Stepwise Design Methodology and Heterogeneous Integration Routine of Air-Cooled SiC Inverter for Electric Vehicle

Carrying on SiC devices, the air-cooled inverter of the electric vehicle (EV) can eliminate the traditional complicated liquid-cooling system in order to obtain a light and compact performance of the powertrain, which is considered as the trend of next-generation EV. However, the air-cooled SiC inverter lacks strategic design methodology and heterogeneous integration routine for critical components. In this article, a stepwise design methodology is proposed for the air-cooled SiC inverter in the power module, dc-link capacitor, and heat sink levels. In the power module level, an electrical-thermal-mechanical multiphysics model is proposed. The multidimension stress distribution principles in a six-in-one SiC power module are demonstrated. An improved power module is presented and confirmed by using the observed multiphysics design principles. In the dc-link capacitor level, ripple modeling of the inverter and capacitor are created. Considering the tradeoffs among ripple voltage, ripple current, and cost, optimal strategies to determine the material and minimize the capacitance of the dc-link capacitor are proposed. In the heat sink level, thermal resistance of air-cooled heat sink is modeled. Structure and material properties of the heat sink are optimally designed by using a comprehensive electro-thermal analysis. Based on the optimal design results, the prototypes of the customized SiC power module and heterogeneously integrated air-cooled inverter are fabricated. Experimental results are presented to demonstrate the feasibility of the designed and manufactured air-cooled SiC inverter.Ministry of Education (MOE)Nanyang Technological UniversityThis work was supported in part by the National Natural Science Foundation of China under Grant 51607016, in part by the National Key Research and Development Program of China under Grant 2017YFB0102303, and in part by the Singapore ACRF Tier 1 Grant RG 85/18. The work of X. Zhang was supported by the NTU Startup Grant (SCOPES)

Dynamics of cold pulses induced by super-sonic molecular beam injection in the EAST tokamak

Evolution of electron temperature, electron density and its fluctuation with high spatial and temporal resolutions are presented for the cold pulse propagation induced by super-sonic molecular beam injection (SMBI) in ohmic plasmas in the EAST tokamak. The non-local heat transport occurs for discharges with plasma current $I_p$=450 kA ($q_{95}\sim5.55$), and electron density $n_{e0}$ below a critical value of $(1.35\pm0.25)\times10^{19}~\mathrm{m^{-3}}$. In contrary to the response of core electron temperature and electron density (roughly 10 ms after SMBI), the electron density fluctuation in the plasma core increases promptly after SMBI and reaches its maximum around 15 ms after SMBI. The electron density fluctuation in the plasma core begins to decrease before the core electron temperature reaches its maximum (roughly 30 ms). It was also observed that the turbulence perpendicular velocity close to the inversion point of the temperature perturbation changes sign after SMBI

Salmonella produce microRNA-like RNA fragment Sal-1 in the infected cells to facilitate intracellular survival.

Salmonella have developed a sophisticated machinery to evade immune clearance and promote survival in the infected cells. Previous studies were mostly focused on either bacteria itself or host cells, the interaction mechanism of host-pathogen awaits further exploration. In the present study, we show that Salmonella can exploit mammalian cell non-classical microRNA processing machinery to further process bacterial small non-coding RNAs into microRNA-like fragments. Sal-1, one such fragment with the highest copy number in the infected cells, is derived from Salmonella 5-leader of the ribosomal RNA transcript and has a stem structure-containing precursor. Processing of Sal-1 precursors to mature Sal-1 is dependent on host cell Argonaute 2 (AGO2) but not Dicer. Functionally, depleting cellular Sal-1 strongly renders the Salmonella bacteria less resistant to the host defenses both in vitro and in vivo. In conclusion, we demonstrate a novel strategy for Salmonella evading the host immune clearance, in which Salmonella produce microRNA-like functional RNA fragments to establish a microenvironment facilitating bacterial survival

A novel solution for seepage problems using physics-informed neural networks

A Physics-Informed Neural Network (PINN) provides a distinct advantage by synergizing neural networks' capabilities with the problem's governing physical laws. In this study, we introduce an innovative approach for solving seepage problems by utilizing the PINN, harnessing the capabilities of Deep Neural Networks (DNNs) to approximate hydraulic head distributions in seepage analysis. To effectively train the PINN model, we introduce a comprehensive loss function comprising three components: one for evaluating differential operators, another for assessing boundary conditions, and a third for appraising initial conditions. The validation of the PINN involves solving four benchmark seepage problems. The results unequivocally demonstrate the exceptional accuracy of the PINN in solving seepage problems, surpassing the accuracy of FEM in addressing both steady-state and free-surface seepage problems. Hence, the presented approach highlights the robustness of the PINN and underscores its precision in effectively addressing a spectrum of seepage challenges. This amalgamation enables the derivation of accurate solutions, overcoming limitations inherent in conventional methods such as mesh generation and adaptability to complex geometries

Holistically-Attracted Wireframe Parsing

This paper presents a fast and parsimonious parsing method to accurately and robustly detect a vectorized wireframe in an input image with a single forward pass. The proposed method is end-to-end trainable, consisting of three components: (i) line segment and junction proposal generation, (ii) line segment and junction matching, and (iii) line segment and junction verification. For computing line segment proposals, a novel exact dual representation is proposed which exploits a parsimonious geometric reparameterization for line segments and forms a holistic 4-dimensional attraction field map for an input image. Junctions can be treated as the "basins" in the attraction field. The proposed method is thus called Holistically-Attracted Wireframe Parser (HAWP). In experiments, the proposed method is tested on two benchmarks, the Wireframe dataset, and the YorkUrban dataset. On both benchmarks, it obtains state-of-the-art performance in terms of accuracy and efficiency. For example, on the Wireframe dataset, compared to the previous state-of-the-art method L-CNN, it improves the challenging mean structural average precision (msAP) by a large margin ($2.8\%$ absolute improvements) and achieves 29.5 FPS on single GPU ($89\%$ relative improvement). A systematic ablation study is performed to further justify the proposed method.Comment: Accepted by CVPR 202

Learning Profitable NFT Image Diffusions via Multiple Visual-Policy Guided Reinforcement Learning

We study the task of generating profitable Non-Fungible Token (NFT) images from user-input texts. Recent advances in diffusion models have shown great potential for image generation. However, existing works can fall short in generating visually-pleasing and highly-profitable NFT images, mainly due to the lack of 1) plentiful and fine-grained visual attribute prompts for an NFT image, and 2) effective optimization metrics for generating high-quality NFT images. To solve these challenges, we propose a Diffusion-based generation framework with Multiple Visual-Policies as rewards (i.e., Diffusion-MVP) for NFT images. The proposed framework consists of a large language model (LLM), a diffusion-based image generator, and a series of visual rewards by design. First, the LLM enhances a basic human input (such as "panda") by generating more comprehensive NFT-style prompts that include specific visual attributes, such as "panda with Ninja style and green background." Second, the diffusion-based image generator is fine-tuned using a large-scale NFT dataset to capture fine-grained image styles and accessory compositions of popular NFT elements. Third, we further propose to utilize multiple visual-policies as optimization goals, including visual rarity levels, visual aesthetic scores, and CLIP-based text-image relevances. This design ensures that our proposed Diffusion-MVP is capable of minting NFT images with high visual quality and market value. To facilitate this research, we have collected the largest publicly available NFT image dataset to date, consisting of 1.5 million high-quality images with corresponding texts and market values. Extensive experiments including objective evaluations and user studies demonstrate that our framework can generate NFT images showing more visually engaging elements and higher market value, compared with SOTA approaches

Holistically-Attracted Wireframe Parsing: From Supervised to Self-Supervised Learning

This article presents Holistically-Attracted Wireframe Parsing (HAWP), a method for geometric analysis of 2D images containing wireframes formed by line segments and junctions. HAWP utilizes a parsimonious Holistic Attraction (HAT) field representation that encodes line segments using a closed-form 4D geometric vector field. The proposed HAWP consists of three sequential components empowered by end-to-end and HAT-driven designs: (1) generating a dense set of line segments from HAT fields and endpoint proposals from heatmaps, (2) binding the dense line segments to sparse endpoint proposals to produce initial wireframes, and (3) filtering false positive proposals through a novel endpoint-decoupled line-of-interest aligning (EPD LOIAlign) module that captures the co-occurrence between endpoint proposals and HAT fields for better verification. Thanks to our novel designs, HAWPv2 shows strong performance in fully supervised learning, while HAWPv3 excels in self-supervised learning, achieving superior repeatability scores and efficient training (24 GPU hours on a single GPU). Furthermore, HAWPv3 exhibits a promising potential for wireframe parsing in out-of-distribution images without providing ground truth labels of wireframes.Comment: Journal extension of arXiv:2003.01663; Accepted by IEEE TPAMI; Code is available at https://github.com/cherubicxn/haw