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1. Energy-Efficient LSTM Inference Accelerator for Real-Time Causal Prediction

   https://doi.org/10.1145/3495006  

   Chen, Zhe; Blair, Hugh T.; Cong, Jason (September 2022, ACM Transactions on Design Automation of Electronic Systems)

   Ever-growing edge applications often require short processing latency and high energy efficiency to meet strict timing and power budget. In this work, we propose that the compact long short-term memory (LSTM) model can approximate conventional acausal algorithms with reduced latency and improved efficiency for real-time causal prediction, especially for the neural signal processing in closed-loop feedback applications. We design an LSTM inference accelerator by taking advantage of the fine-grained parallelism and pipelined feedforward and recurrent updates. We also propose a bit-sparse quantization method that can reduce the circuit area and power consumption by replacing the multipliers with the bit-shift operators. We explore different combinations of pruning and quantization methods for energy-efficient LSTM inference on datasets collected from the electroencephalogram (EEG) and calcium image processing applications. Evaluation results show that our proposed LSTM inference accelerator can achieve 1.19 GOPS/mW energy efficiency. The LSTM accelerator with 2-sbit/16-bit sparse quantization and 60% sparsity can reduce the circuit area and power consumption by 54.1% and 56.3%, respectively, compared with a 16-bit baseline implementation. 

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2. Fast small signal stability assessment using deep convolutional neural networks

   https://doi.org/10.1016/j.epsr.2024.110853  

   Bogodorova, Tetiana; Osipov, Denis; Vanfretti, Luigi (October 2024, Electric Power Systems Research)

   Golpira, Hemin (Ed.)

   The paper proposes an approach for fast small signal stability assessment on a short data window using deep learning algorithms. This paper shows that the proposed deep convolutional neural networks (CNNs)-based assessment approach is faster than traditional methods (i.e. Prony’s method). The evaluated CNNs are fully convolutional network (FCN), CNN with sub-sampling steps performed through max pooling (Time LeNet), time CNN, fully convolutional network with attention mechanism (Encoder), and CNN with a shortcut residual connection (ResNet). The proposed approach is validated on different synthetic measurement data sets generated from the IEEE 9-bus system that is used as a reference, and further applied to a 769-bus system representing a region in the U. S. Eastern Interconnection. We show that precision and recall are more informative metrics than accuracy for the reliability of the stability assessment process using the proposed methodology. In addition, the method’s efficiency is compared to classical Prony method. 

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3. Online predictive connected nd automated eco-driving on signalized arterials considering traffic control devices and road geometry constraints under uncertain traffic conditions

   https://doi.org/https://doi.org/10.1016/j.trb.2020.12.009  

   Zhao, S; Zhang, K. (March 2021, Transportation research)

   null (Ed.)

   For energy-efficient Connected and Automated Vehicle (CAV) Eco-driving control on signalized arterials under uncertain traffic conditions, this paper explicitly considers traffic control devices (e.g., road markings, traffic signs, and traffic signals) and road geometry (e.g., road shapes, road boundaries, and road grades) constraints in a data-driven optimization-based Model Predictive Control (MPC) modeling framework. This modeling framework uses real-time vehicle driving and traffic signal data via Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communications. In the MPC-based control model, this paper mathematically formulates location-based traffic control devices and road geometry constraints using the geographic information from High-Definition (HD) maps. The location-based traffic control devices and road geometry constraints have the potential to improve the safety, energy, efficiency, driving comfort, and robustness of connected and automated driving on real roads by considering interrupted flow facility locations and road geometry in the formulation. We predict a set of uncertain driving states for the preceding vehicles through an online learning-based driving dynamics prediction model. We then solve a constrained finite-horizon optimal control problem with the predicted driving states to obtain a set of Eco-driving references for the controlled vehicle. To obtain the optimal acceleration or deceleration commands for the controlled vehicle with the set of Eco-driving references, we formulate a Distributionally Robust Stochastic Optimization (DRSO) model (i.e., a special case of data-driven optimization models under moment bounds) with Distributionally Robust Chance Constraints (DRCC) with location-based traffic control devices and road geometry constraints. We design experiments to demonstrate the proposed model under different traffic conditions using real-world connected vehicle trajectory data and Signal Phasing and Timing (SPaT) data on a coordinated arterial with six actuated intersections on Fuller Road in Ann Arbor, Michigan from the Safety Pilot Model Deployment (SPMD) project. 

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4. Time Series Forecasting for Structures Subjected to Nonstationary Inputs

   https://doi.org/10.1115/SMASIS2021-68338  

   Chowdhury, Puja; Conrad, Philip; Bakos, Jason D.; Downey, Austin (October 2021, ASME 2021 Conference on Smart Materials, Adaptive Structures and Intelligent Systems)

   Abstract In this paper, a method for real-time forecasting of the dynamics of structures experiencing nonstationary inputs is described. This is presented as time series predictions across different timescales. The target applications include hypersonic vehicles, space launch systems, real-time prognostics, and monitoring of high-rate and energetic systems. This work presents numerical analysis and experimental results for the real-time implementation of a Fast Fourier Transform (FFT)-based approach for time series forecasting. For this preliminary study, a testbench structure that consists of a cantilever beam subjected to nonstationary inputs is used to generate experimental data. First, the data is de-trended, then the time series data is transferred into the frequency domain, and measures for frequency, amplitude, and phase are obtained. Thereafter, select frequency components are collected, transformed back to the time domain, recombined, and then the trend in the data is restored. Finally, the recombined signals are propagated into the future to the selected prediction horizon. This preliminary time series forecasting work is done offline using pre-recorded experimental data, and the FFT-based approach is implemented in a rolling window configuration. Here learning windows of 0.1, 0.5, and 1 s are considered with different computation times simulated. Results demonstrate that the proposed FFT-based approach can maintain a constant prediction horizon at 1 s with sufficient accuracy for the considered system. The performance of the system is quantified using a variety of metrics. Computational speed and prediction accuracy as a function of training time and learning window lengths are examined in this work. The algorithm configuration with the shortest learning window (0.1 s) is shown to converge faster following the nonstationary when compared to algorithm configuration with longer learning windows. 

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5. Developing an eco-driving strategy in a hybrid traffic network using reinforcement learning

   https://doi.org/10.1177/00368504241263406  

   Jamil, Umar; Malmir, Mostafa; Chen, Alan; Filipovska, Monika; Xie, Mimi; Ding, Caiwen; Jin, Yu-Fang (July 2024, Science Progress)

   Eco-driving has garnered considerable research attention owing to its potential socio-economic impact, including enhanced public health and mitigated climate change effects through the reduction of greenhouse gas emissions. With an expectation of more autonomous vehicles (AVs) on the road, an eco-driving strategy in hybrid traffic networks encompassing AV and human-driven vehicles (HDVs) with the coordination of traffic lights is a challenging task. The challenge is partially due to the insufficient infrastructure for collecting, transmitting, and sharing real-time traffic data among vehicles, facilities, and traffic control centers, and the following decision-making of agents involved in traffic control. Additionally, the intricate nature of the existing traffic network, with its diverse array of vehicles and facilities, contributes to the challenge by hindering the development of a mathematical model for accurately characterizing the traffic network. In this study, we utilized the Simulation of Urban Mobility (SUMO) simulator to tackle the first challenge through computational analysis. To address the second challenge, we employed a model-free reinforcement learning (RL) algorithm, proximal policy optimization, to decide the actions of AV and traffic light signals in a traffic network. A novel eco-driving strategy was proposed by introducing different percentages of AV into the traffic flow and collaborating with traffic light signals using RL to control the overall speed of the vehicles, resulting in improved fuel consumption efficiency. Average rewards with different penetration rates of AV (5%, 10%, and 20% of total vehicles) were compared to the situation without any AV in the traffic flow (0% penetration rate). The 10% penetration rate of AV showed a minimum time of convergence to achieve average reward, leading to a significant reduction in fuel consumption and total delay of all vehicles. 

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