bluuebunny/crossref_metadata_2025 huggingface dataset:
Fabrication and application of Fe2O3-decorated carbon nanotube fibers via instantaneous Joule-heating method
Authors: Xue Zhiping , Lu Jing
Published: July 2022

Fe2O3-decorated carbon nanotube fibers (Fe2O3/CNT fibers) exhibit synergistic properties and can be used in flexible electrochemical devices. One of the greatest challenges is to synthesize homogeneous Fe2O3 on CNT fibers. In this paper, we have anchored Fe2O3 nanocrystals compactly and uniformly in CNT fibers via the instantaneous Joule-heating method. By regulating the current intensity, iron catalysts in CNT fibers can be directly converted into Fe2O3 nanocrystals. This method can also prepare Fe2O3 particles of different sizes by adjusting the current value. The distinct structure of Fe2O3/CNT fibers contributed to their excellent electrochemical performance. Because cobaltocene and nickelocene can also be used as catalysts to prepare CNT fibers, this method is expected to be a universal method for the composite of transition metal oxide and CNT fibers.
Uterine slow wave: directionality and changes with imminent delivery
Authors: Albaladejo-Belmonte Monica , Prats-Boluda Gema , Ye-Lin Yiyao , Garfield Robert E , Garcia-Casado Javier
Published: July 2022

Objective. The slow wave (SW) of the electrohysterogram (EHG) may contain relevant information on the electrophysiological condition of the uterus throughout pregnancy and labor. Our aim was to assess differences in the SW as regards the imminence of labor and the directionality of uterine myoelectrical activity. Approach. The SW of the EHG was extracted from the signals of the Icelandic 16-electrode EHG database in the bandwidth [5, 30] mHz and its power, spectral content, complexity and synchronization between the horizontal (X) and vertical (Y) directions were characterized by the root mean square (RMS), dominant frequency (domF), sample entropy (SampEn) and maximum cross-correlation (CCmax) of the signals, respectively. Significant differences between parameters at time-to-delivery (TTD) ≤7 versus >7 days and between the horizontal versus vertical directions were assessed. Main results. The SW power significantly increased in both directions as labor approached (TTD ≤ 7d versus >7d (mean±SD):

R
M

S

x

 = 0.12 ± 0.10 versus 0.08 ± 0.06 mV;

R
M

S

y

 = 0.12 ± 0.09 versus 0.08 ± 0.05 mV), as well as the dominant frequency in the horizontal direction (

d
o
m

F

x

= 9.1 ± 1.3 versus 8.5 ± 1.2mHz) and the synchronization between both directions (

C

C

max

= 0.44 ± 0.16 versus 0.36 ± 0.14). Furthermore, its complexity decreased in the vertical direction (

S
a
m
p
E

n

y

= 6.13·10−2 ± 8.7·10−3 versus 6.50·10−2 ± 8.3·10−3), suggesting a higher cell-to-cell electrical coupling. Whereas there were no differences between the SW features in both directions in the general population, statistically significant differences were obtained between them in individuals in many cases. Significance. Our results suggest that the SW of the EHG is related to bioelectrical events in the uterus and could provide objective information to clinicians in challenging obstetric scenarios.
Large-area regular periodic surface structures on 4H-SiC induced by defocused femtosecond laser
Authors: He Ziyu , Xie Xiaozhu , Long Jiangyou , Liu Tao , Zhang Zucheng , Lai Qing
Published: July 2022

Femtosecond (fs) laser has been proved to induce periodic surface structure formation with various periods. Previous studies employed complex optical means and specific processing environments to induce nano-sized periodic surface structures. In this study, a defocused fs laser was utilized to realize a high-stability and straightforward method for generating periodic structures on silicon carbide (SiC) surfaces. Due to its more uniform energy distribution, the defocused laser effectively attenuates the unstable fluid flow caused by the Marangoni convection, thereby improving the consistency of laser- induced periodic surface structure morphology and size. The effects of laser processing parameters on the ripple period, density, and morphology were systematically investigated. Moreover, an appropriate physical process was adopted to explain the formation of the nanostructures. A high laser pulse number was considered the key to forming large-area dense nanoripples, which possessed a smooth edge profile and similar period. Under optimal parameters, large-area regular nanoripples with periods of ∼100 nm could be induced on the SiC surface by a defocused fs laser method. Meanwhile, a two-step laser-inducing method was proposed to fabricate an array of two-dimensional square-shaped nanocolumns with a size of 60 nm × 60 nm. The two-step laser-inducing method realized tuning in the surface nanostructure morphology and localized nanostructures rewriting by adjusting the laser processing parameters. This large-area periodic structure inducing method showed the possibility of making laser-writing technology to be flexible, straightforward and, hence, competitive for advanced industrial application based on surface nanostructuring.
Cosh-Gaussian laser pulse influenced electron acceleration in an ion channel
Authors: Ghotra Harjit Singh
Published: July 2022

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

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

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

At 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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.
A Transformer Network Architecture for Dermoscopy Image Segmentation
Authors: Wei Lixing
Published: July 2022

Aiming at the problems of irregular shape and blurred boundary of skin lesions in skin lesions images, this paper proposes a skin lesion segmentation algorithm combining CNN and Transformer. Firstly, Resnet is used as the backbone feature extraction network to extract features, and the extracted feature map sequence is used as the input of Transformer. A new structural boundary attention gate is added to Transformer to extract enough local details to deal with fuzzy boundaries. Finally, DenseASPP is used to enhance features Represents and processes multi-scale information, and proposes an improved loss function, the purpose of which is to make the model pay attention to the boundary region when calculating the loss function. The experimental results show that the dice value and JI value of the network on the ISIC2017 dataset are 0.854534 and 0.767901, respectively, and the dice value and JI value on the ISIC2018 dataset are 0.908548 and 0.843689, respectively, which achieves good results compared to other advanced models. Its effectiveness is proved by comparing with different models and showing the effect.
Railway Platform Clearance Measurement Method Based on Monocular Computer Vision Assisted with Structured Light
Authors: Liang Man , Wang Wei , Wu Yuxin , Peng Yahui
Published: July 2022

Railway platform clearance is directly related to the safe operation of trains and the personal safety of passengers. Precise, efficient, and real-time platform clearance inspection is of importance to the safety of railway operations. Considering the shortcomings of the contact measurement method, such as low efficiency, low precision, and many manual operations, a platform clearance measurement system based on monocular computer vision assisted with structured light is proposed. The coordinate of corner points from the calibration ruler is determined by the LSD algorithm to compute a transformation matrix from Image Coordinate System (ICS) to World Coordinate System (WCS). Then, based on homography transformation, the world coordinates of the platform are obtained by the transformation matrix; and used to calculate different types of clearances. The experiments show that the precision can reach 5mm by using the system. It can effectively meet the requirements of railway operation and have a broad prospect.
JPEG Codec Based on ASIMD Parallel Acceleration
Authors: Yao Zhiyuan , Zhao Hongyi
Published: July 2022

The system is based on JPEG image encoding and decoding. The frame skipping algorithm is used to quickly obtain the pictures collected by the camera in r eal time. Through the neural network code, the collected low illumination pictures are enhanced. Through the bottom assembly language, the collected original image is compressed into JPEG and saved. Then the JPEG image is read and decompressed into the original image. The underlying assembly statements are vectorized through ASIMD instructions and accelerated in parallel through multithreading. Finally, the number of video frames displayed is equal to the number of video frames collected by the camera. The acceleration effect is very good.
Mechanism Analysis of Influence of Doubly Fed Wind Power Connection on Transient Power Angle Stability
Authors: Qi Ning , Deng Huiqiong , Ma Ruohan
Published: July 2022

In recent years, due to the continuous expansion of the scale of wind power, its impact on the traditional power grid has begun to receive attention. This paper takes the doubly fed wind power grid connected system as the research object. Based on the transient response characteristics of a doubly-fed wind turbine, replacing the turbine with an earth impedance and shrinking the power network. Relying on EEAC, the relationship between the system power angular acceleration and the variable wind power replacement ratio is quantified. The analysis shows that as the proportion of wind power replacement gradually rises, the amplitude of the equivalent power angle fluctuation of the system gradually decreases. That is, with the increase of wind power scale, the transient power angle stability of the system gradually improves. Finally, the CEPRI-36 node system under PSASP simulation platform is taken as an example to verify the effectiveness of the theoretical analysis.
Vehicle recognition based on Haar features and Adaboost cascade classifier
Authors: Tian M Z , Liu L , Lu J Y , Cheng Y
Published: July 2022

With the progress of world science and economic development, more and more industries are developing towards intelligence and automation. In the field of intelligent driving, the intelligent vehicle environment perception method based on machine vision has become a hot research topic. Based on monocular vision system, aiming at the requirements of different target features and detection accuracy and efficiency, this paper improves the Haar feature and Adaboost cascade classifier recognition algorithm combined with gray symmetry method to adapt to the recognition environment required by vehicles. The measured results show that the improved vehicle identification method combined with the tracking method based on Kalman filter can reduce the misjudgment rate of vehicles and has good real-time performance.
Flink-based Heterogeneous Database Synchronization Adapter Design
Authors: Xiong Shiyong , Zhang Mingxiang , Wu RongSen
Published: July 2022

The existing data synchronization scheme can satisfy the data synchronization between homogeneous table databases. Still, it restricts the table structures that can be synchronized and cannot solve the table heterogeneous database synchronization problem. In this paper, based on the existing data synchronization tools, we analyze the key problems in data synchronization, such as data type differences, data storage differences, data expression differences, etc. To design a synchronization model for these problems, this paper investigates the implementation of Flink CDC and the design idea of the Binlog model and designs a highly available and easily scalable heterogeneous database synchronization model by combining the actual data synchronization scenarios and traditional synchronization ideas.
Modeling analysis of automatic parking path planning for wheeled AGV
Authors: Zhao Y J , Ma S W , Cheng Y
Published: July 2022

Path planning is an important part of vehicle automatic parking. In this paper, the vehicle kinematics model is built based on the Ackerman steering principle, and the automatic parking path planning is explored. The starting conditions of one-step vertical parking are determined, the kinematic constraints are summarized, the parking tracking scheme is designed, and the parking path planning model is established. The parking path is fitted by B-spline curve to obtain the path optimization function. The parking space coordinate system is established according to the vertical parking space, and the parking path data obtained by simulation are input into the vehicle controller for one-step vertical parking test. The test results show that the error between the real vehicle test parking trajectory and the set trajectory is within the allowable range by using the parking path planning algorithm, indicating that the parking path planning strategy is effective.
Enhancement of Brain Network of Alpha Band in Motor Imagery Based on Transcranial Electrical Stimulation
Authors: Peng Maoqin , Qin Yun , Lai Danwei , Liu Zhihong , Gao Dongrui , Liu Tiejun
Published: July 2022

Brain network function underlies the sensorimotor rhythms in MI and involved in BCI performance among different individuals. This article aimed to improve the MI-BCI applicability by enhancing the brain network metrics of the alpha band, which includes the brain network using imaginary coherence, and the local and global network properties based on the graph theory. The MI tasks with transcranial electrical stimulation (tES) was applied to enhance the brain metrics. The results suggested that both tACS and tDCS can enhance the connection strength within the contralateral hemisphere and can improve the brain network metrics in the contralateral of the brain. This study established the relationship between tES and network properties, and provided research guidance for the application of tES in BCI.
Forward looking sonar image segmentation based on empirical mode decomposition
Authors: Tan Xinyu , Wang Chengjie , Chen Tianshun , Shen Liran
Published: July 2022

Due to the low imaging quality and serious noise pollution of forward-looking sonar images, traditional image segmentation methods based on edge information or statistical information are difficult to obtain high precision and robust segmentation results. Therefore, it is very complicated to segment forward-looking sonar images. Based on the analysis of the characteristics of forward-looking sonar, a new image segmentation method for forward-looking sonar is proposed.Firstly, 2d maximum entropy segmentation principle combined with chicken flock optimization algorithm is used to remove the background of the forward-looking sonar image.Then, based on 2d empirical mode decomposition, appropriate eigenmode functions are selected to denoise and enhance the forward-looking sonar image.Finally, the reconstructed forward-looking sonar image is segmented by enhanced fuzzy C-means clustering, and the segmented forward-looking sonar image result is obtained. This method can not only suppress noise interference effectively, but also protect the details of image edge for segmentation.
A Method of Realizing the Reliable Operation of the Variable Frequency Drive – Hot Backup Switching
Authors: Sun Jing
Published: July 2022

In this paper, a method to realize the reliable operation of the variable frequency drive(VFD)is proposed: Hot Backup switching, and the electrical design, software design and test results are given. And meanwhile, also state the switching control principle, clearly indicate the distinctive difference and improved performance between traditional switching mode and Hot Backup switching mode. At the end, gain the conclusion based on test results and extend to bigger size of motor.
Pedestrian re-recognition method based on dual attention and GRU
Authors: Wu Zhipeng
Published: July 2022

In view of the existing problems of pedestrian re-recognition in real scenes, such as camera Angle change, pedestrian posture change and object occlusion, in order to improve the accuracy of pedestrian re-recognition and enhance the ability of pedestrian feature representation, a pedestrian re-recognition method combining double attention and gated cycle unit (GRU) was established based on ResNet-50. The global structure information of pedestrian images can be better captured by channel attention and spatial attention, and the temporal information of pedestrian images can be obtained by GRU. Combined with the triad loss training network of difficult sample sampling and the cross-entropy loss training network based on label smoothing, the experiments were carried out on dataset Market1501 and CUHK03, and the accuracy of rank-1 reached 96.2% and 76.7%, respectively, which increased by 2% and 2.9% compared with the original ResNet-50 backbone network. Experimental results show that the proposed model can effectively improve the accuracy of pedestrian re-recognition.
Compressed Sensing Reconstruction Based on Residual Block and Dense Block
Authors: Liu Xiaoyan , Liu Yuhong
Published: July 2022

Existing compressed sensing image reconstruction algorithms have a large amount of computation and poor reconstruction quality. In order to solve the above problems, this paper proposes an image compressed sensing reconstruction network (RD-Net) based on residual blocks and dense blocks. The sampling network divides the input image into blocks with a block size of and uses convolution checks. These image blocks are compressed and sampled to obtain compressed sensing measurements. The initial reconstruction network uses Reshape+Concat to generate the initial reconstructed image. The deep reconstruction network is composed of residual blocks and dense blocks, which improves the quality of reconstructed images and also improves the running speed. Experimental results show that RD-Net improves the quality of reconstructed images.
Study on the operating condition diagnosis method of hydropower station equipment based on fuzzy clustering analysis
Authors: Zhao Yuzhong , Tang Jian , Wang Jun , Wang Jie , Qiu Mianqian
Published: July 2022

The current operation status diagnosis of hydropower station equipment is mainly based on the independent analysis of the equipment, ignoring the correlation between the equipment, resulting in a large diagnostic error. In order to improve the above problems, a diagnosis method based on fuzzy clustering analysis is studied. This paper deals with association analysis and error data discrimination of equipment state data in operation collection of hydropower station, and selects operation data preliminarily. Using gravitational search algorithm for fuzzy c-means algorithm of the initial fuzzy membership degree matrix, improved fuzzy clustering algorithm is used to analyze the running status data clustering at a time. After using the encoder to extract data, secondary cluster, the abnormal data and equipment state match, the final diagnosis results are obtained. In the case verification experiment, the diagnostic accuracy rate of the method is as high as 94.83%, and the diagnostic efficiency is higher and the real-time performance is better.
Prediction Technology of Uncontrolled Ammunition Landing Point Based on Bi-LSTM
Authors: Zhang Yijie , Wang Gen , Dang Yuanjie , Xi Junjie , Lin Ying , Miao Haochun
Published: July 2022

Landing point prediction is a commonly used algorithm in the field of guidance and control, and has high control accuracy. However, for the prediction of landing points, most algorithms do not realize end-to-end calculation, and the real-time calculation is poor. Aiming at the problem of poor real-time performance of landing calculation, a landing state prediction model based on Bi-LSTM (Bi-directional Long Short-Term Memory) neural network is proposed, and a variable step learning strategy is introduced on this basis. The model is built by taking the state of the ammunition at a certain moment (ballistic inclination, velocity, height) as the model input, and taking the landing point and landing velocity as the output of the model. And carry out simulation verification, and compare the training data fitting accuracy with the Back Propagation (BP) neural network and the LSTM neural network, as well as the complete ballistic fitting accuracy. The results show that the proposed model has a good effect in solving the problem of ammunition drop state prediction.
Exploration and research about key technologies concerning Deep Learning models targeting Mobile Terminals
Authors: Wang Yang , Yi Bohai , Yu Kexin
Published: July 2022

With the rapid development of artificial intelligence and the popularity of mobile devices, mobile deep learning model technology has become a research hotspot in recent years. This paper studies the realization of the mobile terminal deep learning model from the optimization techniques of the deep learning model and the framework of deep learning. This review sorts out the model optimization techniques of pruning, quantization, and model knowledge distillation of deep learning models, and analyzes the lightweight deep learning models and deep learning frameworks suitable for mobile terminals. From the perspective of deep learning model compression, this paper provides multi-granularity pruning, pruning combined with batch normalization factor and filter correlation, joint dynamic pruning, pruning based on cross-entropy; a multi-module feature training method based on knowledge distillation, and an optimized model training strategy based on self-distillation; local quantization, exponential quantization. From the perspective of directly adopting a deep learning framework, this paper compares four different frameworks (Caffe/Caffe2, TensorFlow, Keras and Pytorch) introduced by different companies. The benefits of two other frameworks, TensorFlow Lite and FeatherCNN, are also mentioned. From the perspective of lightweight deep learning model design, this paper analyzes the design of three lightweight models such as SqueezeNet, MobileNet, and ShuffleNet, and compares their performance parameters such as accuracy gap and calculation speed with conventional models such as AlexNet and GoogleNet. Finally, the paper looks ahead to future directions in the field and what the authors believe are important ideas that may help inspire new ideas.
Heavy-load train model based on RBF neural network refers to the adaptive brake control strategy
Authors: Yang Xinyi , Zheng Yunshui
Published: July 2022

Heavy-load train braking control is an important automatic driving function. The braking mode mainly used in China is air braking, which has great nonlinear characteristics. In order to reduce the influence of system uncertainty on control, improve the control accuracy and ensure the safety and efficiency of train transportation, an adaptive brake control strategy is designed.Firstly, establish the state space model of braking system according to the dynamic process of overloaded train braking, use the adaptive method and design the RBF model to track the train speed. Finally, the actual data from CK1466.1 to CK1431.3 in the line is used as the reference model. Experimental results The RBF neural network model designed by the reference adaptive controller realizes the tracking control of a given speed curve with high accuracy, verifying the effectiveness of the strategy.
Upgraded pulsed magnetic field generator for Shenguang-II laser facility toward 30 T
Authors: Hu Peng , Zhao Jia-yi , Wang Jin-can , Zhang Zhen-chi , Tang Hui-bo , Hu Guang-yue
Published: July 2022

The pulsed magnetic field device for Shenguang-II (SG-II)
laser facility was upgraded to obtain stronger magnetic field with
longer rising time, and to facilitate single person operation. The
upgraded magnetic field device adopts a modular design that allows
for easy adjustment of individual component and parallel connection
of multiple devices. Both the capacitor bank and the gas switch
operate in atmosphere environment, and are no longer sealed in
enclosure filled with insulating gas. Combined with plug-and-play
design, the installment and operation are simplified markedly. The
capacitor bank module can be expanded flexibly up to six times the
original device. Besides stronger magnetic field with longer
duration, large capacitance also allows the device to be compatible
with various forms of magnetic field coils and extend the range of
magnetic field parameters. Pulsed magnetic fields of 9.6 T and
12.6 T with >1 μs rising time are generated using double-
and triple-turn Cu coils of 12 mm diameter at 30 kV charging
voltage, at which the intensity of magnetic field is mainly limited
by the yield strength of Cu coils. Using an inductively coupled coil
made of higher yield strength material of BeCu, the magnetic field
strength is further increased to 32 T with >3 μs rising time
at 35 kV charging voltage.
Mechanical characterization of an APPLE undulator at TPS
Authors: Chang Cheng-Hsing , Chung Ting-Yi , Hwang Ching-Shiang
Published: July 2022

Because of a strong demand for circularly polarized
radiation, elliptically polarized undulators (EPU) are widely used
in synchrotron light source nowadays. At Taiwan Photon Source (TPS)
with a 3-GeV ring, the EPU plays an important role to fulfill the
needs of the user community for EUV to soft X-ray. Up to phase II,
in total five EPU have been installed. Because multiple operating
modes are necessary, the EPU suffers changes in attractive and
repulsive forces in three directions, in contrast to a general ID,
which suffers only a vertical attraction. A mechanical structure of
the EPU is required that can withstand multiple stresses and
eliminate mechanical deformation. In addition, the EPU, a purely
magnetic structure, is sensitive to high-permeability material,
which makes the overall structure a heterogeneous material,
resulting in a problem with uneven thermal expansion and contraction
because of changes in ambient temperature. Here we discuss and
propose solutions to resist force and temperature variations of EPU
mechanical structures, which includes mechanical simulation and
inspection.