Virtual Synchronous Generator Operation of Full Converter Wind Turbine ‒ Control and Testing in a Hardware Based Emulation Platform

Wind is one of the most promising renewable energy forms that can be harvested to into the electrical power system. The installation has been rising worldwide in the past and will continue to steadily increase. The high penetration of wind energy has bought about a number of difficulties to the power system operation due to its stochastic nature, lack of exhibited inertia, and differing responses to the traditional energy sources in grid disturbances. Various grid support functions are then proposed to resolve the issues. One solution is to allow the renewable energy sources to behave like a traditional synchronous generator in the system, as a virtual synchronous generator (VSG). On the other hand, testing the control of the future power grid with high penetration renewable often relies on digital simulation or hardware-based experiments. But they either suffer from fidelity and numerical stability issues, or are bulky and inflexible. A power electronics based power system emulation platform is built in the University of Tennessee. This Hardware Testbed (HTB) allows testing of both system level and component level controls, with a good balance between the fidelity of the hardware-based testing platform, and the coverage of the digital simulation.This dissertation proposal investigates the VSG operation of the full converter wind turbine (FCWT), focusing on its control and testing in the HTB. Specifically, a FCWT emulator was developed using a single converter to include its physical model and control strategies. The existing grid support functions are also included to demonstrate their feasibility.The comprehensive VSG controls are then proposed for a FCWT with short term energy storage. The dynamic response of the FCWT can be comparable to the traditional generation during grid disturbance. The control can also allow the FCWT to be dispatched by the system operator, and even operate stand-alone without other grid sources.To study the system response under faults, a short circuit fault emulator was developed in the HTB platform. Four basic types of the short circuit faults with various fault impedance can be emulated using the emulator. The power system transient stability in terms of critical clearing time can be measured using the developed fault emulator

Effects Of Algae Feeding On Mouse Metabolome

University of Minnesota M.S. thesis. September 2017. Major: Nutrition. Advisor: Chi Chen. 1 computer file (PDF); ix, 110 pages.Algae have been investigated and developed as a source of food, dietary supplement, and biofuel, due to their chemical and nutrient composition. Algae consumption carries algal proteins, polyunsaturated fatty acids (PUFAs), vitamins, dietary fibers, and bioactive compounds into the biological systems of humans and animals, and therefore are expected to elicit metabolic and physiological responses. Numerous efforts have been undertaken to understand the health-promoting effects of algae consumption, such as their hypolipidemic, antioxidant, anti-obesity and anti-cancer properties. However, the metabolic events in algae-elicited effects were not examined in details in spite of the fact that these benefits are largely based on the metabolic interactions between algal components and the biological system. In this study, the influences of consuming green algae (Scenedesmus sp.) on the metabolic status of young mice was investigated through growth performance, blood chemistry, and liquid chromatography-mass spectrometry (LC-MS)-based metabolomics. Compared to the control diet, 5% algae promoted growth performance while 20% algae suppressed it. The growth performance was significantly increased by 5% algae but decreased by 20% algae feeding. Serum glucose, triacylglycerols (TAG), and blood urea nitrogen (BUN) levels were not affected by both treatments, but serum cholesterol level was dramatically decreased by 20% algae feeding. Metabolomic analysis of liver, serum, feces and urine samples revealed diverse influences of algae feeding on mouse metabolome, which are represented by the features as follows: 1). Urinary vitamins and fecal pigments are identified as robust exposure markers of algae feeding. 2). Despite the high-level protein in algae, the impacts of algae feeding on free amino acids in serum and the liver were quite limited. 3). Algae feeding increased the PUFA levels in serum and liver lipidomes and the free fatty acids in feces. 4). 5% algae increased the level of reduced glutathione (GSH) in the liver while 20% algae increased the level of oxidized glutathione (GSSG) in the liver and the levels of aldehydic lipid oxidation products (LOPs) in the liver and urine. 5). 5% algae selective increased the levels of intermediate metabolites, including adenosine monophosphate (AMP), adenylsuccinic acid, dephospho-CoA, and nicotinamide, in the liver while 20% algae increased the levels of carnitine and carnitine derivatives in the liver. 6). Algae feeding dramatically altered the microbial metabolism, as reflected by the increases in short-chain fatty acids (SCFAs) and primary bile salts in feces, the increases of branched fatty acids in urine, the decreases of secondary bile acids in feces, and the decrease of p-cresol metabolites in urine. Overall, multiple correlations between metabolite markers and growth performance in algae feeding were established in this study and could serve as a foundation for further mechanistic investigations on the biological effects of algae feeding

VoiceFlow: Efficient Text-to-Speech with Rectified Flow Matching

Although diffusion models in text-to-speech have become a popular choice due to their strong generative ability, the intrinsic complexity of sampling from diffusion models harms their efficiency. Alternatively, we propose VoiceFlow, an acoustic model that utilizes a rectified flow matching algorithm to achieve high synthesis quality with a limited number of sampling steps. VoiceFlow formulates the process of generating mel-spectrograms into an ordinary differential equation conditional on text inputs, whose vector field is then estimated. The rectified flow technique then effectively straightens its sampling trajectory for efficient synthesis. Subjective and objective evaluations on both single and multi-speaker corpora showed the superior synthesis quality of VoiceFlow compared to the diffusion counterpart. Ablation studies further verified the validity of the rectified flow technique in VoiceFlow.Comment: 4 figure, 5 pages, submitted to ICASSP 202

X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval

Video-text retrieval has been a crucial and fundamental task in multi-modal research. The development of video-text retrieval has been considerably promoted by large-scale multi-modal contrastive pre-training, which primarily focuses on coarse-grained or fine-grained contrast. However, cross-grained contrast, which is the contrast between coarse-grained representations and fine-grained representations, has rarely been explored in prior research. Compared with fine-grained or coarse-grained contrasts, cross-grained contrast calculate the correlation between coarse-grained features and each fine-grained feature, and is able to filter out the unnecessary fine-grained features guided by the coarse-grained feature during similarity calculation, thus improving the accuracy of retrieval. To this end, this paper presents a novel multi-grained contrastive model, namely X-CLIP, for video-text retrieval. However, another challenge lies in the similarity aggregation problem, which aims to aggregate fine-grained and cross-grained similarity matrices to instance-level similarity. To address this challenge, we propose the Attention Over Similarity Matrix (AOSM) module to make the model focus on the contrast between essential frames and words, thus lowering the impact of unnecessary frames and words on retrieval results. With multi-grained contrast and the proposed AOSM module, X-CLIP achieves outstanding performance on five widely-used video-text retrieval datasets, including MSR-VTT (49.3 R@1), MSVD (50.4 R@1), LSMDC (26.1 R@1), DiDeMo (47.8 R@1) and ActivityNet (46.2 R@1). It outperforms the previous state-of-theart by +6.3%, +6.6%, +11.1%, +6.7%, +3.8% relative improvements on these benchmarks, demonstrating the superiority of multi-grained contrast and AOSM.Comment: 13 pages, 6 figures, ACMMM2

3D-STMN: Dependency-Driven Superpoint-Text Matching Network for End-to-End 3D Referring Expression Segmentation

In 3D Referring Expression Segmentation (3D-RES), the earlier approach adopts a two-stage paradigm, extracting segmentation proposals and then matching them with referring expressions. However, this conventional paradigm encounters significant challenges, most notably in terms of the generation of lackluster initial proposals and a pronounced deceleration in inference speed. Recognizing these limitations, we introduce an innovative end-to-end Superpoint-Text Matching Network (3D-STMN) that is enriched by dependency-driven insights. One of the keystones of our model is the Superpoint-Text Matching (STM) mechanism. Unlike traditional methods that navigate through instance proposals, STM directly correlates linguistic indications with their respective superpoints, clusters of semantically related points. This architectural decision empowers our model to efficiently harness cross-modal semantic relationships, primarily leveraging densely annotated superpoint-text pairs, as opposed to the more sparse instance-text pairs. In pursuit of enhancing the role of text in guiding the segmentation process, we further incorporate the Dependency-Driven Interaction (DDI) module to deepen the network's semantic comprehension of referring expressions. Using the dependency trees as a beacon, this module discerns the intricate relationships between primary terms and their associated descriptors in expressions, thereby elevating both the localization and segmentation capacities of our model. Comprehensive experiments on the ScanRefer benchmark reveal that our model not only set new performance standards, registering an mIoU gain of 11.7 points but also achieve a staggering enhancement in inference speed, surpassing traditional methods by 95.7 times. The code and models are available at https://github.com/sosppxo/3D-STMN

Semi-Supervised Panoptic Narrative Grounding

Despite considerable progress, the advancement of Panoptic Narrative Grounding (PNG) remains hindered by costly annotations. In this paper, we introduce a novel Semi-Supervised Panoptic Narrative Grounding (SS-PNG) learning scheme, capitalizing on a smaller set of labeled image-text pairs and a larger set of unlabeled pairs to achieve competitive performance. Unlike visual segmentation tasks, PNG involves one pixel belonging to multiple open-ended nouns. As a result, existing multi-class based semi-supervised segmentation frameworks cannot be directly applied to this task. To address this challenge, we first develop a novel SS-PNG Network (SS-PNG-NW) tailored to the SS-PNG setting. We thoroughly investigate strategies such as Burn-In and data augmentation to determine the optimal generic configuration for the SS-PNG-NW. Additionally, to tackle the issue of imbalanced pseudo-label quality, we propose a Quality-Based Loss Adjustment (QLA) approach to adjust the semi-supervised objective, resulting in an enhanced SS-PNG-NW+. Employing our proposed QLA, we improve BCE Loss and Dice loss at pixel and mask levels, respectively. We conduct extensive experiments on PNG datasets, with our SS-PNG-NW+ demonstrating promising results comparable to fully-supervised models across all data ratios. Remarkably, our SS-PNG-NW+ outperforms fully-supervised models with only 30% and 50% supervision data, exceeding their performance by 0.8% and 1.1% respectively. This highlights the effectiveness of our proposed SS-PNG-NW+ in overcoming the challenges posed by limited annotations and enhancing the applicability of PNG tasks. The source code is available at https://github.com/nini0919/SSPNG.Comment: ACM MM 202

Semi-analytical stiffness model of bolted joints in machine tools considering the coupling effect

This study proposes an improved semi-analytical approach for contact stiffness modeling of bolted joints in a machine tool system. First, nonlinear contact stress distribution within a single-bolted joint is obtained from the simulation results of finite element analysis software. Second, employing the Hertz contact theory and fractal theory, the contact stiffness model of a single asperity is formulated, affording analytical expressions for normal and tangential contact stiffnesses of a single-bolted joint by integrating multi-asperities in the contact area. Subsequently, considering two test specimens as illustrations, the mode shapes and natural frequencies of the proposed model and modal analysis tests are compared, and the influence of coupling effects between two adjacent bolts is illustrated. The maximum error in the natural frequencies of the proposed approach is < 2.73% relative to the experimental results. Finally, the measurements of frequency response functions on a box-in-box precision horizontal machine tool are conducted to demonstrate the accuracy and efficiency of the proposed model. The proposed model is highly efficient in revealing the influence of microcontact factors on the contact stiffness of bolted joints and in guiding the optimal functional design of bolt arrangements under the framework of virtual machine tools

X-Mesh: Towards Fast and Accurate Text-driven 3D Stylization via Dynamic Textual Guidance

Text-driven 3D stylization is a complex and crucial task in the fields of computer vision (CV) and computer graphics (CG), aimed at transforming a bare mesh to fit a target text. Prior methods adopt text-independent multilayer perceptrons (MLPs) to predict the attributes of the target mesh with the supervision of CLIP loss. However, such text-independent architecture lacks textual guidance during predicting attributes, thus leading to unsatisfactory stylization and slow convergence. To address these limitations, we present X-Mesh, an innovative text-driven 3D stylization framework that incorporates a novel Text-guided Dynamic Attention Module (TDAM). The TDAM dynamically integrates the guidance of the target text by utilizing text-relevant spatial and channel-wise attentions during vertex feature extraction, resulting in more accurate attribute prediction and faster convergence speed. Furthermore, existing works lack standard benchmarks and automated metrics for evaluation, often relying on subjective and non-reproducible user studies to assess the quality of stylized 3D assets. To overcome this limitation, we introduce a new standard text-mesh benchmark, namely MIT-30, and two automated metrics, which will enable future research to achieve fair and objective comparisons. Our extensive qualitative and quantitative experiments demonstrate that X-Mesh outperforms previous state-of-the-art methods.Comment: Technical repor