Learning Rigid Image Registration - Utilizing Convolutional Neural Networks for Medical Image Registration

Many traditional computer vision tasks, such as segmentation, have seen large step-changes in accuracy and/or speed with the application of Convolutional Neural Networks (CNNs). Image registration, the alignment of two or more images to a common space, is a fundamental step in many medical imaging workflows. In this paper we investigate whether these techniques can also bring tangible benefits to the registration task. We describe and evaluate the use of convolutional neural networks (CNNs) for both mono- and multi- modality registration and compare their performance to more traditional schemes, namely multi-scale, iterative registration. This paper also investigates incorporating inverse consistency of the learned spatial transformations to impose additional constraints on the network during training and investigate any benefit in accuracy during detection. The approaches are validated with a series of artificial mono-modal registration tasks utilizing T1-weighted MR brain i mages from the Open Access Series of Imaging Studies (OASIS) study and IXI brain development dataset and a series of real multi-modality registration tasks using T1-weighted and T2-weighted MR brain images from the 2015 Ischemia Stroke Lesion segmentation (ISLES) challenge. The results demonstrate that CNNs give excellent performance for both mono- and multi- modality head and neck registration compared to the baseline method with significantly fewer outliers and lower mean errors

Prototype of the SST-1M Telescope Structure for the Cherenkov Telescope Array

A single-mirror small-size (SST-1M) Davies-Cotton telescope with a dish diameter of 4 m has been built by a consortium of Polish and Swiss institutions as a prototype for one of the proposed small-size telescopes for the southern observatory of the Cherenkov Telescope Array (CTA). The design represents a very simple, reliable, and cheap solution. The mechanical structure prototype with its drive system is now being tested at the Institute of Nuclear Physics PAS in Krakow. Here we present the design of the prototype and results of the performance tests of the structure and the drive and control system.Comment: In Proceedings of the 34th International Cosmic Ray Conference (ICRC2015), The Hague, The Netherlands. All CTA contributions at arXiv:1508.0589

Modal analysis of the certain membrane disc coupling

The membrane disc coupling has the ability to transmit higher power under high-speed rotation. It also has the ability of compensation angular and radial displacement. The finite element model of the coupling was established and vibrational characteristics were analyzed. The modal experiments were carried out with hammering method, the modal parameters were obtained and the correct of the simulations was verified under the same constraints with the FEM model. The results showed that simulating results agreed well with that of the experiments

Measuring Catastrophic Forgetting in Neural Networks

Deep neural networks are used in many state-of-the-art systems for machine perception. Once a network is trained to do a specific task, e.g., bird classification, it cannot easily be trained to do new tasks, e.g., incrementally learning to recognize additional bird species or learning an entirely different task such as flower recognition. When new tasks are added, typical deep neural networks are prone to catastrophically forgetting previous tasks. Networks that are capable of assimilating new information incrementally, much like how humans form new memories over time, will be more efficient than re-training the model from scratch each time a new task needs to be learned. There have been multiple attempts to develop schemes that mitigate catastrophic forgetting, but these methods have not been directly compared, the tests used to evaluate them vary considerably, and these methods have only been evaluated on small-scale problems (e.g., MNIST). In this paper, we introduce new metrics and benchmarks for directly comparing five different mechanisms designed to mitigate catastrophic forgetting in neural networks: regularization, ensembling, rehearsal, dual-memory, and sparse-coding. Our experiments on real-world images and sounds show that the mechanism(s) that are critical for optimal performance vary based on the incremental training paradigm and type of data being used, but they all demonstrate that the catastrophic forgetting problem has yet to be solved.Comment: To appear in AAAI 201

Vibration characteristics analysis of a centrifugal impeller

Crack fault of a centrifugal impeller occurred while working. The finite element model of the impeller was established and vibrational characteristics were analyzed for to find the reason of the crack fault in this paper. The resonance frequencies of the impeller were identified with the help of Campbell diagrams. The modal experiments were carried out with hammering method, the modal parameters were obtained and the correct of the simulations was verified under the same constraints with the FEM model. Computation effort was saved while accuracy was retained making use of the cyclic symmetry technique in ANSYS. The results showed that simulating results agreed well with that of the experiments

Semantically Invariant Text-to-Image Generation

Image captioning has demonstrated models that are capable of generating plausible text given input images or videos. Further, recent work in image generation has shown significant improvements in image quality when text is used as a prior. Our work ties these concepts together by creating an architecture that can enable bidirectional generation of images and text. We call this network Multi-Modal Vector Representation (MMVR). Along with MMVR, we propose two improvements to the text conditioned image generation. Firstly, a n-gram metric based cost function is introduced that generalizes the caption with respect to the image. Secondly, multiple semantically similar sentences are shown to help in generating better images. Qualitative and quantitative evaluations demonstrate that MMVR improves upon existing text conditioned image generation results by over 20%, while integrating visual and text modalities.Comment: 5 papers, 5 figures, Published in 2018 25th IEEE International Conference on Image Processing (ICIP

Toward Abstraction from Multi-modal Data: Empirical Studies on Multiple Time-scale Recurrent Models

The abstraction tasks are challenging for multi- modal sequences as they require a deeper semantic understanding and a novel text generation for the data. Although the recurrent neural networks (RNN) can be used to model the context of the time-sequences, in most cases the long-term dependencies of multi-modal data make the back-propagation through time training of RNN tend to vanish in the time domain. Recently, inspired from Multiple Time-scale Recurrent Neural Network (MTRNN), an extension of Gated Recurrent Unit (GRU), called Multiple Time-scale Gated Recurrent Unit (MTGRU), has been proposed to learn the long-term dependencies in natural language processing. Particularly it is also able to accomplish the abstraction task for paragraphs given that the time constants are well defined. In this paper, we compare the MTRNN and MTGRU in terms of its learning performances as well as their abstraction representation on higher level (with a slower neural activation). This was done by conducting two studies based on a smaller data- set (two-dimension time sequences from non-linear functions) and a relatively large data-set (43-dimension time sequences from iCub manipulation tasks with multi-modal data). We conclude that gated recurrent mechanisms may be necessary for learning long-term dependencies in large dimension multi-modal data-sets (e.g. learning of robot manipulation), even when natural language commands was not involved. But for smaller learning tasks with simple time-sequences, generic version of recurrent models, such as MTRNN, were sufficient to accomplish the abstraction task.Comment: Accepted by IJCNN 201

Closed-form solution of curved beam model of elastic mechanical wheel

Based on the Timoshenko curved beam theory, a novel and feasible closed-form solution was proposed to deal with the internal mechanics characteristics of mechanical elastic wheel (MEW). With the Laplace transformation and boundary conditions, the governing differential equations was reduced to a single equation in regard to the rotation angle of curved beam, so as to reveal the relationship among the radial deformation, the tangential deformation and the curved angle. Furthermore, by adopting the Frobenius theory and the Green function, six normalized solutions of equations, the general solution and the free vibration of system equations were obtained. In the end, structure mechanics and vibration modal experiments were carried out and the results show that the analytical model is applicable for the experimental results

Offline and online detection of damage using autoregressive models and artificial neural networks

Peer reviewedPostprin

Influence of Interfacial Dynamics and Multi-Dimensional Coupling from Isolator Brackets on Exhaust Isolation System Performance

An automotive exhaust structure is a primary structure-borne noise path by which vibratory forces from the powertrain are transmitted to the vehicle body. The exhaust structure is typically connected to the vehicle body through a system of brackets containing elastomeric isolators, serving as the principal means of vibration isolation. In exhaust isolator system design, the isolator brackets are often modeled as simple springs. This approach neglects the effects of interfacial dynamics and multi-dimensional coupling, which result from distributed mass and stiffness throughout the isolator brackets. Accordingly, the objective of this research is to better understand how the interfacial dynamics and multi-dimensional coupling of the isolator brackets affect the exhaust isolation system performance in the 0-100 Hz range. Therefore, models with a proper representation of these interfacial dynamics and multi-dimensional coupling are created using finite element analysis (FEA) and then parameterized into multi-dimensional lumped parameter models through correlation of static and modal testing on the components and assembled system. The dynamic responses from the models for the exhaust structure and isolator brackets are then combined into a system-level model through a frequency-response-function-based substructuring method. A design study is conducted on the system-level model by systematically changing component parameters and evaluating the effect on the transmitted vertical body forces. The results show that the inclusion of these interfacial dynamics have nominal influence on isolation performance; however, the coupling terms show an observable influence, typically increasing the force transmitted to the vehicle body. In addition, the study identified additional design modifications that could improve isolation performance, such as an increase in isolator material loss factor and an increase in the isolator fore-aft stiffness. Although the results are specific to this isolation system design, the modeling procedure outlined has the potential to be used early in the vehicle design process to identify improvements to other baseline designs.NSF I/UCRC Smart Vehicle Concepts CenterTenneco, Inc.A three-year embargo was granted for this item.Academic Major: Mechanical Engineerin