Efficient and Robust Driver Fatigue Detection Framework Based on the Visual Analysis of Eye States

Fatigue detection based on vision is widely employed in vehicles due to its real-time and reliable detection results. With the coronavirus disease (COVID-19) outbreak, many proposed detection systems based on facial characteristics would be unreliable due to the face covering with the mask. In this paper, we propose a robust visual-based fatigue detection system for monitoring drivers, which is robust regarding the coverings of masks, changing illumination and head movement of drivers. Our system has three main modules: face key point alignment, fatigue feature extraction and fatigue measurement based on fused features. The innovative core techniques are described as follows: (1) a robust key point alignment algorithm by fusing global face information and regional eye information, (2) dynamic threshold methods to extract fatigue characteristics and (3) a stable fatigue measurement based on fusing percentage of eyelid closure (PERCLOS) and proportion of long closure duration blink (PLCDB). The excellent performance of our proposed algorithm and methods are verified in experiments. The experimental results show that our key point alignment algorithm is robust to different scenes, and the performance of our proposed fatigue measurement is more reliable due to the fusion of PERCLOS and PLCDB

Early On-Orbit Performance of the Visible Infrared Imaging Radiometer Suite Onboard the Suomi National Polar-Orbiting Partnership (S-NPP) Satellite

The Visible Infrared Imaging Radiometer Suite (VIIRS) is one of the key environmental remote-sensing instruments onboard the Suomi National Polar-Orbiting Partnership spacecraft, which was successfully launched on October 28, 2011 from the Vandenberg Air Force Base, California. Following a series of spacecraft and sensor activation operations, the VIIRS nadir door was opened on November 21, 2011. The first VIIRS image acquired signifies a new generation of operational moderate resolution-imaging capabilities following the legacy of the advanced very high-resolution radiometer series on NOAA satellites and Terra and Aqua Moderate-Resolution Imaging Spectroradiometer for NASA's Earth Observing system. VIIRS provides significant enhancements to the operational environmental monitoring and numerical weather forecasting, with 22 imaging and radiometric bands covering wavelengths from 0.41 to 12.5 microns, providing the sensor data records for 23 environmental data records including aerosol, cloud properties, fire, albedo, snow and ice, vegetation, sea surface temperature, ocean color, and nigh-time visible-light-related applications. Preliminary results from the on-orbit verification in the postlaunch check-out and intensive calibration and validation have shown that VIIRS is performing well and producing high-quality images. This paper provides an overview of the onorbit performance of VIIRS, the calibration/validation (cal/val) activities and methodologies used. It presents an assessment of the sensor initial on-orbit calibration and performance based on the efforts from the VIIRS-SDR team. Known anomalies, issues, and future calibration efforts, including the long-term monitoring, and intercalibration are also discussed

Detecting Invalid Associations between Fare Machines and Metro Stations Using Smart Card Data

Data quality is essential for its authentic usage in analysis and applications. The large volume of automated collection data inevidently suffers from data quality issues including data missing and invalidity. This paper deals with an invalid data problem in the automated fare collection (AFC) database caused by the erroneous association between the fare machines and metro stations, e.g., a fare machine located at Station A is wrongly associated with Station B in the AFC database. It could lead to inappropriate fare charges in a distance-based fare system and cause analysis bias for planning/operation practice. We propose a tensor decomposition and isolation forest-based approach to detect and correct the invalid associated fare machines in the system. The tensor decomposition extracts features of passenger flows and travel times passing through fare machines. The isolation forest coupled with a neural network (NN) takes these features as inputs to detect the wrongly associated fare machines and infer the correct association stations. Case studies using data from a metro system show that the proposed detection approach achieves over 90% accuracy in detecting the invalid associations for up to 35% invalid associations. The inferred association has a 90% accuracy even when the invalid association ratio reaches 40%. The proposed data-driven invalid data detection method is useful for large-scale data management in terms of data quality check and fix

Recovering the Association Between Unlinked Fare Machines and Stations Using Automated Fare Collection Data in Metro Systems

Data quality is the foundation of data-driven applications in transportation. Data problems such as missing and invalid data could sharply reduce the performance of the methods used in these applications. Although there exist plenty of studies related to data quality issues, they only focus on missing or invalid data caused by infrastructure failures (e.g., loop detector malfunction). In general, there is a lack of attention to data quality issues from insufficient data management. This paper proposes a tensor decomposition based framework to tackle a specific missing data problem which occurs when the machine-station dictionary of an automated fare collection system database is incomplete. In such cases, there is a large amount of loss of origin/destination information as the affected machines are not linked to any station. Consequently, all associated transactions may miss the origin/destination information. The proposed framework recovers the dictionary by capturing features of the passenger flow passing through the unlinked fare machine. Evaluation results show that the proposed approach could recover the missing data with high accuracy even when several fare machines are not linked to a station. The framework could also support other beneficial applications

Co3S4@Li7P3S11 Hexagonal Platelets as Cathodes with Superior Interfacial Contact for All-Solid-State Lithium Batteries

Poor solid-solid contact between an electrode and solid electrolyte is a great challenge for all-solid-state lithium batteries (ASSLBs) which results in limited ion transport and eventually leads to rapid capacity fading. Twodimensional (2D) materials have incomparable advantage in the construction of the desired interface because of their flat surface and large specific surface area. In order to realize intimate interfacial contact and superior ion transport, monodisperse 2D Co3S4 hexagonal platelets as cathodes for all ASSLBs are synthesized through a series of topological reactions followed with in situ coating of tiny Li7P3S11 using a liquid-phase method. The unique 2D hexagonal platelets are favorable for in situ solid electrolyte coating. Moreover, the well-designed interfacial structure can make the electrode materials contact with solid electrolytes more closely, contributing to a remarkable improvement on electrochemical performance. ASSLBs employing the Co3S4@Li7P3S11 composite platelets as a cathode deliver a large reversible capacity of 685.9 mA h g(-1) at 0.5 A g(-1) for 50 cycles. Even at a high current density of 1 A g(-1), the Co3S4@Li7P3S11 composite cathode still exhibits a high capacity of 457.3 mA h g(-1) after 100 cycles. This work provides a simple strategy to design the composite electrode with intimate contact and superior ion transport via morphology controlling