Offshore Wind Turbine Interaction with Floating Freshwater Ice on the Great Lakes.

Developing offshore wind energy has become more and more serious worldwide in recent years. Many of the promising offshore wind farm locations are in cold regions that may have ice cover during wintertime. The challenge of possible ice loads on off- shore wind turbines raises the demand of modeling capacity of dynamic wind turbine response under the joint action of ice, wind, wave and current. The simulation software FAST is an open source CAE package maintained by the National Renewable Energy Laboratory. In this thesis, a new module of FAST for assessing the dynamic response of offshore wind turbines subjected to ice forcing is presented. In the ice module, six models are included which involve both prescribed forcing and coupled response. The division of ice models is based on different ice failure modes and ice-structure interaction characteristics. Among these six models, two new analytical models of ice loading are presented. One is a new analytical model to simulate time-dependent ice forces limited by ice failure with the ice considered to fail in multiple zones non-simultaneously. The other model generates time-dependent ice forces on sloping structures due to bending failure. Besides ice loading models, since ice thickness is an important input parameter when predicting ice loads, several analytical models that predict ice thickness change over time as a function of air temperatures are also presented in this thesis.PhDNaval Architecture & Marine EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107300/1/ybingbin_1.pd

Deep and Surface Sensor Modalities for Myo-intent Detection

Electromyography is the gold-standard among sensors for prosthetic control. However, stable and reliable myocontrol remains an unsolved problem in the community. Amid improvements currently under investigation, one focuses on alternative or complementary sensors. In this study, we compare different techniques, recording surface and deep muscle activity. Ten subjects were involved in an experiment in which three different modalities were attached on their forearm: force myography, electro-impedance tomography and ultrasound. They were asked to perform wrist and grasp movements. For the first time, we evaluate and compare in an offline analysis these three different modalities while recording several hand gestures

Ice Nonsimultaneous Failure, Bending and Floe Impact Modeling for Simulating Wind Turbine Dynamics Using FAST

Many promising locations for developing offshore wind en-ergy are in cold regions. This type of environment introduces one important technological challenge for offshore wind turbine de-sign: the impact of floating surface ice. Recent developments to add an ice-loading module to the wind turbine computer-aided-engineering tool FAST are described in this paper. These efforts enable FAST, developed and maintained by the National Renew-able Energy Laboratory, to simulate the impact of ice on off-shore wind turbines. The ice-loading module includes different ice mechanics models that address various ice properties, fail-ure modes, and ice-structure interaction mechanisms. In a previ-ous OMAE symposium paper, models for quasi-static crushing, transient dynamic ice breakage, and random forcing for the ice module were described. In this paper, three new models are presented. One model evaluates the ice-loading effective pressure reduction caused by ice nonsimultaneous failure in discrete local zones across the contact area. The second model generates time-dependent ice forces on conical structures caused by bending failure. The third model is used to simulate large ice floe interaction with wind turbine support systems. This third model describes ice forces that are limited by momentum or splitting failure of ice floes. These models are integrated in the FAST modularization frame-work and allow for the simulation of coupled ice force, ice floe motion, and wind turbine structure response. This paper also presents example numerical simulation results of wind turbine dynamics using FAST coupled with these three new models

Towards catheter tracking and data-based catheter steering

Minimally invasive surgery (MIS) is an important approach for reducing injuries of the body, allowing faster recovery and healing, and is considered to be safer than open surgeries. Especially for the cardiovascular operations, catheter based diagnosis and therapy are becoming more popular these days. This paper presents an approach of tendon-driven catheter steering by using a joint probability density based catheter model. For tracking the catheter in a 3D rigid mockup, a Qualisys motion tracking system is used. The catheter steering is evaluated in simulation on a mesh generated from the real CT image data.status: publishe

Towards Autonomous Robotic Catheter Navigation Using Reinforcement Learning

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Development of an Innovative Sleeve-Based Robotic Catheter Driver

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Lesion Segmentation in Ultrasound Using Semi-pixel-wise Cycle Generative Adversarial Nets

Breast cancer is the most common invasive cancer with the highest cancer occurrence in females. Handheld ultrasound is one of the most efficient ways to identify and diagnose the breast cancer. The area and the shape information of a lesion is very helpful for clinicians to make diagnostic decisions. In this study we propose a new deep-learning scheme, semi-pixel-wise cycle generative adversarial net (SPCGAN) for segmenting the lesion in 2D ultrasound. The method takes the advantage of a fully convolutional neural network (FCN) and a generative adversarial net to segment a lesion by using prior knowledge. We compared the proposed method to a fully connected neural network and the level set segmentation method on a test dataset consisting of 32 malignant lesions and 109 benign lesions. Our proposed method achieved a Dice similarity coefficient (DSC) of 0.92 while FCN and the level set achieved 0.90 and 0.79 respectively. Particularly, for malignant lesions, our method increases the DSC (0.90) of the fully connected neural network to 0.93 significantly (p < 0.001). The results show that our SPCGAN can obtain robust segmentation results. The framework of SPCGAN is particularly effective when sufficient training samples are not available compared to FCN. Our proposed method may be used to relieve the radiologists' burden for annotation