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The precise positioning of the firing explosion point is an effective means to test the technical performance of artillery weapons and equipment warfare, but when the CCD single pole distance method is directly used to detect the explosion point, it is often affected by the obstacles in the shooting visual area, terrain and landform, and the detection and positioning accuracy is difficult to meet the evaluation requirements. To this end, the double CCD intersection method is first used to locate the explosive point, and the target recognition and positioning algorithm is used to realize the visual explosion point positioning, but this method also has the phenomenon of missing detection of the bomb point and ricochet explosion point. Then, the sensor is used to collect GIS signals at the point of explosion, which can well detect the location of missed bounces and ricochets, but there is also the cost of time registration and spatial coordinate system registration. In order to give full play to the complementary advantages of the two positioning methods, this paper proposes to establish a fusion localization model. Experiments have verified that the fusion positioning method improves the accuracy of the detection of the explosion point of traditional single live shooting, and can provide a reference for the positioning detection of related points of interest.

This paper uses numerical simulation software to study the bearing characteristics of screw pile with pitch after installation. The selected soil is single layer sandy soil, and the blade is buried in deep burial mode. Based on the numerical simulation results, the vertical bearing characteristics of single spiral pile and ordinary bladeless pile are compared, and the excellent compression and pull-out characteristics of spiral pile are revealed. Then, the influences of different pitch sizes, blade diameters, tilt angles, blade numbers and blade spacing on spiral single pile were compared and analyzed. The results show that single screw pile has higher bearing capacity and bearing efficiency. The vertical bearing capacity of single inclined screw pile has no obvious improvement compared with that of straight pile.

Under the background of continuous development in our country, traditional English education can no longer meet the needs of modern times. Through the evaluation of English autonomous learning effect of artificial intelligence and the analysis of the influencing factors of adaptability, the teaching effect of English class is improved and the students’ awareness of autonomous learning is cultivated. In the pilot study, students now have an overall level of adaptation (3

The existing convolutional neural network (CNN) models have shown excellent performance in remote sensing scene classification. However, the structure of such models is becoming more and more complex, and the learning of low-level features is difficult to interpret. To address this problem, in this study, we introduce lie group machine learning into the CNN model, try to combine both approaches to extract more distinguishing ability and effective features, and propose a novel network model, namely, the lie group regional influence network (LGRIN). First, manifold space samples of the lie group are obtained by mapping, and then, the features of the lie group are extracted after the operations of image decomposition and integral image calculation. Second, the multidilation pooling is integrated into the CNN architecture. At the same time, the image regional influence network module is designed to guide the attention of the classification model by using the regional-level supervision of the decomposition. Finally, the fusion features are classified, and the predicted results are obtained. Our model takes full advantage of regional influence, lie group kernel function, and lie group feature learning. Moreover, our model produces satisfactory performance on three public and challenging data sets: Aerial Image Dataset (AID), UC Merced, and NWPU-RESISC45. The experimental results verify that, compared with the state-of-the-art methods, this method is more explanatory and achieves higher accuracy.

At present, there are some new research ideas in the design of luffing mechanism of portal crane, such as genetic algorithm and particle swarm optimization algorithm. The development of design software adopts Visual C++ or Visual Basic, MATLAB and other tools. These methods are advanced but inefficient because extra time and cost are required. This paper proposes a modern design method to complete the optimization of the four-bar luffing mechanism in the portal crane, which can simplify the calculation and improve the efficiency. The motion track description and mechanical properties of the four-bar luffing mechanism are obtained by the geometric analysis and the load analysis. The luffing curves, which can be re-drawn by modifying the initial parameters, are given to verify the optimized results. Results demonstrate that the modern design method can accomplish the optimal design effectively without extra time and cost, and this finding will be provided as the design guidance for the luffing mechanism.

The key problem of remote sensing image classification is to understand the semantic content of the images effectively. Up to now, most methods are to improve the accuracy of the final classification through the convolutional neural networks model. However, the structure of the whole model is complex and contains a large number of parameters. To address this problem, in this study, we proposed a lightweight method of the intrinsic mean within Lie groups, which can achieve high accuracy. We apply the computational advantage of Lie group manifold space and design the kernel function that is suitable for both Lie group sample and vector sample. Experiments are performed on two publicly available and challenging data sets [aerial image dataset (AID) and Northwestern Polytechnical University-REmote Sensing Image Scene Classification, contains 45 scene classes (NWPU-RESISC45)], and the results show that our method is more accurate than the state-of-the-art methods.

Rechargeable aqueous zinc-ion batteries (ZIBs) have become a research hotspot in recent years, due to their huge potential for high-energy, fast-rate, safe and low-cost energy storage. To realize good electrochemical properties of ZIBs, cathode materials with prominent Zn2+ storage capability are highly needed. Herein, we report a promising ZIB cathode material based on electrochemically induced transformation of vanadium oxides. Specifically, K2V6O16·1.5H2O nanofibers were synthesized through a simple stirring method at near room temperature and then used as cathode materials for ZIBs in different electrolytes. The cathode presented superior Zn2+ storage capability in Zn(OTf)2 aqueous electrolyte, including high capacity of 321 mAh/g, fast charge/discharge ability (96 mAh/g delivered in 35 s), high energy density of 235 Wh/kg and good cycling performance. Mechanism analysis evidenced that in Zn(OTf)2 electrolyte, Zn2+ intercalation in the first discharge process promoted K2V6O16·1.5H2O nanofibers to transform into Zn3+xV2O7(OH)2·2H2O nanoflakes, and the latter served as the Zn2+-storage host in subsequent charge/discharge processes. Benefiting from open-framework crystal structure and sufficiently exposed surface, the Zn3+xV2O7(OH)2·2H2O nanoflakes exhibited high Zn2+ diffusion coefficient, smaller charge-transfer resistance and good reversibility of Zn2+ intercalation/de-intercalation, thus leading to superior electrochemical performance. While in ZnSO4 aqueous electrolyte, the cathode material cannot sufficiently transform into Zn3+xV2O7(OH)2·2H2O, thereby corresponding to inferior electrochemical behaviors. Underlying mechanism and influencing factors of such a transformation phenomenon was also explored. This work not only reports a high-performance ZIB cathode material based on electrochemically induced transformation of vanadium oxides, but also provides new insights into Zn2+-storage electrochemistry.

The fiber Bragg grating sensor is widely used in strain monitoring of large metal structure and trend to replace the resistance strain gauge due to its advantages of strong stability, high measurement accuracy, multiple points measuring, strong environmental suitability and long transmission distance. The temperature-induced strain, which can have the same order of magnitudes as the mechanically-induced strain, will cause great errors in the strain monitoring. Therefore, the temperature compensation for the sensors is essential to guarantee the measurement accuracy. The existing theoretical models and experiment platforms for analyzing the temperature compensation are established by assuming that the testing temperature is constant. However, the surrounding temperature of some large metal structure is not stable, and the effect of temperature change cannot be neglected. This paper aims to establish an analytic model and an experiment platform to compare the temperature compensation of the fiber bragg grating sensor and the resistance strain gauge. The superiority of the temperature compensation for the fiber bragg grating sensor is verified. The result provides theoretical support for choosing the fiber bragg grating sensor in the long-time strain monitoring.

Remote sensing scene classification is used to label specific semantic categories for images. The current methods have achieved competitive performances, but they are only for Euclidean space samples. As a result, their representations are not robust for non-Euclidean space samples, which affects the classification accuracy. In this letter, we introduce the Lie group manifold into the traditional feature representation method and propose a novel intrinsic mean representation method within the Lie group. At the same time, the kernel function based on the sample of the Lie group is designed to further improve the robustness and accuracy of classification. In addition, our method achieves satisfactory performance on two public and challenging remote sensing data sets of UC Merced and NWPU-RESISC45.

Few-layer Ti3C2Tx MXene is synthesized from multi-layered Ti3C2Tx via a flash freezing-assisted delamination process. During the flash freezing process, the water molecules in the interlayers of multi-layered MXene are induced to rearrange and produce volume expansion, thus notably expand the MXenes’ interlayer distance to form few-layer MXene. The synthesized few-layer Ti3C2Tx MXene nanosheets display a very small thickness (less than 5 Ti3C2 atom-layers) and expanded interlayer spacing. Consequently, the few-layer Ti3C2Tx exhibits enhanced capacitance (255 F g−1 vs. 177 F g−1 for the multi-layered Ti3C2Tx) and significantly optimized rate capability (150 F g−1 at 200 mV s−1 vs. 25 F g−1 for the multi-layered Ti3C2Tx), because redox-active sites in the few-layer MXene are easily accessible to electrolyte ions. Moreover, an asymmetric supercapacitor is constructed using the few-layer Ti3C2Tx negative electrode and an activated carbon fiber positive electrode. The asymmetric supercapacitor presents a high energy density of 17.9 Wh kg−1 and a high power density of 14 kW kg−1, which is inseparable from its wide voltage window of 1.4 V and the good rate performance of the few-layer Ti3C2Tx MXene electrode. Overall, the flash freezing-assist delamination provides an effective and environmental-friendly strategy to synthesize few-layer MXene materials for high-rate electrochemical energy storage.

In practical application, the data are imbalanced, it is difficult to find the balanced, rather skewed data is the common occurrence. This poses a severe challenge to the classification algorithm. At present, imbalanced data classification methods are mainly for binary classes designed, and it is difficult to extend them to multiple classes. In this study, we introduced Lie group machine learning and proposed a semi-supervised learning algorithm based on the linear Lie group. First, the sample set is represented by a matrix, the isomorphism(or homomorphism)-GL(n) linear Lie group of the corresponding learning system is found, and the labeled data are used to represent the object to be learned by linear Lie group. Then, according to the algebraic structure of the linear Lie group, it is marked by the group method. We performed experiments on 18 benchmark multi-class imbalanced datasets to demonstrate the performance of our proposed method and measured the performance of multi-class imbalanced data using four state-of-the-art learning algorithms (mean of accuracy, mean of f-measure, and mean of area under the curve). The experimental results demonstrate that the proposed method is effective and improves the performance.

Recent years, the research on object classification based on three-dimensional (3D) point cloud pays more attention to extract the features from point sets directly. PointNet++ is the latest network structure for 3D classification which has achieved acceptable results. Although it has achieved acceptable results, there are still two problems: (i) The farthest point sampling (FPS) algorithm in PointNet++ ignores the fact that the feature of each point contributes differently to the classification and segmentation tasks. Therefore, FPS cannot guarantee that the selected point sets can correctly represent the main features of the object. (ii) The multi-scale grouping and multi-resolution grouping in PointNet++ do not consider the features between different levels of the same point. In order to solve these problems, the authors propose the point-selection structure which can calculate the importance of each point's feature. In addition, multi-level-point-feature fusion module is proposed to extract the features of the point at all levels and fuse them as a new feature of that point. In this Letter, they make some experiments on ModelNet40 and ScanNet datasets, which achieves better results compared to the state-of-the-art methods.

As for the sensorless control of interior permanent magnet synchronous motor (IPMSM), conventional high frequency (HF) pulsating signal injection method is widely used, which uses band-pass filter (BPF) and low-pass-filter (LPF) based phase-locked-loop (PLL), however it may face the problems of poor stability and long convergence time. Therefore, an improved signal demodulation algorithm based on moving-average-filter (MAF)-PLL is proposed. MAF, which makes full use of the HF signal periodicity and half-wave symmetry, has higher bandwidth and better harmonics suppression ability than BPF or LPF. Furthermore, a proportional-integral (PI) regulator is used to gain the rotor speed and an integrator is applied to estimate rotor position. Compared with the conventional method, the proposed method gets rid of BPF and LPF, contributing to improving the stability and dynamic performance. Besides, undetermined parameters are reduced to one, i.e., window length of MAF, avoiding complex parameters design and selection. The proposed HF pulsating injection method has simpler signal demodulation process and is easy to implement. Finally, the experimental results have verified the feasibility and effectiveness of proposed method.

Diffusion-induced stress caused by the insertion and extraction of lithium ions can result in the swelling, fracture, and even pulverization of the battery electrodes. However, only a few previous studies consider the phenomenon of phase transformation in an electrode and rarely take the impacts of cylindrical shape with transversely isotropic properties into account. In this paper, by researching the electronic reaction and diffusion process, a new theoretical model is established to study the stress level and mechanical behavior in cylindrical electrodes with phase transformation under galvanostatic operation. From the model, the brittle center and edge cracks are analyzed to investigate the influence of the initiation position on crack propagation. The tangential stress plays an important role in cracking on the electrodes. Furthermore, it is found for the center crack that it tends to grow more easily in the first insertion when the crack locates at the phase interface position, while for the edge crack, it tends to grow more easily in the early stage of lithium ion extraction. Moreover, manufacturing the electrodes with the appropriate property ratios, the diffusion-induced stress level and brittle crack-induced stress intensity factor value may decrease, and the electrode fracture phenomenon could be alleviated to some degree. Overall, our work provides a theoretical basis for the electrode phase transformation and cracking when the battery is working, and it may help us understand more about the internal mechanical behavior of the battery electrodes.

Highlights This work starts the research of pseudocapacitive oxide materials for multivalent Zn2+ storage.The constructed RuO2·H2O||Zn systems exhibit outstanding electrochemical performance, including a high discharge capacity, ultrafast charge/discharge capability, and excellent cycling stability.The redox pseudocapacitive behavior of RuO2·H2O for Zn2+ storage is revealed. Electronic supplementary material The online version of this article (10.1007/s40820-019-0328-3) contains supplementary material, which is available to authorized users., Rechargeable aqueous zinc-ion hybrid capacitors and zinc-ion batteries are promising safe energy storage systems. In this study, amorphous RuO2·H2O for the first time was employed to achieve fast and ultralong-life Zn2+ storage based on a pseudocapacitive storage mechanism. In the RuO2·H2O||Zn zinc-ion hybrid capacitors with Zn(CF3SO3)2 aqueous electrolyte, the RuO2·H2O cathode can reversibly store Zn2+ in a voltage window of 0.4–1.6 V (vs. Zn/Zn2+), delivering a high discharge capacity of 122 mAh g−1. In particular, the zinc-ion hybrid capacitors can be rapidly charged/discharged within 36 s with a very high power density of 16.74 kW kg−1 and a high energy density of 82 Wh kg−1. Besides, the zinc-ion hybrid capacitors demonstrate an ultralong cycle life (over 10,000 charge/discharge cycles). The kinetic analysis elucidates that the ultrafast Zn2+ storage in the RuO2·H2O cathode originates from redox pseudocapacitive reactions. This work could greatly facilitate the development of high-power and safe electrochemical energy storage. Electronic supplementary material The online version of this article (10.1007/s40820-019-0328-3) contains supplementary material, which is available to authorized users.

Multivalent ion storage mechanism is applied to construct high-performance aqueous zinc-ion hybrid supercapacitors (ZHSs). The constructed MnO2 nanorods//activated carbon (AC) ZHSs with ZnSO4 aqueous electrolyte are significantly different from the common MnO2//AC asymmetric supercapacitors with Na2SO4 electrolyte in electrochemical behaviors and energy storage mechanism. The ZHSs show the maximum specific capacity of 54.1 mA h g−1 and maximum energy density of 34.8 W h kg−1 at the optimal mass ratio of AC to MnO2. The ZHSs are capable of being charged/discharged rapidly within only 2–17 s, delivering a large power of 3.3–13.0 kW kg−1. Reversible insertion/extraction of Zn2+ into/from MnO2 nanorods and ion adsorption/desorption on AC particle surface, as well as partially reversible formation/dissolution of Zn4(OH)6SO4·nH2O participates on both electrodes, are established as the energy storage mechanism of the ZHSs. Electrochemical performance of the ZHSs can be further optimized through electrolyte composition regulation. Addition of Mn2+ cations into ZnSO4 electrolyte leads to significantly enhanced energy density (58.6 W h kg−1) of the ZHSs, while anion replacement of SO42- by CF3SO3- suppresses manganese dissolution and Zn4(OH)6SO4·nH2O formation, thus resulting in good cycling stability with 93.4% capacity retention over 5000 charge/discharge cycles. Overall, the ZHSs based on multivalent ion storage are promising to be applied as high-performance, extremely safe and eco-friendly energy storage devices for wearable/portable electronics and hybrid electric vehicles.

Silicon cylindrical anodes suffer from chemo-mechanical degradation and unpredictable fracture problems in high performance lithium-ion batteries, which resulting in capacity fade. A new fracture model based on core-shell structure is derived in this work, which combines diffusion-induced stress and surface stress, to demonstrate the mechanical behavior including the evolution of diffusion-induced stress and stress intensity factor in the hollow cylindrical anode during lithiation and delithiation. By evaluating the evolution of the radial and circumferential cracks, we find that maximum stress intensity factor for the radial crack is larger than the circumferential crack, both for internal and external cracks, which indicates that the radial crack is more dangerous than the circumferential crack for a cylindrical anode. Furthermore, the critical sizes of the hollow cylindrical anode among various crack configurations are obtained. The radial cracks caused by the hoop stress are found to be more essential in determining critical size of the electrode. The present work provides the fracture behavior in Si cylindrical anode during lithiation, which is helpful to the understanding the chemo-mechanical degradation.