bluuebunny/crossref_metadata_2025_split huggingface dataset:
Study of An Environmentally Friendly Oil Pipeline Cleaning Technology Based on Self-Propelled Intelligent Robot
Authors: He Nana , Li Taofeng 
Published: November 2020

Abstract
Polymers, coke, oil dirt, limescale, sediments and rust corrosives, which are adhered to pipe wall, will be generated during the oil pipeline transport, thus leading to degradation of production efficiency, increasing the energy consumption and seriously impacting oil transport quality, efficiency and safety. What’s worse, this will result in blockage and thus interrupt the flow and suspend the production. Based on modern artificial intelligence (AI) and new-type environmental protection technologies, a self-propelled robot of intelligent manipulation is proposed and enters oil pipeline to conduct environmentally friendly cleaning using high-pressure water jet, and the cleaning wastewater can be recycled. This environmentally friendly intelligent pipeline robot is applicable to detection and high-pressure water jet cleaning of circular variable-sectional-diameter pipelines in closed environment. Furthermore, it is capable of online pipe wall quality detection for horizontal, vertical and turning circular pipelines, and also cleaning effect evaluation. According to the operating principle, an environmentally friendly oil pipeline cleaning technology based on intelligent robot is proposed by combining the technical characteristics in the existing oil pipeline design and construction, in an effort to provide a reference for intelligent cleaning and automatic visual inspection and evaluation of oil pipelines.
Design of Filtering Power Divider with Wide Stopband Based on QMSIW
Authors: Cheng Yi , Huang Zhixiang , Wang Zhongmiao , Wang Chao , Li Xuemei 
Published: November 2020

Abstract
A miniaturized filtering power divider with wide stopband based on quarter-mode substrate integrated waveguide (QMSIW) is proposed. The physical size of the QMSIW is reduced by 3/4 comparing with the traditional SIW structure, which can achieve miniaturization, and its electromagnetic field distribution has little effect. Using the metal interference column and the etched U-shaped groove to adjust the resonance frequency, so that the main mode and the first higher mode form a passband, meanwhile, suppressing the second higher mode. The simulation results show that the center frequency is 4.42 GHz with insertion loss of 0.45 dB, the 3-dB bandwidth is 4.12-4.63 GHz with a fractional bandwidths of 9.7%, and there is a great stopband up to 11 GHz with 15 dB rejection level. Besides, the return loss of passband is better than 17.5 dB. To verify the validity of this design, a prototype is fabricated and measured. The measured results demonstrate that it has a good agreement with the design simulations.
A Low SAR, Small size, High gain, Circularly polarized Antenna for Wearable Application
Authors: Wang Chao , Zhang Liang , Cheng Yi , Li XueMei , Wu Xianliang 
Published: November 2020

Abstract
A planar, small size, high gain, low specific absorption rate (SAR) and circularly-polarized (CP) wearable antenna is proposed in this paper. The antenna is fabricated on a substrate with a dielectric constant of 3.5 with a thickness of 0.5 mm and sized at 42×42 mm only. Both the simulation results show that the overlap bandwidth of impedance and axial ratio of the proposed antenna is 13.7% (5.15-5.91 GHz). To isolate the antenna against the influence of the human body, a artificial magnetic conductor (AMC) plane is added on the reverse side of the radiator. The AMC structure effectively isolates the human body from the radiation of the antenna and reduces the specific absorption rate (SAR) of it by more than 96.1% at 5.5 GHz, which improves the antenna gain and the peak gain reaches 5.09 dBi at 5.5 GHz. The wearable performance of the antenna was analyzed, and it is observed that it can sustain good performance even under human body loading. Beside, the antenna has a small size, which makes it ideal for wearable applications.
Spacecraft in Orbit Fault Prediction Based on Deep Machine Learning
Authors: Zhang Xiangyan , Wang Xiaochen , Tian Huadong 
Published: November 2020

Abstract
Spacecraft is playing a more and more important role in military reconnaissance, environmental disaster reduction and other fields in china. The research on fault prediction technology based deep machine learning will realize the transformation from traditional post fault processing to prior system maintenance. In this paper, combined with different in orbit fault modes of spacecraft and the complexity of the fault modes, multi kind of prediction methods for single parameter and multi parameter faults are proposed. Based on this, the in orbit fault prediction system is constructed, and for the results of a typical in orbit case show that the prediction results are good. In the future, it can be considered to further expand the fault prediction methods of the system, such as modeling the in orbit data directly by the graph neural network algorithm, so as to further improve the prediction accuracy.
A Design of Hign Gain Wideband Circular Polarization Antenna Based on All-Metal Vivaldi Antenna
Authors: Li Xuemei , Huang Zhixiang , Liu Dui , Wang Chao , Cheng Yi 
Published: November 2020

Abstract
This paper designs a broadband circularly polarized antenna covering the 25 GHz to 31 GHz band. The Vivaldi antenna is used as the basic structure to generate broadband electromagnetic waves; circular polarization is achieved by feeding through a circularly polarized bridge; a metal choke ring is used at the bottom to ensure the low side lobes of the antenna. The antenna consists of four basic Vivaldi antennas, a metal choke ring, a grounded substrate, and a feed network. The results show that the antenna covers the entire frequency band when the axial ratio is lower than 3 dB, and the impedance matching is better. The use of a choke ring structure ensures that the antenna electromagnetic waves are radiated upward, resulting in higher gain and lower side lobes. The highest gain in the included frequency band is 11.4 dB, and the simulation result is good. The antenna has high gain and low sidelobe characteristics.
An Optimized LightGBM Model for Fraud Detection
Authors: Huang K 
Published: November 2020

Abstract
The rapid development of e-commerce and the growing popularity of credit cards have made online transactions smooth and convenient. However, large numbers of online transactions are also the targets of online credit card fraud, which aggregate to enormous losses annually. In response to this trend, many machine learning and deep learning methods have been proposed to solve this problem. Unfortunately, most models have been developed on small datasets and require tedious fine-tuning processes. In this paper, a LightGBM-based method for fraud detection is proposed. The dataset used for this study is the IEEE-CIS Fraud Detection dataset provided by Vesta Corporation, which includes over 1 million samples. Experiments have shown that the LightGBM-based method outperforms most classical methods based on Support Vector Machine, XGBoost, or Random Forest. Besides, effective feature engineering methods for feature selection and Bayesian fine-tuning for automatic hyperparameter searching are also proposed.
Structural damage detection system based on FPGA and EMI method
Authors: Huang Dehang , Li Tianli , Tao Yongxiang 
Published: November 2020

Abstract
This paper designs a miniaturized structure health detection system based on the electro-mechanical impedance (EMI) method. The system uses two orthogonal signals of the same frequency as the excitation source, which effectively solves the problem of determining the phase difference in phase detection. According to the characteristics of the piezoelectric sensor, the FPGA is used as the main control chip, the measurement frequency range is adjustable by the user, and transmitted through the wireless serial port. The admittance curve of the piezoelectric sensor is displayed on the host computer, and real-time calculation of the RMSD value and relative RMSD value of the admittance curve. Compared with the current impedance analyzer on the market, it has the characteristics of wide measurement frequency range, small size and low cost.
Automatic machine learning Framework for Forest fire forecasting
Authors: Qu Jintao , Cui Xiaohui 
Published: November 2020

Abstract
Based on the automatic machine learning framework, combined with the characteristics of forest fire meteorological data and the adaptive requirements of forest fire prediction, this paper optimizes the data preprocessing, parameter learning, loss function and other links of auto-sklearn, builds a forest fire risk prediction framework with regional adaptive characteristics. Based on the forest meteorological fire risk data, a forest fire risk prediction model with regional characteristics and self-learning characteristics is constructed to solve the problems of low compatibility of the existing machine learning methods with binary unbalanced forest fire data, improve the accuracy of forest fire prediction and provide decision-making basis for forestry risk management. The comparative analysis results show that the prediction accuracy of the improved framework in different test sets is improved by 13% on average. Compared with the existing machine learning model for forest fire prediction, the prediction accuracy of the framework proposed in this paper is comprehensively better than the existing methods in terms of real forest fire data.
Autonomous Driving System Design for Formula Racing
Authors: Zhang Zheng , Gao Peifeng , He Zilong , Chen Hongbin , Wang Jianfeng 
Published: November 2020

Abstract
This paper mainly introduces automatic driving schemes and algorithms for formula racing car. Formula racing is a good platform for testing the extreme performance of vehicles, but there is a risk of harming the drivers. We propose an automatic driving scheme to avoid such problems. The solution we proposed integrates three modules: environment perception, path planning and vehicle control. Using lidar, camera and GPS/INS integrated navigation as sensors, an innovative data fusion method is proposed. This paper proposes a new path planning method, which uses Delaunay triangulation and topological map-based path search and evaluation to obtain the best path. Finally, a control model based on pure pursuit algorithm is used to control the vehicle precisely. The whole system is developed based on ROS which has a unique loose coupling. This scheme is proved to have high accuracy and good robustness by simulation analysis and multiple vehicle tests. The proposed solution has good portability, and it can also be applied to the field of unmanned passenger cars, robots and drones after appropriate modifications.
A cherry tomato classification-picking Robot based on the K-means algorithm
Authors: Zhou Wenting , Meng Fanwen , Li Kun 
Published: November 2020

Abstract
In order to improve the automation level of cherry tomato harvesting, this paper proposes an cherry tomato picking robot. The robot includes four parts: a vision camera, a mechanical arm, a picking claw and a tracked AGV. The vision camera learns and classifies different varieties of cherry tomatoes based on the K-means algorithm. The color patch-based visual tracking algorithm accurately locates the ripe tomatoes, and the robotic arm drives the claws to pick them accurately. The tracked AGV adopts the original difference height intelligent tracking method, which has low cost and high adaptability to the working environment. It is verified by experiment that the cherry tomato classification-picking robot can effectively identify and locate different types of cherry tomatoes. The single-fruit tomato picking operation takes about 20s, the success rate is over 80%.
Multi-scale Feature Fusion for 3D Saliency Detection
Authors: Pan Gang , Wang An-Zhi , Xu Bao-Lei , Ou Weihua 
Published: November 2020

Abstract
3D saliency detection aims to take advantage of the disparity map, depth map and color information to automatically detect informative objects from natural scenes. Although studies have concentrated on this issue in recent years, there are challenges such as how to leverage disparity map or depth map effectively to compute depth-induced saliency, and how to fuse optimally multiple visual features and cues. A novel 3D saliency detection approach is proposed, which fuses local contrast, region contrast, texture feature, depth cue, and location cue into a unified saliency computation framework. Results show that the proposed approach achieves significant and consistent improvements on other advanced methods in the RGBD1000 datasets.
Image recognition algorithm based on spatial transform convolutional neural network
Authors: Xing Zhen-Zhen 
Published: November 2020

Abstract
Aiming at the problem of classification and recognition of noisy handwritten digits, a connection method is proposed to add a spatial transformation network to a convolutional neural network. The spatial transformation network can not only obtain the output results, but also understand the parts of the input data that have the greatest influence on the results and perform feature extraction, which can improve the performance of the model and enhance the interpretability of the model. The experiment was performed on the Cluttered MNIST dataset, and the effects of the conventional convolutional neural network and the improved neural network with spatial transformation network were compared. The experimental results show that the prediction accuracy of convolutional neural networks based on spatial transformation can reach 97.65%, and the results obtained by this method are better than those of conventional convolutional neural networks.
Research on multi-factor Identification model of acceptance content of Work order complaint
Authors: Huaiying Shang , Yan Liu , Zheng Angang , Qi Zhang , Min Ren 
Published: November 2020

Abstract
In this paper, combined with the power business demand, the purpose of this paper is to break the blind area of customers’ demand for electricity, so as to improve the management level of users’ electricity demand, and realize the mining of work order types of hot complaint business. In order to obtain the information behind the complaint work order, the power customer complaint work order is deeply mined. The weight value of keywords is calculated based on TF-IDF algorithm, and the text classifier model is constructed based on SVM classification algorithm. This study aims to provide differentiated service strategies for different types of power customers by controlling the main problems of current power customer complaints, so as to improve customer satisfaction and loyalty.
Application of IAFSA-BP neural network in face orientation recognition
Authors: Wang Hai-Jun , Jin Tao , Zhang Yi-Ting 
Published: November 2020

Abstract
In order to improve the recognition accuracy of BP neural network in face orientation recognition, an improved artificial fish swarm algorithm is proposed to optimize the weights and thresholds of BP neural network face orientation recognition model. The improved artificial fish swarm algorithm is based on the standard algorithm, introducing adaptive factors to make the horizon and step size adaptive change, and at the same time learning the solution before the result announcement, so as to improve the accuracy of the final result. Finally, the experimental results show that the effective combination of the improved artificial fish swarm algorithm and the BP neural network algorithm can improve the output accuracy of face orientation recognition of the BP neural network, and the running speed of the improved algorithm is significantly higher than that of the standard artificial fish swarm algorithm.
A lightweight convolutional neural network model for target recognition
Authors: He Chunqian , Li Dongsheng , Wang Siqi 
Published: November 2020

Abstract
Convolutional neural networks have achieved excellent performance in a wide range of applications, but the huge resource consumption makes a great challenge to their application on mobile terminals and embedded devices. In order to solve such problems, it is necessary to balance the size, speed and accuracy of the network model. This study proposed a new shallow neural network on the bases of ResNet and DenseNet. We use different size convolution kernels to obtain feature maps and then concat them. Afterwards we build two convolution layers to reduce the size of the feature maps and increase the depth of the network. By stacking this structure, we get our net model. Experiments show that our nine-layers network recognition performance is better than 18-layers ResNet and 19-layers DenseNet, and its training time is shorter. The final recognition rate of our network is 97.37%, ResNet recognition rate is 96.93%, and DenseNet is 96.31%.
Better Modeling the Hierarchical Structure of Language for Sentence Semantic Matching
Authors: Pei Zhongmin , Li Changqun , Xiong Wei , Luo Zhangkai 
Published: November 2020

AbstractSentence semantic matching requires understanding the semantic relationship between two sentences, which is widely used in various natural language tasks such as paraphrase identification, natural language inference and question answering. Usually, the underlying structure of sentence is not strictly sequential, this structure is hierarchical. This paper studies how to better model the hierarchical structure of sentence for semantic matching. Recent research suggests that BERT implicitly captures classical, tree-like structures. Based on this, this paper proposes to further enhance the strength of modeling hierarchical structure of language with an advanced variant of LSTM – Ordered Neurons LSTM(ON-LSTM), which introduces a syntax-oriented inductive bias to perform the composition of classical tree structure. Experimental results demonstrate that the proposed approach, which enhances hierarchical structure of language, significantly improves the performance of sentence semantic matching.
Pitch Angle: A Newfound Influential Trait for Image-Based Facial Beauty Perception
Authors: Guo Hao , Han Wenming , Chen Fangmei , Sun Fuming 
Published: November 2020

Abstract
One of the most crucial issues in facial beauty study is to find the traits, which determine the perceived attractiveness of a face. During the decades, many traits have been studied extensively, such as proportions, averageness, and symmetry. However, facial beauty seems to be more complex than these traits. In this paper, a face image is represented by a series of interpretable parameters, which are learnt by a CNN based 3DMM fitting algorithm. Based on the SCUT-FBP5500 dataset, we explore the relationship between those parameters and the human rated facial beauty scores. We observe that the pitch angle parameter has a strong correlation with the beauty score. In order to study the causal relationship between the pitch angle and facial beauty, face images with different pitch angles were generated as the stimuli and 39 volunteers were invited to a rating experiment. The results show that the change of the pitch angle has a significant effect on the perceived facial beauty. This finding may explain that head pose plays an important role in the self-portrait shot.
Evolvable Image Filter based on Rotation Invariant Pixel Correlations
Authors: Li Xiuling , Tu Ke , Jiang Decai , Wang Ting , Li Zhengdong 
Published: November 2020

Abstract
As one of the most typical nonlinear filters, weighted median filter has considerable filter behaviour by assigning weights. But each pixel has only one weight in the traditional weighted median filter. Correlations between pixel and other pixels under the filtering template are not considered. A novel rotation-invariant image filter model is proposed here which has a stable filtering performance when filtering on rotated and reversed images, symmetry of the correlations is utilized to reduce the complexity. Cartesian Genetic Programming is adopted to evolve image filter operators. Experiments about rotated images show that this rotation invariant model exhibits robust characteristics. Image denoising of different images show that it has better properties of edge or details preservation, efficient noise attenuation.
A staked discriminative auto-encoder based on center loss for radar target HRRP recognition
Authors: Li Rui , Wang Xiaodan , Quan Wen , Zhang Guoling , Xiang Qian 
Published: November 2020

Abstract
In the radar automatic target recognition filed, extracting representative features from the radar high resolution range profile (HRRP) is the key issues, which determines the accuracy and reliability of radar target recognition. In this paper, we propose a novel stacked discriminative auto-encoder(S-disAE), the center loss is integrated into auto-encoder, it can force the learned feature with large distance close to their class feature representation center, so as to reduce the intra-class variations while keeping the features of different classes separable. In pre-training stage of our discriminative auto-encoder(disAE), we combine the mean square error loss and center loss to learn the main factors of input raw data and minimize the intra-class variations of the feature, in fine-tuning stage, we also combine the softmax cross entropy loss and center loss to improve the classification accuracy. We conduct several experiments on the simulated HRRP data, the results demonstrate that the proposed algorithm can extract discriminant features and improve the HRRP recognition accuracy.
Summary of Object Recognition
Authors: Li Shiji , Chen Hong , Wang Qing , An Jiahui , Li Jiayue 
Published: November 2020

Abstract
Object recognition is one of the classic problems in computer vision. It is very important for computers to recognize the common objects in life like the human brain and the human eye. This is also an important step in the development of computers in the direction of intelligence. This article summarizes the current research on object recognition, and elaborates on several aspects of object feature extraction, object feature matching and object recognition methods.
Water System Segmentation Method of High Resolution Remote Sensing Image Based on eCognition
Authors: Xue Bei , Lin Xiaohu 
Published: November 2020

Abstract
High-resolution remote sensing images contain abundant ground object information. In this paper, water system elements are segmented and interpreted based on eCognition platform. Three segmentation methods, multi-scale image segmentation, quadtree segmentation and checkerboard segmentation, and their segmentation principles are introduced. The advantages and disadvantages of different segmentation methods are obtained through experimental analysis, and the optimal segmentation parameters of multi-scale segmentation are obtained by setting different segmentation parameter experiments. This paper is of guiding significance for the interpretation of water system data and the dynamic update of geographical survey elements in later remote sensing images.