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Found 38 papers, 10 papers with code
Safe Data-Driven Predictive Control
In the realm of control systems, model predictive control (MPC) has exhibited remarkable potential; however, its reliance on accurate models and substantial computational resources has hindered its broader application, especially within real-time nonlinear systems.
Model-free Vehicle Rollover Prevention: A Data-driven Predictive Control Approach
Vehicle rollovers pose a significant safety risk and account for a disproportionately high number of fatalities in road accidents.
Data-Enabled Predictive Control for Flexible Spacecraft
Spacecraft are vital to space exploration and are often equipped with lightweight, flexible appendages to meet strict weight constraints.
Foundation Model-Based Apple Ripeness and Size Estimation for Selective Harvesting
The Fuji-Ripeness-Size Dataset and the apple detection and size estimation algorithms are made publicly available, which provides valuable benchmarks for future studies in automated and selective harvesting.
UI-TARS: Pioneering Automated GUI Interaction with Native Agents
This paper introduces UI-TARS, a native GUI agent model that solely perceives the screenshots as input and performs human-like interactions (e. g., keyboard and mouse operations).
Deep DeePC: Data-enabled predictive control with low or no online optimization using deep learning
With the trained neural network, the Deep DeePC framework is formed for online control implementation.
Robust Iterative Learning for Collaborative Road Profile Estimation and Active Suspension Control in Connected Vehicles
This paper presents the development of a new collaborative road profile estimation and active suspension control framework in connected vehicles, where participating vehicles iteratively refine the road profile estimation and enhance suspension control performance through an iterative learning scheme.
Online Reduced-Order Data-Enabled Predictive Control
By exploiting the minimum non-zero singular value of the Hankel matrix, the developed online DeePC selectively integrates informative data and effectively captures evolving system dynamics.
Differentially-Private Distributed Model Predictive Control of Linear Discrete-Time Systems with Global Constraints
Distributed model predictive control (DMPC) has attracted extensive attention as it can explicitly handle system constraints and achieve optimal control in a decentralized manner.
Performance Evaluation of Semi-supervised Learning Frameworks for Multi-Class Weed Detection
In this study, we assess the effectiveness of a semi-supervised learning framework for multi-class weed detection, employing two well-known object detection frameworks, namely FCOS and Faster-RCNN.
Back-stepping Experience Replay with Application to Model-free Reinforcement Learning for a Soft Snake Robot
In this paper, we propose a novel technique, Back-stepping Experience Replay (BER), that is compatible with arbitrary off-policy reinforcement learning (RL) algorithms.
Cyclic Learning for Binaural Audio Generation and Localization
Visual sounding object localization establishes the correspondence between specific visual objects and sound modalities which provides object-aware guidance to improve binaural generation performance.
Cross-modal Generative Model for Visual-Guided Binaural Stereo Generation
To this end, a metric to measure the spatial perception of audio is proposed for the first time.
SoybeanNet: Transformer-Based Convolutional Neural Network for Soybean Pod Counting from Unmanned Aerial Vehicle (UAV) Images
Soybeans are a critical source of food, protein and oil, and thus have received extensive research aimed at enhancing their yield, refining cultivation practices, and advancing soybean breeding techniques.
Large Language Models and Foundation Models in Smart Agriculture: Basics, Opportunities, and Challenges
These models are trained on a large amount of data from multiple domains and modalities.
Challenges and Opportunities for Second-life Batteries: A Review of Key Technologies and Economy
Due to the increasing volume of Electric Vehicles in automotive markets and the limited lifetime of onboard lithium-ion batteries (LIBs), the large-scale retirement of LIBs is imminent.
Communication-Efficient Decentralized Multi-Agent Reinforcement Learning for Cooperative Adaptive Cruise Control
Connected and autonomous vehicles (CAVs) promise next-gen transportation systems with enhanced safety, energy efficiency, and sustainability.
A Unified Framework for Online Data-Driven Predictive Control with Robust Safety Guarantees
Despite great successes, model predictive control (MPC) relies on an accurate dynamical model and requires high onboard computational power, impeding its wider adoption in engineering systems, especially for nonlinear real-time systems with limited computation power.
Overtaking-enabled Eco-approach Control at Signalized Intersections for Connected and Automated Vehicles
To potentially mitigate the negative effect of preceding vehicles on eco-driving control at the signalized intersection, this paper proposes an overtakingenabled eco-approach control (OEAC) strategy.
Extended Neighboring Extremal Optimal Control with State and Preview Perturbations
In this work, an extended NE (ENE) framework is developed to systematically adapt the nominal control to both state and preview perturbations.
Physics-Augmented Data-EnablEd Predictive Control for Eco-driving of Mixed Traffic Considering Diverse Human Behaviors
In this paper, we employ a data-enabled predictive control (DeePC) scheme to address the eco-driving of mixed traffic flows with diverse behaviors of human drivers.
Label-Efficient Learning in Agriculture: A Comprehensive Review
In addition, a systematic review of various agricultural applications exploiting these label-efficient algorithms, such as precision agriculture, plant phenotyping, and postharvest quality assessment, is presented.
O2RNet: Occluder-Occludee Relational Network for Robust Apple Detection in Clustered Orchard Environments
One key technology to fully enable efficient automated harvesting is accurate and robust apple detection, which is challenging due to complex orchard environments that involve varying lighting conditions and foliage/branch occlusions.
Dimension Reduction for Efficient Data-Enabled Predictive Control
In this letter, we propose a simple yet effective singular value decomposition (SVD) based strategy to reduce the optimization problem dimension in data-enabled predictive control (DeePC).
Deep Data Augmentation for Weed Recognition Enhancement: A Diffusion Probabilistic Model and Transfer Learning Based Approach
In this paper, we present the first work of applying diffusion probabilistic models (also known as diffusion models) to generate high-quality synthetic weed images based on transfer learning.
Data-Enabled Predictive Control for Fast Charging of Lithium-Ion Batteries with Constraint Handling
Fast charging of lithium-ion batteries has gained extensive research interests, but most of existing methods are either based on simple rule-based charging profiles or require explicit battery models that are non-trivial to identify accurately.
Privacy-Preserving Data-Enabled Predictive Leading Cruise Control in Mixed Traffic
Data-driven predictive control of connected and automated vehicles (CAVs) has received increasing attention as it can achieve safe and optimal control without relying on explicit dynamical models.
A Nonlinear Lateral Controller Design for Vehicle Path-following with an Arbitrary Sensor Location
This paper investigates the lateral control problem in vehicular path-following when the feedback sensor(s) are mounted at an arbitrary location in the longitudinal symmetric axis.
Privacy-Preserving Nonlinear Cloud-based Model Predictive Control via Affine Masking
We show that the proposed privacy scheme does not affect the MPC performance and it preserves the privacy of the plant such that the eavesdropper is unable to identify the actual value or even estimate a rough range of the private state and input signals.
Multi-agent Reinforcement Learning for Cooperative Lane Changing of Connected and Autonomous Vehicles in Mixed Traffic
In this paper, we formulate the lane-changing decision making of multiple AVs in a mixed-traffic highway environment as a multi-agent reinforcement learning (MARL) problem, where each AV makes lane-changing decisions based on the motions of both neighboring AVs and HDVs.
Cloud-Assisted Collaborative Road Information Discovery with Gaussian Process: Application to Road Profile Estimation
Promising results on extensive simulations and hardware-in-the-loop experiments show that the proposed collaborative estimation can significantly enhance estimation and iteratively improve the performance from vehicle to vehicle, despite vehicle heterogeneity, model uncertainty, and measurement noises.
Cloud-Assisted Nonlinear Model Predictive Control for Finite-Duration Tasks
In this paper, we propose a novel cloud-assisted model predictive control (MPC) framework in which we systematically fuse a cloud MPC that uses a high-fidelity nonlinear model but is subject to communication delays with a local MPC that exploits simplified dynamics (due to limited computation) but has timely feedback.
Simultaneous Suspension Control and Energy Harvesting through Novel Design and Control of a New Nonlinear Energy Harvesting Shock Absorber
In this paper, we propose a new EHSA design -- inerter pendulum vibration absorber (IPVA) -- that integrates an electromagnetic rotary EHSA with a nonlinear pendulum vibration absorber.
Deep Multi-agent Reinforcement Learning for Highway On-Ramp Merging in Mixed Traffic
On-ramp merging is a challenging task for autonomous vehicles (AVs), especially in mixed traffic where AVs coexist with human-driven vehicles (HDVs).
DeepApple: Deep Learning-based Apple Detection using a Suppression Mask R-CNN
Robotic apple harvesting has received much research attention in the past few years due to growing shortage and rising cost in labor.
A Unified Linear Speedup Analysis of Federated Averaging and Nesterov FedAvg
For strongly convex and convex problems, we also characterize the corresponding convergence rates for the Nesterov accelerated FedAvg algorithm, which are the first linear speedup guarantees for momentum variants of FedAvg in convex settings.
Autonomous Driving using Safe Reinforcement Learning by Incorporating a Regret-based Human Lane-Changing Decision Model
The predicted decisions are incorporated in the safety constraints for reinforcement learning in training and in implementation.
Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Control
Reinforcement learning (RL) is a promising data-driven approach for adaptive traffic signal control (ATSC) in complex urban traffic networks, and deep neural networks further enhance its learning power.
