Controller Strategy for Open-Winding Brushless Doubly-Fed Wind Power Generator with Common Mode Voltage Elimination

This paper presents the theoretical derivation and implementation of a novel direct power control for open-winding brushless doubly-fed reluctance generator (OW-BDFRG). As one of the promising brushless candidates, the OW-BDFRG is characterized with two stator windings fed by a dual controllable two-level three-phase converters through a common DC bus with common mode voltage elimination. The parameter-free control strategy is designed to obtain maximum power point tracking with variable speed constant frequency (VSCF) for wind energy conversion systems (WECSs). Compared to the traditional three-level converter systems, the DC bus voltage, AC-side voltage and capacity ratings of the proposed converter system are notably high while the reliability, redundancy and fault tolerance are significantly improved. Effectiveness, correctness and robustness of the proposed control strategy and the common mode voltage elimination scheme are evaluated and confirmed through simulation and experimental tests on a 42 kW generator prototype typical for VSCF-WECS

Intriguing Property and Counterfactual Explanation of GAN for Remote Sensing Image Generation

Generative adversarial networks (GANs) have achieved remarkable progress in the natural image field. However, when applying GANs in the remote sensing (RS) image generation task, an extraordinary phenomenon is observed: the GAN model is more sensitive to the size of training data for RS image generation than for natural image generation. In other words, the generation quality of RS images will change significantly with the number of training categories or samples per category. In this paper, we first analyze this phenomenon from two kinds of toy experiments and conclude that the amount of feature information contained in the GAN model decreases with reduced training data. Then we establish a structural causal model (SCM) of the data generation process and interpret the generated data as the counterfactuals. Based on this SCM, we theoretically prove that the quality of generated images is positively correlated with the amount of feature information. This provides insights for enriching the feature information learned by the GAN model during training. Consequently, we propose two innovative adjustment schemes, namely Uniformity Regularization (UR) and Entropy Regularization (ER), to increase the information learned by the GAN model at the distributional and sample levels, respectively. We theoretically and empirically demonstrate the effectiveness and versatility of our methods. Extensive experiments on three RS datasets and two natural datasets show that our methods outperform the well-established models on RS image generation tasks. The source code is available at https://github.com/rootSue/Causal-RSGAN

A Unified GAN Framework Regarding Manifold Alignment for Remote Sensing Images Generation

Generative Adversarial Networks (GANs) and their variants have achieved remarkable success on natural images. However, their performance degrades when applied to remote sensing (RS) images, and the discriminator often suffers from the overfitting problem. In this paper, we examine the differences between natural and RS images and find that the intrinsic dimensions of RS images are much lower than those of natural images. As the discriminator is more susceptible to overfitting on data with lower intrinsic dimension, it focuses excessively on local characteristics of RS training data and disregards the overall structure of the distribution, leading to a faulty generation model. In respond, we propose a novel approach that leverages the real data manifold to constrain the discriminator and enhance the model performance. Specifically, we introduce a learnable information-theoretic measure to capture the real data manifold. Building upon this measure, we propose manifold alignment regularization, which mitigates the discriminator's overfitting and improves the quality of generated samples. Moreover, we establish a unified GAN framework for manifold alignment, applicable to both supervised and unsupervised RS image generation tasks

Optimised Power Error Comparison Strategy for Direct Power Control of the Open-winding Brushless Doubly-Fed Wind Power Generator

This paper presents the conceptual analysis and comparative simulation and experimental evaluation of a novel power error comparison direct power control (PEC-DPC) strategy of the open-winding brushless doubly-fed reluctance generator (OW-BDFRG) for wind energy conversion systems (WECSs). As one of the promising candidates for limited speed range application of pump-alike and wind turbine with partially-rated converter. The emerging OW-BDFRG employed for the proposed PEC-DPC is fed via dual low-cost two-level converters, while the DPC concept is derived from the fundamental dynamic analyses between the calculated and controllable electrical power and flux of the BDFRG with two stators measurable voltage and current. Compared to the traditional two-level and three-level converter systems, the OW-BDFRG requires lower rated capacity of power devices and switching frequency converter, though have more flexible switching mode, higher reliability, redundancy and fault tolerance capability. The performance correctness and effectiveness of the proposed DPC strategy with the selected and optimised switching vector scheme are evaluated and confirmed through computer simulation studies and experimental measurements on a 25 kW generator test rig

Unbiased Image Synthesis via Manifold-Driven Sampling in Diffusion Models

Diffusion models are a potent class of generative models capable of producing high-quality images. However, they can face challenges related to data bias, favoring specific modes of data, especially when the training data does not accurately represent the true data distribution and exhibits skewed or imbalanced patterns. For instance, the CelebA dataset contains more female images than male images, leading to biased generation results and impacting downstream applications. To address this issue, we propose a novel method that leverages manifold guidance to mitigate data bias in diffusion models. Our key idea is to estimate the manifold of the training data using an unsupervised approach, and then use it to guide the sampling process of diffusion models. This encourages the generated images to be uniformly distributed on the data manifold without altering the model architecture or necessitating labels or retraining. Theoretical analysis and empirical evidence demonstrate the effectiveness of our method in improving the quality and unbiasedness of image generation compared to standard diffusion models

Introducing Expertise Logic into Graph Representation Learning from A Causal Perspective

Benefiting from the injection of human prior knowledge, graphs, as derived discrete data, are semantically dense so that models can efficiently learn the semantic information from such data. Accordingly, graph neural networks (GNNs) indeed achieve impressive success in various fields. Revisiting the GNN learning paradigms, we discover that the relationship between human expertise and the knowledge modeled by GNNs still confuses researchers. To this end, we introduce motivating experiments and derive an empirical observation that the human expertise is gradually learned by the GNNs in general domains. By further observing the ramifications of introducing expertise logic into graph representation learning, we conclude that leading the GNNs to learn human expertise can improve the model performance. By exploring the intrinsic mechanism behind such observations, we elaborate the Structural Causal Model for the graph representation learning paradigm. Following the theoretical guidance, we innovatively introduce the auxiliary causal logic learning paradigm to improve the model to learn the expertise logic causally related to the graph representation learning task. In practice, the counterfactual technique is further performed to tackle the insufficient training issue during optimization. Plentiful experiments on the crafted and real-world domains support the consistent effectiveness of the proposed method

Water budget record from variable infiltration capacity (VIC) model

No abstract available

Contrasting fate of western Third Pole's water resources under 21st century climate change

Seasonal melting of glaciers and snow from the western Third Pole (TP) plays important role in sustaining water supplies downstream. However, the future water availability of the region, and even today’s runoff regime, are both hotly debated and inadequately quantified. Here, we characterize the contemporary flow regimes and systematically assess the future evolution of total water availability, seasonal shifts, and dry and wet discharge extremes in four most meltwater-dominated basins in the western TP, by using a process-based, well-established glacier-hydrology model, well-constrained historical reference climate data, and the ensemble of 22 global climate models with an advanced statistical downscaling and bias correction technique. We show that these basins face sharply diverging water futures under 21st century climate change. In RCP scenarios 4.5 and 8.5, increased precipitation and glacier runoff in the Upper Indus and Yarkant basins more than compensate for decreased winter snow accumulation, boosting annual and summer water availability through the end of the century. In contrast, the Amu and Syr Darya basins will become more reliant on rainfall runoff as glacier ice and seasonal snow decline. Syr Darya summer river-flows, already low, will fall by 16–30% by end-of-century, and striking increases in peak flood discharge (by >60%), drought duration (by >1 month) and drought intensity (by factor 4.6) will compound the considerable water-sharing challenges on this major transboundary river

Quantitative Trait Locus Mapping of Soybean Maturity Gene E6

Soybean [ Glycine max (L.) Merr.] sensitivity to photoperiod determines adaptation to a specific range of latitudes for soybean cultivars. When temperate-adapted soybean cultivars are grown in low latitude under short day conditions, they flower early, resulting in low grain yield, and consequently limiting their utility in tropical areas. Most cultivars adapted to low-latitude environments have the trait of delayed flowering under short day conditions, and this trait is commonly called long juvenile (LJ). In this study, the E6 locus, the classical locus conditioning the LJ trait, was molecularly mapped on Gm04 near single-nucleotide polymorphism marker HRM101. Testcross, genetic mapping, and sequencing suggest that the E6 and J loci might be tightly linked. Genetic interaction evaluation between E6 and E1 suggests that E6 has a suppressive effect on E1 and that the function of E6 is dependent on E1. The tagging markers for E6 are very useful for molecular breeding for wide adaptation and stable productivity of soybean under lowlatitude environments. Molecular identification and functional characterization of the E6 gene will greatly facilitate the understanding of the genetic and molecular mechanisms underlying the LJ trait