期刊文献+
共找到5篇文章
< 1 >
每页显示 20 50 100
Weighted Forwarding in Graph Convolution Networks for Recommendation Information Systems
1
作者 Sang-min Lee Namgi Kim 《Computers, Materials & Continua》 SCIE EI 2024年第2期1897-1914,共18页
Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet.Graph Convolution Network(GCN)algorithms have been ... Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet.Graph Convolution Network(GCN)algorithms have been employed to implement the RIS efficiently.However,the GCN algorithm faces limitations in terms of performance enhancement owing to the due to the embedding value-vanishing problem that occurs during the learning process.To address this issue,we propose a Weighted Forwarding method using the GCN(WF-GCN)algorithm.The proposed method involves multiplying the embedding results with different weights for each hop layer during graph learning.By applying the WF-GCN algorithm,which adjusts weights for each hop layer before forwarding to the next,nodes with many neighbors achieve higher embedding values.This approach facilitates the learning of more hop layers within the GCN framework.The efficacy of the WF-GCN was demonstrated through its application to various datasets.In the MovieLens dataset,the implementation of WF-GCN in LightGCN resulted in significant performance improvements,with recall and NDCG increasing by up to+163.64%and+132.04%,respectively.Similarly,in the Last.FM dataset,LightGCN using WF-GCN enhanced with WF-GCN showed substantial improvements,with the recall and NDCG metrics rising by up to+174.40%and+169.95%,respectively.Furthermore,the application of WF-GCN to Self-supervised Graph Learning(SGL)and Simple Graph Contrastive Learning(SimGCL)also demonstrated notable enhancements in both recall and NDCG across these datasets. 展开更多
关键词 Deep learning graph neural network graph convolution network graph convolution network model learning method recommender information systems
下载PDF
GPU Accelerated Marine Data Visualization Method 被引量:1
2
作者 LI Bo CHEN Ge +2 位作者 TIAN Fenglin SHAO Baomin JI Pengbo 《Journal of Ocean University of China》 SCIE CAS 2014年第6期964-970,共7页
The study of marine data visualization is of great value. Marine data, due to its large scale, random variation and multiresolution in nature, are hard to be visualized and analyzed. Nowadays, constructing an ocean mo... The study of marine data visualization is of great value. Marine data, due to its large scale, random variation and multiresolution in nature, are hard to be visualized and analyzed. Nowadays, constructing an ocean model and visualizing model results have become some of the most important research topics of ‘Digital Ocean'. In this paper, a spherical ray casting method is developed to improve the traditional ray-casting algorithm and to make efficient use of GPUs. Aiming at the ocean current data, a 3D view-dependent line integral convolution method is used, in which the spatial frequency is adapted according to the distance from a camera. The study is based on a 3D virtual reality and visualization engine, namely the VV-Ocean. Some interactive operations are also provided to highlight the interesting structures and the characteristics of volumetric data. Finally, the marine data gathered in the East China Sea are displayed and analyzed. The results show that the method meets the requirements of real-time and interactive rendering. 展开更多
关键词 marine data visualization techniques and methodologies spherical ray casting line integral convolution multiquadric method VV-Ocean
下载PDF
Marine target detection based on Marine-Faster R-CNN for navigation radar plane position indicator images 被引量:2
3
作者 Xiaolong CHEN Xiaoqian MU +2 位作者 Jian GUAN Ningbo LIU Wei ZHOU 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2022年第4期630-643,共14页
As a classic deep learning target detection algorithm,Faster R-CNN(region convolutional neural network)has been widely used in high-resolution synthetic aperture radar(SAR)and inverse SAR(ISAR)image detection.However,... As a classic deep learning target detection algorithm,Faster R-CNN(region convolutional neural network)has been widely used in high-resolution synthetic aperture radar(SAR)and inverse SAR(ISAR)image detection.However,for most common low-resolution radar plane position indicator(PPI)images,it is difficult to achieve good performance.In this paper,taking navigation radar PPI images as an example,a marine target detection method based on the Marine-Faster R-CNN algorithm is proposed in the case of complex background(e.g.,sea clutter)and target characteristics.The method performs feature extraction and target recognition on PPI images generated by radar echoes with the convolutional neural network(CNN).First,to improve the accuracy of detecting marine targets and reduce the false alarm rate,Faster R-CNN was optimized as the Marine-Faster R-CNN in five respects:new backbone network,anchor size,dense target detection,data sample balance,and scale normalization.Then,JRC(Japan Radio Co.,Ltd.)navigation radar was used to collect echo data under different conditions to build a marine target dataset.Finally,comparisons with the classic Faster R-CNN method and the constant false alarm rate(CFAR)algorithm proved that the proposed method is more accurate and robust,has stronger generalization ability,and can be applied to the detection of marine targets for navigation radar.Its performance was tested with datasets from different observation conditions(sea states,radar parameters,and different targets). 展开更多
关键词 Marine target detection Navigation radar Plane position indicator(PPI)images convolutional neural network(CNN) Faster R-CNN(region convolutional neural network)method
原文传递
High-Accuracy Polishing Technique Using Dwell Time Adjustment
4
作者 S.Satake K.Yamamoto S.Igarashi 《Communications in Computational Physics》 SCIE 2006年第4期701-715,共15页
Two algorithms for dwell time adjustment are evaluated under the same polishing conditions that involve tool and work distributions.Both methods are based on Preston’s hypothesis.The first method is a convolution alg... Two algorithms for dwell time adjustment are evaluated under the same polishing conditions that involve tool and work distributions.Both methods are based on Preston’s hypothesis.The first method is a convolution algorithm based on the Fast Fourier Transform.The second is an iterative method based on a constraint problem,extended from a one-dimensional formulation to address a two-dimensional problem.Both methods are investigated for their computational cost,accuracy,and polishing shapes.The convolution method has high accuracy and high speed.The constraint problem on the other hand is slow even when it requires larger memory and thus is more costly.However,unlike the other case a negative region in the polishing shape is not predicted here.Furthermore,new techniques are devised by combining the two methods. 展开更多
关键词 POLISHING surface grinding dwell time convolution method fast Fourier transform constraint problem
原文传递
INVERSE RADON TRANSFORM WITH ONE-DIMENSIONAL WAVELET TRANSFORM 被引量:2
5
作者 渠刚荣 《Acta Mathematicae Applicatae Sinica》 SCIE CSCD 2000年第1期70-77,共8页
In this paper, the wavelet inverse formula of Radon transform is obtained with onedimensional wavelet. The convolution back-projection method of Radon transform is derived from this inverse formula. An asymptotic rel... In this paper, the wavelet inverse formula of Radon transform is obtained with onedimensional wavelet. The convolution back-projection method of Radon transform is derived from this inverse formula. An asymptotic relation between wavelet inverse formula of Radon transform and convolution-back projection algorithm of Radon transform in 2 dimensions is established. 展开更多
关键词 Radon transform wavelet transform wavelet inversion formula convolution back-projection method
全文增补中
上一页 1 下一页 到第
使用帮助 返回顶部