Free vibration studies of functionally graded magneto-electro-elastic plates/shells by using solid-shel ements

In this article, free vibration studies on functionally graded magneto-electro-elastic plates and cylindrical shells have been carried out by means of finite element method. The functionally graded material is assumed to be exponential in the thickness direction. The present finite element is formulated on the basis of assumed natural strain, enhanced assumed strain method and using displacement components, electric potential and magnetic potentials as nodal degrees of freedom. This element can be used as solid element and can also be applied to model thin curved shell structures. Numerical studies include the influence of the different exponential factor, magnetic and piezoelectric effect on the natural frequencies. Obtained numerical results are in good agreement with the semi-analytical finite element solutions available in the literature

TransGrasp: Grasp Pose Estimation of a Category of Objects by Transferring Grasps from Only One Labeled Instance

Grasp pose estimation is an important issue for robots to interact with the real world. However, most of existing methods require exact 3D object models available beforehand or a large amount of grasp annotations for training. To avoid these problems, we propose TransGrasp, a category-level grasp pose estimation method that predicts grasp poses of a category of objects by labeling only one object instance. Specifically, we perform grasp pose transfer across a category of objects based on their shape correspondences and propose a grasp pose refinement module to further fine-tune grasp pose of grippers so as to ensure successful grasps. Experiments demonstrate the effectiveness of our method on achieving high-quality grasps with the transferred grasp poses. Our code is available at https://github.com/yanjh97/TransGrasp.Comment: Accepted to European Conference on Computer Vision (ECCV) 202

High temperature lead-free relaxor ferroelectric: intergrowth Aurivillius phase BaBi2Nb2O9−Bi4Ti3O12 ceramics

High temperature lead-free relaxor ferroelectric: intergrowth Aurivillius phase BaBi2Nb2O9−Bi4Ti3O12 ceramic

Is DNS Ready for Ubiquitous Internet of Things?

The vision of the Internet of Things (IoT) covers not only the well-regulated processes of specific applications in different areas but also includes ubiquitous connectivity of more generic objects (or things and devices) in the physical world and the related information in the virtual world. For example, a typical IoT application, such as a smart city, includes smarter urban transport networks, upgraded water supply, and waste-disposal facilities, along with more efficient ways to light and heat buildings. For smart city applications and others, we require unique naming of every object and a secure, scalable, and efficient name resolution which can provide access to any object\u27s inherent attributes with its name. Based on different motivations, many naming principles and name resolution schemes have been proposed. Some of them are based on the well-known domain name system (DNS), which is the most important infrastructure in the current Internet, while others are based on novel designing principles to evolve the Internet. Although the DNS is evolving in its functionality and performance, it was not originally designed for the IoT applications. Then, a fundamental question that arises is: can current DNS adequately provide the name service support for IoT in the future? To address this question, we analyze the strengths and challenges of DNS when it is used to support ubiquitous IoT. First, we analyze the requirements of the IoT name service by using five characteristics, namely security, mobility, infrastructure independence, localization, and efficiency, which we collectively refer to as SMILE. Then, we discuss the pros and cons of the DNS in satisfying SMILE in the context of the future evolution of the IoT environment

Microstructure and electrical properties of Aurivillius phase (CaBi2Nb2O9)1-x(BaBi2Nb2O9)x solid solution

Microstructure and electrical properties of Aurivillius phase (CaBi2Nb2O9)1-x(BaBi2Nb2O9)x solid solutio

Combining Past, Present and Future: A Self-Supervised Approach for Class Incremental Learning

Class Incremental Learning (CIL) aims to handle the scenario where data of novel classes occur continuously and sequentially. The model should recognize the sequential novel classes while alleviating the catastrophic forgetting. In the self-supervised manner, it becomes more challenging to avoid the conflict between the feature embedding spaces of novel classes and old ones without any class labels. To address the problem, we propose a self-supervised CIL framework CPPF, meaning Combining Past, Present and Future. In detail, CPPF consists of a prototype clustering module (PC), an embedding space reserving module (ESR) and a multi-teacher distillation module (MTD). 1) The PC and the ESR modules reserve embedding space for subsequent phases at the prototype level and the feature level respectively to prepare for knowledge learned in the future. 2) The MTD module maintains the representations of the current phase without the interference of past knowledge. One of the teacher networks retains the representations of the past phases, and the other teacher network distills relation information of the current phase to the student network. Extensive experiments on CIFAR100 and ImageNet100 datasets demonstrate that our proposed method boosts the performance of self-supervised class incremental learning. We will release code in the near future