bluuebunny/crossref_metadata_2025_split huggingface dataset:
Drift orbit islands of energetic particles due to 3D fields in ITER
Authors: Liu Yueqiang , Li L. , Loarte A. , Pinches S.D. , Polevoi A. 
Published: August 2021

Abstract
In this paper, we report a comparative study of the orbit islands formed by the guiding center drift motion of test energetic particles (EPs), including both high-energy deuterium ions and fusion-born α-particles, with respect to the magnetic islands formed by tracing the field lines, for an ITER plasma representing the 15 MA baseline scenario. The particle drift orbit is modified by the presence of static resonant magnetic perturbations (RMPs) of different toroidal mode numbers n (n = 1 and 3 in this study), forming orbit islands in the Poincaré plane by passing EPs. The key findings are as follows. (i) With an n = 1 RMP field, the size of the orbit islands combined with the total RMP field, including the plasma response, is about three times smaller than that obtained by assuming the corresponding vacuum field. (ii) The orbit island size is not sensitive to the EP energy, and is comparable to those of the magnetic islands. (iii) Passing EPs with outward radial orbital drift form orbit islands that shift inward with respect to the corresponding magnetic islands, and vice versa. This radial shift, ΔΨ, measured in the normalized equilibrium poloidal flux, is quantified as ΔΨ ≃ 0.03X for the ITER baseline plasma and assuming n = 1 RMPs, with

X
≡

(

M
/

M

p

)

1
/
2

(

E

[

M
e
V

]

)

1
/
2

Z

−
1

, where M is the particle mass, M
p is the proton mass, E is the particle energy, and Z is the particle charge number. (iv) Trapped EPs do not form drift orbit islands, even in the presence of the 3D fields produced by the 90 kAt ELM control coil current in ITER, and thus possess good confinement properties. Nevertheless, the deformation of the trapped particle drift orbits in the Poincaré plane shows that the canonical toroidal angular momentum of EPs is no longer conserved in the presence of the RMP fields.
Continuum limit of lattice quasielectron wavefunctions
Authors: Patra Aniket , Hillebrecht Birgit , Nielsen Anne E B 
Published: August 2021

Abstract
Trial states describing anyonic quasiholes in the Laughlin state were found early on, and it is therefore natural to expect that one should also be able to create anyonic quasielectrons. Nevertheless, the existing trial wavefunctions for quasielectrons show behaviors that are not compatible with the expected topological properties or their construction involves ad hoc elements. It was shown, however, that for lattice fractional quantum Hall systems, it is possible to find a relatively simple quasielectron wavefunction that has all the expected properties (2018 New J. Phys.
20 033029). This naturally poses the question: what happens to this wavefunction in the continuum limit? Here we demonstrate that, although one obtains a finite continuum wavefunction when the quasielectron is on top of a lattice site, such a limit of the lattice quasielectron does not exist in general. In particular, if the quasielectron is put anywhere else than on a lattice site, the lattice wavefunction diverges when the continuum limit is approached. The divergence can be removed by projecting the state on the lowest Landau level, but we find that the projected state does also not have the properties expected for anyonic quasielectrons. We hence conclude that the lattice quasielectron wavefunction does not solve the difficulty of finding trial states for anyonic quasielectrons in the continuum.
Research on Anti-lock Braking System of Electro-mechanical Braking Vehicle Based on Feature Extraction
Authors: Liu Xuebin , Ma Chenbin , Jiang Yu , Zhang Lingli , Xue Yangang 
Published: August 2021

Abstract
With the increase of vehicle speed, the increase of road traffic density and people’s higher requirements for vehicle safety, the Anti-lock Braking System (ABS) has become an important safety device on vehicles. It not only has the braking function of the common braking system, but also can prevent the wheels from locking. Automobile ABS is an important device that affects the safety of automobile braking system and driving safety. It can shorten braking distance and improve directional stability and operability during braking. Electro-mechanical braking control system adopts advanced electro-mechanical braking technology and is applied to automobile braking control system together with anti-lock braking system. Emb (electro-mechanical brake) uses electric energy as the energy source of the braking system, and uses the motor to drive the actuator to generate braking force, which responds quickly and can effectively improve the active safety of automobiles. In this paper, based on feature extraction algorithm, the brake pressure estimation method of EMB actuator is proposed, and the anti-lock braking control algorithm of automobile is analyzed.
Research on Optimization Method of Computer Network Service Quality Based on Feature Matching Algorithm
Authors: Lu Haifeng , Li Manman , Zhang Yajuan 
Published: August 2021

Abstract
On the whole, the main basis for strengthening the quality of network service is the construction of network system, reasonable arrangement of network and adjustment of information resources. In order to enhance the quality of network services, we should minimize the problems. In order to improve the quality of network, we should study more new network technologies and make up for network loopholes in time. In this study, in order to solve the efficiency problem brought by mass candidate services to quality-aware service selection, a service selection strategy based on feature matching algorithm is proposed. Based on statistical method, a probability matrix describing the mapping relationship between quality demand pattern and quality-oriented service pattern is established, and the service selection algorithm is improved by using the probability matrix. A series of experiments on artificial simulated data sets generated from real service data sets prove the feasibility and effectiveness of the proposed method.
Ultimate strength analysis of longitudinal bending of hull girder based on image analysis
Authors: Xu Zhenhao 
Published: August 2021

Abstract
In the traditional code, the allowable stress method is used to check the total longitudinal strength of hull girder, which has the characteristics of simple and quick calculation. However, this method is based on the assumption of elastic materials, and can not truly reflect the actual bearing capacity of the structure. Based on image analysis, a practical method for calculating the ultimate longitudinal strength of ship hull is studied. The average stress-strain relationship of the elements constituting the cross section of the hull girder under axial pressure is given, and the progressive collapse behavior of the hull girder in longitudinal bending is analyzed. Finally, the reliability analysis method for the ultimate strength of the hull girder in longitudinal bending is put forward, and a real ship example is given. The research results show that the proposed method is not only suitable for the safety assessment of ship structures in the conceptual design stage, but also meets the requirements of rapid prediction of real ship wave loads and structural strength reliability analysis.
Research and Application of Analysis System in Material Field by Digital Audit and Artificial Intelligence
Authors: Fan Ruixia , Song Junguo 
Published: August 2021

Abstract
In the material management process of large power companies, there are frequent withdrawals from the library to modify the age of the warehouse, resulting in a substantial backlog of materials. In view of the huge amount of data and multiple analysis dimensions in the process of auditing and screening such problems, the research and development is based on digitalization. The automatic analysis system in the field of audit materials, through the comprehensive cross-analysis of engineering information, financial data, and material data, can quickly determine the data of the false profit pool, and significantly improve the quality and efficiency of the audit. According to the screening results, the material management department is urged to deploy the stockpiled materials in the library to promote the efficient turnover of materials.
Research on Test Method of Smart Anti-Towed Radar Active Decoy in Complex Electromagnetic Environment
Authors: Zhao Bo , Wang Duo 
Published: August 2021

Abstract
As an effective jamming method, towed radar active decoy, poses a great threat to monopulse radar seekers, and has attracted more and more attention from various countries. This article analyzes the interference mechanism and main parameters of towed radar active decoys, and provides a theoretical basis for the use of towed decoys. In view of its technical characteristics, combined with technical and tactical countermeasure strategies and technical approaches, research the test range to carry out countermeasures against towed radar active decoys, and provide theoretical basis and reference value for the use of towed radar active decoy countermeasures in the range.
New Artificial Intelligence Technology Applied in Automobile Lithium Battery Manufacturing
Authors: Wang Zhaiyi 
Published: August 2021

Abstract
In order to improve the estimation accuracy of the state of charge (SOC) of electric vehicle power batteries, this paper is based on artificial intelligence technology for lithium-ion battery model and parameter identification algorithm, adaptive unscented Kalman filter algorithm and SOC estimation based on battery model fusion Algorithm for research. The simulation results show that the SOC error estimated by the artificial intelligence adaptive Kalman filter method is less than 2.4%, which effectively reduces the impact of unknown interference noise on the battery management system when the electric vehicle is driving. The SOC estimation accuracy is higher than that of the extended Kalman method, and has good robustness.
Research on Intelligent Evaluation System of Influence Model Using Cosine Similarity
Authors: Li Zhitao , Wang Xin , Li Xiaoling 
Published: August 2021

Abstract
Nowadays, music plays an increasingly important role in social development. As time goes by, music influences music, artists, and the times. The purpose of this paper is to create a model to quantify the influence of previously created music on new music and music artists. Firstly, this paper constructs two music influence networks based on artists and genres and defines and explains some meaningful parameters of music influence networks. The parameters of this paper describe the influence of communication between artists, and then this paper calculates and explains the parameters in the subnet. Next, this paper uses cosine similarity to measure similarity between two music samples and innovatively uses variance as the weight of music features to identify differences between music. Then the similarity measure is constructed based on matrix norm. Finally, this paper concludes that artists within schools are more similar than artists between schools.
Evaluation of Systems Current Status by PCA-RBF Neural Network and Novel Fuzzy Intelligence Method
Authors: Li Zimu , Luo Lan , Lu Yuqing 
Published: August 2021

Abstract
The health and the sustainability of a higher education system are crucial to the prosperity of a nation. It is crucial to effectively evaluate a higher education system in various aspects and promptly adjust the corresponding educational policies. Firstly, 23 higher education quality indicators with respect to more than 1,000 universities are carefully collected and converted to the corresponding indicators of 40 countries/regions. Moreover, such indicators are normalized by the range transformation method in order to facilitate subsequent analyses. Secondly, a novel fuzzy intelligence (F&I) method is proposed to model the health states of different higher education systems based on radial basis function (RBF) neural networks. This method firstly reduces the input indicators’ dimension by the principal component analysis (PCA) technique. Using the PCA results as the input layer, the RBF neural network is carefully trained and the F&I score of each country/region is therefore obtained. Next, the hierarchical cluster analysis is carried out to depict the health state of each county/region. 1, 5, 5 and 29 countries/regions are categorized as healthy, good, general and unhealthy, respectively.
A Text Category Detection and Information Extraction Algorithm with Deep Learning
Authors: Wu Xiaohan , Wu Zejun , Feng Yuqi 
Published: August 2021

Abstract
In order to solve the problem that the text classification model based on neural network is easy to over-fit and ignore the key words in sentences in the training process, a Bi-GRU Chinese text classification model based on hierarchical Attention mechanism is proposed. The model introduces the idea of layering, uses bi-directional gated cyclic neural network to learn the text representation at word level and sentence level, uses Self-Attention hierarchical model to obtain the information of the influence of words and sentences on text classification, shares the weight between embedded layer and softmax layer by binding, and uses AMSBound optimization method to obtain the optimal weight matrix quickly and effectively while reducing the parameters in the model. Two commonly used Chinese data sets, FudanSet and THUCNews, are tested on the long Chinese text classification data set FudanSet. The experimental results show that the accuracy, recall rate and F-score of this model are better than Text-CNN model, Attention-BiLSTM model and Bi-GRU_CNN model, and the accuracy, recall rate and F-score index are improved by 5.9%, 5.8% and 4.6%, respectively.
Assessment and Information System Establishment of the COVID-19 Impacts and countermeasures: Gray Prediction Model Applied in Analysis and Prediction
Authors: Liu Yushan 
Published: August 2021

Abstract
The outbreak of COVID-19 has had a huge impact on China’s economic and social development, among which the tertiary industry has been severely impacted. As the epidemic prevention and control in China has achieved initial success and entered the normal prevention and control stage, it is very necessary to analyze the damage situation of industries directly affected by the epidemic. According to the historical data of various industries in China in the past five years, a grey prediction model was established to predict the normal development law of some economic indicators without an epidemic situation. Compared with the actual values in the first two quarters of 2020, we can estimate the economic and social losses caused by the COVID-19 epidemic. The epidemic has had the most serious impact on the tertiary industry, with retail, tourism, and catering sectors were hit hard. With the effective control of the epidemic, China’s overall economic performance in the second quarter rose steadily. Many enterprises in the comprehensive service sector have been upgraded and transformed during the epidemic. From the current perspective, the epidemic will not have a serious impact on economic development throughout the year.
Analysis and innovation prospect of intelligent ship visual tracking and recognition system
Authors: Yu Shiguan , Zhang Hongshuo , Mu Shilong , Liu Shizhong , Ding Haojie 
Published: August 2021

Abstract
In recent years, due to the rise of the concept of intelligent ship and the development of the intelligent ship, the ship intelligent has become a global shipping the trend of The Times, with the rapid development of maritime shipping industry, sea lanes and port ship intensity increasing, shipping safety risk increasingly improve, intelligent traffic regulation becomes increasingly important task, shipping information acquisition is more accurate. Ship tracking and identification system is not only the core of shipping safety supervision system, but also has an effective role in the collection of ship navigation information and intelligent supervision of navigation traffic. Computer vision is a technology developed rapidly in recent years, as the current research hotspot, with its access to large amount of information, and the advantages of fast response speed, strong anti-jamming capability compared with traditional of AIS and radar easily affected by obstacles, affected by the operator subjective behavior of communication system device, it can well realize the target ship recognition and tracking, to the modern channel intelligent regulation of ship sailing information collection, transportation, ship navigation safety are very important significance. This article mainly from the principle of development background significance, system characteristics, the visual technology, tracking algorithm for visual identification and tracking system to do a detailed description of principle, on the basis of AIS and radar, based on traditional visual algorithm combined with some data and AIS system on personal improvement ideas on the application of the system innovation, and finally puts forward personal has been developed out of a visual tracking innovative device of the ship.
Tire Defect Detection Using Adaptive Dictionary
Authors: Xiang Yuanyuan , Li Shengbao , Yin Chuanming 
Published: August 2021

Abstract
This paper proposes a new algorithm for detecting tire defects, which is based on principal component analysis and adaptive dictionary learning. Because the defects on the side of the tire are very small and difficult to detect, the dictionary is directly obtained from the test image itself instead of the reference image, thereby improving the soul of adapting to changes in lighting intensity and texture. When using the learned dictionary to represent the test image, patches involving abnormalities in the test image may have larger reconstruction errors than normal errors. Through threshold operation, the defect area can be segmented from the residual image. Compared with the wavelet-based method and the component decomposition method, the experimental results of test images with defects show that the algorithm can adapt to changing light intensity and texture, and also shows more accurate defect detection results.
Attention Generating Target Images with Labels for Unsupervised Domain Adaptation
Authors: Fu Shuai , Ye YiFan , Chen Jing 
Published: August 2021

Abstract
Domain adaptation has recently become a popular and successful field of solving the dependence of the deep learning model on the training set. Previous works proposed unsupervised learning methods based on Generative Adversarial Networks (GANs), which solved the mixed target domain labels problem caused by the neglect of class-level matching, by generating target pairs. And these well-designed models have successfully achieved great performance on different datasets. Moreover, due to the recent widespread use of attention mechanism, we intuitively introduce attention mechanism to the previous model. Our new structure is based on GANs containing attention mechanism and well-designed upampling module, which make our model more robust. Demonstrated by extensive experiments, our new model outperforms the previous works on several standard domain adaption datasets.
Research on Forecast Model Based on BP Neural Network Algorithm
Authors: Xu Qinfeng , Kong Wenju 
Published: August 2021

Abstract
Back propagation Neural Network is a well-known method of prediction. Due to the development of the neural network technology, there has been an increasing amount of neural network application research in the last decade, with a majority of application focusing on financial decisions. Firstly, this paper demonstrates the algorithm of back propagation Neural Network. Then, the application of Back propagation algorithm in prediction sector is explained in detail through the design of pre-warning model of financial distress for real estate industry in China. Meanwhile Z-score model is utilized to produce the only output nodes of Back propagation Neural Network. The research can provide evidence of forecast performance based on BP Neural network technique.
Adaptive Teacher Finetune: Towards high-performance knowledge distillation through adaptive fine-tuning
Authors: Hou Zhenyan , Fan Wenxuan 
Published: August 2021

Abstract
Knowledge distillation is a widely used method to transfer knowledge from a large model to a small model. Traditional methods use pre-trained large models to supervise the training of small models, called Offline Knowledge Distillation, However, the structural gap between teachers and students limits its performance. After that, Oneline Knowledge Distillation retrained the teacher-student network from the beginning and the method of echo teaching greatly improved the performance. But there is very little work to explore the difference between the two. In this paper, we first point out that the essential difference between Offline and Oneline Knowledge Distillation is actually whether the weight of the teacher-student network has a process of mutual adaptation. If they adopt the teacher network and the student network jointly train to implement Offline Knowledge Distillation, there is no obvious difference in the final performance, no matter whether it is a joint distillation training. This shows that teacher-student network adaptation is important for Knowledge Distillation. Then, we propose an Adaptive Teacher Finetune (ATF) to adapt the teacher model to the student network. It will use student model information for Tinetune during the Offline Knowledge Distillation process. With normalized logical distribution and alpha-divergence, the performance improvement of ATF clearly exceeds the existing Offline and Oneline Knowledge Distillation method. Extensive experiments conducted on cifar and ImageNet support our aforementioned analysis and conclusions. With the newly introduced ATF, we obtained state-of-the-art performance on ResNet 18 on ImageNet.
Adaptive trading system based on LSTM neural network
Authors: Li Yueqiu , You Chunming 
Published: August 2021

Abstract
With the continuous development of statistics and computer technology, the methods of technical analysis are also constantly expanding. A large number of statistical and mathematical methods have been applied to the analysis of stocks, which greatly expands the tools available for stock analysis. With the rapid development of deep learning and the great progress of computer hardware technology, statistical learning algorithms have been widely used in big data processing. Including convolutional neural network model and cyclic neural network model, have shown great advantages in the processing of time series such as images, speech and text. In today’s financial market, due to the existence of a large number of assets and fast information flow, people are more and more inclined to use quantitative investment. Quantitative investment as a means of financial sector, the New Deal, which is mainly composed of fundamental analysis and technical analysis from the traditional investment method, its core is to use mathematical model set up trading strategy, and with the help of computer technology on the financial data for quantitative analysis, and judgment, found that financial market rules, eliminating dependence on the experience of the people in the course of investment and emotional impact, so as to guide investment decisions. In order to improve the prediction ability of LSTM neural network model, this paper chooses the stack LSTM neural network model, and combines the stack LSTM neural network model with ADAM algorithm to achieve better prediction results. The empirical results show that the prediction effect of ADAM-based stack LSTM neural network is better than that of ADAM-based LSTM neural network.