Hotspot defect detection (HDD) of photovoltaic (PV) modules is one of the daily inspections of PV power stations. It aims to detect hotspot defects from the infrared images(IFIs), which are captured ...by the unmanned aerial vehicles at about 20 ms. The backgrounds in the IFIs are complex, which results in the difficulties of detecting hotspots in PV modules, especially the tiny-size hotspots. Therefore, a segmentation-before-detection method is proposed for HDD in this paper. In specific, the regions of PV modules in the IFIs are first extracted by an improved semantic segmentation model, and then hotspot defects are detected from the segmented regions by a developed object detection model. The semantic segmentation model is named Attention DeepLab, which has been developed by an attention module. And, the object detection model is derived from YOLOv5s. Three optimization schemes are proposed to increase the detection accuracy for tiny-size hotspot defects. The schemes are: (1) appending a prediction head for tiny hotspot defects in the prediction network, (2) revising the path aggregation network in the feature fusion network by merging multiple-scale feature maps to enhance the semantic information, and (3) applying efficient channel attention module to eliminate aliasing in the feature fusion network. Experimental results demonstrate the effectiveness of the proposed method. The mean intersection over union of the semantic segmentation model of PV modules is 97.8%, and the average precision of the HDD is 89.6%.
We evaluate the interaction effects of rising life expectancy and the public pension burden on economic growth by proposing a theoretical model based on an overlapping generations framework. Testable ...implications from the theoretical model are that the public pension burden impairs the positive effect of increased life expectancy on the aggregate savings rate in the same period and reduces the positive effect of life expectancy on the economic growth rate in the next period. Meanwhile, rising life expectancy intensifies the negative effect of the public pension burden on the aggregate savings rate and economic growth. A panel data set on OECD countries is used to provide empirical support for these predictions. Our results reveal the complicated relationships among rising life expectancy, a high public pension burden, and growth. It would be valuable for the government sector to adjust its spending to deal with fiscal pressure from public pensions.
This paper presents a disturbance observer-based model predictive of super-twisting control for Soft Open Point (SOP). First, with the consideration of the disturbances caused by parameter mismatches ...and unmodelled dynamics, a super-twisting sliding-mode observer (STO) is proposed to observe the disturbances, and the observed disturbances are introduced into the inner-loop as the compensation to improve the anti-disturbance of SOP system. Second, the outer-loop controller is designed by applying the super-twisting sliding-mode control (STC) approach to improve the dynamic performance and robustness. Third, to deal with large current harmonics by traditional model predictive control (MPC), a Three-Vector-based MPC (TV-MPC) is proposed to increase the number of voltage vectors in a sampling time. Finally, it is verified by simulations that the proposed method can reduce current harmonics, DC-side voltage setting time and improve the dynamic performance of SOP system effectively. In case of parameter mismatches, the proposed observer can observe the disturbances correctly to enhance the robustness of the SOP system.
Image super-resolution (SR) is an important image processing technique in computer vision. Although the convolutional neural network has developed rapidly and made some breakthroughs in the field of ...super-division, there are still some problems when images are magnified at large upscaling factors. Recently, generative adversarial network is popular, but the structural similarity (SSIM) between the super-resolution (SR) image generated by GAN network and high-resolution (HR) image is always unsatisfactory. In this paper, we propose a pixel-level self-paced adversarial network with multiple attention (PSPA) method to reduce the noise of SR image and increase its structural similarity with HR image. The combination of multiple attentions makes the model grasp the global information and restore the detail texture more accurately. The PSPA network can make the model notice the position with a large difference between the pixel values of SR and HR images and speed up the gradient descent speed. Our method shows excellent performance on Set5, Set14 and BSD100 datasets and overcomes many popular algorithms.
With the widespread use of modular multi-stage converters, the demands on their stability are increasing. In particular, problems such as open-circuit and short-circuit faults in their submodules ...have also attracted considerable attention from all walks of life. On this basis, a machine learning-based fault self-test and sub-module tracking strategy as well as innovative machine learning algorithms are proposed. Starting from the output characteristics of the sub-module, the harmonic components are analysed, the eigenvalues of the current system sub-module during normal operation and during faults are extracted, the eigenvalues are quickly categorised, and after categorisation, a new support vector machine model is put into place for machine learning. The trained machine model is finally embedded on top of the MCU integrated system and a communication transmission module is added on top of it, which can quickly determine the fault item in time when the system is running fault and reduce the maintenance cost later.
The rhizospheric bacterium Pseudomonas protegens Pf-5 can colonize the seed and root surfaces of plants, and can protect them from pathogen infection. Secondary metabolites, including lipopeptides ...and polyketides produced by Pf-5, are involved in its biocontrol activity. We isolated a crude extract from Pf-5. It exhibited significant surface activity and strong antibacterial activity against Pantoea ananatis DZ-12, which causes maize brown rot on leaves. HPLC analysis combined with activity tests showed that the polyketide pyoluteorin in the crude extract participated in the suppression of DZ-12 growth, and that the lipopeptide orfamide A was the major biosurfactant in the crude extract. Further studies indicated that the pyoluteorin in the crude extract significantly suppressed the biofilm formation of DZ-12, and it induced the accumulation of reactive oxygen species in DZ-12 cells. Scanning electron microscopy and transmission electron microscopy observation revealed that the crude extract severely damaged the pathogen cells and caused cytoplasmic extravasations and hollowing of the cells. The pathogenicity of DZ-12 on maize leaves was significantly reduced by the crude extract from Pf-5 in a dose-dependent manner. The polyketide pyoluteorin had strong antibacterial activity against DZ-12, and it has the potential for development as an antimicrobial agent.
This paper proposes an extended state observer-based ultra-local model-free three-vector predictive control method for Soft Open Point (SOP). First, the Ultra-Local Model-Free Predictive Control ...(ULMFPC) method is proposed to improve the robustness of the system, which only uses the input and output of the outer-loop, and any other parameters are not involved. Second, considering parameter perturbations and external disturbances in the SOP system, an expansion state observer (ESO) is established to observe the SOP system’s total perturbations and the perturbations are compensated in real-time to improve the system. Third, to solve the problem of significant current harmonics in traditional model predictive control (MPC), a three-vector MPC method (TV-MPC) is adopted to reduce the total harmonic distortion rate (THD) of the current. Finally, it is verified by simulation that the proposed method can effectively reduce the current harmonics of the SOP system, rate value setting time, and improve the dynamic performance effectively. When perturbations occur in the system, the proposed method can improve the anti-interference and robustness of the system.
Granite-related uranium ore is an important uranium resource type in China and worldwide. Whether the uranium geochemical theory “U6+ oxidative migration and U4+ reductive precipitation” is ...applicable to the granite-related uranium mineralization theory has not been determined. Detailed field and petrographic work, as well as scanning electron microscopy energy spectrum analysis, are conducted in this study to analyze the relationship between uranium minerals and pyrite from different ore types and evaluate the mechanism for the precipitation and enrichment of uranium in the Mianhuakeng uranium deposit of northern Guangdong. Uranium ore bodies in the Mianhuakeng deposit generally occur as vein-filling or vein-disseminated types. Four different kinds of ores are recognized: fluorite, carbonate, siliceous, and reddening types. Despite differences in the mineral assemblages, veined ores share similar characteristics and show that uranium minerals (1) occur in the central part or periphery of vein-filling ores or in interphase arrangements with syn-ore fluorite, quartz, or calcite veins; (2) occur as veinlets or are disseminated in cataclastic altered granite; (3) are inlaid with gangue minerals, primarily calcite, fluorite, and microcrystalline quartz; and (4) are closely associated with pyrite in aggregates or relatively independent states, forming straight boundaries with syn-ore gangue minerals that have euhedral and intact crystals and show mosaic growth features. All these results indicate that both pyrite and uranium minerals are co-crystallized products of the ore-forming fluid. Combined with previous research suggesting that the reducing fluid was sourced from mantle, this study shows that decreased pressure and temperature, as well as changes in pH and the solubility (saturation) of changes, rather than the redox reaction, caused the uranium precipitation in the Mianhuakeng deposit.
Reactive oxygen species (ROS) are now recognized as important second messengers with roles in many aspects of signaling during leukemogenesis. They serve as critical cell signaling molecules that ...regulate the activity of various enzymes including tyrosine phosphatases. ROS can induce inactivation of tyrosine phosphatases, which counteract the effects of tyrosine kinases. ROS increase phosphorylation of many proteins including signal transducer and activator of transcription-5 (STAT5) via Janus kinases (JAKs). STAT5 is aberrantly activated through phosphorylation in many types of cancer and this constitutive activation is associated with cell survival, proliferation, and self-renewal. Such leukemic activation of STAT5 is rarely caused by mutation of the STAT5 gene itself but instead by overactive mutant receptors with tyrosine kinase activity as well as JAK, SRC family protein tyrosine kinases (SFKs), and Abelson murine leukemia viral oncogene homolog (ABL) kinases. Interestingly, STAT5 suppresses transcription of several genes encoding antioxidant enzymes while simultaneously enhancing transcription of NADPH oxidase. By doing so, STAT5 activation promotes an overall elevation of ROS level, which acts as a feed-forward loop, especially in high risk Fms-related tyrosine kinase 3 (FLT3) mutant leukemia. Therefore, efforts have been made recently to target ROS in cancer cells. Drugs that are able to either quench ROS production or inversely augment ROS-related signaling pathways both have potential as cancer therapies and may afford some selectivity by activating feedback inhibition of the ROS-STAT5 kinome. This review summarizes the cooperative relationship between ROS and STAT5 and explores the pros and cons of emerging ROS-targeting therapies that are selective for leukemia characterized by persistent STAT5 phosphorylation.
Separating induction motor noise sources can provide an important reference basis for induction motor condition detection, noise reduction treatment, and fault diagnosis. Induction motors have ...different types of noise sources that partially overlap, and most radiate outward through the housing, so it is difficult to separate these noise sources. Therefore, a single-channel induction motor noise source separation and identification method, based on adaptive scale-space modal extraction (ASSME) is proposed. Firstly, the adaptive scale-space mode extraction method is proposed by constructing the electromagnetic feature scale space and the adaptive penalty factor. The simulation results show that this method solves over-decomposition problems in the classical scale-space variational mode decomposition and the difficulty in balancing the harmonic and shock modes. Secondly, motor noise experiments are conducted to construct blind source separation multi-channel inputs using the adaptive scale-space modal extraction method, judging the validity of the modal components using correlation and the variance contribution rate. Finally, robust independent component analysis (RobustICA) is used to extract independent noise components and identify these noise sources by power spectral density and envelope analysis. The results show that the multi-channel input signals obtained by the proposed method are more accurate and practical than those obtained by other methods. The independent components extracted through this noise source separation method are: electromagnetic noise of different orders, aerodynamic noise, and switching frequency noise.
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