Modeling and Performance Investigation of a Rotor with Dissimilar Bearing Support System

Different types of bearings have different dynamic characteristics. By using one type of bearing at one end of a rotor and another type of bearing at the other end of the rotor, it is possible to exploit the advantages of both types in the same system. One example of such combination is a bronze bushing and active magnetic bearing (AMB). In the available literature, there are examples of such systems but are not fully explored with regard to how to model the system to fully utilize both support type properties. This thesis investigates the modeling and performance of such a dissimilar bearing support system. An experimental test rig with a rotor supported at one end by AMB and at the other end by bushing is modeled with two different methods, i.e., approximate analytical approach and finite element analysis (FEA). A cost function minimizing AMB controller design method is used for both system models, resulting in two controllers of the same form. Both controllers are implemented on the experimental test rig. AMB suspension is achieved, steady state orbits are measured at several selected constant speeds. Then experimental results are compared to numerical simulations and recommendations made regarding the utilization of these dissimilar bearing support

Modeling and Performance Investigation of a Rotor with Dissimilar Bearing Support System

Different types of bearings have different dynamic characteristics. By using one type of bearing at one end of a rotor and another type of bearing at the other end of the rotor, it is possible to exploit the advantages of both types in the same system. One example of such combination is a bronze bushing and active magnetic bearing (AMB). In the available literature, there are examples of such systems but are not fully explored with regard to how to model the system to fully utilize both support type properties. This thesis investigates the modeling and performance of such a dissimilar bearing support system. An experimental test rig with a rotor supported at one end by AMB and at the other end by bushing is modeled with two different methods, i.e., approximate analytical approach and finite element analysis (FEA). A cost function minimizing AMB controller design method is used for both system models, resulting in two controllers of the same form. Both controllers are implemented on the experimental test rig. AMB suspension is achieved, steady state orbits are measured at several selected constant speeds. Then experimental results are compared to numerical simulations and recommendations made regarding the utilization of these dissimilar bearing support

Kernel Learning in Ridge Regression "Automatically" Yields Exact Low Rank Solution

We consider kernels of the form $(x,x') \mapsto \phi(\|x-x'\|^2_\Sigma)$ parametrized by $\Sigma$. For such kernels, we study a variant of the kernel ridge regression problem which simultaneously optimizes the prediction function and the parameter $\Sigma$ of the reproducing kernel Hilbert space. The eigenspace of the $\Sigma$ learned from this kernel ridge regression problem can inform us which directions in covariate space are important for prediction. Assuming that the covariates have nonzero explanatory power for the response only through a low dimensional subspace (central mean subspace), we find that the global minimizer of the finite sample kernel learning objective is also low rank with high probability. More precisely, the rank of the minimizing $\Sigma$ is with high probability bounded by the dimension of the central mean subspace. This phenomenon is interesting because the low rankness property is achieved without using any explicit regularization of $\Sigma$, e.g., nuclear norm penalization. Our theory makes correspondence between the observed phenomenon and the notion of low rank set identifiability from the optimization literature. The low rankness property of the finite sample solutions exists because the population kernel learning objective grows "sharply" when moving away from its minimizers in any direction perpendicular to the central mean subspace.Comment: Add code links and correct a figur

Experimental investigation of dielectric barrier impact on breakdown voltage enhancement of copper wire-plane electrode systems

Non-pressurized air is extensively used as basic insulation media in medium / high voltage equipments. An inherent property of air-insulated designs is that the systems tend to become physically large. Application of Dielectric barrier can increase the breakdown voltage and therefore decrease the size of the equipments. In this paper, the impact of dielectric barrier on breakdown voltage enhancement of a copper wire-plane system is investigated. For this purpose, the copper wire is covered with different dielectric materials. Depending on the air gap and dielectric strength of the barrier the breakdown can be initiated in the solid or gas dielectric. Theoretically, free charges are affected by the electric field between the electrodes and accumulated at the dielectric surface, this leads to the reduction of electric field in air gap and enhancement of the ifield in the dielectric layer. Therefore, with appropriate selection of the barrier thickness and material, it is possible to increase the breakdown voltage of the insulation system. The influence of different parameters like inter-electrode spacing, and dielectric material on the break-down voltage is investigated for applied 50 Hz AC and DC voltages. The results indicate that up to 240% increase of the breakdown voltage can be achieved

Metal Additive Manufacturing Parts Inspection using Convolutional Neural Network

Metal additive manufacturing (AM) is gaining increasing attention from academia and industry due to its unique advantages compared to the traditional manufacturing process. Parts quality inspection is playing a crucial role in theAMindustry, which can be adopted for product improvement. However, the traditional inspection process has relied on manual recognition, which could suffer from low efficiency and potential bias. This study presented a convolutional neural network (CNN) approach toward robust AM quality inspection, such as good quality, crack, gas porosity, and lack of fusion. To obtain the appropriate model, experiments were performed on a series of architectures. Moreover, data augmentation was adopted to deal with data scarcity. L2 regularization (weight decay) and dropout were applied to avoid overfitting. The impact of each strategy was evaluated. The final CNN model achieved an accuracy of 92.1%, and it took 8.01 milliseconds to recognize one image. The CNN model presented here can help in automatic defect recognition in the AM industry

Federated Pseudo Modality Generation for Incomplete Multi-Modal MRI Reconstruction

While multi-modal learning has been widely used for MRI reconstruction, it relies on paired multi-modal data which is difficult to acquire in real clinical scenarios. Especially in the federated setting, the common situation is that several medical institutions only have single-modal data, termed the modality missing issue. Therefore, it is infeasible to deploy a standard federated learning framework in such conditions. In this paper, we propose a novel communication-efficient federated learning framework, namely Fed-PMG, to address the missing modality challenge in federated multi-modal MRI reconstruction. Specifically, we utilize a pseudo modality generation mechanism to recover the missing modality for each single-modal client by sharing the distribution information of the amplitude spectrum in frequency space. However, the step of sharing the original amplitude spectrum leads to heavy communication costs. To reduce the communication cost, we introduce a clustering scheme to project the set of amplitude spectrum into finite cluster centroids, and share them among the clients. With such an elaborate design, our approach can effectively complete the missing modality within an acceptable communication cost. Extensive experiments demonstrate that our proposed method can attain similar performance with the ideal scenario, i.e., all clients have the full set of modalities. The source code will be released.Comment: 10 pages, 5 figures

TRIE++: Towards End-to-End Information Extraction from Visually Rich Documents

Recently, automatically extracting information from visually rich documents (e.g., tickets and resumes) has become a hot and vital research topic due to its widespread commercial value. Most existing methods divide this task into two subparts: the text reading part for obtaining the plain text from the original document images and the information extraction part for extracting key contents. These methods mainly focus on improving the second, while neglecting that the two parts are highly correlated. This paper proposes a unified end-to-end information extraction framework from visually rich documents, where text reading and information extraction can reinforce each other via a well-designed multi-modal context block. Specifically, the text reading part provides multi-modal features like visual, textual and layout features. The multi-modal context block is developed to fuse the generated multi-modal features and even the prior knowledge from the pre-trained language model for better semantic representation. The information extraction part is responsible for generating key contents with the fused context features. The framework can be trained in an end-to-end trainable manner, achieving global optimization. What is more, we define and group visually rich documents into four categories across two dimensions, the layout and text type. For each document category, we provide or recommend the corresponding benchmarks, experimental settings and strong baselines for remedying the problem that this research area lacks the uniform evaluation standard. Extensive experiments on four kinds of benchmarks (from fixed layout to variable layout, from full-structured text to semi-unstructured text) are reported, demonstrating the proposed method's effectiveness. Data, source code and models are available

E2-AEN: End-to-End Incremental Learning with Adaptively Expandable Network

Expandable networks have demonstrated their advantages in dealing with catastrophic forgetting problem in incremental learning. Considering that different tasks may need different structures, recent methods design dynamic structures adapted to different tasks via sophisticated skills. Their routine is to search expandable structures first and then train on the new tasks, which, however, breaks tasks into multiple training stages, leading to suboptimal or overmuch computational cost. In this paper, we propose an end-to-end trainable adaptively expandable network named E2-AEN, which dynamically generates lightweight structures for new tasks without any accuracy drop in previous tasks. Specifically, the network contains a serial of powerful feature adapters for augmenting the previously learned representations to new tasks, and avoiding task interference. These adapters are controlled via an adaptive gate-based pruning strategy which decides whether the expanded structures can be pruned, making the network structure dynamically changeable according to the complexity of the new tasks. Moreover, we introduce a novel sparsity-activation regularization to encourage the model to learn discriminative features with limited parameters. E2-AEN reduces cost and can be built upon any feed-forward architectures in an end-to-end manner. Extensive experiments on both classification (i.e., CIFAR and VDD) and detection (i.e., COCO, VOC and ICCV2021 SSLAD challenge) benchmarks demonstrate the effectiveness of the proposed method, which achieves the new remarkable results