Einsatz eines Hochspannungs-Gleichstrom-Übertragung-Systems zum netzstabilisierenden Anschluss von Offshore-Windparks an das Elektroenergiesystem

Die vorliegende Arbeit befasst sich mit der Hochspannungs-Gleichstrom-Übertragung (HGÜ) zur Einspeisung der elektrischen Energie von Offshore-Windparks an das Elektroenergiesystem (EES). Parallel zu Windparks wird der traditionelle Generator mit dem öffentlichen Netz gebunden. Auf der Basis der vorhandenen Literatur werden Regelungssynthesemethoden für den HGÜ-Wechselrichter vorgeschlagen. Bei Kurzschlusseintritt auf der Leitung können Leistungsschwingungen angeregt werden. Diese werden durch den Onshore-Wechselrichter gedämpft. Der Schwerpunkt wird auf die Dämpfung der Leistungsschwingung gelegt. Die Regelungsstrukturen des Offshore- und Onshore-Wechselrichters sind kaskadenförmig. Zur Regelung der Spannungen an dem Filterkondensator wird eine Kaskadenregelung mit unterlagerter Stromregelung in d-q-Koordinaten vorgeschlagen. Der Wirkstromsollwert der inneren Stromregelung des Onshore-Wechselrichters wird durch die überlagerte Zwischenkreisspannungsregelung bereitgestellt. Durch eine Strombegrenzungsregelung (anti-windup) wird der Wechselrichterschutz realisiert. Zum Schutz der Überspannung der Zwischenkreiskapazität des Onshore-Wechselrichters wird mit einem DC-Chopper ein zusätzlicher Strompfad geschaffen. Zur Netzstabilitätsuntersuchung des Energieversorgungssystems wird der gesteuerte Onshore-Wechselrichter der HGÜ als ein paralleles Netzwerkelement betrachtet, mit dessen Hilfe die Wirkleistungsschwankung gedämpft wird. Wie durch Simulationsergebnisse bewiesen wird, wird die Dämpfung der Leistungsschwingung durch die Wirk- oder Blindleistungseinspeisung vom Onshore-Wechselrichter realisiert.This thesis focused on the high-voltage direct current (HVDC) for supply of electrical energy from offshore wind farms to the electrical power system (EPS). Based on the existing literatures, control methods for HVDC inverters are proposed in this thesis to connect wind farms (paralleled to the traditional generator) to the power grid and stabilize the power grid. When short circuit occurs in the network, power oscillation can be excited, which will be attenuated by the onshore inverter. In another words, the stabilization of the network connection is achieved by power oscillation damping. In the model of this thesis, the control structures of the offshore and onshore inverter are cascaded. In order to control the voltage of capacitor of the offshore inverter, a cascade control is suggested to be used with inner current control in d-q-coordinates. For the inner current control of the onshore inverter, the rated value of the active current is determined by the other DC link voltage regulator. And through a current limit control (anti-windup), the protection of the onshore inverter could be realized. Meanwhile, the DC link capacity of the onshore inverter could be protected by a DC chopper to avoid overload. The controllable onshore inverter of HVDC, which also can be regarded as a paralleled network component, is used to study the energy supply system stability. With the aid of this element, the active power fluctuation is damped. All of these are proved by simulation results, and the damping of the oscillation power indeed can be realized through the active or reactive power supplied by control of onshore inverter

A Scheme to fabricate magnetic graphene-like cobalt nitride CoN4monolayer proposed by first-principles calculations

We propose a scheme to fabricate the cobalt nitride CoN4 monolayer, a magnetic graphene-like two-dimensional material, in which all Co and N atoms are in a plane. Under the pressure above 40 GPa, the bulk CoN4 is stabilized in a triclinic phase. With the pressure decreasing, the triclinic phase of CoN4 is transformed into an orthorhombic phase, and the latter is a layered compound with large interlayer spacing. At ambient condition, the weak interlayer couplings are so small that single CoN4 layer can be exfoliated by the mechanical method

BEV-DG: Cross-Modal Learning under Bird's-Eye View for Domain Generalization of 3D Semantic Segmentation

Cross-modal Unsupervised Domain Adaptation (UDA) aims to exploit the complementarity of 2D-3D data to overcome the lack of annotation in a new domain. However, UDA methods rely on access to the target domain during training, meaning the trained model only works in a specific target domain. In light of this, we propose cross-modal learning under bird's-eye view for Domain Generalization (DG) of 3D semantic segmentation, called BEV-DG. DG is more challenging because the model cannot access the target domain during training, meaning it needs to rely on cross-modal learning to alleviate the domain gap. Since 3D semantic segmentation requires the classification of each point, existing cross-modal learning is directly conducted point-to-point, which is sensitive to the misalignment in projections between pixels and points. To this end, our approach aims to optimize domain-irrelevant representation modeling with the aid of cross-modal learning under bird's-eye view. We propose BEV-based Area-to-area Fusion (BAF) to conduct cross-modal learning under bird's-eye view, which has a higher fault tolerance for point-level misalignment. Furthermore, to model domain-irrelevant representations, we propose BEV-driven Domain Contrastive Learning (BDCL) with the help of cross-modal learning under bird's-eye view. We design three domain generalization settings based on three 3D datasets, and BEV-DG significantly outperforms state-of-the-art competitors with tremendous margins in all settings.Comment: Accepted by ICCV 202

Farewell to Mutual Information: Variational Distillation for Cross-Modal Person Re-Identification

The Information Bottleneck (IB) provides an information theoretic principle for representation learning, by retaining all information relevant for predicting label while minimizing the redundancy. Though IB principle has been applied to a wide range of applications, its optimization remains a challenging problem which heavily relies on the accurate estimation of mutual information. In this paper, we present a new strategy, Variational Self-Distillation (VSD), which provides a scalable, flexible and analytic solution to essentially fitting the mutual information but without explicitly estimating it. Under rigorously theoretical guarantee, VSD enables the IB to grasp the intrinsic correlation between representation and label for supervised training. Furthermore, by extending VSD to multi-view learning, we introduce two other strategies, Variational Cross-Distillation (VCD) and Variational Mutual-Learning (VML), which significantly improve the robustness of representation to view-changes by eliminating view-specific and task-irrelevant information. To verify our theoretically grounded strategies, we apply our approaches to cross-modal person Re-ID, and conduct extensive experiments, where the superior performance against state-of-the-art methods are demonstrated. Our intriguing findings highlight the need to rethink the way to estimate mutua

Image Understands Point Cloud: Weakly Supervised 3D Semantic Segmentation via Association Learning

Weakly supervised point cloud semantic segmentation methods that require 1\% or fewer labels, hoping to realize almost the same performance as fully supervised approaches, which recently, have attracted extensive research attention. A typical solution in this framework is to use self-training or pseudo labeling to mine the supervision from the point cloud itself, but ignore the critical information from images. In fact, cameras widely exist in LiDAR scenarios and this complementary information seems to be greatly important for 3D applications. In this paper, we propose a novel cross-modality weakly supervised method for 3D segmentation, incorporating complementary information from unlabeled images. Basically, we design a dual-branch network equipped with an active labeling strategy, to maximize the power of tiny parts of labels and directly realize 2D-to-3D knowledge transfer. Afterwards, we establish a cross-modal self-training framework in an Expectation-Maximum (EM) perspective, which iterates between pseudo labels estimation and parameters updating. In the M-Step, we propose a cross-modal association learning to mine complementary supervision from images by reinforcing the cycle-consistency between 3D points and 2D superpixels. In the E-step, a pseudo label self-rectification mechanism is derived to filter noise labels thus providing more accurate labels for the networks to get fully trained. The extensive experimental results demonstrate that our method even outperforms the state-of-the-art fully supervised competitors with less than 1\% actively selected annotations

Directional enhancement of selected high-order-harmonics from intense laser irradiated blazed grating targets

Relativistically intense laser solid target interaction has been proved to be a promising way to generate high-order harmonics, which can be used to diagnose ultrafast phenomena. However, their emission direction and spectra still lack tunability. Based upon two-dimensional particle-in-cell simulations, we show that directional enhancement of selected high-order-harmonics can be realized using blazed grating targets. Such targets can select harmonics with frequencies being integer times of the grating frequency. Meanwhile, the radiation intensity and emission area of the harmonics are increased. The emission direction is controlled by tailoring the local blazed structure. Theoretical and electron dynamics analysis for harmonics generation, selection and directional enhancement from the interaction between multi-cycle laser and grating target are carried out. These studies will benefit the generation and application of laser plasma-based high order harmonics

knnAUC: an open-source R package for detecting nonlinear dependence between one continuous variable and one binary variable

Abstract Background Testing the dependence of two variables is one of the fundamental tasks in statistics. In this work, we developed an open-source R package (knnAUC) for detecting nonlinear dependence between one continuous variable X and one binary dependent variables Y (0 or 1). Results We addressed this problem by using knnAUC (k-nearest neighbors AUC test, the R package is available at https://sourceforge.net/projects/knnauc/ ). In the knnAUC software framework, we first resampled a dataset to get the training and testing dataset according to the sample ratio (from 0 to 1), and then constructed a k-nearest neighbors algorithm classifier to get the yhat estimator (the probability of y = 1) of testy (the true label of testing dataset). Finally, we calculated the AUC (area under the curve of receiver operating characteristic) estimator and tested whether the AUC estimator is greater than 0.5. To evaluate the advantages of knnAUC compared to seven other popular methods, we performed extensive simulations to explore the relationships between eight different methods and compared the false positive rates and statistical power using both simulated and real datasets (Chronic hepatitis B datasets and kidney cancer RNA-seq datasets). Conclusions We concluded that knnAUC is an efficient R package to test non-linear dependence between one continuous variable and one binary dependent variable especially in computational biology area.https://deepblue.lib.umich.edu/bitstream/2027.42/146514/1/12859_2018_Article_2427.pd

Dissection of the mechanism of traditional Chinese medical prescription-Yiqihuoxue formula as an effective anti-fibrotic treatment for systemic sclerosis

BACKGROUND: Systemic sclerosis (SSc) is a connective tissue fibrotic disease for which there is no effective treatment. Traditional Chinese Medicine (TCM), such as the Yiqihuoxue formula used in Shanghai TCM-integrated Hospital, has shown the efficacy of anti-fibrosis in clinical applications. This study was aiming to dissect the anti-fibrotic mechanism of Yiqihuoxue treatment for SSc. METHODS: Bleomycin-induced mice and SSc dermal fibroblasts were treated with Yiqihuoxue decoction; NIH-3T3 fibroblasts were exposed to exogenous TGF-β1, and then cultured with or without Yiqihuoxue decoction. Luciferase reporter gene assay was used to determine the activity of Smad binding element (SBE). Quantitative reverse transcription-polymerase chain reaction (RT-PCR) was used to examine the mRNA levels of extracellular matrix (ECM) genes. The protein levels of type I collagen, Smad3 and phosphorylated-Smad3 (p-Smad3) were detected by western blotting. Student’s t-tests were used to determine the significance of the results. RESULTS: Bleomycin-induced mice, SSc dermal fibroblasts and TGF-β1-induced NIH/3T3 fibroblasts showed higher levels of ECM gene transcriptions and collagen production. In addition, the phosphorylation level of Smad3 and activity of SBE were significantly increased after exogenous TGF-β1 induction. Whereas, Yiqihuoxue treatment could obviously attenuate fibrosis in bleomycin-induced mice, down regulate ECM gene expressions and collagen production in SSc dermal fibroblasts and TGF-β1-induced NIH/3T3 fibroblasts. Furthermore, the aberrantly high phosphorylation level of Smad3 and activity of SBE in the TGF-β1-induced NIH/3T3 fibroblasts were also dramatically decreased by Yiqihuoxue treatment. CONCLUSIONS: Yiqihuoxue treatment could effectively reduce collagen production via down-regulating the phosphorylation of Smad3 and then the activity of SBE, which are involved in the TGF-β pathway and constitutively activated in the progression of SSc