inTformer: A Time-Embedded Attention-Based Transformer for Crash Likelihood Prediction at Intersections Using Connected Vehicle Data

The real-time crash likelihood prediction model is an essential component of the proactive traffic safety management system. Over the years, numerous studies have attempted to construct a crash likelihood prediction model in order to enhance traffic safety, but mostly on freeways. In the majority of the existing studies, researchers have primarily employed a deep learning-based framework to identify crash potential. Lately, Transformer has emerged as a potential deep neural network that fundamentally operates through attention-based mechanisms. Transformer has several functional benefits over extant deep learning models such as Long Short-Term Memory (LSTM), Convolution Neural Network (CNN), etc. Firstly, Transformer can readily handle long-term dependencies in a data sequence. Secondly, Transformer can parallelly process all elements in a data sequence during training. Finally, Transformer does not have the vanishing gradient issue. Realizing the immense possibility of Transformer, this paper proposes inTersection-Transformer (inTformer), a time-embedded attention-based Transformer model that can effectively predict intersection crash likelihood in real-time. The proposed model was evaluated using connected vehicle data extracted from INRIX's Signal Analytics Platform. The data was parallelly formatted and stacked at different timesteps to develop nine inTformer models. The best inTformer model achieved a sensitivity of 73%. This model was also compared to earlier studies on crash likelihood prediction at intersections and with several established deep learning models trained on the same connected vehicle dataset. In every scenario, this inTformer outperformed the benchmark models confirming the viability of the proposed inTformer architecture.Comment: 29 pages, 7 figures, 9 table

Real-Time Crash Risk Estimation: Are All Freeways Created Equal?

Underground loop detectors have been recently used by many researchers to investigate the links with real-time crash risk and the traffic data. An issue that has been raised but not explicitly addressed in these studies is how the results from one freeway might transfer to another. This study attempts to look at the relationship between crash risk and real-time traffic variables from a freeway corridor (I4 eastbound in Orlando, FL) and attempts to apply the models to three other freeway corridors (I-4 westbound, and I-95 north and southbound). Traffic data used in the study were collected using loop as well as radar detectors already installed on these freeways. The traffic information was collected for crash as well as random non-crash cases so that a binary classification approach may be adopted. The Random Forest based models provide a list of significant variables based on the mean average reduction in the Gini indices to the overall forest classification. The period between 5-10 minutes before and 10-15 minutes before the crash were taken into consideration to allow for the model to be developed so as to facilitate the issuance of warning in advance. Average occupancy of upstream station and average speed and coefficient of variation of volume for downstream stations were observed to better the classification trees. Application of multilayer perceptron neural network models showed that while the model developed for I-4 corridor works reasonably well for the I-4 westbound data the performance is not as good for the I-95 sections. It indicates that the same model for crash risk identification may only work for corridors with very similar travel patterns. Keywords: Real-time crash risk, transferability, freeway safety, random forest, neural network

A Proposed ANN-Based Acceleration Control Scheme for Soft Starting Induction Motor

In this article, a new soft starting control scheme based on an artificial neural network (ANN) is presented for a three-phase induction motor (IM) drive system. The main task of the control scheme is to keep the accelerating torque constant at a level based on the value of reference acceleration. This is accomplished by the proper choice of the firing angles of thyristors in the soft starter. Using the ANN approach, the complexity of the online determination of the thyristors firing angles is resolved. The IM torque-speed characteristic curves are firstly used to train the ANN model. Secondly, the IM- soft starter system is modeled using MATLAB/SIMULINK. To prove the effectiveness of the proposed ANN-based acceleration control scheme, different reference accelerations and loading conditions are applied and investigated. Finally, a laboratory prototype of 3 kW soft starter is implemented. The proposed control scheme is executed in a real-time environment using a digital signal processor (Model: TMS320F28335). The simulation and real-time results significantly confirm that the proposed controller can efficiently reduce the IM starting current and torque pulsations. This in turn ensures a smooth acceleration of the IM during the starting process. Moreover, the proposed control scheme has the superiority over several soft starting control schemes since it has a simple control circuit configuration, less required sensors, and low computational burden of the control algorithm. © 2021 Institute of Electrical and Electronics Engineers Inc.. All rights reserved

Noninvasive Detection of Fibrosis Applying Contrast-Enhanced Cardiac Magnetic Resonance in Different Forms of Left Ventricular Hypertrophy Relation to Remodeling

ObjectivesWe aimed to evaluate the incidence and patterns of late gadolinium enhancement (LGE) in different forms of left ventricular hypertrophy (LVH) and to determine their relation to severity of left ventricular (LV) remodeling.BackgroundLeft ventricular hypertrophy is an independent predictor of cardiac mortality. The relationship between LVH and myocardial fibrosis as defined by LGE cardiovascular magnetic resonance (CMR) is not well understood.MethodsA total of 440 patients with aortic stenosis (AS), arterial hypertension (AH), or hypertrophic cardiomyopathy (HCM) fulfilling echo criteria of LVH underwent CMR with assessment of LV size, weight, function, and LGE. Patients with increased left ventricular mass index (LVMI) resulting in global LVH in CMR were included in the study.ResultsCriteria were fulfilled by 83 patients (56 men, age 57 ± 14 years; AS, n = 21; AH, n = 26; HCM, n = 36). Late gadolinium enhancement was present in all forms of LVH (AS: 62%, AH: 50%; HCM: 72%, p = NS) and was correlated with LVMI (r = 0.237, p = 0.045). There was no significant relationship between morphological obstruction and LGE. The AS subjects with LGE showed higher LV end-diastolic volumes than those without (1.0 ± 0.2 ml/cm vs. 0.8 ± 0.2 ml/cm, p < 0.015). Typical patterns of LGE were observed in HCM but not in AS and AH.ConclusionsFibrosis as detected by CMR is a frequent feature of LVH, regardless of its cause, and depends on the severity of LV remodeling. As LGE emerges as a useful tool for risk stratification also in nonischemic heart diseases, our findings have the potential to individualize treatment strategies

An open-source software tool for the generation of relaxation time maps in magnetic resonance imaging

BACKGROUND: In magnetic resonance (MR) imaging, T1, T2 and T2* relaxation times represent characteristic tissue properties that can be quantified with the help of specific imaging strategies. While there are basic software tools for specific pulse sequences, until now there is no universal software program available to automate pixel-wise mapping of relaxation times from various types of images or MR systems. Such a software program would allow researchers to test and compare new imaging strategies and thus would significantly facilitate research in the area of quantitative tissue characterization. RESULTS: After defining requirements for a universal MR mapping tool, a software program named MRmap was created using a high-level graphics language. Additional features include a manual registration tool for source images with motion artifacts and a tabular DICOM viewer to examine pulse sequence parameters. MRmap was successfully tested on three different computer platforms with image data from three different MR system manufacturers and five different sorts of pulse sequences: multi-image inversion recovery T1; Look-Locker/ TOMROP T1; modified Look-Locker inversion recovery (MOLLI) T1; single-echo T2/ T2*; and multi-echo T2/ T2*. Computing times varied between 2 and 113 seconds. Estimates of relaxation times compared favorably to those obtained from non-automated curve fitting. Completed maps were exported in DICOM format and could be read in standard software packages used for analysis of clinical and research MR data. CONCLUSIONS: MRmap is a flexible cross-platform research tool that enables accurate mapping of relaxation times from various pulse sequences. The software allows researchers to optimize quantitative MR strategies in a manufacturer-independent fashion. The program and its source code were made available as open-source software on the internet