Miracle-dz/UniFAR-FTUs huggingface dataset:
Mechanical Action Mechanism and Crack Response Analysis of Slide Plate of No.12 Turnout

In the course of maintenance and repair, the railway workshops of the Lan-Qing railway found that the slide plate was prone to injury and damage. In order to study the actual stress state of the slide plate during the running of the train, a finite element model of the slide plate and related components was established based on ANSYS, Hypermesh was used for meshing and mesh quality control, and the Mooney-Rivlin constitutive intrinsic model was selected to simulate the rubber plate. The stress state of the slide plate when passing through the turnout in the straight and lateral directions is analyzed respectively. The results show that there is stress concentration at the root of the tongue depressor of the slide plate, and large deformation exists in the middle of the base plate, the front end of the platform plate and the position of the guide iron when passing through the turnout directly and laterally. The stress state of the slide plate is simulated when the rail brace fails, and the maximum stress is found to be 279.24 MPa, which exceeds the material yield limit. Therefore, we should pay attention to the inspection of the condition of the rail brace during the maintenance and repair. Presenting cracks at the weld seam location, it is found that the crack has a significant effect on the dewelding of the slide plate, and the heel position is the weakness of the slide plate. When the crack length reaches 1.2 mm, there is a tendency to crack at the weld seam position of the slide plate, and when the crack length continues to increase, the crack will further expand and jeopardizing structural safety. The research results can provide a theoretical basis for the improvement, disease prevention and maintenance of the slide plate.
A Lightweight Two-Stream Model for Driver Emotion Recognition

In recent years, more and more attention has been paid to the traffic safety of drivers. Studies show that the motor vehicle speed will affect the incidence of traffic accidents and traffic accident casualty rate. In this paper, machine learning vision technology is used for real-time recognition of driver emotions, and a lightweight SIC-NET+RC-NET dual-stream recognition model is designed to cope with different scenarios of vehicle speed during driving. SIC-NET is a lightweight CNN network model, which greatly reduces the number of model parameters and computational power requirements, sacrifices part of accuracy, and greatly improves the real-time performance of model recognition. To improve the residual convolutional network model of inception blocks, RC-NET nested multiple micro-convolutional neural networks are used to identity mapping between different inception blocks to enhance the network training effect, expand network width and convolutional depth, and alleviate network degradation. This article stipulates that when the vehicle speed is greater than 60km/h, SIC-NET is used. When the vehicle speed is less than or equal to 60km/h, SIC-NET+RC-NRT is used. The FER2013 dataset was used for testing and validation in this paper. When SIC-NET is used, the model has better real-time performance and can provide a more timely warning when the vehicle speed is higher. When SIC-NET+RC-NET is used, the accuracy of the model in the FER2013 dataset is 69.65%, and the model can better cope with the scenario of a low vehicle speed. The experiment shows that the lightweight dual-flow model has good robustness and identification effect, which solves the problem of using the same model for different vehicle speeds and can effectively ensure the safety of the driver’s life and property.
Comparison of Crown Health Assessment Using Forest Health Monitoring and Remote Sensing Techniques (Case Study: KTH Lestari Jaya 8, KPHL Kota Agung Utara, Lampung)

Tree crown is one of the main components that must be monitored to assess the health condition of the forest. The crown condition is described by five ecological parameters namely : live crown ratio, foliage transparency, crown density, crown diameter, and the dieback. The method used to assess the canopy condition was the Forest Health Monitoring (FHM) method and remote sensing with the Normalize Difference Vegetation Index (NDVI) transformation. This study was conducted with the aim of being able to determine the condition of tree crown in protected forests using the FHM and remote sensing methods so that a comparison of the assessment results of the two methods can be carried out. This research was carried out at HKm Beringin Jaya, a managed area of KTH Lestari Jaya 8 which is located in Sumberejo District, Tanggamus Regency. The results of this study are that the Visual Crown Ratio value obtained ranges from 2.50 - 3.25. NDVI value describing the condition of the tree crown ranges from 0.75-0.83. The conclusion obtained from this study is that with the FHM technique, the health condition of the tree crown obtained is moderate to good, while with remote sensing techniques, the condition of the tree crown is in a very good condition.
Compensatory Fault-Tolerant Control Toward the Manipulator in a High-Throughput Preparation System

A Deviation-Division Fault-Tolerant Control (D-D FTC) scheme which can produce convergent compensating control inputs for the faulty yet partially controllable actuators in finite steps is proposed in this paper. Based on the D-D FTC scheme, the implementation of the robot manipulator in a high-throughput preparation system is derived. Moreover, the simulation is given to illustrate the effectiveness of the proposed scheme.
Nanostructures of silicon anodes in Li-ion batteries

As the application scenarios of lithium-ion batteries expand to many fields including electric vehicles and wearable devices, the energy density of current Li-ion batteries should be improved for satisfying the raising demand. In recent years, various methods have been gradually intensified, in which battery anode materials have received widespread attention. One of the most effective ways for improving battery performance is the use of silicon with different nanostructures, such as structures with different dimensions and different elemental doping, as the anode material, which can effectively improve the stability of solid electrolyte layers, enhance the number of reversible cycles and reversible capacity. This review summarizes the latest advances in silicon nanostructured anodes for lithium-ion batteries including nitrogen-doped carbon-caged silicon nanoparticles, silicon nanotubes made of layered CaSiO3, layered porous silicon encapsulated in carbon nanotube cages, silicon nanoparticles encapsulated in carbon-coated mesoporous silicon shells, and three-dimensional hierarchical porous structures. These nanostructures with excellent electrochemical properties can provide directions for the evolution of high-performance lithium-ion batteries.
Land cover classification of remote sensing images based on improved DeeplabV3+ network

For land survey and land planning, the precise segmentation of land cover based on remote sensing images is extremely important. Based on the high-resolution satellite remote sensing images and the corresponding land cover GIS vector data in Hangzhou, for the categorization and change detection of urban land cover, a unique convolutional neural network is suggested. The data source is from high-resolution satellite remote sensing images, the semantic segmentation model DeeplabV3+ is improved. The original backbone network Xception is replaced by MobileNetV2 with smaller parameters, and add the channel attention module after the multi-scale module. In addition, to deal with the effect of category imbalance on model results, the focus loss function is introduced for network training. The pixel accuracy and average intersection ratio of 77.63% and 67.46% respectively, which are better than the commonly used semantic segmentation models DeeplabV3 +, UNet, and PSPNet models, and the model size is only 20.1MB. The experimental results show that the model is effective in redecing the number of model paramenters and improving the segmentation effect.
An Improved CLIQUE Algorithm

CLIQUE is a clustering algorithm based on grid and density, which is sensitive to grid division parameter M and density threshold R, and has poor clustering accuracy. This paper proposes an improved algorithm, which is not sensitive to the initial grid division parameter M, and can automatically split the grid. Combined with the K-means algorithm, the clustering effect is improved. The F value on the S1 dataset is 36.9% higher than that of the classic CLIQUE algorithm.
The Discovery of a Low‐Redshift, Red Quasar in the 2MASS Prototype Survey

A prototype camera designed to test the observing and data reduction techniques for the 2 Micron All Sky Survey (2MASS) has been used to search for extremely red objects. Selection criteria based on the combination of near‐IR and visible light observations help separate the vast majority of ordinary stars from more exotic objects. Visible spectroscopic observations for objects selected using these criteria in an area of just a few tenths of a square degree has resulted in the discovery of a quasar with a redshift of 0.147. The number of quasars expected at Ks in the 2MASS survey based on typical quasar colors and on the surface density of optically selected samples is ∼0.17 deg−2. The 2MASS survey will result in a sample of quasars relatively unbiased by the presence of dust and will allow a reliable determination of whether there is a significant population of quasars with internal dust absorption.
Improving the performance of Uyghur speech recognition based on Factorized Time-Delay Neural Network

Speech recognition systems have low accuracy in recognizing the Uyghur language, a low-resource language, due to its strong language specificity and few public training datasets. Given this problem, considering the characteristics of Uyghur, we use morpheme units to build a language model and use mixture data augmentation methods to expand the training data. A 9-layer TDNN-F is applied, which can effectively utilize contextual information. An optimal 9.88% WER (Word Error Rate) is achieved in experiments on the open-source dataset THUYVG-20. Compared to the baseline system of this dataset, the WER is reduced by 6.7%, which significantly improves the accuracy of the Uyghur speech recognition, and provides a reference in other low-resource languages for speech recognization.
Reservoirs Optimal Operation Based on Reinforcement Learning

Reinforcement learning has been widely used to solve the problem of optimal operation of a single reservoir. However, the research on the combination of reinforcement learning and optimal operation of reservoir groups is still blank. The complexity of reservoir groups brings difficulties to the application of reinforcement learning. In order to solve the complex problem of optimal operation of the reservoir group, Q-learning combined with the penalty method is used to generate the optimal operation scheme of the reservoir group by exploring the optimization Q value of the operation sequence. The experimental results show that this method can achieve the optimal solution in theory. Furthermore, Q-learning combined with a feasible direction method is used to establish time feasible state table and state-feasible action hash table according to the constraints of the discrete four-reservoirs problem, so that the Q-learning algorithm can shorten the optimization time and obtain the optimal solution.
Effects of a porous boron nitride nanofiber on nano-mechanical properties in shallow surface of WC-Si3N4 composite

The nanoindentation technique has been applied to materials research for decades, such as WC, Si3N4, and other ceramics or composites. However, nano-mechanical properties in shallow surfaces were often unstable, especially for ceramics-based composites. In this paper, the mechanical properties of WC-10 wt. % Si3N4 without and with the addition of ultrafine porous boron nitride nanofiber were investigated by nanoindentation continuous stiffness measurement in this study. The effects of the porous fiber on elastic modulus and hardness were revealed, which led to more stable and regular profiles of the elastic modulus and hardness versus displacement curves. The curves were cut out and shown before the 30 nm displacement of the tip, which displayed the regularity of the profiles caused by the porous fiber more directly. The values were statistically analyzed at intervals of 10 nm after 30 nm displacement, and then lower standard deviations were observed. The different Si3N4 cracks made from Vickers indentation on the composites were photographed and associated with the regularity of the curves after the fiber addition. It’s considered that the regularity effects on mechanical properties of the porous boron nitride nanofiber on ceramics composites can be helpful in thin film studies, which are also in difficulties of mechanical properties scattering on the nanometer scale using continuous stiffness measurement.
3D human pose estimation based on negative exponential reduction Gaussian kernel

Human pose estimation has become an important research direction in the field of motion recognition. 3D human pose estimation adds depth information to 2D pose estimation, which is more widely used. In this paper, the weight of each voxel is calculated in the 3D discrete space by projecting the joint point heatmap to directly estimate the 3D human pose. To improve the accuracy of 3D human pose estimation, the Gaussian kernel of heatmap with the variable size is reduced by a negative exponent in the process of training. The dilated convolution of a small convolution kernel is used to replace the large convolution kernel to solve the problem of large computation overhead when detecting key points in discrete 3D space. Experimental results show that this method is effective and can accurately estimate the 3D pose in multi view images.
Planetary Line-to-accretion Luminosity Scaling Relations: Extrapolating to Higher-order Hydrogen Lines

Aoyama et al. provided scaling relations between hydrogen-line luminosities and the accretion luminosity for planetary-mass objects. These fits should be an improvement over blind extrapolations of stellar relations. The fits go up only to the n = 8 electron energy level, but higher-n Balmer lines have been observed in the near-UV with UVES. We extend the scaling relations to higher-n levels for the Balmer and other series by fitting the fit coefficients (a, b) themselves and extrapolating them. Within the assumption of an accretion shock as the source of line emission, these fits should be robust for accreting planetary-mass objects.
Research on the Visual Servoing method based on semantic segmentation results of metal objects in a complex lighting environment

The problem of feature recognition and servo tracking for semantic segmentation results of metal parts under complex and variable lighting conditions is addressed. In this paper, we propose an algorithm that incorporates template matching and image-based visual servoing based on semantic segmentation. The method adopts a geometry-based template matching method to stably identify the image features of the pixel region obtained from semantic segmentation, introduces a feature pyramid dimensionality reduction method to improve the processing speed, and then introduces the extracted image moment features into the image-based visual servoing to operate the robotic arm to perform the servo process on the target.
A deep learning method for X-ray image safety detection: YOLO-T

There are many problems in X-ray image dangerous goods target recognition with existing technology, such as low degree of automation, slow detection time, easy to misjudge under occlusion interference, etc. Based on the above problems, this paper proposes a multi-objective intelligent security inspection method for X-ray images based on the YOLO-T deep learning network. By adding the optimized Transformer structure to the YOLO architecture, this method can better solve the above problems. In order to better carry out the experiment, we proposed a set of X-ray safety detection data set GDXray-Expanded containing multiple categories of dangerous goods, and tested several versions of the deep learning network model of the YOLO series on this basis. Experiments show that the existing YOLO series algorithms still cannot solve the problem that dangerous goods in X-ray images are easy to be misjudged under occlusion interference. The YOLO-T method proposed in this paper solves this problem well, and in the big data set test, the maximum mAP can reach 97.73%, which is 7.66%, 16.47%, and 7.11% higher than the three methods of YOLO v2, YOLO v3, and YOLO v4 respectively, and has achieved the most competitive performance in the detection of seven categories of dangerous goods. To sum up, the YOLO-T network proposed in this paper mainly solves a series of problems in the field of dangerous goods target recognition and detection in X-ray security inspection images and has a high engineering application prospect in the field of X-ray security inspection.
An underwater image enhancement method based on color correction and local unsharp masking

The underwater images collected by optical cameras have different problems due to the camera equipment, underwater environment and light source, which lead to the problems of chromatic aberration and low contrast of the images collected by ordinary people for subsequent use. The reasons are that the reflection of light by impurities in the water and the absorption efficiency of different wavelengths of light under water, and under different depths of field conditions often leads to color Bias and contrast are low. To solve this problem, we adopt a method based on color correction and local unsharp masking for the fusion of underwater images. Firstly, color is improved by correcting for color shift using the red channel and gray world methods, combined with CLAHE. secondly, for the low contrast problem, the adaptive mask coefficient and the local unsharp mask are used to separate the background and foreground, which enhances the recognition of underwater target and background, and effectively improves the contrast of images. Target. Finally, the weighted fusion of Gaussian pyramid is completed by using the method of combining four weights. The experimental results show that the algorithm proposed in this paper has a strong generalization ability, and can improve the quality of images in different underwater scenes which is according to different objective evaluation indicators, the algorithm in this paper also achieves the best results.
On-printed circuit board emulator with controllability of pinched hysteresis loop for nanoscale TiO 2 thin-ﬁlm memristor device

Since real memristor devices are still not commer-cially available to most researchers, modeling a memristor is an effective method to explore its properties. In this paper, a ﬂux-controlled memristor emulator circuit that can cor-rectly mimic the memristive behavior of a real nanoscale TiO 2 thin-ﬁlm memristor device is presented. The mathematical equations for the proposed emulator are explicitly derived, and the design parameters for the circuit in which the emulator works as a passive memristor with positive memductance are discussed. In addition, the proposed emulator can produce various v – i hysteretic behaviors by controlling the nonlinear polynomial cubic function between the ﬂux and charge inside. The results from numerical simulations in PSpice and MATLAB, as well as the measured results from an implemented emulator circuit on a printed circuit board using off-the-shelf electronics components, demonstrate that a controllable emulator can actually be constructed. This study serves as a foundation for understanding and designing different emulators for nanoscale TiO 2 thin-ﬁlm memristors at the laboratory level.
ournal of C osmology and A stroparticle P hysics

Data analysis in cosmology requires reliable covariance matrices. Covariance matrices derived from numerical simulations often require a very large number of realizations to be accurate. When a theoretical model for the covariance matrix exists, the parameters of the model can often be ﬁt with many fewer simulations. We write a likelihood-based method for performing such a ﬁt. We demonstrate how a model covariance matrix can be tested by examining the appropriate χ 2 distributions from simulations. We show that if model covariance has amplitude freedom, the expectation value of second moment of χ 2 distribution with a wrong covariance matrix will always be larger than one using the true covariance matrix. By combining these steps together, we provide a way of producing reliable covariances without ever requiring running a large number of simulations. We demonstrate our method on two examples. First, we measure the two-point correlation function of halos from a large set of 10000 mock halo catalogs. We build a model covariance with 2 free parameters, which we ﬁt using our procedure. The resulting best-ﬁt model covariance obtained from just 100 simulation realizations proves to be as reliable as the numerical covariance matrix built from the full 10000 set. We also test our method on a setup where the covariance matrix is large by measuring the halo bispectrum for thousands of triangles for the same set of mocks. We build a block diagonal model covariance with 2 free parameters as an improvement over the diagonal Gaussian covariance. Our model covariance passes the χ 2 test only partially in this case, signaling that the model is insuﬃcient even using free parameters, but signiﬁcantly improves over the Gaussian one.
Precise Measurement of Time Dependent Cell Growth and Detection of Partial Drug Resistance in Infectious Microbial

: Drug resistance has become a serious public health problem. Accurate assessment of the drug-induced proliferation (DIP) rate is essential in identifying partial drug resistance. The turbidity assay is a continuous assay and has been used for years to track the bacteria growth, but the method has relative low sensitivity and high experimental error. In addition, most current cell proliferation assays are endpoint assays that are not ideal for tracking minor changes over time. Here, we report an EZMTT dye-based detection method which provides 10 to 30-fold better sensitivity than the turbidity assay. Simple mixing of the EZMTT dye with the bacteria in the presence of an inhibitor allowed precise measurement of the drug-induced proliferation rate in high-through-put (HTS) mode; the resulting growth curves show critical characteristics of exponential growth including the cell density requirement and the doubling time. Within 4-6 hours, the simple procedure not only reliably detects all drug resistance found by the traditional method, but also partial drug resistance of infectious bacteria (with 5-20% growth), which current MIC-based clinical diagnostic methods failed to identify. In conclusion, this simple EZMTT method provides rapid and precise measurement of the drug-induced proliferation rate, and identifies 10-30% more partially drug resistant bacteria than current clinical diagnostic methods. A combination of the EZMTT dye-based detection and the DPI method has great potential to solve the unmet medical need to battle against drug resistance.