中国科学院机构知识库网格系统: 检索

Hybrid deep learning-based identification of microseismic events in TBM tunnelling 期刊论文 OAI收割
MEASUREMENT, 2024, 卷号: 238, 页码: 20
作者: Yin, Xin

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收藏 | 浏览/下载：1/0 | 提交时间：2025/06/27

TBM tunnelling Microseismic monitoring Microseismic identification Deep learning Multi-algorithm fusion

The multi-scale fusion reconstruction algorithm of CT and CL 期刊论文 OAI收割
Physica Scripta, 2023, 卷号: 98, 期号: 10
作者: Jia,Tong; Wei,Cunfeng; Zhu,Min; Shi,Rongjian; Wang,Zhe

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收藏 | 浏览/下载：33/0 | 提交时间：2023/10/25

computed tomography computed laminography multi-scale fusion reconstruction algorithm paleontological fossils multilayer printed circuit boards

Multi-focus image fusion dataset and algorithm test in real environment 期刊论文 OAI收割
FRONTIERS IN NEUROROBOTICS, 2022, 卷号: 16, 页码: 8
作者: Liu, Shuaiqi; Peng, Weijian; Jiang, Wenjing; Yang, Yang; Zhao, Jie

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收藏 | 浏览/下载：36/0 | 提交时间：2022/12/27

image fusion multi-focus image fusion dataset image preprocessing multi-focus image fusion algorithm test real environment

A real-time prediction method for tunnel boring machine cutter-head torque using bidirectional long short-term memory networks optimized by multi-algorithm 期刊论文 OAI收割
JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING, 2022, 卷号: 14, 期号: 3, 页码: 798
作者: Huang, Xing; Zhang, Quantai; Liu, Quansheng; Liu, Xuewei; Liu, Bin

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收藏 | 浏览/下载：12/0 | 提交时间：2023/08/02

Tunnel boring machine (TBM) Real-time cutter-head torque prediction Bidirectional long short-term memory (BLSTM) Bayesian optimization Multi-algorithm fusion optimization Incremental learning

The Assessment of Upper-Limb Spasticity Based on a Multi-Layer Process Using a Portable Measurement System 期刊论文 OAI收割
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2021, 卷号: 29, 页码: 2242-2251
作者: Wang, Chen; Peng, Liang; Hou, Zeng-Guang; Zhang, Pu

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收藏 | 浏览/下载：23/0 | 提交时间：2021/12/28

Spasticity quantification portable assessment device modified genetic algorithm ensemble empirical mode decomposition (EEMD) multi-layer fusion

Unsupervised variational auto-encoder hash algorithm based on multi-channel feature fusion 会议论文 OAI收割
Osaka, Japan, 2020-05-19
作者: Wang, Huanting; Qu, Bo; Lu, Xiaoqiang; Chen, Yaxiong

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收藏 | 浏览/下载：27/0 | 提交时间：2020/08/21

Multi-channel feature fusion Unsupervised hashing algorithm VAE Image retrieval

Infrared and visual image fusion using LNSST and an adaptive dual-channel PCNN with triple-linking strength 期刊论文 OAI收割
Neurocomputing, 2018, 卷号: 310, 页码: 135-147
作者: Cheng, B. Y.; Jin, L. X.; Li, G. N.

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收藏 | 浏览/下载：32/0 | 提交时间：2019/09/17

LNSST ATD-PCNN Image fusion Singular value decomposition Auxiliary linking strength Triple-linking strength sparse representation shearlet transform multi-focus feature-extraction neural-network domain algorithm decomposition Computer Science

Study on time registration method for photoelectric theodolite data fusion (EI CONFERENCE) 会议论文 OAI收割
10th World Congress on Intelligent Control and Automation, WCICA 2012, July 6, 2012 - July 8, 2012, Beijing, China
Yang H.-T.; Gao H.-B.

收藏 | 浏览/下载：19/0 | 提交时间：2013/03/25

In range measurement theodolite and radar constitute a real-time tracking system at different sites to track the same target in the air and get useful information exactly and timely. As the optical theodolite and radar have different sampling frequency and measurement system the data is sent to the fusion center is asynchronous. This paper proposed a time registration method based on multi-sensor data using Wavelet neural network algorithm which not only better solved the basic problems of theodolite fusion tracking but also improve the efficiency of data fusion. Simulation experiment and comparison with other time registration method have shown the advantage of this method. 2012 IEEE.

基于进化算法的工件视觉定位及其在工业机器人中的应用 学位论文 OAI收割
工学博士, 中国科学院自动化研究所: 中国科学院研究生院, 2011
作者: 刘伟

收藏 | 浏览/下载：69/0 | 提交时间：2015/09/02

进化算法工件视觉定位多特征融合监督学习高精度装配 Evolutionary Algorithm Visual localization Multi-feature fusion Supervised learning High-precision assembly

Multi-focus image fusion algorithm based on adaptive PCNN and wavelet transform (EI CONFERENCE) 会议论文 OAI收割
International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Imaging Detectors and Applications, May 24, 2011 - May 26, 2011, Beijing, China
Wu Z.-G.; Wang M.-J.; Han G.-L.

收藏 | 浏览/下载：78/0 | 提交时间：2013/03/25

Being an efficient method of information fusion image fusion has been used in many fields such as machine vision medical diagnosis military applications and remote sensing.In this paper Pulse Coupled Neural Network (PCNN) is introduced in this research field for its interesting properties in image processing including segmentation target recognition et al. and a novel algorithm based on PCNN and Wavelet Transform for Multi-focus image fusion is proposed. First the two original images are decomposed by wavelet transform. Then based on the PCNN a fusion rule in the Wavelet domain is given. This algorithm uses the wavelet coefficient in each frequency domain as the linking strength so that its value can be chosen adaptively. Wavelet coefficients map to the range of image gray-scale. The output threshold function attenuates to minimum gray over time. Then all pixels of image get the ignition. So the output of PCNN in each iteration time is ignition wavelet coefficients of threshold strength in different time. At this moment the sequences of ignition of wavelet coefficients represent ignition timing of each neuron. The ignition timing of PCNN in each neuron is mapped to corresponding image gray-scale range which is a picture of ignition timing mapping. Then it can judge the targets in the neuron are obvious features or not obvious. The fusion coefficients are decided by the compare-selection operator with the firing time gradient maps and the fusion image is reconstructed by wavelet inverse transform. Furthermore by this algorithm the threshold adjusting constant is estimated by appointed iteration number. Furthermore In order to sufficient reflect order of the firing time the threshold adjusting constant is estimated by appointed iteration number. So after the iteration achieved each of the wavelet coefficient is activated. In order to verify the effectiveness of proposed rules the experiments upon Multi-focus image are done. Moreover comparative results of evaluating fusion quality are listed. The experimental results show that the method can effectively enhance the edge details and improve the spatial resolution of the image. 2011 SPIE.