Hybrid framework for single-pointer meter identification.

Automated identification of single-pointer meter identification in substations is widely used in the construction of digital substations and it must accurately identify the value of the pointer meter. Current single-pointer meter identification methods are not universally applicable and can only identify one type of meter. In this study, we present a hybrid framework for single-pointer meter identification. First, the input image of the single-pointer meter is modeled to gain a priori knowledge, including the template image, dial position information, the pointer template image, and scale value positions. Based on a convolutional neural network to generate the input image and the template image feature points, image alignment is then applied through a feature point match to mitigate slight changes in the camera angle. Next, a pixel loss-free method of arbitrary point image rotation correction is presented for rotation template matching. Finally, by rotating the input gray mask image of the dial and matching it to the pointer template to get the optimal rotation angle, the meter value is calculated. The experimental findings demonstrate the method's effectiveness in identifying nine different types of single-pointer meters in substations with various ambient illuminations. This study provides a feasible reference for substations to identify the value of different types of single-pointer meters.

Enhanced Poly(Propylene Carbonate) with Thermoplastic Networks: A One-Pot Synthesis from Carbon Dioxide, Propylene Oxide, and a Carboxylic Dianhydride.

Thermally and mechanically enhanced poly(propylene carbonate) (PPC) with networks was prepared by adding a cyclic carboxylic dianhydride, bicyclo(2,2,2)oct-7-ene-2,3,5,6-tetracarboxylic dianhydride (BTCDA), in the CO2/propylene oxide (PO) copolymerization. The obtained copolymers were characterized by FT-IR, ¹H NMR, DSC, and TGA. The gel, melt flow rate, hot-set elongation, and tensile properties were also measured. The formation of networks was confirmed by the presence of gel and the shape recovery after the hot-set elongation test. The minimum permanent deformation of the copolymer is 3.8% and that of PPC is 4539% higher than this value. The results show that BTCDA units are inserted into the backbone of PPC, and the PPC chains are connected successfully owing to cyclic multifunctional anhydride groups in BTCDA. With increasing feed molar ratio of BTCDA to PO from 1 to 4%, the yield strength of copolymers increases from 18.1 to 37.4 MPa compared to 12.9 MPa of PPC. The 5% weight-loss degradation temperatures and maximum weight-loss degradation temperatures greatly increase up to 276.4 and 294.7 °C, respectively, which are 58.6 °C and 55.1 °C higher than those of PPC. These enhanced properties originate from the formation of crosslinks by the rigid and bulky multifunctional dianhydride.