Physical Sciences and Mathematics Commons^™

A Nonlinear Systems Framework For Cyberattack Prevention For Chemical Process Control Systems, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

Recent cyberattacks against industrial control systems highlight the criticality of preventing future attacks from disrupting plants economically or, more critically, from impacting plant safety. This work develops a nonlinear systems framework for understanding cyberattack-resilience of process and control designs and indicates through an analysis of three control designs how control laws can be inspected for this property. A chemical process example illustrates that control approaches intended for cyberattack prevention which seem intuitive are not cyberattack-resilient unless they meet the requirements of a nonlinear systems description of this property.

State Measurement Spoofing Prevention Through Model Predictive Control Design, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

Security of chemical process control systems against cyberattacks is critical due to the potential for injuries and loss of life when chemical process systems fail. A potential means by which process control systems may be attacked is through the manipulation of the measurements received by the controller. One approach for addressing this is to design controllers that make manipulating the measurements received by the controller in any meaningful fashion very difficult, making the controllers a less attractive target for a cyberattack of this type. In this work, we develop a model predictive control (MPC) implementation strategy that incorporates Lyapunov-based stability …

3d Face Reconstruction And Emotion Analytics With Part-Based Morphable Models, Hai Jin

Wayne State University Dissertations

3D face reconstruction and facial expression analytics using 3D facial data are new

and hot research topics in computer graphics and computer vision. In this proposal, we first

review the background knowledge for emotion analytics using 3D morphable face model, including

geometry feature-based methods, statistic model-based methods and more advanced

deep learning-bade methods. Then, we introduce a novel 3D face modeling and reconstruction

solution that robustly and accurately acquires 3D face models from a couple of images

captured by a single smartphone camera. Two selfie photos of a subject taken from the

front and side are used to guide our …

Sofie: Smart Operating System For Internet Of Everything, Jie Cao

Wayne State University Dissertations

The proliferation of Internet of Things and the success of rich cloud services have pushed the

horizon of a new computing paradigm, Edge computing, which calls for processing the data at

the edge of the network. Applications such as cloud offloading, smart home, and smart city

are idea area for Edge computing to achieve better performance than cloud computing. Edge

computing has the potential to address the concerns of response time requirement, battery life

constraint, bandwidth cost saving, as well as data safety and privacy.

However, there are still some challenges for applying Edge computing in our daily life. The …

Integrated Strategies For Sustainable Wastewater-Based Algal Biofuel Production And Environmental Mitigation In The Us, Javad Roostaei

Wayne State University Dissertations

Integration of algae cultivation with wastewater treatment has received increasing interest as a cost-effective strategy for biofuel production. However, there has been no full assessment of algal biofuel production with wastewater on macro-scale by taking into account wastewater resources, land availability, CO2 emission resources, and geographic variation. This research addressed and evaluated the use of wastewater for algae cultivation, in terms of modeling and laboratory experiments. The first goal of this research was to develop a spatially explicit lifecycle model, by integrating life cycle assessment (LCA), and Geographic Information Systems (GIS) analysis, for the evaluation of the environmental and economic …

Data-Driven Modeling For Decision Support Systems And Treatment Management In Personalized Healthcare, Milad Zafar Nezhad

Wayne State University Dissertations

Massive amount of electronic medical records (EMRs) accumulating from patients and populations motivates clinicians and data scientists to collaborate for the advanced analytics to create knowledge that is essential to address the extensive personalized insights needed for patients, clinicians, providers, scientists, and health policy makers. Learning from large and complicated data is using extensively in marketing and commercial enterprises to generate personalized recommendations. Recently the medical research community focuses to take the benefits of big data analytic approaches and moves to personalized (precision) medicine. So, it is a significant period in healthcare and medicine for transferring to a new paradigm. …

Representation Learning With Convolutional Neural Networks, Haotian Xu

Wayne State University Dissertations

Deep learning methods have achieved great success in the areas of Computer Vision and Natural Language Processing. Recently, the rapidly developing field of deep learning is concerned with questions surrounding how we can learn meaningful and effective representations of data. This is because the performance of machine learning approaches is heavily dependent on the choice and quality of data representation, and different kinds of representation entangle and hide the different explanatory factors of variation behind the data.

In this dissertation, we focus on representation learning with deep neural networks for different data formats including text, 3D polygon shapes, and brain …

Predictable Reliability In Inter-Vehicle Communications, Chuan Li

Wayne State University Dissertations

Predictably reliable communication in wireless networked sensing and control systems (WSC) is a basic enabler for performance guarantee. Yet current research efforts are either focus on maximizing throughput or based on inaccurate interference modelling methods, which yield unsatisfactory results in terms of communication reliability. In this dissertation, we discuss techniques that enable reliable communication in both traditional wireless sensor networks and highly mobile inter-vehicle communication networks. We focus our discussion on traditional wireless sensor networks in Chapter 2 where we discuss mechanisms that enable predictable and reliable communications with no centralized infrastructures. With the promising results in Chapter 2, we …

Scalable And Secure Provenance Querying For Scientific Workflows And Its Application In Autism Study, Fahima Amin Bhuyan

Wayne State University Dissertations

In the era of big data, scientific workflows have become essential to automate scientific experiments and guarantee repeatability. As both data and workflow increase in their scale, requirements for having a data lineage management system commensurate with the complexity of the workflow also become necessary, calling for new scalable storage, query, and analytics infrastructure. This system that manages and preserves the derivation history and morphosis of data, known as provenance system, is essential for maintaining quality and trustworthiness of data products and ensuring reproducibility of scientific discoveries. With a flurry of research and increased adoption of scientific workflows in processing …

The Impact Of Machine Learning Algorithms On Benchmarking Process In Healthcare Service Delivery, Egbe-Etu Emmanuel Etu

Wayne State University Theses

Currently, organizations have adopted and implemented a variety of innovative quality management philosophies, approaches, and techniques to stay competitive in an ever-changing global economy. Benchmarking is one of such techniques deployed by organizations to stay competitive. The motivation for this research stems from a real-world problem being faced by hospitals in the healthcare industry who have amassed a ton of data and want to embark on benchmarking project to assess the performance of the emergency departments due to challenges faced with poor management of operations which has led to high patient boarding rates, high patient wait-times, poor quality service, low …

System Support For Stream Processing In Collaborative Cloud-Edge Environment, Quan Zhang

Wayne State University Dissertations

Stream processing is a critical technique to process huge amount of data in real-time manner.

Cloud computing has been used for stream processing due to its unlimited computation

resources. At the same time, we are entering the era of Internet of Everything (IoE). The emerging

edge computing benefits low-latency applications by leveraging computation resources at

the proximity of data sources. Billions of sensors and actuators are being deployed worldwide

and huge amount of data generated by things are immersed in our daily life. It has become

essential for organizations to be able to stream and analyze data, and provide low-latency …

Deep Learning Methods For Visual Object Recognition, Zeyad Hailat

Wayne State University Dissertations

Convolutional neural networks (CNNs) attain state-of-the-art performance on various classification tasks assuming a sufficiently large number of labeled training examples. Unfortunately, curating sufficiently large labeled training dataset requires human involvement, which is expensive, time-consuming, and susceptible to noisy labels. Semi-supervised learning methods can alleviate the aforementioned problems by employing one of two techniques. First, utilizing a limited number of labeled data in conjunction with sufficiently large unlabeled data to construct a classification model. Second, exploiting sufficiently large noisy label training data to learn a classification model. In this dissertation, we proposed a few new methods to mitigate the aforementioned problems. …

Identification Of Streptococcus Pyogenes Using Raman Spectroscopy, Ehsan Majidi

Wayne State University Dissertations

Despite the attention that Raman Spectroscopy has gained recently in the area of pathogen identification, the spectra analyses techniques are not well developed. In most scenarios, they rely on expert intervention to detect and assign the peaks of the spectra to specific molecular vibration. Although some investigators have used machine-learning techniques to classify pathogens, these studies are usually limited to a specific application, and the generalization of these techniques is not clear. Also, a wide range of algorithms have been developed for classification problems, however, there is less insight to applying such methods on Raman spectra. Furthermore, analyzing the Raman …

Real-Time Guarantees For Wireless Networked Sensing And Control, Yu Chen

Wayne State University Dissertations

Wireless networks are increasingly being explored for mission-critical sensing and control in emerging domains such as connected and automated vehicles, Industrial 4.0, and smart city. In wireless networked sensing and control (WSC) systems, reliable and real- time delivery of sensed data plays a crucial role for the control decision since out-of-date information will often be irrelevant and even leads to negative effects to the system. Since WSC differs dramatically from the traditional real-time (RT) systems due to its wireless nature, new design objective and perspective are necessary to achieve real-time guarantees.

First, we proposed Optimal Node Activation Multiple Access (ONAMA) …

Qualitative Change Detection Approach For Preventive Therapies, Cristina Mitrea

Wayne State University Dissertations

Currently, most diseases are diagnosed only after disease-associated changes have occurred. In this PhD dissertation, we propose a paradigm shift from treating the disease to maintaining the healthy state. The proposed approach is able to identify when systemic qualitative changes in biological systems happen, thus opening the possibility of therapeutic interventions before the occurrence of symptoms. The change detection method exploits knowledge from biological networks and longitudinal data using a system impact analysis approach. This approach is validated on eight datasets, for seven different model organisms and eight biological phenomena. On these data, our proposed method performs well, consistently identifying …