跳至主導覽
跳至搜尋
跳過主要內容
國立中央大學 首頁
說明與常見問題
English
中文
首頁
人才檔案
研究單位
研究計畫
研究成果
資料集
榮譽/獲獎
學術活動
新聞/媒體
影響
按專業知識、姓名或所屬機構搜尋
DOA Estimation Based on Convolutional Autoencoder in the Presence of Array Imperfections
Dah Chung Chang
, Yan Ting Liu
通訊工程學系
研究成果
:
雜誌貢獻
›
期刊論文
›
同行評審
2
引文 斯高帕斯（Scopus）
總覽
指紋
指紋
深入研究「DOA Estimation Based on Convolutional Autoencoder in the Presence of Array Imperfections」主題。共同形成了獨特的指紋。
排序方式
重量
按字母排序
Keyphrases
Direction of Arrival Estimation
100%
Convolutional
100%
Autoencoder
100%
Convolutional Autoencoder
100%
Array Imperfections
100%
AE Technique
75%
Model-based Method
50%
Deep Neural Network
50%
Neural Network Method
25%
Node number
25%
Neural Network
25%
Direction of Arrival
25%
Number of Connections
25%
Estimation Accuracy
25%
Non-ideality
25%
Antenna System
25%
Multiple Components
25%
Numerical Evaluation
25%
Number of Parameters
25%
Accurately Model
25%
Noise Effects
25%
Local Features
25%
Low Signal-to-noise Ratio
25%
Convolution Operation
25%
Array Model
25%
Concentrated Distributions
25%
Antenna Array Geometry
25%
Engineering
Direction-of-Arrival Estimation
100%
Autoencoder
100%
Deep Neural Network
100%
Signal-to-Noise Ratio
20%
Nodes
20%
Limitations
20%
Direction of Arrival
20%
Antenna Arrays
20%
Antenna System
20%
Numerical Evaluation
20%
Subregions
20%
Ideal Design
20%
Computer Science
direction-of-arrival
100%
Autoencoder
100%
Deep Neural Network
100%
Neural Network
20%
array antenna
20%
Multiple Component
20%
Noise-to-Signal Ratio
20%
local feature
20%
Estimation Accuracy
20%
Antenna System
20%
Mathematics
Deep Neural Network
100%
Neural Network
20%
Noise Ratio
20%
Chemical Engineering
Deep Neural Network
100%
Neural Network
20%
Material Science
Signal-to-Noise Ratio
100%