Physical Sciences and Mathematics Commons^™

Towards An Experimental Bibliography Of Hemispheric Reconstruction Newspapers, Joshua Ortiz Baco, Benjamin Charles Germain Lee, Jim Casey, Sarah H. Salter

Criticism

Digital collections of newspapers have drawn broader attention to the fragmented and scattered print histories of minoritized communities. Attempts to survey these histories through bibliography, however, quickly meet with a fundamental problem: the practice of bibliographic description calls for creating a static record of social affiliations. Given the overwhelming scholarly consensus that categories such as race, ethnicity, and language are socially constructed, this article introduces an experimental bibliographic method for mapping the vast landscape of historical newspapers. This method extends the machine learning affordances of a recent project called Newspaper Navigator to enumerate the newspapers in Chronicling America according to …

Control Implemented On Quantum Computers: Effects Of Noise, Nondeterminism, And Entanglement, Kip Nieman, Keshav Kasturi Rangan, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

Quantum computing has advanced in recent years to the point that there are now some quantum computers and quantum simulators available to the public for use. In addition, quantum computing is beginning to receive attention within the process systems engineering community for directions such as machine learning and optimization. A logical next step for its evaluation within process systems engineering is for control, specifically for computing control actions to be applied to process systems. In this work, we provide some initial studies regarding the implementation of control on quantum computers, including the implementation of a single-input/single-output proportional control law on …

Actuator Cyberattack Handling Using Lyapunov-Based Economic Model Predictive Control, Keshav Kasturi Rangan, Henrique Oyama, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

Cybersecurity has gained increasing interest as a consequence of the potential impacts of cyberattacks on profits and safety. While attacks can affect various components of a plant, prior work from our group has focused on the impact of cyberattacks on control components such as process sensors and actuators and the development of detection strategies for cybersecurity derived from control theory. In this work, we provide greater focus on actuator attacks; specifically, we extend a detection and control strategy previously applied for sensor attacks and based on an optimization-based control technique called Lyapunov-based economic model predictive control (LEMPC) to detect attacks …

Test Methods For Image-Based Information In Next-Generation Manufacturing, Henrique Oyama, Dominic Messina, Renee O'Neill, Samantha Cherney, Minhazur Rahman, Keshav Kasturi Rangan, Govanni Gjonaj, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

Typical control designs in the process systems engineering literature have assumed that the primary sensing methodologies are traditional instruments such as thermocouples. Dig- italization is changing the landscape for manufacturing, and data-based sensing modalities (e.g., image-based sensing) are becoming of greater interest for plant control. These considerations require novel test/evaluation solutions. For example, process systems engineering researchers may wish to test image-based sensors in simulation. In this work, we provide preliminary thoughts on how image-based technologies might be evaluated via simulation for process systems.

Quantum Computing And Resilient Design Perspectives For Cybersecurity Of Feedback Systems, Keshav Kasturi Rangan, Jihan Abou Halloun, Henrique Oyama, Samantha Cherney, Ilham Azali Assoumani, Nazir Jairazbhoy, Helen Durand, Simon Ka Ng

Chemical Engineering and Materials Science Faculty Research Publications

Cybersecurity of control systems is an important issue in next-generation manufac- turing that can impact both operational objectives (safety and performance) as well as process designs (via hazard analysis). Cyberattacks differ from faults in that they can be coordinated efforts to exploit system vulnerabilities to create otherwise unlikely hazard scenarios. Because coordination and targeted process manipulation can be characteristics of attacks, some of the tactics previously analyzed in our group from a control system cybersecurity perspective have incorporated randomness to attempt to thwart attacks. The underlying assumption for the generation of this randomness has been that it can be achieved …

Challenges And Opportunities For Next-Generation Manufacturing In Space, Kip Nieman, A. F. Leonard, Katie Tyrell, Dominic Messina, Rebecca Lopez, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

With commercial space travel now a reality, the idea that people might spend time on other planets in the future seems to have greater potential. To make this possible, however, there needs to be flexible means for manufacturing in space to enable tooling or resources to be created when needed to handle unexpected situations. Next-generation manufacturing paradigms offer significant potential for the kind of flexibility that might be needed; however, they can result in increases in computation time compared to traditional control methods that could make many of the computing resources already available on earth attractive for use. Furthermore, resilience …

The Short-Term Effects Of Fine Airborne Particulate Matter And Climate On Covid-19 Disease Dynamics, El Hussain Shamsa, Kezhong Zhang

Medical Student Research Symposium

Background: Despite more than 60% of the United States population being fully vaccinated, COVID-19 cases continue to spike in a temporal pattern. These patterns in COVID-19 incidence and mortality may be linked to short-term changes in environmental factors.

Methods: Nationwide, county-wise measurements for COVID-19 cases and deaths, fine-airborne particulate matter (PM_2.5), and maximum temperature were obtained from March 20, 2020 to March 20, 2021. Multivariate Linear Regression was used to analyze the association between environmental factors and COVID-19 incidence and mortality rates in each season. Negative Binomial Regression was used to analyze daily fluctuations of COVID-19 cases …

On-Line Process Physics Tests Via Lyapunov-Based Economic Model Predictive Control And Simulation-Based Testing Of Image-Based Process Control, Henrique Oyama, A. F. Leonard, Minhazur Rahman, Govanni Gjonaj, Michael Williamson, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

Next-generation manufacturing involves increasing use of automation and data to enhance process efficiency. An important question for the chemical process industries, as new process systems (e.g., intensified processes) and new data modalities (e.g., images) are integrated with traditional plant automation concepts, will be how to best evaluate alternative strategies for data-driven modeling and synthesizing process data. Two methods which could be used to aid in this are those which aid in testing data-based techniques on-line, and those which enable various data-based techniques to be assessed in simulation. In this work, we discuss two techniques in this domain which can be …

Lyapunov-Based Economic Model Predictive Control For Detecting And Handling Actuator And Simultaneous Sensor/Actuator Cyberattacks On Process Control Systems, Henrique Oyama, Dominic Messina, Keshav Kasturi Rangan, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

The controllers for a cyber-physical system may be impacted by sensor measurement cyberattacks, actuator signal cyberattacks, or both types of attacks. Prior work in our group has developed a theory for handling cyberattacks on process sensors. However, sensor and actuator cyberattacks have a different character from one another. Specifically, sensor measurement attacks prevent proper inputs from being applied to the process by manipulating the measurements that the controller receives, so that the control law plays a role in the impact of a given sensor measurement cyberattack on a process. In contrast, actuator signal attacks prevent proper inputs from being applied …

Segmentation Of Intracranial Structures From Noncontrast Ct Images With Deep Learning, Evan Porter

Wayne State University Dissertations

Presented in this work is an investigation of the application of artificially intelligent algorithms, namely deep learning, to generate segmentations for the application in functional avoidance radiotherapy treatment planning. Specific applications of deep learning for functional avoidance include generating hippocampus segmentations from computed tomography (CT) images and generating synthetic pulmonary perfusion images from four-dimensional CT (4DCT).A single institution dataset of 390 patients treated with Gamma Knife stereotactic radiosurgery was created. From these patients, the hippocampus was manually segmented on the high-resolution MR image and used for the development of the data processing methodology and model testing. It was determined that …

Adversarial Machine Learning For Advanced Medical Imaging Systems, Xin Li

Wayne State University Dissertations

Although deep neural networks (DNNs) have achieved significant advancement in various challenging tasks of computer vision, they are also known to be vulnerable to so-called adversarial attacks. With only imperceptibly small perturbations added to a clean image, adversarial samples can drastically change models’ prediction, resulting in a significant drop in DNN’s performance. This phenomenon poses a serious threat to security-critical applications of DNNs, such as medical imaging, autonomous driving, and surveillance systems. In this dissertation, we present adversarial machine learning approaches for natural image classification and advanced medical imaging systems.

We start by describing our advanced medical imaging systems to …

Defending Against Adversarial Attacks On Medical Imaging Ai Systems, Xin Li

Wayne State University Theses

Although deep learning systems trained on medical images have shown state-of-the-art performance in many clinical prediction tasks, recent studies demonstrate that these systems can be fooled by carefully crafted adversarial images. It has raised concerns on the practical deployment of deep learning based medical image classification systems. Although an array of defense techniques have been developed and proved to be effective in computer vision, defending against adversarial attacks on medical images remains largely an uncharted territory due to their unique challenges: crafted adversarial noises added to a highly standardized medical image can make it a hard sample for model to …

Integrated Cyberattack Detection And Handling For Nonlinear Systems With Evolving Process Dynamics Under Lyapunov-Based Economic Model Predictive Control, Keshav Kasturi Rangan, Henrique Oyama, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

Safety-critical processes are becoming increasingly automated and connected. While automation can increase effciency, it brings new challenges associated with guaranteeing safety in the presence of uncertainty especially in the presence of control system cyberattacks. One of the challenges for developing control strategies with guaranteed safety and cybersecurity properties under suffcient conditions is the development of appropriate detection strategies that work with control laws to prevent undetected attacks that have immediate closed-loop stability consequences. Achieving this, in the presence of uncertainty brought about by plant/model mismatch and process dynamics that can change with time, requires a fundamental understanding of the characteristics …

Versatility Of Low-Power Wide-Area Network Applications, Dali Ismail

Wayne State University Dissertations

Low-Power Wide-Area Network (LPWAN) is regarded as the leading communication technology for wide-area Internet-of-Things (IoT) applications. It offers low-power, long-range, and low-cost communication. With different communication requirements for varying IoT applications, many competing LPWAN technologies operating in both licensed (e.g., NB-IoT, LTE-M, and 5G) and unlicensed (e.g., LoRa and SigFox) bands have emerged. LPWANs are designed to support applications with low-power and low data rate operations. They are not well-designed to host applications that involve high mobility, high traffic, or real-time communication (e.g., volcano monitoring and control applications).With the increasing number of mobile devices in many IoT domains (e.g., agricultural …

Software As A Service: The Mediating Role Of Consequences Of Saas Diffusion On Firm Performance, Cristina Marie-Mccarthy Recchia

Wayne State University Dissertations

ABSTRACTSOFTWARE AS A SERVICE: THE MEDIATING ROLE OF CONSEQUENCES OF SAAS DIFFUSION ON FIRM PERFORMANCE by CRISTINA MARIE-MCCARTHY RECCHIA DECEMBER 2021 Advisor: Dr. Ratna Babu Chinnam Major: Industrial Engineering Degree: Doctor of Philosophy There are ample studies that support a positive link between information technology and firm performance. Bharadwaj (2000) and Chae (2014, 2018) are two examples that provided a foundation for this work. These scholars looked at how capabilities associated with information technology contribute to improved financial performance using a specific set of financial ratios. In addition, there are studies that examine a positive link between Software-as-a-Service (SaaS) and …

Integrated Cyberattack Detection And Resilient Control Strategies Using Lyapunov-Based Economic Model Predictive Control, Henrique Oyama, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

The use of an integrated system framework, characterized by numerous cyber/physical components (sensor measurements, signals to actuators) connected through wired/wireless networks, has not only increased the ability to control industrial systems, but also the vulnerabilities to cyberattacks. State measurement cyberattacks could pose threats to process control systems since feedback control may be lost if the attack policy is not thwarted. Motivated by this, we propose three detection concepts based on Lyapunov‐based economic model predictive control (LEMPC) for nonlinear systems. The first approach utilizes randomized modifications to an LEMPC formulation online to potentially detect cyberattacks. The second method detects attacks when …

Mitigating Safety Concerns And Profit/Production Losses For Chemical Process Control Systems Under Cyberattacks Via Design/Control Methods, Helen Durand, Matthew Wegener

Chemical Engineering and Materials Science Faculty Research Publications

One of the challenges for chemical processes today, from a safety and profit standpoint, is the potential that cyberattacks could be performed on components of process control systems. Safety issues could be catastrophic; however, because the nonlinear systems definition of a cyberattack has similarities to a nonlinear systems definition of faults, many processes have already been instrumented to handle various problematic input conditions. Also challenging is the question of how to design a system that is resilient to attacks attempting to impact the production volumes or profits of a company. In this work, we explore a process/equipment design framework for …

Responsive Economic Model Predictive Control For Next-Generation Manufacturing, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

There is an increasing push to make automated systems capable of carrying out tasks which humans perform, such as driving, speech recognition, and anomaly detection. Automated systems, therefore, are increasingly required to respond to unexpected conditions. Two types of unexpected conditions of relevance in the chemical process industries are anomalous conditions and the responses of operators and engineers to controller behavior. Enhancing responsiveness of an advanced control design known as economic model predictive control (EMPC) (which uses predictions of future process behavior to determine an economically optimal manner in which to operate a process) to unexpected conditions of these types …

Process Data Analytics Using Deep Learning Techniques, Majid Moradi Aliabadi

Wayne State University Theses

In chemical manufacturing plants, numerous types of data are accessible, which could be process operational data (historical or real-time), process design and product quality data, economic and environmental (including process safety, waste emission and health impact) data. Effective knowledge extraction from raw data has always been a very challenging task, especially the data needed for a type of study is huge. Other characteristics of process data such as noise, dynamics, and highly correlated process parameters make this more challenging.

In this study, we introduce an attention-based RNN for multi-step-ahead prediction that can have applications in model predictive control, fault diagnosis, …

Representation Learning With Autoencoders For Electronic Health Records, Najibesadat Sadatijafarkalaei

Wayne State University Theses

Increasing volume of Electronic Health Records (EHR) in recent years provides great opportunities for data scientists to collaborate on different aspects of healthcare research by applying advanced analytics to these EHR clinical data. A key requirement however

is obtaining meaningful insights from high dimensional, sparse and complex clinical data. Data science approaches typically address this challenge by performing feature learning in order to build more reliable and informative feature representations from clinical data followed by supervised learning. In this research, we propose a predictive modeling approach based on deep feature representations and word embedding techniques. Our method uses different deep …

Tiling Optimization For Nested Loops On Gpus, Yuanzhe Li

Wayne State University Dissertations

Optimizing nested loops has been considered as an important topic and widely studied in parallel programming. With the development of GPU architectures, the performance of these computations can be significantly boosted with the massively parallel hardware.

General matrix-matrix multiplication is a typical example where executing such an algorithm on GPUs outperforms the performance obtained on other multicore CPUs. However, achieving ideal performance on GPUs usually requires a lot of human effort to manage

the massively parallel computation resources. Therefore, the efficient implementation of optimizing nested loops on GPUs became a popular topic in recent years. We present our work based …

Low-Power Wide-Area Network Design, Md Mahbubur Rahman

Wayne State University Dissertations

Low-Power Wide-Area Network (LPWAN) is an enabling technology for long-range, low-power, and low-cost Internet of Things (IoT) and Cyber-Physical Systems (CPS) applications. Due to their escalating demand in the IoT/CPS applications, recently, multiple LPWAN technologies have been developed that operate in the cellular/licensed (e.g., 5G, LTE Cat M1, and NB-IoT) and unlicensed/ISM (e.g., LoRa and SigFox) bands. To avoid the crowd in the limited ISM band (where most LPWANs operate) and the cost of the licensed band, we propose a novel LPWAN technology called Sensor Network Over White Spaces (SNOW) by utilizing the TV white spaces. White spaces refer to …

Real-Time Control Over Wireless Networks, Venkata Prashant Modekurthy

Wayne State University Dissertations

Industrial internet of Things (IIoT) are gaining popularity for use in large-scale applications such as oil-field management (e.g., 74×8km2 East Texas Oil-field), smart farming, smart manufac- turing, smart grid, and data center power management. These applications require the wireless stack to provide a scalable, reliable, low-power and low-latency communication. To realize a predictable and reliable communication in a highly unreliable wireless environment, industrial wireless standards use a centralized wireless stack design. In a centralized wireless stack design, a central manager generates routes and a communication schedule for a multi-channel time divi- sion multiple access communication (TDMA) based medium access control …

Towards Personalized Medicine: Computational Approaches For Drug Repurposing And Cell Type Identification, Azam Peyvandipour

Wayne State University Dissertations

The traditional drug discovery process is extremely slow and costly. More than 90% of drugs fail to pass beyond the early stage of development and toxicity tests, and many of the drugs that go through early phases of the clinical trials fail because of adverse reactions, side effects, or lack of efficiency. In spite of unprecedented investments in research and development (R&D), the number of new FDA-approved drugs remains low, reflecting the limitations of the current R&D model.

In this context, finding new disease indications for existing drugs sidesteps these issues and can therefore increase the available therapeutic choices at …

Catgame: A Tool For Problem Solving In Complex Dynamic Systems Using Game Theoretic Knowledge Distribution In Cultural Algorithms, And Its Application (Catneuro) To The Deep Learning Of Game Controller, Faisal Waris

Wayne State University Dissertations

Cultural Algorithms (CA) are knowledge-intensive, population-based stochastic optimization methods that are modeled after human cultures and are suited to solving problems in complex environments. The CA Belief Space stores knowledge harvested from prior generations and re-distributes it to future generations via a knowledge distribution (KD) mechanism. Each of the population individuals is then guided through the search space via the associated knowledge. Previously, CA implementations have used only competitive KD mechanisms that have performed well for problems embedded in static environments. Relatively recently, CA research has evolved to encompass dynamic problem environments. Given increasing environmental complexity, a natural question arises …

Machine Learning In Manufacturing: Review, Synthesis, And Theoretical Framework, Ajit Sharma, Zhibo Zhang, Rahul Rai

Business Administration Faculty Research Publications

There has been a paradigmatic shift in manufacturing as computing has transitioned from the programmable to the cognitive computing era. In this paper we present a theoretical framework for understanding this paradigmatic shift in manufacturing and the fast evolving role of artificial intelligence. Policy, Strategic and Operational implications are discussed. Implications for the future of strategy and operations in manufacturing are also discussed. Future research directions are presented.

Securing Arm Platform: From Software-Based To Hardware-Based Approaches, Zhenyu Ning

Wayne State University Dissertations

With the rapid proliferation of the ARM architecture on smart mobile phones and Internet of Things (IoT) devices, the security of ARM platform becomes an emerging problem. In recent years, the number of malware identified on ARM platforms, especially on Android, shows explosive growth. Evasion techniques are also used in these malware to escape from being detected by existing analysis systems.

In our research, we first present a software-based mechanism to increase the accuracy of existing static analysis tools by reassembleable bytecode extraction. Our solution collects bytecode and data at runtime, and then reassemble them offline to help static analysis …

Process/Equipment Design Implications For Control System Cybersecurity, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

An emerging challenge for process safety is process control system cybersecurity. An attacker could gain control of the process actuators through the control system or communication policies within control loops and potentially drive the process state to unsafe conditions. Cybersecurity has traditionally been handled as an information technology (IT) problem in the process industries. In the literature for cybersecurity specifically of control systems, there has been work aimed at developing control designs that seek to fight cyberattacks by either giving the system appropriate response mechanisms once attacks are detected or seeking to make the attacks difficult to perform. In this …

Machine Learning Methods For The Analysis Of Clinical Conversation, Md Mehedi Hasan

Wayne State University Dissertations

Motivational Interviewing (MI) is an evidence-based communication technique to increase intrinsic motivation and self-efficacy for behavior change. This goal is achieved through the exploration of the patient's own desires, ability, reasons, need for and commitment to the targeted behavior change. However, communication science approaches to understanding the efficacy of MI are inherently limited by traditional qualitative coding methods which is a time-consuming and resource-intensive process. Thus, an efficient method is required to automate the coding process which will accelerate the pace of communication research in behavioral science. The specific provider behaviors responsible for the elicitation of change talk, are also …

Utilizing Knowledge Bases In Information Retrieval For Clinical Decision Support And Precision Medicine, Saeid Balaneshinkordan

Wayne State University Dissertations

Accurately answering queries that describe a clinical case and aim at finding articles in a collection of medical literature requires utilizing knowledge bases in capturing many explicit and latent aspects of such queries. Proper representation of these aspects needs knowledge-based query understanding methods that identify the most important query concepts as well as knowledge-based query reformulation methods that add new concepts to a query. In the tasks of Clinical Decision Support (CDS) and Precision Medicine (PM), the query and collection documents may have a complex structure with different components, such as disease and genetic variants that should be transformed to …