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1. Development of a real-time solver for the local eigenvalue modification procedure

   https://doi.org/10.1117/12.2613208  

   Ogunniyi, Emmanuel; Downey, Austin R.; Bakos, Jason (April 2022, SPIE Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2022)

   Zonta, Daniele; Su, Zhongqing; Glisic, Branko (Ed.)

   Real-time model updating for active structures experiencing high-rate dynamic events such as; hypersonic vehicles, active blast mitigation, and ballistic packages require that continuous changes in the structure’s state be updated on a timescale of 1 ms or less. This requires the development of real-time model updating techniques capable of tracking the structure’s state. The Local Eigenvalue Modification Procedure (LEMP) is a structural dynamic modification procedure that converts the computationally intensive global eigenvalue problem used in modal analysis into a set of second-order equations that are more readily handled. Implementation of LEMP for tracking a structure’s state results in secular equations that must be solved to obtain the modified eigenvalues of the structure’s state. In this work, the roots of the secular equations are solved iteratively using a divide and conquer approach, leading to faster root convergence. The present study reports on developing a real-time computing module to perform LEMP in the context of real-time model updating with a stringent timing constraint of 1 ms or less. In this preliminary work, LEMP is applied to tracking the condition of a numerical cantilever beam structure, which depicts changes in a structure’s state as a change in the roller position. A discussion of variations in timing results and accuracy are discussed. 

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2. Adaptive data collection for robust learning across multiple distributions

   Zang, Chengbo; Turkcan, Mehme; Zussman, Gil; Kostic, Zoran; Ghaderi, Javad (May 2025, in Proc. ICML’25, 2025)

   We propose a framework for adaptive data collection aimed at robust learning in multi-distribution scenarios under a fixed data collection budget. In each round, the algorithm selects a distribution source to sample from for data collection and updates the model parameters accordingly. The objective is to find the model parameters that minimize the expected loss across all the data sources. Our approach integrates upper-confidence-bound (UCB) sampling with online gradient descent (OGD) to dynamically collect and annotate data from multiple sources. By bridging online optimization and multi-armed bandits, we provide theoretical guarantees for our UCB-OGD approach, demonstrating that it achieves a minimax regret of O(T 1 2 (K ln T) 1 2 ) over K data sources after T rounds. We further provide a lower bound showing that the result is optimal up to a ln T factor. Extensive evaluations on standard datasets and a real-world testbed for object detection in smartcity intersections validate the consistent performance improvements of our method compared to baselines such as random sampling and various active learning methods. 

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3. Collaborative Semantic Data Fusion with Dynamically Observable Decision Processes

   Burks, L.; Ahmed, N. (February 2020, 2019 22th International Conference on Information Fusion (FUSION))

   null (Ed.)

   This work presents novel techniques for tightly integrated online information fusion and planning in human-autonomy teams operating in partially known environments. Motivated by dynamic target search problems, we present a new map-based sketch interface for online soft-hard data fusion. This interface lets human collaborators efficiently update map information and continuously build their own highly flexible ad hoc dictionaries for making language-based semantic observations, which can be actively exploited by autonomous agents in optimal search and information gathering problems. We formally link these capabilities to POMDP algorithms for optimal planning under uncertainty, and develop a new Dynamically Observable Monte Carlo planning (DOMCP) algorithm as an efficient means for updating online sampling-based planning policies for POMDPs with non-static observation models. DOMCP is validated on a small scale robot localization problem, and then demonstrated with our new user interface on a simulated dynamic target search scenario in a partially known outdoor environment. 

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4. Multi-Time Resolution Ensemble LSTMs for Enhanced Feature Extraction in High-Rate Time Series

   https://doi.org/10.3390/s21061954  

   Barzegar, Vahid; Laflamme, Simon; Hu, Chao; Dodson, Jacob (March 2021, Sensors)

   null (Ed.)

   Systems experiencing high-rate dynamic events, termed high-rate systems, typically undergo accelerations of amplitudes higher than 100 g-force in less than 10 ms. Examples include adaptive airbag deployment systems, hypersonic vehicles, and active blast mitigation systems. Given their critical functions, accurate and fast modeling tools are necessary for ensuring the target performance. However, the unique characteristics of these systems, which consist of (1) large uncertainties in the external loads, (2) high levels of non-stationarities and heavy disturbances, and (3) unmodeled dynamics generated from changes in system configurations, in combination with the fast-changing environments, limit the applicability of physical modeling tools. In this paper, a deep learning algorithm is used to model high-rate systems and predict their response measurements. It consists of an ensemble of short-sequence long short-term memory (LSTM) cells which are concurrently trained. To empower multi-step ahead predictions, a multi-rate sampler is designed to individually select the input space of each LSTM cell based on local dynamics extracted using the embedding theorem. The proposed algorithm is validated on experimental data obtained from a high-rate system. Results showed that the use of the multi-rate sampler yields better feature extraction from non-stationary time series compared with a more heuristic method, resulting in significant improvement in step ahead prediction accuracy and horizon. The lean and efficient architecture of the algorithm results in an average computing time of 25 μμs, which is below the maximum prediction horizon, therefore demonstrating the algorithm’s promise in real-time high-rate applications. 

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5. Efficient Centroid-Linkage Clustering

   Bateni, MH; Dhulipala, L; Fletcher, W; Gowda, K; Hershkowitz, DE; Jayaram, R; Lacki, J (December 2024, NeurIPS 2024)

   We give an algorithm for Centroid-Linkage Hierarchical Agglomerative Clustering (HAC), which computes a $$c$$-approximate clustering in roughly $$n^{1+O(1/c^2)}$$ time. We obtain our result by combining a new Centroid-Linkage HAC algorithm with a novel fully dynamic data structure for nearest neighbor search which works under adaptive updates. We also evaluate our algorithm empirically. By leveraging a state-of-the-art nearest-neighbor search library, we obtain a fast and accurate Centroid-Linkage HAC algorithm. Compared to an existing state-of-the-art exact baseline, our implementation maintains the clustering quality while delivering up to a $$36\times$$ speedup due to performing fewer distance comparisons. 

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