scholarly journals Research on Defect Detection of Electric Energy Metering Box Based on YOLOv5

2021 ◽  
Vol 2087 (1) ◽  
pp. 012081
Author(s):  
Yong Yu ◽  
Yanchao Sun ◽  
Chunxue Zhao ◽  
Chong Qu
Keyword(s):  
Data Storage ◽  
Electric Energy ◽  
Detection Accuracy ◽  
Damage State ◽  
Data Set ◽  
Operation And Maintenance ◽  
Average Accuracy ◽  
Energy Metering ◽  
Inspection Work ◽  
Power Metering

Abstract The manual inspection for the damage state of the electric energy metering box consumes a lot of time, the workload is large, and the data storage is difficult. In order to solve these problems, this paper proposes an automatic detection method for the damage state of the electric energy metering box based on the YOLOv5 algorithm. The actual metering box pictures taken by the operation and maintenance inspectors are used as the training set, LabelImage is used to annotate the data set, and YOLOv5s model is used to train the data set. The experimental results show that the method proposed in this paper can accurately mark the position of the metering box lid and accurately predict its damage state. The average accuracy reaches 98%, which can meet the requirements for the detection accuracy of the power metering box damage state in the operation and maintenance inspection work.

Analysis of the Energy Metering Problem in Dynamic Environment

2014 ◽  
Vol 668-669 ◽  
pp. 673-676
Author(s):  
Zhuo Wang ◽  
Hai Bao
Keyword(s):  
Steady State ◽  
Electric Power ◽  
Electric Energy ◽  
Dynamic Environment ◽  
First Order ◽  
Energy Metering ◽  
Analytic Expressions ◽  
Engineering Environment ◽  
Linear Elements ◽  
Power Metering

The traditional electric power metering theory is a kind of no-error metering theory in sinusoidal steady-state circuit. However, the applying condition is too rigor, and the engineering environment is hard to fulfil, the application premise should be extended to dynamic. Ideal linear elements are adopted to build a first-order dynamic circuit. And the analytic expressions of the capacitor cumulative electric energy in the charging process are derived theoretically. It points out that the cumulative electric energy of capacitor in dynamic circuit is a nonzero value. This fully demonstrates that the energy metering principle brings error when it is used in dynamic environment.

Data Storage Strategy of Electric Energy Metering Production and Dispatching Platform under Intensification Mode

2012 ◽  
Vol 614-615 ◽  
pp. 1710-1715
Author(s):  
Xiao Shu Huang ◽  
Yang Shao ◽  
Yang Wen ◽  
Liang Zhang
Keyword(s):  
Data Storage ◽  
High Speed ◽  
Electric Energy ◽  
Data Retrieval ◽  
Data Partitioning ◽  
Operating Efficiency ◽  
Sampled Data ◽  
Time Data ◽  
Energy Metering ◽  
High Speed Data

Analyse the electric energy metering production mode and data characteristics of production and dispatching platform mode under intensification mode. Put forward a method of data partitioning by archived data zone, real-time data zone, sampled data zone and business-management data zone. Form the device’s owner identification by device table number shifting strategy and realize the high-speed data retrieval between every two of the zones by using the index of device to monitoring dot and main table retrieval. This strategy has been used in developing provincial production and dispatching platform of STATE GRID and raised the operating efficiency of the platform.

scholarly journals Target Detection-Based Tree Recognition in a Spruce Forest Area with a High Tree Density—Implications for Estimating Tree Numbers

2021 ◽  
Vol 13 (6) ◽  
pp. 3279
Author(s):  
Mirzat Emin ◽  
Erpan Anwar ◽  
Suhong Liu ◽  
Bilal Emin ◽  
Maryam Mamut ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Target Detection ◽  
Tianshan Mountain ◽  
Detection Accuracy ◽  
Spruce Forest ◽  
Monitoring Methods ◽  
Data Set ◽  
Accuracy And Precision ◽  
Average Accuracy ◽  
Aerial Vehicle

Here, unmanned aerial vehicle (UAV) remote sensing and machine vision were used to automatically, accurately, and efficiently count Tianshan spruce and improve the efficiency of scientific forest management, focusing on a typical Tianshan spruce forest on Tianshan Mountain, middle Asia. First, the UAV in the sampling area was cropped from the image, and a target-labeling tool was used. The Tianshan spruce trees were annotated to construct a data set, and four models were used to identify and verify them in three different areas (low, medium, and high canopy closures). Finally, the combined number of trees was calculated. The average accuracy of the detection frame, mean accuracy and precision (mAP), was used to determine the target detection accuracy. The Faster Region Convolutional Neural Network (Faster-RCNN) model achieved the highest accuracies (96.36%, 96.32%, and 95.54% under low, medium, and high canopy closures, respectively) and the highest mAP (85%). Canopy closure affected the detection and recognition accuracy; YOLOv3, YOLOv4, and Faster-RCNN all showed varying spruce recognition accuracies at different densities. The accuracy of the Faster-RCNN model decreased by at least 0.82%. Combining UAV remote sensing with target detection networks can identify and quantify statistics regarding Tianshan spruce. This solves the shortcomings of traditional monitoring methods and is significant for understanding and monitoring forest ecosystems.

scholarly journals Implementation of a Modified Faster R-CNN for Target Detection Technology of Coastal Defense Radar

2021 ◽  
Vol 13 (9) ◽  
pp. 1703
Author(s):  
He Yan ◽  
Chao Chen ◽  
Guodong Jin ◽  
Jindong Zhang ◽  
Xudong Wang ◽  
...  
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Real Data ◽  
Detection Performance ◽  
Detection Accuracy ◽  
Constant False Alarm Rate ◽  
Data Set ◽  
Detection Technology ◽  
Coastal Defense

The traditional method of constant false-alarm rate detection is based on the assumption of an echo statistical model. The target recognition accuracy rate and the high false-alarm rate under the background of sea clutter and other interferences are very low. Therefore, computer vision technology is widely discussed to improve the detection performance. However, the majority of studies have focused on the synthetic aperture radar because of its high resolution. For the defense radar, the detection performance is not satisfactory because of its low resolution. To this end, we herein propose a novel target detection method for the coastal defense radar based on faster region-based convolutional neural network (Faster R-CNN). The main processing steps are as follows: (1) the Faster R-CNN is selected as the sea-surface target detector because of its high target detection accuracy; (2) a modified Faster R-CNN based on the characteristics of sparsity and small target size in the data set is employed; and (3) soft non-maximum suppression is exploited to eliminate the possible overlapped detection boxes. Furthermore, detailed comparative experiments based on a real data set of coastal defense radar are performed. The mean average precision of the proposed method is improved by 10.86% compared with that of the original Faster R-CNN.

scholarly journals Entropy Based Features Distribution for Anti-DDoS Model in SDN

2021 ◽  
Vol 13 (3) ◽  
pp. 1522
Author(s):  
Raja Majid Ali Ujjan ◽  
Zeeshan Pervez ◽  
Keshav Dahal ◽  
Wajahat Ali Khan ◽  
Asad Masood Khattak ◽  
...  
Keyword(s):  
Network Security ◽  
False Positive ◽  
Denial Of Service ◽  
Network Services ◽  
Detection Accuracy ◽  
Data Sets ◽  
Traffic Patterns ◽  
Average Accuracy ◽  
Ddos Detection ◽  
Quantitative Results

In modern network infrastructure, Distributed Denial of Service (DDoS) attacks are considered as severe network security threats. For conventional network security tools it is extremely difficult to distinguish between the higher traffic volume of a DDoS attack and large number of legitimate users accessing a targeted network service or a resource. Although these attacks have been widely studied, there are few works which collect and analyse truly representative characteristics of DDoS traffic. The current research mostly focuses on DDoS detection and mitigation with predefined DDoS data-sets which are often hard to generalise for various network services and legitimate users’ traffic patterns. In order to deal with considerably large DDoS traffic flow in a Software Defined Networking (SDN), in this work we proposed a fast and an effective entropy-based DDoS detection. We deployed generalised entropy calculation by combining Shannon and Renyi entropy to identify distributed features of DDoS traffic—it also helped SDN controller to effectively deal with heavy malicious traffic. To lower down the network traffic overhead, we collected data-plane traffic with signature-based Snort detection. We then analysed the collected traffic for entropy-based features to improve the detection accuracy of deep learning models: Stacked Auto Encoder (SAE) and Convolutional Neural Network (CNN). This work also investigated the trade-off between SAE and CNN classifiers by using accuracy and false-positive results. Quantitative results demonstrated SAE achieved relatively higher detection accuracy of 94% with only 6% of false-positive alerts, whereas the CNN classifier achieved an average accuracy of 93%.

scholarly journals An Optimized Stacking Ensemble Model for Phishing Websites Detection

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1285
Author(s):  
Mohammed Al-Sarem ◽  
Faisal Saeed ◽  
Zeyad Ghaleb Al-Mekhlafi ◽  
Badiea Abdulkarem Mohammed ◽  
Tawfik Al-Hadhrami ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forests ◽  
Ensemble Method ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Ensemble Model ◽  
Security Attacks ◽  
Data Set ◽  
Machine Learning Methods ◽  
Ensemble Machine Learning

Security attacks on legitimate websites to steal users’ information, known as phishing attacks, have been increasing. This kind of attack does not just affect individuals’ or organisations’ websites. Although several detection methods for phishing websites have been proposed using machine learning, deep learning, and other approaches, their detection accuracy still needs to be enhanced. This paper proposes an optimized stacking ensemble method for phishing website detection. The optimisation was carried out using a genetic algorithm (GA) to tune the parameters of several ensemble machine learning methods, including random forests, AdaBoost, XGBoost, Bagging, GradientBoost, and LightGBM. The optimized classifiers were then ranked, and the best three models were chosen as base classifiers of a stacking ensemble method. The experiments were conducted on three phishing website datasets that consisted of both phishing websites and legitimate websites—the Phishing Websites Data Set from UCI (Dataset 1); Phishing Dataset for Machine Learning from Mendeley (Dataset 2, and Datasets for Phishing Websites Detection from Mendeley (Dataset 3). The experimental results showed an improvement using the optimized stacking ensemble method, where the detection accuracy reached 97.16%, 98.58%, and 97.39% for Dataset 1, Dataset 2, and Dataset 3, respectively.

MobileNet-Yolo Based wildlife detection model: A case study in yunnan tongbiguan nature reserve, China

2021 ◽  
pp. 1-11
Author(s):  
Tingting Zhao ◽  
Xiaoli Yi ◽  
Zhiyong Zeng ◽  
Tao Feng
Keyword(s):  
Real Time ◽  
Nature Reserve ◽  
Complex Model ◽  
Training Data ◽  
Mean Average Precision ◽  
Detection Accuracy ◽  
High Definition ◽  
Average Precision ◽  
Data Set ◽  
Real Time Detection

YTNR (Yunnan Tongbiguan Nature Reserve) is located in the westernmost part of China’s tropical regions and is the only area in China with the tropical biota of the Irrawaddy River system. The reserve has abundant tropical flora and fauna resources. In order to realize the real-time detection of wild animals in this area, this paper proposes an improved YOLO (You only look once) network. The original YOLO model can achieve higher detection accuracy, but due to the complex model structure, it cannot achieve a faster detection speed on the CPU detection platform. Therefore, the lightweight network MobileNet is introduced to replace the backbone feature extraction network in YOLO, which realizes real-time detection on the CPU platform. In response to the difficulty in collecting wild animal image data, the research team deployed 50 high-definition cameras in the study area and conducted continuous observations for more than 1,000 hours. In the end, this research uses 1410 images of wildlife collected in the field and 1577 wildlife images from the internet to construct a research data set combined with the manual annotation of domain experts. At the same time, transfer learning is introduced to solve the problem of insufficient training data and the network is difficult to fit. The experimental results show that our model trained on a training set containing 2419 animal images has a mean average precision of 93.6% and an FPS (Frame Per Second) of 3.8 under the CPU. Compared with YOLO, the mean average precision is increased by 7.7%, and the FPS value is increased by 3.

Research the Test Method of the Voltage Transformer Secondary Load in the Three-Phase Three-Wire Electric Energy Metering Device

2014 ◽  
Vol 687-691 ◽  
pp. 3110-3115
Author(s):  
Gu Li ◽  
Zi Ming Fu ◽  
Jie Feng Yan ◽  
Bing Wen Li ◽  
Zhi Rong Cen
Keyword(s):  
Power Supply System ◽  
Electric Energy ◽  
Test Methods ◽  
Test Method ◽  
Testing Methods ◽  
Voltage Transformer ◽  
Secondary Load ◽  
Three Phase ◽  
Energy Metering ◽  
Definition Of

This paper analyzes and studies the definition of the voltage transformer secondary load, examines the practical purposes of the measured values of the voltage transformer secondary load, and presents a variety of testing methods to analyze and compare the differences. This paper gives the test methods of the voltage transformer secondary load when the connection of the voltage transformer is the Y / Y in a three-phase three-wire power supply system, filling the blank of this type of test method in the industry. When other units within the industry carry out such work, the conclusions of this paper are available for reference, and the conclusions of this paper can be referred when drafting relevant regulations in the future.

Detection of COVID-19 cases through X-ray images using hybrid deep neural network

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rajit Nair ◽  
Santosh Vishwakarma ◽  
Mukesh Soni ◽  
Tejas Patel ◽  
Shubham Joshi
Keyword(s):  
Real Time ◽  
Binary Classification ◽  
Data Set ◽  
Content Type ◽  
X Ray ◽  
Internal Parameters ◽  
Average Accuracy ◽  
Chest X Ray ◽  
Multi Class Classification

Purpose The latest 2019 coronavirus (COVID-2019), which first appeared in December 2019 in Wuhan's city in China, rapidly spread around the world and became a pandemic. It has had a devastating impact on daily lives, the public's health and the global economy. The positive cases must be identified as soon as possible to avoid further dissemination of this disease and swift care of patients affected. The need for supportive diagnostic instruments increased, as no specific automated toolkits are available. The latest results from radiology imaging techniques indicate that these photos provide valuable details on the virus COVID-19. User advanced artificial intelligence (AI) technologies and radiological imagery can help diagnose this condition accurately and help resolve the lack of specialist doctors in isolated areas. In this research, a new paradigm for automatic detection of COVID-19 with bare chest X-ray images is displayed. Images are presented. The proposed model DarkCovidNet is designed to provide correct binary classification diagnostics (COVID vs no detection) and multi-class (COVID vs no results vs pneumonia) classification. The implemented model computed the average precision for the binary and multi-class classification of 98.46% and 91.352%, respectively, and an average accuracy of 98.97% and 87.868%. The DarkNet model was used in this research as a classifier for a real-time object detection method only once. A total of 17 convolutionary layers and different filters on each layer have been implemented. This platform can be used by the radiologists to verify their initial application screening and can also be used for screening patients through the cloud. Design/methodology/approach This study also uses the CNN-based model named Darknet-19 model, and this model will act as a platform for the real-time object detection system. The architecture of this system is designed in such a way that they can be able to detect real-time objects. This study has developed the DarkCovidNet model based on Darknet architecture with few layers and filters. So before discussing the DarkCovidNet model, look at the concept of Darknet architecture with their functionality. Typically, the DarkNet architecture consists of 5 pool layers though the max pool and 19 convolution layers. Assume as a convolution layer, and as a pooling layer. Findings The work discussed in this paper is used to diagnose the various radiology images and to develop a model that can accurately predict or classify the disease. The data set used in this work is the images bases on COVID-19 and non-COVID-19 taken from the various sources. The deep learning model named DarkCovidNet is applied to the data set, and these have shown signification performance in the case of binary classification and multi-class classification. During the multi-class classification, the model has shown an average accuracy 98.97% for the detection of COVID-19, whereas in a multi-class classification model has achieved an average accuracy of 87.868% during the classification of COVID-19, no detection and Pneumonia. Research limitations/implications One of the significant limitations of this work is that a limited number of chest X-ray images were used. It is observed that patients related to COVID-19 are increasing rapidly. In the future, the model on the larger data set which can be generated from the local hospitals will be implemented, and how the model is performing on the same will be checked. Originality/value Deep learning technology has made significant changes in the field of AI by generating good results, especially in pattern recognition. A conventional CNN structure includes a convolution layer that extracts characteristics from the input using the filters it applies, a pooling layer that reduces calculation efficiency and the neural network's completely connected layer. A CNN model is created by integrating one or more of these layers, and its internal parameters are modified to accomplish a specific mission, such as classification or object recognition. A typical CNN structure has a convolution layer that extracts features from the input with the filters it applies, a pooling layer to reduce the size for computational performance and a fully connected layer, which is a neural network. A CNN model is created by combining one or more such layers, and its internal parameters are adjusted to accomplish a particular task, such as classification or object recognition.

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