scholarly journals Dutomatic Detection Technology of Intelligent Meter Based On Machine Vision

2021 ◽  
Vol 2143 (1) ◽  
pp. 012041
Author(s):  
Zhilong Zhang ◽  
Lin Yang ◽  
Hong Su ◽  
Weiguang Wang ◽  
Tao Jiang
Keyword(s):  
Machine Vision ◽  
Detection Rate ◽  
Practical Significance ◽  
Smart Meter ◽  
Smart Meters ◽  
Low Contrast ◽  
Morphological Gradient ◽  
Appearance Quality ◽  
Detection Technology

Abstract In view of the low detection rate of LCD appearance quality of smart meters in the current low contrast environment, this study puts forward the research of intelligent meters automation based on machine vision, and puts forward corresponding solutions and strategies according to this problem. Firstly, the image of the LCD area of the smart meter is extracted. On this basis, the image of the LCD screen of the ammeter is enhanced by the method of wavelet transform. Then, the LCD area of the smart meter is accurately divided. Then the morphological gradient is used to reconstruct the character information, and finally realize the automatic detection of intelligent electricity. The experimental results show that the method proposed in this paper has certain feasibility and effectiveness, improves the accuracy of appearance detection of smart meter, and has certain practical significance in power industry.

Research on Rice Detection Technology Based on Machine Vision

2014 ◽  
Vol 1030-1032 ◽  
pp. 1788-1791 ◽  
Author(s):  
Zhi Fu Xu ◽  
Liang Qi Zhu ◽  
Xiao Yan Shi ◽  
Jun Liu ◽  
Can Liu ◽  
...  
Keyword(s):  
Machine Vision ◽  
Human Error ◽  
Grain Shape ◽  
Manual Measurement ◽  
Appearance Quality ◽  
Accurate Detection ◽  
Detection Technology ◽  
Low Efficiency ◽  
Detection Effect

Detection of rice kernel domestic mainly rely on manual measurement using a ruler or vernier caliper tool, which use the ruler measurement of human error, and the measurement of the twisted grain rice vernier caliper is limited. Manual measurement are difficult problems, but also low efficiency. This study analyzes the current research on appearance quality of rice by using machine vision technology mainly focuses on the aspects of rice kernel, chalkiness, yellow rice and other characteristics, realized the accurate detection and obtain rice information quickly by using the machine vision technology, improves the speed and precision of detection, especially detection effect the grain shape distortions.

Resistance Monitor and Control for Vacuum Evaporation of Metals and Carbon

1976 ◽  
Vol 34 ◽  
pp. 572-573
Author(s):  
Russell L. Steere ◽  
Eric F. Erbe ◽  
J. Michael Moseley
Keyword(s):  
Electronic Device ◽  
Point Sources ◽  
High Contrast ◽  
Variable Resistor ◽  
Quality Of Results ◽  
Low Contrast ◽  
Evaporation Source ◽  
And Control ◽  
At Will

We have designed and built an electronic device which compares the resistance of a defined area of vacuum evaporated material with a variable resistor. When the two resistances are matched, the device automatically disconnects the primary side of the substrate transformer and stops further evaporation.This approach to controlled evaporation in conjunction with the modified guns and evaporation source permits reliably reproducible multiple Pt shadow films from a single Pt wrapped carbon point source. The reproducibility from consecutive C point sources is also reliable. Furthermore, the device we have developed permits us to select a predetermined resistance so that low contrast high-resolution shadows, heavy high contrast shadows, or any grade in between can be selected at will. The reproducibility and quality of results are demonstrated in Figures 1-4 which represent evaporations at various settings of the variable resistor.

A Survey on Internet of Things : Applications and Layered Wise Security Issues

2019 ◽  
pp. 171-180
Author(s):  
Uppuluri Sirisha ◽  
G. Lakshme Eswari
Keyword(s):  
Quality Of Life ◽  
Internet Of Things ◽  
Security And Privacy ◽  
Global Network ◽  
Business Growth ◽  
Paper Briefly ◽  
Smart Meters ◽  
Security Issues ◽  
Data Points

This paper briefly introduces Internet of Things(IOT) as a intellectual connectivity among the physical objects or devices which are gaining massive increase in the fields like efficiency, quality of life and business growth. IOT is a global network which is interconnecting around 46 million smart meters in U.S. alone with 1.1 billion data points per day[1]. The total installation base of IOT connecting devices would increase to 75.44 billion globally by 2025 with a increase in growth in business, productivity, government efficiency, lifestyle, etc., This paper familiarizes the serious concern such as effective security and privacy to ensure exact and accurate confidentiality, integrity, authentication access control among the devices.

scholarly journals Nondestructive Detection of the Gel State of Preserved Eggs Based on Dielectric Impedance

Foods ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 394
Author(s):  
Cheng-Han Li ◽  
Chun-Hung Hsieh ◽  
Cheng-Chu Hung ◽  
Ching-Wei Cheng
Keyword(s):  
Decay Rate ◽  
Mechanical Vibration ◽  
Scientific Basis ◽  
Nondestructive Method ◽  
Inspection System ◽  
Nyquist Diagram ◽  
Detection Technology ◽  
The Difference ◽  
The Individual

After completing the production of preserved eggs, traditionally, the degree of gelling is judged by allowing workers to tap the preserved eggs with their fingers and sense the resulting oscillations. The amount of oscillation is used for the quality classification. This traditional method produces varying results owing to the differences in the sensitivity of the individual workers, who are not objective. In this study, dielectric detection technology was used to classify the preserved eggs nondestructively. The impedance in the frequency range of 2–300 kHz was resolved into resistance and reactance, and was plotted on a Nyquist diagram. Next, the diagram curve was fitted in order to obtain the equivalent circuit, and the difference in the compositions of the equivalent circuits corresponding to gelled and non-gelled preserved eggs was analyzed. A preserved egg can be considered an RLC series circuit, and its decay rate is consistent with the decay rate given by mechanical vibration theory. The Nyquist diagrams for the resistance and reactance of preserved eggs clearly showed that the resistance and reactance of gelled and non-gelled eggs were quite different, and the classification of the eggs was performed using Bayesian network (BN). The results showed that a BN classifier with two variables, i.e., resistance and reactance, can be used to classify preserved eggs as gelled or non-gelled, with an accuracy of 81.0% and a kappa value of 0.62. Thus, a BN classifier based on resistance and reactance demonstrates the ability to classify the quality of preserved egg gel. This research provides a nondestructive method for the inspection of the quality of preserved egg gel, and provides a theoretical basis for the development of an automated preserved egg inspection system that can be used as the scientific basis for the determination of the quality of preserved eggs.

scholarly journals Molecular Hydrogen Maintains the Storage Quality of Chinese Chive through Improving Antioxidant Capacity

Plants ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 1095
Author(s):  
Ke Jiang ◽  
Yong Kuang ◽  
Liying Feng ◽  
Yuhao Liu ◽  
Shu Wang ◽  
...  
Keyword(s):  
Antioxidant Capacity ◽  
Molecular Hydrogen ◽  
Practical Significance ◽  
Total Phenolic ◽  
Guaiacol Peroxidase ◽  
Storage Quality ◽  
Decay Index ◽  
Chinese Chive ◽  
Dpph Scavenging

Chinese chive usually becomes decayed after a short storage time, which was closely observed with the redox imbalance. To cope with this practical problem, in this report, molecular hydrogen (H2) was used to evaluate its influence in maintaining storage quality of Chinese chive, and the changes in antioxidant capacity were also analyzed. Chives were treated with 1%, 2%, or 3% H2, and with air as the control, and then were stored at 4 ± 1 °C. We observed that, compared with other treatment groups, the application of 3% H2 could significantly prolong the shelf life of Chinese chive, which was also confirmed by the obvious mitigation of decreased decay index, the loss ratio of weight, and the reduction in soluble protein content. Meanwhile, the decreasing tendency in total phenolic, flavonoid, and vitamin C contents was obviously impaired or slowed down by H2. Results of antioxidant capacity revealed that the accumulation of reactive oxygen species (ROS) and hydrogen peroxide (H2O2) was differentially alleviated, which positively matched with 2,2-Diphenyl-1-picrylhydrazyl (DPPH) scavenging activity and the improved activities of antioxidant enzymes, including superoxide dismutase (SOD), guaiacol peroxidase (POD), catalase (CAT), and ascorbate peroxidase (APX). Above results clearly suggest that postharvest molecular hydrogen application might be a potential useful approach to improve the storage quality of Chinese chive, which is partially achieved through the alleviation of oxidative damage happening during the storage periods. These findings also provide potential theoretical and practical significance for transportation and consumption of perishable vegetables.

scholarly journals A Machine Vision Rapid Method to Determine the Ripeness Degree of Olive Lots

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2940
Author(s):  
Luciano Ortenzi ◽  
Simone Figorilli ◽  
Corrado Costa ◽  
Federico Pallottino ◽  
Simona Violino ◽  
...  
Keyword(s):  
Machine Vision ◽  
Nearest Neighbors ◽  
Calibration Method ◽  
Mobile App ◽  
Harvest Time ◽  
Automatic Assessment ◽  
K Nearest Neighbors ◽  
Olive Ripeness ◽  
Olive Ripening

The degree of olive maturation is a very important factor to consider at harvest time, as it influences the organoleptic quality of the final product, for both oil and table use. The Jaén index, evaluated by measuring the average coloring of olive fruits (peel and pulp), is currently considered to be one of the most indicative methods to determine the olive ripening stage, but it is a slow assay and its results are not objective. The aim of this work is to identify the ripeness degree of olive lots through a real-time, repeatable, and objective machine vision method, which uses RGB image analysis based on a k-nearest neighbors classification algorithm. To overcome different lighting scenarios, pictures were subjected to an automatic colorimetric calibration method—an advanced 3D algorithm using known values. To check the performance of the automatic machine vision method, a comparison was made with two visual operator image evaluations. For 10 images, the number of black, green, and purple olives was also visually evaluated by these two operators. The accuracy of the method was 60%. The system could be easily implemented in a specific mobile app developed for the automatic assessment of olive ripeness directly in the field, for advanced georeferenced data analysis.

scholarly journals Inspection of Transparent Objects with Varying Light Scattering Using a Frangi Filter

2021 ◽  
Vol 7 (2) ◽  
pp. 27
Author(s):  
Dieter P. Gruber ◽  
Matthias Haselmann
Keyword(s):  
Light Scattering ◽  
Machine Vision ◽  
Nearest Neighbor ◽  
Production Testing ◽  
Transparent Objects ◽  
Frangi Filter ◽  
Anomaly Score ◽  
Difference Images ◽  
New Machine

This paper proposes a new machine vision method to test the quality of a semi-transparent automotive illuminant component. Difference images of Frangi filtered surface images are used to enhance defect-like image structures. In order to distinguish allowed structures from defective structures, morphological features are extracted and used for a nearest-neighbor-based anomaly score. In this way, it could be demonstrated that a segmentation of occurring defects is possible on transparent illuminant parts. The method turned out to be fast and accurate and is therefore also suited for in-production testing.

scholarly journals Evaluation Model of Operation State Based on Deep Learning for Smart Meter

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4674
Author(s):  
Qingsheng Zhao ◽  
Juwen Mu ◽  
Xiaoqing Han ◽  
Dingkang Liang ◽  
Xuping Wang
Keyword(s):  
Deep Learning ◽  
Power Consumption ◽  
Detection Efficiency ◽  
Evaluation Model ◽  
Smart Meter ◽  
Smart Meters ◽  
Abnormal Detection ◽  
Prediction Residual ◽  
Threshold Setting ◽  
Training Efficiency

The operation state detection of numerous smart meters is a significant problem caused by manual on-site testing. This paper addresses the problem of improving the malfunction detection efficiency of smart meters using deep learning and proposes a novel evaluation model of operation state for smart meter. This evaluation model adopts recurrent neural networks (RNN) to predict power consumption. According to the prediction residual between predicted power consumption and the observed power consumption, the malfunctioning smart meter is detected. The training efficiency for the prediction model is improved by using transfer learning (TL). This evaluation uses an accumulator algorithm and threshold setting with flexibility for abnormal detection. In the simulation experiment, the detection principle is demonstrated to improve efficient replacement and extend the average using time of smart meters. The effectiveness of the evaluation model was verified on the actual station dataset. It has accurately detected the operation state of smart meters.

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