Accurate prediction of vehicle acceleration has significant practical applications. Deep learning, as one of the methods for acceleration prediction, has shown promising applications in acceleration prediction. However, due to the influence of multip...
An adaptive constant acceleration (ACA) model is proposed for the maneuvering target tracking problem. Based on the Taylor series expansion of acceleration, we establish the relationship between the Jerk and the velocity as well as the acceleration s...
With the popularity of smartphones, a large number of “phubbers” have emerged who are engrossed in their phones regardless of the situation. In response to the potential dangers that phubbers face while traveling, this paper proposes a mu...
Extreme rainstorms are difficult to predict and often result in catchment-scale rainfall flooding, leading to substantial economic losses globally. Enhancing the numerical computational efficiency of flood models is essential for improving flood fore...
This study investigates the development of a tire wear model using finite element techniques. Experimental testing was conducted using the Hoosier R25B slick tire mounted onto a Mustang Dynamometer (MD-AWD-500) in the Automotive Center of Excellence,...
In order to obtain precise kinematic global positioning systems (GPS) in medium to large scale networks, the atmospheric effects from tropospheric and ionospheric delays need to be properly modeled and estimated. It is also preferable to use multiple...
This paper addresses the modeling challenge of significant asymmetry between acceleration and deceleration processes in car-following behavior by proposing an Asymmetric Acceleration and Deceleration Car Following (AAD-CF) model. The model characteri...
In marine environments, ships are bound to be disturbed by several external factors, which can cause stochastic fluctuations and strong nonlinearity in the ship motion. Predicting ship motion is pivotal to ensuring ship safety and providing early war...
The simulation of massive multiple-input multiple-output (MIMO) channel models is becoming increasingly important for testing and validation of fifth-generation new radio (5G NR) wireless networks and beyond. However, simulation performance tends to...
The Haiyang 2B (HY-2B) satellite requires precise orbit determination (POD) products for geodetic remote sensing techniques. An improved set of reduced-dynamic (RD) orbit solutions was generated from the onboard Global Positioning System (GPS) measur...
Track quality instruments use low-cost accelerometers placed on or attached to the floors of operating trains, and these instruments collect substantial amounts of data over short inspection periods. The measurements collected by the instruments are...
We examined the neuro/axono-protective potential of a novel poly (ADP-ribose) polymerase (PARP) inhibitor L-2286 in a rat impact acceleration brain injury model. Male Wistar rats (n = 70) weighing 300-350 grams were used to determine the most effecti...
Battery manufacturers and device companies often test batteries at high temperature to accelerate the degradation process. The data collected from these accelerated tests are then used to determine battery performance and reliability over specified n...
Sustainability-oriented start-ups are fundamental to developing solutions for, and to fostering, a societal transition towards a low carbon society. In this context, social impact accelerators (SIAs) are organizations specializing in accelerating the...
This paper proposes an enhanced vibration-based damage detection index leveraging autoregressive moving average (ARMA) time-series modeling. The method relies on the fact that material deterioration alters the vibration features of the structure. Thu...
Large language models (LLMs) have emerged as powerful tools for natural language processing tasks, revolutionizing the field with their ability to understand and generate human-like text. As the demand for more sophisticated LLMs continues to grow, t...
The SCHISM model is widely used for ocean numerical simulations, but its computational efficiency is constrained by the substantial resources it requires. To enhance its performance, this study develops GPU–SCHISM, a GPU-accelerated parallel ve...
The systolic array provides extremely high efficiency for running matrix multiplication and is one of the mainstream architectures of today’s deep learning accelerators. In order to develop efficient accelerators, people usually employ simulato...
Convolutional neural networks (CNNs) have made impressive achievements in image classification and object detection. For hardware with limited resources, it is not easy to achieve CNN inference with a large number of parameters without external stora...
This study presents a universal reconfigurable hardware accelerator for efficient processing of sparse decision trees, artificial neural networks and support vector machines. The main idea is to develop a hardware accelerator that will be able to dir...
17 February 2023
Electric vehicles, known for their low-noise emission, are popular and widespread in metropolises in China, and they provide an opportunity for a reduction in environmental noise from vehicles. To understand the noise from electric vehicles better, t...
This paper aims to improve the accuracy of reliability estimates and the failure time prediction for products exhibiting nonlinear degradation behavior under constant-stress accelerated degradation test (CSADT). To achieve this, a novel degradation m...
There are many different mathematical models that can be used to describe relations between energy machines in the power-split hybrid drive system. Usually, they are created based on simulations or measurements in bench (laboratory) conditions. In th...
Driving behavior is one of the most critical factors in traffic accidents. Accurate vehicle acceleration prediction approaches can promote the development of Advanced Driving Assistance Systems (ADAS) and improve traffic safety. However, few predicti...
In this article, we take stock of the findings from conceptual and empirical work on the role of transition initiatives for accelerating transitions as input for modeling acceleration dynamics. We applied the qualitative modeling approach of causal l...
Theoretical life prediction of tribo-pairs such as seals, bearings and gears with the failure form of wear under mixed lubrication depends on quantitative analysis of wear. Correspondingly, the wear life test depends on an accelerated wear test metho...
Model averaging has become a crucial statistical methodology, especially in situations where numerous models vie to elucidate a phenomenon. Over the past two decades, there has been substantial advancement in the theory of model averaging. However, a...
The Expectation-Maximisation (EM) algorithm is widely used for maximum likelihood estimation in incomplete data problems such as mixture modelling, but it often converges slowly, particularly when mixture components overlap substantially. This study...
The paper presents experimental data and a model of an electromagnetic rail accelerator. The model includes an equivalent circuit, magnetic field in the system and movement of the projectile (that is solved separately) which is computed numerically....
As the size of deep learning models continues to expand, the elongation of inference time has gradually evolved into a significant challenge to efficiency and practicality for autoregressive models. This work introduces a hybrid model acceleration st...
The expectation–maximization (EM) algorithm is an important numerical method for maximum likelihood estimation in incomplete data problems. However, convergence of the EM algorithm can be slow, and for this reason, many EM acceleration techniqu...
Survival analysis is a set of methods for statistical inference concerning the time until the occurrence of an event. One of the main objectives of survival analysis is to evaluate the effects of different covariates on event time. Although the propo...
Many fields including clinical and manufacturing areas usually perform life-testing experiments and accelerated failure time models (AFT) play an essential role in these investigations. In these models the covariate causes an accelerant effect on the...
For analyzing recurrent event data, we consider a generalization of the classical accelerated failure time model. In the proposed approach, the general function is no longer assumed to be a singleton but allowed to be time-varying. This is in the sam...
Multimodal medical image fusion provides vital anatomical and pathological details for clinical diagnosis. However, existing diffusion algorithms often struggle with prolonged inference times and local structure loss. To address these critical issues...
Previous studies have described vehicle acceleration profiles in non-car-following scenarios; however, the underlying mechanisms governing these profiles remain incompletely understood. This study aims to enhance the understanding of these mechanisms...
Considering that the degradation of ball screw grease involves fractal characteristics, which exhibit long-term dependency and autocorrelation, a multivariate accelerated degradation modeling and reliability assessment method based on the fractional...
Degradation models are central to the lifetime prediction of electromagnetic relays. Coefficients of degradation models under accelerated degradation test (ADTs) can be obtained experimentally, and it is customary to map these coefficients back to th...
The gravity forward modeling algorithm is a compute-intensive method and is widely used in scientific computing, particularly in geophysics, to predict the impact of subsurface structures on surface gravity fields. Traditional implementations rely on...
Uncertainty from heterogeneous degradation paths, limited experimental samples, and exogenous perturbations often complicates accelerated lifetime modeling and prediction. To confront these intertwined challenges, a Wiener process-based robust framew...
This work presents a semi-quantitative spectroscopic approach, including FTIR–ATR and Raman spectroscopies, for the biochemical analysis of red blood cells (RBCs) supported by the biochemical, morphological and rheological reference techniques. This...
The accelerated lifetime test is a widely used and effective approach in reliability analysis because of its shorter testing duration. In this study, product lifetimes are assumed to follow the Gompertz distribution. This article primarily focuses on...
This paper presents a GPU (Graphics Processing Unit)-accelerated and LTS (Local-time-Step)-based finite volume Shallow Water Model (SWM). The model performance is compared against the other five model versions (Single CPU versions with/without LTS, M...
To evaluate the lifetime and reliability of long-life, high-reliability products under limited resources, accelerated degradation testing (ADT) technology has been widely applied. Furthermore, the Bayesian evaluation method for ADT can comprehensivel...
Several standard fatigue testing methods are used to determine the fatigue stress-life prediction model (S-N curve) and the endurance limit of Reinforced Concrete (RC) beams, including the application of constant cyclic tension-tension loads at diffe...
This paper analyzes the behavior of a three-phase induction motor (IM) during voltage sags in the supply network and its subsequent re-acceleration following voltage recovery. A dynamic mathematical model based on the two-axis (d,q) representation of...
This study aims to propose a flexible, fully parametric hazard-based regression model for censored time-to-event data with crossing survival curves. We call it the accelerated hazard (AH) model. The AH model can be written with or without a baseline...
Modelling floods and flood-related disasters has become priority for many researchers and practitioners. Currently, there are several options that can be used for modelling floods in urban areas and the present work attempts to investigate effectiven...
In this article, we examined the behavior of dark energy (DE) and the cosmic acceleration in the framework of
In biomedical research, identifying genes associated with diseases is of paramount importance. However, only a small fraction of genes are related to specific diseases among the multitude of genes. Therefore, gene selection and estimation are necessa...
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