More Like this

1. Experimental Study on Digital Image Correlation for Deep Learning-Based Damage Diagnostic

   https://doi.org/10.1007/978-3-030-12115-0_28  

   Gulgec, Nur Sila; Takáč, Martin; Pakzad, Shamim N. (May 2019, Proceedings of the Society for Experimental Mechanics)

   Large quantities of data which contain detailed condition information over an extended period of time should be utilized to prioritize infrastructure repairs. As the temporal and spatial resolution of monitoring data drastically increase by advances in sensing technology, structural health monitoring applications reach the thresholds of big data. Deep neural networks are ideally suited to use large representative training datasets to learn complex damage features. In the previous study of authors, a real-time deep learning platform was developed to solve damage detection and localization challenge. The network was trained by using simulated structural connection mimicking the real test object with a variety of loading cases, damage scenarios, and measurement noise levels for successful and robust diagnosis of damage. In this study, the proposed damage diagnosis platform is validated by using temporally and spatially dense data collected by Digital Image Correlation (DIC) from the specimen. Laboratory testing of the specimen with induced damage condition is performed to evaluate the performance and efficiency of damage detection and localization approach. 

   more » « less
2. A Deep Reinforcement Learning Based Approach for Home Energy Management System

   https://doi.org/10.1109/ISGT45199.2020.9087647  

   Li, Hepeng; Wan, Zhiqiang; He, Haibo (February 2020, IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT 2020))

   Home energy management system (HEMS) enables residents to actively participate in demand response (DR) programs. It can autonomously optimize the electricity usage of home appliances to reduce the electricity cost based on time-varying electricity prices. However, due to the existence of randomness in the pricing process of the utility and resident's activities, developing an efficient HEMS is challenging. To address this issue, we propose a novel home energy management method for optimal scheduling of different kinds of home appliances based on deep reinforcement learning (DRL). Specifically, we formulate the home energy management problem as an MDP considering the randomness of real-time electricity prices and resident's activities. A DRL approach based on proximal policy optimization (PPO) is developed to determine the optimal DR scheduling strategy. The proposed approach does not need any information on the appliances' models and distribution knowledge of the randomness. Simulation results verify the effectiveness of our proposed approach. 

   more » « less
3. Probabilistic seismic damage and loss assessment methodology for wastewater network incorporating modeling uncertainty and damage correlations

   https://doi.org/10.1177/87552930231180642  

   Alam, Mohammad S; Simpson, Barbara G; Barbosa, Andre R; Jung, Jaehoon; Parulekar, Nishant (August 2023, Earthquake Spectra)

   Maintaining the functionality of wastewater networks is critical to individual well-being, business continuity, public health, and safety. However, seismic damage and loss assessments of wastewater networks traditionally use fragility functions based on median repair rates without considering relevant sources of uncertainty and correlations of damage when estimating potential damage states and pipe repairs. This study presents a probabilistic methodology to incorporate modeling uncertainty (e.g. model parameter and model class uncertainty) and spatial correlations (e.g. spatial auto- and cross-correlation) of pipe repairs. The methodology was applied to a case study backbone system of a wastewater network in Portland, OR, using the expected hazard intensity maps for multiple deterministic earthquake scenarios, including a moment magnitude M6.8 Portland Hills Fault and M8.1, M8.4, M8.7, and M9.0 Cascadia Subduction Zone (CSZ) events. As spatial-correlation models of pipeline damage were non-existent in the literature and local information on costs to repair the pipes was limited at the time of this study, correlation methods and repair costs were proposed to estimate lower and upper bounds of pipe damage and loss. The results show how the consideration of different levels of uncertainty and spatial correlation for pipe repair rate could lead to different probabilistic estimates of damage and loss at the system level of the wastewater network, even though the point estimates, such as the mean and median, remain essentially unaltered. 

   more » « less
4. Risk-aware electricity dispatch with large-scale distributed renewable integration under climate extremes

   https://doi.org/10.1073/pnas.2426620122  

   Xu, Luo; Zeng, Hongtai; Lin, Ning; Yang, Yue; Guo, Qinglai; Poor, H Vincent (May 2025, Proceedings of the National Academy of Sciences)

   Distribution networks, with large-scale integration of distributed renewable resources, particularly rooftop solar photovoltaic systems, represent the most extensive yet vulnerable components of modern electric power systems during climate extremes such as hurricanes. However, existing day-ahead electricity dispatch approaches primarily focus on the transmission network and lack the capability to manage the spatiotemporal risks associated with the vast distribution networks, which can potentially lead to significant power imbalances due to the mismatches between scheduled generation and actual demand. To address this increasingly critical gap under intensifying climate extremes and growing distributed renewable integration, we introduce Risk-aware Electricity Dispatch under Climate Extremes with Renewable integration (REDUCER), a risk-aware day-ahead electricity dispatch model that incorporates high-resolution spatiotemporal risk analysis for distribution networks with large-scale distributed renewable integration into an Entropic Value-at-Risk-constrained mixed-integer convex optimization framework. Applied to the 2022 Puerto Rico power grid under Hurricane Fiona, the proposed REDUCER model is seen to effectively manage these risks with substantially less reliance on additional flexibility resources to cope with power imbalances, reducing overall operational costs by about 30% under extreme cases compared to standard unit commitment strategies already informed by average demand loss. Also, the proposed REDUCER model consistently demonstrates its effectiveness in managing the increasing temporal net demand variability introduced by growing large-scale distributed solar integration while maintaining minimal operational costs. This model offers a practical solution for cost-effective and resilient electricity dispatch of modern power systems with large-scale renewable integration facing intensifying climate risks. 

   more » « less
5. Channel Aware Greedy Algorithm for MISO Cache-Aided Communication

   https://doi.org/10.1109/SPAWC51304.2022.9834007  

   Bergel, Itsik; Mohajer, Soheil (January 2022, 2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC))

   We present novel schemes for cache-aided communication over networks with a multi-antenna base station (BS) that serves multiple single-antenna users. The schemes are based on a greedy scheduling [1], which simultaneously transmits coded messages to disjoint groups of users. The proposed algorithms use the channel state information to opportunistically choose the groups to be served together and to allocate power to each coded message in order to minimize the overall communication delay. Numerical study shows that the new schemes outperform the previously known schemes. 

   more » « less