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查看 Scopus 资料
卫 晋
计算机学院
h-index
31
引用
3
H-指数
根据储存在 Pure 的刊物以及来自 Scopus 的引用文献数量计算
2007
2024
每年的科研成果
概览
指纹图谱
合作网络
科研成果
(12)
指纹图谱
深入其中 Jin Wei 为活跃的研究主题。这些主题标签来自此人的成果。它们共同形成唯一的指纹。
分类
加权
按字母排序
Computer Science
Processing System
62%
Parallel Processing Systems
37%
High-Performance Embedded Computing
37%
Transfer Learning
37%
Beam Forming
37%
Tracking Algorithm
37%
Feature Selection
37%
Restoration Algorithm
37%
Prediction Error
37%
Image Processing
37%
Convolutional Neural Network
37%
Convolutional Neural Network
37%
Image Restoration
37%
Generative Adversarial Networks
37%
Feature Extraction
28%
Training Sample
18%
Prediction Time
18%
image feature
18%
Multiscale Edge
18%
Heterogeneous System
12%
Input/Output
12%
agent architecture
12%
Processing Unit
12%
Software Implementation
12%
Resource Access
12%
Motion Model
9%
Perceptual Information
9%
Special Feature
9%
Contextual Information
9%
Training Model
9%
Edge Information
9%
Region Feature
9%
Discriminator
9%
Network Parameter
9%
Evaluation Criterion
9%
False Negative
9%
Distance Calculation
9%
Neural Network Training
9%
Prediction Value
9%
Classification Performance
9%
Experimental Result
9%
Structural Similarity
9%
Multi-Classifiers
9%
Candidate Model
9%
peak signal to noise ratio
9%
Suspicious Region
9%
Parsec Benchmark
7%
Control Granularity
7%
Engineering
Processing System
100%
Image Processing
74%
Feature Extraction
65%
Beam Forming
37%
Image Restoration
37%
Restoration Algorithm
37%
Prediction Error
37%
Convolutional Neural Network
37%
Medical Image
37%
Tracking Algorithm
37%
Transfer Learning
37%
Multiscale Edge
25%
Network Model
25%
Signal Processing System
12%
Optimised Design
12%
Radar Signal Processing
12%
Field Programmable Gate Arrays
12%
Signal-to-Noise Ratio
12%
Peak Signal
12%
Damage Area
12%
Template Matching
12%
Real Image
12%
Structural Similarity
12%
Radar Systems
12%
Edge Information
12%
Similarity Index
12%
Generated Image
12%
Heterogeneous System
12%
Processing Unit
12%
Discriminator
12%
Set Filter
9%
Target Vehicle
9%
Similarities
9%
False Negative
9%
Motion Model
9%
Network Parameter
9%
Calculation Accuracy
9%
Actual Value
7%
Optimal Feature
7%
Experimental Result
7%
Error Image
7%
Classification Performance
7%
Neural Network Training
7%
Image Region
7%
Suspicious Region
7%
