Sentiment Analysis of Nepali COVID19 Tweets Using NB, SVM AND LSTM
Volume-3 | Issue-3
Deniable Authentication Encryption for Privacy Protection using Blockchain
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Blockchain-Enabled Federated Learning on Kubernetes for Air Quality Prediction Applications
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Smart Fashion: A Review of AI Applications in Virtual Try-On & Fashion Synthesis
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Volume-3 | Issue-1
Multi-scale CNN Approach for Accurate Detection of Underwater Static Fish Image
Volume-3 | Issue-3
Real Time Anomaly Detection Techniques Using PySpark Frame Work
Volume-2 | Issue-1
Deniable Authentication Encryption for Privacy Protection using Blockchain
Volume-3 | Issue-3
Smart Fashion: A Review of AI Applications in Virtual Try-On & Fashion Synthesis
Volume-3 | Issue-4
Sentiment Analysis of Nepali COVID19 Tweets Using NB, SVM AND LSTM
Volume-3 | Issue-3
Audio Tagging Using CNN Based Audio Neural Networks for Massive Data Processing
Volume-3 | Issue-4
Frontiers of AI beyond 2030: Novel Perspectives
Volume-4 | Issue-4
Smart Medical Nursing Care Unit based on Internet of Things for Emergency Healthcare
Volume-3 | Issue-4
Early Stage Detection of Crack in Glasses by Hybrid CNN Transformation Approach
Volume-3 | Issue-4
ARTIFICIAL INTELLIGENCE APPLICATION IN SMART WAREHOUSING ENVIRONMENT FOR AUTOMATED LOGISTICS
Volume-1 | Issue-2
Deep Convolution Neural Network Model for Credit-Card Fraud Detection and Alert
Volume-3 | Issue-2
Volume - 2 | Issue - 2 | june 2020
Published
18 May, 2020
The study of a robotic arm copied with 3D-printer combines computer vision system with tracking algorithm is proposed in the paper. Moreover, the designing to the intelligent vehicle system with the integration of electromechanical for planning to apply it to the operations in various fields is presented too. The main purpose of this work tries to avoid the complicated process with traditional manual adjustment or teaching. It is expected to achieve the purpose that the robotic arm can grab the target automatically, classify the target and place it in the specified area, and even accurately realize the classification through training to distinguish the characteristics of the target. Eventually, the mechanical arm's movement behavior is able to be corrected through a real-time image data feedback control system. In words, with the experiment that the computer vision system is used to assist the robotic arm to detect the color and position of the target. By adding color features for algorithm training as well as through human-machine collaboration, which approves that the proposed algorithm has well known that the accuracy of target tracking definitely depends on both of two parameters include "object locations" and the "illustration direction" of light source. The difference will far from 75.2% to 89.0%.
KeywordsRobotic arm Computer vision system Tracking algorithm Real-time object detection
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