Detection of Abnormal Vibration Dampers on Transmission Lines in UAV Remote Sensing Images with PMA-YOLO

The accurate detection and timely replacement of abnormal vibration dampers on transmission lines are critical for the safe and stable operation of power systems. Recently, unmanned aerial vehicles (UAVs) have become widely used to inspect transmission lines. In this paper, we constructed a data set of abnormal vibration dampers (DAVDs) on transmission lines in images obtained by UAVs. There are four types of vibration dampers in this data set, and each vibration damper may be rusty, defective, or normal. The challenges in the detection of abnormal vibration dampers on transmission lines in the images captured by UAVs were as following: the images had a high resolution as well as the objects of vibration dampers were relatively small and sparsely distributed, and the backgrounds of cross stage partial networks of the images were complex due to the fact that the transmission lines were erected in a variety of outdoor environments. Existing methods of ground-based object detection significantly reduced the accuracy when dealing with complex backgrounds and small objects of abnormal vibration dampers detection. To address these issues, we proposed an end-to-end parallel mixed attention You Only Look Once (PMA-YOLO) network to improve the detection performance for abnormal vibration dampers. The parallel mixed attention (PMA) module was introduced and integrated into the YOLOv4 network. This module combines a channel attention block and a spatial attention block, and the convolution results of the input feature maps in parallel, allowing the network to pay more attention to critical regions of abnormal vibration dampers in complex background images. Meanwhile, in view of the problem that abnormal vibration dampers are prone to missing detections, we analyzed the scale and ratio of the ground truth boxes and used the K-means algorithm to re-cluster new anchors for abnormal vibration dampers in images. In addition, we introduced a multi-stage transfer learning strategy to improve the efficiency of the original training method and prevent overfitting by the network. The experimental results showed that the for PMA-YOLO in the detection of abnormal vibration dampers reached 93.8% on the test set of DAVD, 3.5% higher than that of YOLOv4. When the multi-stage transfer learning strategy was used, the was improved by a further 0.2%.

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Seismic trace interpolation for irregularly spatial sampled data using convolutional autoencoder

Geophysics ◽

10.1190/geo2018-0699.1 ◽

2020 ◽

Vol 85 (2) ◽

pp. V119-V130 ◽

Cited By ~ 2

Author(s):

Yingying Wang ◽

Benfeng Wang ◽

Ning Tu ◽

Jianhua Geng

Keyword(s):

Transfer Learning ◽

Field Data ◽

Learning Strategy ◽

Complete Data ◽

Training Data ◽

Sampled Data ◽

Data Set ◽

Network Training ◽

Convolutional Autoencoder ◽

Irregularly Sampled Data

Seismic trace interpolation is an important technique because irregular or insufficient sampling data along the spatial direction may lead to inevitable errors in multiple suppression, imaging, and inversion. Many interpolation methods have been studied for irregularly sampled data. Inspired by the working idea of the autoencoder and convolutional neural network, we have performed seismic trace interpolation by using the convolutional autoencoder (CAE). The irregularly sampled data are taken as corrupted data. By using a training data set including pairs of the corrupted and complete data, CAE can automatically learn to extract features from the corrupted data and reconstruct the complete data from the extracted features. It can avoid some assumptions in the traditional trace interpolation method such as the linearity of events, low-rankness, or sparsity. In addition, once the CAE network training is completed, the corrupted seismic data can be interpolated immediately with very low computational cost. A CAE network composed of three convolutional layers and three deconvolutional layers is designed to explore the capabilities of CAE-based seismic trace interpolation for an irregularly sampled data set. To solve the problem of rare complete shot gathers in field data applications, the trained network on synthetic data is used as an initialization of the network training on field data, called the transfer learning strategy. Experiments on synthetic and field data sets indicate the validity and flexibility of the trained CAE. Compared with the curvelet-transform-based method, CAE can lead to comparable or better interpolation performances efficiently. The transfer learning strategy enhances the training efficiency on field data and improves the interpolation performance of CAE with limited training data.

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Electrical DebtRank Algorithm–Based Identification of Vulnerable Transmission Lines in Power Systems

Frontiers in Energy Research ◽

10.3389/fenrg.2021.786439 ◽

2021 ◽

Vol 9 ◽

Author(s):

Lijuan Li ◽

Yiwei Zeng ◽

Jie Chen ◽

Yue Li ◽

Hai Liu ◽

...

Keyword(s):

Power Systems ◽

Power System ◽

Transmission Lines ◽

Large Scale ◽

Operation Mode ◽

System Structure ◽

Stable Operation ◽

Dual Model ◽

Line Load ◽

Definition Of

With the increase of complexity of the power system structure and operation mode, the risk of large-scale power outage accidents rises, which urgently need an accuracy algorithm for identifying vulnerabilities and mitigating risks. Aiming at this, the improved DebtRank (DR) algorithm is modified to adapt to the property of the power systems. The overloading state of the transmission lines plays a notable role of stable operation of the power systems. An electrical DR algorithm is proposed to incorporate the overloading state to the identification of vulnerable lines in the power systems in this article. First, a dual model of power system topology is established, the nodes of which represent the lines in the power systems. Then, besides the normal state and failure state having been considered, the definition of the overloading state is also added, and the line load and network topology are considered in the electrical DR algorithm to identify vulnerable lines. Finally, the correctness and reasonability of the vulnerable lines of the power systems identified by the electrical DR algorithm are proved by the comparative analysis of cascade failure simulation, showing its better advantages in vulnerability assessment of power systems.

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Transient Stability Improvement of the Power Systems

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v12.i3.pp916-923 ◽

2018 ◽

Vol 12 (3) ◽

pp. 916

Author(s):

Zaid H. Al-Tameemi ◽

Hayder H. Enawi ◽

Karrar M. Al-Anbary ◽

Hussam M. Almukhtar

Keyword(s):

Power Systems ◽

Power System ◽

Transmission Lines ◽

Transient Stability ◽

Electrical Power ◽

Limiting Factor ◽

Stable Operation ◽

Electric Power Networks ◽

Matlab Package ◽

The Stability

<p>During the last few decades, electrical power demand enlarged significantly whereas power production and transmission expansions has been brutally restricted as a result of restricted resources as well as ecological constrains. Consequently, many transmission lines have been profoundly loading so the stability of power system became as Limiting factor for transferring electrical power. So, maintaining a secure and stable operation of the electric power networks is deemed an imporatant and challenge issue.transient stability of a power system has been gained a considerable attention from researchers dute to it importance . Therefore,this paper sheds light on A substantial number of the adopted techniques, including an inclease in inertia constant of generator, shunt capacitor, reduction reactance of the transmission line to acheive this purpose. A 7-Machine CIGRE system has been considered a case study. Matlab package has been employed to implement this study. The simulation results show that the transient stability of the repective system enhanced considerably with these techniques.</p>

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A deep transfer learning approach for wearable sleep stage classification with photoplethysmography

npj Digital Medicine ◽

10.1038/s41746-021-00510-8 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Mustafa Radha ◽

Pedro Fonseca ◽

Arnaud Moreau ◽

Marco Ross ◽

Andreas Cerny ◽

...

Keyword(s):

Transfer Learning ◽

Learning Strategy ◽

Sleep Stage ◽

Sleep Apnoea ◽

Training Data ◽

Data Set ◽

Data Intensive ◽

Deep Recurrent Neural Network ◽

Stage Classification ◽

Sleep Stage Classification

AbstractUnobtrusive home sleep monitoring using wrist-worn wearable photoplethysmography (PPG) could open the way for better sleep disorder screening and health monitoring. However, PPG is rarely included in large sleep studies with gold-standard sleep annotation from polysomnography. Therefore, training data-intensive state-of-the-art deep neural networks is challenging. In this work a deep recurrent neural network is first trained using a large sleep data set with electrocardiogram (ECG) data (292 participants, 584 recordings) to perform 4-class sleep stage classification (wake, rapid-eye-movement, N1/N2, and N3). A small part of its weights is adapted to a smaller, newer PPG data set (60 healthy participants, 101 recordings) through three variations of transfer learning. Best results (Cohen’s kappa of 0.65 ± 0.11, accuracy of 76.36 ± 7.57%) were achieved with the domain and decision combined transfer learning strategy, significantly outperforming the PPG-trained and ECG-trained baselines. This performance for PPG-based 4-class sleep stage classification is unprecedented in literature, bringing home sleep stage monitoring closer to clinical use. The work demonstrates the merit of transfer learning in developing reliable methods for new sensor technologies by reusing similar, older non-wearable data sets. Further study should evaluate our approach in patients with sleep disorders such as insomnia and sleep apnoea.

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DermoExpert: Skin lesion classification using a hybrid convolutional neural network through segmentation, transfer learning, and augmentation

10.1101/2021.02.02.21251038 ◽

2021 ◽

Author(s):

Md. Kamrul Hasan ◽

Md. Toufick E Elahi ◽

Md. Ashraful Alam ◽

Md. Tasnim Jawad

Keyword(s):

Neural Network ◽

Skin Lesion ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Web Application ◽

Learning Strategy ◽

Characteristic Curve ◽

Feature Maps ◽

Lesion Recognition ◽

Lesion Classification

AbstractBackground and ObjectiveAlthough automated Skin Lesion Classification (SLC) is a crucial integral step in computeraided diagnosis, it remains challenging due to inconsistency in textures, colors, indistinguishable boundaries, and shapes.MethodsThis article proposes an automated dermoscopic SLC framework named Dermoscopic Expert (DermoExpert). The DermoExpert consists of preprocessing and hybrid Convolutional Neural Network (hybrid-CNN), leveraging a transfer learning strategy. The proposed hybrid-CNN classifier has three different feature extractor modules taking the same input images, which are fused to achieve better-depth feature maps of the corresponding lesion. Those unique and fused feature maps are classified using different fully connected layers, which are then ensembled to predict the lesion class. We apply lesion segmentation, augmentation, and class rebalancing in the proposed preprocessing. We have also employed geometry- and intensity-based augmentations and class rebalancing by penalizing the majority class’s loss and combining additional images to the minority classes to enhance lesion recognition outcomes. Moreover, we leverage the knowledge from a pre-trained model to build a generic classifier, although small datasets are being used. In the end, we design and implement a web application by deploying the weights of our DermoExpert for automatic lesion recognition.ResultsWe evaluate our DermoExpert on the ISIC-2016, ISIC-2017, and ISIC-2018 datasets, where the DermoExpert has achieved the area under the receiver operating characteristic curve (AUC) of 0.96, 0.95, and 0.97, respectively. The experimental results defeat the recent state-of-the-art by the margins of 10.0 % and 2.0 % respectively for the ISIC-2016 and ISIC-2017 datasets in terms of AUC. The DermoExpert also outperforms by a border of 3.0 % for the ISIC-2018 dataset concerning a balanced accuracy.ConclusionSince our framework can provide better-classification outcomes on three different test datasets, it can lead to better-recognition of melanoma to assist dermatologists. Our source code and segmented masks for the ISIC-2018 dataset will be publicly available for further improvements.

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Overview of Subsynchronous Oscillation in Grid-connected Wind Farm

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) ◽

10.2174/2352096513999200407103526 ◽

2020 ◽

Vol 13 (7) ◽

pp. 969-979

Author(s):

Xu Pei-Zhen ◽

Lu Yong-Geng ◽

Cao Xi-Min

Keyword(s):

Power Systems ◽

Wind Turbine ◽

Large Scale ◽

Wind Farm ◽

Wind Farms ◽

Induction Generator ◽

Future Research ◽

Stable Operation ◽

Subsynchronous Oscillation ◽

Different Types

Background: Over the past few years, the subsynchronous oscillation (SSO) caused by the grid-connected wind farm had a bad influence on the stable operation of the system and has now become a bottleneck factor restricting the efficient utilization of wind power. How to mitigate and suppress the phenomenon of SSO of wind farms has become the focus of power system research. Methods: This paper first analyzes the SSO of different types of wind turbines, including squirrelcage induction generator based wind turbine (SCIG-WT), permanent magnet synchronous generator- based wind turbine (PMSG-WT), and doubly-fed induction generator based wind turbine (DFIG-WT). Then, the mechanisms of different types of SSO are proposed with the aim to better understand SSO in large-scale wind integrated power systems, and the main analytical methods suitable for studying the SSO of wind farms are summarized. Results: On the basis of results, using additional damping control suppression methods to solve SSO caused by the flexible power transmission devices and the wind turbine converter is recommended. Conclusion: The current development direction of the SSO of large-scale wind farm grid-connected systems is summarized and the current challenges and recommendations for future research and development are discussed.

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Fig Plant Segmentation from Aerial Images Using a Deep Convolutional Encoder-Decoder Network

Remote Sensing ◽

10.3390/rs11101157 ◽

2019 ◽

Vol 11 (10) ◽

pp. 1157 ◽

Cited By ~ 8

Author(s):

Jorge Fuentes-Pacheco ◽

Juan Torres-Olivares ◽

Edgar Roman-Rangel ◽

Salvador Cervantes ◽

Porfirio Juarez-Lopez ◽

...

Keyword(s):

Precision Agriculture ◽

Image Data ◽

Ground Truth ◽

Aerial Images ◽

Aerial Image ◽

Data Set ◽

Visual Appearance ◽

Aerial Robots ◽

Lighting Conditions ◽

Convolutional Encoder

Crop segmentation is an important task in Precision Agriculture, where the use of aerial robots with an on-board camera has contributed to the development of new solution alternatives. We address the problem of fig plant segmentation in top-view RGB (Red-Green-Blue) images of a crop grown under open-field difficult circumstances of complex lighting conditions and non-ideal crop maintenance practices defined by local farmers. We present a Convolutional Neural Network (CNN) with an encoder-decoder architecture that classifies each pixel as crop or non-crop using only raw colour images as input. Our approach achieves a mean accuracy of 93.85% despite the complexity of the background and a highly variable visual appearance of the leaves. We make available our CNN code to the research community, as well as the aerial image data set and a hand-made ground truth segmentation with pixel precision to facilitate the comparison among different algorithms.

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A Self-Spatial Adaptive Weighting Based U-Net for Image Segmentation

Electronics ◽

10.3390/electronics10030348 ◽

2021 ◽

Vol 10 (3) ◽

pp. 348

Author(s):

Choongsang Cho ◽

Young Han Lee ◽

Jongyoul Park ◽

Sangkeun Lee

Keyword(s):

Image Segmentation ◽

Medical Image ◽

Medical Image Segmentation ◽

Feature Maps ◽

Data Set ◽

Feature Map ◽

Adaptive Weighting ◽

Spatially Adaptive ◽

Wide Range ◽

Decoder Architecture

Semantic image segmentation has a wide range of applications. When it comes to medical image segmentation, its accuracy is even more important than those of other areas because the performance gives useful information directly applicable to disease diagnosis, surgical planning, and history monitoring. The state-of-the-art models in medical image segmentation are variants of encoder-decoder architecture, which is called U-Net. To effectively reflect the spatial features in feature maps in encoder-decoder architecture, we propose a spatially adaptive weighting scheme for medical image segmentation. Specifically, the spatial feature is estimated from the feature maps, and the learned weighting parameters are obtained from the computed map, since segmentation results are predicted from the feature map through a convolutional layer. Especially in the proposed networks, the convolutional block for extracting the feature map is replaced with the widely used convolutional frameworks: VGG, ResNet, and Bottleneck Resent structures. In addition, a bilinear up-sampling method replaces the up-convolutional layer to increase the resolution of the feature map. For the performance evaluation of the proposed architecture, we used three data sets covering different medical imaging modalities. Experimental results show that the network with the proposed self-spatial adaptive weighting block based on the ResNet framework gave the highest IoU and DICE scores in the three tasks compared to other methods. In particular, the segmentation network combining the proposed self-spatially adaptive block and ResNet framework recorded the highest 3.01% and 2.89% improvements in IoU and DICE scores, respectively, in the Nerve data set. Therefore, we believe that the proposed scheme can be a useful tool for image segmentation tasks based on the encoder-decoder architecture.

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