scholarly journals Detection method based on improved faster R-CNN for pin defect in transmission lines

2021 ◽  
Vol 300 ◽  
pp. 01011
Author(s):  
Jun Wu ◽  
Sheng Cheng ◽  
Shangzhi Pan ◽  
Wei Xin ◽  
Liangjun Bai ◽  
...  
Keyword(s):  
Transmission Line ◽  
Defect Detection ◽  
Transmission Lines ◽  
False Positive Rate ◽  
Detection Algorithm ◽  
Detection Model ◽  
Positive Rate ◽  
Aerial Vehicle ◽  
The Stability ◽  
Small Targets

Defects such as insulator, pins, and counterweight in highvoltage transmission lines affect the stability of the power system. The small targets such as pins in the unmanned aerial vehicle (UAV) inspection images of transmission lines occupy a small proportion in the images and the characteristic representations are poor which results a low defect detection rate and a high false positive rate. This paper proposed a transmission line pin defect detection algorithm based on improved Faster R-CNN. First, the pre-training weights with higher matching degree are obtained based on transfer learning. And it is applied to construct defect detection model. Then, the regional proposal network is used to extract features in the model. The results of defect detection are obtained by regression calculation and classification of regional characteristics. The experimental results show that the accuracy of the pin defect detection of the transmission line reaches 81.25%

scholarly journals Hue–saturation–intensity and texture feature-based cloud detection algorithm for unmanned aerial vehicle images

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142090353
Author(s):  
Wang Yi ◽  
Zhang Jing ◽  
Gao Shuang
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Target Identification ◽  
False Positive Rate ◽  
Texture Feature ◽  
Detection Algorithm ◽  
Cloud Detection ◽  
Detection Algorithms ◽  
Saturation Intensity ◽  
Positive Rate ◽  
Aerial Vehicle

There are a large number of cloud-covered areas in most unmanned aerial vehicle images and lead to the loss of information in the image and affect image post procession such as image fusion and target identification. Finding the cloud-occluded area in an image is a key step in image processing. Based on the differences of color and texture characteristics between cloud and ground, a cloud detection algorithm for the unmanned aerial vehicle images is proposed. Simulation results show that the proposed algorithm is better than the classical cloud detection algorithms in accuracy rate, false-positive rate, and kappa coefficient.

scholarly journals The Development of a Defect Detection Model from the High-Resolution Images of a Sugarcane Plantation Using an Unmanned Aerial Vehicle

Information ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 136
Author(s):  
Bhoomin Tanut ◽  
Panomkhawn Riyamongkol
Keyword(s):  
Feature Extraction ◽  
High Resolution ◽  
Defect Detection ◽  
Extraction Methods ◽  
Detection Algorithm ◽  
K Nearest Neighbors ◽  
Color Selection ◽  
Detection Model ◽  
Aerial Vehicle ◽  
Processing Techniques

This article presents a defect detection model of sugarcane plantation images. The objective is to assess the defect areas occurring in the sugarcane plantation before the harvesting seasons. The defect areas in the sugarcane are usually caused by storms and weeds. This defect detection algorithm uses high-resolution sugarcane plantations and image processing techniques. The algorithm for defect detection consists of four processes: (1) data collection, (2) image preprocessing, (3) defect detection model creation, and (4) application program creation. For feature extraction, the researchers used image segmentation and convolution filtering by 13 masks together with mean and standard deviation. The feature extraction methods generated 26 features. The K-nearest neighbors algorithm was selected to develop a model for the classification of the sugarcane areas. The color selection method was also chosen to detect defect areas. The results show that the model can recognize and classify the characteristics of the objects in sugarcane plantation images with an accuracy of 96.75%. After the comparison with the expert surveyor’s assessment, the accurate relevance obtained was 92.95%. Therefore, the proposed model can be used as a tool to calculate the percentage of defect areas and solve the problem of evaluating errors of yields in the future.

Study on the characteristics of secondary arc current of UHV high compensation degree TCSC line under the fine-tuning mode

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Baina He ◽  
Yadi Xie ◽  
Jingru Zhang ◽  
Nirmal-Kumar C. Nair ◽  
Xingmin He ◽  
...  
Keyword(s):  
Transmission Line ◽  
Transmission Lines ◽  
Stability Margin ◽  
Transmission Capacity ◽  
Fine Tuning ◽  
Series Compensation ◽  
Arc Current ◽  
Compensation Device ◽  
The Stability ◽  
Recovery Voltage

Abstract In the transmission line, the series compensation device is often used to improve the transmission capacity. However, when the fixed series capacitor (FSC) is used in high compensation series compensation device, the stability margin cannot meet the requirements. Therefore, thyristor controlled series compensator (TCSC) is often installed in transmission lines to improve the transmission capacity of the line and the stability of the system. For cost considerations, the hybrid compensation mode of FSC and TCSC is often adopted. However, when a single-phase grounding fault occurs in a transmission line with increased series compensation degree, the unreasonable distribution of FSC and TCSC will lead to the excessive amplitude of secondary arc current, which is not conducive to rapid arc extinguishing. To solve this problem, this paper is based on 1000 kV Changzhi-Nanyang-Jingmen UHV series compensation transmission system, using PSCAD simulation program to established UHV series compensation simulation model, The variation law of secondary arc current and recovery voltage during operation in fine tuning mode after adding TCSC to UHV transmission line is analyzed, and the effect of increasing series compensation degree on secondary arc current and recovery voltage characteristics is studied. And analyze the secondary arc current and recovery voltage when using different FSC and TCSC series compensation degree schemes, and get the most reasonable series compensation configuration scheme. The results show that TCSC compensation is more beneficial to arc extinguishing under the same series compensation. Compared with several series compensation schemes, it is found that with the increase of the proportion of TCSC, the amplitude of secondary arc current and recovery voltage vary greatly. Considering various factors, the scheme that is more conducive to accelerating arc extinguishing is chosen.

scholarly journals ScoreNet: A neural network-based post-processing model for identifying epileptic seizure onset and offset in EEGs

2020 ◽  
Author(s):  
Poomipat Boonyakitanont ◽  
Apiwat Lek-uthai ◽  
Jitkomut Songsiri
Keyword(s):  
Neural Network ◽  
Epileptic Seizure ◽  
False Positive Rate ◽  
Imbalanced Data ◽  
Detection Algorithm ◽  
Processing Technique ◽  
Post Processing ◽  
Scalp Eeg ◽  
Positive Rate ◽  
Eeg Recordings

AbstractThis article aims to design an automatic detection algorithm of epileptic seizure onsets and offsets in scalp EEGs. A proposed scheme consists of two sequential steps: the detection of seizure episodes, and the determination of seizure onsets and offsets in long EEG recordings. We introduce a neural network-based model called ScoreNet as a post-processing technique to determine the seizure onsets and offsets in EEGs. A cost function called a log-dice loss that has an analogous meaning to F1 is proposed to handle an imbalanced data problem. In combination with several classifiers including random forest, CNN, and logistic regression, the ScoreNet is then verified on the CHB-MIT Scalp EEG database. As a result, in seizure detection, the ScoreNet can significantly improve F1 to 70.15% and can considerably reduce false positive rate per hour to 0.05 on average. In addition, we propose detection delay metric, an effective latency index as a summation of the exponential of delays, that includes undetected events into account. The index can provide a better insight into onset and offset detection than conventional time-based metrics.

The Study of the Ontology and Context Verification Based Intrusion Detection Model

2014 ◽  
Vol 644-650 ◽  
pp. 3338-3341 ◽  
Author(s):  
Guang Feng Guo
Keyword(s):  
Intrusion Detection ◽  
Knowledge Base ◽  
False Positive ◽  
Intrusion Detection System ◽  
Detection System ◽  
False Positive Rate ◽  
False Positives ◽  
The Real ◽  
Detection Model ◽  
Positive Rate

During the 30-year development of the Intrusion Detection System, the problems such as the high false-positive rate have always plagued the users. Therefore, the ontology and context verification based intrusion detection model (OCVIDM) was put forward to connect the description of attack’s signatures and context effectively. The OCVIDM established the knowledge base of the intrusion detection ontology that was regarded as the center of efficient filtering platform of the false alerts to realize the automatic validation of the alarm and self-acting judgment of the real attacks, so as to achieve the goal of filtering the non-relevant positives alerts and reduce false positives.

scholarly journals A monocular-based navigation approach for unmanned aerial vehicle safe and autonomous transmission-line inspection

2019 ◽  
Vol 16 (1) ◽  
pp. 172988141982994 ◽  
Author(s):  
Xiaolong Hui ◽  
Jiang Bian ◽  
Xiaoguang Zhao ◽  
Min Tan
Keyword(s):  
Transmission Line ◽  
Unmanned Aerial Vehicle ◽  
Transmission Lines ◽  
Spatial Distance ◽  
Vanishing Point ◽  
Transmission Tower ◽  
Flight Direction ◽  
Field Environment ◽  
Smooth Tracking ◽  
Aerial Vehicle

This article presents a monocular-based navigation approach for unmanned aerial vehicle safe and continuous inspection along one side of transmission lines. To this end, a navigation model based on the transmission tower and the transmission-line vanishing point was proposed, and the following three key issues were addressed. First, a deep-learning-based object detection and a fast and smooth tracking algorithm based on the kernelized correlation filter were combined to locate transmission tower timely and reliably. Second, the vanishing point of transmission lines was computed and optimized to provide unmanned aerial vehicle with a robust and precise flight direction. Third, to keep a stable safe distance from transmission lines, the transmission lines were first rectified by optimizing a homography matrix to eliminate the parallel distortion, and then their interval variation was estimated for reflecting the spatial distance variation. Finally, the real distance from transmission tower was measured by the triangulation across multiple views. The proposed navigation approach and the designed UAV platform were tested in a field environment, which achieved an encouraging result. To the best of authors’ knowledge, this article marks the first time that a safe and continuous navigation approach along one side of transmission lines is put forward and implemented.

scholarly journals Research on a Surface Defect Detection Algorithm Based on MobileNet-SSD

2018 ◽  
Vol 8 (9) ◽  
pp. 1678 ◽  
Author(s):  
Yiting Li ◽  
Haisong Huang ◽  
Qingsheng Xie ◽  
Liguo Yao ◽  
Qipeng Chen
Keyword(s):  
Defect Detection ◽  
Surface Defect ◽  
Surface Defects ◽  
Detection Method ◽  
Detection Algorithm ◽  
Single Shot ◽  
Detection Model ◽  
Machine Learning Methods ◽  
Network Methods ◽  
Surface Defect Detection

This paper aims to achieve real-time and accurate detection of surface defects by using a deep learning method. For this purpose, the Single Shot MultiBox Detector (SSD) network was adopted as the meta structure and combined with the base convolution neural network (CNN) MobileNet into the MobileNet-SSD. Then, a detection method for surface defects was proposed based on the MobileNet-SSD. Specifically, the structure of the SSD was optimized without sacrificing its accuracy, and the network structure and parameters were adjusted to streamline the detection model. The proposed method was applied to the detection of typical defects like breaches, dents, burrs and abrasions on the sealing surface of a container in the filling line. The results show that our method can automatically detect surface defects more accurately and rapidly than lightweight network methods and traditional machine learning methods. The research results shed new light on defect detection in actual industrial scenarios.

scholarly journals A Study on an Out-of-Step Detection Algorithm Using the Time Variation of Complex Power: Part I, Mathematical Modeling

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4065
Author(s):  
You-Jin Lee ◽  
Jeong-Yong Heo ◽  
O-Sang Kwon ◽  
Chul-Hwan Kim
Keyword(s):  
Transmission Lines ◽  
Smart Grids ◽  
Detection Algorithm ◽  
Grid System ◽  
Step Detection ◽  
Power Swing ◽  
Complex Power ◽  
Power Part ◽  
Micro Grid ◽  
The Stability

Power quality and stability have become the most important issues in power system operations, Micro Grids, and Smart Grids. Sensitive equipment can be seriously damaged when exposed to unstable power swing conditions. An unstable system may cause serious damage to Micro Grid System elements such as generators, transformers, transmission lines, and so forth. Therefore, out-of-step detection is essential for the safe operation of a Micro Grid system. In general, Equal Area Criterion (EAC) is a method for evaluating the stability of Smart Grid systems. However, EAC can be performed only if it is possible to analyze the active power and generator angle. This paper presents an analysis of the trajectory of complex power using a mathematical model. The variation of complex power is analyzed using a mathematical method, and then the relationship between complex power and EAC is presented, and a simulation performed. Later, in part II, a novel out-of-step detection algorithm based on part I will be presented and tested.

scholarly journals Malicious Domain Names Detection Algorithm Based on N-Gram

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Hong Zhao ◽  
Zhaobin Chang ◽  
Guangbin Bao ◽  
Xiangyan Zeng
Keyword(s):  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Detection Algorithm ◽  
Internet Security ◽  
Domain Name ◽  
Domain Names ◽  
Malicious Attack ◽  
Positive Rate ◽  
N Gram

Malicious domain name attacks have become a serious issue for Internet security. In this study, a malicious domain names detection algorithm based on N-Gram is proposed. The top 100,000 domain names in Alexa 2013 are used in the N-Gram method. Each domain name excluding the top-level domain is segmented into substrings according to its domain level with the lengths of 3, 4, 5, 6, and 7. The substring set of the 100,000 domain names is established, and the weight value of a substring is calculated according to its occurrence number in the substring set. To detect a malicious attack, the domain name is also segmented by the N-Gram method and its reputation value is calculated based on the weight values of its substrings. Finally, the judgment of whether the domain name is malicious is made by thresholding. In the experiments on Alexa 2017 and Malware domain list, the proposed detection algorithm yielded an accuracy rate of 94.04%, a false negative rate of 7.42%, and a false positive rate of 6.14%. The time complexity is lower than other popular malicious domain names detection algorithms.

Research on Distributed Intrusion Detection Model Based on Information Fusion

2010 ◽  
Vol 121-122 ◽  
pp. 528-533
Author(s):  
Ping Du ◽  
Wei Xu
Keyword(s):  
Intrusion Detection ◽  
Information Fusion ◽  
Detection System ◽  
False Positive Rate ◽  
Prototype System ◽  
Security Risk ◽  
Source Information ◽  
Detection Model ◽  
Distributed Intrusion Detection ◽  
Positive Rate

The research actuality of Intrusion Detection System(IDS) were analyzed, Due to the defects of IDS such as high positive rate of IDS and incapable of effective detection of dispersed coordinated attacks on the time and space, the ideas of the multi-source information fusion were introduced in the paper, a multi-level IDS reasoning framework and prototype system were presented. The prototype adds analysis engine to the existing IDS Sensor, We used Bayesian Network as a tool for multi-source information fusion, and we used goal-tree to analyze the attempts of coordinated attacks and quantify the security risk of system. Compared to the existing IDS, the prototype is more integrated and more capable in finding coordinated attacks with lower false positive rate.

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