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Found 28 papers, 5 papers with code
Pre-Trained Large Language Model Based Remaining Useful Life Transfer Prediction of Bearing
Accurately predicting the remaining useful life (RUL) of rotating machinery, such as bearings, is essential for ensuring equipment reliability and minimizing unexpected industrial failures.
A Deep Learning-Based Method for Power System Resilience Evaluation
Statistics-based methods offer a retrospective analysis of system performance without requiring a physical model, while simulation-based methods necessitate detailed physical system information and often simplify real-world scenarios.
Integrating Cascade Pumped Micro-Hydro Storage: A Sustainable Approach to Energy and Water Management
This study addresses these challenges by proposing a cascade-pumped micro-hydro storage (CPMHS) system that leverages intermediate reservoirs to bridge long horizontal distances, enabling efficient energy transfer and storage.
Deep Learning-Based Electricity Price Forecast for Virtual Bidding in Wholesale Electricity Market
Based on the price spread prediction results, several trading strategies are proposed and the most effective strategy for maximizing cumulative profit under realistic market conditions is identified through backtesting.
Graph-based Simulation Framework for Power Resilience Estimation and Enhancement
The increasing frequency of extreme weather events poses significant risks to power distribution systems, leading to widespread outages and severe economic and social consequences.
MetaFollower: Adaptable Personalized Autonomous Car Following
To the best of our knowledge, this is the first car-following model aiming to achieve fast adaptation by considering both driver and temporal heterogeneity based on meta-learning.
Video Frame Interpolation for Polarization via Swin-Transformer
Video Frame Interpolation (VFI) has been extensively explored and demonstrated, yet its application to polarization remains largely unexplored.
Research on material identification of mobile phones falling to the ground
The method determines the material of the mobile phone falling to the ground according to the data of the mobile phone accelerometer and can obtain the ground material of the mobile phone falling through a large number of user data.
Rényi Neural Processes
Neural Processes (NPs) are deep probabilistic models that represent stochastic processes by conditioning their prior distributions on a set of context points.
Deep Learning-Based Weather-Related Power Outage Prediction with Socio-Economic and Power Infrastructure Data
This paper presents a deep learning-based approach for hourly power outage probability prediction within census tracts encompassing a utility company's service territory.
Active Learning for NLP with Large Language Models
To reduce the labeling cost and enhance the sample efficiency, Active Learning (AL) technique can be used to label as few samples as possible to reach a reasonable or similar results.
AccidentGPT: Accident Analysis and Prevention from V2X Environmental Perception with Multi-modal Large Model
For human-driven vehicles, we offer proactive long-range safety warnings and blind-spot alerts while also providing safety driving recommendations and behavioral norms through human-machine dialogue and interaction.
Data-driven Traffic Simulation: A Comprehensive Review
The primary objective of this paper is to review current research efforts and provide a futuristic perspective that will benefit future developments in the field.
Study on the Time Domain Precision Evolution Mechanism of CNC Machine Tool Feed Systems Based on Acceleration and Deceleration Capability Indicator
This paper innovatively proposes the need to consider the coupling effects among subsystems, directing the optimization design of CNC machine tool feed systems towards time domain dynamic precision.
Decoupling control parameter method to study the coupling characteristics of subsystems in the feed system
Currently, the influence of changes in control parameters on the matching characteristics of each subsystem was not yet considered when studying the coupling relationship between subsystems.
A review of dynamics design methods for high-speed and high-precision CNC machine tool feed systems
With the development of CNC machine tools toward high speed and high precision, the traditional static design methods can hardly meet the demand.
Evolving Testing Scenario Generation Method and Intelligence Evaluation Framework for Automated Vehicles
Current testing scenarios typically employ predefined or scripted BVs, which inadequately reflect the complexity of human-like social behaviors in real-world driving scenarios, and also lack a systematic metric for evaluating the comprehensive intelligence of AVs.
The Neural Process Family: Survey, Applications and Perspectives
We shed light on their potential to bring several recent advances in other deep learning domains under one umbrella.
Automatic Classification of Bug Reports Based on Multiple Text Information and Reports' Intention
The innovation is that when categorizing bug reports, in addition to using the text information of the report, the intention of the report (i. e. suggestion or explanation) is also considered, thereby improving the performance of the classification.
Orthogonal Stochastic Configuration Networks with Adaptive Construction Parameter for Data Analytics
As a randomized learner model, SCNs are remarkable that the random weights and biases are assigned employing a supervisory mechanism to ensure universal approximation and fast learning.
Contrastive Graph Learning for Population-based fMRI Classification
Nonetheless, existing contrastive methods generate resemblant pairs only on pixel-level features of 3D medical images, while the functional connectivity that reveals critical cognitive information is under-explored.
TransFollower: Long-Sequence Car-Following Trajectory Prediction through Transformer
Through cross-attention between encoder and decoder, the decoder learns to build a connection between historical driving and future LV speed, based on which a prediction of future FV speed can be obtained.
Contrastive Cycle Adversarial Autoencoders for Single-cell Multi-omics Alignment and Integration
Our approach can efficiently map the above data with high sparsity and noise from different spaces to a low-dimensional manifold in a unified space, making the downstream alignment and integration straightforward.
Global Convolutional Neural Processes
Latent neural process, a member of NPF, is believed to be capable of modelling the uncertainty on certain points (local uncertainty) as well as the general function priors (global uncertainties).
MSFM: Multi-Scale Fusion Module for Object Detection
Specifically, the input of the module will be resized into different scales on which position and semantic information will be processed, and then they will be rescaled back and combined with the module input.
NP-PROV: Neural Processes with Position-Relevant-Only Variances
Neural Processes (NPs) families encode distributions over functions to a latent representation, given context data, and decode posterior mean and variance at unknown locations.
Safe, Efficient, and Comfortable Velocity Control based on Reinforcement Learning for Autonomous Driving
A model used for velocity control during car following was proposed based on deep reinforcement learning (RL).
Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning
This study demonstrates that reinforcement learning methodology can offer insight into driver behavior and can contribute to the development of human-like autonomous driving algorithms and traffic-flow models.
