Kenneth A. Loparo - US grants

This research considers the analysis and control of a class of nonlinear systems with bounded and switching control functions. Such systems have application to a number of areas such as power systems, spacecraft dynamics and switched electrical networks. Problems to be studied include controllability, attainability and optimal control problems where the control appears linearly in the cost for bounded control functions and minimum switch control problems for bang-bang control systems. A systematic investigation of these problems for linear-analytic two dimensional systems and bilinear systems in higher dimensions will be investigated.

Funding is requested to support acquisition of workstations (1 Texas Instruments Explorer LX, and 3 Digital Equipment Corp. AI VAXstation-GPX) to upgrade the computing facilities at the Center for Automation and Intelligent Systems Research. This upgrade will benefit various research projects emphasizing artificial intelligence tools and techniques for enhancing the productivity of manufacturing industry. Some of the current projects are: investigation of meta-interpreters for expert systems, introspection capabilities in Rummelhart associative networks for pattern recognition and mechanization of discovery, a domain-independent expert system for scheduling, tactile sensing for robotic manipulation, and the use of expert systems to improve productivity in the production of "high-technology" metal parts with challenging specifications.

This proposal is concerned with the restoration of service in a power system that has lost some or all of its load. In a blackout or brownout situation, the system may separate into separate islands and it is necessary to first restore the load within the islands and then restore the interconnections among the islands. The restoration is proposed to be done with a hierarchical multi-layer approach consisting of a direct control layer, an optimizing layer, and an adaptive layer. The focus here is on the last two layers with the problem formulated as a multi-stage multi-objective sequential decision problem. The problem of power system restoration after a blackout is an important one but has been very difficult to formulate in a rigorous mathematical way. A new formulation is to be developed in this approach. Any success at better restoration techniques will lead to shorter periods of power outages.

Hobbs 9521603 Existing planning tools do not calculate expected production costs while considering transmission limitations. Further, present frameworks for comparing distributed resources against transmission and distribution (T&D) investments and centralized resources lack a theoretical basis for the appropriate development and use of marginal cost information. We propose the following research efforts to fill those gaps: * Development and demonstration of methods for assessing the effect of transmission limits upon expected costs. The methods would exploit a multiarea probabilistic production costing framework incorporating DC and AC network representations. Existing probabilistic multiarea methods, aside from inefficient Monte Carlo approaches, do not include DC or AC flow models. This work would be of benefit to utilities by allowing them to assess the long run implications of resources and transmission decisions for production costs while rigorously considering transmission limitations and the entire range of possible load and equipment states. It could also benefit non-utility providers of electric power, large power consumers, and regulators by providing them a means of better understanding the spatial cost structure of the bulk power market. * Creation of a theoretically defensible yet practical framework for using such information for evaluating resources and transactions. Decomposition methods would be used to develop theoretically optimal approaches for coordinating models for generation planning, bulk power system operation, and transmission and distribution planning. Guidelines and procedures would be defined for theoretically correct calculation of marginal costs and coordination of models. This effort would be especially valuable to the many electric utilities who are beginning to evaluate the merits of dist ributed versus centralized generation and demand-side resources. Some utilities are presently testing ad hoc procedures involving coordination of distribution planning methods with bulk power planning and power flow models. ****

Induction motors have found considerable applications in industry due to their reliability, ruggedness and relatively low cost. Because there is no mechanical commutation and no sparks are produced, induction motors have found broad applications in volatile environments. In spite of these advantages, the induction motor presents challenging problems in the detection, diagnosis and isolation of motor faults and the control of the electrical machine.

According to previous studies which have examined the reliability of induction motors and their associated failure modes, the three most probable failures are: mechanical failures in the rotor shaft bearings, short circuits in the stator windings, and rotor bar failures. There is currently a significant need for the development of detection and diagnosis algorithms that can monitor, in real-time, the health of an induction motor.

The problem of modeling induction motors with a saturating magnetic circuit has always been an important subject. It is well recognized that a linear magnetic circuit machine model does not capture all the dynamics of a real machine, especially the cross-saturation effects. Although saturation modeling has been used for predicting the transient behavior of the motor, most of the previous work on control does not consider the effects of saturation on motor performance. In controller synthesis, the common assumption of the linearity of the magnetic circuit is often justified by including the flux magnitude in the outputs to be regulated. During machine transients the flux magnitude can exceed these saturation limits and the performance of the machine can be seriously compromised. Also in many variable torque applications, it is desirable to operate in the magnetic saturation region to allow the machine to develop higher torque.

This proposal outlines a research program focusing on the development and evaluation (simulation and experimental) of algorithms for the detection and diagnosis of the most common types of motor faults and for the design of advanced control algorithms under a wide variety of motor operating conditions (e.g. magnetic and actuator saturation) and applications.

Context Statement
(Sensor and Sensor Networks Panel, June 12 and 13, 2003)

Approximately 930 proposals were submitted in response to the Sensors and Sensor Networks Solicitation (NSF 03-512) during fiscal year 2003. A total of 469 of these proposals were considered in the Small-Team competition. All proposals were grouped according to their relevance to one of the three general topical areas identified in the solicitation. This proposal is one of 30 considered in a panel on June 12-13, 2003 on the subject of Designs, Materials and Concepts for New Biological Sensors and Sensing Systems. This panel was jointly run by program directors from the Experimental and Integrated Activities Division of CISE and the Bioengineering and Environmental Systems Division of ENG. The panel included sixteen panelists, all technical experts, who were invited to NSF and who reflect the range of expertise needed for the proposals under consideration. The panelists had reviewed the proposals in their areas of expertise, and sent their individual reviews via FastLane, prior to coming to the panel meeting. Proposals were reviewed and evaluated against both merit review criteria established by the National Science Board, namely, "What is the intellectual merit of the proposed activity?" and "What are the broader impacts of the proposed activity?" In addition, the proposals were assessed for relevance to the Program Solicitation (NSF 03-512).

At least three panelists provided written evaluations for each proposal. The written evaluations were presented to the panelists who debated the strengths and weaknesses of the proposals, which were then assigned preliminary ratings. The panel discussions concerning the proposals were documented by a panel Recorder, who submitted the summary of the discussion to the panel for unanimous approval. After all the proposals had been reviewed and rated, the panel placed the proposals into two categories: (1) Recommended and (2) Not Recommended for funding. The panel provided sufficient information for the Program Director to make a recommendation.

DESCRIPTION (provided by applicant): The objective of the proposed planning activity, and the broad, long-term goal of our proposed Case Integrative Cancer Biology Program (ICBP), is to develop a fully integrated interdisciplinary team of systems scientists and cancer biologists that can address the complex biological problem of cancer using systems approaches. This effort is being built on a cancer research forte at the Case School of Medicine and pioneering research in systems theory and mathematical control in the Case School of Engineering. The scope of this work is composed of five integrated projects that include research, data and model sharing, and educational activities. The projects will produce a predictive in silico model of deoxynucleotide metabolism that will facilitate drug and radiation dose time course optimizations in future therapies of mismatch repair defective (MMR-) malignancies. The projects will investigate two basic approaches for selectively killing MMR defective cells. In one approach (Project 1), cells that are MMR defective due to either methylation silencing or genetic mutations are targeted; in the other approach (Project 2), only methylation silenced MMR defective cells are targeted. In both approaches, the strategy is to preferentially accumulate drug into DNA of MMR defective cells. In the first approach, IdUrd accumulates preferentially in the DNA of MMR defective cells and after an appropriate amount of incorporation, cells are exposed to radiation to selectively kill MMR- cells. In the second approach, FdCyd is first used to load FdUrd selectively into the DNA of cells MMR defective due to methylation, and after sufficient loading, dH4Urd (an inhibitor of cytidine deaminase) is then used to redirect FdCyd into DNA where it acts as a demethylating agent that reverses MMR competence and thus creates a catastrophic spike of DNA double strand breaks (DSBs). Through an iterative process that involves model development and systems analysis, experimentation and data collection, model testing and validation, and a detailed study of coordination and control between the salvage and de novo deoxynucleotide synthesis pathways (Project 3), we will produce a deoxynucleotide metabolism model in R and make it publicly available in both R and Systems Biology Markup Language (Project 4). To educate oncologists and engineers, we will develop a graduate level course sequence in Integrative Cancer Biology (Project 5). Accomplishing these projects will produce building blocks needed for subsequent translational cancer research studies. At the completion of this three-year project, we will have developed a strong interdisciplinary team that will be capable of advancing the study of cancer as a problem of complex biological systems.

DESCRIPTION (provided by applicant): The long-term goal of the proposed research is the development of improved chemotherapy and/or radiation therapy treatment strategies for mismatch repair deficient cancers based on a comprehensive modeling and systems and control framework using a systems biology approach. As a first step in achieving this long-term goal, the proposed research is directed at developing synergistic experimental and computational frameworks that will enable the study of the cellular and molecular aspects of mismatch repair. The experimental data and models developed in this work will provide the necessary foundation for the subsequent efforts directed at the development of improved treatment strategies for resistant (damage tolerant) cancers. A stochastic hybrid model for the mismatch repair process will be developed using data from mismatch repair reconstitution experiments with purified proteins. The model will capture the basic dynamics of the biochemical mechanisms governing the mismatch repair pathway, and in silico experiments with the model will be used to improve the understanding of the mismatch repair process and to generate further hypotheses that can be tested experimentally. The model will then be modified using the data from these cellular experiments. The mismatch repair pathway will be reconstituted in vitro using purified proteins to obtain kinetic parameter estimates for the computational model from measurements on protein activity. A cellular experimental model, consisting of genetically manipulated MMR+ versus MMR- cells, will be used to test and further validate the computational model with respect to treatment responses. PUBLIC HEALTH RELEVANCE: One of the DNA repair mechanisms will be studied both experimentally and computationally to improve the understanding of the repair process dynamics under treatment conditions. A detailed understanding of these treatment responses using computational models will provide valuable therapeutic information in the treatment of drug resistant cancers.

Smart and connected systems are changing communities, work, and the home profoundly.  Automation over the past few decades has radically reshaped manufacturing, employment in manufacturing industries, and their local communities. The Internet of Things (IoT) will likely accelerate change, opening new opportunities and presenting new challenges. This grant brings together academic researchers in science, engineering, and business management at two universities, educators, community members and city leaders, and small-to-mid-sized manufacturers (SMM), all within a neighborhood of Cleveland, Ohio, to plan for research, education, and workforce development in their community.

This project develops action plans for research, education, and policy recommendations to benefit SMMs and the local community.  The plans reflect an understanding of the investment in time and resources required to leverage IoT in order for the SMMs to transform themselves into "smart" manufacturers with increased productivity and energy efficiency to better capitalize on new markets.  Within the local community, the plans reflect an understanding of the potential role of IoT in workforce education, recruitment, and retention. IoT has the potential to improve the quality of life of a community.  This project brings these different perspectives together, creating a new language for engagement among stakeholders and creating new opportunities in a community for public-private investment in IoT-enabled technologies and applications. This project in a neighborhood of Ohio may serve as a model for small towns and communities to work and plan with their local universities and manufacturers to achieve the societal benefits from leveraging IoT technologies.

Industrial energy consumption is significant and accounts for approximately one-third of total U.S. energy consumptions. For the energy currently consumed in industry, about 64.4% is wasted, based on the U.S. Department of Energy's statistical data. There is a great potential to improve the energy efficiency of industrial operations to reduce the energy consumption, lower the utility cost and cut greenhouse gas emissions. This project is to plan for the establishment of an Industry-University Cooperative Research Center, by reaching out to potential manufacturing companies, utility companies and energy service companies which are interested in conducting collaborative research with CWRU faculty on industrial energy efficiency. The vision of the planned IUCRC is to establish a national/international research center dedicated to advancing the knowledge and techniques needed to improve industrial energy efficiency to enhance the competitiveness of U.S. manufacturing industry in the global economy and to mitigate the carbon emissions from U.S. industrial sector. Simultaneously, the proposed IUCRC will develop advanced educational program to train a large cohort of engineering students. This planned IUCRC will generate significant impacts on the industrial operations and academic research on industrial energy efficiency, through joint efforts with Purdue University.

The proposed IUCRC at CWRU will focus on improving energy efficiency of manufacturing companies, in collaborations with utility companies and energy service companies. The research objectives of this IUCRC are to: (1). Develop scientific modeling tools and experimental techniques for identifying the in-depth energy saving opportunities in industrial manufacturing systems. (2). Develop applicable control algorithms and tools for effectively reducing the energy consumption in industrial manufacturing systems. (3). Develop data analytics approaches and decision-support tools for supporting policy-making and strategy-planning on Industrial Energy Efficiency. (4). Develop educational programs for workforce training on Industrial Energy Efficiency. The planning project will leverage the expertise of CWRU faculty members in industrial energy efficiency, manufacturing system optimization, adaptive process control, and energy data analytics, to harness existing relationships of CWRU with industry to identify possible partners for the IUCRC and utilize an inclusive feedback process between researchers and potential members to determine the strategic direction of research projects to be conducted. The research results obtained from the center will provide novel insights into current industrial operations for in-depth energy savings, plant-wide implementations, and public policy support.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

This planning grant award will be used to study the feasibility of establishing a multi-university-industry Center for High-Assurance Secure Systems and Internet-of-Things (CHASSI) that will focus on areas where both security and high assurance are necessary to support operations of high mission criticality, due to safety or economic impact. Examples include medical devices, manufacturing, the energy grid, real-time financial markets, construction, and defense. Combining security and high-assurance is hard, however, intentionally combining them will lead to new models, techniques, designs, architectures, and systems that will be applicable across a range of important U.S. industries.

CHASSI has five sites: University of Kansas, University of Minnesota, Syracuse University, Case Western Reserve University (CWRU), and Indiana University. CHASSI research falls into four main thrusts: (1) Architectures, design and formal modeling for systems-level security, privacy, stability, and performance; (2) Secure communication, sensing, and devices; (3) Scalable trust and privacy; and (4) Human behavior for privacy and security. CWRU brings expertise in industrial controls, Internet-of-Things (IoT), Industrial Internet-of-Things (IIoT), and manufacturing and energy applications. Complementary expertise at the other sites includes mission assurance and systems security, assurance of medical devices, networking, cyber-physical systems, mobile security, and human factors.

CHASSI faculty members will gain an understanding of the specific interests and actual needs/barriers of industrial companies. Likewise, companies will benefit from exposure to: cutting-edge university research across all sites; networking with and learning best practices from other industry colleagues in and out of their sector; students who may be potential hires; and faculty that might perform center projects or proprietary research. During the planning period, CWRU will explore ways to advance diversity and outreach with the Women in Science and Engineering Roundtable (WISER) and Women in Technology initiatives through recruiting prospective students, educating current students, and identifying student researchers.

The collaborators from this multi-university-industry Center will host a single Center-wide repository at: http://chassi.ku.edu. This shared repository will include meeting materials, program information, publications, etc., and will be made available for a minimum of five years after the conclusion of the award or until the Center transitions to the next phase.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

The coronavirus disease (COVID-19) pandemic has exposed a critical set of vulnerabilities that have impacted community resilience in responding to escalating societal, economic, and behavioral issues. Unfortunately, there are no established solutions or proven models for us to depend on to tackle the complex challenges with significant uncertainties and unknowns. This project engages novel disciplinary perspectives to help address the devastating effects caused by COVID-19, i.e., leveraging the extracted information of experiences, ideas and support from positive-energy communities who are successfully navigating threats that can be transformed and transferred into actionable information to assist vulnerable communities to cope, progress and move forward. More specifically, by advancing artificial intelligence (AI) innovations, the goal of this project is to design and develop an AI-driven paradigm for collective and collaborative community resilience in responses to a variety of crises and exposed vulnerabilities in the COVID-19 era and beyond. With additional validation, this research will provide foundation to assist the federal and state governments, corporations, societal leaders to develop and implement strategies that will guide local and regional communities, and the nation into a successful new normal future.


This exploratory yet transformative high risk-high payoff work that involves radically different approaches will have three main research components. First, the research team will construct a novel attributed heterogeneous information network (AHIN) to comprehensively model the up-to-date multi-source pandemic related data for abstract representation. Second, to understand how users interact and how information are propagated within and cross-community in social media, the team will develop an innovative nonnegative matrix factorization regularized deep graph learning model for community detection in the AHIN by considering the heterogeneity of the network. Third, the team will propose an integrated adversarial disentangler to separate the distinct, informative factors of variations hidden in the milieu to learn post embeddings for emotion and topic analysis for community classification and framing, and thus to derive supportive and constructive information for community resilience improvement. The developed AI-driven paradigm in this project will provide in-depth insights and customized guidance that can help public health experts, social workers, law enforcement, economists, and policy makers in decision-making and also enable a conceptual framework for the development of resilient community engagement strategies in responses to a variety of crises created by COVID-19 and future natural or health-related disasters. The research will be beneficial to multidisciplinary areas, including data mining, machine learning, epidemiology, economics, social and behavioral sciences. The outcomes of this project will be made publicly accessible and broadly distributed. The project will integrate research with education through curriculum development, the participation of underrepresented groups, and student mentoring activities.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

The fast evolving and deadly outbreak of coronavirus disease (COVID-19) has created one of the most challenging issues facing global public health. According to the Centers for Disease Control and Prevention (CDC), before a vaccine or drug becomes widely available, community mitigation, which is a set of actions that persons and communities can take to help slow the spread of respiratory virus infections, is the most readily available interventions to help slow transmission of the virus in communities. A growing number of areas are reporting community transmission of the virus, which would represent a significant turn for the worse in the battle against the novel coronavirus; this points to an urgent need for expanded surveillance so we can better understand the spread of COVID-19 and better respond with actionable strategies for community mitigation. By advancing capabilities of artificial intelligence (AI) and leveraging the large-scale and real-time data generated from heterogeneous sources, the goal of this project is to design and develop an AI- and data-driven integrated framework to provide real-time hierarchical community-level risk assessment to help combat the COVID-19 pandemic.

The research will have three main parts. First, the research team will construct a novel heterogeneous graph architecture to comprehensively model the large-scale and real-time pandemic related data collected from multiple sources. Second, the team will develop conditional generative adversarial nets for graph enrichment to address the challenge of limited data that might be available for learning. Third, the team will develop algorithms to model potential community transmission routes and design an innovative heterogeneous graph auto-encoder model for hierarchical community-level risk assessment. Through the potential community transmission route modeling, the developed framework will facilitate a predictive understanding of the spread of the virus; by providing the dynamic and real-time COVID-19 risk assessment, the planned work will enable the general public to select appropriate actions for protection while minimizing disruptions to daily life to the extent possible (i.e., mitigate the negative effects of COVID-19 on public health, society, and the economy). The planned research will benefit intelligent information management where multiple data sources are involved and secure and trustworthy cyberspace with applications such as malware detection and mitigation. The project integrates research with education through curriculum development, the participation of underrepresented groups, and student mentoring activities.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Infectious disease outbreaks, such as the novel coronavirus disease (COVID-19) pandemic, entailed localized conditions with evolution in time and space present a daunting task for policy and decision makers in finding optimal non-pharmaceutical intervention (NPI) strategies at different scales that balance epidemiological benefits and socioeconomic costs. To help tackle this challenging problem, by harnessing the data revolution and advancing capabilities of artificial intelligence (AI), this multidisciplinary project aims to design and develop a data-driven and AI-augmented framework that is tailored to the evolving localized conditions and enables expert-in-the-loop for adaptive NPIs to effectively respond to the dynamics of epidemic while balancing the multidimensional socioeconomic impacts. The proposed work will not only benefit local and federal governments, regional communities, corporations, societal leaders and the public by assisting with effective responses to the public health issues while mitigating negative socioeconomic impacts and various induced crises, but will also facilitate the development of robust science-based decision support systems responding to future natural or man-made disasters. The research will be beneficial to multidisciplinary areas, including data science, machine learning, epidemiology, economics, social and behavioral sciences. The outcomes (e.g., open-source code, data, and models) will be made publicly accessible and broadly distributed through publications, media presses, etc. This project will integrate research with education, including novel curriculum development, student mentoring, professional training and workforce development, and K-12 outreach activities aimed at underrepresented groups.

To combat infectious disease outbreaks with robust response planning, this project includes four interconnected research components to develop an intelligent and interactive decision support framework that allows in silico exploration of extensive possible NPIs prior to the potential field implementation phase. First, the team will develop a novel spatial-temporal heterogeneous graph model to abstract dynamics of harnessed multi-source data. Second, the team will develop new techniques to learn node (i.e., area) representations over the constructed graph by integrating both spatial and temporal dependencies while preserving the heterogeneity. Third, based on the learned node representations, given a set of NPIs, the team will design and develop an innovative NPI-aware multi-head transformer for multi-task prediction (i.e., forecasting epidemic dynamics and associated socioeconomic impacts). Fourth, based on the predictions, the team will develop a novel multi-agent reinforcement learning model with inverse reward learning to enable expert-in-the-loop in finding optimal sequential NPIs that balance epidemiological benefits and socioeconomic costs under certain constraints and objectives set by policy and decision makers. The research will advance the field of information integration and informatics through the development of a series of original works including novel deep graph learning techniques with the context of heterogeneous and dynamic graph structures, which will also provide foundational work for addressing similar challenges for future natural or man-made disasters.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Smart and connected technologies have the potential to dramatically reshape the manufacturing landscape, affecting not only the way products are manufactured but also how the workforce is trained, providing new opportunities for employment and improving the economic viability and sustainability of communities where manufacturing companies reside. The Industrial Internet of Things (IIoT) infrastructure and integration of technologies from AI and Machine Learning is accelerating such changes, while capital investments to engage Small-to-Midsized-Manufacturers (SMMs) with IIoT and Industry 4.0 (I4.0) have remained a daunting task that impacts the economic viability of many communities. This project represents a collaborative effort among academic researchers in engineering, education, and management at two universities, educators in a local community college, community advocates and stakeholders, economic development partners, and local SMMs. The goal is to create a smart and connected community in Northeast Ohio by forging a strategic alliance between the community and SMMs that engage with IIoT technologies. Collectively, research and development efforts will be directed to transforming participating SMMs into “smart” and connected enterprises with enhanced adaptivity, competitiveness, and resilience to new supply chain dynamics. This approach will enhance the competitiveness of the local SMM and at the same time prepare a workforce to meet the challenges of the introduced technology. Such transformation will position the community for economic growth through a “smart and connected” local industrial base and upskilled workforce. The blueprint of this approach will also be transferrable to other communities.

The collaborative action plan consists of: (1) use-inspired innovative research, (2) education, workforce training and development, and (3) policy recommendations on community behavior and organizational development. Since effective transformation of manufacturing data to actionable information is key to successful implementation of I4.0 technologies, research on artificial intelligence (AI) for real-time operation monitoring and asset tracking will be conducted to improve quality control and productivity. Through process-embedded sensing, and edge- and cloud-computing, a customized learning and intervention platform will be created to facilitate digital transformation of the workforce from experience-based operation to data-guided optimization. Research on transfer learning will facilitate knowledge translation across machine equipment both within an SMM and to other SMMs across the organizational boundaries to create “spill-over” learning effects that promote the creation of a collaborative SMM network that assists each other in times of need and opportunity. Collaborations between academia and community developers will create pathways for curriculum development and internships that facilitate workforce training and employment opportunities. Ultimately, the collective action plan will promote research on both fundamental and practical problems, and benefit education, workforce development, and economic advancement in both Northeast Ohio and other regions across the country where SMMs play a critical role in the wealth generation and wellbeing of the community.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.