Search Results for author:
Found 14 papers, 6 papers with code
Data-Driven Ergonomic Risk Assessment of Complex Hand-intensive Manufacturing Processes
We develop a data-driven ergonomic risk assessment system with a special focus on hand and finger activity to better identify and address ergonomic issues related to hand-intensive manufacturing processes.
Human-Inspired Topological Representations for Visual Object Recognition in Unseen Environments
Visual object recognition in unseen and cluttered indoor environments is a challenging problem for mobile robots.
A Distance Correlation-Based Approach to Characterize the Effectiveness of Recurrent Neural Networks for Time Series Forecasting
We empirically show that the RNN activation layers learn the lag structures of time series well.
Persistent Homology Meets Object Unity: Object Recognition in Clutter
Recognition of occluded objects in unseen and unstructured indoor environments is a challenging problem for mobile robots.
F2BEV: Bird's Eye View Generation from Surround-View Fisheye Camera Images for Automated Driving
We take the first step in addressing this challenge and introduce a baseline, F2BEV, to generate discretized BEV height maps and BEV semantic segmentation maps from fisheye images.
Topologically Persistent Features-based Object Recognition in Cluttered Indoor Environments
Recognition of occluded objects in unseen indoor environments is a challenging problem for mobile robots.
Visual Object Recognition in Indoor Environments Using Topologically Persistent Features
For performance evaluation, in addition to a widely used shape dataset and a benchmark indoor scenes dataset, we collect a new dataset, comprising scene images from two different environments, namely, a living room and a mock warehouse.
A Multi-Task Learning Approach for Human Activity Segmentation and Ergonomics Risk Assessment
We propose a new approach to Human Activity Evaluation (HAE) in long videos using graph-based multi-task modeling.
Deep Learning-Based Semantic Segmentation of Microscale Objects
Accurate estimation of the positions and shapes of microscale objects is crucial for automated imaging-guided manipulation using a non-contact technique such as optical tweezers.
Toward Ergonomic Risk Prediction via Segmentation of Indoor Object Manipulation Actions Using Spatiotemporal Convolutional Networks
Automated real-time prediction of the ergonomic risks of manipulating objects is a key unsolved challenge in developing effective human-robot collaboration systems for logistics and manufacturing applications.
A Hierarchical Bayesian Linear Regression Model with Local Features for Stochastic Dynamics Approximation
One of the challenges in model-based control of stochastic dynamical systems is that the state transition dynamics are involved, and it is not easy or efficient to make good-quality predictions of the states.
Predicting shim gaps in aircraft assembly with machine learning and sparse sensing
This new approach is based on the assumption that patterns exist in shim distributions across aircraft, which may be mined and used to reduce the burden of data collection and processing in future aircraft.
A Note on Community Trees in Networks
We introduce the concept of community trees that summarizes topological structures within a network.
Sparse-TDA: Sparse Realization of Topological Data Analysis for Multi-Way Classification
Topological data analysis (TDA) has emerged as one of the most promising techniques to reconstruct the unknown shapes of high-dimensional spaces from observed data samples.
