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	Intelligent fault diagnosis of train bearing based on ISTOA-VMD and SE-WDCNN 期刊论文 OAI收割 JOURNAL OF VIBRATION AND CONTROL, 2023, 页码: 12 作者: He, Deqiang; Zou, Xueyan; Jin, Zhenzhen; Yan, Jingren; Ren, Chonghui \| 收藏 \| 浏览/下载：6/0 \| 提交时间：2023/12/07 intelligent fault diagnosis train bearing improved sooty tern optimization algorithm variational mode decomposition deep convolutional neural network
	A Dead Reckoning Calibration Scheme Based on Optimization with an Adaptive Quantum-Inspired Evolutionary Algorithm for Vehicle Self-Localization 期刊论文 OAI收割 ENTROPY, 2022, 卷号: 24 作者: Yu, Biao; Zhu, Hui; Xue, Deyi; Xu, Liwei; Zhang, Shijin \| 收藏 \| 浏览/下载：14/0 \| 提交时间：2022/12/23 adaptive quantum-inspired evolutionary algorithm dead reckoning intelligent vehicle optimization parameter calibration
	Reliability-Aware and Deadline-Constrained Mobile Service Composition Over Opportunistic Networks 期刊论文 OAI收割 IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2021, 卷号: 18, 期号: 3, 页码: 1012-1025 作者: Peng, Qinglan; Xia, Yunni; Zhou, MengChu; Luo, Xin; Wang, Shu \| 收藏 \| 浏览/下载：77/0 \| 提交时间：2021/08/20 Reliability Mobile handsets Mobile applications Device-to-device communication Cloud computing Service computing Quality of service Intelligent optimization Krill-Herd algorithm mobile computing mobile opportunistic network mobile service composition service reliability
	Fine-Grained Resource Provisioning and Task Scheduling for Heterogeneous Applications in Distributed Green Clouds 期刊论文 OAI收割 IEEE/CAA Journal of Automatica Sinica, 2020, 卷号: 7, 期号: 5, 页码: 1380-1393 作者: Haitao Yuan; MengChu Zhou; Qing Liu; Abdullah Abusorrah \| 收藏 \| 浏览/下载：15/0 \| 提交时间：2021/03/11 Bees algorithm data centers distributed green cloud (DGC) energy optimization intelligent optimization simulated annealing task scheduling machine learning
	Drag-reduction design on high-speed trains with intelligent optimization algorithm 会议论文 OAI收割 Ajaccio, Corsica, France, 8-11 April 2014 作者: Yang GW(杨国伟); Yao SB(姚拴宝); Guo DL(郭迪龙) \| 收藏 \| 浏览/下载：26/0 \| 提交时间：2018/11/08 Aerodynamic drag Aerodynamics Ant colony optimization Computational fluid dynamics Deformation Design Drag reduction Interpolation Iterative methods Radial basis function networks Railroad cars Railroad transportation Railroads Aerodynamic optimization Grid deformation High speed train (HST) Intelligent optimization algorithm Kriging surrogate model
	The registration of aerial infrared and visible images (EI CONFERENCE) 会议论文 OAI收割 2010 International Conference on Educational and Information Technology, ICEIT 2010, September 17, 2010 - September 19, 2010, Chongqing, China 作者: Liu J.; Wang Y.; Wang Y.; Wang Y.; Wang Y. 收藏 \| 浏览/下载：14/0 \| 提交时间：2013/03/25 In order to solve the registration problem of different source image existed on aerial image fusion algorithms based on Particle Swarm Optimization (PSO) are applied as search strategy in this paper and Alignment Metric (AM) is used as judgment. This study has realized the different source image registration of infrared and visible light with high speed high accuracy and high reliability. Basically with little restriction of gray level properties a new alignment measure is applied which can efficiently measure the image registration extent and tolerate noise well. Even more the intelligent optimization algorithm - Particle Swarm Optimization (PSO) is combined to improve the registration precision and rate of infrared and visible light. Experimental results indicate that the study attains the registration accuracy of pixel level and every registration time is cut down over 40 percent compared to traditional method. The match algorithm based on AM solves the registration problem that greater differences between different source images are existed on gray and characteristic. At the same time the adoption of combining the intelligent optimization algorithms significantly improves the searching efficiency and convergence speed of the algorithms and the registration result has higher accuracy and stability which builds up solid foundation for different source image fusion. The method in this paper has a magnificent effect and is easy for application and very suitable for engineering use. 2010 IEEE.
	Optimizing control mode of optical payloads based on multi-population genetic algorithm (EI CONFERENCE) 会议论文 OAI收割 2009 IEEE International Conference on Mechatronics and Automation, ICMA 2009, August 9, 2009 - August 12, 2009, Changchun, China Xu W.; Jin G. 收藏 \| 浏览/下载：21/0 \| 提交时间：2013/03/25 Optimizing the control mode of optical payload could improve the payload's work efficiency. When the mathematic model of payload's control mode was established optimization problems could be boiled down to seek the maximal value of the function with many variables. This paper put forward a calculational method to base the mathematic model the calculational method was multi-population genetic algorithm (MPGA). After the 0-1 variables and real multidimensional ones had been coded separately the algorithm established the populations independently made the multidimensional variables as the centrosome and combined 0-1 ones to work out the maximal value of the function which was established to describe the control mode of the optical payload. Moreover the emulational experiment had been done with the material arithmetic operators. The result indicates that the method using MPGA can hurdle the disadvantage of traditional ones which calculate slowly and get into local best value trap easily . It has the characters that not only fits large scale area scout but also gets the best value in full scale rapidly so this algorithm can better satisfy the technology demand for the intelligent control of optical payload. 2009 IEEE.