Control design for PMM-based generator fed by active front-end rectifier in more-electric aircraft

The future aircraft electrical power system is expected to be more efficient, safer, simpler in servicing and easier in maintenance. As a result, many existing hydraulic and pneumatic power driven systems are being replaced by their electrical counterparts. This trend is known as a move towards the More-Electric Aircraft (MEA). As a result, a large number of new electrical loads have been introduced in order to power many primary functions including actuation, de-icing, cabin airconditioning, and engine start. Therefore electric power generation systems have a key role in supporting this technological trend. Advances in modern power electronics allow the concept of starter/generator (S/G) which enables electrical engine start and power generation using the same electrical machine. This results in substantial improvements in power density and reduced overall weight. One of the potential S/G solutions is to employ a permanent magnet machine (PMM) controlled by active front-end rectifier (AFE). Operation of the PMM as a generator at wide range of speed that is dictated by the engine and electrical loads connected to the aircraft bus require careful design of the controllers. Corresponding plant models are derived and verified with simulations using developed models in Matlab/Simulink. The relevant controllers are designed based on the derived plants and operating points. The controllers are tested with Simulink models and experimentally using a scaled prototype of the investigated generator system

MP2: A Momentum Contrast Approach for Recommendation with Pointwise and Pairwise Learning

Binary pointwise labels (aka implicit feedback) are heavily leveraged by deep learning based recommendation algorithms nowadays. In this paper we discuss the limited expressiveness of these labels may fail to accommodate varying degrees of user preference, and thus lead to conflicts during model training, which we call annotation bias. To solve this issue, we find the soft-labeling property of pairwise labels could be utilized to alleviate the bias of pointwise labels. To this end, we propose a momentum contrast framework (MP2) that combines pointwise and pairwise learning for recommendation. MP2 has a three-tower network structure: one user network and two item networks. The two item networks are used for computing pointwise and pairwise loss respectively. To alleviate the influence of the annotation bias, we perform a momentum update to ensure a consistent item representation. Extensive experiments on real-world datasets demonstrate the superiority of our method against state-of-the-art recommendation algorithms.Comment: This paper was accepted at SIGIR 202

Nanoindentation Characterization of a Ternary Clay-Based Composite Used in Ancient Chinese Construction

Ternary clay-based composite material (TCC), composed of lime, clay and sand, and usually modified with sticky rice and other organic compounds as additives, was widely used historically in Chinese construction and buildings due to its high mechanical performance. In this study, to gain an insight into the micromechanical mechanism of this cementitious material, the nanomechanical properties and volume fraction of mechanically different phases of the binder matrix are derived from the analysis of grid nanoindentation tests. Results show that there are five distinct mechanical phases, where the calcium silicate hydrate (C-S-H) and geopolymer present in the binder matrix are almost identical to those produced in ordinary Portland cement (OPC) and alkali-activated fly-ash geopolymer materials in nano-mechanical performance. The nano-mechanical behavior of calcite produced by the carbonation of lime in this binder is close to the calcite porous outer part of some sea urchin shells. Compared to OPC, the C-S-H contained in the TCC has a relatively lower ratio of indentation modulus to indentation hardness, implying a relatively lower resistance to material fracture. However, the geopolymer and calcite, at nearly the same volume content as the C-S-H, help to enhance the strength and durability of the TCC by their higher energy resistance capacity or higher strength compared to the C-S-H. Rediscovering of TCC offers a potential way to improve modern concrete’s strength and durability through synergy of multi-binders and the addition of organic materials if TCC can be advanced in terms of its workability and hardening rate

Strength-Adaptive Adversarial Training

Adversarial training (AT) is proved to reliably improve network's robustness against adversarial data. However, current AT with a pre-specified perturbation budget has limitations in learning a robust network. Firstly, applying a pre-specified perturbation budget on networks of various model capacities will yield divergent degree of robustness disparity between natural and robust accuracies, which deviates from robust network's desideratum. Secondly, the attack strength of adversarial training data constrained by the pre-specified perturbation budget fails to upgrade as the growth of network robustness, which leads to robust overfitting and further degrades the adversarial robustness. To overcome these limitations, we propose \emph{Strength-Adaptive Adversarial Training} (SAAT). Specifically, the adversary employs an adversarial loss constraint to generate adversarial training data. Under this constraint, the perturbation budget will be adaptively adjusted according to the training state of adversarial data, which can effectively avoid robust overfitting. Besides, SAAT explicitly constrains the attack strength of training data through the adversarial loss, which manipulates model capacity scheduling during training, and thereby can flexibly control the degree of robustness disparity and adjust the tradeoff between natural accuracy and robustness. Extensive experiments show that our proposal boosts the robustness of adversarial training

Evaluation of Performance of Background Traffic-based CMT-SCTP with Active Queue Management Algorithms

Abstract Existing researches on performance analysis of SCTP's Concurrent Multipath Transfer (CMT-SCTP) usually use DropTail algorithm as queue management algorithm without considering the impact of the background traffic. However, the background traffic of realistic network environments has an important impact on the QoS of SCTP. Besides, more and more Active Queue Management (AQM) algorithms have been proposed as a router-based mechanism for early congestion detection to keep the stability of the whole network. This paper investigates the effect of background traffic on the performance of CMT-SCTP, and evaluates the performance of CMT-SCTP under two realistic simulation topologies with reasonable background traffic and different AQM algorithms in NS-2. The simulation results show that: 1) the performance of CMT-SCTP depends on characteristic of background traffic; and 2) the different AQM algorithms used as queue management algorithm under same background traffic have the different effects. Finally, this paper summarizes the proposals to satisfy the QoS requirements in terms of throughput, end-to-end packet delay and loss rate. Since CMT-PF2 is recommended by RFC4960 but without taking impact of cross traffic into account. In the second part, we use the most promising topology which meets the developing network and base on result of analysis mentioned in the first part to analyze the performance CMT-PF1/2/3/4 played respectively, in this part, the most common scenario, symmetric CMT-SCTP, is adopted and CMT-PF algorithm is turned on. A conclusion had been nailed down that, CMT-PF3 can get more advantage in terms of average throughput than CMT-PF2 which is recommended by RFC4960. Per reasonable analyzing, we lastly recommend a more reasonable resolution for realistic network in order to reaching more satisfied QoS

The influence of adatom diffusion on the formation of skyrmion lattice in sub-monolayer Fe on Ir(111)

Room temperature grown Fe monolayer (ML) on the Ir(111) single crystal substrate has attracted great research interests as nano-skyrmion lattice can form under proper growth conditions. The formation of the nanoscale skyrmion, however, appears to be greatly affected by the diffusion length of the Fe adatoms on the Ir(111) surface. We made this observation by employing spin-polarized scanning tunneling microscopy to study skyrmion formation upon systematically changing the impurity density on the substrate surface prior to Fe deposition. Since the substrate surface impurities serve as pinning centers for Fe adatoms, the eventual size and shape of the Fe islands exhibit a direct correlation with the impurity density, which in turn determines whether skyrmion can be formed. Our observation indicates that skyrmion only forms when the impurity density is below 0.006/nm2, i.e., 12 nm averaged spacing between the neighboring defects. We verify the significance of Fe diffusion length by growing Fe on clean Ir(111) substrate at low temperature of 30 K, where no skyrmion was observed to form. Our findings signify the importance of diffusion of Fe atoms on the Ir(111) substrate, which affects the size, shape and lattice perfection of the Fe islands and thus the formation of skyrmion lattice

Creation of nano-skyrmion lattice in Fe/Ir(111) system using voltage pulse

Magnetic ultrathin films grown on heavy metal substrates often exhibit rich spin structures due to the competition between various magnetic interactions such as Heisenberg exchange, Dzyaloshinskii-Moriya interaction and higher-order spin interactions. Here we employ spin-polarized scanning tunneling microscopy to study magnetic nano-skyrmion phase in Fe monolayer grown on Ir(111) substrate. Our observations show that the formation of nano-skyrmion lattice in the Fe/Ir(111) system depends sensitively on the growth conditions and various non-skyrmion spin states can be formed. Remarkably, the application of voltage pulses between the tip and the sample can trigger a non-skyrmion to skyrmion phase transition. The fact that nano-skyrmions can be created using voltage pulse indicates that the balance between the competing magnetic interactions can be affected by an external electric field, which is highly useful to design skyrmion-based spintronic devices with low energy consumption

Identification of molecular subtypes, risk signature, and immune landscape mediated by necroptosis-related genes in non-small cell lung cancer

BackgroundNon-small cell lung cancer (NSCLC) is a highly heterogeneous malignancy with an extremely high mortality rate. Necroptosis is a programmed cell death mode mediated by three major mediators, RIPK1, RIPK3, and MLKL, and has been shown to play a role in various cancers. To date, the effect of necroptosis on NSCLC remains unclear.MethodsIn The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we downloaded transcriptomes of lung adenocarcinoma (LUAD) patients and their corresponding clinicopathological parameters. We performed multi-omics analysis using consensus clustering based on the expression levels of 40 necroptosis-related genes. We constructed prognostic risk models and used the receiver operating characteristic (ROC) curves, nomograms, and survival analysis to evaluate prognostic models.ResultsWith the use of consensus clustering analysis, two distinct subtypes of necroptosis were identified based on different mRNA expression levels, and cluster B was found to have a better survival advantage. Correlation results showed that necroptosis was significantly linked with clinical features, overall survival (OS) rate, and immune infiltration. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis confirmed that these differential genes were valuable in various cellular and biological functions and were significantly enriched in various pathways such as the P53 signaling pathway and cell cycle. We further identified three genomic subtypes and found that gene cluster B patients had better prognostic value. Multivariate Cox analysis identified the 14 best prognostic genes for constructing prognostic risk models. The high-risk group was found to have a poor prognosis. The construction of nomograms and ROC curves showed stable validity in prognostic prediction. There were also significant differences in tumor immune microenvironment, tumor mutational burden (TMB), and drug sensitivity between the two risk groups. The results demonstrate that the 14 genes constructed in this prognostic risk model were used as tumor prognostic biomarkers to guide immunotherapy and chemotherapy. Finally, we used qRT-PCR to validate the genes involved in the signature.ConclusionThis study promotes our new understanding of necroptosis in the tumor microenvironment of NSCLC, mines prognostic biomarkers, and provides a potential value for guiding immunotherapy and chemotherapy