OpenDriver: an open-road driver state detection dataset

In modern society, road safety relies heavily on the psychological and physiological state of drivers. Negative factors such as fatigue, drowsiness, and stress can impair drivers' reaction time and decision making abilities, leading to an increased incidence of traffic accidents. Among the numerous studies for impaired driving detection, wearable physiological measurement is a real-time approach to monitoring a driver's state. However, currently, there are few driver physiological datasets in open road scenarios and the existing datasets suffer from issues such as poor signal quality, small sample sizes, and short data collection periods. Therefore, in this paper, a large-scale multimodal driving dataset for driver impairment detection and biometric data recognition is designed and described. The dataset contains two modalities of driving signals: six-axis inertial signals and electrocardiogram (ECG) signals, which were recorded while over one hundred drivers were following the same route through open roads during several months. Both the ECG signal sensor and the six-axis inertial signal sensor are installed on a specially designed steering wheel cover, allowing for data collection without disturbing the driver. Additionally, electrodermal activity (EDA) signals were also recorded during the driving process and will be integrated into the presented dataset soon. Future work can build upon this dataset to advance the field of driver impairment detection. New methods can be explored for integrating other types of biometric signals, such as eye tracking, to further enhance the understanding of driver states. The insights gained from this dataset can also inform the development of new driver assistance systems, promoting safer driving practices and reducing the risk of traffic accidents. The OpenDriver dataset will be publicly available soon

A Theoretically Guaranteed Deep Optimization Framework for Robust Compressive Sensing MRI

Magnetic Resonance Imaging (MRI) is one of the most dynamic and safe imaging techniques available for clinical applications. However, the rather slow speed of MRI acquisitions limits the patient throughput and potential indi cations. Compressive Sensing (CS) has proven to be an efficient technique for accelerating MRI acquisition. The most widely used CS-MRI model, founded on the premise of reconstructing an image from an incompletely filled k-space, leads to an ill-posed inverse problem. In the past years, lots of efforts have been made to efficiently optimize the CS-MRI model. Inspired by deep learning techniques, some preliminary works have tried to incorporate deep architectures into CS-MRI process. Unfortunately, the convergence issues (due to the experience-based networks) and the robustness (i.e., lack real-world noise modeling) of these deeply trained optimization methods are still missing. In this work, we develop a new paradigm to integrate designed numerical solvers and the data-driven architectures for CS-MRI. By introducing an optimal condition checking mechanism, we can successfully prove the convergence of our established deep CS-MRI optimization scheme. Furthermore, we explicitly formulate the Rician noise distributions within our framework and obtain an extended CS-MRI network to handle the real-world nosies in the MRI process. Extensive experimental results verify that the proposed paradigm outperforms the existing state-of-the-art techniques both in reconstruction accuracy and efficiency as well as robustness to noises in real scene

Fate of particles released by a puff–dispersion with different air distributions

Well-mixed assumption normally has flaws in the space with continuous-releasing particle sources. For transient point or puff sources, however, particle concentration might vary differently among locations during emission periods and afterwards. This study measures whether and how rapidly ventilation systems can distribute particles emitted from puff-like sources in an indoor space. The impact of ventilation pattern (over-head mixing ventilation and displacement ventilation), particle size (0.77, 2.5 and 7 μm) and source location are also examined. The results show that particles with sizes of 0.77 μm and 2.5 μm can be distributed uniformly by both mixing ventilation and displace ventilation shortly (within a few minutes) after particle injection is terminated, regardless of particle source locations with the absence of obstructed airflow. This paper validates the well-mixed assumption when assessing long-term human exposure to puff-generated particles in the indoor environment. With regard to puff sources, the spatial concentration enhancement in human microenvironment/breathing zone might not be as significant as continuous-releasing particle sources

Predicted percentage dissatisfied with vertical temperature gradient

A vertical thermally stratified environment provides opportunities for improved ventilation effectiveness and energy efficiency, but vertical temperature gradient can also cause local thermal discomfort. ASHRAE 55 and ISO 7730 prescribe a 3 °C/m limit between head and feet for seated persons. However, an increasing amount of evidence suggests that this limit is too restrictive. To revisit how vertical temperature gradient affects local thermal comfort, we conducted laboratory tests with four nominal vertical temperature gradients (0.4, 2.9, 5.9, and 8.4 °C/m). Ninety-eight seated college-age students participated in a blind within-subject experiment. Cold-feet discomfort is more frequently rated than warm-head discomfort with increasing temperature gradients. By using logistic regression modeling, we show that the whole-body dissatisfaction increases only slightly (< 10 %) with vertical temperature gradient, even up to 8.4 °C/m. Sex does not significantly affect the results except at 8.4 °C/m. Acceptable vertical temperature gradient changes with thermal sensation votes. The results suggest that the vertical temperature gradient could be increased to 5 °C/m between head and feet when the subject is thermally neutral

Development of TiA1-based alloys using suspended droplet alloying

In this project a novel combinatorial synthesis method, Suspended Droplet Alloying (SDA), has been developed for the rapid production of small, bulk alloy samples for the large EU FP7 collaborative project, Accelerated Metallurgy. SDA can produce a discrete mm-sized fully dense alloy button with precise stoichiometry in several minutes. Samples of many different alloy systems have been produced by SDA but this thesis will only present work on the Ti-Al-V, Ti-Al-Fe and Ti-Al-Nb alloy systems. Ti-Al-Nb alloy samples are difficult to make due to the high melting point of Nb, so the SDA process parameters have been optimized in order to make homogeneous Ti-Al-Nb alloys. The fundamentals of the SDA process have been studied in terms of the formation of the droplets and the consistency of the process. Splats deposited by the impact of individual alloy droplets have also been investigated. Finally, SDA has been used to explore the influence of a fourth elemental addition to a Ti-46Al-8Nb alloy. The elements added are V, Hf, Cr and Zr. It has been found that the addition of V can increase the ductility of Ti-46Al-8Nb alloy significantly

Advances to ASHRAE Standard 55 to encourage more effective building practice

ASHRAE Standard 55 has been evolving in recent years to encourage more sustainable building designs and operational practices. A series of changes address issues for which past design practice has been deficient or overly constrained. Some of the changes were enabled by findings from field studies of comfort and energy-efficiency, and others by new developments in the design- and building-management professions.  The changes have been influencing practice and spurring follow-on research.The Standard now addresses effects of elevated air movement, solar gain on the occupant, and draft at the ankles, each with several impacts on energy-efficient design and operation. It also addresses the most important source of discomfort in modern buildings, the large inter- and intra-personal variability in thermal comfort requirements, by classifying the occupants’ personal control and adaptive options in a form that can be used in building rating systems. In order to facilitate design, new computer tools extend the use of the standard toward direct use in designers’ workflow. The standard also includes provisions for monitoring and evaluating buildings in operation. This paper summarizes these developments and their underlying research, and attempts to look ahead