Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1364/ao.25.004076 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Driving Intention Recognition of Electric Wheel Loader Based on Fuzzy Control.
Sensors (Basel)
December 2024
College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China.
Energy conservation and emission reduction is a common concern in various industries. The construction process of electric wheel loaders has the advantages of being zero-emission and having a high energy efficiency, and has been widely recognized by the industry. The frequent shift in wheel loader working processes poses a serious challenge to the operator.
View Article and Find Full Text PDFApplication of AERMOD dispersion model for assessment PM concentrations from mobile sources in Kuala Lumpur Metropolitan City, Malaysia.
Environ Monit Assess
September 2024
Geography Section, School of Humanities, Universiti Sains Malaysia, 11800, Penang, Malaysia.
Deterioration of air quality in Kuala Lumpur caused by mobile sources and traffic-related activities with interaction with climatic conditions and dispersion in the atmosphere. This study was focused on predicting the averaged 1-h concentration of particulate matter (PM) that was emitted from private cars in Kuala Lumpur by applying the air dispersion model American Meteorological Society (AMS)/United States Environmental Protection Agency (EPA) Regulatory Model (AERMOD) in 2014. The AERMOD model indicates that private cars in Kuala Lumpur recorded 1-h concentration of PM below the Malaysian Ambient Air Quality Guidelines (RMAQG) in 2014.
View Article and Find Full Text PDFCollaborative augmented reconstruction of 3D neuron morphology in mouse and human brains.
Nat Methods
October 2024
New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China.
Digital reconstruction of the intricate 3D morphology of individual neurons from microscopic images is a crucial challenge in both individual laboratories and large-scale projects focusing on cell types and brain anatomy. This task often fails in both conventional manual reconstruction and state-of-the-art artificial intelligence (AI)-based automatic reconstruction algorithms. It is also challenging to organize multiple neuroanatomists to generate and cross-validate biologically relevant and mutually agreed upon reconstructions in large-scale data production.
View Article and Find Full Text PDFAutomatic driving image matching via Random Sample Consensus (RANSAC) and Spectral Clustering (SC) with monocular camera.
Rev Sci Instrum
August 2024
Mobile Department, Xiaomi Technology Co., Ltd., No. 33 Xierqi Middle Road, Haidian District, 100085 Beijing, China.
In today's big data era, with the development of the Internet of Things (IoT) technology and the trend of autonomous driving prevailing, visual information has shown a blowout increase, but most image matching algorithms have problems such as low accuracy and low inlier rates, resulting in insufficient information. In order to solve the problem of low image matching accuracy and low inlier rate in the field of autonomous driving, this research innovatively applies spectral clustering (SC) in the field of data analysis to image matching in the field of autonomous driving, and a new image matching algorithm "SC-RANSAC" based on SC and Random Sample Consensus (RANSAC) is proposed. The datasets in this research are collected based on the monocular cameras of autonomous driving cars.
View Article and Find Full Text PDFA new CNN-BASED object detection system for autonomous mobile robots based on real-world vehicle datasets.
Heliyon
August 2024
Dept. of Electrical and Computer Engineering, Universitas Syiah Kuala, Banda Aceh, 23111, Indonesia.
Recently, autonomous mobile robots (AMRs) have begun to be used in the delivery of goods, but one of the biggest challenges faced in this field is the navigation system that guides a robot to its destination. The navigation system must be able to identify objects in the robot's path and take evasive actions to avoid them. Developing an object detection system for an AMR requires a deep learning model that is able to achieve a high level of accuracy, with fast inference times, and a model with a compact size that can be run on embedded control systems.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!