Search Results for author:
Found 9 papers, 5 papers with code
MSCMHMST: A traffic flow prediction model based on Transformer
This study proposes a hybrid model based on Transformers, named MSCMHMST, aimed at addressing key challenges in traffic flow prediction.
Drone Data Analytics for Measuring Traffic Metrics at Intersections in High-Density Areas
This study employed over 100 hours of high-altitude drone video data from eight intersections in Hohhot to generate a unique and extensive dataset encompassing high-density urban road intersections in China.
Real-Time Vehicle Detection and Urban Traffic Behavior Analysis Based on UAV Traffic Videos on Mobile Devices
This work integrates drone technology, iOS development, and deep learning techniques to integrate traffic video acquisition, object detection, object tracking, and traffic behavior analysis functions on mobile devices.
Data Smoothing Filling Method based on ScRNA-Seq Data Zero-Value Identification
However, due to the limitations of the sequencing technique, the scRNA-seq data are represented with sparsity, whichcontains missing gene values, i. e., zero values, called dropout.
Deep Q-Network Based Decision Making for Autonomous Driving
This paper introduces a method for safely navigating an autonomous vehicle in highway scenarios by combining deep Q-Networks and insight from control theory.
MFPP: Morphological Fragmental Perturbation Pyramid for Black-Box Model Explanations
Deep neural networks (DNNs) have recently been applied and used in many advanced and diverse tasks, such as medical diagnosis, automatic driving, etc.
PipeNet: Selective Modal Pipeline of Fusion Network for Multi-Modal Face Anti-Spoofing
Face anti-spoofing has become an increasingly important and critical security feature for authentication systems, due to rampant and easily launchable presentation attacks.
Channel Pruning via Optimal Thresholding
By using OT, most negligible or unimportant channels are pruned to achieve high sparsity while minimizing performance degradation.
NAMF: A Non-local Adaptive Mean Filter for Salt-and-Pepper Noise Removal
In this paper, a novel algorithm called a non-local adaptive mean filter (NAMF) for removing salt-and-pepper (SAP) noise from corrupted images is presented.
