can be combined together into a certain structure to form a composite service. Since there are a large number of atom services having the same function, the atom service is selected to participate in the composite service so as to fulfill users＇ will. In this paper a method based on discrete particle swarm optimization（DPSO） is proposed to tackle this problem. The method aims at selecting atom services from service repositories to constitute the composite service, satisfying the MSU＇s requirement on QoS. Since the QoS criteria include location-aware criteria and location-independent criteria, this method aims to get the composite service with the highest location-aware criteria and the best-match location-independent criteria. Simulations show that the DPSO has a better performance compared with the standard particle swarm optimization（PSO） and genetic algorithm（GA）.展开更多
文摘Gelatincarbonyl iron composite particle was prepared by micro emulsion method. The analysis of scanning electron microscope(SEM) shows that the ultrafine particles are spheroids coated by gelatin, and the average sizes of particles are 310μm. The specific saturation magnetization σ_s is 130.9A·m2/kg, coercivity H_c is 0.823A/m, and residual magnetism r is 4.98Am2/kg for the composite particles. It is shown that the particles possess properties of soft magnetic. The yield stress of magnetorheological fluid(MRF) with composite particle reaches 70kPa at 0.5T magnetic induction. Magnetorheological effects are superior in lower magnetic field intensity and the subsidence stability of the MRF is excellent compared with pure carbonyl iron powder.
基金supported by the National Natural Science Foundation of China(61573283)
文摘This paper addresses the problem of service composition in military organization cloud cooperation（MOCC）. Military service providers（MSP） cooperate together to provide military resources for military service users（MSU）. A group of atom services, each of which has its level of quality of service（QoS）, can be combined together into a certain structure to form a composite service. Since there are a large number of atom services having the same function, the atom service is selected to participate in the composite service so as to fulfill users＇ will. In this paper a method based on discrete particle swarm optimization（DPSO） is proposed to tackle this problem. The method aims at selecting atom services from service repositories to constitute the composite service, satisfying the MSU＇s requirement on QoS. Since the QoS criteria include location-aware criteria and location-independent criteria, this method aims to get the composite service with the highest location-aware criteria and the best-match location-independent criteria. Simulations show that the DPSO has a better performance compared with the standard particle swarm optimization（PSO） and genetic algorithm（GA）.
