Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic license plate recognition using transformer

Automatic license plate recognition (ALPR) is widely used in Intelligent Traffic System (ITS). Deep learning has become the mainstream technology for ALPR. At present, the most advanced ALPR technologies are all designed based on convolutional neural networks (CNNs). From 2020, the visual Transformer technology has been introduced into the computer vision (CV) filed, and has achieved excellent performance in many CV tasks. However, there are few researches on ALPR using visual transformer technology. In order to study the license plate detection and recognition using visual transformer, we evaluate the performance of two representative object detection visual Transformer methods including DETR and Deformable DETR on three license plate datasets. Experimental results show that the visual transformer methods can achieve good detection results, but their detection and recognition performance is worse than that of some SOTA object detection methods based on CNNs. In the future, with the emergence of new visual Transformer methods and the construction of some large-scale license plate datasets, visual transformer will achieve better performance of license plate detection and recognition.
Time series prediction and application based on multi-kernel support vector regression

In this paper, we propose a new MRMRKA-SVR-GARCH model to predict the volatility of time series. This model is a combination of the MRMRKA-SVR model and the GARCH model. Since SVR-GARCH model has been successfully employed to forecast volatility. Empirical results show that the multi-kernel support vector regression based on minimal redundancy maximal correlation criteria and kernel target alignment (MRMRKA-SVR) tend to beat single-kernel SVR models in terms of forecasting accuracy. First, we use the MRMRKA algorithm to select a set of kernels to establish a multi-kernel, and establish the MRMRKA-SVR model. Then we use MRMRKA-SVR to estimate the volatility equation of GARCH model, and establish MRMRKA-SVR-GARCH model. Furthermore, the models’ predictive ability was evaluated using three empirical analyses on China Unicom's stock price data and stock price data of ICBC. Under different basic kernel numbers m, compare the prediction errors between the proposed model and the SVR-GARCH model based on the mixed Gaussian kernels in the two stock data, the results show that the prediction accuracy of the MRMRKA-SVR-GARCH model fluctuates slightly with the change of m , and the MAE and RMSE of the MRMRKA-SVR-GARCH model are lower than the mixed-Gaussian-kernel SVR-GARCH model. Description MRMRKA-SVR-GARCH model managed to outperform the mixed-Gaussian-kernel SVR-GARCH model. Therefore, the MRMRKA-SVR-GARCH model can provide a certain degree of reference in analyzing the trend and prediction of volatility, and has high feasibility and effectiveness.
Design and development of equipment data management visualization system for intelligent KPI evaluation

To address the problems of data acquisition and integration of heterogeneous equipment such as CK40 CNC lathe, KUKA industrial robot and Siemens PLC for logistics control, interconnection and interoperability between heterogeneous equipment and visual management of equipment data in the discrete manufacturing workshops, this paper proposes a data acquisition and integration method based on OPC UA and studies a data visualization and management method for equipment KPI. This study establishes an OPC UA information model based on the characteristics of discrete manufacturing heterogeneous equipment, develops an OPC UA aggregation server and client using the OPC UA open source library, and develops an equipment data management visualization system for intelligent KPI evaluation using Java. Finally, based on the field experiment platform, it is verified that the system is feasible in dealing with data acquisition and integration of heterogeneous equipment in discrete manufacturing workshops, equipment KPI evaluation, and data visualization.
Improved YOLOv5 network for agricultural pest detection

Agriculture is the source of food, clothing, and the foundation of human existence. In recent years, various countries have been increasing investment in agricultural production and actively carrying out agricultural pest control work. Therefore, how effectively realizing pest identification is a top priority at present. Traditional identification methods have disadvantages, such as time-consuming and laborious, untimely diagnosis, and limited diagnosis range. With the development of modern digital technology, image processing technology develops rapidly, which opens up a new way for pest identification. This paper proposes an improved network model for pest identification using the YOLOv5 target detection algorithm. Firstly, the data sets of pests and diseases are collected and marked. Then, an improved anchor frame size is proposed to make it more suitable for the data set used in this paper. Finally, an improved network structure of YOLOv5 is proposed, which improves the ability of the network to capture characteristic information. The experimental results show that the mean Average Precision (mAP@0.5) of the improved network model reaches 79.7%. At the same time, compared with Faster R-CNN, Dynamic R-CNN, Double-Head R-CNN, YOLOv3, and YOLOv5, the mAP@0.5 is improved by 8.5%, 8.1%, 8.8%, 9.3%, and 3.7% respectively.
GCN-based group level analysis of brain functional connectivity in fMRI

Brain functional network describes the functional connectivity (FC) between brain regions, and hence provides a crucial way for analyzing brain diseases. In order to explore neural mechanism of a brain disease, statistical test method is usually used to obtain the FC differences between normal group and abnormal group. However, it is difficult for statistical test method to utilize features from brain region nodes and brain connection edges simultaneously. In this study, we develop a method based on graph convolution network (GCN) for brain functional connectivity analysis in functional magnetic resonance imaging (fMRI). Graph convolution is used to extract the features from brain region nodes and brain connection edges simultaneously, and the interpretability of GCN is applied to obtain the FC differences between different groups. The proposed method is able to analyze the brain functional connectivity more comprehensively and can be a supplement to traditional statistical test method. A task-state public fMRI data set including healthy group, severe traumatic brain injury (TBI) patient group was used for training and testing of the models. And a statistical test method was used as baseline in the performance evaluation. The results showed that the proposed GCN-based method outperformed the statistical baseline method. This method has potential to find more useful FC when we analyzing the neural mechanisms of brain diseases.
Research on discrepancy discrimination and auxiliary decision making of electric power standard terms

The standard clauses used in the electric power industry often contain inconsistencies, making it difficult for professionals to select appropriate terms in their work. In this paper, we propose a discrepancy discrimination model based on knowledge graph and natural language processing, using standard clauses of power equipment as the dataset. Furthermore, we construct an ensemble learning model to assist decision-making in the electric power field. The experimental results show that the proposed method is effective in terms of precision and recall. The discrepancy discrimination model has a precision rate of 76.45% and recall rate of 84.72%, while the auxiliary decision-making model has a precision rate of 75.3%. The method is able to effectively identify inconsistencies in equipment standards and provide decision-making advice, providing a valuable reference for standardizing the content of standard terms in the industry.
Blind spectrum sensing algorithm based on cyclic spectrum and deep learning

With the Internet of Things and big data advancing so quickly, spectrum resources are increasingly in short supply. For unauthorized users, to achieve reliable and stable wireless communication, they must rely on spectrum sensing technology. Traditional spectrum sensing methods have poor performance in situations with a low signal-to-noise ratio, but deep learning's evolution can effectively solve this problem. However, with the improvement of perception accuracy in practical application, the neural network now has many layers and has many large model parameters. , which makes the actual deployment of the algorithm require high computing power equipment. To address the issues with conventional spectrum sensing models' excessive resource consumption, poor real time performance, and limited detection accuracy, a deep learning and cyclic spectrum-based approach for blind spectrum sensing is suggested. In this method, a lightweight sensing network based on MobileNet V2 is built, and the fast Fourier transformation accumulation method is used to extract the features of the signals, which are normalized and sent into the network for training. Experimental results show that this algorithm can sense many kinds of digital modulation signals, and the perceived performance is better than other algorithms.
Optimize the application of attention mechanism in complex labels text detection algorithms

The label is used to mark the object information or classification of the logo, complex label then contains more layout information than ordinary labels. These include Chinese and English characters, pictures, symbols and barcodes. When text detection is performed in complex labels, the detection capability of the algorithm is reduced and is prone to feature overlap resulting in missed and false detection. In this paper, the original YOLOv5 algorithm is optimized by an improved channel attention mechanism to form the new algorithm SA-YOLOv5. The experimental results show that the SA-YOLOv5 model can effectively improve detection efficiency and solve the problem of false and missed detections. The average detection accuracy mAP value is 92.24%, which is 0.68 percentage points higher than the original algorithm, proving the effectiveness of the algorithm.
A CatBoost-based data driven method for equipment effectiveness assessment and prediction

Simulation-based design plays an important role in the study and manufacturing of weapon equipment, in which effectiveness evaluation is one of essential tasks. While a variety of effectiveness evaluation approaches have been proposed, they are becoming incapable with the increase in the complexity of weapon equipment systems. In this case, data driven methods have been explored to address the effectiveness evaluation issue. In this paper, we propose a new CatBoost based data driven method for assessing and predicting the effectiveness of equipment and give a procedure for guiding the use of this method. We illustrate our method with an air defense missile weapon system, which demonstrates the validity of our method compared with state-of-the-art methods.
Active scene reconstruction via self-rotation driven by optimized information theory

With the increased demands of digital twins and virtual reality, autonomous scene reconstruction has gradually become an essential direction in the field of 3D vision. However, efficient and accurate reconstruction with RGBD sensors remains challenging due to the high nonlinearity of camera pose optimization caused by the complexity of the exploration trajectory. Inspired by the working principle of Lidar and panorama, we introduce self-rotation for robust reconstruction. This design can reduce the optimization freedom and expand the visible range of the robot in a specific area, which can significantly improve reconstruction accuracy. To ensure accuracy and efficiency simultaneously, we present a two-branch framework based on the information theory to drive the robot for active scene reconstruction. The scanning branch finds the optimal goal position for self-rotation scanning, and the exploration branch is optimized to find an efficient exploration path which helps the scanning branch find more global optimal. Many experimental results show that our method effectively improves the quality and efficiency of scene reconstruction compared to the current SOTA method (the average integrity is from 47.28% to 84.96%, and the average time consuming is from 315.4s to 189s).
SCGRNet: shape completion-guided registration network for face point cloud

Point cloud registration is a fundamental problem in computer vision and robotics, and has been widely used in various applications, including 3D reconstruction, simultaneous localization and mapping, and autonomous driving. Over the last decades, numerous researchers have devoted themselves to tackling this challenging problem. With the success of deep learning in geometric vision tasks, various types of deep learning-based point cloud registration methods have been proposed, and some of them exhibit good performance. However, their performance degrades rapidly when the overlap between point clouds is reduced. In this paper, we propose a shape completion-guided registration network for face point cloud registration, which takes face shape as a prior knowledge to guide the network to learn a complete face features at the low overlap. Another noteworthy point is that we use the Regularized Projective Manifold Gradient (RPMG) layer to bridge the gap between the neural network output measured in Euclidean space and the rotation parameters described in the SO(3) manifold. In addition, our proposed method infers the translation parameters from the two complementary point clouds and the predicted rotation rather than estimates them directly. For the problem of low overlap point cloud registration, our method can effectively improve the registration accuracy compared to the current state-of-the-art methods. The rotation error and translation error are, respectively, less than 2 degrees and 2mm on the FaceScape-based low overlap face registration dataset.
Anomaly detection of terminal access based on LSTM and ResNet

Massive wireless debugging terminals and complex and diverse access requirements pose significant challenges to the secure access of substation terminal equipment. It is crucial to detect anomaly network traffic to ensure the security of terminal access to the substation. At present, network traffic anomaly detection based on traditional deep learning often has the problem of low computational efficiency or weak representation ability. Given the low computational efficiency of traditional deep learning, the residual network is used to extract spatial features of data, which can effectively improve convergence speed and time efficiency. Aiming at the problem of weak representation ability of traditional machine learning methods, the long short-term memory network (LSTM) is used to improve the representation ability to learn while learning the temporal characteristics of traffic and prevent the gradient from disappearing and network degradation. Experimental results show that compared with the traditional deep learning method, the accuracy of the proposed method is improved, the F1 score reaches 90.09, and the AUC is up to 0.981. By improving anomaly detection accuracy, the paper further guarantees terminal security.
A method for extracting flash point cloud feature on the surface of wind turbine blade based on difference of normal vector

The surface automatic processing of large wind turbine blades has always been an industry problem. The RGBD camera can be used to scan the blade surface to obtain a 3D point cloud of complex curved surfaces. However, the point cloud contains a lot of invalid information and is unorganized data, which makes it difficult to segment the point cloud of the blade. In this paper, we propose a method based on difference of normal vector to extract the blade flash feature of wind turbine blade. Firstly, the point cloud obtained by the RGBD camera is filtered to obtain a pure wind turbine blade point cloud. Then, the difference of normal vector of the wind turbine blade point cloud is calculated to separate the flash feature from the blade surface. Finally, a pure flash point cloud is segmented by clustering, which provides guidance data for automatic grinding of flash. Through experiments, it is proved that this method can accurately segment the blade flash feature on huge wind turbine blade without a large amount of data for model training. And the separation effect is not affected by the scanning angle, sampling noise, etc. It has strong robustness.
Crop planting recommendation algorithm based on ensemble learning

In this paper, we propose a crop planting recommendation algorithm based on ensemble learning to recommend the most suitable crops for farmers based on environmental characteristics and soil element content, which can achieve scientific planting and crop yield increase. Firstly, we adjust the scaling ratio of N (nitrogen), P (phosphor) and K (potassium) elements, which play an important role in crop growth, and use KNN (K-Nearest Neighbor), XGBoost and RF (Random Forest) as weak learners, and use GA (Genetic Algorithm) to optimize the important parameters for KNN, XGBoost and RF, and combine these three weak learners to obtain the ensemble learning model through a soft voting mechanism. After training and testing on the Kaggle public dataset, the accuracy of the crop planting recommendation algorithm based on ensemble learning can reach 94.36%.
An improved OCT retinal image denoising algorithm based on variational image decomposition

Optical Coherence Tomography (OCT) has become an important auxiliary diagnostic technology in the field of fundus disease detection due to its advantages of high resolution and high penetration depth. However, speckle noise exists in retinal images acquired by OCT. It is a difficult problem for OCT image processing technology to keep the middle layer structure information in the process of OCT image denoising. A novel OCT retinal image denoising method based on Gaussian mixture model and variational image decomposition is proposed. Firstly, the proposed BL-G-BM3D variational image decomposition model is used to initially denoise OCT images, and then the Gaussian mixture model is used to cluster the initial denoising results to obtain binary masks that can distinguish background and structure. Finally, the final denoising results are obtained by multiplying the mask image with the initial denoising results. An OCT retinal image with high noise and low contrast was tested and compared with five commonly used denoising methods. The results show that the proposed method can achieve both de-noising effect and laminar structure preservation for high-noise OCT retinal images.
Research on realistic effect generation algorithm of rendering images based on GAN

Saving algorithmic overhead for realistic effect generation is always a hot topic in graphics research. The algorithm in this paper applies the method of the Generative Adversarial Network (GAN) to the study of realistic computer graphics, proposing a new solution to reduce the algorithm overhead. We adopt conditional GAN and add an additional module before the generator to provide the lighting information of the current scene. This extra module represents the illuminating direction with grayscale gradient maps of different angles helping the network get lighting information through image input. In addition, we optimize the loss function by adding the L1 loss and the feature perception loss to improve the generative effect of our network. In the feature perception loss, we use a pre-trained VGG network to calculate the detail feature gap between images, to help the model generate images with better light and shadow effects. Our algorithm can add realistic effects to the existing coarse-rendered image according to the lighting conditions and obtain the corresponding fine-rendered image in single light scenes. The extensive experimental results show that our algorithm has a good post-processing effect on realistic rendering, and the time overhead of the algorithm is independent of the complexity of the scene. From the robustness test, we can know that our network also has a good generalization ability.
Influence of couple-stress on stress concentration around a spheroidal cavity.

In this paper, the tension problem of a cylinder with spheroidal cavity is analyzed by the use of the indirect fictitious-boundary integral method in the linear couple-stress theory. The stress concentration factors and the stress distributions on a net section are obtained for various values of parameters, such as the characteristic length l, the diameter of a cylinder and the flatness ratio of a cavity. The results of the infinite problem with a spherical cavity are comparerd with the others. From the results, it is shown that, with increasing l, the axial stress concentration factor decreases monotonously, however, the hoop stress concentration factor decreases in the case of large stress gradient but increases in the case of small one.
Modeling and optimizing PE utilization rate for systolic array based CNN accelerators

Due to its efficiency, energy-saving, and abundant data reuse, systolic array has been a popular choice for Convolutional Neural Network (CNN) accelerators. Dataflow of the systolic array defines computation mapping strategy and memory access and it is one of the most important design points of accelerators. Most conventional accelerator designs choose a single dataflow and optimize around it. This may influence the Processing Element (PE) utilization rate and cause waste of computing resources and energy. This work introduces a self-paced method to alleviate this problem. We analyse and quantify the PE utilization rate related to the three basic dataflows and build a model called PEU-sim to explore workload-oriented flexible dataflow. Experiments show by combining three dataflows, we are able to raise more than 10% of PE utilization rate for most neural networks and we get the highest of 12.4% for MobileNet.
Temporal and Spatial Expression of Parasitism-Related Olfactory Genes in Larvae of the Ectoparasitoid Dastarcus helophoroides (Coleoptera: Bothrideridae)

Abstract Dastarcus helophoroides (Fairmaire) (Coleoptera: Bothrideridae) is an ectoparasitoid of the pine sawyer beetle, Monochamus alternatus Hope (Coleoptera: Cerambycidae). A sensitive and precise olfactory system is required for the accurate location of the coleopteran host by D. helophoroides neonates. Herein, we characterized the relative expression patterns of six representative olfactory-related genes at different stages of M. alternatus parasitism and in different body regions of D. helophoroides neonates. The genes encoding chemosensory protein 2 (DhelCSP2), odorant receptor 2 (DhelOR2), and ionotropic receptor 2 (DhelIR2) were significantly upregulated before parasitization was initiated, whereas the genes encoding odorant binding protein 8 (DhelOBP8), gustatory receptor 5 (DhelGR5), and sensory neuron membrane protein 1 (DhelSNMP1) were significantly upregulated 4–5 d after initiation of parasitism. In D. helophoroides neonates, four genes (DhelOBP8, DhelCSP2, DhelOR2, and DhelIR2) were significantly upregulated in the head compared with the thoracoabdominal region, and one gene (DhelGR5) was significantly upregulated in the thoracoabdominal area compared with the head. Double-stranded RNAs (dsRNAs) targeting the six olfactory-related genes were synthesized and delivered to D. helophoroides neonates via immersion. After dsRNA treatment, the transcript levels of four olfactory-related genes (DhelOBP8, DhelCSP2, DhelOR2, and DhelSNMP1) were significantly reduced compared with that of the controls. These results provide a basis for further functional explorations of D. helophoroides olfactory genes, which may lead to the development of improved biological pest control methods using D. helophoroides larvae.