15 citations · 73 across the 24 of their papers we have counts for
24 papers
SEED-SET: Scalable Evolving Experimental Design for System-level Ethical Testing
Anjali Parashar, Yingke Li, Eric Yang Yu +4
As autonomous systems such as drones, become increasingly deployed in high-stakes, human-centric domains, it is critical to evaluate the ethical alignment since failure to do so im…
Distributed Koopman Learning using Partial Trajectories for Control
Wenjian Hao, Zehui Lu, Devesh Upadhyay +1
This paper proposes a distributed data-driven framework for dynamics learning, termed distributed deep Koopman learning using partial trajectories (DDKL-PT). In this framework, eac…
Deep Koopman-based Control of Quality Variation in Multistage Manufacturing Systems
Zhiyi Chen, Harshal Maske, Devesh Upadhyay +3
This paper presents a modeling-control synthesis to address the quality control challenges in multistage manufacturing systems (MMSs). A new feedforward control scheme is developed…
Deep Koopman Learning using Noisy Data
Wenjian Hao, Devesh Upadhyay, Shaoshuai Mou
This paper proposes a data-driven framework to learn a finite-dimensional approximation of a Koopman operator for approximating the state evolution of a dynamical system under nois…
Efficient Motion Planning for Manipulators with Control Barrier Function-Induced Neural Controller
Mingxin Yu, Chenning Yu, M-Mahdi Naddaf-Sh +3
Sampling-based motion planning methods for manipulators in crowded environments often suffer from expensive collision checking and high sampling complexity, which make them difficu…
C3D: Cascade Control with Change Point Detection and Deep Koopman Learning for Autonomous Surface Vehicles
Jianwen Li, Hyunsang Park, Wenjian Hao +10
In this paper, we discuss the development and deployment of a robust autonomous system capable of performing various tasks in the maritime domain under unknown dynamic conditions.…
Inherent Diverse Redundant Safety Mechanisms for AI-based Software Elements in Automotive Applications
Mandar Pitale, Alireza Abbaspour, Devesh Upadhyay
This paper explores the role and challenges of Artificial Intelligence (AI) algorithms, specifically AI-based software elements, in autonomous driving systems. These AI systems are…
Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selection
Liangqi Yuan, Dong-Jun Han, Su Wang +2
Multimodal federated learning (MFL) aims to enrich model training in FL settings where clients are collecting measurements across multiple modalities. However, key challenges to MF…
Stochastic Deep Koopman Model for Quality Propagation Analysis in Multistage Manufacturing Systems
Zhiyi Chen, Harshal Maske, Huanyi Shui +6
The modeling of multistage manufacturing systems (MMSs) has attracted increased attention from both academia and industry. Recent advancements in deep learning methods provide an o…
Targeted collapse regularized autoencoder for anomaly detection: black hole at the center
Amin Ghafourian, Huanyi Shui, Devesh Upadhyay +3
Autoencoders have been extensively used in the development of recent anomaly detection techniques. The premise of their application is based on the notion that after training the a…
Robust Multiview Multimodal Driver Monitoring System Using Masked Multi-Head Self-Attention
Yiming Ma, Victor Sanchez, Soodeh Nikan +3
Driver Monitoring Systems (DMSs) are crucial for safe hand-over actions in Level-2+ self-driving vehicles. State-of-the-art DMSs leverage multiple sensors mounted at different loca…
FIR-based Future Trajectory Prediction in Nighttime Autonomous Driving
Alireza Rahimpour, Navid Fallahinia, Devesh Upadhyay +1
The performance of the current collision avoidance systems in Autonomous Vehicles (AV) and Advanced Driver Assistance Systems (ADAS) can be drastically affected by low light and ad…
A Novel Two-level Causal Inference Framework for On-road Vehicle Quality Issues Diagnosis
Qian Wang, Huanyi Shui, Thi Tu Trinh Tran +4
In the automotive industry, the full cycle of managing in-use vehicle quality issues can take weeks to investigate. The process involves isolating root causes, defining and impleme…
Using simulation to quantify the performance of automotive perception systems
Zhenyi Liu, Devesh Shah, Alireza Rahimpour +3
The design and evaluation of complex systems can benefit from a software simulation - sometimes called a digital twin. The simulation can be used to characterize system performance…
Model Monitoring and Robustness of In-Use Machine Learning Models: Quantifying Data Distribution Shifts Using Population Stability Index
Aria Khademi, Michael Hopka, Devesh Upadhyay
Safety goes first. Meeting and maintaining industry safety standards for robustness of artificial intelligence (AI) and machine learning (ML) models require continuous monitoring f…
Quantum-Inspired Optimization over Permutation Groups
Rathi Munukur, Bhaskar Roy Bardhan, Devesh Upadhyay +1
Quantum-inspired optimization (QIO) algorithms are computational techniques that emulate certain quantum mechanical effects on a classical hardware to tackle a class of optimizatio…
A Survey on Evaluation Metrics for Synthetic Material Micro-Structure Images from Generative Models
Devesh Shah, Anirudh Suresh, Alemayehu Admasu +2
The evaluation of synthetic micro-structure images is an emerging problem as machine learning and materials science research have evolved together. Typical state of the art methods…
Real-Time Driver Monitoring Systems through Modality and View Analysis
Yiming Ma, Victor Sanchez, Soodeh Nikan +3
Driver distractions are known to be the dominant cause of road accidents. While monitoring systems can detect non-driving-related activities and facilitate reducing the risks, they…
Towards Accurate and Robust Classification in Continuously Transitioning Industrial Sprays with Mixup
Hongjiang Li, Huanyi Shui, Alemayehu Admasu +2
Image classification with deep neural networks has seen a surge of technological breakthroughs with promising applications in areas such as face recognition, medical imaging, and a…
Virtual Rings on Highways: Traffic Control by Connected Automated Vehicles
Tamas G. Molnar, Michael Hopka, Devesh Upadhyay +2
This work gives introduction to traffic control by connected automated vehicles. The influence of vehicle control on vehicular traffic and traffic control strategies are discussed…
BTranspose: Bottleneck Transformers for Human Pose Estimation with Self-Supervised Pre-Training
Kaushik Balakrishnan, Devesh Upadhyay
The task of 2D human pose estimation is challenging as the number of keypoints is typically large (~ 17) and this necessitates the use of robust neural network architectures and tr…
Stochastic Adversarial Koopman Model for Dynamical Systems
Kaushik Balakrishnan, Devesh Upadhyay
Dynamical systems are ubiquitous and are often modeled using a non-linear system of governing equations. Numerical solution procedures for many dynamical systems have existed for s…
NeuralPDE: Automating Physics-Informed Neural Networks (PINNs) with Error Approximations
Kirill Zubov, Zoe McCarthy, Yingbo Ma +11
Physics-informed neural networks (PINNs) are an increasingly powerful way to solve partial differential equations, generate digital twins, and create neural surrogates of physical…
Deep Adversarial Koopman Model for Reaction-Diffusion systems
Kaushik Balakrishnan, Devesh Upadhyay
Reaction-diffusion systems are ubiquitous in nature and in engineering applications, and are often modeled using a non-linear system of governing equations. While robust numerical…