The full-cycle discrete Fourier transform (FCDFT) is commonly used to estimate phasors in numerical relays because of its immunity to harmonics and accurate identification of a decaying dc component (DDC) in a fault current. For fast protection of power systems, it is necessary to speed up the phasor estimation by using a data window below one cycle. The conventional least squares (LS) methods below one cycle approximate the DDC as the first several terms of the Taylor series expansion, which degrades the phasor-estimation accuracy. This paper proposes an LS method that uses Fourier analysis for approximation of the DDC to speed up phasor estimation without accuracy degradation. The phasor-estimation accuracy of the proposed method is investigated for the data-window length in simulations using synthetic data of fault currents containing DDC, harmonics, and measurement noise. The phasor-estimation speed is also investigated with respect to the delay of the antialiasing filter in the numerical relay. The proposed method is compared with the conventional LS and FCDFT methods in simulations using synthetic fault-current data. The feasibility of the proposed method is demonstrated through the phasor estimation of simulated transmission-line faults.

To protect our planet, the material recycling of domestic waste is necessary. Since the COVID-19 pandemic began, the volume of domestic waste has surged overwhelmingly, and many countries suffered from poor waste management. Increased demand for food delivery and online shopping led to a huge surge in plastic and paper waste which came from natural resources. To reduce the consumption of resources and protect the environment from pollution, such as that from landfills, waste should be recycled. One of precious recyclable materials from household waste is liquid cartons that are made of high-quality paper. To promote sustainable recycling, this paper proposes a vision-based inspection module based on convolutional neural networks via transfer learning (CNN-TL) for collecting liquid packaging cartons in the reverse vending machine (RVM). The RVM is an unmanned automatic waste collector, and thus it needs the intelligence to inspect whether a deposited item is acceptable or not. The whole processing algorithm for collecting cartons, including the inspection step, is presented. When the waste is inserted into the RVM by a user after scanning the barcode on the waste, it is relocated to the inspection module, and the item is weighed. To develop the inspector, an experimental set-up with a video camera was built for image data generation and preparation. Using the image data, the inspection agent was trained. To make a good selection for the model, 17 pretrained CNN models were evaluated, and DenseNet121 was selected. To access the performance of the cameras, four different types were also evaluated. With the same CNN model, this paper found the effect of the number of training epochs being set to 10, 100, and 500. In the results, the most accurate agent was the 500-epoch model, as expected. By using the RVM process logic with this model, the results showed that the accuracy of detection was over 99% (overall probability from three inspections), and the time to inspect one item was less than 2 s. In conclusion, the proposed model was verified for whether it would be applicable to the RVM, as it could distinguish liquid cartons from other types of paper waste.

Background: A polarization-directed flat (PDF) lens acts as a converging lens with a focal length (f) > 0 and a diverging lens with f < 0, depending on the polarization state of the incidental light. To produce a multifocal lens with two focal lengths, a PDF and a converging lens having shorter focal length were combined. In this study, we tested a bifocal PDF to determine its potential as a new multifocal intraocular lens (IOL). Methods: Constructed a multifocal lens with a PDF lens (f = +/− 100 mm) and a converging lens (f = + 25 mm). In an optical bench test, we measured the defocus curve to test the multifocal function. The multifocal function and optical quality of the lens in various situations were tested. An Early Treatment Diabetic Retinopathy Study (ETDRS) chart as a near target and a building as a distant target were photographed using a digital single-lens reflex (DSLR) camera. Both lenses (multifocal and monofocal) were tested under the same conditions. Results: For the 0 D and − 20 D focal points, the multifocal lens showed sharp images in the optical bench test. In the DSLR test using the multifocal lens, the building appeared slightly blurry compared with the results using the monofocal lens. With the multifocal lens, the ETDRS chart’s images became blurry as the ETDRS chart’s distance decreased, but became very clear again at a certain position. Conclusions: We confirmed the multifocal function of the multifocal lens using a PDF lens. This lens can be used as a multifocal IOL in the future.