Porous media play a vital role in daily life and industrial production, but their flow resistance calculation is always a difficult at the same time a hot topic of research. In this paper, a ...tube-sphere combination model is established based on the actual porous media structure and flow characteristics. The model considers the flow resistance of porous media as the superposition of tube flow viscous resistance, the resistance of flowing around a small ball and through the variable diameter of a channel. From this, a new resistance equation without any empirical constant is derived. Based on 35 sets of experimental data collected from the literature, this paper compares and verifies the derived formula, the classical Ergun equation and the Carman equation under small Reynolds number. The experimental parameters include particle size range: 0.0375–56.8 mm; porosity range: 0.32–0.882; Reynolds number range: 0.006–10 730. It is found that in most cases, the derived formula has better adaptability than the Ergun equation and is equivalent to the prediction accuracy of the Carman formula in a Darcy regime. However, for the pre-Darcy regime with smaller Reynolds number, the experimental data itself are quite different, and the calculation results of the three formulas are quite different from the experimental value. The maximum error of the Ergun equation is 49%, that of the Kozeny–Carman equation is 39%, and that of the derivation equation is 37%, indicating that the region may have completely different resistance mechanisms.
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In this paper, a ground target extraction system for a novel LiDAR, airborne streak tube imaging LiDAR (ASTIL), is proposed. This system depends on only a single echo and a single data source, and ...can achieve fast ground target extraction. This system consists of two modules: Autofocus SSD (Single Shot MultiBox Detector) and post-processing. The Autofocus SSD proposed in this paper is used for object detection in the ASTIL echo signal, and its prediction speed exceeds that of the original SSD by a factor of three. In the post-processing module, we describe in detail how the echoes are processed into point clouds. The system was tested on a test set, and it can be seen from a visual perspective that satisfactory results were obtained for the extraction of buildings and trees. The system mAPIoU=0.5 is 0.812, and the FPS is greater than 34. The results prove that this ASTIL processing system can achieve fast ground target extraction based on a single echo and a single data source.
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Airborne streak tube imaging LiDAR (STIL) consists of several different data-generating subsystems and introduces system errors each time it is installed on an aircraft. These errors change with each ...installation, which makes the parametric calibration of the LiDAR meaningless. In this study, we propose a high-precision reconstruction method for point clouds that can be used without calibrating the system parameters. In essence, after each remote sensing measurement, a self-checking process is performed with experimental data to replace the fixed system parameters. In this process, the splicing error of the same region measured under different conditions is used as a criterion to optimize the reconstruction parameters via a particle swarm optimization (PSO) algorithm. For a detection distance of 3000 m, the elevation error of the point cloud reconstruction reaches more than 1 m if the placement parameters are not optimized; after optimization, the elevation error can be controlled within 0.3 m.
Ternary cathode materials hold great promise in the lithium battery market, yet analyses regarding their calcination process in simulation and on-site experimentation, are few. In this paper, a ...multiphysics-coupled computational fluid dynamics (CFD) model was developed to simulate the primary calcination process (The actual production process is divided into two calcinations, because the production conditions affect the measurement, this paper focuses on the analysis of the primary calcination process) of lithium battery raw materials (namely, Ni0.8Co0.1Mn0.1(OH)2 and LiOH∙H2O mixture, hereinafter referred to as raw materials) under oxidizing atmosphere conditions. The process involves fluid flow, heat and mass transfer, and chemical reactions. A new method which combined steady-state calculation with transient calculation was adopted. Firstly, the steady-state simulation of the whole furnace was carried out, and then the transient simulation of each furnace area is carried out consequently, the heat and mass transfer processes of raw materials in different furnace zones were solved, ultimately achieving the simulation of the entire furnace calcination process. On-site black box experiments were performed to obtain the temperature evolution during the calcination process. Meanwhile, compared with the calculated results of the model, it was found that the average hit rate with an absolute error of less than ±20 °C above 300 °C can reach 86.9%, confirming the applicability of the model. The multiphysics-coupled CFD model simultaneously solves the oxygen concentration. The process parameters were analyzed based on the model, providing a fundamental method for the improvement of the performance of lithium battery cathode materials calcination process.
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Owing to the special working systems of streak tube imaging lidar (STIL), the time and space dimensions are coupled together on the streak images. This coupling can cause measurement errors in 3D ...point clouds and can make measurement results more complicated to calibrate than other kinds of lidars. This paper presents a method to generate a time calibration array and an angle calibration array to separate the offset of the streak into time dimension and space dimension. The time and space information of the signal at any position on the streak image can be indexed through these two arrays. A validation experiment on aircraft was carried out, and the range error of the 3D point cloud was improved from 0.41 m to 0.27 m using the proposed calibration method. Thus, using the proposed calibration method can improve the accuracy of the point cloud produced by STIL.
At present, some researchers believe that Darcy's law may not be valid, which may be the root cause of complex porous media problems. Porous media flow model is generally regarded as pipe flow, but ...in theory, it is necessary to consider it as bypass flow. However, there are few studies in this area. According to the seepage characteristics, we established the model of flowing around a single sphere in porous media and derived the resistance correlation of Darcy velocity (Re < 1). However, the Darcy experimental data analysis shows that the predicted value of the flow around the sphere is far lower than the experimental value, which proves that the assumption of the model of flowing around a single sphere is unreasonable. Even for the seepage of the extremely low Reynolds number, the flow interference around the sphere should be taken into account. Therefore, when the interference coefficient is 45, our formulas are superior to the classical Kozeny-Carman equation and Ergun equation in the prediction of all Darcy's experimental data and Charles Ritter's experimental data. Ergun equation has the largest error.
The research on 811 ternary cathode materials is mainly based on synthesis and modification. However, the preparation process of these materials is accompanied by complex chemical reactions, and the ...reaction process and corresponding kinetic analysis have not been widely explored. Under different oxygen concentrations, this study analyzed the chemical reaction mechanism of the raw material's (namely, Ni0.8Co0. 1Mn0. 1(OH)2 and LiOH·H2O mixture, which is referred to as the raw material hereinafter) calcination process by non-isothermal thermogravimetry, differential scanning calorimetry, and in situ X-ray diffraction. Based on the obtained data, multiple heating rate methods were used to calculate the reaction mechanism functions and kinetic parameters at each stage as well as the corresponding activation energy and pre-exponential factor. Results showed that four chemical reactions occurred successively during the calcination process of the raw materials with each corresponding to a different kinetic function, pre-exponential factor, and activation energy. Comparing the calcination characteristics under different oxygen concentrations showed that the activation energy was the smallest when the oxygen concentration was 60%.
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8.
Hardness-Aware Deep Metric Learning Zheng, Wenzhao; Chen, Zhaodong; Lu, Jiwen ...
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR),
2019-June
Conference Proceeding
Open access
This paper presents a hardness-aware deep metric learning (HDML) framework. Most previous deep metric learning methods employ the hard negative mining strategy to alleviate the lack of informative ...samples for training. However, this mining strategy only utilizes a subset of training data, which may not be enough to characterize the global geometry of the embedding space comprehensively. To address this problem, we perform linear interpolation on embeddings to adaptively manipulate their hard levels and generate corresponding label-preserving synthetics for recycled training, so that information buried in all samples can be fully exploited and the metric is always challenged with proper difficulty. Our method achieves very competitive performance on the widely used CUB-200-2011, Cars196, and Stanford Online Products datasets.
The rapid development of deep neural networks (DNNs) in recent years can be attributed to the various techniques that address gradient explosion and vanishing. In order to understand the principle ...behind these techniques and develop new methods, plenty of metrics have been proposed to identify networks that are free of gradient explosion and vanishing. However, due to the diversity of network components and complex serial-parallel hybrid connections in modern DNNs, the evaluation of existing metrics usually requires strong assumptions, complex statistical analysis, or has limited application fields, which constraints their spread in the community. In this paper, inspired by the Gradient Norm Equality and dynamical isometry, we first propose a novel metric called Block Dynamical Isometry, which measures the change of gradient norm in individual blocks. Because our Block Dynamical Isometry is norm-based, its evaluation needs weaker assumptions compared with the original dynamical isometry. To mitigate challenging derivation, we propose a highly modularized statistical framework based on free probability. Our framework includes several key theorems to handle complex serial-parallel hybrid connections and a library to cover the diversity of network components. Besides, several sufficient conditions for prerequisites are provided. Powered by our metric and framework, we analyze extensive initialization, normalization, and network structures. We find that our Block Dynamical Isometry is a universal philosophy behind them. Then, we improve some existing methods based on our analysis, including an activation function selection strategy for initialization techniques, a new configuration for weight normalization, a depth-aware way to derive coefficients in SeLU, and initialization/weight normalization in DenseNet. Moreover, we propose a novel normalization technique named second moment normalization, which has 30 percent fewer computation overhead than batch normalization without accuracy loss and has better performance under micro batch size. Last but not least, our conclusions and methods are evidenced by extensive experiments on multiple models over CIFAR-10 and ImageNet.
Object detection has developed rapidly with the help of deep learning technologies recent years. However, object detection on drone view remains challenging due to two main reasons: (1) It is ...difficult to detect small-scale objects lacking detailed information. (2) The diversity of camera angles of drones brings dramatic differences in object scale. Although feature pyramid network (FPN) alleviates the problem caused by scale difference to some extent, it also retains some worthless features, which wastes resources and slows down the speed. In this work, we propose a novel High-Resolution Feature Pyramid Network (HR-FPN) to improve the detection accuracy of small-scale objects and avoid feature redundancy. The key components of HR-FPN include a high-resolution feature alignment module (HRFA), a high-resolution feature fusion module (HRFF) and a multi-scale decoupled head (MSDH). HRFA feeds multi-scale features from backbone into parallel resampling channels to obtain high-resolution features at the same scale. HRFF establishes a bottom-up path to distribute context-rich low-level semantic information to all layers that are then aggregated into classification feature and localization feature. MSDH cope with the scale difference of objects by predicting the categories and locations corresponding to different scales of objects separately. Moreover, we train model by scale-weighted loss to focus more on small-scale objects. Extensive experiments and comprehensive evaluations demonstrate the effectiveness and advancement of our approach.
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