Skip to main navigation
Skip to search
Skip to main content
Dongguk University Home
Search content at Dongguk University
Home
Profiles
Research units
Research output
Press/Media
View Scopus Profile
Prince
Assistant professor
Department of Industrial and Systems Engineering
Email
ee.prince
ieee
org
h-index
242
Citations
8
h-index
Calculated based on number of publications stored in Pure and citations from Scopus
2020
2026
Research activity per year
Overview
Fingerprint
Network
Research output
(20)
Similar Profiles
(2)
Fingerprint
Dive into the research topics where Prince is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Engineering
Convolutional Neural Network
100%
Induction Motor
78%
Energy Engineering
67%
Air Flow
62%
Feature Extraction
50%
Artificial Neural Network
45%
Long Short-Term Memory
41%
Deep Learning Method
36%
Recurrent Neural Network
33%
Artificial Intelligence
30%
Fault Diagnosis
30%
Noise Level
28%
Bandpass Filter
25%
Fast Fourier Transform
25%
Extended Kalman Filter
25%
Accurate Prediction
25%
Axle Load
25%
Energy Efficiency
25%
Transfer Learning
25%
Squirrel Cage Induction Motor
25%
Earth Wall
25%
Microgrid
25%
Induction Machine
25%
Energy Conservation
25%
Comprehensive Review
25%
Genetic Algorithm
18%
Field-Oriented Control
15%
Experimental Result
14%
Mean Absolute Error
13%
Systems Performance
13%
Mechanically Stabilized Earth
12%
Recurrent
12%
Electric Vehicle
12%
Multiscale
12%
Sliding Mode
12%
Fault Detection and Diagnosis
12%
Motor Drive
11%
Deep Neural Network
10%
Rotor Speed
10%
Fault Detection
9%
Nearest Neighbor
8%
Retaining Walls
8%
Research Work
8%
Metrics
8%
Operational Cost
8%
Convergence Rate
8%
Input Data
6%
Efficient Model
6%
Current Signal
6%
Charging Time
6%
Computer Science
Fault detection
46%
Convolutional Neural Network
37%
Experimental Result
28%
Long Short-Term Memory Network
27%
Recurrent Neural Network
25%
Artificial Neural Network
25%
Noise Sensitivity
25%
Transfer Learning
25%
Neural Network
25%
Convolutional Neural Network
25%
Fault Diagnosis
25%
Feature Extraction
21%
Deep Learning Method
12%
Deep Learning Model
12%
Condition Monitoring
9%
Feature Selection
9%
Average Accuracy
6%
Preprocessing
6%
Analysis Technique
6%
local feature
5%
Detection Accuracy
5%
