Driving Risk Assessment Using Non-Negative Matrix Factorization With Driving Behavior Records

Aggressive driving behavior (ADB) is a major cause of traffic accidents. As ADB is controllable, ADB-based driving risk assessment is an effective method for drivers and transportation companies to ensure driving safety. Conventionally, the relationships between ADBs and accident-related records are analyzed when assessing driving risk. However, such records typically overlook driver responsibility for driving risks and depend considerably on the person producing the data (e.g., police officers or insurance managers). Foremost, conventional approaches do not consider non-accident situations that comprise most driving scenarios. Thus, we propose a novel driving risk assessment method that uses only ADB data. In this method, interpretable latent risk factors are extracted from ADB data via sparse non-negative matrix factorization (NMF), and then the driving risk score is computed on a scale of 0-100. The proposed method was validated by adopting a real-world application to assess the driving risk of bus drivers in South Korea and by conducting an evaluation performed by transportation experts in conjunction with the Korea Transportation Safety Authority. Results revealed that the proposed method can discriminate between high-and low-risk driving, thus providing clear guidelines to improve driving. Then, the proposed driving risk score assessment method using NMF was compared with existing machine learning-based risk assessment methods. The proposed method outperformed the conventional methods in terms of driving risk discrimination and interpretability. This study can provide risk assessment guidelines based on driving behavior records and contribute to the application of machine learning in transportation safety management

Explaining Convolutional Neural Networks through Attribution-Based Input Sampling and Block-Wise Feature Aggregation

As an emerging field in Machine Learning, Explainable AI (XAI) has been offering remarkable performance in interpreting the decisions made by Convolutional Neural Networks (CNNs). To achieve visual explanations for CNNs, methods based on class activation mapping and randomized input sampling have gained great popularity. However, the attribution methods based on these techniques provide lower resolution and blurry explanation maps that limit their explanation power. To circumvent this issue, visualization based on various layers is sought. In this work, we collect visualization maps from multiple layers of the model based on an attribution-based input sampling technique and aggregate them to reach a fine-grained and complete explanation. We also propose a layer selection strategy that applies to the whole family of CNN-based models, based on which our extraction framework is applied to visualize the last layers of each convolutional block of the model. Moreover, we perform an empirical analysis of the efficacy of derived lower-level information to enhance the represented attributions. Comprehensive experiments conducted on shallow and deep models trained on natural and industrial datasets, using both ground-truth and model-truth based evaluation metrics validate our proposed algorithm by meeting or outperforming the state-of-the-art methods in terms of explanation ability and visual quality, demonstrating that our method shows stability regardless of the size of objects or instances to be explained.Comment: 9 pages, 9 figures, Accepted at the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21

Large Linear Magnetoresistance in Heavily-Doped Nb:SrTiO\u3csub\u3e3\u3c/sub\u3e Epitaxial Thin Films

Interaction between electrons has long been a focused topic in condensed-matter physics since it has led to the discoveries of astonishing phenomena, for example, high-Tc superconductivity and colossal magnetoresistance (CMR) in strongly-correlated materials. In the study of strongly-correlated perovskite oxides, Nb-doped SrTiO3 (Nb:SrTiO3) has been a workhorse not only as a conducting substrate, but also as a host possessing high carrier mobility. In this work, we report the observations of large linear magnetoresistance (LMR) and the metal-to-insulator transition (MIT) induced by magnetic field in heavily-doped Nb:STO (SrNb0.2Ti0.8O3) epitaxial thin films. These phenomena are associated with the interplay between the large classical MR due to high carrier mobility and the electronic localization effect due to strong spin-orbit coupling, implying that heavily Nb-doped Sr(Nb0.2Ti0.8)O3 is promising for the application in spintronic devices

A Multi-Epoch, Simultaneous Water and Methanol Maser Survey toward Intermediate-Mass Young Stellar Objects

We report a multi-epoch, simultaneous 22 GHz H2O and 44 GHz class I CH3OH maser line survey towards 180 intermediate-mass young stellar objects, including 14 Class 0, 19 Class I objects, and 147 Herbig Ae/Be stars. We detected H2O and CH3OH maser emission towards 16 (9%) and 10 (6%) sources with one new H2O and six new CH3OH maser sources. The detection rates of both masers rapidly decrease as the central (proto)stars evolve, which is contrary to the trends in high-mass star-forming regions. This suggests that the excitations of the two masers are closely related to the evolutionary stage of the central (proto)stars and the circumstellar environments. H2O maser velocities deviate on average 9 km s^-1 from the ambient gas velocities whereas CH3OH maser velocities match quite well with the ambient gas velocities. For both maser emissions, large velocity differences (|v_{H2O} - v_{sys} | > 10 km s^-1 and |v_{CH3OH} - v_{sys}| > 1 km s^-1) are mostly confined to Class 0 objects. The formation and disappearance of H2O masers is frequent and their integrated intensities change by up to two orders of magnitude. In contrast, CH3OH maser lines usually show no significant change in intensity, shape, or velocity. This is consistent with the previous suggestion that H2O maser emission originates from the base of an outflow while 44 GHz class I CH3OH maser emission arises from the interaction region of the outflow with the ambient gas. The isotropic maser luminosities are well correlated with the bolometric luminosities of the central objects. The fitted relations are L_{H2O} = 1.71 * 10^{-9} (L_{bol})^{0.97} and L_{CH3OH} = 1.71 * 10^{-10} (L_{bol})^{1.22}.Comment: Accepted to ApJS, 40 pages, 9 figures, 9 table

An Experimental Analysis of Water–Air Two-Phase Flow Pattern and Air Entrainment Rate in Self-Entrainment Venturi Nozzles

For self-entrainment venturi nozzles, the effects of nozzle shapes and operating conditions on the water–air two-phase flow pattern, and the characteristics of the air entrainment rate have been investigated. A rectangular venturi nozzle with width and height dimensions of 3 mm and 0.5 mm was used with a vertically downward flow direction. The pressure ratio, which is the ratio of the inlet and outlet pressures, water flow rate, and diverging angle were set as experimental parameters. From the flow visualization, annular and bubbly flows were observed. In the case of bubbly flow, the more bubbles that are generated with a higher water flow rate, the smaller the pressure ratio. In the case of annular flow, the increased pressure ratio and water flow rate induce the breakup of air core in the diverging area and make the interfacial oscillation stronger, which finally causes the flow transition from annular to bubbly flow, by accompanying a sharp increase in the air entrainment rate. During this flow transition, the frictional pressure drop of the two-phase flow is reduced, showing that a two-phase multiplier gets smaller

Fully Solution-Processable Photoelectrochemical Cells for Bias-Free Solar Water Splitting

Solar production of hydrogen, the so-called artificial photosynthesis is getting more and more attention as a promising solution to modern energy and environmental problems. In principle, photoelectrochemical cells allows efficient production and separation of hydrogen by visible-light driven water electrolysis. However, conventional PEC devices have many limitations for their application such as low performance, low stability, complicate fabrication process, and the use of expensive material (e.g., Pt and Ru). To address these problems, we developed a fully solution-processable photoelectrochemical cells with cheap and abundant materials for bias-free solar water splitting. Unassisted solar water oxidation was enabled by constructing a PEC cell composed of BiVO4 photoanode and Cu2O photocathode modified by layer-by-layer assembly. It is found that the deposition of molecular polyelectrolytes and electrocatalysts on each electrode significantly improve their photoelectrochemical properties in terms of both performance and stability. We believe that the present study provides an insight and flexibility in the design and fabrication of future PEC devices

Surfactant-controlled synthesis of polygonal-stacked Cu2O as advanced anode materials for Li-ion batteries

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Polygonal-stacked Cu2O synthesized by surfactant-controlled Benedict’s reaction for advanced anode materials lithium-ion batteries

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