Wheel Weighing Meter of Continuous Rail Based on BP Neural Network and Symmetric Moving Average Filter

Regularly detecting the wheel weight of EMU (Electric Multiple Units) is of great significance for maintaining the safety of railway transportation. The existing wheel weighing instruments are mostly the rail-broken type, which is easy to cause safety risks in long-term use. A rail-continues wheel weighing meter is introduced to solve the problem. The rail-continues wheel weight meter introduces a composite sensor structure, using BP neural network to search the optimal sensor factors and introduces a parameter to eliminate the interference of the EMU driving speed. To process the output of the BP neural network, a symmetric moving average filtering algorithm is proposed. The experimental results show that the wheel weighing meter introduced in this paper has high precision and stability. The weighing error of the continuous rail wheel weighing meter is 0.24%

MSDH: matched subspace detector with heterogeneous noise

The matched subspace detector (MSD) is a classical subspace-based method for hyperspectral subpixel target detection. However, the model assumes that noise has the same variance over different bands, which is usually unrealistic in practice. In this letter, we relax the equal variance assumption and propose a matched subspace detector with heterogeneous noise (MSDH). In essence, the noise variances are different for different bands and they can be estimated by using iteratively reweighted least squares methods. Experiments on two benchmark real hyperspectral datasets demonstrate the superiority of MSDH over MSD for subpixel target detection

Conformal Isometry of Lie Group Representation in Recurrent Network of Grid Cells

The activity of the grid cell population in the medial entorhinal cortex (MEC) of the mammalian brain forms a vector representation of the self-position of the animal. Recurrent neural networks have been proposed to explain the properties of the grid cells by updating the neural activity vector based on the velocity input of the animal. In doing so, the grid cell system effectively performs path integration. In this paper, we investigate the algebraic, geometric, and topological properties of grid cells using recurrent network models. Algebraically, we study the Lie group and Lie algebra of the recurrent transformation as a representation of self-motion. Geometrically, we study the conformal isometry of the Lie group representation where the local displacement of the activity vector in the neural space is proportional to the local displacement of the agent in the 2D physical space. Topologically, the compact abelian Lie group representation automatically leads to the torus topology commonly assumed and observed in neuroscience. We then focus on a simple non-linear recurrent model that underlies the continuous attractor neural networks of grid cells. Our numerical experiments show that conformal isometry leads to hexagon periodic patterns in the grid cell responses and our model is capable of accurate path integration. Code is available at \url{https://github.com/DehongXu/grid-cell-rnn}

Conformal Normalization in Recurrent Neural Network of Grid Cells

Grid cells in the entorhinal cortex of the mammalian brain exhibit striking hexagon firing patterns in their response maps as the animal (e.g., a rat) navigates in a 2D open environment. The responses of the population of grid cells collectively form a vector in a high-dimensional neural activity space, and this vector represents the self-position of the agent in the 2D physical space. As the agent moves, the vector is transformed by a recurrent neural network that takes the velocity of the agent as input. In this paper, we propose a simple and general conformal normalization of the input velocity for the recurrent neural network, so that the local displacement of the position vector in the high-dimensional neural space is proportional to the local displacement of the agent in the 2D physical space, regardless of the direction of the input velocity. Our numerical experiments on the minimally simple linear and non-linear recurrent networks show that conformal normalization leads to the emergence of the hexagon grid patterns. Furthermore, we derive a new theoretical understanding that connects conformal normalization to the emergence of hexagon grid patterns in navigation tasks

APANet: Adaptive Prototypes Alignment Network for Few-Shot Semantic Segmentation

Few-shot semantic segmentation aims to segment novel-class objects in a given query image with only a few labeled support images. Most advanced solutions exploit a metric learning framework that performs segmentation through matching each query feature to a learned class-specific prototype. However, this framework suffers from biased classification due to incomplete feature comparisons. To address this issue, we present an adaptive prototype representation by introducing class-specific and class-agnostic prototypes and thus construct complete sample pairs for learning semantic alignment with query features. The complementary features learning manner effectively enriches feature comparison and helps yield an unbiased segmentation model in the few-shot setting. It is implemented with a two-branch end-to-end network (\ie, a class-specific branch and a class-agnostic branch), which generates prototypes and then combines query features to perform comparisons. In addition, the proposed class-agnostic branch is simple yet effective. In practice, it can adaptively generate multiple class-agnostic prototypes for query images and learn feature alignment in a self-contrastive manner. Extensive experiments on PASCAL-5 i and COCO-20 i demonstrate the superiority of our method. At no expense of inference efficiency, our model achieves state-of-the-art results in both 1-shot and 5-shot settings for few-shot semantic segmentation

APANet: Adaptive Prototypes Alignment Network for Few-Shot Semantic Segmentation

Few-shot semantic segmentation aims to segment novel-class objects in a given query image with only a few labeled support images. Most advanced solutions exploit a metric learning framework that performs segmentation through matching each query feature to a learned class-specific prototype. However, this framework suffers from biased classification due to incomplete feature comparisons. To address this issue, we present an adaptive prototype representation by introducing class-specific and class-agnostic prototypes and thus construct complete sample pairs for learning semantic alignment with query features. The complementary features learning manner effectively enriches feature comparison and helps yield an unbiased segmentation model in the few-shot setting. It is implemented with a two-branch end-to-end network (\ie, a class-specific branch and a class-agnostic branch), which generates prototypes and then combines query features to perform comparisons. In addition, the proposed class-agnostic branch is simple yet effective. In practice, it can adaptively generate multiple class-agnostic prototypes for query images and learn feature alignment in a self-contrastive manner. Extensive experiments on PASCAL-5 i and COCO-20 i demonstrate the superiority of our method. At no expense of inference efficiency, our model achieves state-of-the-art results in both 1-shot and 5-shot settings for few-shot semantic segmentation

Effects of fully open-air [CO2] elevation on leaf photosynthesis and ultrastructure of Isatis indigotica Fort

Traditional Chinese medicine relies heavily on herbs, yet there is no information on how these herb plants would respond to climate change. In order to gain insight into such response, we studied the effect of elevated [CO2] on Isatis indigotica Fort, one of the most popular Chinese herb plants. The changes in leaf photosynthesis,chlorophyll fluorescence, leaf ultrastructure and biomass yield in response to elevated [CO2] (550619 mmol mol–1) were determined at the Free-Air Carbon dioxide Enrichment (FACE) experimental facility in North China. Photosynthetic ability of I. indigotica was improved under elevated [CO2]. Elevated [CO2] increased net photosynthetic rate (PN), water use efficiency (WUE) and maximum rate of electron transport (Jmax) of upper most fully-expended leaves, but not stomatal conductance (gs), transpiration ratio (Tr) and maximum velocity of carboxylation (Vc,max). Elevated [CO2] significantly increased leaf intrinsic efficiency of PSII (Fv’/Fm’) and quantum yield of PSII(WPSII), but decreased leaf non-photochemical quenching (NPQ), and did not affect leaf proportion of open PSII reaction centers (qP) and maximum quantum efficiency of PSII (Fv/Fm). The structural chloroplast membrane, grana layer and stroma thylakoid membranes were intact under elevated [CO2], though more starch grains were accumulated within the chloroplasts than that of under ambient [CO2]. While the yield of I. indigotica was higher due to the improved photosynthesis under elevated [CO2], the content of adenosine, one of the functional ingredients in indigowoad root was not affected

Klein-Nishina effects on the high-energy afterglow emission of gamma-ray bursts

Extended high-energy(>100MeV) gamma-ray emission that lasts much longer than the prompt sub-MeV emission has been detected from quite a few gamma-ray bursts (GRBs) by Fermi Large Area Telescope (LAT) recently. A plausible scenario is that this emission is the afterglow synchrotron emission produced by electrons accelerated in the forward shocks. In this scenario, the electrons that produce synchrotron high-energy emission also undergo inverse-Compton (IC) loss and the IC scattering with the synchrotron photons should be in the Klein-Nishina regime. Here we study effects of the Klein-Nishina scattering on the high-energy synchrotron afterglow emission. We find that, at early times the Klein-Nishina suppression effect on those electrons that produce the high-energy emission is usually strong and therefore their inverse-Compton loss is small with a Compton parameter Y < a few for a wide range of parameter space. This leads to a relatively bright synchrotron afterglow at high energies that can be detected by Fermi LAT. As the Klein-Nishina suppression effect weakens with time, the inverse-Compton loss increases and could dominate over the synchrotron loss in some parameter space. This will lead to a faster temporal decay of the high-energy synchrotron emission than what is predicted by the standard synchrotron model, which may explain the observed rapid decay of the early high-energy gamma-ray emission in GRB090510 and GRB090902B.Comment: 8 page (emulateapj style), 8 figures, submitted to Ap

Probing Thermal Electrons in GRB Afterglows

Particle-in-cell simulations have unveiled that shock-accelerated electrons do not follow a pure power-law distribution, but have an additional low-energy "thermal" part, which owns a considerable portion of the total energy of electrons. Investigating the effects of these thermal electrons on gamma-ray burst (GRB) afterglows may provide valuable insights into the particle acceleration mechanisms. We solve the continuity equation of electrons in the energy space, from which multi-wavelength afterglows are derived by incorporating processes including synchrotron radiation, synchrotron self-absorption, synchrotron self-Compton scattering, and gamma-gamma annihilation. First, there is an underlying positive correlation between temporal and spectral indices due to the cooling of electrons. Moreover, thermal electrons would result in the simultaneous non-monotonic variation in both spectral and temporal indices at multi-wavelength, which could be individually recorded by the 2.5-meter Wide Field Survey Telescope and Vera Rubin Observatory Legacy Survey of Space and Time (LSST). The thermal electrons could also be diagnosed from afterglow spectra by synergy observation in the optical (with LSST) and X-ray bands (with the Microchannel X-ray Telescope on board the Space Variable Objects Monitor). Finally, we use Monte Carlo simulations to obtain the distribution of peak flux ratio ($R_{\rm X}$) between soft and hard X-rays, and of the time delay ($\Delta t$) between peak times of soft X-ray and optical light curves. The thermal electrons significantly raise the upper limits of both $R_{\rm X}$ and $\Delta t$. Thus the distribution of GRB afterglows with thermal electrons is more dispersive in the $R_{\rm X} - \Delta t$ plane.Comment: 17 pages, 15 figure

Fatigue Detection for Ship OOWs Based on Input Data Features, from The Perspective of Comparison with Vehicle Drivers: A Review

Ninety percent of the world’s cargo is transported by sea, and the fatigue of ship officers of the watch (OOWs) contributes significantly to maritime accidents. The fatigue detection of ship OOWs is more difficult than that of vehicles drivers owing to an increase in the automation degree. In this study, research progress pertaining to fatigue detection in OOWs is comprehensively analysed based on a comparison with that in vehicle drivers. Fatigue detection techniques for OOWs are organised based on input sources, which include the physiological/behavioural features of OOWs, vehicle/ship features, and their comprehensive features. Prerequisites for detecting fatigue in OOWs are summarised. Subsequently, various input features applicable and existing applications to the fatigue detection of OOWs are proposed, and their limitations are analysed. The results show that the reliability of the acquired feature data is insufficient for detecting fatigue in OOWs, as well as a non-negligible invasive effect on OOWs. Hence, low-invasive physiological information pertaining to the OOWs, behaviour videos, and multisource feature data of ship characteristics should be used as inputs in future studies to realise quantitative, accurate, and real-time fatigue detections in OOWs on actual ships