Analysis of the Electrical and Thermal Properties for Magnetic Fe3O4-Coated SiC-Filled Epoxy Composites.

Orderly arranged Silicon carbide (SiC)/epoxy (EP) composites were fabricated. SiC was made magnetically responsive by decorating the surface with iron oxide (Fe3O4) nanoparticles. Three treatment methods, including without magnetization, pre-magnetization and curing magnetization, were used to prepare SiC/EP composites with different filler distributions. Compared with unmodified SiC, magnetic SiC with core-shell structure was conducive to improve the breakdown strength of SiC/EP composites and the maximum enhancement rate was 20.86%. Among the three treatment methods, SiC/EP composites prepared in the curing-magnetization case had better comprehensive properties. Under the action of magnetic field, magnetic SiC were orderly oriented along the direction of an external field, thereby forming SiC chains. The magnetic alignment of SiC restricted the movement of EP macromolecules or polar groups to some extent, resulting in the decrease in the dielectric constant and dielectric loss. The SiC chains are equivalent to heat flow channels, which can improve the heat transfer efficiency, and the maximum improvement rate was 23.6%. The results prove that the orderly arrangement of SiC had a favorable effect on dielectric properties and thermal conductivity of SiC/EP composites. For future applications, the orderly arranged SiC/EP composites have potential for fabricating insulation materials in the power electronic device packaging field

Global health effects of future atmospheric mercury emissions

Mercury is a potent neurotoxin that poses health risks to the global population. Anthropogenic mercury emissions to the atmosphere are projected to decrease in the future due to enhanced policy efforts such as the Minamata Convention, a legally-binding international treaty entered into force in 2017. Here, we report the development of a comprehensive climate-atmosphere-land-ocean-ecosystem and exposure-risk model framework for mercury and its application to project the health effects of future atmospheric emissions. Our results show that the accumulated health effects associated with mercury exposure during 2010–2050 are $19 (95% confidence interval: 4.7–54) trillion (2020 USD) realized to 2050 (3% discount rate) for the current policy scenario. Our results suggest a substantial increase in global human health cost if emission reduction actions are delayed. This comprehensive modeling approach provides a much-needed tool to help parties to evaluate the effectiveness of Hg emission controls as required by the Minamata Convention

A New Real-Time Detection and Tracking Method in Videos for Small Target Traffic Signs

It is a challenging task for self-driving vehicles in Real-World traffic scenarios to find a trade-off between the real-time performance and the high accuracy of the detection, recognition, and tracking in videos. This issue is addressed in this paper with an improved YOLOv3 (You Only Look Once) and a multi-object tracking algorithm (Deep-Sort). First, data augmentation is employed for small sample traffic signs to address the problem of an extremely unbalanced distribution of different samples in the dataset. Second, a new architecture of YOLOv3 is proposed to make it more suitable for detecting small targets. The detailed method is (1) removing the output feature map corresponding to the 32-times subsampling of the input image in the original YOLOv3 structure to reduce its computational costs and improve its real-time performances; (2) adding an output feature map of 4-times subsampling to improve its detection capability for the small traffic signs; (3) Deep-Sort is integrated into the detection method to improve the precision and robustness of multi-object detection, and the tracking ability in videos. Finally, our method demonstrated better detection capabilities, with respect to state-of-the-art approaches, which precision, recall and mAP is 91%, 90%, and 84.76% respectively

Fault Detection and Identification in MMCs Based on DSCNNs

Fault detection and location is one of the critical issues in engineering applications of modular multilevel converters (MMCs). At present, MMC fault diagnosis based on neural networks can only locate the open-circuit fault of a single submodule. To solve this problem, this paper proposes a fault detection and localization strategy based on a depthwise separable convolutional (DSC) neural network. By inputting the bridge arm circulating current and the submodule capacitor voltage into two serially connected neural networks, not only can this method achieve the classification of submodule open-circuit faults, submodule block short-circuit faults, and bridge arm inductance faults in MMCs, but it can also locate the switch where open-circuit faults occur. The simulation experimental results show that the proposed method achieves fault classification and locates multiple submodule open-circuit faults in the same bridge arm. This method achieves accuracies of ≥99% and 87.7% for the single-point and multi-point open-circuit fault localization in MMCs, respectively, which is better than some benchmark achievements in the current literature in terms of detection accuracy, and speed, and it has fewer model parameters and better real-time performance

Mono- and Bi-Molecular Adsorption of SF6 Decomposition Products on Pt Doped Graphene: A First-Principles Investigation

Based on the first-principles of density functional theory, the SF6 decomposition products including single molecule (SO2F2, SOF2, SO2), double homogenous molecules (2SO2F2, 2SOF2, 2SO2) and double hetero molecules (SO2 and SOF2, SO2 and SO2F2, SOF2 and SO2F2) adsorbed on Pt doped graphene were discussed. The adsorption parameters, electron transfer, electronic properties and energy gap was investigated. The adsorption of SO2, SOF2 and SO2F2 on the surface of Pt-doped graphene was a strong chemisorption process. The intensity of chemical interactions between the molecule and the Pt-graphene for the above three molecules was SO2F2 > SOF2 > SO2. The change of energy gap was also studied and according to the value of energy gap, the conductivity of Pt-graphene before and after adsorbing different gas molecules can be evaluated

Synergistic treatment of SF6 by dielectric barrier discharge/γ-Al2O3 catalysis

SF6 dielectric barrier discharge (DBD) degradation technology has been a hot spot and difficult problem in environmental protection, because SF6 has very high global warming potential and long atmospheric lifetime. To further improve the destruction and removal efficiency and energy yield of SF6 by DBD, the effects of the synergetic degradation of SF6 by dielectric barrier discharge/γ-Al2O3 were studied under different gases and catalyst masses. Ar was the background gas in the reaction. The initial concentration of SF6 was 2%, and the feed gas was water vapor or oxygen. The experimental results showed the evident synergistic effect of a suitable amount of γ-Al2O3 catalyst and DBD plasma on SF6 removal when the water vapor or oxygen was applied. When the catalyst mass was 5 g, the energy density was 43.5 J/mL, and the feed gas was water vapor, the optimal destruction and removal efficiency of discharge catalytic synergistic system reached 90.13%. This value was 15.5% higher than that of individual DBD degradation. Moreover, the energy yield reached 14 g/kWh, which was approximately 21% higher than that without the catalyst

Impedance Modeling and Stability Analysis of DFIG-Based Wind Energy Conversion System Considering Frequency Coupling

Impedance-based stability analysis is an effective method for addressing a new type of SSO accidents that have occurred in recent years, especially those caused by the control interaction between a DFIG and the power grid. However, the existing impedance modeling of DFIGs is mostly focused on a single converter, such as the GSC or RSC, and the influence between the RSC and GSC, as well as the frequency coupling effect inside the converter are usually overlooked, reducing the accuracy of DFIG stability analysis. Hence, the entire impedance is proposed in this paper for the DFIG-based WECS, taking coupling factors into account (e.g., DC bus voltage dynamics, asymmetric current regulation in the dq frame, and PLL). Numerical calculations and HIL simulations on RT-Lab were used to validate the proposed model. The results indicate that the entire impedance model with frequency coupling is more accurate, and it is capable of accurately predicting the system’s possible resonance points

A holistic state estimation framework for active distribution network with battery energy storage system

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).Battery energy storage systems (BESSs) are expect‐ ed to play a crucial role in the operation and control of active distribution networks (ADNs). In this paper, a holistic state esti‐ mation framework is developed for ADNs with BESSs integrat‐ ed. A dynamic equivalent model of BESS is developed, and the state transition and measurement equations are derived. Based on the equivalence between the correction stage of the iterated extended Kalman filter (IEKF) and the weighted least squares (WLS) regression, a holistic state estimation framework is pro‐ posed to capture the static state variables of ADNs and the dy‐ namic state variables of BESSs, especially the state of charge (SOC). A bad data processing method is also presented. The simulation results show that the proposed holistic state estima‐ tion framework improves the accuracy of state estimation as well as the capability of bad data detection for both ADNs and BESSs, providing comprehensive situational awareness for the whole system

Robust Stereo Visual SLAM for Dynamic Environments With Moving Object

The accuracy of localization and mapping of automated guided vehicles (AGVs) using visual simultaneous localization and mapping (SLAM) is significantly reduced in a dynamic environment compared to a static environment due to incorrect data association caused by dynamic objects. To solve this problem, a robust stereo SLAM algorithm based on dynamic region rejection is proposed. The algorithm first detects dynamic feature points from the fundamental matrix of consecutive frames and then divides the current frame into superpixels and labels its boundaries with disparity. Finally, dynamic regions are obtained from dynamic feature points and superpixel boundaries types; only the static area is used to estimate the pose to improve the localization accuracy and robustness of the algorithm. Experiments show that the proposed algorithm outperforms ORB-SLAM2 in the KITTI dataset, and the absolute trajectory error in the actual dynamic environment can be reduced by 84% compared with the conventional ORB-SLAM2, which can effectively improve the localization and mapping accuracy of AGVs in dynamic environments

Composite membranes anchoring phosphotungstic acid by β-cyclodextrins modified halloysite nanotubes

Anchoring phosphotungstic acid (HPW) on insoluble inorganic fillers is a facile and effective approach to suppress its leakage in the proton exchange membrane. In this work, we introduced β-cyclodextrin (β-CD) on halloysite nanotubes (HNTs) with the aid of polydopamine coating to prepare water-insoluble β-CD-DHNTs, which was dispersed in the SPEEK matrix to anchor HPW. Both HPW and β-CD-DHNTs are well dispersed in the SPEEK matrix because of the hydrogen bonding complexation between [PW12O40]3- and β-CD. For the SPEEK/β-CD-DHNTs/HPW composite membranes, the proton conductivity increased with the increase of HPW content, reaching the maximum of ~120% increase relative to that of the SPEEK membrane. The HPW immobilized by the β-CD and polydopamine on the nanotubes could provide more active sites, resulting in the construction of more continuous proton transfer pathways. Therefore, as compared to the SPEEK membrane, lower activation energy for the proton transport was observed for the composite membrane, which also exhibited stable proton conductivity during the water immersion test for 30 days