bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cosh-Gaussian laser pulse influenced electron acceleration in an ion channel
Authors: Ghotra Harjit Singh 
Published: July 2022

Abstract
The electron acceleration in a prepared ion channel is studied theoretically by using a radially polarized (RP) cosh-Gaussian (ChG) laser pulse. The peculiar propagation properties of ChG laser cause it to focus early and over a shorter time than a Gaussian laser pulse, making it suitable for accelerating electrons to extremely high energies over a small duration. The electrostatic field formed by an ion channel prevents electrons from escaping the interaction zone due to their transverse oscillations, whereas the decentering parameter of the ChG laser pulse influences electron energy gain. This combined role of RP-ChG laser and the effect of an ion channel causes an enhancement in the electron energy gain significantly to the order of GeV with laser intensity (∼

10

20

W

c

m

−
2

) in an ion density channel (∼

10

22

m

−
3

) with decentered parameter (∼2.15) for an incident laser pulse at initial phase (

ψ
0

=
π

).
Enhanced dilution step and gravimetric preparation uncertainty for low-amount fraction standard gas mixtures using a newly developed automatic weighing system for a mini cylinder
Authors: Kim Jeong Eun , Lee Jin Bok , Kim Jin Seog , Yang Inseok , Hong Kiryong 
Published: July 2022

Abstract
The gravimetric method is widely used to prepare standard gas mixtures in gas cylinders. Several dilution steps are required to prepare a low-amount fraction gas mixture, such as at μmol mol−1 level, using the gravimetric method. We have developed a new automatic weighing system (AWS) that is applicable to a mini cylinder (100 mL or less) to reduce the number of dilution steps. The developed mini-AWS is operated by an automatic cylinder exchange mechanism that is similar to the existing AWS. However, the minimum measurable mass was decreased to measure a small amount of target gas; therefore, the gas mixture can be prepared directly at the μmol mol−1 scale. This study evaluated the fundamental performance of the developed mini-AWS, and we gravimetrically prepared several gas mixtures of nominal 100 μmol mol−1 carbon monoxide (CO) in nitrogen (N2). The uncertainties of prepared gas mixtures were consistent within 0.1% of the relative expanded uncertainty. They were then compared to the standard gas mixture, which was gravimetrically prepared using the existing AWS. The compared results agreed within their expanded uncertainties (k = 2).
A lightweight learning-based decoding algorithm for intraneural vagus nerve activity classification in pigs
Authors: Pollina Leonardo , Vallone Fabio , Ottaviani Matteo M , Strauss Ivo , Carlucci Lucia , Recchia Fabio A , Micera Silvestro , Moccia Sara 
Published: July 2022

Abstract

Objective. Bioelectronic medicine is an emerging field that aims at developing closed-loop neuromodulation protocols for the autonomic nervous system (ANS) to treat a wide range of disorders. When designing a closed-loop protocol for real time modulation of the ANS, the computational execution time and the memory and power demands of the decoding step are important factors to consider. In the context of cardiovascular and respiratory diseases, these requirements may partially explain why closed-loop clinical neuromodulation protocols that adapt stimulation parameters on patient’s clinical characteristics are currently missing. Approach. Here, we developed a lightweight learning-based decoder for the classification of cardiovascular and respiratory functional challenges from neural signals acquired through intraneural electrodes implanted in the cervical vagus nerve (VN) of five anaesthetized pigs. Our algorithm is based on signal temporal windowing, nine handcrafted features, and random forest (RF) model for classification. Temporal windowing ranging from 50 ms to 1 s, compatible in duration with cardio-respiratory dynamics, was applied to the data in order to mimic a pseudo real-time scenario. Main results. We were able to achieve high balanced accuracy (BA) values over the whole range of temporal windowing duration. We identified 500 ms as the optimal temporal windowing duration for both BA values and computational execution time processing, achieving more than 86% for BA and a computational execution time of only ∼6.8 ms. Our algorithm outperformed in terms of BA and computational execution time a state of the art decoding algorithm tested on the same dataset (Vallone et al 2021 J. Neural Eng.
18 0460a2). We found that RF outperformed other machine learning models such as support vector machines, K-nearest neighbors, and multi-layer perceptrons. Significance. Our approach could represent an important step towards the implementation of a closed-loop neuromodulation protocol relying on a single intraneural interface able to perform real-time decoding tasks and selective modulation of the VN.
Improved FairMOT multi-target tracking method for complex scenes
Authors: Qiang Dong , Wang Zhangang 
Published: July 2022

AbstractAt present, the popularization of augmented reality technology on personal terminals is no longer hindered by inherent computing devices due to the substantial improvement in the performance of intelligent terminal processors. However, in the practical application of augmented reality technology, there are still various external disturbances in the real environment, such as perspective changes, motion poses and other potential variables, so it will cause unstable target detection, target registration distortion, virtual and real space superposition of varying degrees of severity. Using TBD strategy as the basis of multi-target tracking technology can further improve the efficiency of multi-target tracking method if it is effectively combined with neural network algorithm. Based on the improved FairMOT neural network multi-target tracking algorithm, this paper mainly studies the specific application of deep learning algorithms in the field of multi-target tracking. Experiments have shown that the improved multi-scale feature fusion network and the optimized loss function of the FairMOT tracking algorithm have good real-time performance and high accuracy for multi-category target detection. The method of stable tracking of multi-category targets is completed in the scene of this paper, which improves the tracking performance, and finally achieves stable tracking through neural network learning and training.
Building extraction algorithm from remote sensing images based on improved DeepLabv3+ network
Authors: Han Jiapeng , Wang Zhenzhou , Wang Yun , Hou Weimin 
Published: July 2022

Abstract
With the development of deep learning, quickly extracting high-precision building information from remote sensing images has become the research focus of intelligent application and processing of remote sensing data. Aiming at the problems of slow extraction speed and incomplete edge segmentation in building extraction in remote sensing images, a building extraction algorithm of remote sensing images based on an improved deeplabv3+ network is proposed. The more lightweight network MobileNetv3 is used to replace the original deeplabv3+ semantic segmentation model feature extraction backbone network Xception, and the standard convolution in the hole space pyramid pooling module is replaced with deep separable convolution, which reduces the amount of calculation and improves the training speed.DAMM (Dual Attention Mechanism Module) is connected in parallel with ASPP (Atous Spatial Pyramid Pooling) to improve the segmentation accuracy of edge targets. The model is verified on WHU and Massachusetts data sets. The results show that the number of training parameters and training time of the model are reduced, and the accuracy of the building extraction is effectively improved, which can meet the requirements of rapid extraction of high-precision buildings.
Point cloud semantic segmentation based on pillarpointnet
Authors: Gan Xingli , Shi Hao , Hu Chunlei , Deng DianFu , Yang Shan 
Published: July 2022

Abstract
Aiming at the problem that the deep neural network pointnet does not introduce local features, the segmentation accuracy is not high and the segmentation efficiency of pointnet++ is low. On the basis of pointnet, a two-way feature extraction method, pillarpointnet, is proposed. Our network is divided into upper and lower parallel paths. The upper path is a simplified version of pointnet, which is used to extract global features. In the lower path, we use pillars instead of voxels to reduce the problem of wasting computing power due to inconsistency in density. The features of each voxel are then assigned to the points within the grid to represent the domain features of the points. Finally, the local features, global features and the points are concatenated and put into the segmentation network. The final experimental results show that compared to some current state-of-the-art segmentation networks, pillarpointnet maintains a good balance between speed and accuracy.
Traffic sign detection algorithm based on improved Tiny-YOLOv4
Authors: Zhang Xiaoxue , Huang Wei 
Published: July 2022

Abstract
In order to balance the three requirements of high detection accuracy, fast detection speed and small model capacity, this paper proposes a traffic sign detection algorithm based on improved Tiny-YOLOv4. The backbone feature extraction network and feature pyramid network of original algorithm are improved, a new feature extraction module CRBottle is constructed and introduced into the network, and spatial pyramid pooling is added to enhance the ability of the network to extract features. Change to larger scale 52×52 and 104×104 prediction feature layers to improve the detection accuracy of small target traffic signs. The experimental results show that the improved algorithm achieves an average accuracy of 88.9% and a model memory capacity of 10.8MB. Compared with the original algorithm, the detection accuracy is increased by 13.4%, the model memory capacity is reduced by 11.8MB, the detection speed reaches 200FPS, and the comprehensive performance of the improved algorithm is better than other detection algorithms.
A Bayesian optimization-based LSTM model for DGA domain name identification approach
Authors: Niu Youfeng , Guan Mingxi , Yuan Wenhao , Chen Yilin , Chen Lingyi , Yu Qiming 
Published: July 2022

Abstract
In recent years, with the rapid development and rise of mobile Internet, network security issues have also posed a great threat to people. Botnets are an important problem faced by current network security. DNS protocol-based botnets widely use domain generation algorithm (DGA), which can randomly change the domain name to hide itself, and therefore is very likely to threaten people’s network security. In this paper, we use the domain names of the top 1 million websites in the Alexa global ranking as white samples, and for the DGA sample data, we use the open data of 360netlab as black samples. The character sequence model is used for feature extraction, and the LSTM with Bayesian optimization neural network is used to optimize the hyperparameter combination, which finally makes the accuracy of the model above 97%, and the model has superior performance to compare with the conventional model, which can effectively improve the accuracy of DGA detection and recognition.
Research on UWB fusion location algorithm
Authors: Zhang PingJun , Hu DeYang , Hu PengJun 
Published: July 2022

Abstract
In order to solve the problem that ultra-wideband (ultra-wideband, UWB) positioning is greatly affected by the environment in the indoor environment of mobile robot, an indoor positioning algorithm based on UWB, odometer and inertial measurement unit is proposed. Taking advantage of the high positioning accuracy of UWB in indoor environment and the high accuracy of odometer positioning in a small range, the adaptive parameter equation of error is constructed by using linear regression function. The weight coefficient is obtained by combining the position data solved by UWB and the previous moment fusion algorithm to solve the position difference and the mileage displacement formula, and the weight value of angle increment is given according to the state of the system. Through the calculation, the displacement increment matrix of each measurement of the system is obtained, and the final linear optimal value is obtained by substituting the extended Kalman filter algorithm. Compared with the single UWB positioning method, the results show that the system can maintain good accuracy and robustness, and the mean error of the whole system is less than 8cm, which provides a solution for the single UWB positioning of mobile robot affected by NLOS error.
Improved ViBe algorithm based on multi-frame combined with adaptive threshold
Authors: Wang Shuhai , Zhou Yijia , Bai Fenglin , Li Junnan , Li Hongzhe 
Published: July 2022

Abstract
For moving human target detection under video surveillance, the traditional vibe algorithm is often disturbed by environmental changes, resulting in “ghosts”, incomplete detection results and “holes” in the human body. An improved vibe algorithm based on multi frame combined with adaptive threshold is proposed. Firstly, the sample set of vibe algorithm is expanded to 24 fields to reduce the possibility of pixel misclassification; Secondly, the historical pixel queue is introduced, and the initialization background model without ghost is obtained according to the change of foreground and background pixels in time domain; Finally, the distance determination threshold is dynamically adjusted by using the gray characteristic convergence and divergence, and the adaptive update factor of the model is calculated by introducing two fixed parameters to optimize the update rate of the background model. The experimental results show that the algorithm is not only suitable for general scenes, but also for dynamic complex scenes, it can accurately detect the foreground of moving human targets, eliminate “ghosts”, obtain complete moving targets, avoid the appearance of “holes”, improve the accuracy of detecting foreground human targets in dynamic complex environments, and enhance the robustness of the algorithm.
Next basket recommendation based on graph attention network and transformer
Authors: Liu Tong , Liu Bojun 
Published: July 2022

Abstract
To solve the problem that when using recurrent neural network will forget the previously learned basket information and cannot reflect the user’s real purchase intention. A next basket recommendation based on graph attention network (GAT) and transformer model is proposed. Construct the item-basket relationship graph and use GAT to learn the basket interactive characteristics, then model the user interest representation, and finally obtain the item probability distribution through deep neural network. The experimental results on two real world datasets show that the proposed model outperforms state-of-the-art existing basket recommendation models. Through ablation experiments and hyperparameter experiments, the effectiveness of each module and the influence of model parameters are proved.
Short term photovoltaic power prediction based on FWA-CNN-GRU neural network
Authors: Wang Xiaohuan , Zhao Quanming , Li Ke , Yang Tianyi , Fan Chuang 
Published: July 2022

Abstract
In order to achieve accurate prediction of short-term photovoltaic power generation, this paper establishes a photovoltaic power prediction model based on FWA-CNN-GRU through the combination and optimization of neural network. Firstly, this paper crunches data and establishes the test sets and training sets. Then, the prediction model based on CNN-GRU is built through Convolutional Neural Networks (CNN) and Gated Recurrent Unit (GRU) neural network. Meanwhile, the Fireworks Algorithm (FWA) is introduced to search the optimal value of the super parameters of the network model. A prediction model that is based on FWA-CNN-GRU is established. Finally, the prediction is made according to different seasons. The experimental comparison shows that the average Root Mean Square Error and Mean Absolute Error of the optimized model are reduced by 9.190% and 6.095% respectively, and the average coefficient of determination reaches 96.83%. It shows that FWA-CNN-GRU model has higher prediction accuracy and can provide a theoretical basis for the operation and regulation of power grid.
Real-time grasping detection method based on attention residual block and multi-scale receptive field
Authors: Zhang L , Li M , Jia T , Wang G 
Published: July 2022

Abstract
In order to achieve high-precision and high-speed grasping detection, in this paper, we propose a lightweight grasping network model with the combination of attention residual block and multi-scale receptive field, which directly predicts the grasping pose of the manipulator from n-channel grasping scene images. The proposed grasping network structure extracts features from the input image based on the residual block introduced with the attention mechanism, and combines the receptive field module to improve the discriminability of the multi-scale features of the image. The network model is trained and verified on the Cornell grasping dataset. The results show that the multi-modal image information can improve the accuracy of grasping detection. With RGB-D image as input, the grasping accuracy rate of both image-wise and object-wise can reach 97.7%, which is better than other current grasping detection algorithms. Meanwhile, the detection speed of the algorithm is 17ms, which ensures the real-time performance of grasping detection tasks.
Graph Enhanced Representation and Reasoning Model for Tabular Fact Verification
Authors: Tang Yaling , Yang Peng 
Published: July 2022

Abstract
Tabular fact verification is a challenging task that requires obtaining relevant evidence from the table and utilizing them to verify a given claim. The main difficulty in tabular fact verification is that traditional language models cannot capture the underlying information carried in tabular data. To solve this problem, we propose GraERR, a Graph Enhanced Representation and Reasoning Model for Tabular Fact Verification. It consists of two modules: a data initial representation module based on the DeBERTa model and a graph-augmented representation and inference module. The former improves the inference ability of the model with a DeBERTa model pre-trained on the natural language inference dataset. The graph enhancement module enhances the model’s ability to learn table representations by effectively integrating textual and structural information in tables. The best results obtained experimentally on the InfoTabs dataset demonstrate the effectiveness of the GECMT model on the tabular fact verification task.
Model optimization strategies based on deep neural networks Learning and application of pruning optimization algorithms
Authors: Ni Ni , Xu Shaoshao 
Published: July 2022

Abstract
Deep learning is becoming increasingly important in the context of machine learning, with notable performance achieved in many experiments and attempts. However, if one wants to port a deep learning network from a computationally powerful CPU platform to a small embedded mobile device, it will be constrained by various aspects such as power consumption storage. This paper addresses this problem by simplifying the model, i.e. by model compression. Specifically, by collating work on compression pruning techniques from recent years, the importance of the parameter weights of the data model in the training phase is analysed and then the model is tested with new data in the inference phase to reduce the parameter weights and avoid over-parameterisation, thus achieving model optimisation. Ultimately, an effective idea for model pruning was derived: using the absolute magnitude of the parameters and feature outputs to measure their importance, while analysing the effect of parameter clipping on the loss function. This paper provides a summary distillation of relevant model compression work in recent years to provide a reference for research enthusiasts in the field of pruning and to pave the way for further research.
Speed sensorless direct voltage control of brushless doubly-fed machine independent generation system based on improved particle swarm optimization algorithm
Authors: Zhou Jing , Chai Lin 
Published: July 2022

Abstract
The speed information is obtained by sensors in the control strategy of brushless doubly-fed machine (BDFM) independent power generation system. However, the use of sensors increases the hardware cost of the system, reduces the reliability and the sensors are difficult to adapt to the harsh environment such as high temperature and high humidity. In order to solve the above problems, a speed sensorless direct voltage control method for brushless doubly-fed machine independent generation system is proposed. At the same time, in order to improve the control quality, the improved particle swarm optimization algorithm is used to optimize the control parameters of the system. Simulation results show that the BDFM independent power generation system can still maintain excellent performance and can meet the performance requirements in various operating conditions with the control method proposed in this paper.
Optimized wind-light-storage configuration based on Homer pro
Authors: Hou YiSen , Yan Yan 
Published: July 2022

Abstract
With the increasing shortage of energy and increasing demand for electricity, renewable energy microgrids have become the future trend of power grids. However, due to the strong uncertainty of its power generation, improving the stability of power system operation and reducing the cost of microgrid configuration have become important issues for the optimization of microgrid configuration. To address the above problems, this paper proposes a wind-light storage system, and establishes a model for optimal allocation of microgrid capacity with the minimum net present value of this system as the objective function. The relevant parameters are entered into Homer Pro software, which performs simulation calculations to obtain the configuration solution with the best economy and performance.
Multi-compartment vehicle routing optimization based on a hybrid Genetic Algorithm
Authors: Chen Liyin , Pan Fubin , Yi Junmin 
Published: July 2022

Abstract
To solve the problem of efficient distribution of convenience store chains, this paper creates a multi-compartment vehicle routing problem model with time- windows (MCVRP-TW) to meet the goal of minimizing the total distribution cost. The solution is based on a hybrid genetic algorithm, which using the “destruction” and “repair” operators of the large-scale neighborhood search algorithm and the Metropolis criterion of simulated annealing for local search operations. The results show that the proposed multi-compartment vehicle routing model with time windows and the hybrid algorithm can meet the requirement of practical route design and reduce the total distribution cost.