Knowledge-Driven Semantic Segmentation for Waterway Scene Perception

Semantic segmentation as one of the most popular scene perception techniques has been studied for autonomous vehicles. However, deep learning-based solutions rely on the volume and quality of data and knowledge from specific scene might not be incorporated. A novel knowledge-driven semantic segmentation method is proposed for waterway scene perception. Based on the knowledge that water is irregular and dynamically changing, a Life Time of Feature (LToF) detector is designed to distinguish water region from surrounding scene. Using a Bayesian framework, the detector as the likelihood function is combined with U-Net based semantic segmentation to achieve an optimized solution. Finally, two public datasets and typical semantic segmentation networks, FlowNet, DeepLab and DVSNet are selected to evaluate the proposed method. Also, the sensitivity of these methods and ours to dataset is discussed

Thickness-dependent magnetic properties in Pt[CoNi]n multilayers with perpendicular magnetic anisotropy

We systematically investigated the Ni and Co thickness-dependent perpendicular magnetic anisotropy (PMA) coefficient, magnetic domain structures, and magnetization dynamics of Pt(5 nm)/[Co(t_Co nm)/Ni(t_Ni nm)]5/Pt(1 nm) multilayers by combining the four standard magnetic characterization techniques. The magnetic-related hysteresis loops obtained from the field-dependent magnetization M and anomalous Hall resistivity (AHR) \r{ho}_xy found that the two serial multilayers with t_Co = 0.2 and 0.3 nm have the optimum PMA coefficient K_U well as the highest coercivity H_C at the Ni thickness t_Ni = 0.6 nm. Additionally, the magnetic domain structures obtained by Magneto-optic Kerr effect (MOKE) microscopy also significantly depend on the thickness and K_U of the films. Furthermore, the thickness-dependent linewidth of ferromagnetic resonance is inversely proportional to K_U and H_C, indicating that inhomogeneous magnetic properties dominate the linewidth. However, the intrinsic Gilbert damping constant determined by a linear fitting of frequency-dependent linewidth does not depend on Ni thickness and K_U. Our results could help promote the PMA [Co/Ni] multilayer applications in various spintronic and spin-orbitronic devices.Comment: 17 pages, 4 figure

Complex-Valued Multi-Scale Fully Convolutional Network with Stacked-Dilated Convolution for PolSAR Image Classification

Polarimetric synthetic aperture radar (PolSAR) image classification is a pixel-wise issue, which has become increasingly prevalent in recent years. As a variant of the Convolutional Neural Network (CNN), the Fully Convolutional Network (FCN), which is designed for pixel-to-pixel tasks, has obtained enormous success in semantic segmentation. Therefore, effectively using the FCN model combined with polarimetric characteristics for PolSAR image classification is quite promising. This paper proposes a novel FCN model by adopting complex-valued domain stacked-dilated convolution (CV-SDFCN). Firstly, a stacked-dilated convolution layer with different dilation rates is constructed to capture multi-scale features of PolSAR image; meanwhile, the sharing weight is employed to reduce the calculation burden. Unfortunately, the labeled training samples of PolSAR image are usually limited. Then, the encoder–decoder structure of the original FCN is reconstructed with a U-net model. Finally, in view of the significance of the phase information for PolSAR images, the proposed model is trained in the complex-valued domain rather than the real-valued domain. The experiment results show that the classification performance of the proposed method is better than several state-of-the-art PolSAR image classification methods

Development of a DP980 steel with low cooling rate requirement

DP980 is a promising light-weightening material in car body. To avoid high investment of strong cooling system, a new DP980 steel with low cooling rate requirement was developed. The mechanical properties and microstructure were analyzed under different manufacturing process. It could be concluded that the chemical composition design should be reasonable and of low cost to achieve both high strength and also austenite to martensite transformation at low cooling rate. Strength increased with coiling temperature decreasing during hot rolling, and higher annealing temperature and lower over aging temperature were favourable to higher strength. The austenite-martensite transforming could be completed at even lower rapid cooling rate of 20°C/s. Through optimized manufacturing process parameters, the new DP steel product with good mechanical properties could be obtained successfully, which provided a new option for normal production line to produce ultra high strength steel

Ship Intention Prediction at Intersections Based on Vision and Bayesian Framework

Due to the high error frequency of the existing methods in identifying a ship’s navigational intention, accidents frequently occur at intersections. Therefore, it is urgent to improve the ability to perceive ship intention at intersections. In this paper, we propose an algorithm based on the fusion of image sequence and radar information to identify the navigation intention of ships at intersections. Some existing algorithms generally use the Automatic Identification System (AIS) to identify ship intentions but ignore the problems of AIS delay and data loss, resulting in unsatisfactory effectiveness and accuracy of intention recognition. Firstly, to obtain the relationship between radar and image, a cooperative target composed of a group of concentric circles and a central positioning radar angle reflector is designed. Secondly, the corresponding relationship of radar and image characteristic matrix is obtained after employing the RANSAC method to fit radar and image detection information; then, the homographic matrix is solved to realize radar and image data matching. Thirdly, the YOLOv5 detector is used to track the ship motion in the image sequence. The visual measurement model based on continuous object tracking is established to extract the ship motion parameters. Finally, the motion intention of the ship is predicted by integrating the extracted ship motion features with the position information of the shallow layer using a Bayesian framework. Many experiments on real data sets show that our proposed method is superior to the most advanced method for ship intention identification at intersections

Multichannel semi-supervised active learning for PolSAR image classification

Deep neural networks have recently been extensively utilized for Polarimetric synthetic aperture radar (PolSAR) image classification. However, this heavily relies on extensive labeled data which is both costly and labor-intensive. To lower the collection of labeling data and enhance the classification performance, a novel multichannel semi-supervised active learning (MSSAL) method is proposed for PolSAR image classification. First, a multichannel strategy-based committee model with cooperative representation classification is presented to explore more effective information in the limited training data. Second, a loss prediction (LP) module is designed to identify the most informative pixels, and an ensemble learning (EL) strategy is designed to select the pixels with the highest confidence. Then, the deep neural network is fine-tuned with the obtaining target pixels through LP and EL in each iteration. Finally, the trained deep model predicts labels for all unlabeled data, outputting the final classification results. The proposed method is evaluated on three real-world PolSAR datasets, demonstrating superior performance to other PolSAR image classification methods with limited labeled samples

Understanding the structural features of high-amylose maize starch through hydrothermal treatment

In this study, high-amylose starches were hydrothermally-treated and the structural changes were monitored with time (up to 12 h) using scanning electron microscopy (SEM), confocal laser scanning microscopy (CLSM), small-angle X-ray scattering (SAXS), X-ray diffraction (XRD), and differential scanning calorimetry (DSC). When high-amylose starches were treated in boiling water, half-shell-like granules were observed by SEM, which could be due to the first hydrolysis of the granule inner region (CLSM). This initial hydrolysis could also immediately (0.5 h) disrupt the semi-crystalline lamellar regularity (SAXS) and dramatically reduce the crystallinity (XRD); but with prolonged time of hydrothermal treatment (≥2 h), might allow the perfection or formation of amylose single helices, resulting in slightly increased crystallinity (XRD and DSC). These results show that the inner region of granules is composed of mainly loosely-packed amylopectin growth rings with semi-crystalline lamellae, which are vulnerable under gelatinization or hydrolysis. In contrast, the periphery is demonstrated to be more compact, possibly composed of amylose and amylopectin helices intertwined with amylose molecules, which require greater energy input (higher temperature) for disintegration

The Environmental and Socioeconomic Effects and Prediction of Patients With Tuberculosis in Different Age Groups in Southwest China: A Population-Based Study

BackgroundWhile the End Tuberculosis (TB) Strategy has been implemented worldwide, the cause of the TB epidemic is multifactorial and not fully understood. ObjectiveThis study aims to investigate the risk factors of TB and incorporate these factors to forecast the incidence of TB infection across different age groups in Sichuan, China. MethodsCorrelation and linear regression analyses were conducted to assess the relationships between TB cases and ecological factors, including environmental, economic, and social factors, in Sichuan Province from 2006 to 2017. The transfer function-noise model was used to forecast trends, considering both time and multifactor effects. ResultsFrom 2006 to 2017, Sichuan Province had a reported cumulative incidence rate of 1321.08 cases per 100,000 individuals in male patients and 583.04 cases per 100,000 individuals in female patients. There were significant sex differences in the distribution of cases among age groups (trend χ225=12,544.4; P<.001). Ganzi Tibetan Autonomous Prefecture had the highest incidence rates of TB in both male and female patients in Sichuan. Correlation and regression analyses showed that the total illiteracy rate and average pressure at each measuring station (for individuals aged 15-24 years) were risk factors for TB. The protective factors were as follows: the number of families with the minimum living standard guarantee in urban areas, the average wind speed, the number of discharged patients with invasive TB, the number of people with the minimum living standard guarantee in rural areas, the total health expenditure as a percentage of regional gross domestic product, and being a single male individual (for those aged 0-14 years); the number of hospitals and number of health workers in infectious disease hospitals (for individuals aged 25-64 years); and the amount of daily morning and evening exercise, the number of people with the urban minimum living standard guarantee, and being married (for female individuals aged ≥65 years). The transfer function-noise model indicated that the incidence of TB in male patients aged 0-14 and 15-24 years will continue to increase, and the incidence of TB in female patients aged 0-14 and ≥65 years will continue to increase rapidly in Sichuan by 2035. ConclusionsThe End TB Strategy in Sichuan should consider environmental, educational, medical, social, personal, and other conditions, and further substantial efforts are needed especially for male patients aged 0-24 years, female patients aged 0-14 years, and female patients older than 64 years