bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimization for Hot-film Anti-icing Structure by BPNN and GA
Authors: Liu Jie , Ke Peng 
Published: March 2021

Abstract
A coupled method combining the Back Propagation Neural Network (BPNN) and Genetic Algorithm (GA) was developed to optimize a 2D design of aero-engine inlet anti-icing structure, which has a cover on the film heating ejection slot. The optimal goal is to maximize the heating effectiveness which was used to assess the performance of hot-air film. The film-heating ejection angle and the cover opening angle were selected as the design variables to be optimized. The training and testing samples employed in BPNN were obtained by numerical simulation, after which the objective function of GA was predicted. With a given flow rate of bled air, the optimal values of the two design variables were achieved as 22.6° and 15.1°, respectively. Compared to the previous optimal result of other researchers, the heating performance was improved by 16.7% with rapid progress. The result of this study illustrates that this hybrid optimal method can meet the accuracy requirements with high time-efficiency for optimization problems in aeronautics engineering.
Thunder Signal Detection via Deep Learning
Authors: Zhang Han , Yan Biwu , Gu Shanqiang , An Chao , Li Jian , Wu Min , Wang Yu , Xu Heng 
Published: March 2021

AbstractIn this paper, a thunder signal detection method is proposed based on the deep learning framework. The recorded thunder signal is segment-wise acquired, stored and pre-processed. In each frame, we use Mel Frequency Cepstrum Coefficient (MFCC) to extract the features of the thunder signal, which is consistent with the frequency characteristics of human perception. We then use the MFCC features derived in each frame to form a 3-channel tensor data, which is used as the further input to the designed convolutional neural network (CNN). The goal of CNN is to classify the existence of thunder for a single data frame. To improve the robustness of CNN, we included other confusing signals that are similar to thunder signals in the training and testing datasets. On the testing dataset, our proposed method outperforms the state-of-art methods in terms of accuracy, sensitivity, and specificity. Our proposed deep-learning-based thunder detection method not only increases the real-time performance of the lighting location system with thunder signals but also further improves the accuracy of other sound alarm systems.
Crowd Density Estimation Based on Multi-Column Hybrid Convolutional Network
Authors: Guo Linlin , Zhou Weimin 
Published: March 2021

Abstract
This paper studies an accurate counting model for dealing with highly crowded people, multi-column hybrid convolutional neural network model. The model is mainly composed of three parts. The first part uses the first ten layers of VGG-16 convolutional network for image feature extraction. The middle layer is a dilated convolution with three rows of “jaggy” dilation rates, and each row uses the Resnet-block connection method, which is used primarily to perceive human head features of different sizes. Compared with a variety of image up-sampling ways, in the third part of the model, this paper tries to use a combination of bilinear interpolation and convolution to up-sample image features, and research shows that this method effectively reduces the model error. In this experiment, the average absolute error (MAE), mean square error (MSE) and average relative error (MRE) are used as evaluation indicators, and experiments on the ShanghaiTech dataset proves that the network works well.
A Dual Branch Attention Network for Multiple Degraded Image Restoration
Authors: Jia R M , Wang D , Li T 
Published: March 2021

Abstract
In this paper, we propose a novel dual branch attention network (DBA-Net), which can efficiently restore multiple degradation images. Most methods of image restoration focus on a single degradation factor, such as blur, noise, raindrop, etc. However, these methods frequently fail to real degraded images, because the real images usually contain different types of degradation. Our network backbone consists of two branches, the residual branch and the information distillation branch, which have different receptive fields. We design a gate module to choose useful feature maps from the two branches. Then, an improved multi-channel attention selection module is proposed to allow the network to learn more features of real results. On DIV2K datasets, which contains different degradation types (noise, blur, and compression loss) and diverse degradation levels (mild, moderate and severe), our results on PSNR and SSIM outperform other advanced algorithms. In visual performance, the texture of objects in DBA-Net images is more similar to the original image.
Questions and Answers on Legal Texts Based on BERT-BiGRU
Authors: Zhang Na-Na , Xing Yinan 
Published: March 2021

Abstract
Using text question and answer technology to improve the efficiency of judicial personnel in the process of case handling can greatly reduce labour costs. This paper proposes a hybrid neural network model that combines pre-training model BERT and Bi-GRU. The model first uses the pre-training model to learn powerful semantic capabilities, then combines Bi-GRU to learn the semantic information between the text and the question, finally gets the answer corresponding to the legal text. The experimental results on the CJRC data set show that compared with the basic baseline model, the algorithm in this paper can effectively improve the accuracy and F1 value.
A Comparison of Machine Learning Algorithms for Automatic Cloud Resource Scaling on a Multi-Tenant Platform
Authors: Lee Chun-Hsiang , He Zhengda , Li Zhaofeng , Lu Xu , Wang Jian , Wu Chao 
Published: March 2021

Abstract
In an online multi-tenant machine learning platform, the system manager would dynamically load the computing resource according to the tenant’s demand. With cloud computing services, the platform can rent or release computing resources dynamically to fulfill tenant usage which minimizes resource consumption and ensure scalability. Currently, many cloud-based services providers are using the rule-based, threshold auto-scaling mechanism. However, the rule-based method is not efficient, as the nature of availability and cost-reducing violate each other in this method, especially with the sudden increase or variation of the demand. In this paper, we compare several machine-learning-based predictive algorithms to build models based on the information of the system used to predict future usage demands. Decisions made based on this prediction save over 80% cloud resource consumption compared to the rule-based method.
Multi Information Fusion Diagnosis Method for Diesel Engine Based on Optimized ANFIS
Authors: Han Yuting , Fei Jingzhou , Wang Zhongwei 
Published: March 2021

Abstract
A novel diagnosis method based on optimized subtraction clustering ANFIS (adaptive neural fuzzy inference system) algorithm is proposed in order to improve the accuracy and reliability of diesel engine fault diagnosis. The improved analytic hierarchy process (AHP) and subtractive clustering algorithm are combined to form a new ANFIS network suitable for multi information fusion diagnosis. The initial clustering centers of subtractive clustering algorithm and reasoning rules of ANFIS are automatically optimized by AHP algorithm without relying on expert experience. The effectiveness of the novel algorithm is investigated on the example of multi information fusion diagnosis of diesel engine, and the results indicate that the proposed method can eliminate the disadvantages of more inference rules, slow convergence speed and low diagnostic accuracy of the conventional ANFIS algorithm under multiple input parameters, which means this new method can effectively improve the accuracy of diesel engine fault diagnosis with the advantages of more fusion parameters and less calculation.
An Improved Decision-Making Approach Based on Interval-valued Fuzzy Soft Set
Authors: Zhang Qiansheng , Sun Dongfang 
Published: March 2021

Abstract
Some operators and laws of the interval-valued fuzzy soft sets are first introduced in this paper. Then an improved decision-making approach is proposed by employing the comparable table of interval-valued fuzzy soft set. At last, one numerical example is given to demonstrate the effectiveness of the presented decision approach.
A Siamese Network Tracking Algorithm Based on Hierarchical Attention Mechanism
Authors: Zhang Hu , Hu Dong , Qiu Yingcan 
Published: March 2021

Abstract
A siamese network tracking algorithm based on hierarchical attention mechanism is proposed in this paper. In order to obtain more robust target tracking results, different layer features are fused effectively. In the process of extracting features, attention mechanism is used to recalibrate the feature map, and AdaBoost algorithm is used to weight the target feature map, which improves the reliability of the response map. Besides, the Inception module is also introduced which not only increases the width of the network and the adaptability of the siamese network to the scale, but also reduces the parameters and improves the speed of network training. Experimental results show that this method can effectively solve the impact of background clutter and improve the accuracy of tracking.
Real-time Railway Transit Management Based on Multi-Agent System (MAS)
Authors: Guo Yida , Zhang Cheng 
Published: March 2021

Abstract
Because of environmental concerns, increasing attention has been given to developing energy-efficient technologies. As an essential public transportation method, metro transit is facing increasing pressure. Disturbances may cause the offline-optimized timetable and speed trajectory to be invalid. The present paper proposes a multi-agent system (MAS) train control method, in which each train is controlled by an agent. Each train agent has a simulation platform and an optimization platform. The simulation platform collects information such as track slop, speed limitation, speed trajectories of neighboring trains from a few neighboring agents. The simulation platform sends a signal with the collected information to the optimization platform when it detects a disturbance. Afterward, the optimization platform performs energy-aimed optimization to the timetable and corresponding speed trajectory based on a combination of a trained Neuro Network and a Mixed Integer Linear Programming (MILP) model. The test result shows an encouraging balance in optimization time and accuracy. The case study result proves that the proposed approach could provide a more energy-efficient control strategy when disturbances occur.
Learning Collision Avoidance of Ship Manoeuvring based on Gated Recurrent Unit
Authors: Sun Wenli , Gao Xu 
Published: March 2021

Abstract
Unmanned surface vehicle (USV) has progressed quickly in recent decades, with widespread research and practical applications in academic and industry circles. Collision avoidance is a fundamental capability of USVs. It is extremely challenging to develop an ideal and sophisticated collision avoidance algorithm for USV in complex environments and practical offshore situations. However, a supervised learning method provides the USV a way of learning the process of avoidance and imitating a human pilot maneuvering to navigate in the real environment. This study analyzes the relative relationships and features of the own and target ships in the avoidance process firstly. And then a Gated Recurrent Unit (GRU) recurrent neural network model is constructed. Maneuvering commands during avoidance by human pilots are utilized as tags for training. Finally, the validity of method is proven by performing navigation experiments. In particularly, we also compare the effectiveness of GRU with Long Short-Term Memory (LSTM) network which is also a kind of recurrent neural network. The experimental results indicate that the proposed GRU model is better than the LSTM model, and the USV can autonomously navigate during the collision avoidance process by using the well-trained GRU model, reaching a level similar to that of human pilots.
Algorithm of Vehicle’s Data Cleaning and Monitoring
Authors: Li Hao , Ma Di , Yan Zhijiang , Fu Jie , Zeng Mengjie , Bao Wuzhida 
Published: March 2021

Abstract
The aim of this paper is to screen out abnormal data which is caused by human factors and natural factors. We proposed an algorithm of vehicle’s data cleaning and monitoring. First, the valid data is filtered out by an improved DBSCAN method, through data analysis, and then get the threshold range. After that, it screens out the abnormal data in the valid data through the threshold range. Finally, the abnormal data is classified and counted according to the factors which was stipulated by the enterprise. The results show that the proposed algorithm can simpler and faster to process the abnormal data than the other similar algorithm.
Long-Term Electricity Consumption Forecasting in China — Based on a Combined Model of KPCA and Linear Regression
Authors: Huang Zili , Zhang Haochen , Qiu Chenxi , Liu Jia 
Published: March 2021

Abstract
Total electricity consumption is a barometer of a country’s economy. Long-term forecasting of total electricity consumption in the whole society can effectively track a country’s economic development and monitor the implementation of energy conservation and emission reduction policies. How to effectively forecast the long-term total electricity consumption is an important topic in the academic and industrial fields. The combined model of kernel principal component analysis (KPCA) and linear regression (LR) proposed in this paper can accurately predict the changes in total electricity consumption over time, even if the sample size is small. Meanwhile, the model results have strong interpretability and practical value. Further, through the correlation analysis of principal components obtained from KPCA dimensionality reduction, this paper finds that the most important features affecting the total electricity consumption are the economy feature and production efficiency feature. Finally, this paper predicts that China’s total social electricity consumption will reach 1.83 trillion KWH in 2035, which is more optimistic than the prediction of Oxford experts, which is consistent with the reality that China has achieved an overall victory in the fight against COVID-19.
Effect of Pitch Control on the Performance of an Offshore Floating Multi-Wind-Turbine Platform
Authors: Bashetty Srikanth , Ozcelik Selahattin 
Published: March 2021

Abstract
The extraction of wind energy from more reliable deep-water offshore wind resources can be advanced by using a multi-wind turbine platform that can reduce mooring and installation costs. This paper describes the results of research aimed at designing a novel semi-submersible platform for placing multiple wind turbines. The offshore floating multi-wind-turbine platform (OFMWTP) hosts five 8 MW wind turbines which is proposed for installing on the gulf coast of the United States. The principal problems that have been addressed here include analyzing the effects of blade pitch control on the performance of the OFMWTP in the above-rated wind speed operating conditions. In this process, the adaptive control techniques vary the blade pitch angle for generating optimum power production and reduce platform motions. The coupled dynamics of the wind turbines with the platform are formulated considering the aerodynamic, hydrostatic, hydrodynamic, and mooring forces. In this study, the adaptive control algorithm designed in previous work is used for the blade pitch control. The wind speed distribution with 15% turbulence intensity and irregular waves with zero degrees incident wave angle is used for simulating the operating conditions. The performance of the adaptive control is compared to a baseline proportional-integral (PI) controller. Simulation results showed that the adaptive controller significantly improves the rotor speed regulation and reduces the fluctuations in generated output power under varying operating conditions.
Dynamic Fusion of Electromyographic and Electroencephalographic Data towards Use in Robotic Prosthesis Control
Authors: Pritchard Michael , Weinberg Abraham Itzhak , Williams John A R , Campelo Felipe , Goldingay Harry , Faria Diego R 
Published: March 2021

Abstract
We demonstrate improved performance in the classification of bioelectric data for use in systems such as robotic prosthesis control, by data fusion using low-cost electromyography (EMG) and electroencephalography (EEG) devices. Prosthetic limbs are typically controlled through EMG, and whilst there is a wealth of research into the use of EEG as part of a brain-computer interface (BCI) the cost of EEG equipment commonly prevents this approach from being adopted outside the lab. This study demonstrates as a proof-of-concept that multimodal classification can be achieved by using low-cost EMG and EEG devices in tandem, with statistical decision-level fusion, to a high degree of accuracy. We present multiple fusion methods, including those based on Jensen-Shannon divergence which had not previously been applied to this problem. We report accuracies of up to 99% when merging both signal modalities, improving on the best-case single-mode classification. We hence demonstrate the strengths of combining EMG and EEG in a multimodal classification system that could in future be leveraged as an alternative control mechanism for robotic prostheses.
MIL-Standards Verification of Battery Control for More Electric Aircraft Application
Authors: Russo Antonio , Canciello Giacomo , Cavallo Alberto 
Published: March 2021

Abstract
As a consequence of the increase of air traffic, the innovative topic of the More Electric Aircraft (MEA) has received increasing attention. In this paper, the control of a bidirectional DC/DC converter for battery management in the MEA framework is described. A detailed simulator and simulation campaign have been designed in order to verify the satisfaction of the MIL-STD-704F standard which regulates the behaviour of electric devices on-board the aircraft.
The Capacity Load Model of K-Uniform Hyper-Network based on Equal Load Distribution
Authors: Chen Yang , Ma Xiujuan , Ma Fuxiang , Liu Qian , Cheng Wenxin 
Published: March 2021

Abstract
Cascading failures occur frequently in many network systems. In this paper, we propose a hyper-network Capacity Load Model based on hyper-edge degrees load to explore the influence of cascading failures on some K-uniform hyper-networks. In simulation, we attack the hyper-edge which has largest hyper-edge degree. When the hyper-edge fails, its load will be equally distributed to adjacent hyper-edges. Through simulation analysis, we get the relationship between the capacity-load model parameters and the robustness of the k-uniform hyper-network. The results show the robustness of the hyper-networks is enhanced with the increase of its hyper-edge capacity. In addition, simulation result show that the robustness of K-uniform hyper-networks is strongest at α=1.
Research on the Electromagnetic Interference of Yanmenguan DC Grounding Electrode on West-East Transmission Gas Pipeline
Authors: Shi Weiguang , Wang Kesheng , Liu Wei , Li Pingwei , Cai Xinjing 
Published: March 2021

Abstract
There was a west-east gas transmission pipeline nearby the Yanmenguan DC grounding electrode. In order to assess the electromagnetic interference of Yanmenguan DC grounding electrode on the pipeline, an electromagnetic simulation model was established. The pipeline to earth potential was analyzed. The effects of insulation layers and insulation joints on the pipeline to earth potential were investigated. It can provide theorical support for the pratical operation of gas pipelines.
Calculation of Corrosion Depth of Wind Power Generators and AC Substations Induced by HVDC Grounding Electrode
Authors: Feng Lijun , Hao Chenglong , Zhao Yuting , Guo Dongqing , Cai Xinjing 
Published: March 2021

Abstract
At present, grounding potential rise is often used as the evaluation index to assess the electromagnetic intererence induced by HVDC grounding electrodes. It will lead to too stringent standards, for HVDC grounding electrodes are considered as long-term operation. Actuallly, HVDC grounding electrodes operates only in the test run stage, planned maintence and forced outage. In this paper corrosion depth is adopted as the evaluation index to assess the electromagnetic intererence. The method to calculate the corrosion depth of wind power generators and AC substation is described. The corrosion depth of windfarms and AC substation nearby Yanmenguan converter station is derived. The effects of material properties, distance and soil resistivity on corrosion depth are also studied.
Performance Degradation Prediction of IF Conversion Circuit Based on DE-GWO-SVM Model
Authors: Zhu Y , Fu M R , Bu X Z , Hu J 
Published: March 2021

Abstract
Due to the complex structure, nonlinearity and tolerance of IF conversion circuit, the failure probability of IF conversion circuit is greatly increased. To solve the above problems, a differential evolution adaptive grey wolf algorithm (DE-GWO) was proposed to optimize the parameters of support vector machine model, and the model was applied to the fault diagnosis of IF conversion circuit for the first time. Firstly, the energy feature of the output signal of IF conversion circuit was extracted by wavelet packet decomposition, which effectively reduced the dimension of feature vector. Secondly, the improved grey wolf algorithm was used to optimize the parameters of SVM model and establish DE-GWO-SVM fault diagnosis model; Finally, taking the IF conversion circuit as an example, the fault diagnosis experiment was carried out and compared with other methods. Comparison results show that the fault diagnosis accuracy rate of this method reaches 97.33%. Compared with the traditional methods, this method can better improve the fault diagnosis rate and shorten the diagnosis time.
Calculation of Electromagnetic Thrust for Long Primary Double Sided Linear Induction Motors with Secondary of Arbitrary Length
Authors: Zhang Qian , Liu Huijuan , Zhang Zhenyang , Song Tengfei 
Published: March 2021

Abstract
The expressions of the air gap flux density and secondary current density of long primary double-sided linear induction motor (LPDLIM) with arbitrary secondary length are derived by the numerical method, which with the secondary as the moving reference frame. The effects of the forward and backward components of end effects on the normal travelling wave and the secondary current density are analyzed respectively, and their temporal and spatial distributions are presented. And then the electromagnetic thrust and its ripple expression considering the longitudinal end effect of the air gap magnetic field and the components of the secondary current density are solved. The slip characteristics distribution of each electromagnetic thrust and thrust ripple components, and the contribution of end effect to resultant thrust and force ripple of the motor with arbitrary secondary length are investigated. Finally, the thrust characteristics of the LPDLIM with arbitrary length secondary are verified by the results of the FEM model. The calculation shows that the appropriate secondary length can reduce the thrust ripple of the LPDLIM.