机构

• 高能物理研究所 [13]
• 长春光学精密机械与物... [1]
• 上海应用物理研究所 [1]
• 自动化研究所 [1]

采集方式

• OAI收割 [15]
• iSwitch采集 [1]

内容类型

• 期刊论文 [15]
• 会议论文 [1]

发表日期

• 2021 [1]
• 2019 [1]
• 2018 [2]
• 2015 [2]
• 2012 [3]
• 2010 [1]
• 更多

学科主题

• Physics [8]

筛选

浏览/检索结果: 共16条，第1-10条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 Dynamically Optimizing Network Structure Based on Synaptic Pruning in the Brain 期刊论文  OAI收割 FRONTIERS IN SYSTEMS NEUROSCIENCE, 2021, 卷号: 15, 页码: 8 作者:  Zhao, Feifei;  Zeng, Yi   |  收藏  |  浏览/下载：33/0  |  提交时间：2021/08/15 synaptic pruning  developmental neural network  optimizing network structure  accelerating learning  compressing network   Radio-frequency design of a new C-band variable power splitter 期刊论文  OAI收割 NUCLEAR SCIENCE AND TECHNIQUES, 2019, 卷号: 30, 期号: 6, 页码: — 作者:  Li, ZB;  Grudiev, A;  Fang, WC;  Gu, Q;  Zhao, ZT   |  收藏  |  浏览/下载：59/0  |  提交时间：2019/12/30 ACCELERATING STRUCTURE  OPTIMIZATION   Studies on the s-band bunching system with the hybrid bunching-accelerating structure 期刊论文  iSwitch采集 Nuclear instruments & methods in physics research section a-accelerators spectrometers detectors and associated equipment, 2018, 卷号: 888, 页码: 64-69 作者:  Pei, Shi-Lun;  Gao, Bin 收藏  |  浏览/下载：97/0  |  提交时间：2019/04/23 Beam dynamics  Bunching system  Beam capturing efficiency  Hybrid bunching-accelerating structure  Industrial linac   Studies on the S-band bunching system with the Hybrid Bunching-accelerating Structure 期刊论文  OAI收割 NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT, 2018, 卷号: 888, 页码: 64-69 作者:  Gao B(高斌);  Gao, B;  Pei, SL;  Pei SL(裴士伦)   |  收藏  |  浏览/下载：15/0  |  提交时间：2019/09/24 Beam dynamics  Bunching system  Beam capturing efficiency  Hybrid Bunching-accelerating Structure  Industrial linac   S-band J-type waveguide feeding accelerating structure for free electron laser 期刊论文  OAI收割 HIGH POWER LASER AND PARTICLE BEAMS, 2015, 卷号: 27, 期号: 4, 页码: 45109 作者:  Hou M(侯汨);  Song NB(宋迺斌);  He X(贺祥);  Zhao FL(赵风利);  Pei SL(裴士伦) 收藏  |  浏览/下载：29/0  |  提交时间：2016/04/18 Accelerating gradient  Accelerating structure  Cylindrical cavities  Mechanical fabrication  Racetrack cavities  Rotational symmetries  Symmetric coupled  Transverse momenta   Accelerating structure design and fabrication for KIPT and PAL XFEL 期刊论文  OAI收割 CHINESE PHYSICS C, 2015, 卷号: 39, 期号: 5, 页码: 57006 作者:  Hou M(侯汨);  He X(贺祥);  Pei SL(裴士伦);  Nei B(那斌);  Chi YL(池云龙) 收藏  |  浏览/下载：23/0  |  提交时间：2016/04/18 S-band  accelerating structure  BBU (beam break-up) effect  neutron source   Design studies on the ERL-FEL test facility at IHEP; Beijing 期刊论文  OAI收割 中国物理C, 2012, 期号: 5, 页码: 469-474 作者:  Wang SH(王书鸿);  Wang JQ(王九庆);  Chen SY(陈森玉);  Chi YL(池云龙);  Wang GW(王光伟) 收藏  |  浏览/下载：54/0  |  提交时间：2015/12/07 ERL  test facility  DC gun  CW superconducting accelerating structure  CSR  BBU   Design studies on the ERL-FEL test facility at IHEP, Beijing 期刊论文  OAI收割 CHINESE PHYSICS C, 2012, 卷号: 36, 期号: 5, 页码: 469-474 作者:  Wang SH(王书鸿);  Wang JQ(王久庆);  Chen SY(陈森玉);  Wang, SH;  Wang, JQ 收藏  |  浏览/下载：60/0  |  提交时间：2016/04/08 ERL  test facility  DC gun  CW superconducting accelerating structure  CSR  BBU   RF-thermal-structural-RF coupled analysis on a travelling wave disk-loaded accelerating structure 期刊论文  OAI收割 中国物理C, 中国物理C, 2012, 2012, 期号: 6, 页码: 555-560, 555-560 作者:  Pei SL(裴士伦);  Chi YL(池云龙);  Zhang JR(张敬如);  Hou M(侯汨);  Li XP(李小平)   |  收藏  |  浏览/下载：27/0  |  提交时间：2015/12/14 RF-thermal-structural-RF coupled analysis  travelling wave  disk-loaded accelerating structure  normal conducting  ANSYS  RF-thermal-structural-RF coupled analysis  travelling wave  disk-loaded accelerating structure  normal conducting  ANSYS   The costs prediction of AOD furnace based on improved RBF neural network (EI CONFERENCE) 会议论文  OAI收割 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering, CMCE 2010, August 24, 2010 - August 26, 2010, Changchun, China Na T.; Zhang D.-J.; Hui L. 收藏  |  浏览/下载：16/0  |  提交时间：2013/03/25 In order to predict the cost  a model of cost prediction was set up based on adaptive hierarchical genetic algorithm and RBF neural network. Hierarchical genetic algorithm could optimize the topology and the parameters simultaneously. Compared with simple genetic algorithm  it has more efficiency in not only accelerating and stabilizing the parameters training but also determining the structure of the network. Adaptive crossover and mutation probability could accelerate the speed and avoid prematurity. The model was tested by five samples. The results showed that the prediction model has high prediction accuracy  which indicated that it was applicable to predict the cost by the model. 2010 IEEE.