Miracle-dz/UniFAR-FTUs huggingface dataset:
Intelligent Redesign for Thermal Robustness of Electromagnet

In order to solve the failure problem of the electromagnet coil under the thermal load, the analysis and robustness optimization basis of the electromagnet magnetic-thermal coupling system are integrated, and an intelligent redesign model for the thermal robustness of the electromagnet is proposed. This paper takes solenoid electromagnet as the research object, takes the main structural parameters of the coil as the control factor, and takes the random thickness of the wire insulation paint film caused by the uncertainty of the process conditions as the noise factor. Under the restriction of the allowable temperature rise of the electromagnet that does not cause damage to the coil insulation, a multi-factor weighted summation of the electromagnet's thermal robustness intelligent redesign evaluation function is proposed. Through orthogonal experiments, key structural variables that are sensitive to the thermal performance of the system are obtained, combined with robustness optimization, a design scheme with the smallest variation of the system's magnetic and thermal comprehensive performance indicators under the combined disturbance of key structural parameters is obtained. The optimization research results show that the wire diameter as a function factor of the main structural parameters of the coil is the most important factor affecting the performance of the electromagnet and the working temperature rise under the magnetic-thermal coupling. Intelligent redesign improves the thermal robustness of the electromagnet while reducing the surface temperature rise of the electromagnet by 1.54 percent. Compared with the traditional design optimization process, the intelligent redesign method proposed in this paper can be implemented quickly and flexibly under the premise of ensuring the accuracy of electromagnetic calculation power, making it more operability than the finite element method. This method has certain reference value for the customization and rapid and intelligent engineering design of similar electromagnet products.
Multi-level Efficient Perception Network for Grain's Edge Detection of Cross-polarized Petrographic Images

The edge detection of mineral grains in petrographic images is the first step of the analysis of petrographic images, which provides cues about the grain's size, shape, and composition. The challenge of automatic methods lies in the ambiguous edges of adjacent grains, as well as the various colors and intensities of grain under different circumstances. In this paper, a multi-level efficient perception network (MLEP) for the grain edge detection of cross-polarized petrographic images is proposed to address the above problem. The multi-angle inputs fusion block (MAIF) takes advantage of sequential petrographic images to generate edge-enhanced features, followed by the modified EfficientNetV2 and the proposed BiDecoder to obtain an affluent and hierarchal representation of edge features. The cross-polarized petrographic image datasets, named CPPID, are generated and carefully annotated. The proposed MLEP is tested on CPPID and achieves 0.888 F1-scores. Experimental results demonstrate the effectiveness of the proposed model, which outperforms four classical CNN-based edge detection models by a large margin.
The Object Detection Method for Pedestrian Video Based on Improved YOLOv3

With the development of smart city and the popularization and application of video surveillance, pedestrian target detection has played a more and more important role in real life. Compared with the traditional pedestrian detection al-gorithm, the pedestrian target detection technology based on deep learning has higher detection accuracy, however, there are still some problems in the current target detection algorithms, such as the inaccurate location of the target, the more missed detection and the unimproved detection accuracy. First of all, in the image division size, this paper finally selected 10 × 10 as the image division size. Secondly, this paper improves the K-MEANS clustering algorithm by adding data filtering process before clustering to make the initial size of the network candidate box more fit to the characteristics of pedestrian targets. Finally, this paper compares the improved detection module’s YOLOv3 with the original YOLOv3 on the VOC Data to verify the generality of this improvement.
On the "Clausal-Connective" and "Nominal-Connective" ka 'or'in Japanese

This paper focuses on the two possible forms of constructions involving disjunction: clausal disjunctive in which ka 'or' takes clauses and nominal disjunctive in which ka 'or' takes nominal elements. I first claim that Japanese clausal disjunctive and nominal disjunctive should not be analyzed as coming from the same source (i.e. nominal disjunctive is not derived from clausal disjunctive through ellipsis). After presenting the difference between clausal disjunctive and nominal disjunctive, I give two possible analyses of the two types of ka ‘or’: the focus analysis and the wh analysis. The main proposal of this paper is that while both analyses have problems and require amendment, the wh analysis better explains the data and goes well with the claim that nominal disjunctive does not involve ellipsis.
Driving Style Identification Model based on XGBoost

During the transition from man-machine co-driving to automatic driving, identifying the driving style of the driver accurately according to the different driving behaviors of the driver is an effective means to formulate a reasonable man-machine driving strategy. For this reason, this paper proposes a driving style identification model based on XGBoost algorithm. This paper extracts the driving habits of the driver based on the INTERACTION dataset, and uses principal component analysis (PCA) to reduce the dimensionality of the data; uses the Fuzzy C-means (FCM) clustering method to calibrate three different driving styles to provide a basis for the construction of the classifier; The Extreme Gradient Boosting (XGBoost) driving style identification model is designed to identify three driving styles; the hierarchical k-fold cross-validation method is used to evaluate its performance. After verification and analysis, the XGBoost model constructed in this paper can accurately identify different driving styles, and the comprehensive accuracy rate for different driving styles is as high as 96.67%. The driving style identification model based on XGBoost improves the accuracy of driving style recognization and lays a foundation for man-machine coordinated driving strategy braking for different driving styles.
Improved B0 Field Estimation Near Air/Tissue Interface And Its Application In Single-Shot Echo-Planar Readout Based Diffusion Imaging

The image quality of single-shot echo-planar imaging (ssEPI) is affected by static magnetic field B0 inhomogeneity induced distortion. Despite previous correction methods, the B0 inhomogeneity near air/tissue interface remains a challenge. In this article, we introduce an approach to improve B0 estimation near air/tissue interface based on a high resolution structural image, then further investigate whether it would improve the correction of ssEPI based diffusion tensor imaging by analytical point spread function (PSF) method with the proposed B0 estimation. Phantom and in vivo brain experiments both demonstrated that B0 accuracy was improved near the air/tissue interface compared to the low resolution B0, and PSF corrections were superior to field map corrections, especially on signal intensity restoration.
Research on Spectral Characteristics of Phase-Shifted Fiber Bragg Grating and Its Defence Applications

Phase-shifted fiber Bragg grating (PSFBG), as an important basic optical component, has been widely used in the fabrication of optical devices due to its narrow linewidth and good filtering characteristics. Fiber lasers and fiber sensors which composed of them have important application prospects in defence fields. According to transfer matrix method, the transmission characteristics of PSFBG is analyzed theoretically. Through the simulation of different phase shift, fiber grating length and refractive index modulation depth, the main effect factors of transmission window position, linewidth and reflectivity are obtained. Finally, the applications of PSFBG in synthetic aperture ladar, fiber hydrophone and three-dimensional microscale sensing are introduced and analyzed.
Application Research of Asset Allocation Model in Performance Analysis of China's Oil Industry

In this paper, factor analysis is used to analyze the performance of listed companies in China's oil industry. The research shows that the performance of oil companies can be evaluated from four aspects: solvency, growth, management and profitability, and solvency is particularly important. Then according to the performance of listed companies in the oil industry, this paper analyzes the application of asset allocation model in the performance of listed companies in China's oil industry and finds that: MV model is more suitable for the asset allocation in China's oil industry, and the asset allocation strategy with variable mean and variance can overcome the simple EW (or SW) strategy.
Automatic Segmentation of Cerebrovascular Based on Deep Learning

Cerebrovascular disease (CVD) is one of the primary causes of death and disability globally. The occurrence of many categories of CVD is accompanied by vessel deformations. So rapid and accurate cerebrovascular segmentation can help clinicians and researchers analyze vascular morphology to prevent and treat CVDs better. Existing deep learning-based methods require a lot of human- annotated data for model training. At present, these manually labelled cerebrovascular data are limited by the quantity and accessibility of data samples. Therefore, creating a vessel segmentation model with strong generalization ability and finite annotations is one of the challenges to be solved urgently. In this article, we propose an optimized U-net network architecture, which is based on multi-scale input and residuals, for automatic vessel segmentation in time-of-flight magnetic resonance angiography (TOF-MRA) images of CVD patients and normal subjects. The experiment uses TOF-MRA images of external independent datasets (not involved in model training) to compare the vessel segmentation effects. The proposed vessel segmentation framework achieves an average dice coefficient of training and validation sets reached 0.947 and 0.927, respectively. The results of this study show that the introduction of multi-scale input and residuals in the U-net network can improve the performance of this model and maintain generalization capability. In the external independent dataset, the function reproducibility is more effective than some existing deep learning-based methods.
Sequential Recommendation with Multiple Contrast Signals

Sequential recommendation has become a trending research topic for its capability to capture dynamic user intents based on historical interaction sequence. To train a sequential recommendation model, it is a common practice to optimize the next-item recommendation task with a pairwise ranking loss. In this paper, we revisit this typical training method from the perspective of contrastive learning and find it can be taken as a specialized contrastive learning task conceptually and mathematically, named context-target contrast. Further, to leverage other self-supervised signals in user interaction sequences, we propose another contrastive learning task to encourage sequences after augmentation, as well as sequences with the same target item, to have similar representations, called context-context contrast. A general framework, ContraRec, is designed to unify the two kinds of contrast signals, leading to a holistic joint-learning framework for sequential recommendation with different contrastive learning tasks. Besides, various sequential recommendation methods (e.g., GRU4Rec, Caser, and BERT4Rec) can be easily integrated as the base sequence encoder in our ContraRec framework. Extensive experiments on three public datasets demonstrate that ContraRec achieves superior performance compared to state-of-the-art sequential recommendation methods.
Rapid calculation method of pore section hydraulic conductivity based on convolution neural network

Structural differences in pore space are the direct factors affecting fluid movement in porous media, while the shape of the pore cross-section determines the process and state of fluid movement, which is closely related to the conductivity in fluid media. Considering the traditional engineering calculations such as pore network models and finite element methods use shape approximation to describe pore cross-sections, which lose part of the shape information. To address the above problems, we proposes a computational method based on convolution neural network to accurately describe the pore cross-section shape by extracting the cross-section shape features and correct the computational misalignment problem of the traditional method. In order to ensure the universality of the method to different rock types, we extract (3779) 2D pore cross-sections from the 3D X-ray images of Bethemier and Limestone samples as the sample set for the training of the convolution neural network model. Finally, the accuracy of the model prediction results and the efficiency comparison with the mainstream methods are given, proving that the method proposed in this paper outperforms other methods in terms of accuracy and efficiency. This work is of significance for oil and gas field exploitation.
A Prior-knowledge guided Multi-scale Deep Network for Speech Emotion Recognition

Speech emotion recognition (SER) is a challenging task, whose performance always depends on the effectiveness of its features for classification. At present, many researchers use signal processing or data-driven deep learning methods to obtain expressive features. However, most of them are single-scale and only represent local speech information, and the methods cannot fully learn the underlying knowledge either. Therefore, we propose a multi-scale convolutional recurrent neural network with attention mechanism (AMCRNN) for multi-scale features for more comprehensive expression. What's more, we introduce prior-knowledge to guide our model discriminant learning. The proposed model is evaluated over two datasets including CHEAVD2.0 and IEMOCAP, and the results show that our method can achieve the comparable performance.
A Novel Efficient Coupling Algorithm for Adaptive Fiber Coupling

The high-speed free space optical communication systems based on the fiber techniques play more and more important roles nowadays, the laser beam propagating in the free space should be coupled into the single-mode fiber (SMF) at the input of the receiver. However, propagation through the atmospheric turbulence degrades the laser beam's spatial coherence and the jitters of the launch platform also decrease the convergent efficiency. Based on the existing adaptive coupling system, this paper studies the coupling algorithm based on the gradient descent (GD). We propose a novel algorithm that estimating the gradient through the simultaneous perturbation stochastic approximation (SPSA) and combining it with some traditional deep learning algorithms. Besides, we compare the coupling efficiency and speed of these algorithms. The simulation results demonstrate that the new algorithm combining the SPSA and the iteration point update strategy of Adagrad (SP-Adagrad) algorithm has a great advantage in coupling performance and is robust compared with other traditional algorithms, which improves the performance of the adaptive fiber coupling system in coupling speed and stability.
Low-cost and lightweight indoor positioning based on computer vision

Location Based Service (LBS) is becoming increasingly valuable in many indoor application scenarios. However, existing indoor positioning technologies are either too costly or have high computational volumes. It is a challenge to achieve positioning both at low cost and low computation. To address this problem, we pro-pose an indoor positioning algorithm based on computer vision, which can achieve localization at a low-cost and low computational complexity. In the proposed method, the lightweight MobileNet network is used to build an image classification model to train and optimize the collected dataset, then the predicted positions are obtained by inputting the images of the locations to be measured into the trained model. The experimental results demonstrate that the MobileNet model has lower computational effort than the classical convolutional neural network CNN. The designed computer vision-based method can successfully recognize images for indoor positioning, which is highly practical.
A Novel Approach for Early Recognition of Cataract using VGG-16 and Custom User-based Region of Interest

Cataract is a severe eye disease and is one of the main causes of vision impairment across the world. There have been many researches about cataract prediction using machine and deep learning state-of-the-art methods but all of these methods rely on automatic image classification without using the knowledge of the clinicians. This study proposes a novel approach that incorporates artificial intelligence with the human intelligence of experienced medical professionals. Our model allows manual selection of the region in the fundus image that is most likely to have cataract and gives it more importance as compared to the rest of the region in the image. After that, a VGG-16 model was applied to classify the input images. Accuracy was chosen as the performance metric and the maximum accuracy observed for the model was 98.83%.
Investigating The Possible Effect of Gak-3βinhibitor on Macrophage Transformation Via TLR3 and 4 Signaling Pathway

While a great number of immunotherapy strategies are promising in treating many types of cancer via targeting and strengthening adaptive immune responses, finding antitumor strategies through facilitating the innate immune response is still an urgent need in treating broader types of tumor. Previous studies have shown that GSK-3β could affect the expression of toll-like receptors (TLRs) which aids in the transformation of M2 macrophages to M1, slowing the development of cancer tumors. In this study, we analyzed the potential immunotherapy approach for treating glioblastoma, an aggressive brain cancer that is highly refractory to conventional treatment by using GSK-3β inhibitors to manipulate macrophage polarization via TLR-3 and TLR-4. The success of this study could bring up a new target for regulating the tumor progression.
Multi-Feature Fusion via 2D and 3D Networks for Action Recognition

Human action recognition is a hot topic in the field of computer vision and its progress can promote the development of social intelligence and security. In order to improve the performance of action recognition, the multi-feature fusion method is proposed. RGB information contains information including the texture and color of the object and background and optical flow information contains the motion information of the object and background. 2D CNN has excellent ability to process single frame of information, and 3D CNN can process information of multiple frames and overall temporal information. Multi-feature fusion can make full use of their complementarity. In this work, RGB information and optical flow information were input into 2D ResNet-18 and 3D ResNet-18, and then their softmax scores were linearly fused for action recognition. In order to find better weights, we set up different loss functions and used the tree-structured Parzen estimator (TPE) for parameters searching. We conducted experiments on the baseline data set UCF101, and pre-trained the 2D and 3D RGB networks on the Kinetics data set to further improve the fusion results on UCF101. The top-1 accuracy of the 2D and 3D optical flow network and the 2D and 3D RGB fine-tuning network we obtained were 50.942%, 54.386%, 66.743% and 70.816%. With TPE, the final accuracy reached 76.265%.
Study on Seismic Behavior of Cross-shaped BRB based on Full Lightweight Concrete as Filler Material

A novel type of cross-shaped buckling restrained brace (BRB) was developed. Full lightweight concrete was used as filler material. To study the effects of the width-to-thickness ratio (10 and14) of inner core and filler materials (ordinary concrete, full light-weight concrete) on the seismic performance of buckling restrained braces, low cyclic reciprocating loading was used to simulate horizontal seismic loading on 4 specimens. The failure mode, hysteretic curves, and skeleton curves were compared and analyzed. The test results indicated that the hysteretic curve of each specimen was plump. The energy dissipation coefficient increased with the increase of displacement amplitude. The width-to-thickness ratio rather than filler material has a great influence on the energy dissipation capacity of BRBs.The specimen with the width-thickness ratio of 10 has better energy dissipation capacity and cumulative plastic deformation capacity, which reflects better fatigue performance. It is suggested that the width-to-thickness ratio of core material should be selected between 5 to 10.
Application of K-Nearest Neighbor Algorithm Based on Orthogonal Wavelet Feature Extraction In Fault Diagnosis

K-Nearest Neighbor (KNN) learning, a commonly used supervised learning method, finds the k closest training samples in the training set based on distance measurements of a given test sample. The learning of these k "neighboring" training samples reveals the intrinsic nature and laws of the data, and thus distinguishes the operating state of the machine. In this paper, an Orthogonal Wavelet Transform K-Nearest Neighbor (OWTKNN) diagnosis method has been proposed to extract the peak-to-peak values of each detailed signal as training samples using the Orthogonal Wavelet Transform (OWT) method and classify them by the KNN method. The classification results of the data obtained through rolling bearing tests show that the method can handle complex mechanical vibration signals in a more effective way than KNN without extracted eigenvalues, as it significantly improves the classification effects of unknown fault data, shortening classification time, and improving intelligent diagnosis efficiency.