Data Science Methods for Analyzing Nanomaterial Images and Videos

A large amount of nanomaterial characterization data has been routinely collected by using electron microscopes and stored in image or video formats. A bottleneck in making effective use of the image/video data is the lack of the development of sophisticated data science methods capable of unlocking valuable material pertinent information buried in the raw data. To address this problem, the research of this dissertation begins with understanding the physical mechanisms behind the concerned process to determine why the generic methods fall short. Afterwards, it designs and improves image processing and statistical modeling tools to address the practical challenges. Specifically, this dissertation consists of two main tasks: extracting useful information from images or videos of nanomaterials captured by electron microscopes, and designing analytical methods for modeling/monitoring the dynamic growth of nanoparticles. In the first task, a two-pipeline framework is proposed to fuse two kinds of image information for nanoscale object detection that can accurately identify and measure nanoparticles in transmission electron microscope (TEM) images of high noise and low contrast. To handle the second task of analyzing nanoparticle growth, this dissertation develops dynamic nonparametric models for time-varying probability density functions (PDFs) estimation. Unlike simple statistics, a PDF contains fuller information about the nanoscale objects of interests. Characterizing the dynamic changes of the PDF as the nanoparticles grow into different sizes and morph into different shapes, the proposed nonparametric methods are capable of analyzing an in situ TEM video to delineate growth stages in a retrospective analysis, or tracking the nanoparticle growth process in a prospective analysis. The resulting analytic methods have applications in areas beyond the nanoparticle growth process such as the image-based process control tasks in additive manufacturing

Structuring NPD processes: advancements in test scheduling and activity sequencing

Ph.DDOCTOR OF PHILOSOPH

Development of Ultrasonic Devices for Non-destructive Testing: Ultrasonic Vibro-tactile Sensor and FPGA-Based Research Platform

This thesis is focused on the development of ultrasonic devices for industrial non-destructive testing (NDT). Ultrasound is generated from mechanical vibrations and then propagates through the medium. Ultrasonic devices can make use of the ultrasound in both aspects, vibrations and propagations, to perform inspections of the objects. To this end, two devices were developed in this research, each pertaining to NDT of the objects. The first device is the vibro-tactile sensor which aims to estimate the elastic modules of soft materials with minimally invasive technique. Inspired by load sensitivity studies in the high-power ultrasonic applications, vibration characteristics in resonance were utilized to perform the inspection. Only a minimal force to ensure contact with the object surface needs to be applied for a vibro-tactile sensor to perform inspection of the object; hence, it can be used for in-vivo measurement of the soft materials’ elastic moduli without causing severe surface deformation. The design and analysis of the device were carried out using the electro-mechanical analogy to address the electro-mechanical nature of piezoelectric devices. The designed vibro-tactile sensor resonates at ~40 kHz and can be applied to differentiate the elastic modulus of isotropic soft samples with a range from 10 kPa to 70 kPa. The second device developed is a field-programmable development platform for ultrasonic pulse-echo testing. Ultrasonic testing, utilizing sound wave propagation, is a widely used technique in the industry. The commercially available equipment for industrial NDT is highly dependent on the competence of the inspector and rarely provides the access to raw data. For successful transition from traditional labor-intensive manufacturing to the next generation “smart factory” where intelligent machines replace human labor, inspection equipment with automated in-line data collection and processing capability is highly needed. To this end, a flexible platform which provides the access to raw data for algorithm development and implementation should be established. Therefore, an affordable, versatile, and researcher-friendly development platform based on field-programmable gate array (FPGA) was developed in the research. Both hardware and software development tools and procedures were discussed. In the lab experiment, the developed prototype exhibited its competence in NDT applications and successfully carried out hardware-based auto-detection algorithm for mm-level defects on steel and aluminum specimens. Comparisons with commercial systems were provided to guide future development

Measurement of Charge Distributions in a Bubbling Fluidized Bed Using Wire-Mesh Electrostatic Sensors

In order to maintain safe and efficient operation of a fluidized bed, electrostatic charges in the bed should be monitored continuously. Electrostatic sensors with wire-mesh electrodes are introduced in this paper to measure the charge distribution in the cross section of the fluidized bed. A Finite Element Model is built to investigate the sensing characteristics of the wire-mesh sensors. In comparison with conventional electrostatic sensors, wire-mesh sensors have higher and more uniform sensitivity distribution. Based on the induced charges on the electrodes and the sensitivity distributions of the sensors, the charge distribution in the cross section of the fluidized bed is reconstructed. However, it is difficult to directly measure the induced charges on the electrodes. A charge calibration process is conducted to establish the relationship between the induced charge on the electrode and the electrostatic signal. Experimental studies of charge distribution measurement were conducted on a lab-scale bubbling fluidized bed. The electrostatic signals from the wire-mesh sensors in the dense phase and splash regions of the bed for different fluidization air flow rates were obtained. Based on the results obtained from the charge calibration process, the estimated induced charges on the electrodes are calculated from the Root Mean Square values of the electrostatic signals. The characteristics of the induced charges on the electrodes and the charge distribution in the cross section under different flow conditions are investigated, which proves that wire-mesh electrostatic sensors are able to measure the charge distribution in the bubbling fluidized bed

GCformer: An Efficient Framework for Accurate and Scalable Long-Term Multivariate Time Series Forecasting

Transformer-based models have emerged as promising tools for time series forecasting. However, these model cannot make accurate prediction for long input time series. On the one hand, they failed to capture global dependencies within time series data. On the other hand, the long input sequence usually leads to large model size and high time complexity. To address these limitations, we present GCformer, which combines a structured global convolutional branch for processing long input sequences with a local Transformer-based branch for capturing short, recent signals. A cohesive framework for a global convolution kernel has been introduced, utilizing three distinct parameterization methods. The selected structured convolutional kernel in the global branch has been specifically crafted with sublinear complexity, thereby allowing for the efficient and effective processing of lengthy and noisy input signals. Empirical studies on six benchmark datasets demonstrate that GCformer outperforms state-of-the-art methods, reducing MSE error in multivariate time series benchmarks by 4.38% and model parameters by 61.92%. In particular, the global convolutional branch can serve as a plug-in block to enhance the performance of other models, with an average improvement of 31.93\%, including various recently published Transformer-based models. Our code is publicly available at https://github.com/zyj-111/GCformer

The Design And Validations Of The Ultrasonic Tactile Sensor

An ultrasonic tactile sensor that can measure the stiffness of the tissue was developed. By combining analytical and numerical approaches, efficient design methodology was presented. The electrical and mechanical performance of developed sensor was experimentally validated

Estimation of shear stress by using a myocardial bridge-mural coronary artery simulating device

Background: This study was aimed at developing a myocardial bridge-mural coronary artery simulative device and analyzing the relationship between shear stress on the mural coronary artery and atherosclerosis. Methods: A myocardial bridge-mural coronary artery simulative device was used to simulate experiments in vitro. In the condition of maintaining any related parameters such as system temperature, average flow rate, and heart rate, we calculated and observed changes in proximal and distal mean values, and oscillatory value of shear stress on the mural coronary artery by regulating the compression level of the myocardial bridge to the mural coronary artery. Results: Under 0% compression, no significant differences were observed in distal and proximal mean values and oscillatory value of the shear stress on the mural coronary artery. With the increase in the degree of compression, the mean shear stress at the distal end was greater than that at the proximal end, but the oscillatory value of the shear stress at the proximal end was greater than that at the distal end. Conclusions: The experimental results of this study indicate that myocardial bridge compression leads to abnormal hemodynamics at the proximal end of the mural coronary artery. This abnormal phenomenon is of great significance in the study of atherosclerosis hemodynamic pathogenesis, which has potential clinical value for pathological effects and treatments of myocardial bridg

Enhanced nucleation and precipitation hardening in Al-Mg-Si(-Cu) alloys with minor Cd additions

This work reports a novel effect of impurity element Cd on enhancing the precipitation kinetics and increasing the peak hardness of Al–Mg–Si(–Cu) alloys during artificial ageing. It is found that the number density of age hardening Mg–Si(–Cu) precipitates is greatly increased by Cd addition (~0.06 at.%) at both the under-aged and peak-aged stages. A systematic study on the precipitation behaviour by high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) shows that most Mg–Si(–Cu) precipitates in the Cd-containing Al–Mg–Si alloys are associated with Cd-rich precipitates and have highly disordered structures. It is also found that the formation of Q'/C-like sub-units in Mg–Si(–Cu) precipitates is significantly promoted by Cd additions. To explore the nucleation mechanism under the influence of Cd addition, atom probe tomography (APT) is applied to study the solute clustering behaviour in the early stages of artificial ageing, and density functional theory (DFT) calculations are used to evaluate the binding energies of different solute-vacancy complexes and therefore the formation kinetics of Mg–Si–Cd clusters.acceptedVersio

A novel method for in vivo measurement of dynamic ischiofemoral space based on MRI and motion capture

Purpose: To use a novel in vivo method to simulate a moving hip model. Then, measure the dynamic bone-to-bone distance, and analyze the ischiofemoral space (IFS) of patients diagnosed with ischiofemoral impingement syndrome (IFI) during dynamic activities.Methods: Nine healthy subjects and 9 patients with IFI were recruited to collect MRI images and motion capture data. The motion trail of the hip during motion capture was matched to a personalized 3D hip model reconstructed from MRI images to get a dynamic bone model. This personalized dynamic in vivo method was then used to simulate the bone motion in dynamic activities. Validation was conducted on a 3D-printed sphere by comparing the calculated data using this novel method with the actual measured moving data using motion capture. Moreover, the novel method was used to analyze the in vivo dynamic IFS between healthy subjects and IFI patients during normal and long stride walking.Results: The validation results show that the root mean square error (RMSE) of slide and rotation was 1.42 mm/1.84° and 1.58 mm/2.19°, respectively. During normal walking, the in vivo dynamic IFS was significantly larger in healthy hips (ranged between 15.09 and 50.24 mm) compared with affected hips (between 10.16 and 39.74 mm) in 40.27%–83.81% of the gait cycle (p = 0.027). During long stride walking, the in vivo dynamic IFS was also significantly larger in healthy hips (ranged between 13.02 and 51.99 mm) than affected hips (between 9.63 and 44.22 mm) in 0%–5.85% of the gait cycle (p = 0.049). Additionally, the IFS of normal walking was significantly smaller than long stride walking during 0%–14.05% and 85.07%–100% of the gait cycle (p = 0.033, 0.033) in healthy hips. However, there was no difference between the two methods of walking among the patients.Conclusions: This study established a novel in vivo method to measure the dynamic bone-to-bone distance and was well validated. This method was used to measure the IFS of patients diagnosed with IFI, and the results showed that the IFS of patients is smaller compared with healthy subjects, whether in normal or long stride walking. Meanwhile, IFI eliminated the difference between normal and long stride walking