scholarly journals Sliding wire detection system of electric lock screw tool based on motor characteristics

2021 ◽  
Vol 2137 (1) ◽  
pp. 012009
Author(s):  
Ning Zhang ◽  
Yinxin Yan ◽  
Houcheng Yang ◽  
Zhangsi Yu
Keyword(s):  
Real Time ◽  
Detection System ◽  
Low Cost ◽  
High Sensitivity ◽  
High Accuracy ◽  
Normal Operation ◽  
Automatic Assembly ◽  
The Real ◽  
Current Change ◽  
Real Time Detection

Abstract This paper presents a sliding wire detection system of electric screw locking tool based on the characteristics of motor. The system can judge whether the screw has sliding wire through the current change of motor during normal operation, and realize the real-time detection and alarm of sliding wire. The system has the advantages of high sensitivity, low cost and high accuracy. It can be widely used in automatic assembly and other fields.

Low-Cost DSP-Based Equipment for the Real-Time Detection of Transients in Power Systems

2004 ◽  
Vol 53 (4) ◽  
pp. 933-939 ◽  
Author(s):  
M. Artioli ◽  
G. Pasini ◽  
L. Peretto ◽  
R. Sasdelli ◽  
F. Filippetti
Keyword(s):  
Power Systems ◽  
Real Time ◽  
Low Cost ◽  
The Real ◽  
Real Time Detection

scholarly journals Multiscale Parallel Algorithm for Early Detection of Tomato Gray Mold in a Complex Natural Environment

2021 ◽  
Vol 12 ◽  
Author(s):  
Xuewei Wang ◽  
Jun Liu
Keyword(s):  
Parallel Algorithm ◽  
Real Time ◽  
Natural Environment ◽  
Plant Disease ◽  
Feature Fusion ◽  
Detection System ◽  
Gray Mold ◽  
Disease Detection ◽  
The Real ◽  
Real Time Detection

Plant disease detection technology is an important part of the intelligent agricultural Internet of Things monitoring system. The real natural environment requires the plant disease detection system to have extremely high real time detection and accuracy. The lightweight network MobileNetv2-YOLOv3 model can meet the real-time detection, but the accuracy is not enough to meet the actual needs. This study proposed a multiscale parallel algorithm MP-YOLOv3 based on the MobileNetv2-YOLOv3 model. The proposed method put forward a multiscale feature fusion method, and an efficient channel attention mechanism was introduced into the detection layer of the network to achieve feature enhancement. The parallel detection algorithm was used to effectively improve the detection performance of multiscale tomato gray mold lesions while ensuring the real-time performance of the algorithm. The experimental results show that the proposed algorithm can accurately and real-time detect multiscale tomato gray mold lesions in a real natural environment. The F1 score and the average precision reached 95.6 and 93.4% on the self-built tomato gray mold detection dataset. The model size was only 16.9 MB, and the detection time of each image was 0.022 s.

scholarly journals Small Scaled Autonomous Vehicle

2019 ◽  
Vol 8 (11S) ◽  
pp. 911-914
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Low Cost ◽  
Autonomous Vehicle ◽  
High Accuracy ◽  
The Real ◽  
Output Layer ◽  
Input Layer ◽  
Video Feed

This paper presents a self-driving car using low cost convolutional neural network based platform. The project replicates the real autonomous vehicle/car into a small scaled remote controlled (RC) car. The CNN (convolutional neural network) provides a high accuracy prediction. A small camera is mounted on the RC car for capturing real time video feed to firstly train and then to predict the direction. The camera provides video feed to the input layer of the CNN that has hidden layers, that analyzes and compute the data to predict the output from an output layer that tells rc car to move in predicted direction

scholarly journals Real-Time Detection on SPAD Value of Potato Plant Using an In-Field Spectral Imaging Sensor System

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3430 ◽  
Author(s):  
Ning Liu ◽  
Gang Liu ◽  
Hong Sun
Keyword(s):  
Real Time ◽  
Potato Plant ◽  
Vegetation Index ◽  
Detection System ◽  
Automatic Segmentation ◽  
The Real ◽  
Detection Model ◽  
Potato Plants ◽  
Spad Value ◽  
Real Time Detection

In this study, a SPAD value detection system was developed based on a 25-wavelength spectral sensor to give a real-time indication of the nutrition distribution of potato plants in the field. Two major advantages of the detection system include the automatic segmentation of spectral images and the real-time detection of SPAD value, a recommended indicating parameter of chlorophyll content. The modified difference vegetation index (MDVI) linking the Otsu algorithm (OTSU) and the connected domain-labeling (CDL) method (MDVI–OTSU–CDL) is proposed to accurately extract the potato plant. Additionally, the segmentation accuracy under different modified coefficients of MDVI was analyzed. Then, the reflectance of potato plants was extracted by the segmented mask images. The partial least squares (PLS) regression was employed to establish the SPAD value detection model based on sensitive variables selected using the uninformative variable elimination (UVE) algorithm. Based on the segmented spectral image and the UVE–PLS model, the visualization distribution map of SPAD value was drawn by pseudo-color processing technology. Finally, the testing dataset was employed to measure the stability and practicality of the developed detection system. This study provides a powerful support for the real-time detection of SPAD value and the distribution of crops in the field.

Real-time Detection of Aortic Valve in Echocardiography using Convolutional Neural Networks

2020 ◽  
Vol 16 (5) ◽  
pp. 584-591 ◽  
Author(s):  
Muhammad Hanif Ahmad Nizar ◽  
Chow Khuen Chan ◽  
Azira Khalil ◽  
Ahmad Khairuddin Mohamed Yusof ◽  
Khin Wee Lai
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Heart Disease ◽  
Aortic Valve ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Valvular Heart Disease ◽  
Detection System ◽  
Processing Unit ◽  
Real Time Detection

Background: Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection. Methods: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos. Results: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models. Conclusion: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.

scholarly journals A hybrid upconversion nanoprobe for ratiometric detection of aliphatic biogenic amines in aqueous medium

2021 ◽  
Author(s):  
Shilpi Jaiswal ◽  
Subhankar Kundu ◽  
Sujoy Bandyopadhyay ◽  
Abhijit Patra
Keyword(s):  
Aqueous Medium ◽  
Biogenic Amines ◽  
Real Time ◽  
Inorganic Hybrid ◽  
The Real ◽  
Ratiometric Detection ◽  
Real Time Detection

An organic–inorganic hybrid upconversion nanoprobe was developed for the real-time detection of aliphatic biogenic amines in an aqueous medium, adulterated milk, and rotten fish.

scholarly journals Biosensors for the Multiplex Detection of Inflammatory Disease Biomarkers

Biosensors ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 11
Author(s):  
Ali Mobasheri
Keyword(s):  
Real Time ◽  
Inflammatory Disease ◽  
Multiplex Detection ◽  
The Real ◽  
Disease Biomarkers ◽  
Real Time Detection ◽  
Biochemical Substance ◽  
Analytical Device

A biosensor is an analytical device used for the real-time detection and measurement of a chemical or biochemical substance [...]

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