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Found 7 papers, 2 papers with code
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.
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.
Adaptive Leading Cruise Control in Mixed Traffic Considering Human Behavioral Diversity
A comprehensive simulation is conducted to statistically verify the positive impacts of CAV on the holistic energy efficiency of the mixed traffic flow with uncertain and diverse human driving behaviors.
Decentralized On-Ramp Merging Control of Connected and Automated Vehicles in the Mixed Traffic Using Control Barrier Functions
We first formulate the optimal merging control problem, which includes the constraints of safety and vehicle dynamics, with the objectives of minimizing travel time and energy consumption.
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.
