Explore the vast range of titles available.

This paper proposes a multi-port DC transformer for boosting and flexible interconnection in large-scale DC collection systems. It can transmit power and adjust port current, while also having a relatively simple control strategy. The engineering practicality of the proposed DC transformer can be well verified through the simulation of a 4 GW DC collection system. In this application scenario, the proposed DC transformer can operate stably and safely under both dynamic and steady-state conditions.

Based on the fundamentals of electromagnetics, this clear and concise text explains basic and applied principles of transformer and inductor design for power electronic applications. It details both the theory and practice of inductors and transformers employed to filter currents, store electromagnetic energy, provide physical isolation between circuits, and perform stepping up and down of DC and AC voltages. The authors present a broad range of applications from modern power conversion systems. They provide rigorous design guidelines based on a robust methodology for inductor and transformer design.  They offer real design examples, informed by proven and working field examples. Key features include:  * emphasis on high frequency design, including optimisation of the winding layout and treatment of non-sinusoidal waveforms * a chapter on planar magnetic with analytical models and descriptions of the processing technologies * analysis of the role of variable inductors, and their applications for power factor correction and solar power * unique coverage on the measurements of inductance and transformer capacitance, as well as tests for core losses at high frequency * worked examples in MATLAB, end-of-chapter problems, and an accompanying website containing solutions, a full set of instructors' presentations, and copies of all the figures. Covering the basics of the magnetic components of power electronic converters, this book is a comprehensive reference for students and professional engineers dealing with specialised inductor and transformer design. It is especially useful for senior undergraduate and graduate students in electrical engineering and electrical energy systems, and engineers working with power supplies and energy conversion systems who want to update their knowledge on a field that has progressed considerably in recent years.

[This corrects the article DOI: 10.1371/journal.pone.0335447.].

Power transformers are the most important assets of electric power substations. The reliability in the operation of electric power transmission and distribution is due to the correct operation and maintenance of power transformers. The parameters that are most used to assess the health status of power transformers are dissolved gas analysis (DGA), oil quality analysis (OQA) and content of furfuraldehydes (FFA) in oil. The parameter that currently allows for simple online monitoring in an energized transformer is the DGA. Although most of the DGA continues to be done in the laboratory, the trend is online DGA monitoring, since it allows for detection or diagnosis of the faults throughout the life of the power transformers. This study presents a review of the main DGA monitors, single- or multi-gas, their most important specifications, accuracy, repeatability and measurement range, the types of installation, valve or closed loop, and number of analogue inputs and outputs. This review shows the differences between the main existing DGA monitors and aims to help in the selection of the most suitable DGA monitoring approach according to the needs of each case.

Vision transformers have become popular as a possible substitute to convolutional neural networks (CNNs) for a variety of computer vision applications. These transformers, with their ability to focus on global relationships in images, offer large learning capacity. However, they may suffer from limited generalization as they do not tend to model local correlation in images. Recently, in vision transformers hybridization of both the convolution operation and self-attention mechanism has emerged, to exploit both the local and global image representations. These hybrid vision transformers, also referred to as CNN-Transformer architectures, have demonstrated remarkable results in vision applications. Given the rapidly growing number of hybrid vision transformers, it has become necessary to provide a taxonomy and explanation of these hybrid architectures. This survey presents a taxonomy of the recent vision transformer architectures and more specifically that of the hybrid vision transformers. Additionally, the key features of these architectures such as the attention mechanisms, positional embeddings, multi-scale processing, and convolution are also discussed. In contrast to the previous survey papers that are primarily focused on individual vision transformer architectures or CNNs, this survey uniquely emphasizes the emerging trend of hybrid vision transformers. By showcasing the potential of hybrid vision transformers to deliver exceptional performance across a range of computer vision tasks, this survey sheds light on the future directions of this rapidly evolving architecture.

The mainstream crowd counting methods usually utilize the convolution neural network (CNN) to regress a density map, requiring point-level annotations. However, annotating each person with a point is an expensive and laborious process. During the testing phase, the point-level annotations are not considered to evaluate the counting accuracy, which means the point-level annotations are redundant. Hence, it is desirable to develop weakly-supervised counting methods that just rely on count-level annotations, a more economical way of labeling. Current weakly-supervised counting methods adopt the CNN to regress a total count of the crowd by an image-to-count paradigm. However, having limited receptive fields for context modeling is an intrinsic limitation of these weakly-supervised CNN-based methods. These methods thus cannot achieve satisfactory performance, with limited applications in the real world. The transformer is a popular sequence-to-sequence prediction model in natural language processing (NLP), which contains a global receptive field. In this paper, we propose TransCrowd, which reformulates the weakly-supervised crowd counting problem from the perspective of sequence-to-count based on transformers. We observe that the proposed TransCrowd can effectively extract the semantic crowd information by using the self-attention mechanism of transformer. To the best of our knowledge, this is the first work to adopt a pure transformer for crowd counting research. Experiments on five benchmark datasets demonstrate that the proposed TransCrowd achieves superior performance compared with all the weakly-supervised CNN-based counting methods and gains highly competitive counting performance compared with some popular fully-supervised counting methods.

A health index method is a useful tool for the transformer assessment condition. This method has been used in several previous studies. However, most of them have not observed the health index decreasing rate as an aspect to improve the transformer condition assessment. A health index method with different approaches is proposed, considering the values and decreasing rate to assess the transformer condition. Inspection data from in‐service and out‐of‐service 150 kV power transformers provided by the Indonesian electric company are included. The correlation between operating age and Health Index value and Health Index decreasing rate was also observed. With increasing operation age, the Health Index value tends to decrease with a correlation coefficient R 2 of 0.631. Further analysis was conducted to power transformers with historical data of 3 years or more, which showed that the tendency of the Health Index value decreasing rate is higher with older transformers. This paper also illustrates the Health Index analysis of 35 out‐of‐service transformers, resulting in a more suitable Health Index value compared to the previous approach. This study proposes the transformer risk assessment based on its health index value and the decreasing rate.

The relationship between the resource of a power transformer and the resource characteristics of a liquid dielectric is shown. Determined the key quality indicators, which allow to control the resource of the liquid dielectric. For the first time, “The method of experimental determination of the liquid dielectric resource and measures for its restoration” was developed.

The resolution of feature maps is a critical factor for accurate medical image segmentation. Most of the existing Transformer-based networks for medical image segmentation adopt a U-Net-like architecture, which contains an encoder that converts the high-resolution input image into low-resolution feature maps using a sequence of Transformer blocks and a decoder that gradually generates high-resolution representations from low-resolution feature maps. However, the procedure of recovering high-resolution representations from low-resolution representations may harm the spatial precision of the generated segmentation masks. Unlike previous studies, in this study, we utilized the high-resolution network (HRNet) design style by replacing the convolutional layers with Transformer blocks, continuously exchanging feature map information with different resolutions generated by the Transformer blocks. The proposed Transformer-based network is named the high-resolution Swin Transformer network (HRSTNet). Extensive experiments demonstrated that the HRSTNet can achieve performance comparable with that of the state-of-the-art Transformer-based U-Net-like architecture on the 2021 Brain Tumor Segmentation dataset, the Medical Segmentation Decathlon’s liver dataset, and the BTCV multi-organ segmentation dataset.

Increase in global energy demand and constraints from fossil fuels have encouraged a growing share of renewable energy resources in the utility grid. Accordingly, an increased penetration of direct current (DC) power sources and loads (e.g., solar photovoltaics and electric vehicles) as well as the necessity for active power flow control has been witnessed in the power distribution networks. Passive transformers are susceptible to DC offset and possess no controllability when employed in smart grids. Solid state transformers (SSTs) are identified as a potential solution to modernize and harmonize alternating current (AC) and DC electrical networks and as suitable solutions in applications such as traction, electric ships, and aerospace industry. This paper provides a complete overview on SST: concepts, topologies, classification, power converters, material selection, and key aspects for design criteria and control schemes proposed in the literature. It also proposes a simple terminology to identify and homogenize the large number of definitions and structures currently reported in the literature.