Blind Inpainting with Object-aware Discrimination for Artificial Marker Removal

Medical images often contain artificial markers added by doctors, which can negatively affect the accuracy of AI-based diagnosis. To address this issue and recover the missing visual contents, inpainting techniques are highly needed. However, existing inpainting methods require manual mask input, limiting their application scenarios. In this paper, we introduce a novel blind inpainting method that automatically completes visual contents without specifying masks for target areas in an image. Our proposed model includes a mask-free reconstruction network and an object-aware discriminator. The reconstruction network consists of two branches that predict the corrupted regions with artificial markers and simultaneously recover the missing visual contents. The object-aware discriminator relies on the powerful recognition capabilities of the dense object detector to ensure that the markers of reconstructed images cannot be detected in any local regions. As a result, the reconstructed image can be close to the clean one as much as possible. Our proposed method is evaluated on different medical image datasets, covering multiple imaging modalities such as ultrasound (US), magnetic resonance imaging (MRI), and electron microscopy (EM), demonstrating that our method is effective and robust against various unknown missing region patterns

Sequential Recommendation Based on Objective and Subjective Features

Nowadays, sequential recommender systems are widely used in E-commerce fields to capture consumers’ dynamic preferences in short terms. Existing transformer-based recommendation models mainly consider consumer preference for the products and some related features, such as price. However, besides such objective features, some subjective features, such as consumers’ preference for product quality, also affect consumers’ purchase decisions. In this paper, we design a Sequential Recommender system based on Objective and Subjective features (SROS). We construct subjective features by using natural language processing to analyze online consumer reviews. Then we design a feature-level multi-head self-attention to explore the interactions between objective features and subjective features and capture consumers’ dynamic preferences for them among different purchases. Experimental results on real-world datasets demonstrate the effectiveness of the proposed model

Microgrid distribution system dynamic reactive power optimization based on improved particle swarm algorithms

Abstract Due to the low accuracy and convergence of existing particle swarm algorithm in the micro power dynamic reactive power optimization in distribution system, this paper proposes an improved particle swarm algorithm based on the state of the particle and inertia weight optimization. This algorithm first adjusts the status of the states of the particles. Then using Sigmoid mapping to optimize the search ability of the inertia weight in particle swarms algorithm. Finally, using the optimal learning strategies to improve the convergence of particle swarm optimization algorithm. Through simulation experiments, the proposed improving particle swarm algorithm based on particle state and inertia weight optimization owing better convergence than traditional particle swarm optimization. Only small error was obtained during dynamic reactive power optimization in micro power distribution system

Numerical analysis and experiment of sandwich T-joint structure reinforced by composite fasteners

This study presents an investigation into the failure mechanism and strength improvement of a sandwich composite T-joint bonded and reinforced by fasteners made of thermoplastic composite. The T-joint subjected to pulling load was analysed by numerical simulation and experiment methods. Cohesive zone model (CZM) and Hashin damage model were used in the FE analysis to simulate the crack propagation and composite fastener damage. According to the results, the composite joint reinforcement is mainly attributed to the resistance of composite fasteners to shear failure of the bonded interface. Following the interface delamination and crack propagation in mode II failure, fracture of the composite fasteners occurred in transverse shear mode. The results show nearly 19% increase of bonding strength for the T-joint reinforced by composite fasteners of 5 mm diameter compared to the T-joint without fasteners. After the interface delamination, pull-out failure of fasteners was also observed and correlated to the numerical model considering material property reduction due to the sparse fibre tows in the fastener head forming and T-joint assembly. The investigation was extended to a parametric study of diameter and fibre orientation of the composite fasteners. The results show that the T-joint reinforced by composite fasteners of 6.28 mm diameter and lay-up can achieve the same strength and 44% weight saving compared to a titanium fastener opponent of 5 mm diameter

A Survey of Multi-task Learning in Natural Language Processing: Regarding Task Relatedness and Training Methods

Multi-task learning (MTL) has become increasingly popular in natural language processing (NLP) because it improves the performance of related tasks by exploiting their commonalities and differences. Nevertheless, it is still not understood very well how multi-task learning can be implemented based on the relatedness of training tasks. In this survey, we review recent advances of multi-task learning methods in NLP, with the aim of summarizing them into two general multi-task training methods based on their task relatedness: (i) joint training and (ii) multi-step training. We present examples in various NLP downstream applications, summarize the task relationships and discuss future directions of this promising topic.Comment: Accepted to EACL 2023 as regular long pape

Transonic flutter characteristic of an airfoil with morphing devices

An investigation into transonic flutter characteristic of an airfoil conceived with the morphing leading and trailing edges has been carried out. Computational fluid dynamics (CFD) is used to calculate the unsteady aerodynamic force in transonic flow. An aerodynamic reduced order model (ROM) based on autoregressive model with exogenous input (ARX) is used in the numerical simulation. The flutter solution is determined by eigenvalue analysis at specific Mach number. The approach is validated by comparing the transonic flutter characteristics of the Isogai wing with relevant literatures before applied to a morphing airfoil. The study reveals that by employing the morphing trailing edge, the shock wave forms and shifts to the trailing edge at a lower Mach number, and aerodynamic force stabilization happens earlier. Meanwhile, the minimum flutter speed increases and transonic dip occurs at a lower Mach number. It is also noted that leading edge morphing has negligible effect on the appearance of the shock wave and transonic flutter. The mechanism of improving the transonic flutter characteristics by morphing technology is discussed by correlating shock wave location on airfoil surface, unsteady aerodynamics with flutter solutio

Composites joints reinforced by composite rivets

This paper presents an investigation into the mechanical behaviour of composite joints reinforced by using a novel composite rivet made of rolled laminates. Two typical joints have been modelled using three-dimensional solid finite element model in the study. The first type is a composites single lap joint bonded and reinforced by a composite rivet compared with the joint reinforced by a titanium bolt subjected to tensile load. The results are also compared with an adhesive bonded joint as reference. The second type of joint model is a wing box section with skin-rib joint reinforced by composite rivet subjected to a pulling load. A range of adhesive damage was modelled up to 50% (undamaged WBDM, WBDM I 16%, WBDM II 33% and WBDM III 50% respectively) of the bonding area. The results show that the rivets located in the regions where the adhesive bonding failed will carry higher stress and make more contribution to the structure integrity. Although the titanium rivets provide better mechanical performance to carry more load, composite rivets offer an alternative adequate reinforcement to delay the bonding failure and safeguard the structure

Investigation of adhesive joining strategies for the application of a multi-material light rail vehicle

© 2021 The Author(s). Licensee MDPI, Basel, Switzerland. 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/To meet the high demand for lightweight energy-efficient and safe structures for transport applications, a current state-of-the-art light rail vehicle structure is under development that adopts a multi-material design strategy. This strategy creates the need for advanced multi-material joining technologies. The compatibility of the adhesive with a wide range of material types and the possibility of joining multi-material structures is also a key advantage to its success. In this paper, the feasibility of using either epoxy or polyurethane adhesive joining techniques applied to the multi-material vehicle structure is investigated. Importantly, consideration is given to the effect of variation in bond thickness for both families of structural adhesives. Multi-material adhesively bonded single lap joints with different adhesives of controlled bond thicknesses were manufactured and tested in order to experimentally assess the shear strength and stiffness. The torsional stiffness and natural frequency of the vehicle were modelled using a global two-dimensional finite element model (FEM) with different adhesive properties, and the obtained vehicle performances were further explained by the coupon-level experimental tests. The results showed that the vehicle using polyurethane adhesive with a target bond thickness of 1.0 mm allowed for optimal modal frequency and weight reduction.Peer reviewe