Numerical study of molten and semi-molten ceramic impingement by using coupled Eulerian and Lagrangian method

Large temperature gradients are present within ceramic powder particles during plasma spray deposition due to their low thermal conductivity. The particles often impinge at the substrate in a semi-molten form which in turn substantially affects the final characteristics of the coating being formed. This study is dedicated to a novel modeling approach of a coupled Eulerian and Lagrangian (CEL) method for both fully molten and semi-molten droplet impingement processes. The simulation provides an insight to the deformation mechanism of the solid core YSZ and illustrates the freezing-induced break-up and spreading at the splat periphery. A 30 μm fully molten YSZ particle and an 80 μm semi-molten YSZ particle with different core sizes and initial velocity ranging from 100 to 240 m/s were examined. The flattened degree for both cases were obtained and compared with experimental and analytical data

Measurements of Recommendation Network Structure in a Package Tour E-commerce Platform

The economic impact of recommendation networks in an e-commerce platform is attracting increasing interests from researchers. Various indicators adopted from social network measuring are used to describe the features of products recommendation networks. However, systematic measurements are still scarce in existing studies. This paper summarized three dimensions of measurement for products recommendation network, i.e., centrality, connection and size. Furthermore, the measurements were used to examine the structures of recommendation network in a packaged tour e-commerce platform from their evenness, clustering and scale. Our study mainly contributes to the emerging literature on products recommendation networks by offering three dimensions of measurements to describe the network structure

A Simple Framework for Multi-mode Spatial-Temporal Data Modeling

Spatial-temporal data modeling aims to mine the underlying spatial relationships and temporal dependencies of objects in a system. However, most existing methods focus on the modeling of spatial-temporal data in a single mode, lacking the understanding of multiple modes. Though very few methods have been presented to learn the multi-mode relationships recently, they are built on complicated components with higher model complexities. In this paper, we propose a simple framework for multi-mode spatial-temporal data modeling to bring both effectiveness and efficiency together. Specifically, we design a general cross-mode spatial relationships learning component to adaptively establish connections between multiple modes and propagate information along the learned connections. Moreover, we employ multi-layer perceptrons to capture the temporal dependencies and channel correlations, which are conceptually and technically succinct. Experiments on three real-world datasets show that our model can consistently outperform the baselines with lower space and time complexity, opening up a promising direction for modeling spatial-temporal data. The generalizability of the cross-mode spatial relationships learning module is also validated

Rearrange Indoor Scenes for Human-Robot Co-Activity

We present an optimization-based framework for rearranging indoor furniture to accommodate human-robot co-activities better. The rearrangement aims to afford sufficient accessible space for robot activities without compromising everyday human activities. To retain human activities, our algorithm preserves the functional relations among furniture by integrating spatial and semantic co-occurrence extracted from SUNCG and ConceptNet, respectively. By defining the robot's accessible space by the amount of open space it can traverse and the number of objects it can reach, we formulate the rearrangement for human-robot co-activity as an optimization problem, solved by adaptive simulated annealing (ASA) and covariance matrix adaptation evolution strategy (CMA-ES). Our experiments on the SUNCG dataset quantitatively show that rearranged scenes provide an average of 14% more accessible space and 30% more objects to interact with. The quality of the rearranged scenes is qualitatively validated by a human study, indicating the efficacy of the proposed strategy.Comment: 7 pages, 7 figures; Accepted by ICRA 202

A Localization-to-Segmentation Framework for Automatic Tumor Segmentation in Whole-Body PET/CT Images

Fluorodeoxyglucose (FDG) positron emission tomography (PET) combined with computed tomography (CT) is considered the primary solution for detecting some cancers, such as lung cancer and melanoma. Automatic segmentation of tumors in PET/CT images can help reduce doctors' workload, thereby improving diagnostic quality. However, precise tumor segmentation is challenging due to the small size of many tumors and the similarity of high-uptake normal areas to the tumor regions. To address these issues, this paper proposes a localization-to-segmentation framework (L2SNet) for precise tumor segmentation. L2SNet first localizes the possible lesions in the lesion localization phase and then uses the location cues to shape the segmentation results in the lesion segmentation phase. To further improve the segmentation performance of L2SNet, we design an adaptive threshold scheme that takes the segmentation results of the two phases into consideration. The experiments with the MICCAI 2023 Automated Lesion Segmentation in Whole-Body FDG-PET/CT challenge dataset show that our method achieved a competitive result and was ranked in the top 7 methods on the preliminary test set. Our work is available at: https://github.com/MedCAI/L2SNet.Comment: 7 pages,3 figure

Enhancing Infrared Small Target Detection Robustness with Bi-Level Adversarial Framework

The detection of small infrared targets against blurred and cluttered backgrounds has remained an enduring challenge. In recent years, learning-based schemes have become the mainstream methodology to establish the mapping directly. However, these methods are susceptible to the inherent complexities of changing backgrounds and real-world disturbances, leading to unreliable and compromised target estimations. In this work, we propose a bi-level adversarial framework to promote the robustness of detection in the presence of distinct corruptions. We first propose a bi-level optimization formulation to introduce dynamic adversarial learning. Specifically, it is composited by the learnable generation of corruptions to maximize the losses as the lower-level objective and the robustness promotion of detectors as the upper-level one. We also provide a hierarchical reinforced learning strategy to discover the most detrimental corruptions and balance the performance between robustness and accuracy. To better disentangle the corruptions from salient features, we also propose a spatial-frequency interaction network for target detection. Extensive experiments demonstrate our scheme remarkably improves 21.96% IOU across a wide array of corruptions and notably promotes 4.97% IOU on the general benchmark. The source codes are available at https://github.com/LiuZhu-CV/BALISTD.Comment: 9 pages, 6 figure

Highly sensitive, broadband microwave frequency identification using a chip-based Brillouin optoelectronic oscillator

Detection and frequency estimation of radio frequency (RF) signals are critical in modern RF systems, including wireless communication and radar. Photonic techniques have made huge progress in solving the problem imposed by the fundamental trade-off between detection range and accuracy. However, neither fiber-based nor integrated photonic RF signal detection and frequency estimation systems have achieved wide range and low error with high sensitivity simultaneously in a single system. In this paper, we demonstrate the first Brillouin opto-electronic oscillator (B-OEO) based on on-chip stimulated Brillouin scattering (SBS) to achieve RF signal detection. The broad tunability and narrowband amplification of on-chip SBS allow for the wide-range and high-accuracy detection. Feeding the unknown RF signal into the B-OEO cavity amplifies the signal which is matched with the oscillation mode to detect low-power RF signals. We are able to detect RF signals from 1.5 to 40 GHz with power levels as low as −67 dBm and a frequency accuracy of ± 3.4 MHz. This result paves the way to compact, fully integrated RF detection and channelization.Australian Research Council (ARC) Linkage grant (LP170100112) with Harris Corporation. U.S. Air Force (USAF) through AFOSR/AOARD (FA2386-16-1-4036); U.S. Office of Naval Research Global (ONRG) (N62909-18-1-2013)

3D vision-guided pick-and-place using kuka LBR iiwa robot

This paper presents the development of a control system for vision-guided pick-and-place tasks using a robot arm equipped with a 3D camera. The main steps include camera intrinsic and extrinsic calibration, hand-eye calibration, initial object pose registration, objects pose alignment algorithm, and pick-and-place execution. The proposed system allows the robot be able to pick and place object with limited times of registering a new object and the developed software can be applied for new object scenario quickly. The integrated system was tested using the hardware combination of kuka iiwa, Robotiq grippers (two finger gripper and three finger gripper) and 3D cameras (Intel real sense D415 camera, Intel real sense D435 camera, Microsoft Kinect V2). The whole system can also be modified for the combination of other robotic arm, gripper and 3D camera