Stress and frequency optimization of prismatic sandwich beams with joints: Performance improvements through topology optimization

Prismatic sandwich panels fabricated from metals offer a compelling alternative to more traditional panels across diverse industries, primarily due to their superior strength-to-weight ratio. Although several core types were proposed in the past, further improvements in performance could be achieved by devising the topology of the core through a topology optimization framework, which is explored in this article for the first time. Another novelty is the inclusion of joints between the sandwich beams and its surroundings in the analysis and optimization. Stress is minimized under uniform pressure loading on the beams and natural frequency maximized using the Method of Moving Asymptotes. The results are compared with X-core, Y-core, corrugated-core, and web-core sandwich beams, a few conventional prismatic sandwich types, which are optimized using a prominent global evolutionary algorithm. Manufacturing requirements are considered through practical limitations on the design variables. It is shown that structures produced by topology optimization outperform the conventional sandwich beams by up to 44% at intermediate to high mass levels, where volume fraction is between 0.2 and 0.4, but often through increased topological complexity. The new core topologies bear a certain resemblance with the conventional core types, underscoring engineering ingenuity that went into their development over the years. The topology of the optimized joints differs from the conventional joint. The results also show some limitations of the topology optimization framework, for example that it does not offer better-performing beams for volume fractions below 0.2.Comment: 17 pages, 3 tables, 19 figures, journal pape

Recursive Cascaded Networks for Unsupervised Medical Image Registration

We present recursive cascaded networks, a general architecture that enables learning deep cascades, for deformable image registration. The proposed architecture is simple in design and can be built on any base network. The moving image is warped successively by each cascade and finally aligned to the fixed image; this procedure is recursive in a way that every cascade learns to perform a progressive deformation for the current warped image. The entire system is end-to-end and jointly trained in an unsupervised manner. In addition, enabled by the recursive architecture, one cascade can be iteratively applied for multiple times during testing, which approaches a better fit between each of the image pairs. We evaluate our method on 3D medical images, where deformable registration is most commonly applied. We demonstrate that recursive cascaded networks achieve consistent, significant gains and outperform state-of-the-art methods. The performance reveals an increasing trend as long as more cascades are trained, while the limit is not observed. Code is available at https://github.com/microsoft/Recursive-Cascaded-Networks.Comment: Accepted to ICCV 201

GFlowCausal: Generative Flow Networks for Causal Discovery

Causal discovery aims to uncover causal structure among a set of variables. Score-based approaches mainly focus on searching for the best Directed Acyclic Graph (DAG) based on a predefined score function. However, most of them are not applicable on a large scale due to the limited searchability. Inspired by the active learning in generative flow networks, we propose a novel approach to learning a DAG from observational data called GFlowCausal. It converts the graph search problem to a generation problem, in which direct edges are added gradually. GFlowCausal aims to learn the best policy to generate high-reward DAGs by sequential actions with probabilities proportional to predefined rewards. We propose a plug-and-play module based on transitive closure to ensure efficient sampling. Theoretical analysis shows that this module could guarantee acyclicity properties effectively and the consistency between final states and fully-connected graphs. We conduct extensive experiments on both synthetic and real datasets, and results show the proposed approach to be superior and also performs well in a large-scale setting

MaskFlownet: Asymmetric Feature Matching with Learnable Occlusion Mask

Feature warping is a core technique in optical flow estimation; however, the ambiguity caused by occluded areas during warping is a major problem that remains unsolved. In this paper, we propose an asymmetric occlusion-aware feature matching module, which can learn a rough occlusion mask that filters useless (occluded) areas immediately after feature warping without any explicit supervision. The proposed module can be easily integrated into end-to-end network architectures and enjoys performance gains while introducing negligible computational cost. The learned occlusion mask can be further fed into a subsequent network cascade with dual feature pyramids with which we achieve state-of-the-art performance. At the time of submission, our method, called MaskFlownet, surpasses all published optical flow methods on the MPI Sintel, KITTI 2012 and 2015 benchmarks. Code is available at https://github.com/microsoft/MaskFlownet.Comment: CVPR 2020 (Oral

Negative obstacle detection on open pit roads based on multi-feature fusion

With the gradual implementation of intelligent mine concept, intelligence and unmanned operation are gradually implemented in mining area. Unmanned driving of open pit mine trucks is increasingly becoming the main focus of intelligent mine construction. In order to solve the safety problems of the overturn of unmanned vehicles and heavy-duty trucks due to irregular negative obstacles appearing in some parts of road surface such as potholes and collapses in open pit mines, and to improve the safe driving coefficient in mines, a multi-feature fusion method of detecting negative obstacles in open pit mine roads is proposed. The method uses the BiFPN feature fusion module to improve the weight proportion of small-scale negative obstacle detection, introduces the spatial and channel dual attention mechanism to improve the feature extraction and feature fusion ability of negative obstacle edges, so as to improve the detection accuracy of small-scale negative obstacles on the road. Also, the SIoU Loss is adopted as the loss function of the model bounding box, the Anchor by using the K-means++ method is used to improve the convergence speed and boundary frame localization effect of the obstacle detection model, the hyperparameters are optimized based on genetic algorithm to make the model more suitable for the mine scene, and finally the fast and accurate recognition of negative obstacles on the mine road is realized. The experiments show that the detection model can quickly and accurately identify the negative road obstacle targets in the complex background of the open pit mine, and the detection accuracy, recall rate, and mAP of the negative road obstacle targets reach 96.9%, 89.9%, and 95.3%, respectively, and the size of the model is only 12.7 MB. Compared with other mainstream detection networks, the network model is more suitable for the safety needs of unstructured road driving in open pit mining areas under complex environment, and the robustness of the detection model is good, which can be adapted to a variety of situations in open pit mining areas, providing a feasible method for the detection of negative obstacles on unstructured roads in open pit mining areas where the actual environment is complex and variable, and providing some safety warnings for the safety of unmanned trucks in open pit mines

Improved Conditional Generative Adversarial Net Classification For Spoken Language Recognition

Recent research on generative adversarial nets (GAN) for language identification (LID) has shown promising results. In this paper, we further exploit the latent abilities of GAN networks to firstly combine them with deep neural network (DNN)-based i-vector approaches and then to improve the LID model using conditional generative adversarial net (cGAN) classification. First, phoneme dependent deep bottleneck features (DBF) combined with output posteriors of a pre-trained DNN for automatic speech recognition (ASR) are used to extract i-vectors in the normal way. These i-vectors are then classified using cGAN, and we show an effective method within the cGAN to optimize parameters by combining both language identification and verification signals as supervision. Results show firstly that cGAN methods can significantly outperform DBF DNN i-vector methods where 49-dimensional i-vectors are used, but not where 600-dimensional vectors are used. Secondly, training a cGAN discriminator network for direct classification has further benefit for low dimensional i-vectors as well as short utterances with high dimensional i-vectors. However, incorporating a dedicated discriminator network output layer for classification and optimizing both classification and verification loss brings benefits in all test cases

The environmental dependence of Spitzer dusty Supernovae

Thanks to the mid-infrared capability offered by Spitzer, systematic searches of dust in SNe have been carried out over the past decade. Studies have revealed the presence of a substantial amount of dust over a broad range of SN subtypes. How normal SNe present mid-IR excess at later time and turn out to be dusty SNe can be affected by several factors, such as mass-loss history and envelope structure of progenitors and their explosion environment. All these can be combined and related to their environmental properties. A systematic analysis of SNe that exploded under a dusty environment could be of critical importance to measure the properties of the dust-veiled exploding stars, and whether such an intense dust production process is associated with the local environment. In this work, we firstly use the IFS data to study the environmental properties of dusty SNe compared to those of normal ones, and analyze correlations between the environmental properties and their dust parameters. We find that dusty SNe have a larger proportion located at higher SFR regions compared to the normal types. The occurrence of dusty SNe is less dependent on metallicity, with the oxygen abundance spanning from subsolar to oversolar metallicity. We also find the host extinction of dusty SNe scatters a lot, with about 40% of dusty SN located at extremely low extinction environments, and another 30% of them with considerably high host extinction of E(B-V)>0.6 mag.Comment: 20 pages, 15 figures, submitted to Ap

Probing the Shock Breakout Signal of SN 2024ggi from the Transformation of Early Flash Spectroscopy

© 2024. The Author(s). Published by the American Astronomical Society. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/We present early-time, hour-to-day cadence spectroscopy of the nearby Type II supernova (SN II) 2024ggi, which was discovered at a phase when the SN shock had just emerged from the red supergiant (RSG) progenitor star. Over the first few days after the first light, SN 2024ggi exhibited prominent narrow emission lines formed through intense and persistent photoionization of the nearby circumstellar material (CSM). In the first 63 hr, spectral lines of He, C, N, and O revealed a rapid rise in ionization as a result of the progressive sweeping up of the CSM by the shock. The duration of the IIn-like spectra indicates a dense and relatively confined CSM distribution extending up to ∼4 × 1014 cm. Spectral modeling reveals that a CSM mass-loss rate at this region exceeding 5 × 10−3 M ⊙ yr−1 is required to reproduce low-ionization emissions, which dramatically exceeds that of an RSG. Analyzing the Hα emission shift implies the velocity of the unshocked outer CSM to be between 20 and 40 km s−1, matching the typical wind velocity of an RSG. The differences between the inner and outer layers of the CSM and an RSG progenitor highlight a complex mass-loss history before the explosion of SN 2024ggi.Peer reviewe