Estimation of the lightning performance of transmission lines with focus on mitigation of flashovers

The growth of transmission networks into remote areas due to renewable generation features new challenges with regard to the lightning protection of transmission systems. Up to now, standard transmission line designs kept outages resulting from lightning strokes to reasonable limits with minor impacts on the power grid stability. However, due to emerging problematic earthing conditions at towers, topographically exposed transmission towers and varying lightning activity, such as encountered at the 400 kV Beauly-Denny transmission line in Scotland, the assessment of the lightning performance of transmission lines in operation and in planning emerges as an important aspect in system planning and operations. Therefore, a fresh approach is taken to the assessment of the lightning performance of transmission lines in planning and construction, as well as possible lightning performance improvements in more detail, based on the current UK/Scottish and Southern Energy 400 kV tower design and overhead line arrangements. The approach employs electromagnetic transient simulations where a novel mathematical description for positive, negative and negative subsequent lightning strokes, which are all scalable with stroke current, is applied. Furtermore, a novel tower foot earthing system model which combines soil ionisation and soil frequency-dependent effect is used. Novel lightning stroke distribution data for Scotland as well as novel cap-and-pin insulators with arcing horn flashover data derived from laboratory experiments are applied. For overhead lines, transmission towers, and flashover mitigation methods describing their physical behaviour in lightning stroke conditions state-of-the-art models are utilised. The investigation features a variety of tower and overhead line arrangements, soil conditions and earthing designs, as well as the evaluation of various measures to improve the performance. Results show that the lightning performance of a transmission line is less dependent on the tower earthing conditions, but more dependent on the degree of lightning activity and stroke amplitude distribution. The assessment of flashover mitigation methods shows that cost-effective and maintenance free solutions, such as underbuilt wires can effectively replace a costly improvement of the tower earthing system. However, in locations where challenging earthing conditions prevail, tower line arresters or counterpoise are the only options to maintain an effective lightning protection

Transformer Utilization in Medical Image Segmentation Networks

Owing to success in the data-rich domain of natural images, Transformers have recently become popular in medical image segmentation. However, the pairing of Transformers with convolutional blocks in varying architectural permutations leaves their relative effectiveness to open interpretation. We introduce Transformer Ablations that replace the Transformer blocks with plain linear operators to quantify this effectiveness. With experiments on 8 models on 2 medical image segmentation tasks, we explore -- 1) the replaceable nature of Transformer-learnt representations, 2) Transformer capacity alone cannot prevent representational replaceability and works in tandem with effective design, 3) The mere existence of explicit feature hierarchies in transformer blocks is more beneficial than accompanying self-attention modules, 4) Major spatial downsampling before Transformer modules should be used with caution.Comment: Accepted in NeurIPS 2022 workshop, Medical Imaging Meets NeurIPS (MedNeurIPS

MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation

There has been exploding interest in embracing Transformer-based architectures for medical image segmentation. However, the lack of large-scale annotated medical datasets make achieving performances equivalent to those in natural images challenging. Convolutional networks, in contrast, have higher inductive biases and consequently, are easily trainable to high performance. Recently, the ConvNeXt architecture attempted to modernize the standard ConvNet by mirroring Transformer blocks. In this work, we improve upon this to design a modernized and scalable convolutional architecture customized to challenges of data-scarce medical settings. We introduce MedNeXt, a Transformer-inspired large kernel segmentation network which introduces - 1) A fully ConvNeXt 3D Encoder-Decoder Network for medical image segmentation, 2) Residual ConvNeXt up and downsampling blocks to preserve semantic richness across scales, 3) A novel technique to iteratively increase kernel sizes by upsampling small kernel networks, to prevent performance saturation on limited medical data, 4) Compound scaling at multiple levels (depth, width, kernel size) of MedNeXt. This leads to state-of-the-art performance on 4 tasks on CT and MRI modalities and varying dataset sizes, representing a modernized deep architecture for medical image segmentation. Our code is made publicly available at: https://github.com/MIC-DKFZ/MedNeXt.Comment: Accepted at MICCAI 202

RecycleNet: Latent Feature Recycling Leads to Iterative Decision Refinement

Despite the remarkable success of deep learning systems over the last decade, a key difference still remains between neural network and human decision-making: As humans, we cannot only form a decision on the spot, but also ponder, revisiting an initial guess from different angles, distilling relevant information, arriving at a better decision. Here, we propose RecycleNet, a latent feature recycling method, instilling the pondering capability for neural networks to refine initial decisions over a number of recycling steps, where outputs are fed back into earlier network layers in an iterative fashion. This approach makes minimal assumptions about the neural network architecture and thus can be implemented in a wide variety of contexts. Using medical image segmentation as the evaluation environment, we show that latent feature recycling enables the network to iteratively refine initial predictions even beyond the iterations seen during training, converging towards an improved decision. We evaluate this across a variety of segmentation benchmarks and show consistent improvements even compared with top-performing segmentation methods. This allows trading increased computation time for improved performance, which can be beneficial, especially for safety-critical applications.Comment: Accepted at 2024 Winter Conference on Applications of Computer Vision (WACV

Whole-body x-ray dark-field radiography of a human cadaver

Background!#!Grating-based x-ray dark-field and phase-contrast imaging allow extracting information about refraction and small-angle scatter, beyond conventional attenuation. A step towards clinical translation has recently been achieved, allowing further investigation on humans.!##!Methods!#!After the ethics committee approval, we scanned the full body of a human cadaver in anterior-posterior orientation. Six measurements were stitched together to form the whole-body image. All radiographs were taken at a three-grating large-object x-ray dark-field scanner, each lasting about 40 s. Signal intensities of different anatomical regions were assessed. The magnitude of visibility reduction caused by beam hardening instead of small-angle scatter was analysed using different phantom materials. Maximal effective dose was 0.3 mSv for the abdomen.!##!Results!#!Combined attenuation and dark-field radiography are technically possible throughout a whole human body. High signal levels were found in several bony structures, foreign materials, and the lung. Signal levels were 0.25 ± 0.13 (mean ± standard deviation) for the lungs, 0.08 ± 0.06 for the bones, 0.023 ± 0.019 for soft tissue, and 0.30 ± 0.02 for an antibiotic bead chain. We found that phantom materials, which do not produce small-angle scatter, can generate a strong visibility reduction signal.!##!Conclusion!#!We acquired a whole-body x-ray dark-field radiograph of a human body in few minutes with an effective dose in a clinical acceptable range. Our findings suggest that the observed visibility reduction in the bone and metal is dominated by beam hardening and that the true dark-field signal in the lung is therefore much higher than that of the bone

Modeling the cosmological co-evolution of supermassive black holes and galaxies: I. BH scaling relations and the AGN luminosity function

We model the cosmological co-evolution of galaxies and their central supermassive black holes (BHs) within a semi-analytical framework developed on the outputs of the Millennium Simulation. This model, described in detail in Croton et al. (2006) and De Lucia & Blaizot (2007), introduces a `radio mode' feedback from Active Galactic Nuclei (AGN) at the centre of X-ray emitting atmospheres in galaxy groups and clusters. Thanks to this mechanism, the model can simultaneously explain: (i) the low observed mass drop-out rate in cooling flows; (ii) the exponential cut-off in the bright end of the galaxy luminosity function; and (iii) the bulge-dominated morphologies and old stellar ages of the most massive galaxies in clusters. This paper is the first of a series in which we investigate how well this model can also reproduce the physical properties of BHs and AGN. Here we analyze the scaling relations, the fundamental plane and the mass function of BHs, and compare them with the most recent observational data. Moreover, we extend the semi-analytic model to follow the evolution of the BH mass accretion and its conversion into radiation, and compare the derived AGN bolometric luminosity function with the observed one. While we find for the most part a very good agreement between predicted and observed BH properties, the semi-analytic model underestimates the number density of luminous AGN at high redshifts, independently of the adopted Eddington factor and accretion efficiency. However, an agreement with the observations is possible within the framework of our model, provided it is assumed that the cold gas fraction accreted by BHs at high redshifts is larger than at low redshifts.Comment: 15 pages, 7 figures, MNRAS submitte