bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimal model for deviation penalty of electricity in joint operation of wind power retailer and heat storage electric boiler
Authors: Liu Xiao , Gao Ciwei , Ding Hengchun , Zhou Lixia , Wang Ting , Ding Jianyong 
Published: March 2020

Abstract
Because of the randomness and fluctuation characteristics of wind power output, there is a deviation between the bidding output and the actual output of the wind power retailer, and it faces with a lot of penalty fees for deviated power. In order to promote clean heating in the northern heating area, the heat storage electric boiler (this article abbreviates as HSEB) have been gradually promoted. Based on the thermal storage capacity of the HSEB, the deviation of wind power output can be reduced to a certain extent. Considering the comfort range of the indoor human body, the indoor heat supply can fluctuate within a certain range, further increasing the regulation capacity of the HSEB. In this study, taking reducing the cost of deviation penalty as the objective function, and a joint operation model of wind power retailer and HSEB is established. By an example, the feasibility of using HSEB to reduce the cost of deviation penalty is verified.
Robust Face detection based on Viola-jones Algorithms
Authors: Xayachack Phonetheva , Zhang Jun 
Published: March 2020

Abstract
This paper describes the experimental methods of using face detection based on MATLAB software. The important things are that interesting in the movement, this face detection are accepted to use four general face detection internationally. We also need to use the Viola and Jones principles in MATLAB for face detection because of its ability to process that image quickly and Very accurate. And this paper aims to study principles and algorithms for active development and improvement.
A 2×2 MIMO Antenna Design with High Isolation and Steady Radiation Performances
Authors: Ding Dawei , Li Dawei , Tang Ting , Min Daiyong , Zhang Qidong 
Published: March 2020

Abstract
This paper introduces a new high-isolation 2×2 multi-input multi-output antenna array design. The antenna array adopts multi-objective optimization method and fragment structure to achieve better performance. The results show that the isolation structure of this design can reduce the mutual coupling between different units of the antenna array. Compared with the traditional mutual coupling suppression technology in the microstrip array antenna, the isolation structure also improves the front-to-back ratio of the array.
Partial discharge classifier based on wavelet theory
Authors: Yun Yuxin , Zhao Xiaoxiao , Zhang YanJie 
Published: March 2020

Abstract
The excursion of fractal parameters is the main obstacle to the application of classifiers trained by fractal-parameter-based features. In order to solve this problem, the characteristics of fractal parameters are studied based on partial discharge ultra-high-frequency signals mathematical models. One solution to this problem based on symlets wavelet has been put forward. And the result of implementation to signals with different signal-to-noise-ratio shows that it’s a promising approach to expand the applicability of the classifier.
An Numerical Analysis Approach for a Novel Electromagnetic Flow Meter with Multi-Electrode
Authors: Li Xuejing 
Published: March 2020

Abstract
Electromagnetic flowmeter (EMFM) plays an important role in technological processes such as the cooling of nuclear reactors. The internal EMFM must have large-size magnet to provide uniform magnetic field. In this paper present a novel electromagnetic flowmeter, which has multi-electrode and non-insulation pipe wall. We present an extended theory and compare it with experimental results. A finite element procedure for the solution of the novel EMFM is presented.
SystemC hardware in the loop simulation scheme
Authors: Zheng Hongyun , Wu Xianghu , Tao Yongchao 
Published: March 2020

Abstract
SystemC is the standard in the field of virtual modeling. With the continuous expansion of semiconductor industry, test verification is a very important part in the whole process of semiconductor. Our research is based on the standard SystemC platform, which is very helpful for the early planning and architecture modeling of SOC architecture, as well as for the later regression testing and upper software development testing. SystemC is a pure software solution, but with more and more customized customer needs, we need semi physical simulation. The so-called hardware in the loop simulation is the integration of software and hardware in a platform for simulation. For example, in the previous pure software simulation tests, all of them are virtual, and the simulation engine is implemented by pure software. It has nothing to do with real world time. There are also real device tests. All devices are pure hardware. If a problem is found, it may affect the overall results. The cost of building hardware again is too high. Therefore, the requirement of hardware in the loop simulation is put forward.
Three dimensional point cloud data reduction algorithm and application in tooth modelling
Authors: Wang Shigang , Peng Shuai , He Jiawen , Pan Bin 
Published: March 2020

Abstract
Reverse engineering is an advanced manufacturing technology, which can design, develop and innovate products. The processing technology of 3D point cloud data is the key of reverse engineering technology, and the simplification technology is an important part of data processing, which plays a decisive role in the quality and efficiency of subsequent surface modelling. The advantages and disadvantages of different point cloud data reduction algorithms are analysed. By combining the efficiency of three-dimensional mesh algorithm with the accuracy of curvature reduction method, a second reduction algorithm is proposed. Firstly, the algorithm uses the non-uniform mesh reduction algorithm of octree to simplify it, to ensure the data of feature points is not damaged, and then uses the curvature reduction method of B-spline surface fitting to simplify it. The experiment of tooth point cloud data reduction shows that this method can retain the details of the original data better on the basis of higher reduction ratio.
Implementation of Digital Image Encryption Algorithm Based on DNA Coding and Chaos Theory
Authors: Li Denghui , Wang Yanhong 
Published: March 2020

Abstract
Because of the large amount of data and high redundancy, digital image can not be encrypted by traditional methods. The method based on DNA and chaos theory can be used for image encryption. The experimental results show that the gray value of the image after encryption is randomly and evenly distributed, which directly loses the features of the original image. The histogram is balanced, the correlation between adjacent pixels is low, and the information entropy is close to the ideal value, which shows a good anti cracking ability and can restore the image well.
GPU-accelerated fast implementation of shortest path algorithm in the noise simulation analysis system
Authors: Qiu Yuxiao , Zou Anxin , Chen Pengcheng , Xu Luwen 
Published: March 2020

Abstract
A graphics processing unit (GPU) framework for the computation of the noise level between the noise source and the receiving point in the noise simulation analysis system is presented. The calculation process of the noise level often encounters a problem of slow convergence, and the calculation amount is large due to problems such as weighted point diffusion, transformation of the potential energy matrix, and gradient degradation. To circumvent these limitations, we devise a GPU-accelerated algorithm to calculate the shortest distance between the noise source and the receiving point, which has been shown to perform 11 times faster than the CPU method. Compare test results between GPU method and CPU method in different mesh density scenarios while maintaining their same calculation accuracy.
A reinforcement learning model based on reward correction for quantitative stock selection
Authors: Chen Haibo , Zhang Chenyu , Li Yunke 
Published: March 2020

Abstract
A novel reinforcement learning model based on reward function correction is proposed to quantify stock selection., In this model, hidden markov model is uesd to predict the trend of the future state and the reward function of reinforcement learning is corrected according to the prediction, which makes it close to the long-term average gradually. Using the method, the prediction accuracy rate of HS300 in China A-market is 76.6%, Sharp ratio is 1.08%, and the average profit rate can reach 32.2%
Research on denoising algorithm of 3D point cloud data based on curvature change
Authors: Wang Shigang , He Jiawen , Peng Shuai , Gao Xueshan 
Published: March 2020

Abstract
This paper focuses on the algorithm of denoising for 3D point cloud data in 3D modelling technology. Through the problem that it is difficult to retain the feature points of traditional 3D point cloud data denoising and the denoising amplitude is too large, taking the change of Gaussian curvature value of sampling points as the basis of region division, an improved denoising algorithm based on the combination of median denoising algorithm and bilateral denoising algorithm is proposed The algorithm is implemented and simulated by pseudo code. The simulation results show that the 3D point cloud data denoising algorithm based on curvature change not only has a higher ability in denoising effect and feature retention effect, but also has a greater advantage in the execution time of the denoising algorithm.
Production and reconstruction of binary Fourier transform computer-generated holograms based on MATLAB
Authors: Zeng Jianhua , Zeng Wei , Yang Jianrong 
Published: March 2020

AbstractBased on MATLAB, the production and reconstruction of binary Fourier transform computer-generated holograms (CGHs) were realized using Roman III roundabout phase encoding method. The principle, method and step of CGHs were discussed. The digital reconstruction of holograms was completed using CGHs technology, and the simulation of whole holographic recording and reconstruction process was realized by a computer. Additionally, effects of the width of a sampling unitsand the threshold coefficientefon the production and reconstruction of CGHs were studied, which can provide a theoretical reference for the improvement of the quality of the reconstruction image. Compared with experiments, the result of the CGHs technology is more intuitive and obvious. It is inferred that CGHs have the potential to be used to image encryptions and communications.
The spectral conjugate gradient method in variational adjoint assimilation for model terrain correction II: Numerical test
Authors: Tao Sulin , Li Yuhong , Mugume Isaac , Shen Shuanghe 
Published: March 2020

Abstract
The performance of the spectral conjugate gradient (SCG) method proposed in part I of this paper is evaluated by comparison with that of the limited-memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS) method and Hager and Zhang’s CG_DESCENT method. The step length is determined by solving the tangent linear model and a line search algorithm with cubic interpolation is introduced for comparison. Numerical tests indicate that the SCG method is effective at correcting the bottom terrain. It is robust, providing small root mean square (RMS) errors and smooth profiles for the corrected bottom terrain for all tests conducted. It has much higher optimizing efficiency than Hager and Zhang’s CG_DESCENT and Liu’s LBFGS methods. The SCG method reduces the norm of the gradient sufficiently and the value of the cost function very quickly. It always provides linear convergence rates, especially when including a regularization term. This study suggests the combined utilization of the SCG method and line search strategy to ensure high efficiency when solving the large-scale unconstrained problems.
Momentum multi-objective optimization algorithm based on black hole algorithm
Authors: Rao Jing , Wu Tao , Chong Wu , Li Yongbo , He Wangyong 
Published: March 2020

Abstract
In this paper, we mainly study the application of a multi-objective optimization algorithm based on the black hole algorithm in motor optimization. Using the combination of global search and local search, the continuous search area is better. The random search method is used in the global search, and a momentum gradient method is used in the local search, which makes the search results have faster convergence rates and easier convergence to the global optimal. A new file management strategy is used to make the optimization results more global and have better generalization ability. in practical application, the design of motor is often affected by manufacturing error, so random noise is added to the selection of optimization results, which can be more in line with the practical application. Finally, the actual motor model and complex function are used to test the performance of the optimization algorithm. Finally, the actual motor model and complex function are used to verify the performance of the optimization algorithm.
CAD secondary development technology based on. NET API
Authors: Zhang Li , Zhang Peng 
Published: March 2020

Abstract
AutoCAD secondary development technology plays an increasingly important role in engineering practice. It can help drafters free from heavy and repetitive tasks and is suitable for different engineering fields. This article details the implementation of secondary development based on the. NET API. This method makes it easier to develop the various plug-ins we need, and to draw various graphics accurately. This paper introduces the development of CAD plug-ins, the drawing of graphics, and the connection between CAD and database. Through the actual development and application, it can be found that the C#-based. NET API technology can easily realize the secondary development of CAD, and the method is easy to grasp and has strong practicability, which can greatly improve work efficiency.
Collision algorithm design based on dynamic rotation model
Authors: Zhai Tingyu , Tian Yi , Wen Zhonghua 
Published: March 2020

Abstract
This article aims to study the inelastic collision between system, based on the daily life of drum ball collision problems, by combining the collision body as well as the impact of instantaneous rotation process of damping kinetic energy consumption situation, designs a Rotating collision model algorithm model of concentric drums collision problem, and then, apply those algorithms to stimulate the size of the drum’s offset that produced by the effect of different tension.
E-commerce evaluation text classification algorithm
Authors: Yang Jingmiao , Yang Hongwei , Zhao Jianping , He Jinlong 
Published: March 2020

Abstract
Based on the research results of the false evaluation and identification of e-commerce shopping, this paper analyzes the characteristics of the evaluation content of the merchant’s brush list. In order to solve the problem that the convolutional neural network (CNN) model is difficult to capture the feature information of the whole evaluation text in the false evaluation identification task, a convolutional belief network (CBN) model based on keyword weighting is proposed. Firstly, the TF-IDF algorithm is used to construct the keyword set, then the word vector is weighted by the keyword vector. Then use restricted boltzmann machine instead of the pooling layer of CNN model to reduce the dimension; finally, the weighted word vector is classified by this algorithm model to complete the recognition task of false evaluation text.
A two-stage ALS point cloud segmentation framework for urban areas
Authors: Han Wei , Huang Daqing , Xu Cheng 
Published: March 2020

Abstract
In this paper, a novel methodology for semantic classification of airborne laser scanning point clouds is introduced in this paper. A two-stage framework integrating point-based classification and cluster-based rules is proposed with a general end to end processing scheme, which is from scattered airborne laser scanning (ALS) data to points with semantic labels. In the first stage, energy function consists of a point-wise-based soft labelings term obtained by random forest classifier and a spatially smooth labelling term. Then, a constrained mean-shift-based clustering algorithm combined with sematic rules is proposed to refine the hard labelings of class-wise point clouds, which can be as a post-processing stage. To verify the effectiveness of our method, the classification results derived for an ALS benchmark dataset are shown in Section 3. Base on the semantic classification results, the two-stage framework can be demonstrated with high accuracy and high plausibility in comparison to some classic approaches.
ACGLM: A Hybrid Approach to Select and Combine Gene Expression Regulation in Cancer Datasets
Authors: Al-bukhaiti Hesham Abdulatef Mohammed , Luo Jiawei 
Published: March 2020

AbstractCancer is one of the causes of death in the world and many genes are involved in it. Transcription factors (TFs) and microRNAs (miRNAs) are primary gene regulators and regulatory mechanisms for cells to define their targets. The study of the Regulatory mechanisms of the two main regulators is complex, but this lead to a deeper interpretation of biological processes. In order to avoid exhaustive search and unnecessary genes, firstly, mRNA expression and miRNA expression are clustered by K-means cluster, then, applied ANOVA test to select significant genes. We proposed a gene regulatory network (GRN) estimation method, using Directed networks with generalized linear regression to predict and explain the relationships between regulators and their targets. Where through GO TERM and KEGG PATHWAY for target genes we got many processes such as cell communication, regulation of the biologic process, biological regulation and cell cycle, DNA replication, and cell cycle, these processes are considered significant to the cancer diseases. by comparing with other methodologies Our approach was better, as well as the results were consistent with the medical literature, where the important regulators in our gene regulatory network have a major role in cancer this explains the efficiency of this approach.
Ant colony algorithm based on data classification
Authors: Tan Wanrong , Gao Jian , Rao Jun 
Published: March 2020

Abstract
Aiming at the drawbacks of ant colony algorithm applied in the medium and large scale traveling salesman problem (TSP), this paper proposes an ant colony algorithm based on data classification (DACO),in which data classification method takes attraction degree as an important consideration.First,the initial data of the selected data set is divided into two categories - candidate class and elimination class according to the law of attraction degree.Then the elimination class is eliminated to reduce the search time.At the same time, the attraction degree can effectively guide the algorithm to search for the optimal solution when the previous pheromone heuristic effect is not significant.In addition, 3-opt local search algorithm is added to further optimize the solution.The experimental results show that the DACO has a better convergence and solution quality compared with other ant colony algorithms.
Optimization of Multi-Intersection Traffic Signal Timing Model Based on Improved Q-Learning
Authors: Yin Mingyue , Wang Yunyun , Li Ziqing 
Published: March 2020

Abstract
With the economic development and the increase of car ownership, major cities in China are facing plenty of problems, such as traffic congestion and environmental pollution caused by traffic congestion. The effective solution to solve this kind of problem is to adopt intelligent control to the traffic signal timing scheme. Based on traditional Q-learning, this paper adds the influence of phase difference and congestion factors, and uses the traffic condition data of adjacent intersections and this intersection to predict the next state, so as to adjust the timing scheme of signal lights at each intersection in the road network. The VISSIM simulation experiments show that this method can effectively reduce the average vehicle delay time and the average number of parking times, alleviate traffic congestion, and improve the traffic efficiency in the overall road network.
A Matrix Aggregation Intelligent Decision Based on H-Convex Combination and Expert Preference
Authors: Li Wei , Zhang Mingsheng , Chen Deqiang , Luo Shan 
Published: March 2020

Abstract
In the field of multi-dimensional intelligent decision-making, the evaluation results rely on the subjective experience of the evaluation experts. However, the evaluation information generated by personal expert preferences can has certain impact on the decision-making results.For this problem, we present an intelligent decision model based on H-convex combination and expert preference. The H-convex combinatorial matrix algorithm is used to assemble the individual judgment matrices, which reduces the inconsistency and non-reciprocity of the group decision matrix. The expert preference quantization formula is used to obtain the preference values of the corresponding experts, which are taken into the H-convex combination as power exponents. This solves the impact of expert preference differences on decision outcomes. The feasibility and effectiveness of the proposed method are verified by some evaluation cases.
Optimization and Parallelization of Fuzzy Clustering Algorithm Based on the Improved Kmeans++ Clustering
Authors: Ma Yu , Cheng Wenjuan 
Published: March 2020

Abstract
In the field of big data, fuzzy clustering algorithm is one of the most widely used clustering algorithm. Although fuzzy c-means (FCM) algorithm performs well, it still is difficult to ascertain the number of clusters and is sensitive to initial clustering center. To solve these problems, an improved fuzzy clustering algorithm based on Spark is proposed in this article. The proposed algorithm integrates the L2 norm and uses the kmeans++ algorithm improved by the Canopy algorithm to initialize the cluster center. Experimental results show that the proposed algorithm performs well in clustering accuracy and computational performance.