11 results match your criteria: "State Grid Shanxi Electric Power Research Institute[Affiliation]"

This paper introduces a novel carbon emission prediction method based on tracking control, leveraging historical CO emission prediction errors and feed-forward integration of electricity consumption data to enhance forecasting accuracy and minimize lag. Comparative analysis with pre-trained models such as LSTM and ARDL using Python showcases the proposed method's substantial reduction in prediction errors compared to singular reliance on electricity data, while also significantly reducing computational time in contrast to LSTM models. The findings establish a valuable reference for policymakers and researchers in refining carbon emission prediction methodologies and formulating effective carbon reduction policies.

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Article Synopsis
  • * It introduces the Abnormal Defect Detection Generative Adversarial Network (ADD-GAN), a novel model designed to generate high-quality defect images from substation equipment, significantly improving the training data available for deep learning.
  • * Experimental results show that models trained on datasets generated by ADD-GAN achieved an impressive mean average precision of 81.5% using the YOLOV7 object detection model, outperforming other existing methods for data generation and augmentation.
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With the progress of the power grid system, the coverage area of cables is widening, and the problem of cable faults is gradually coming to affect people's daily lives. While the vast majority of cable faults are caused by the ablation of the cable buffer layer, polypropylene (PP), as a common cable buffer material, has pyrolysis properties that critically impact cable faults. Studying the semiconductive buffer layer of polypropylene (PP) and its pyrolysis properties allows us to obtain a clearer picture of the pyrolysis products formed during PP ablation.

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The power transformer is vital to the reliability of the power grid which is most commonly insulated with Kraft paper and immersed in mineral oil, among which the aged state of the paper is mainly correlated to the operating life of the transformer. Degree of polymerization (DP) is a direct parameter to assess the aged condition of insulating paper, but existing DP measurement by viscosity methods are destructive and complicated. In this paper, terahertz time-domain spectroscopy (THz-TDS) was introduced to reach rapid, non-destructive detection of the DP of insulating paper.

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Photothermal materials based on organic small molecules have the characteristics of structural diversity and easy modification for solar-driven water evaporation and power generation technology. However, there still exist limitations, such as the utilization of solar energy and photostability. Therefore, it is the focus of current research to design organic photothermal materials with excellent photothermal stability, strong solar absorption capacity, and high photothermal conversion efficiency.

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Despite the extensive research on nanocomposites, a fundamental theory on the interface region is still difficult to achieve. In the present paper, we chose epoxy resin and nano-SiO, nano-SiC, nano-ZnO to prepare three kinds of nanocomposites. The dispersion characteristics at the terahertz range and dielectric permittivity at 1 Hz of epoxy resin-based nanocomposites were investigated.

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The velocity measurement algorithm based on vision is widely used in unmanned aerial vehicle navigation. Under uneven illumination intensity distribution, the traditional Lucas-Kanade (LK) optical flow (OF) algorithm has problems arising from low computational accuracy and poor adaptability. To solve these problems, we propose a monocular vision integrated velocity measurement system based on the square-root cubature Kalman filter (SRCKF).

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The quality of polarization images is easy to be affected by the noise in the image acquired by a polarization camera. Consequently, a de-noising method optimized with a Pulse Coupled Neural Network (PCNN) for polarization images is proposed for a Field-Programmable Gate Array (FPGA)-based polarization camera in this paper, in which the polarization image de-noising is implemented using an adaptive PCNN improved by Gray Wolf Optimization (GWO) and Bi-Dimensional Empirical Mode Decomposition (BEMD). Unlike other artificial neural networks, PCNN does not need to be trained, but the parameters of PCNN such as the exponential decay time constant, the synaptic junction strength factor, and the inherent voltage constant play a critical influence on its de-noising performance.

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6,6'-Dibromided -butyloxycarbonyl isoindigo (Br-TBOCII) has intense fluorescence in the solid state excitation with aggregation-induced emission (AIE), contrary to the classic heavy-atom effect. The unique AIE mechanism is attributed to the Br-Br bonding joint restricting intramolecular motion. Furthermore, the water-soluble nanoparticles Br-TBOCII/Pluronic® 127, possess robust photostability, low toxicity and good cell imaging performance.

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The focus of this study was to observe the mechanical properties of bamboo plastic composites (BPCs) with bamboo pulp fiber (BPF) or white mud (WM). The essential work of fracture (EWF) methodology was used to characterize the impact toughness of BPCs. The results revealed an increase in flexural, tensile and impact properties, when adding the BPF in the BPCs.

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Two new four-coordinate organoboron compounds with 2-(2-hydroxyphenyl)imidazole derivatives as the chelating ligands have been synthesized. They possess high thermal stability and are able to form an amorphous glass state. Crystallographic analyses indicate that the differences in ligand structure cause the change of ππ stacking character.

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