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Articles 1 - 26 of 26
Full-Text Articles in Engineering
Optimizing Electric Vehicle Fleet Integration In Industrial Demand Response: Maximizing Vehicle-To-Grid Benefits While Compensating Vehicle Owners For Battery Degradation, Andre Leippi, Markus Fleschutz, Kevin Davis, Anna-Lena Klingler, Michael D. Murphy
Optimizing Electric Vehicle Fleet Integration In Industrial Demand Response: Maximizing Vehicle-To-Grid Benefits While Compensating Vehicle Owners For Battery Degradation, Andre Leippi, Markus Fleschutz, Kevin Davis, Anna-Lena Klingler, Michael D. Murphy
Publications
This paper addresses the integration of electric vehicle (EV) fleets into industrial smart grids to increase operational flexibility. It focuses on an extended multi-objective optimization problem that minimizes two primary objectives: (i) the electricity expenditure of a company using its employees’ EV batteries as temporary distributed energy storage, and (ii) the costs associated with the degradation of EV batteries, given the additional usage from the company’s perspective. In this paper, the utilization of an EV fleet is simulated at the individual car level over a one-year period. These optimization problems were balanced by using real-time electricity prices and the effective …
A Benchmark Knowledge Graph Of Driving Scenes For Knowledge Completion Tasks, Ruwan Wickramarachchi, Cory Henson, Amit Sheth
A Benchmark Knowledge Graph Of Driving Scenes For Knowledge Completion Tasks, Ruwan Wickramarachchi, Cory Henson, Amit Sheth
Publications
Knowledge graph completion (KGC) is a problem of significant importance due to the inherent incompleteness in knowledge graphs (KGs). The current approaches for KGC using link prediction (LP) mostly rely on a common set of benchmark datasets that are quite different from real-world industrial KGs. Therefore, the adaptability of current LP methods for real-world KGs and domain-specific ap- plications is questionable. To support the evaluation of current and future LP and KGC methods for industrial KGs, we introduce DSceneKG, a suite of real-world driving scene knowledge graphs that are currently being used across various industrial applications. The DSceneKG is publicly …
Visual Causal Question And Answering With Knowledge Graph Link Prediction, Utkarshani Jaimini, Cory Henson, Amit Sheth
Visual Causal Question And Answering With Knowledge Graph Link Prediction, Utkarshani Jaimini, Cory Henson, Amit Sheth
Publications
The ability to answer causal questions is important for any system that requires robust scene under- standing. In this demonstration, we develop a prototype system that leverages our causal link prediction framework, CausalLP. CausalLP framework uses a visual causal knowledge graph and associated knowledge graph embedding for two visual causal question and answering tasks- (i) causal explanation and (ii) causal prediction. In the live demonstration sessions, the participants will be invited to test the efficiency and effectiveness of the system for visual causal question and answering.
Causal Neuro-Symbolic Ai For Root Cause Analysis In Smart Manufacturing, Utkarshani Jaimini, Cory Henson, Amit Sheth
Causal Neuro-Symbolic Ai For Root Cause Analysis In Smart Manufacturing, Utkarshani Jaimini, Cory Henson, Amit Sheth
Publications
Root cause analysis is the process of investigating the cause of a failure and providing measures to prevent future failures. It is an active area of research due to the complexities in manufacturing production lines and the vast amount of data that requires manual inspection. We present a combined approach of causal neuro-symbolic AI for root cause analysis to identify failures in smart manufacturing production lines. We have used data from an industry-grade rocket assembly line and a simulation package to demonstrate the effectiveness and relevance of our approach.
Causal Knowledge Graph For Scene Understanding In Autonomous Driving, Utkarshani Jaimini, Cory Henson, Amit Sheth
Causal Knowledge Graph For Scene Understanding In Autonomous Driving, Utkarshani Jaimini, Cory Henson, Amit Sheth
Publications
The current approaches to autonomous driving focus on learning from observation or simulated data. These approaches are based on correlations rather than causation. For safety-critical applications, like autonomous driving, it’s important to represent causal dependencies among variables in addition to the domain knowledge expressed in a knowledge graph. This will allow for a better understanding of causation during scenarios that have not been observed, such as malfunctions or accidents. The causal knowledge graph, coupled with domain knowledge, demonstrates how autonomous driving scenes can be represented, learned, and explained using counterfactual and intervention reasoning to infer and understand the behavior of …
Ontology Design Metapattern For Relationtype Role Composition, Utkarshani Jaimini, Ruwan Wickramarachchi, Cory Henson, Amit Sheth
Ontology Design Metapattern For Relationtype Role Composition, Utkarshani Jaimini, Ruwan Wickramarachchi, Cory Henson, Amit Sheth
Publications
RelationType is a metapattern that specifies a property in a knowledge graph that directly links the head of a triple with the type of the tail. This metapattern is useful for knowledge graph link prediction tasks, specifically when one wants to predict the type of a linked entity rather than the entity instance itself. The RelationType metapattern serves as a template for future extensions of an ontology with more fine-grained domain information.
Healthcare Assistance Challenges-Driven Neurosymbolic Ai, Kaushik Roy
Healthcare Assistance Challenges-Driven Neurosymbolic Ai, Kaushik Roy
Publications
Although Artificial Intelligence technology has proven effective in providing healthcare assistance by analyzing health data, it still falls short in supporting decision-making. This deficiency largely stems from the predominance of opaque neural networks, particularly in mental health care AI applications, which raise concerns about their unpredictable and unverifiable nature. This skepticism hinders the transition from information support to decision support. This presentation will explore neurosymbolic approaches that combine neural networks with symbolic control and verification mechanisms. These approaches aim to unlock AI’s full potential by enhancing information analysis and decision-making support for healthcare assistance.
Cognitive Manufacturing: Definition And Current Trends, Fadi El Kalach, Ibrahim Yousif, Thorsten Wuest, Amit Sheth, Ramy Harik
Cognitive Manufacturing: Definition And Current Trends, Fadi El Kalach, Ibrahim Yousif, Thorsten Wuest, Amit Sheth, Ramy Harik
Publications
Manufacturing systems have recently witnessed a shift from the widely adopted automated systems seen throughout industry. The evolution of Industry 4.0 or Smart Manufacturing has led to the introduction of more autonomous systems focused on fault tolerant and customized production. These systems are required to utilize multimodal data such as machine status, sensory data, and domain knowledge for complex decision making processes. This level of intelligence can allow manufacturing systems to keep up with the ever-changing markets and intricate supply chain. Current manufacturing lines lack these capabilities and fall short of utilizing all generated data. This paper delves into the …
Farm Electricity System Simulator (Fess): A Platform For Simulating Electricity Utilisation On Dairy Farms, F. Buckley, J. Upton, R. Prendergast, L. Shalloo, Michael D. Murphy
Farm Electricity System Simulator (Fess): A Platform For Simulating Electricity Utilisation On Dairy Farms, F. Buckley, J. Upton, R. Prendergast, L. Shalloo, Michael D. Murphy
Publications
The objective of this paper was to define, validate and demonstrate a model capable of accurately simulating dairy farm electricity consumption across varying herd and parlour sizes, to facilitate research investigating renewable energy systems (RES) and demand side management (DSM). The Farm Electricity System Simulator (FESS) was developed using grey-box modelling techniques utilizing empirical data for parameter tuning. Empirical data were gathered from nine spring calving, pasture based dairy farms located in the Republic of Ireland. A k-means clustering analysis was conducted, separating the farms into three, near homogenous groups, from which representative farms were selected. FESS was trained using …
Optimal Environmental And Economic Performance Trade-Offs For Fifth Generation District Heating And Cooling Network Topologies With Waste Heat Recovery, Michael D. Murphy
Optimal Environmental And Economic Performance Trade-Offs For Fifth Generation District Heating And Cooling Network Topologies With Waste Heat Recovery, Michael D. Murphy
Publications
Network topology greatly influences both the economic and environmental performance of fifth generation district heating and cooling (5GDHC) systems. In this study the optimal trade-offs between the environmental and economic performance of 5GDHC network topologies for a five-building district with waste heat recovery were explored. A life cycle assessment method was used to calculate the total life cycle CO2 emissions (LCCO2) associated with the installation and operation of various network topologies. Twelve months of empirical data from a data center cooling system were analyzed to assess its suitability for integration into a 5GDHC system. The most suitable method for utilizing …
Review Of Methodological Decisions In Life Cycle Assessment (Lca) Of Biorefinery Systems Across Feedstock Categories, James Gaffey, Maurice N. Collins, David Styles
Review Of Methodological Decisions In Life Cycle Assessment (Lca) Of Biorefinery Systems Across Feedstock Categories, James Gaffey, Maurice N. Collins, David Styles
Publications
The application of life cycle assessment (LCA) to biorefineries is a necessary step to estimate their environmental sustainability. This review explores contemporary LCA biorefinery studies, across different feedstock categories, to understand approaches in dealing with key methodological decisions which arise, including system boundaries, consequential or attributional approach, allocation, inventory data, land use changes, product end-of-life (EOL), biogenic carbon storage, impact assessment and use of uncertainty analysis. From an initial collection of 81 studies, 59 were included within the final analysis, comprising 22 studies which involved dedicated feedstocks, 34 which involved residue feedstocks (including by-products and wastes), and a further 3 …
Farmer Perceptions Of Land Cover Classification Of Uas Imagery Of Coffee Agroecosystems In Puerto Rico, Jose Cabrera, Blake Neal, Kevin Adkins, Ronny Schroeder, Gwendolyn Klenke, Shannon Brines, Nayethzi Hernandez, Kevin Li, Riley Glancy, Ivette Perfecto
Farmer Perceptions Of Land Cover Classification Of Uas Imagery Of Coffee Agroecosystems In Puerto Rico, Jose Cabrera, Blake Neal, Kevin Adkins, Ronny Schroeder, Gwendolyn Klenke, Shannon Brines, Nayethzi Hernandez, Kevin Li, Riley Glancy, Ivette Perfecto
Publications
Highly diverse agroecosystems are increasingly of interest as the realization of farms’ invaluable ecosystem services grows. Simultaneously there has been an increased use of uncrewed aerial systems (UAS) in remote sensing as drones offer a finer spatial resolution and faster revisit rate than traditional satellites. With the combined utility of UAS and the attention on agroecosystems, there exists an opportunity to assess UAS practicality in highly biodiverse settings. In this study, we utilized UAS to collect fine-resolution 10-band multispectral imagery of coffee agroecosystems in Puerto Rico. We created land cover maps through a pixel-based supervised classification of each farm and …
Human Factors In Aviation Maintenance: Understanding Errors, Management, And Technological Trends, Rajee Olaganathan
Human Factors In Aviation Maintenance: Understanding Errors, Management, And Technological Trends, Rajee Olaganathan
Publications
Aircraft maintenance and inspection are complex systems that work on a time-based schedule and require teamwork of different professionals to maintain the airworthiness of aircraft. Errors in maintenance and inspection processes cause in-flight engine shutdowns, flight delays, flight cancellation, sometimes resulting in accidents and incidents that cause significant economic consequences. Due to the substantial impact on both safety and financial aspects of an air carrier, this paper focuses on hangar maintenance as the work is carried out across several shifts by different technicians, addressing various human factor issues that contribute to errors. The paper will also briefly discuss shift work …
Commentary: A Patient-Specific Lower Extremity Biomechanical Analysis Of A Knee Orthotic During A Deep Squat Movement, Christine Dailey Walck
Commentary: A Patient-Specific Lower Extremity Biomechanical Analysis Of A Knee Orthotic During A Deep Squat Movement, Christine Dailey Walck
Publications
The recent study by Walck et al., titled “A Patient-Specific Lower Extremity Biomechanical Analysis of a Knee Orthotic during a Deep Squat Movement,” provides a novel insight into the biomechanical impacts of knee orthotics, particularly the non-linear spring-loaded (NLSL) knee joint orthosis (KJO)1 . This commentary aims to delve into the implications, interpretations, and evaluations of the findings, contextualizing them within the broader discourse on knee orthotics.
Immersive Framework For Designing Trajectories Using Augmented Reality, Joseph Anderson, Leo Materne, Karis Cooks, Michelle Aros, Jaia Huggins, Jesika Geliga-Torres, Kamden Kuykendall, David Canales, Barbara Chaparro
Immersive Framework For Designing Trajectories Using Augmented Reality, Joseph Anderson, Leo Materne, Karis Cooks, Michelle Aros, Jaia Huggins, Jesika Geliga-Torres, Kamden Kuykendall, David Canales, Barbara Chaparro
Publications
The intuitive interaction capabilities of augmented reality make it ideal for solving complex 3D problems that require complex spatial representations, which is key for astrodynamics and space mission planning. By implementing common and complex orbital mechanics algorithms in augmented reality, a hands-on method for designing orbit solutions and spacecraft missions is created. This effort explores the aforementioned implementation with the Microsoft Hololens 2 as well as its applications in industry and academia. Furthermore, a human-centered design process and study are utilized to ensure the tool is user-friendly while maintaining accuracy and applicability to higher-fidelity problems.
Experimental Environmental Profiles And Sloshing Dynamics Aboard Zero-G Aircraft, Pedro J. Llanos, Sathya Gangadharan, Kevin Crosby
Experimental Environmental Profiles And Sloshing Dynamics Aboard Zero-G Aircraft, Pedro J. Llanos, Sathya Gangadharan, Kevin Crosby
Publications
This study presents the results of a parabolic flight experiment to study the sloshing dynamics of the magneto-active propellant management device experiment. This device utilizes a magnetoactive membrane and magnets located external to the tank to effectively damp the liquid free surface motion. This research work establishes a benchmark with sloshing analytical formulation and sensor calibration methods that can be used to characterize future research parabolic flights while providing important environmental profiles measured during flight, such as accelerations, pitch angle, velocity, temperature, total volatile content, carbon dioxide, relative humidity, magnetic field, and radiation. Correlation between these flight variables and the …
Experimental Analysis Of The Integrated High-Lift Propulsor, Robert W. Deters, Byron Ward, Shreyas Narsipur
Experimental Analysis Of The Integrated High-Lift Propulsor, Robert W. Deters, Byron Ward, Shreyas Narsipur
Publications
Wind tunnel testing was conducted to evaluate the performance of the Integrated High Lift Propulsor (IHLP), a novel Distributed Electric Propulsion (DEP) system. The IHLP integrates traditional Krueger flap/slat elements with a Distributed Electric Propulsion design, enhancing high lift performance and cruise efficiency compared to conventional pylon-mounted DEP configurations. Starting from a baseline configuration determined from pretest Computational Fluid Dynamics (CFD) analyses, a parametric study was performed to determine the influence on the aerodynamic characteristics (���� , ����, and ����). The study involved variations in flap settings, slat angles, overlap, propeller tilt, and propeller position. The impact of Reynolds number, …
On Progress In Exploring Controlled Viscous Limit-Cycle Oscillations In Modified Glauert Airfoil, Ethan Deweese, Lap Nguyen, Erik Vataker, William Mackunis, Vladimir Golubev, Ron Efrati, Oksana Stalnov
On Progress In Exploring Controlled Viscous Limit-Cycle Oscillations In Modified Glauert Airfoil, Ethan Deweese, Lap Nguyen, Erik Vataker, William Mackunis, Vladimir Golubev, Ron Efrati, Oksana Stalnov
Publications
The paper reports on the progress in the development of a novel robust, nonlinear flow control technology that employs an array of synthetic-jet actuators (SJAs) embedded in 2-DOF, elastically mounted, optimized Modified Glauert (MG) airfoil design in order to control limit cycle oscillations (LCO) at low subsonic flow regimes. The focus here is on the conceptual design of the wind energy harvesting system that employs, e.g., a piezoelectric device to extract energy from plunging LCO, with the closed-loop controller being capable to sustain the required LCO amplitudes over a wide range of wind speeds. The current high-fidelity studies first include …
K-Perm: Personalized Response Generation Using Dynamic Knowledge Retrieval And Persona-Adaptive Queries, Kanak Raj, Kaushik Roy, Vamshi Bonagiri, Priyanshul Govil, Krishnaprasad Thirunarayan, Raxit Goswami, Manas Gaur
K-Perm: Personalized Response Generation Using Dynamic Knowledge Retrieval And Persona-Adaptive Queries, Kanak Raj, Kaushik Roy, Vamshi Bonagiri, Priyanshul Govil, Krishnaprasad Thirunarayan, Raxit Goswami, Manas Gaur
Publications
Personalizing conversational agents can enhance the quality of conversations and increase user engagement. However, they often lack external knowledge to tend to a user’s persona appropriately. This is particularly crucial for practical applications like mental health support, nutrition planning, culturally sensitive conversations, or reducing toxic behavior in conversational agents. To enhance the relevance and comprehensiveness of personalized responses, we propose using a two-step approach that involves (1) selectively integrating user personas and (2) contextualizing the response with supplementing information from a background knowledge source. We develop K-PERM (Knowledge-guided PErsonalization with Reward Modulation), a dynamic conversational agent that combines these elements. …
Causal Event Graph-Guided Language-Based Spatiotemporal Question Answering, Kaushik Roy, Alessandro Oltramari, Yuxin Zi, Chathurangi Shyalika, Vignesh Narayanan, Amit Sheth
Causal Event Graph-Guided Language-Based Spatiotemporal Question Answering, Kaushik Roy, Alessandro Oltramari, Yuxin Zi, Chathurangi Shyalika, Vignesh Narayanan, Amit Sheth
Publications
Large Language Models have excelled at encoding and leveraging language patterns in large text-based corpora for various tasks, including spatiotemporal event-based question answering (QA). However, due to encoding a text-based projection of the world, they have also been shown to lack a fullbodied understanding of such events, e.g., a sense of intuitive physics, and cause-and-effect relationships among events. In this work, we propose using causal event graphs (CEGs) to enhance language understanding of spatiotemporal events in language models, using a novel approach that also provides proofs for the model’s capture of the CEGs. A CEG consists of events denoted by …
Tutorial: Knowledge-Infused Artificial Intelligence For Mental Healthcare, Kaushik Roy
Tutorial: Knowledge-Infused Artificial Intelligence For Mental Healthcare, Kaushik Roy
Publications
Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to platform for supporting patients seeking MHCare. The creation of peer-support groups without social stigma has resulted in patients transitioning from clinical settings to SocMedia supported interactions for quick help. Researchers started exploring SocMedia content in search of cues that showcase correlation or causation between different MH conditions to design better interventional strategies. User-level Classification-based AI systems were designed to leverage diverse SocMedia data from various MH conditions, …
Ontolog Summit 2024 Talk Report: Healthcare Assistance Challenges-Driven Neurosymbolic Ai, Kaushik Roy
Ontolog Summit 2024 Talk Report: Healthcare Assistance Challenges-Driven Neurosymbolic Ai, Kaushik Roy
Publications
Although Artificial Intelligence technology has proven effective in providing healthcare assistance by analyzing health data, it still falls short in supporting decision-making. This deficiency largely stems from the predominance of opaque neural networks, particularly in mental health care AI applications, which raise concerns about their unpredictable and unverifiable nature. This skepticism hinders the transition from information support to decision support. This presentation will explore neurosymbolic approaches that combine neural networks with symbolic control and verification mechanisms. These approaches aim to unlock AI’s full potential by enhancing information analysis and decision-making support for healthcare assistance1.
Neurosymbolic Customized And Compact Copilots, Kaushik Roy, Megha Chakraborty, Yuxin Zi, Manas Gaur, Amit Sheth
Neurosymbolic Customized And Compact Copilots, Kaushik Roy, Megha Chakraborty, Yuxin Zi, Manas Gaur, Amit Sheth
Publications
Large Language Models (LLMs) are credible with open-domain interactions such as question answering, summarization, and explanation generation [1]. LLM reasoning is based on parametrized knowledge, and as a consequence, the models often produce absurdities and inconsistencies in outputs (e.g., hallucinations and confirmation biases) [2]. In essence, they are fundamentally hard to control to prevent off-the-rails behaviors, are hard to fine-tune, customize for tailored needs, prompt effectively (due to the “tug-of-war” between external and parametric memory), and extremely resource-hungry due to the enormous size of their extensive parametric configurations [3,4]. Thus, significant challenges arise when these models are required to perform …
Towards Pragmatic Temporal Alignment In Stateful Generative Ai Systems: A Configurable Approach, Kaushik Roy, Yuxn Zi, Amit Sheth
Towards Pragmatic Temporal Alignment In Stateful Generative Ai Systems: A Configurable Approach, Kaushik Roy, Yuxn Zi, Amit Sheth
Publications
Temporal alignment in stateful generative artificial intelligence (AI) systems remains an underexplored area, particularly beyond goal-driven approaches in planning. Stateful refers to maintaining a persistent memory or “state” across runs or sessions. This helps with referencing past information to make system outputs more contextual and relevant. This position paper proposes a framework for temporal alignment with several configurable toggles. We present four alignment mechanisms: knowledge graph path-based, neural score-based, vector similarity-based, and sequential process-guided alignment. By offering these interchangeable approaches, we aim to provide a flexible solution adaptable to complex and real-world applications. This paper discusses the potential benefits and …
Proknow: Process Knowledge For Safety Constrained And Explainable Question Generation For Mental Health Diagnostic Assistance In The Age Of Large Language Models, Kaushik Roy, Manas Gaur, Misagh Soltani, Vipula Rawte, Ashwin Allen, Amit P. Sheth
Proknow: Process Knowledge For Safety Constrained And Explainable Question Generation For Mental Health Diagnostic Assistance In The Age Of Large Language Models, Kaushik Roy, Manas Gaur, Misagh Soltani, Vipula Rawte, Ashwin Allen, Amit P. Sheth
Publications
Current Virtual Mental Health Assistants (VMHAs) primarily offer counseling and suggestive care but do not assist with patient diagnosis due to their lack of training in safety-constrained and specialized clinical process knowledge, referred to as ProKnow. In this work, we define ProKnow as an ordered set of information aligned with evidence-based guidelines or categories of conceptual understanding used by domain experts. We also introduce a new dataset of diagnostic conversations guided by safety constraints and Pro- Know, known as ProKnow-data. We develop a method for natural language question generation (NLG) designed to interactively gather diagnostic information from patients, termed ProKnow-algo. …
Exploring Alternative Approaches To Language Modeling For Learning From Data And Knowledge, Yuxin Zi, Kaushik Roy, Vignesh Narayanan, Amit Sheth
Exploring Alternative Approaches To Language Modeling For Learning From Data And Knowledge, Yuxin Zi, Kaushik Roy, Vignesh Narayanan, Amit Sheth
Publications
Despite their wide applications to language understanding tasks, large language models (LLMs) still face challenges such as hallucinations - the occasional fabrication of information, and alignment issues - the lack of associations with human-curated world models (e.g., intuitive physics or common-sense knowledge). Additionally, the black-box nature of LLMs makes it highly challenging to train them meaningfully in order to achieve a desired behavior. Specifically, the attempt to adjust LLMs’ concept embedding spaces can be highly intractable, which involves analyzing the implicit impact on LLMs’ numerous parameters and the resulting inductive biases. This paper proposes a novel architecture that wraps powerful …