Ruoqian Liu

A predictive machine learning approach for microstructure optimization and materials design.

This paper addresses an important materials engineering question: How can one identify the complete space (or as much of it as possible) of microstructures that are theoretically predicted to yield the desired combination of properties demanded by a selected application? We present a problem involving design of magnetoelastic Fe-Ga alloy microstructure for enhanced elastic, plastic and magnetostrictive properties. While theoretical models for computing properties given the microstructure are known for this alloy, inversion of these relationships to obtain microstructures that lead to desired properties is challenging, primarily due to the high dimensionality of microstructure space, multi-objective design requirement and non-uniqueness of solutions. These challenges render traditional search-based optimization methods incompetent in terms of both searching efficiency and result optimality. In this paper, a route to address these challenges using a machine learning methodology is proposed. A systematic framework consisting of random data generation, feature selection and classification algorithms is developed. Experiments with five design problems that involve identification of microstructures that satisfy both linear and nonlinear property constraints show that our framework outperforms traditional optimization methods with the average running time reduced by as much as 80% and with optimality that would not be achieved otherwise.

Machine learning approaches for elastic localization linkages in high-contrast composite materials

There has been a growing recognition of the opportunities afforded by advanced data science and informatics approaches in addressing the computational demands of modeling and simulation of multiscale materials science phenomena. More specifically, the mining of microstructure–property relationships by various methods in machine learning and data mining opens exciting new opportunities that can potentially result in a fast and efficient material design. This work explores and presents multiple viable approaches for computationally efficient predictions of the microscale elastic strain fields in a three-dimensional (3-D) voxel-based microstructure volume element (MVE). Advanced concepts in machine learning and data mining, including feature extraction, feature ranking and selection, and regression modeling, are explored as data experiments. Improvements are demonstrated in a gradually escalated fashion achieved by (1) feature descriptors introduced to represent voxel neighborhood characteristics, (2) a reduced set of descriptors with top importance, and (3) an ensemble-based regression technique.

Context Aware Machine Learning Approaches for Modeling Elastic Localization in Three-Dimensional Composite Microstructures

The response of a composite material is the result of a complex interplay between the prevailing mechanics and the heterogenous structure at disparate spatial and temporal scales. Understanding and capturing the multiscale phenomena is critical for materials modeling and can be pursued both by physical simulation-based modeling as well as data-driven machine learning-based modeling. In this work, we build machine learning-based data models as surrogate models for approximating the microscale elastic response as a function of the material microstructure (also called the elastic localization linkage). In building these surrogate models, we particularly focus on understanding the role of contexts, as a link to the higher scale information that most evidently influences and determines the microscale response. As a result of context modeling, we find that machine learning systems with context awareness not only outperform previous best results, but also extend the parallelism of model training so as to maximize the computational efficiency.

A Scalable Hierarchical Clustering Algorithm Using Spark

Clustering is often an essential first step in data mining intended to reduce redundancy, or define data categories. Hierarchical clustering, a widely used clustering technique, can offer a richer representation by suggesting the potential group structures. However, parallelization of such an algorithm is challenging as it exhibits inherent data dependency during the hierarchical tree construction. In this paper, we design a parallel implementation of Single-linkage Hierarchical Clustering by formulating it as a Minimum Spanning Tree problem. We further show that Spark is a natural fit for the parallelization of single-linkage clustering algorithm due to its natural expression of iterative process. Our algorithm can be deployed easily in Amazons cloud environment. And a thorough performance evaluation in Amazons EC2 verifies that the scalability of our algorithm sustains when the datasets scale up.

Real time vehicle speed predition using gas-kinetic traffic modeling

Prediction of the traffic information such as flow, density, speed, and travel time is important for traffic control systems, optimizing vehicle operations, and the individual driver. Prediction of future traffic information is a challenging problem due to many dynamic contributing factors. In this paper, macroscopic and kinetic traffic modeling approaches are investigated. We present a speed prediction algorithm, KTM-SP, based on gas-kinetic traffic modeling. Experimental results show that the proposed algorithm gave good prediction results on real traffic data.

Data samplingschemes formicrostructure designwith vibrational tuning constraints

Microstructures significantly impact the performance of sensitively engineered components, such as wireless impact detectors used in military vehicles or sensors used in aircrafts. These components...

Materials discovery: Understanding polycrystals from large-scale electron patterns

This paper explores the idea of modeling a large image data collection of polycrystal electron patterns, in order to detect insights in understanding materials discovery. There is an emerging interest in applying big data processing, management and modeling methods to scientific images, which often come in a form and with patterns only interpretable to domain experts. While large-scale machine learning approaches have demonstrated certain superiority in analyzing, summarizing, and providing an understandable route to data types like natural images, speeches and texts, scientific images is still a relatively unexplored area. Deep convolutional neural networks, despite their recent triumph in natural image understanding, are still rarely seen adapted to experimental microscopic images, especially in a large scale. To the best of our knowledge, we present the first deep learning solution towards a scientific image indexing problem using a collection of over 300K microscopic images. The result obtained is 54% better than a dictionary lookup method which is state-of-the-art in the materials science society.

Driving course prediction for vehicle handling maneuvers

This paper aims at predicting the future driving course, which we define as a combination of two bifurcating channels - future speed and steering action that in turn derive a future driving trajectory during a curve. In defining the relation of these two channels, human factors, such as the stressfulness, comfort level, and skillfulness of the driver, are paid particular attention to. While the modeling and forecast of speed and steering angle are to some extent separated, a hidden Markov model (HMM) thats designed to mimic drivers intention integrates them by making subjective corrections. The proposed algorithm has been proved effective on realistic driving data based on a prototype vehicle at Ford.

A hybrid system ensemble based time series signal classification on driver alertness detection

This paper presents the methodologies developed for solving IJCNN 2011s Ford Challenge II problem, where the drivers alertness is to be detected employing physiological, environmental and vehicular data acquired during driving. The solution is based on a thorough four-fold framework consisting of temporal processing, feature creation and extraction, and the training and ensemble of several learning systems, such as neural networks, random forest, support vector machine, trained from diverse features. The selection of input features to a learning machine has always been critique on signal classification. In our approach, the employment of Bayesian network filtered out a set of features and has been proved by the ensemble to be effective. The ensemble technique enhanced the performance of individual systems dramatically. The performance acquired on 30% of the test samples reached an accuracy of 78.34%. These results are significant for a real-world vehicular problem and we are quite confident this solution will become one of the top ones on the competition test data.

PinterNet: A thematic label curation tool for large image datasets

Recent progress in big data and computer vision with deep learning models has gained a lot of attention. Deep learning has been performed on tasks such as image classification, object detection, image segmentation, image captioning, visual question and answering, using large collections of annotated images. This calls for more curated large image datasets with clearer descriptions, cleaner contents, and diversified usability. However, the curation and labeling of such datasets can be labor-intensive. In this paper, we present PinterNet, an algorithm for automatic curation and label generation from noisy textual descriptions, and also publish a big image dataset containing over 110K images automatically labeled with their themes. Our dataset is hierarchical in nature, it has high level category information which we refer as verticals with fine-grained thematic labels at lower level. This advocates a new type of hierarchical theme classification problem closer to human cognition and of business value. We provide benchmark performances using deep learning models based on AlexNet architecture with different pre-training schemes for this novel task and new data.