bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Transformer Network Architecture for Dermoscopy Image Segmentation
Authors: Wei Lixing 
Published: July 2022

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.
Tail-stiffness optimization for a flexible robotic fish
Authors: Zou Qianqian , Zhou Chao , Lu Ben , Liao Xiaocun , Zhang Zhuoliang 
Published: July 2022

Abstract
Undulation regulation in a robotic fish propelled by a passive flexible tail is more similar to that of a natural fish than with a rigid tail, owing to the smooth curvature of the flexible tail. Moreover, it has been observed that fish change the stiffness of their bodies to adapt to various swimming states. Inspired by this, a stiffness optimization scheme is explored for a novel elastic tail, which can improve the performance of the robotic fish. Spring steels are used as passive flexible joints of the fishtail; these can be easily expanded into multi-joint structures and the joint stiffness can be altered by changing the joint size. In this study, the Lagrangian dynamic method is employed to establish a dynamic model of the robotic fish in which passive flexible joints are simplified by a pseudo-rigid-body model. In addition, the hydrodynamics of the head and tail are analyzed using the simplified Morison equation and quasi-steady wing theory, respectively. Furthermore, to determine unknown hydrodynamic parameters in the dynamic model, a parameter identification method is applied. The results show that the identified simulation speeds fit the experimental speeds well within a wide range of stiffness values. Finally, to improve performance, the influence of joint stiffness and frequency on swimming speed is investigated based on the identified dynamic model. At each frequency, the optimal joint stiffness distribution is one that reduces the stiffness from the front to the rear. At the maximum driving frequency of 2.5 Hz, the optimal swimming speed is 0.3 body lengths per second, higher than that when rigid joints are used.
Hydrodynamic interaction of dorsal fin and caudal fin in swimming tuna
Authors: Zhang Jun-Duo , Sung Hyung Jin , Huang Wei-Xi 
Published: July 2022

Abstract
Tuna, which are known for high-performance swimming, possess a large crescent dorsal fin (DF) and a caudal fin (CF) that differ from those of other fishes. The hydrodynamic interaction between the DF and CF in tuna, which are represented by two tandem 3D flapping plates, is numerically explored in the present study. Hydrodynamic properties and wake structures of the models with and without a DF are compared to investigate the effects of the DF. The thrust on the CF is substantially enhanced by the DF, whereas the force on the DF is not affected by the CF. The constructive interaction between the leading-edge vortex (LEV) on the CF and the vortices shed from the dorsal fin (DFVs) is identified from 3D wake topology and 2D vorticity distributions. The circulation of spanwise vorticity quantitatively reveals that the LEV on the CF is strengthened by the same-signed DFV. The effect of the flapping phase of the CF is examined. The DF–CF interaction is sensitive to the flapping phase at a short spacing, whereas a long spacing between the two fins enables a robust constructive interaction in tuna swimming. A systematic study is carried out to explore the effects of the Strouhal number (St) and the Reynolds number (Re) on the interaction of the fins. The enhancement of thrust due to the DF is diminished at St = 0.63, whereas the Re does not substantially influence the constructive DF–CF interaction.