bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mask wearing detection algorithm based on improved YOLOv4
Authors: Ye Qing , Zhao Yuqi 
Published: April 2022

Abstract
Since the outbreak of COVID-19, the domestic situation has gradually improved, but small-scale outbreaks are still frequent, the current situation of COVID-19 cannot be eased up. Using target detection algorithms to identify mask wearing to remind people to pay attention to the epidemic, can reduce the probability of cross infection in public places. YOLOv4 is a real-time target detection algorithm with fast detection speed and small size, but it has problems such as insufficient positioning of the bounding box and easy to miss the occluded targets. Based on this, we improve the algorithm and integrates the CBAM attention module into the backbone network. At the same time, the depthwise over-parameterized convolution (DO-Conv) is used to replace the ordinary convolution, which improves the network fitting ability without adding additional parameters. The experimental results show that the improved algorithm has improved the positioning accuracy of the bounding box and detection accuracy.
Data Analysis Methods of Natural Fragment Spatial Distribution
Authors: Lv Juan , Kang Zhenyu , Li Yuwen , Yuan Shuqiang , Cheng Xiangyu , Du Juan 
Published: April 2022

Abstract
This paper aims to explore the simulation method of random spatial distribution of natural fragments. The transformation between three-dimensional spatial coordinates and two-dimensional planar coordinates of fragments is realized by coordinate mapping and inverse mapping, and the random uniform distribution of fragments in the two-dimensional plane is realized by the Monte Carlo stochastic points method, so as to obtain the spatial position distribution of fragments.
Research on power load forecasting algorithm of power system based on artificial firefly
Authors: Chen Haomin , Liang Jinzhao 
Published: April 2022

Abstract
The power system is constantly upgraded with the development of information technology. The digital management of power system effectively improves the operation and maintenance efficiency of power system. Although the current power system algorithms have made great progress, there has been no effective prediction algorithm in the field of load forecasting. Load forecasting is a key operation and planning approach of power system. It affects many decisions of power system, such as economic dispatching, automatic generation control, safety evaluation, maintenance dispatching and energy commercialization [1]. In view of the above problems, the power load forecasting algorithm of power system based on artificial firefly will be studied. In the existing resource management framework of power system digital grid platform, the power load business of power system is studied. The artificial firefly algorithm is used to complete the research of power load forecasting algorithm. Simulation results show that the established algorithm has good time complexity and space complexity, and the accuracy of power load forecasting results is improved by at least 22.8%.
A self-supervised feature selection algorithm for spectral embedding based on block HSIC lasso
Authors: Guo Yajing , Duan Lijuan , Xie Chenyao , Zhang Wenbo 
Published: April 2022

Abstract
With the rapid development of information technology, a large amount of unlabeled high-dimensional data has been generated. To be able to better handle these data, we propose a new self-supervised feature selection algorithm for spectral embedding based on block HSIC lasso (FSSBH). It innovatively applies the HSIC theoretical approach to unlabeled scenarios for feature importance assessment, and performs feature selection by self-supervised learning with the pseudo-label matrix formed by the spectral embedding. The algorithm is compared with five popular algorithms for classification experiments on seven publicly available datasets. The results show the superior performance of the FSSBH algorithm.
Video behavior recognition algorithm based on two-stream heterogeneous convolutional neural network
Authors: Ye Qing , Liang Zhenghao 
Published: April 2022

Abstract
In video behavior recognition, making full use of the spatio-temporal information contained in video frame is the critical point to further improve the recognition accuracy. In our study, we propose a video behavior recognition algorithm based on two-stream heterogeneous network, so that we can further extract the spatio-temporal feature information in the video frame, and finally take full advantage of the spatio-temporal features in the video sequence. In view of the characteristics of RGB and optical flow images, this paper uses DenseNet121 and Inception-V4 to structure a two-stream network, to fully extract the spatio-temporal feature information in the video. In this paper, UCF101 dataset is used to evaluate the algorithm, and the recognition accuracy is 91.7%, which verified the reliability of the proposed algorithm.
An application analysis method based on Clustering
Authors: Duan Rui , Chen Junde 
Published: April 2022

AbstractDepending upon the load intensity and time variation information, we established the indicator system to analyze the of commercial halls, so that the further optimization actions can be taken in practical application scenarios. The representative clustering algorithms including the k-means and AC algorithm are chosen for the comparative analysis, and on this basis, an enhanced k-means algorithm is put forward in this study because the traditional method is sensitive to the initial clustering centroids and lacks the stability. A series of experiments are performed in this work, and the experimental findings reveal the validity of the proposed method. It provides a new idea for the efficiency analysis of commercial halls.
Landslide Hazard Identification Based on Deep Learning and Sentinel-2 Remote Sensing Imagery
Authors: Xia Jun , Liu Hongjiang , Zhu Linfu 
Published: April 2022

Abstract
Landslide is one of the common geological disasters, which seriously threatens human life and property safety. It is particularly important to quickly identify landslide information. This paper takes the Wenchuan earthquake landslide area as the research area, and uses 7 deep learning methods(4-Layer-CNN, AlexNet, ResNet152V2, DenseNet201, InceptionV3, Xception and InceptionResNetV2) to discuss landslide detection methods based on Sentinel-2 remote sensing images. Using the marked landslide and non-landslide sample points, the Sentinel-2 remote sensing image was sliced into 80×80 pixel tiles, and then the deep learning method was used for model training, verification and testing. The results show that : (1) Among the 7 deep learning models, the F1-Score of the DenseNet201 model is the largest, reaching 0.8872, and the RMSE is the smallest 0.2503. It can be seen that the DenseNet model has a good recognition effect on landslide samples, with an accuracy of 0.9172; (2) Second It is InceptionResNetV2, the F1-Score is 0.8721, the RMSE is 0.2721, and the landslide sample recognition accuracy is 0.9012; (3) the worst effect is AlexNet, the minimum F1-Score is only 0.7263, the maximum RMSE is 0.4022, and the accuracy is 0.8295. It can be seen that the deep learning method is applied to Sentinel-2 remote sensing images for landslide image detection, and the accuracy can reach 91.72%, which can quickly and accurately identify landslide information, and improve the method reference and decision basis for disaster prevention and mitigation.
An Effective Approach for Sonar Image Recognition with Improved Efficientdet and Ensemble Learning
Authors: Du Yunben , Lin Wei , Zhong Weibo , Yuan Yu 
Published: April 2022

Abstract
Precisely recognition of underwater targets plays a critical role in the field of underwater unmanned exploration. According to the characteristics of sonar image, in order to improve the accuracy of underwater automatic target recognition, this paper proposes an underwater target sonar image recognition method combined with improved Efficientdet and ensemble learning. According to the regional dominance principle and threshold segmentation method, the sonar image is preprocessed, and preliminary effective features extracted from preprocessed data by Efficientnet backbone, so as to balance the efficiency and power consumption of the algorithm. The feature fusion adopts the improved weighted bi-directional feature pyramid ( BiFPN ) structure to aggregate the global information and strengthen the feature representation of the shallow feature map, which optimizes the problem that the small feature extraction is not ideal due to the low resolution of the sonar image and less detail information. By building a two-level classifier to complete the learning task in an ensemble way, a better classification effect is obtained than a single classifier. This method is applied to the underwater intelligent perception group of the 10th National Marine Aircraft Design and Production Competition in 2021, and won the first prize in the country. The experimental results show that the designed ensemble network model is more accurate than the conventional convolution neural network in identifying underwater targets on the measured sonar image data set while ensuring a certain prediction speed.
Research On Machining Product Quality Prediction Based On Machine Learning
Authors: Lan Chen , Fei Wan 
Published: April 2022

Abstract
Data empowerment has attracted widespread attention from manufacturing companies. Enterprises control the production process by mining the data generated in the processing process, so as to achieve the purpose of improving product quality. This paper mainly uses the classification algorithm in data mining to model the data in the processing process and predict the product quality. Using the accuracy of the model prediction as the evaluation standard, the ID3, CART, SVM, KNN and Adaboost algorithms are compared. The experimental results show that the Adaboost classifier is significantly better than other algorithms in the evaluation index of accuracy, so the Adaboost classifier is selected as the final classifier.
Feature Sharing Attention 3D Face Reconstruction with Unsupervised Learning from In-the-Wild Photo Collection
Authors: Yang Xiaoxiao 
Published: April 2022

Abstract
3D face reconstruction is an active research topic in the field of computer vision research. It has a wide range of application scenarios. The number of 3D face annotation samples is small and the accuracy of the 3D reconstruction algorithm is insufficient. In this paper, we propose a shared attention module and a feature point constraint mechanism, and proposes a 3D face reconstruction algorithm that takes a single face image as input and uses a encoder-decoder network to predict the 3D face. The prediction view and light network introduces the attention mechanism module, which can improve the feature extraction ability of the network. The albedo and depth introduces shared features to help extract richer facial feature information. At the same time, the self-monitoring method of single image input avoids the high requirements of traditional methods for datasets. We conducted comparative experiments and ablation experiments on the BFM, and CelebA face datasets. The experimental results show that compared with Unsup3d and other representative 3D Face reconstruction algorithms, it has a certain improvement in the two evaluation indexes of Scale Invariant Depth Error (SIDE) and Mean Angle Deviation (MAD).
Model Reference Adaptive Speed Observer Control of Permanent Magnet Synchronous Motor Based on Single Neuron PID
Authors: Liu Hao , Zhang Huixian , Zhang He , Chen Gaoqi 
Published: April 2022

Abstract
For the speed sensorless control of permanent magnet synchronous motor (PMSM), a design method based on model reference adaptive observer and single neuron PID speed loop was proposed. In this method, the model reference adaptive observer is used to identify the speed online, the Popov hyperstability is used to determine the reference adaptive law of the speed observer, the neural network module is used to control the q-axis current, and the LMS algorithm is selected to modify the weights of the neural network speed loop online. The simulation results show that the model reference adaptive speed observer control of PMSM based on single neuron PID is effective and robust.
Improved PD of LESO quadrotor control algorithm
Authors: Liu Chunling , Feng Jinlong 
Published: April 2022

Abstract
Aiming at the attitude instability problem caused by the quadrotor’s nonlinearity, strong coupling and susceptibility to external interference, a linear extended state observer (LESO) controller with improved PD was designed. First, the incomplete differential control is formed by adding a first-order inertia system to the differential Elements of the PD algorithm. Then, LESO is used to estimate and compensate the internal and external disturbances in the control system to the original system, which can not only reduce system errors, but also enhance the anti-interference ability of the control system. The MATLAB simulation shows that the improved PD of LESO controller can quickly reach a stable state, effectively improve the stable control ability of the quadrotor’s attitude, with smaller overshoot and better control effect.
Design of interval type-2 fuzzy fractional order PID controller based on particle swarm optimization
Authors: Wu Hao , Zhao Taoyan 
Published: April 2022

Abstract
In this paper, an interval type-2 fuzzy fractional order PID controller (IT2F-FOPIDC) based on particle swarm optimization is proposed. The proposed controller has two adjustable degree of freedom parameters, so the control range of the controller parameters becomes larger and can control the controlled object more flexibly. Aiming at the difficult problem of many controller parameters and tuning, this paper introduces particle swarm optimization algorithm to optimize the controller parameters. In order to avoid the problems of long iteration time and high computational cost caused by Karnik-Mendel reduction algorithm, the enhanced iterative with stop condition algorithm is selected for the proposed controller. Finally, the proposed controller is applied to the second order systems with time delays. Simulation results show that the proposed strategy has faster response time and smaller oscillation amplitude, and its performance is better than fuzzy controller and interval type-2 fuzzy controller.
Optical angular momentum in atomic transitions: a paradox
Authors: Barnett Stephen M , Speirits Fiona C , Babiker Mohamed 
Published: April 2022

Abstract
Stated simply the paradox is as follows: it is clear that the orbital angular momentum of a light beam in its direction of propagation is an intrinsic quantity, and therefore has the same value everywhere in the beam. How then can a Gaussian beam, with precisely zero orbital angular momentum, drive a (single-photon) quadrupole transition which requires the transfer of angular momentum 2ℏ to an absorbing atom?
Engineered assistive materials for 3D bioprinting: support baths and sacrificial inks
Authors: Brunel Lucia G , Hull Sarah M , Heilshorn Sarah C 
Published: April 2022

Abstract
Three-dimensional (3D) bioprinting is a promising technique for spatially patterning cells and materials into constructs that mimic native tissues and organs. However, a trade-off exists between printability and biological function, where weak materials are typically more suited for 3D cell culture but exhibit poor shape fidelity when printed in air. Recently, a new class of assistive materials has emerged to overcome this limitation and enable fabrication of more complex, biologically relevant geometries, even when using soft materials as bioinks. These materials include support baths, which bioinks are printed into, and sacrificial inks, which are printed themselves and then later removed. Support baths are commonly yield-stress materials that provide physical confinement during the printing process to improve resolution and shape fidelity. Sacrificial inks have primarily been used to create void spaces and pattern perfusable networks, but they can also be combined directly with the bioink to change its mechanical properties for improved printability or increased porosity. Here, we outline the advantages of using such assistive materials in 3D bioprinting, define their material property requirements, and offer case study examples of how these materials are used in practice. Finally, we discuss the remaining challenges and future opportunities in the development of assistive materials that will propel the bioprinting field forward toward creating full-scale, biomimetic tissues and organs.
Microwave photonics applications of stimulated Brillouin scattering
Authors: M K Varun , Mishra Akhileshwar , Pant Ravi 
Published: April 2022

Abstract
Generation, processing, and sensing of microwave signals using photonic technologies offer immunity from electromagnetic interference, allow high-speed operation over a wide frequency range, and reduce the overall system weight. Recent developments in integrated photonic technologies further increase the attractiveness of photonics-based radio frequency (RF) systems. Stimulated Brillouin scattering (SBS), due to its inherently narrow bandwidth, wavelength transparent nature, low threshold, and bandwidth reconfigurability, has emerged as a potential contender for microwave photonic (MWP) generation and signal processing. In this article, we present a review of recent developments in the area of SBS-based MWP. We discuss different SBS-based techniques for realizing some of the key MWP signal processing functionalities such as RF generation, filtering, and true time delay. Implementation of these tasks using fiber and on-chip platforms is presented along with the effect of system parameters such as dispersion on their performance. Novel architectures to mitigate these impairments are discussed.
STMP at 10: shaping surface metrology, measurement and phenomena for a decade
Authors: Wood Robert , Costa Henara Lillian 
Published: April 2022

Abstract
Surface Topography: Metrology and Properties (STMP) is reaching its 10th Birthday soon and this paper looks at the inception of the journal and the goals set in the mid 2010s and will look back at what it has published in the most highly cited areas and detail future initiatives to serve the readership. STMP publishes the latest physics, chemistry, life science, materials science and engineering research on applied, functional surfaces. It has published ground-breaking work on surface design, measurement, instrumentation, manufacturing, functionality and modelling as well as cross-disciplinary work on surface and interface engineering across an array of different applications. It is the home of papers from the biannual international conference on Metrology and Properties of Surfaces series. The journal was awarded it’s first impact factor in 2017 and has a current value of 2.038 (2020) and is now working to improve on this. A new initiative for 2021 is to offer a collection of papers from emerging leaders within the scope of the journal.
The TOX subfamily: all-round players in the immune system
Authors: Han Jiawen , Wan Minjie , Ma Zhanchuan , He Ping 
Published: April 2022

AbstractThe thymocyte selection-related HMG box protein (TOX) subfamily comprises evolutionarily conserved DNA-binding proteins, and is expressed in certain immune cell subsets and plays key roles in the development of CD4+ T cells, innate lymphoid cells (ILCs), T follicular helper (Tfh) cells, and in CD8+ T-cell exhaustion. Although its roles in CD4+ T and natural killer (NK) cells have been extensively studied, recent findings have demonstrated previously unknown roles for TOX in the development of ILCs, Tfh cells, as well as CD8+ T-cell exhaustion; however, the molecular mechanism underlying TOX regulation of these immune cells remains to be elucidated. In this review, we discuss recent studies on the influence of TOX on the development of various immune cells and CD8+ T-cell exhaustion and the roles of specific TOX family members in the immune system. Moreover, this review suggests candidate regulatory targets for cell therapy and immunotherapies.
Common colonic community indicators of the suckling pig microbiota where diversity and abundance correlate with performance
Authors: Lee Adam , Le Bon Melanie , Connerton Ian F , Mellits Kenneth H 
Published: April 2022

AbstractThe primary objective of this study was to investigate if common colonic community indicators could be identified from the microbiota of 22-day-old suckling pigs in repeated small-scale trials. A total of three separate trials were conducted at different times in the same year and facility with genetically similar animals. Colonic samples were collected from four pigs in each trial and the microbiome composition assessed by 16s rRNA gene sequencing. Pig weight, average daily gain (ADG), bacterial diversity, and abundance were not significantly different between repeated trials, except for a significant difference in Jaccard Similarity. At genus level, the most abundant taxa identified were Porphyromonadaceae unclassified (15.81%), Ruminococcaceae unclassified, (12.78%), Prevotella (7.26%), Clostridiales unclassified (6.99%), Lactobacillus (6.58%), Phascolarctobacterium (6.52%), and Firmicutes unclassified (5.69%). The secondary objective was to establish if pooled data in terms of microbial diversity and abundance of the colonic microbiota related to weight and ADG. Pig weight at day 22 and ADG positively correlated with α-diversity. Abundance of potential protein digesting and short-chain fatty acid producing operational taxonomic units ascribed to Terrisporobacter, Ruminococcaceae unclassified, Intestinimonas, and Dorea correlated with weight and ADG, suggesting a nutritional role for these common colonic community microbiota members in suckling pigs.
Identification of novel split-GAL4 drivers for the characterization of enteroendocrine cells in the Drosophila melanogaster midgut
Authors: Holsopple Jessica M , Cook Kevin R , Popodi Ellen M 
Published: April 2022

Abstract
The Drosophila melanogaster midgut is commonly studied as a model epithelial tissue for many reasons, one of which is the presence of a diverse population of secretory cells called enteroendocrine cells. Subpopulations of these cells secrete various combinations of peptide hormones which have systemic effects on the organism. Many of these hormones are also produced in the Drosophila brain. The split-GAL4 system has been useful for identifying and manipulating discrete groups of cells, but previously characterized split-GAL4 drivers have not driven expression in high proportions of enteroendocrine cells. In this study, we screened candidate split-GAL4 drivers for enteroendocrine cell expression using known reference drivers for this cell type and discovered a new split-GAL4 driver pair that confers expression in a greater number of enteroendocrine cells than previously characterized driver pairs. The new pair demonstrates less brain expression, thereby providing better tools for disentangling the physiological roles of gut- and brain-secreted peptides. We also identified additional split-GAL4 drivers that promote expression in discrete subpopulations of enteroendocrine cells. Overall, the tools reported here will help researchers better target enteroendocrine cell subpopulations.