25 research outputs found
Dynamic modelling of hexarot parallel mechanisms for design and development
In this research, the kinematics, dynamics, and general closed-form dynamic formulation of the centrifugal high-G hexarot-based manipulators have been developed through the different mathematical modeling techniques. The vibrations of the mechanism have also been investigated
Artificial Intelligence in Assessing Cardiovascular Diseases and Risk Factors via Retinal Fundus Images: A Review of the Last Decade
Background: Cardiovascular diseases (CVDs) continue to be the leading cause
of mortality on a global scale. In recent years, the application of artificial
intelligence (AI) techniques, particularly deep learning (DL), has gained
considerable popularity for evaluating the various aspects of CVDs. Moreover,
using fundus images and optical coherence tomography angiography (OCTA) to
diagnose retinal diseases has been extensively studied. To better understand
heart function and anticipate changes based on microvascular characteristics
and function, researchers are currently exploring the integration of AI with
non-invasive retinal scanning. Leveraging AI-assisted early detection and
prediction of cardiovascular diseases on a large scale holds excellent
potential to mitigate cardiovascular events and alleviate the economic burden
on healthcare systems. Method: A comprehensive search was conducted across
various databases, including PubMed, Medline, Google Scholar, Scopus, Web of
Sciences, IEEE Xplore, and ACM Digital Library, using specific keywords related
to cardiovascular diseases and artificial intelligence. Results: A total of 87
English-language publications, selected for relevance were included in the
study, and additional references were considered. This study presents an
overview of the current advancements and challenges in employing retinal
imaging and artificial intelligence to identify cardiovascular disorders and
provides insights for further exploration in this field. Conclusion:
Researchers aim to develop precise disease prognosis patterns as the aging
population and global CVD burden increase. AI and deep learning are
transforming healthcare, offering the potential for single retinal image-based
diagnosis of various CVDs, albeit with the need for accelerated adoption in
healthcare systems.Comment: 40 pages, 5 figures, 2 tables, 91 reference
Current and future roles of artificial intelligence in retinopathy of prematurity
Retinopathy of prematurity (ROP) is a severe condition affecting premature
infants, leading to abnormal retinal blood vessel growth, retinal detachment,
and potential blindness. While semi-automated systems have been used in the
past to diagnose ROP-related plus disease by quantifying retinal vessel
features, traditional machine learning (ML) models face challenges like
accuracy and overfitting. Recent advancements in deep learning (DL), especially
convolutional neural networks (CNNs), have significantly improved ROP detection
and classification. The i-ROP deep learning (i-ROP-DL) system also shows
promise in detecting plus disease, offering reliable ROP diagnosis potential.
This research comprehensively examines the contemporary progress and challenges
associated with using retinal imaging and artificial intelligence (AI) to
detect ROP, offering valuable insights that can guide further investigation in
this domain. Based on 89 original studies in this field (out of 1487 studies
that were comprehensively reviewed), we concluded that traditional methods for
ROP diagnosis suffer from subjectivity and manual analysis, leading to
inconsistent clinical decisions. AI holds great promise for improving ROP
management. This review explores AI's potential in ROP detection,
classification, diagnosis, and prognosis.Comment: 28 pages, 8 figures, 2 tables, 235 references, 1 supplementary tabl
A Study on the Beech Wood Machining Parameters Optimization Using Response Surface Methodology
The surface quality of wooden products is of great importance to production industries. The best surface quality requires a thorough understanding of the cutting parameters’ effects on the wooden material. In this paper, response surface methodology, which is one of the conventional statistical methods in experiment design, has been used to design experiments and investigate the effect of different machining parameters as feed rate, spindle speed, step over, and depth of cut on surface quality of the beech wood. The mathematical model of the examined parameters and the surface roughness have also been obtained by the method. Finally, the optimal machining parameters have been obtained to achieve the best quality of the machined surface, which reduced the surface roughness up to 4.2 (µm). Each of the machining parameters has a considerable effect on surface quality, although it is noted that the feed rate has the greatest effect
A study on vibrations of hexarot-based high-G centrifugal simulators
This paper investigates the vibrations of hexarot simulators. The generalized modeling of kinematics and dynamics formulation of a hexarot mechanism is addressed. This model considers the flexible manipulator with the base motion. The dynamic formulation has been developed based on the principle of virtual work. The dynamic model consists of the stiffness of the different parts of the mechanism, the effects of gravity and inertia, torque and force related to the joints viscous friction. Finally, the response of the end effector at various frequencies has been presented, and the vibrations of the mechanism and the dynamic stability index have been investigated