Buckling and postbuckling of axially-loaded CNT-reinforced composite cylindrical shell surrounded by an elastic medium in thermal environment

Buckling and postbuckling behaviors of nanocomposite cylindrical shells reinforced by single walled carbon nanotubes (SWCNTs), surrounded by an elastic medium, exposed to a thermal environment and subjected to uniform axial compression are investigated in this paper. Material properties of carbon nanotubes (CNTs) and isotropic matrix are assumed to be temperature dependent, and effective properties of nanocomposite are estimated by extended rule of mixture. The CNTs are embedded into matrix via uniform distribution (UD) or functionally graded (FG) distribution along the thickness direction. Governing equations are based on Donnell’s classical shell theory taking into account von Karman-Donnell nonlinear terms and interaction between the shell and surrounding elastic medium. Three-term form of deflection and stress function are assumed to satisfy simply supported boundary conditions and Galerkin method is applied to obtain load-deflection relation from which buckling and postbuckling behaviors are analyzed. Numerical examples are carried out to analyze the effects of CNT volume fraction and distribution types, geometrical ratios, environment temperature and surrounding elastic foundation on the buckling loads and postbuckling strength of CNTRC cylindrical shells

Enriching Rare Word Representations in Neural Language Models by Embedding Matrix Augmentation

The neural language models (NLM) achieve strong generalization capability by learning the dense representation of words and using them to estimate probability distribution function. However, learning the representation of rare words is a challenging problem causing the NLM to produce unreliable probability estimates. To address this problem, we propose a method to enrich representations of rare words in pre-trained NLM and consequently improve its probability estimation performance. The proposed method augments the word embedding matrices of pre-trained NLM while keeping other parameters unchanged. Specifically, our method updates the embedding vectors of rare words using embedding vectors of other semantically and syntactically similar words. To evaluate the proposed method, we enrich the rare street names in the pre-trained NLM and use it to rescore 100-best hypotheses output from the Singapore English speech recognition system. The enriched NLM reduces the word error rate by 6% relative and improves the recognition accuracy of the rare words by 16% absolute as compared to the baseline NLM.Comment: 5 pages, 2 figures, accepted to INTERSPEECH 201

LAPFormer: A Light and Accurate Polyp Segmentation Transformer

Polyp segmentation is still known as a difficult problem due to the large variety of polyp shapes, scanning and labeling modalities. This prevents deep learning model to generalize well on unseen data. However, Transformer-based approach recently has achieved some remarkable results on performance with the ability of extracting global context better than CNN-based architecture and yet lead to better generalization. To leverage this strength of Transformer, we propose a new model with encoder-decoder architecture named LAPFormer, which uses a hierarchical Transformer encoder to better extract global feature and combine with our novel CNN (Convolutional Neural Network) decoder for capturing local appearance of the polyps. Our proposed decoder contains a progressive feature fusion module designed for fusing feature from upper scales and lower scales and enable multi-scale features to be more correlative. Besides, we also use feature refinement module and feature selection module for processing feature. We test our model on five popular benchmark datasets for polyp segmentation, including Kvasir, CVC-Clinic DB, CVC-ColonDB, CVC-T, and ETIS-LaribComment: 7 pages, 7 figures, ACL 2023 underrevie

Constrained Output Embeddings for End-to-End Code-Switching Speech Recognition with Only Monolingual Data

The lack of code-switch training data is one of the major concerns in the development of end-to-end code-switching automatic speech recognition (ASR) models. In this work, we propose a method to train an improved end-to-end code-switching ASR using only monolingual data. Our method encourages the distributions of output token embeddings of monolingual languages to be similar, and hence, promotes the ASR model to easily code-switch between languages. Specifically, we propose to use Jensen-Shannon divergence and cosine distance based constraints. The former will enforce output embeddings of monolingual languages to possess similar distributions, while the later simply brings the centroids of two distributions to be close to each other. Experimental results demonstrate high effectiveness of the proposed method, yielding up to 4.5% absolute mixed error rate improvement on Mandarin-English code-switching ASR task.Comment: 5 pages, 3 figures, accepted to INTERSPEECH 201

Nonlinear buckling of CNT-reinforced composite toroidal shell segment surrounded by an elastic medium and subjected to uniform external pressure

Buckling and postbuckling behaviors of Toroidal Shell Segment (TSS) reinforced by single-walled carbon nanotubes (SWCNT), surrounded by an elastic medium and subjected to uniform external pressure are investigated in this paper. Carbon nanotubes (CNTs) are reinforced into matrix phase by uniform distribution (UD) or functionally graded (FG) distribution along the thickness direction. Effective properties of carbon nanotube reinforced composite (CNTRC) are estimated by an extended rule of mixture through a micromechanical model. Governing equations for TSSs are based on the classical thin shell theory taking into account geometrical nonlinearity and surrounding elastic medium. Three-term solution of deflection and stress function are assumed to satisfy simply supported boundary condition, and Galerkin method is applied to obtain nonlinear load-deflection relation from which buckling loads and postbuckling equilibrium paths are determined. The effects of CNT volume fraction, distribution types, geometrical ratios and elastic foundation on the buckling and postbuckling behaviors of CNTRC TSSs are analyzed and discussed

Experiment-based Comparative Analysis of Nonlinear Speed Control Methods for Induction Motors

Field-oriented control (FOC) for induction motors is widely used in industrial applications. By using a fast and accurate torque controller based on a stator current controller it is possible to flexibly implement advanced speed control methods to achieve proper performance both in transient and steady-state states. In this study, a deadbeat controller was used for the current loop. The nonlinear methods used for the outer loop controller were backstepping, flatness-based control, and exact feedback linearization with state derivative. The dynamic responses of these three controls were compared through various experimental results. The advantages and disadvantages of the different control structures were analyzed and evaluated in detail. Based on this evaluation, an appropriate scheme can be specified when deployed in practice

Effect of Creep and Shrinkage model in calculation of long-term deflection of three-span solid slab continuous prestressed concrete bridge

Shrinkage and creep effect significantly to the long-term deflection of prestressed concrete bridge. The proper shrinkage and creep models should be developed to meet the requirements of deflection effect calculation. There are many models has been researched and developed. Each specification, such as ASSHTO, Eurocode, ACI and CEB-FIB, has their own model of shrinkage and creep by considering different input parameter. The long-term deflection calculation is also different in each specification as a result. In this paper, several shrinkage and creep models were selected and reviewed to see the difference and compare by using popular concrete grade in Vietnamese bridge building     (C40 and C45, equivalent to 40 and 45 Mpa, respectively). Those selected shrinkage and creep models are applied in calculation of deflection for a typical three-span continuous solid slab prestressed concrete bridge. The calculation result show the significant different of long-term deflection and the ASSHTO shrinkage and creep model show the biggest deflection