Dust properties from GALEX observations of a UV halo around Spica

GALEX has detected ultraviolet halos extending as far as 5$^{\circ}$ around four bright stars (Murthy et al. (2011)). These halos are produced by scattering of starlight by dust grains in thin foreground clouds that are not physically associated with the star. Assuming a simple model consisting of a single layer of dust in front of the star, Murthy et al.(2011) have been able to model these halo intensities and constrain the value of the phase function asymmetry factor $g$ of the scattering grains in the FUV and NUV. However due to the uncertainty in the dust geometry they could not constrain the albedo. In this work we have tried to constrain the optical constants and dust geometry by modeling the UV halo of Spica. Since the halo emission is not symmetric, we have modeled the Northern and Southern parts of the halo separately. To the North of Spica, the best-fit albedo is 0.26$\pm$0.1 and $g$ is 0.58$\pm$0.11 in the FUV at the 90% confidence level. The corresponding limits on the distance and optical depth ($\tau$) of the dust sheet is 3.65$\pm$1.05 pc and 0.047$\pm$0.006 respectively. However, owing to a complicated dust distribution to the South of Spica, we were unable to uniquely constrain the dust parameters in that region. Nevertheless, by assuming the optical constants of the Northern region and assuming a denser medium, we were able to constrain the distance of the dust to 9.5$\pm$1.5 pc and the corresponding $\tau$ to 0.04$\pm$0.01.Comment: 4 pages, accepted for publication in Earth, Planets and Spac

A PHM System Approach: Application to a Simplified Aircraft Bleed System

Regarding Prognostics and Health Management (PHM), the stakes lie in system-level prognostics or even the prognostics of systems of systems, as decisions are usually made at system or platform level. In this paper, a method, which takes into account both the system redundancy and the adaptation of operational modes in degraded functioning, is proposed and formalized. This method makes the system-level prognostics more relevant. The main feature of the method is to re-compute the components Remaining Useful Life (RUL) using the degradation rate associated to the future operating mode(s) due to system reconfiguration. This results in an improvement of both the System (SRUL) and the components . The proposed method is applied on a simplified aircraft bleed valve system to illustrate its effectiveness. This method is primarily destined to aeronautic systems, which are usually resilient. It has not been tested whether or not it could be useful in other fields

Drive in Peace

In this paper, in order to implement a computer vision-based recognition system of driving fatigue. In addition to detecting human face in different light sources and the background conditions, and tracking eyes state combined with fuzzy logic to determine whether the driver of the physiological phenomenon of fatigue from face of detection. Driving fatigue recognition has been valued highly in recent years by many scholars and used extensively in various fields, for example, driver activity tracking, driver visual attention monitoring, and in-car camera systems.In this paper, we use the Windows operating system as the development environment, and utilize PC as the hardware platform. First, the system uses a camera to obtain the frame with a human face to detect, and then uses the frame to set the appropriate skin color scope to find face. Next, we find and mark out the eyes and the lips from the selected face area. Finally, we combine the image processing of eyes features with fuzzy logic to determine the driver's fatigue level, and make the graphical man-machine interface with MiniGUI for users to operate.Along with that we are using Arduino Uno microcontroller which is connected to MQ2-smoke sensor through which we can detect smoke which appears through issue in the car system. The results of experiment show that we achieve this system on PC platform successfully

Reducing False Discoveries in Statistically-Significant Regional-Colocation Mining: A Summary of Results

Given a set S of spatial feature types, its feature instances, a study area, and a neighbor relationship, the goal is to find pairs such that C is a statistically significant regional-colocation pattern in r_{g}. This problem is important for applications in various domains including ecology, economics, and sociology. The problem is computationally challenging due to the exponential number of regional colocation patterns and candidate regions. Previously, we proposed a miner [Subhankar et. al, 2022] that finds statistically significant regional colocation patterns. However, the numerous simultaneous statistical inferences raise the risk of false discoveries (also known as the multiple comparisons problem) and carry a high computational cost. We propose a novel algorithm, namely, multiple comparisons regional colocation miner (MultComp-RCM) which uses a Bonferroni correction. Theoretical analysis, experimental evaluation, and case study results show that the proposed method reduces both the false discovery rate and computational cost

Analyzing Trajectory Gaps for Possible Rendezvous: A Summary of Results

Given trajectory data with gaps, we investigate methods to identify possible rendezvous regions. Societal applications include improving maritime safety and regulations. The challenges come from two aspects. If trajectory data are not available around the rendezvous then either linear or shortest-path interpolation may fail to detect the possible rendezvous. Furthermore, the problem is computationally expensive due to the large number of gaps and associated trajectories. In this paper, we first use the plane sweep algorithm as a baseline. Then we propose a new filtering framework using the concept of a space-time grid. Experimental results and case study on real-world maritime trajectory data show that the proposed approach substantially improves the Area Pruning Efficiency over the baseline technique

Reducing Uncertainty in Sea-level Rise Prediction: A Spatial-variability-aware Approach

Given multi-model ensemble climate projections, the goal is to accurately and reliably predict future sea-level rise while lowering the uncertainty. This problem is important because sea-level rise affects millions of people in coastal communities and beyond due to climate change's impacts on polar ice sheets and the ocean. This problem is challenging due to spatial variability and unknowns such as possible tipping points (e.g., collapse of Greenland or West Antarctic ice-shelf), climate feedback loops (e.g., clouds, permafrost thawing), future policy decisions, and human actions. Most existing climate modeling approaches use the same set of weights globally, during either regression or deep learning to combine different climate projections. Such approaches are inadequate when different regions require different weighting schemes for accurate and reliable sea-level rise predictions. This paper proposes a zonal regression model which addresses spatial variability and model inter-dependency. Experimental results show more reliable predictions using the weights learned via this approach on a regional scale.Comment: 6 pages, 5 figures, I-GUIDE 2023 conferenc