With the fast development of smart grid, the power line mileage and power equipments get rapid growth. The contradiction between the large number of maintenance equipments and the small number of maintenance workers becomes increasingly prominent. Meanwhile, the traditional maintenance mode has the disadvantages of over or under maintenance, which will lead to the increasing failure risk of power transmission system. Faced with these issues, to enhance the intelligence and automation level of power line inspection, many researchers have devoted much effort to the research of automatic power line inspection and some state-of-the-art techniques about power line inspection are proposed to improve the inspection efficiency and quality, such as unmanned aerial vehicle (UAV), image processing, deep architecture, and so on. In this article, we analyzed and summarized the state-of-the-art techniques on power line inspection to provide a valuable reference for the researchers engaged in the smart grid. First, the common inspection tasks on power line inspection are reviewed. Second, the existing inspection platforms are examined in this article. Also, the advantages and disadvantages of different platforms are analyzed accordingly. Third, faced with different inspection tasks, different inspection sensors are integrated into the inspection platforms for data collection. Therefore, the common sensors on inspection platforms are discussed. Finally, to realize automatic power line inspection, different inspection methods are proposed or improved, and these advanced inspection methods are surveyed and discussed.

A Review on State-of-the-Art Power Line Inspection Techniques

To recognize the tender shoots for high-quality tea and to determine the picking points accurately and quickly, this paper proposes a method of recognizing the picking points of the tender tea shoots with the improved YOLO-v3 deep convolutional neural network algorithm. This method realizes the end-to-end target detection and the recognition of different postures of high-quality tea shoots, considering both efficiency and accuracy. At first, in order to predict the category and position of tender tea shoots, an image pyramid structure is used to obtain the characteristic map of tea shoots at different scales. The residual network block structure is added to the downsampling part, and the fully connected part is replaced by a $1\times 1$ convolution operation at the end, ensuring accurate identification of the result and simplifying the network structure. The K-means method is used to cluster the dimension of the target box. Finally, the image data set of picking points for high-quality tea shoots is built. The accuracy of the trained model under the verification set is over 90%, which is much higher than the detection accuracy of the research methods.

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Tender Tea Shoots Recognition and Positioning for Picking Robot Using Improved YOLO-V3 Model

Automatic inspection of insulators from high-voltage transmission lines is of paramount importance to the safety and reliable operation of the power grid. Due to different size insulators and the complex background of aerial images, it is a difficult task to recognize insulators in aerial views. Most of the traditional image processing methods and machine learning methods cannot achieve sufficient performance for insulator detection when diverse background interference is present. In this study, a deep learning method—based on You Only Look Once (YOLO)—will be proposed, capable of detecting insulators from aerial images with complex backgrounds. Firstly, aerial images with common aerial scenes were collected by Unmanned Aerial Vehicle (UAV), and a novel insulator dataset was constructed. Secondly, to enhance feature reuse and propagation, on the basis of YOLOv3 and Dense-Blocks, the YOLOv3-dense network was utilized for insulator detection. To improve detection accuracy for different sized insulators, a structure of multiscale feature fusion was adapted to the YOLOv3-dense network. To obtain abundant semantic information of upper and lower layers, multilevel feature mapping modules were employed across the YOLOv3-dense network. Finally, the YOLOv3-dense network and compared networks were trained and tested on the testing set. The average precision of YOLOv3-dense, YOLOv3, and YOLOv2 were 94.47%, 90.31%, and 83.43%, respectively. Experimental results and analysis validate the claim that the proposed YOLOv3-dense network achieves good performance in the detection of different size insulators amid diverse background interference.

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Improved YOLOv3 Network for Insulator Detection in Aerial Images with Diverse Background Interference

Insulator fault detection is one of the essential tasks for high-voltage transmission lines’ intelligent inspection. In this study, a modified model based on You Only Look Once (YOLO) is proposed for detecting insulator faults in aerial images with a complex background. Firstly, aerial images with one fault or multiple faults are collected in diverse scenes, and then a novel dataset is established. Secondly, to increase feature reuse and propagation in the low-resolution feature layers, a Cross Stage Partial Dense YOLO (CSPD-YOLO) model is proposed based on YOLO-v3 and the Cross Stage Partial Network. The feature pyramid network and improved loss function are adopted to the CSPD-YOLO model, improving the accuracy of insulator fault detection. Finally, the proposed CSPD-YOLO model and compared models are trained and tested on the established dataset. The average precision of CSPD-YOLO model is 4.9% and 1.8% higher than that of YOLO-v3 and YOLO-v4, and the running time of CSPD-YOLO (0.011 s) model is slightly longer than that of YOLO-v3 (0.01 s) and YOLO-v4 (0.01 s). Compared with the excellent object detection models YOLO-v3 and YOLO-v4, the experimental results and analysis demonstrate that the proposed CSPD-YOLO model performs better in insulator fault detection from high-voltage transmission lines with a complex background.

Insulator Faults Detection in Aerial Images from High-Voltage Transmission Lines Based on Deep Learning Model

Deep convolutional neural network for enhancing traffic sign recognition developed on Yolo V4

The reliability of electrical components affects the stable operation of the power system. Electrical components inspection has long been important issues in the intelligent power system. The main problems of traditional recognition methods of electrical components are low detection accuracy and poor real-time performance, which are challenging to extract necessary features from the inspection images. This paper proposes a way to detect the electrical components in the Unmanned Aerial Vehicle (UAV) inspection image based on You Only Look Once (YOLO) V3 algorithm. Due to some of the inspection images are not clear, which result in the reduction of the available dataset. On this basis, we adopt Super-Resolution Convolutional Neural Network (SRCNN) to realize super-resolution reconstruction on the blurred image, which achieves the expansion of the dataset. We compare the performance of the proposed method with other popular recognition methods. The results of experiment verify the effectiveness of the proposed method, and the technique reaches high recognition accuracy, good robustness, and strong real-time performance for UAV power inspection system.

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Research on Recognition Method of Electrical Components Based on YOLO V3

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AIE-Active Photosensitizers: Manipulation of Reactive Oxygen Species Generation and Applications in Photodynamic Therapy

Resveratrol (RES) displays strong antioxidant and anti-inflammatory properties in protecting the animals from various stressors and inflammatory injuries, but its interrelationship with the gut microbiota remained largely unclear. This study was carried out to investigate the effects of dietary RES supplementation on growth performance, antioxidant capacity, intestinal immunity and gut microbiota in yellow-feathered broilers challenged by lipopolysaccharide (LPS). A total of 240 yellow-feathered broilers were randomly assigned to four treatment groups in a 2 × 2 factorial design. The broilers were fed with the control diet or control diet supplemented with 400 mg/kg RES, followed by challenge with LPS or the same amount of saline. Dietary RES supplementation significantly alleviated the decreases in the final body weight (BW), average daily gain (ADG), and ADFI induced by LPS (P < 0.05). LPS challenge significantly increased plasma concentrations of triglyceride, high-density lipoprotein cholesterol (HDL-C), aspartate aminotransferase (AST), and cortisol levels, but decreased triiodothyronine (T3) and insulin levels (P < 0.05). Dietary supplementation with RES significantly reversed the elevated creatinine concentrations and the decreased concentrations of T3 and insulin caused by LPS (P < 0.05). Moreover, dietary RES supplementation significantly increased plasma total antioxidant capacity (T-AOC) and catalase (CAT) activities and superoxide dismutase (SOD) and T-AOC activities in jejunal mucosa and reduced malondialdehyde (MDA) concentration in the plasma (P < 0.05). The reduction in the villus height to crypt depth ratio in duodenum, jejunum and ileum and the shortening of villus height in jejunum and ileum caused by LPS were also alleviated by RES treatment (P < 0.05). Furthermore, the increased concentrations of intestinal tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), and IL-1β caused by LPS were significantly decreased by RES treatment (P < 0.05). Dietary RES treatment increased the mRNA expression of claudin-1, claudin-5, occludin, and zonula occludens-1 (ZO-1), and decreased mRNA expression of IL-1β, IL-8, IL-17, and TNF-α after LPS challenge (P < 0.05). Dietary RES treatments significantly decreased the dominance of cecal microbiota, and increased the Pieiou-e and Simpson index. Moreover, dietary RES supplementation increased relative abundance of UCG_ 009, Erysipelotrichaceae, Christensenellaceae_R-7_group, Anaerotruncus, RF39, and Ruminococcus while decreasing the abundance of Alistipes at genus level. Spearman correlation analysis revealed that the microbes at the order and genus levels significantly correlated with indicators of growth performance, antioxidant capacity, and intestinal health. Collectively, dietary supplementation with 400 mg/kg RES could improve growth performance and antioxidant capacity, and modulate intestinal immunity in yellow-feathered broilers challenged by LPS at early stage, which might be closely associated with the regulation of gut microbiota community composition.

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Effect of dietary resveratrol supplementation on growth performance, antioxidant capacity, intestinal immunity and gut microbiota in yellow-feathered broilers challenged with lipopolysaccharide

Background Hepatocellular carcinoma (HCC) is one of the most common carcinomas all over the world, with high mortality and low survival rate. Notably, many studies have showed that a variety of molecules play vital roles in the progression of HCC. Therefore, it is urgent to find reliable biomarkers to diagnose HCC and developing novel strategies are required for the effective treatment of patients with HCC. Methods This study obtained an HCC cohort from The Cancer Genome Atlas (TCGA). For prognostic analysis, the TCGA cohort was grouped according to different median boundaries. The key module associated with HCC was adopted by Weighted Gene Co-expression Network analysis (WGCNA). We also analyzed the survival ability, functional enrichment, and potential binding proteins of key lncRNAs. The expression of hub lncRNAs in HCC tissues and cell lines was detected by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Cell Counting Kit-8 (CCK-8) and flow cytometry were applied to detect the cell proliferation, apoptosis, and cell cycle. The interaction between NIFK-AS1 and SRSF1 was examined using an RNA pull-down assay. Results The green module is the key module in HCC. NIFK-AS1 was highly expressed in HCC tissues and correlated with a poor prognosis in HCC patients (P=0.008). NIFK-AS1 was also significantly associated with cell mitosis, the cell cycle, and other biological processes. NIFK-AS1 deletion prevented cell proliferation, induced apoptosis, caused G2/M arrest, and affected cell cycle progression. RNA pull-down validated the NIFK-AS1/SRSF10 interaction. The overexpression of NIFK-AS1 was sufficient to rescue the growth of SRSF10 knockdown HepG2 cells. Conclusions This study suggested that NIFK-AS1 promotes HCC cell cycle progression through interaction with SRSF10 and its findings provide new insights into therapeutic targets for HCC.

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A novel biomarker NIFK-AS1 promotes hepatocellular carcinoma cell cycle progression through interaction with SRSF10

https://doi.org/10.1016/j.psj.2022.102370

Research Note: Dietary resveratrol supplementation improves the hepatic antioxidant capacity and attenuates lipopolysaccharide-induced inflammation in yellow-feathered broilers

Zhentao He

Papers

Research on Recognition Method of Electrical Components Based on YOLO V3

AIE-Active Photosensitizers: Manipulation of Reactive Oxygen Species Generation and Applications in Photodynamic Therapy

Effect of dietary resveratrol supplementation on growth performance, antioxidant capacity, intestinal immunity and gut microbiota in yellow-feathered broilers challenged with lipopolysaccharide

A novel biomarker NIFK-AS1 promotes hepatocellular carcinoma cell cycle progression through interaction with SRSF10

Research Note: Dietary resveratrol supplementation improves the hepatic antioxidant capacity and attenuates lipopolysaccharide-induced inflammation in yellow-feathered broilers