Engineering topics

Omitaomu, Olufemi A.

Publications and source records attributed to Omitaomu, Olufemi A..

9 records

DOE OSTI2024

Towards Next-Generation Urban Decision Support Systems through AI-Powered Construction of Scientific Ontology Using Large Language Models—A Case in Optimizing Intermodal Freight Transportation

The incorporation of Artificial Intelligence (AI) models into various optimization systems is on the rise. However, addressing complex urban and environmental management challenges often demands deep expertise in domain science and informatics. This expertise is essential for deriving data and simulation-driven insights that support informed decision-making. In this context, we investigate the potential of leveraging the pre-trained Large Language Models (LLMs) to create knowledge representations for supporting operations research. By adopting ChatGPT-4 API as the reasoning core, we outline an applied workflow that encompasses natural language processing, Methontology-based prompt tuning, and Generative Pre-trained Transformer (GPT), to automate the construction of scenario-based ontologies using existing research articles and technical manuals of urban datasets and simulations. From these ontologies, knowledge graphs can be derived using widely adopted formats and protocols, guiding various tasks towards data-informed decision support. The performance of our methodology is evaluated through a comparative analysis that contrasts our AI-generated ontology with the widely recognized pizza ontology, commonly used in tutorials for popular ontology software. We conclude with a real-world case study on optimizing the complex system of multi-modal freight transportation. Our approach advances urban decision support systems by enhancing data and metadata modeling, improving data integration and simulation coupling, and guiding the development of decision support strategies and essential software components.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2024

Multicriteria screening evaluation of geothermal resources on mine lands for direct use heating

Abstract Direct use of geothermal energy is the oldest and most versatile form of utilizing geothermal energy. In the last decade, this utilization has significantly increased, especially with the installation of geothermal (ground-source) heat pumps. Many current and inactive mine land sites across the U.S. could be redeveloped with clean energy technologies such as direct use geothermal, which would revitalize former mining communities, help with reducing greenhouse gas emissions, and accelerate the transition to a clean energy economy. We present a multicriteria screening framework to evaluate various aspects of direct-use geothermal projects on mine lands. The criteria are divided into three categories: (1) technical potential, (2) demand and benefits, and (3) regulatory and permitting. We demonstrate the framework using publicly available data on a national scale (continental U.S.). Then, using an example of abandoned coal mines in Illinois and focusing on resource potential, we illustrate how this evaluation can be applied at the state or more local scales when a region’s characteristics drive spatial variability estimates. The strength of this approach is the ability to combine seemingly disparate parameters and inputs from numerous sources. The framework is very flexible—additional criteria can be easily incorporated and weights modified if input data support them. Vice versa, the framework can also help identify additional data needed for evaluating those criteria. The multicriteria screening evaluation methodology provides a framework for identifying potential candidates for detailed site evaluation and characterization.

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

EHR-BERT: A BERT-based model for effective anomaly detection in electronic health records

Objective: Physicians and clinicians rely on data contained in electronic health records (EHRs), as recorded by health information technology (HIT), to make informed decisions about their patients. The reliability of HIT systems in this regard is critical to patient safety. Consequently, better tools are needed to monitor the performance of HIT systems for potential hazards that could compromise the collected EHRs, which in turn could affect patient safety. In this paper, we propose a new framework for detecting anomalies in EHRs using sequence of clinical events. This new framework, EHR-Bidirectional Encoder Representations from Transformers (BERT), is motivated by the gaps in the existing deep-learning related methods, including high false negatives, sub-optimal accuracy, higher computational cost, and the risk of information loss. EHR-BERT is an innovative framework rooted in the BERT architecture, meticulously tailored to navigate the hurdles in the contemporary BERT method; thus, enhancing anomaly detection in EHRs for healthcare applications.Methods: The EHR-BERT framework was designed using the Sequential Masked Token Prediction (SMTP) method. This approach treats EHRs as natural language sentences and iteratively masks input tokens during both training and prediction stages. This method facilitates the learning of EHR sequence patterns in both directions for each event and identifies anomalies based on deviations from the normal execution models trained on EHR sequences.Results: Extensive experiments on large EHR datasets across various medical domains demonstrate that EHR-BERT markedly improves upon existing models. It significantly reduces the number of false positives and enhances the detection rate, thus bolstering the reliability of anomaly detection in electronic health records. This improvement is attributed to the model’s ability to minimize information loss and maximize data utilization effectively.Conclusion: EHR-BERT showcases immense potential in decreasing medical errors related to anomalous clinical events, positioning itself as an indispensable asset for enhancing patient safety and the overall standard of healthcare services. The framework effectively overcomes the drawbacks of earlier models, making it a promising solution for healthcare professionals to ensure the reliability and quality of health data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2022

Detecting anomalous sequences in electronic health records using higher-order tensor networks

Detecting anomalous sequences is an integral part of building and protecting modern large-scale health information technology (HIT) systems. These HIT systems generate a large volume of records of patients’ state and significant events, which provide a valuable resource to help improve clinical decisions, patient care processes, and other issues. However, detecting anomalous sequences in electronic health records (EHR) remains a challenge in healthcare applications for several reasons, including imbalances in the data, complexity of relationships between events in the sequence, and the curse of dimensionality. Conventional anomaly detection methods use the finite sequence of events to discriminate sequences. They fail to incorporate salient event details under variable higher-order dependencies (e.g., duration between events) that can provide better discrimination of sequences in their models. To address this problem, we propose event sequence and subsequence anomaly detection algorithms that (1) use network-based representations of interactions in the data, (2) account for variable higher-order dependencies in the data, and (3) incorporate events duration for adequate discrimination of the data. The proposed approach identifies anomalies by monitoring the change in the graph after the test sequence is removed from the network. The change is quantified using graph distance metrics so that dramatic changes in the network can be attributed to the removed sequence. Furthermore, the proposed subsequence algorithm recommends plausible paths and salient information for the detected anomalous subsequences. Our results show that the proposed event sequence anomaly detection algorithm outperforms the baseline methods for both synthetic data and real-world EHR data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2022

Methods and system for siting advanced nuclear reactors and evaluating energy policy concerns

There is a growing sociopolitical desire to develop cleaner energy sources in the United States and maintain energy security. Regardless of politics, many coal-fired electric plants have already been shut down and many utilities are vowing to retire their current coal-fired assets within the next two decades. Replacement power assets require consideration of appropriate siting. A geographic information system (GIS)-based multicriteria decision analysis approach is useful to assist utility and energy companies, as well as policymakers, to evaluate potential areas for siting new plants in the contiguous United States. A GIS-based framework is simply a database of location information that allows for mapping, querying, modeling, and analyzing data based on location. The spatial output can be structured to be visual, allowing for easier analysis of location data. The need to site additional power assets, including renewable resources and clean power sources, such as nuclear, led to the development of the Oak Ridge Siting Analysis for power Generation Expansion (OR-SAGE) tool discussed in this paper. The tool takes inputs such as population growth, water availability, environmental indicators, and tectonic and geological hazards to provide an in-depth visual analysis for siting options. Energy companies and other stakeholders can use OR-SAGE to procure feedback quickly and effectively on land suitability based on technology specific inputs. Policymakers can use OR-SAGE to analyze the impacts of future energy technology decisions, while balancing competing resource use. Overall, this paper discusses the recent use of OR-SAGE for these purposes and plans for future development.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2022

RePOWERD: Restoration of Power Outage from Wide-area Severe Weather Disruptions

The purpose of the RePOWERD project is to develop a probabilistic and a simulation based power restoration forecasting model for tropical storms based on geographic utility service areas, impacts to energy infrastructures and transportation networks, time-varying customer outage number, crew information (i.e., crew size, crew staging requirements), and utility restoration plans. Energy infrastructure is a critical lifeline essential for the United States’ national and economic security. Like other critical infrastructures, this infrastructure is aging and is at a high risk of damages from extreme weather events. Based on the number of reported outages in EAGLE-I during 2020 and 2021, it is evident that the current energy infrastructure is not equipped to handle extreme event impacts and will contribute to significant outages. From a resilience perspective, it is essential to forecast restoration time in the event of a severe weather induced power outage to assist the US Department of Energy, emergency managers, and first responders with resource planning. This project contributes to this crucial need. This report details the models, that were developed in this pilot study, to determine the rate of restoration and estimated time of restoration (ETR) during tropical storm events within counties and utility service areas based on damage to energy infrastructures and total number of customers experiencing outages.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Spatial–Temporal Patterns of Historical, Near-Term, and Projected Drought in the Conterminous United States

Major droughts in the United States have heavily impacted the hydrologic system, negatively effecting energy and food production. Improved understanding of historical drought is critical for accurate forecasts. Data from global climate models (GCMs), commonly used to assess drought, cannot effectively evaluate local patterns because of their low spatial scale. This research leverages downscaled (~4 km grid spacing) temperature and precipitation estimates from nine GCMs’ data under the business-as-usual scenario (Representative Concentration Pathway 8.5) to examine drought patterns. Drought severity is estimated using the Palmer Drought Severity Index (PDSI) with the Thornthwaite evapotranspiration method. The specific objectives were (1) To reproduce historical (1966–2005) drought and calculate near-term to future (2011–2050) drought patterns over the conterminous USA. (2) To uncover the local variability of spatial drought patterns in California between 2012 and 2018 using a network-based approach. Our estimates of land proportions affected by drought agree with the known historical drought events of the mid-1960s, late 1970s to early 1980s, early 2000s, and between 2012 and 2015. Network analysis showed heterogeneity in spatial drought patterns in California, indicating local variability of drought occurrence. The high spatial scale at which the analysis was performed allowed us to uncover significant local differences in drought patterns. This is critical for highlighting possible weak systems that could inform adaptation strategies such as in the energy and agricultural sectors.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Artificial Intelligence Techniques in Smart Grid: A Survey

The smart grid is enabling the collection of massive amounts of high-dimensional and multi-type data about the electric power grid operations, by integrating advanced metering infrastructure, control technologies, and communication technologies. However, the traditional modeling, optimization, and control technologies have many limitations in processing the data; thus, the applications of artificial intelligence (AI) techniques in the smart grid are becoming more apparent. This survey presents a structured review of the existing research into some common AI techniques applied to load forecasting, power grid stability assessment, faults detection, and security problems in the smart grid and power systems. It also provides further research challenges for applying AI technologies to realize truly smart grid systems. Finally, this survey presents opportunities of applying AI to smart grid problems. The paper concludes that the applications of AI techniques can enhance and improve the reliability and resilience of smart grid systems.

energy systems↗

DOE OSTI2020

Planning green infrastructure placement based on projected precipitation data

Continued urbanization has led to tremendous changes on the landscape. These changes have exacerbated the effects of extreme climatic events such as flooding because of constrained water infiltration and increased surface flow. Typical runoff control measures involve sophisticated gray infrastructure that guide excess surface flow into storage and disposal sites. In a dynamic climate system, these measures are not sustainable since they cannot be easily modified to accommodate large volumes of runoff. Green Infrastructure (GI) is an adaptable technique that can be used to minimize runoff, in addition to offering an array of additional benefits (urban heat regulation, aesthetics, improved air quality etc.). Strategic placement of GI is key to achieving maximum utility. While physical site characteristics play a major role in determining suitable GI placement sites, knowledge of future precipitation patterns is crucial to ensure successful flood mitigation. Here, suitable GI sites within the city of Knoxville, Tennessee, were determined based on potential impact of an extreme flood event as indicated by site characteristics. Then, the relative potential likelihood of a flood event was determined based on projected precipitation data and knowledge of existing flood zones. By combining potential impact with likelihood information, low, medium, and high priority GI implementation sites were established. Results indicate that high priority sites are in the central parts of the city with priority decreasing outward. The GI prioritization scheme presented here, offers valuable guidance to city planners and policy makers who wish to exploit the GI approach for flood mitigation.

54 ENVIRONMENTAL SCIENCES↗