This article presents an interacting multiple model (IMM) for short-term prediction and long-term trajectory prediction of an intelligent vehicle. This model is based on vehicle's physics model and maneuver recognition model. The long-term trajectory prediction is challenging due to the dynamical nature of the system and large uncertainties. The vehicle physics model is composed of kinematics and dynamics models, which could guarantee the accuracy of short-term prediction. The maneuver recognition model is realized by means of hidden Markov model, which could guarantee the accuracy of long-term prediction, and an IMM is adopted to guarantee the accuracy of both short-term prediction and long-term prediction. The experiment results of a real vehicle are presented to show the effectiveness of the prediction method.
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http://dx.doi.org/10.1109/TNNLS.2021.3136866 | DOI Listing |
Int J Comput Assist Radiol Surg
January 2025
Advanced Medical Devices Laboratory, Kyushu University, Nishi-ku, Fukuoka, 819-0382, Japan.
Purpose: This paper presents a deep learning approach to recognize and predict surgical activity in robot-assisted minimally invasive surgery (RAMIS). Our primary objective is to deploy the developed model for implementing a real-time surgical risk monitoring system within the realm of RAMIS.
Methods: We propose a modified Transformer model with the architecture comprising no positional encoding, 5 fully connected layers, 1 encoder, and 3 decoders.
J Am Med Dir Assoc
January 2025
Department of Neurology, Renaissance School of Medicine, Stony Brook, NY, United States.
Objectives: Early research reported that older adults who stopped walking when they began a conversation were more likely to fall in the future. As a systematic measure of dual-task performance, Verghese and colleagues developed the Walking While Talking (WWT) test, in which a person walks at a normal pace while reciting alternate letters of the alphabet. The present paper highlights key findings from the 2 decades of research using the WWT test.
View Article and Find Full Text PDFJ Phys Chem A
January 2025
School of Chemistry, University of Bristol, Bristol BS8 1TS, U.K.
In both nature and industry, aerosol droplets contain complex mixtures of solutes, which in many cases include multiple inorganic components. Understanding the drying kinetics of these droplets and the impact on resultant particle morphology is essential for a variety of applications including improving inhalable drugs, mitigating disease transmission, and developing more accurate climate models. However, the previous literature has only focused on the relationship between drying kinetics and particle morphology for aerosol droplets containing a single nonvolatile component.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Seamless Trans-X Lab (STL), School of Integrated Technology, Yonsei University, Incheon 21983, Republic of Korea.
In the domain of autonomous driving, trajectory prediction plays a pivotal role in ensuring the safety and reliability of autonomous systems, especially when navigating complex environments. Unfortunately, trajectory prediction suffers from uncertainty problems due to the randomness inherent in the driving environment, but uncertainty quantification in trajectory prediction is not widely addressed, and most studies rely on deep ensembles methods. This study presents a novel uncertainty-aware multimodal trajectory prediction (UAMTP) model that quantifies aleatoric and epistemic uncertainties through a single forward inference.
View Article and Find Full Text PDFNutrients
December 2024
Department of Endocrinology and Metabolism, Nanfang Hospital, Southern Medical University, Guangzhou 510510, China.
Background: An increased risk of multiple secondary diseases has been observed in individuals with diabetes, which contributes to the growing economic burden. Few studies have established the connection of blood urea nitrogen/albumin (BAR) with diabetes, and its link to subsequent diabetic complications and mortality remains unclear. We aimed to explore the association of BAR with the onset of type 2 diabetes mellitus (T2DM) and its dynamic progression.
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