机构

• 长春光学精密机械与物... [5]
• 金属研究所 [3]
• 自动化研究所 [3]
• 地理科学与资源研究所 [1]
• 计算技术研究所 [1]
• 新疆生态与地理研究所 [1]
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采集方式

• OAI收割 [18]

内容类型

• 期刊论文 [11]
• 会议论文 [7]

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• 2024 [1]
• 2021 [1]
• 2020 [1]
• 2019 [2]
• 2018 [1]
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学科主题

• Geoscience... [1]
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筛选

浏览/检索结果: 共18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 WSMsFNet: Joint the Whole Supervision and Multiscale Fusion Network for Remote Sensing Image Change Detection 期刊论文  OAI收割 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2024, 卷号: 17, 页码: 14656-14669 作者:  Wang, Bin;  Jiang, Xiaohu;  Qin, Pinle;  Zeng, Jianchao   |  收藏  |  浏览/下载：14/0  |  提交时间：2024/10/21 Feature extraction  Transformers  Data mining  Correlation  Semantics  Context modeling  Accuracy  Change detection  convolutional neural network (CNN)  multiscale feature (MSF) fusion  the whole process supervision  transformer   A Graph based Calligraphy Similarity Compare Model 会议论文  OAI收割 Hainan Island, China, 2021-12-06 作者:  Pan, Guoyang;  Yang, Yi;  Li, Meng;  Hu, Xueyang;  Huang, Weixing   |  收藏  |  浏览/下载：23/0  |  提交时间：2022/03/31 calligraphy estimation  graph similarity  image process  graph neural network   Neural-Network-Based Nonlinear Model Predictive Tracking Control of a Pneumatic Muscle Actuator-Driven Exoskeleton 期刊论文  OAI收割 IEEE/CAA Journal of Automatica Sinica, 2020, 卷号: 7, 期号: 6, 页码: 1478-1488 作者:  Yu Cao;  Jian Huang   |  收藏  |  浏览/下载：19/0  |  提交时间：2021/03/11 Echo state Gaussian process  model predictive control  neural network  pneumatic muscle actuators-driven exoskeleton   Deep Hashing Based on VAE-GAN for Efficient Similarity Retrieval 期刊论文  OAI收割 CHINESE JOURNAL OF ELECTRONICS, 2019, 卷号: 28, 期号: 6, 页码: 1191-1197 作者:  Jin, Guoqing;  Zhang, Yongdong;  Lu, Ke   |  收藏  |  浏览/下载：24/0  |  提交时间：2020/12/10 file organisation  image retrieval  learning (artificial intelligence)  neural nets  pairwise hashing learning  semantic perserving feature mapping model  adversarial generative process  image feature vector  hash codes  pairwise ranking loss  generative networks  VAE-GAN based hashing framework  image retrieval  content preserving images  similarity retrieval  variational autoencoder  generative adversarial network  Image retrieval  Learning to hash  Variational autoencoder(VAE)  Generative adversarial network(GAN)   Integrated modeling of coking flue gas indices based on mechanism model and improved neural network 期刊论文  OAI收割 TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL, 2019, 卷号: 41, 期号: 1, 页码: 85-96 作者:  Li, Yaning;  Wang, Xuelei;  Tan, Jie   |  收藏  |  浏览/下载：45/0  |  提交时间：2019/07/12 Coking  process control  neural network (NN)  mechanism model  integrated modeling   Extraction and evaluation of melt pool, plume and spatter information for powder-bed fusion AM process monitoring 期刊论文  OAI收割 MATERIALS & DESIGN, 2018, 卷号: 156, 页码: 458-469 作者:  Zhang, Yingjie;  Hong, Geok Soon;  Ye, Dongsen;  Zhu, Kunpeng;  Fuh, Jerry Y. H.   |  收藏  |  浏览/下载：90/0  |  提交时间：2019/11/11 Additive manufacturing (AM)  Powder-bed fusion  Melt pool, plume and spatter  Statistical process monitoring  Support vector machines (SVM)  Convolutional neural network (CNN)   Multilevel Feature Moving Average Ratio Method for Fault Diagnosis of the Microgrid Inverter Switch 期刊论文  OAI收割 IEEE-CAA JOURNAL OF AUTOMATICA SINICA, 2017, 卷号: 4, 期号: 2, 页码: 177-185 作者:  Huang Zhanjun;  Wang Zhanshan;  Zhang Huaguang   |  收藏  |  浏览/下载：25/0  |  提交时间：2021/02/02 ARTIFICIAL NEURAL NETWORKS  WIND TURBINE SYSTEMS  OPEN-CIRCUIT FAULT  POWER-SYSTEMS  DRIVE SYSTEMS  2-LEVEL  PWM  Absolute average ratio process  fault diagnosis  microgrid inverter  multilevel feature moving average ratio  neural network   基于机器学习模型的沙漠腹地地下水含盐量变化过程及模拟研究/Groundwater salt content change and its simulation based on machine learning model in hinterlands of Taklimakan Desert[J] 期刊论文  OAI收割 生态学报, 2013, 卷号: 33, 期号: 18, 页码: 5874-5882 作者:  范敬龙;  刘海龙;  雷加强;  徐新文;  王桂芬   |  收藏  |  浏览/下载：21/0  |  提交时间：2017/12/29 地下水含盐量 高斯过程 高斯过程隐变量模型 人工神经网络 沙漠腹地 Groundwater Salt Content Gaussian Process Gaussian Process Latent Variable Model Back Propagation Artificial Neural Network Hinterlands Of Taklimakan Desert   Forewarning of sustainable utilization of regional water resources: a model based on BP neural network and set pair analysis 期刊论文  OAI收割 NATURAL HAZARDS, 2012, 卷号: 62, 期号: 1, 页码: 13,115-127 Jin, JL; Wei, YM; Zou, LL; Liu, L; Zhang, WW; Zhou, YL 收藏  |  浏览/下载：37/0  |  提交时间：2012/11/12 Water security management  Water resources sustainable utilization  Forewarning model  Fuzzy analytic hierarchy process  Back-propagation neural network  Set pair analysis  Genetic algorithm   System identification of tracking error and evaluation of tracking performance using BP neural network (EI CONFERENCE) 会议论文  OAI收割 International Symposium on Photoelectronic Detection and Imaging 2009: Advances in Infrared Imaging and Applications, June 17, 2009 - June 19, 2009, Beijing, China Zhang N.; Shen X.-H. 收藏  |  浏览/下载：20/0  |  提交时间：2013/03/25 A novel approach for evaluating the tracking performance of optoelectronic theodolite is proposed. First  an equivalent mathematic model of tracking error is established. Then  the equivalent sine signal is inputted to the equivalent model  and the outputs are sampled. The results of evaluating the tracking performance are obtained based on the statistical calculation of output produced by equivalent model. Equivalent model using the BP (Backprogration) neural network structure is identified. The training method of BP neural network adopts the LM (Levenberg-Marquardt) algorithm for the sake of speeding up training process. The BP neural network is trained and tested by using the training and testing samples gotten from the simulation model of optoelectronic theodolite tracking system under MATLAB/SIMULINK. The estimate errors of equivalent model including average error  maximum error and standard error are 2.5872e-0060  2.8 and 1.9. The results show that the equivalent model identified based on BP neural network meets the needs of evaluating the tracking performance of optoelectronic theodolite. The accurate evaluation of tracking performance is achieved. 2009 SPIE.