bluuebunny/crossref_metadata_2025_split huggingface dataset:
Use of System Dynamics Forecasting Model in Transportation System to Reduce Vehicular Emissions-A Mathematical Approach
Authors: Singh Sandeep , Shukla Bishnu Kant , Sharma Pushpendra Kumar 
Published: June 2020

Abstract
In recent years, the surface water quality has been observed deteriorating due to industrial activities and pollution in the Punjab state. Evaluation of surface water quality is an important issue to assure from its safe and stable use. However, describing quality conditions is generally difficult considering spatial variability of pollutants and a wide range of indicators like biological, physical and chemical substances which can be measured. This paper includes the study of surface water quality parameters in the northern Punjab region. Samples from different sources across northern Punjab were analysed for pH, TDS, TSS, turbidity, DO, chloride content, iron content and hardness of surface water. The pH, TDS, TSS, turbidity, DO, chloride content, iron content and hardness were found in the range of 6.7-8.7, 1358.00-1430.60 mg/l, 135.10-141.21 mg/l, 0.001-0.62 NTU, 0.96-7.80 mg/l, 0.80-15.56 mg/l, 0.01-0.02 mg/l and 1.2-13.9 mg/l respectively. The obtained results were compared with the acceptance limits as given by Indian standard code IS: 10500-2012. The high values of water quality parameters obtained as a result of this study indicates the level of pollution of the different sources of surface water of northern Punjab. At the end the obtained results were critically examined and suggestions were made for better management of surface water resources. An efficient transportation system is vital to economic development and a sustainable environment in a country. India's road transportation contributes to approximately 5.4% of the GDP carrying 65% of freight traffic and 85% of passenger traffic. India's fuel imports are growing at an average of 18% annually for which India spends 3.5% of GDP, thereby posing a major threat to the economy of the country in the future. Also, the use of such a high quantity of fuel causes vehicular pollution, which is responsible for fuel emission in urban areas. This study concentrates on carrying out an interdisciplinary work involving the sectors of transportation, energy, and environment considering the parameters like vehicle model split, fuel consumption and vehicular emissions. This is done by building, System Dynamics (SD) forecasting simulation models using STELLA software to mathematically forecast these parameters in the upcoming years until 2030. Hence, a scenario of augmenting the growth rate of public transportation and simultaneously restricting the growth rate of personalised transportation showed a substantial decrease in fuel consumption and fuel emissions, which eventually resulted in a reduction of fuel cost as well as an increase in the GDP in the transportation system for Chennai city
Emperical Modeling of Growth Parameters in Cellulosimicrobium cellulans during Heavy Metal Tolerance
Authors: Pipil Nikhil Kumar , Chatterjee Sayan , Purty Ram Singh 
Published: June 2020

Abstract
In the present work we have studied the growth kinetics of Cellulosimicrobium cellulans under different metal stress. The strain used was Gram positive and non-pathogenic. Metal studied were Fe, Zn, Pb, Cu, Cd, and Ni. Growth curve of Cellulosimicrobium cellulans was studied under different concentrations of these metals. In the second part of our investigation we tried to established growth equation which can define the growth curve of Cellulosimicrobium cellulans. The three parameters μm, λ, Xm majorly contribute to growth equations. The changes in these parameters affect the location and duration of exponential or log phase t. Thus, we have chosen these factors primarily to model our system. In each case we have observed little deviation from the predicted equation at the maximum error percentage below 10% (α< = 10) R2<0.8. Next we have tried to model the cumulative effect of these metals simultaneously but our model has failed to reach an appreciable correlation with the experimental data set. In this case we may assume that more factors like mole fraction or partial molar volume of metal solution may have contributed to the growth profile.
Interpretation of Weil’s Theorem
Authors: Meng Feilong 
Published: June 2020

Abstract
This is an introductory paper based on general knowledge such as simplicial complex, nerves and their homotopy equivalents in the topology field. It focuses on the theoretical part of one of the four statements on nerve theorem of paper variations on the nerve theorem. One of many ideologies throughout this theorem is to conduct an approximating process on mathematical spaces with restrictions, which is the bridge from a theory to an application of Weil’s theorem and many others. Besides, work done by Kathryn Heal in her paper variations on the nerve theorem has proved the correctness and feasibility about Weil’s theorem. This paper aims to clarify her proof of Weil’s theorem and make it more easier for people to understand.
Analysis and Research of Sorting Algorithm in Data Structure Based on C Language
Authors: Zhu Zhi-Gang 
Published: June 2020

Abstract
In the process of learning the data structure, it is very necessary to master the sorting algorithm, and in the program design, the sorting algorithm is applied frequently. Based on the importance of sorting algorithms, this paper will carefully compare the characteristics of different algorithms, starting with the work efficiency, algorithm implementation, basic ideas, sorting methods and other aspects, and draw conclusions so as to better select sorting algorithms.
Research on Machine Learning and Its Algorithms and Development
Authors: Jin Wei 
Published: June 2020

Abstract
This article analyzes the basic classification of machine learning, including supervised learning, unsupervised learning, and reinforcement learning. It combines analysis on common algorithms in machine learning, such as decision tree algorithm, random forest algorithm, artificial neural network algorithm, SVM algorithm, Boosting and Bagging algorithm, BP algorithm. Through the development of theoretical systems, further improvement of autonomous learning capabilities, the integration of multiple digital technologies, and the promotion of personalized custom services, the purpose is to improve people’s awareness of machine learning and accelerate the speed of popularization of machine learning.
An Uncertain Tuple Density Clustering (UTDC) Algorithm for Uncertain Data
Authors: Li Chunhui , Zheng Linjiang 
Published: June 2020

Abstract
The massive uncertain data generated by network applications has potential value, and it is of great significance to carry out clustering analysis of uncertain data. However, the uncertainty of data brings a serious challenge to traditional clustering algorithms. There are some problems in the existing clustering algorithms for uncertain data. (1) Some algorithms have a lot of meaningless distance calculations when calculating the distance of uncertain objects, and then the calculation complexity of algorithms increases. (2) The data model in clustering algorithms results in the loss of data distribution information, and then the accuracy of the algorithms decreases. In this paper, we propose an uncertain tuple density clustering (UTDC) algorithm for uncertain data. Firstly, we extract the data distribution features of uncertain instances and construct uncertain tuples by introducing the cloud model, which realizes the pruning of clustering objects. Secondly, we apply the EW distance to the traditional density clustering algorithm DBSCAN, which completes the density clustering for uncertain data. The experimental results show that comparing with UK-Means and FDBSCAN, UTDC algorithm effectively reduces the computational complexity and improves the accuracy.
The Discussion on Application of Underground Pipeline 3D Information System in College’s Repair Project
Authors: Feifei Hu 
Published: June 2020

Abstract
A university campus has a high population density. When the construction workers repairs and reconstructs underground pipelines, they must complete the construction as soon as possible to reduce the impact on the work and study of teachers and students. The underground pipeline 3D information system can display the spatial location and attributes of the underground pipeline intuitively, providing the construction workers with operational guidance to ensure that the repair is carried out in an orderly manner.
An efficient and high-precision algorithm on parameters estimation of LFM signal
Authors: Ruan Hang , Zhang Shuxian 
Published: June 2020

Abstract
At present, prevalent algorithms on parameters estimation of linear frequency modulation (LFM) signal include the fractional Fourier transform (FrFT) method, of which the crucial procedure is to estimate the optimal rotation angle. The estimated value of the optimal rotation angle has a great influence on the estimation accuracy of LFM parameters, and it also affects the computational complexity of the algorithm. This paper proposes an algorithm on parameters estimation of LFM signal with high efficiency and precision. Based on the mathematical relationship between the optimal and any two rotation angles of FrFT, the optimal rotation angle is calculated through three FrFT transforms and interpolation, and the LFM signal parameters are estimated with high efficiency and precision. Simulation results verify the effectiveness of the proposed algorithm.
Three-dimensional reconstruction filtering algorithm based on single ray picture of pipe welds
Authors: Yang Xianbiao , Yang Chao , Yang Qingxu , Yue Xianqiang , Shi Renqiang , Shen Qiucheng , Wang Lihui 
Published: June 2020

Abstract
With constraints in single image, such as collinear equations and coplanarity, a real-scale object model can be obtained, and real scenes of regular objects can be reconstructed, by using three-dimensional reconstruction algorithms such as grid processing and interpolation fitting. A 3D reconstruction algorithm based on single pipe weld seam ray picture is proposed. Firstly, recognizing and segmenting the weld image, and using non-linear filtering to enhance the gray level of the image. Secondly, identifying the inner and outer edge positions of the weld, and performing threshold segmentation, morphological filtering, connected area detection, edge detection, line detection, image rotation, and inner wall area extraction. Thirdly, based on the image of the elliptical opening of the weld seam, the inverse perspective transformation is used to restore the formation of the weld seam root and the weld 3D scatter points. Finally, 3D point cloud is meshed, the function of point-to-surface is achieved, and a three-dimensional model of the welding tumor is generated. The experimental results demonstrate that the algorithm can accurately and effectively perform 3D reconstruction of the weld seam based on a single 2D pipe radiograph of the weld seam, and realize accurate identification of the weld nodules at the root of the pipe through-flow weld.
Random Forest Algorithm Based on GAN for Imbalanced Data Classification
Authors: Shu Qihui , Hu Tianyu , Liu Song 
Published: June 2020

Abstract
Data imbalance increases the difficulty of knowledge mining from data. Aiming at the problem of data imbalanced classification, a random forest algorithm based on GAN is proposed, which can effectively classify imbalanced data sets. The GAN model is used to generate a few class samples, which are mixed with the original data samples to form a new data set to reduce the imbalance of data. Then the new data set is divided into several data subsets with a balanced sample distribution. Finally, all the decision trees are collected to form the forest and the classification results are obtained. The random forest algorithm based on GAN improves the classification effect on minority classes, which makes the equilibrium rate of the dataset reach 50%. The algorithm in this paper has performed relevant experiments on multiple imbalanced datasets, the results show that the algorithm has good performance in data imbalance classification. The optimized algorithm after parallelization improves the running speed.
Least-squares method for the diffraction problem of strip gratings
Authors: Li Huimin , Wang Yujie , Zheng Enxi 
Published: June 2020

Abstract
In this paper, we focus on the diffraction problem of periodic strip gratings. A least-squares non-polynomial finite element method is proposed for this problem. Firstly, the computational domain is decomposed. Secondly, a local approximation space is defined by using non-polynomial functions in every subdomain. Then the least-squares finite element method is derived. Finally, numerical results are reported to show the effectiveness and convergence of the least-squares non-polynomial finite element method.
Orthodontic path planning method based on optimized artificial bee colony algorithm
Authors: Li Zhanli , Liu Tongxin , Li Hong-An , Sun Zhihao 
Published: June 2020

Abstract
Aiming at the orthodontic path planning problems, for example, the number is estimated by orthodontists based on experience, and the efficiency of tooth movement path planning on computer is low. This paper proposed a teeth movement path planning method based optimized artificial bee colony algorithm (ABC). At first, according to the age and gender of patients, the constraint amount of tooth movement is automatically determined. Second, by optimizing the observation bees select method, using both roulette and anti-roulette mechanisms replaced roulette mechanisms, this method can avoid the population from falling into local optimality and obtain better distribution. Then, the initial stage number is obtained through interpolation, and combined with the initial food source generation strategy in ABC. Finally, this paper uses the optimized ABC to plan the teeth movement path and obtain the actual amount of orthodontic stages. The experimental result shows that the optimized ABC has better distribution than basic ABC, and the proposed method is closer to clinical orthodontic, reduced the workload of the orthodontist. The data shows this method is more suitable for different patient situations, reduced the orthodontic stage number and the cost of teeth movement effectively.
Research on Error Correction Algorithm of Vehicle SINS and Odometer Integrated Navigation
Authors: Wu Meng , Yin Xun-Feng 
Published: June 2020

Abstract
In order to solve the problem of odometer small differential error and pulse calculation error and improve the precision of SINS and Odometer Integrated Navigation, this paper proposes an error correction algorithm of vehicle SINS and odometer integrated navigation, compares the calculated position of SINS with the calculated position of odometer as the measured value of integrated navigation within the fixed mileage range and presents a method to correct the truncation error of odometer counting. The simulation results show that these methods can quickly separate the sensor errors of the integrated navigation system, have the high positioning and orientation accuracy. Also, these methods can be applied in the practical vehicle SINS system.
Particle Swarm Optimization Algorithm for Container Deployment
Authors: Wu Libin , Xia Hongbin 
Published: June 2020

Abstract
In recent years, with the development of cloud computing, virtualization technology has received widespread attention. As a new representative of virtualization technology, containers have been widely used in software development, operation and maintenance, testing and other aspects, such as microservices and Docker Cloud. In cloud data centers, containers have gradually replaced virtual machines (VMs) as a new carrier for cloud tasks. However, with the increasing number of cloud products, the scale of tasks requested by users in cloud data centers continues to expand. The economic cost of tasks in the process of containerized deployment has become a concern of various cloud service vendors. The container deployment cost usually includes the data exchange cost, the image pull cost and the server energy cost. In order to solve the containerized deployment of application tasks in the container cloud environment with the lowest possible container deployment cost, this paper proposes a new cost calculation model in a container cloud environment, and then presents an improved particles swarm algorithm, namely a particle swarm optimization (PSO) algorithm for container deployment (CD-PSO) to provide the best solution for application task loading. Experimental results show that the proposed algorithm has a lower deployment cost than other scheduling algorithms.
Research status of mobile robot path planning based on genetic algorithm
Authors: Li Yanhui , Huang Zhonghua , Xie Ya 
Published: June 2020

Abstract
With its strong search ability and other characteristics, the genetic algorithm in mobile robot path optimization has been rapidly developed and applied, and has become one of the most successful algorithms to solve the mobile robot path planning problem. Firstly, this paper introduces the basic principle of genetic algorithm and gives a brief overview of its development and improvement strategies. Then, combining several representative improvement strategies, path planning of mobile robot is explained in detail. Finally, combining the theoretical and applied research results of genetic algorithm, the further development of the algorithm is summarized and prospected.
A real-time detection USV algorithm based on bounding box regression
Authors: Zhang Yihong , Wu Sen , Liu ZiHao , Yang Yijin , Zhu Di , Chen Qian 
Published: June 2020

Abstract
With the rapid development of unmanned surface vessels (USV) applications, USV are widely used in military intelligence collection, target monitoring, and shipping services. However, this also brings security issues to shipping and the country. For more efficient detection of USV, in this paper, an enclosing center distance Intersection over Union (E-CIoU) algorithm is proposed for real-time USV detection, where the normalized distance between center points of the smallest enclosing box and target box is combined with bounding box regression. Meanwhile, we designed a new USV data set. The extensive experiments on USV data set have demonstrated that the proposed approach has achieved better convergence speed during training and a significant accuracy prediction.
Automatic Detection of Pointer Automobile Instrument Based on Machine Vision
Authors: Lin Chuanwen , Chen Gang , Gao Xiang , Liu Zhenhua 
Published: June 2020

Abstract
Automotive instrument detection is usually performed manually. Factors such as human subjectivity and external environment have a great influence and are prone to errors. This paper proposes a method for automatic detection of pointer automobile instruments based on machine vision. Firstly, the common part of multiple instrument images with different pointer angles is extracted as the background image; secondly, based on the frame difference method, binarization, morphological operation and other techniques the instrument pointer is identified, and then the intersections of the pointer extension lines are used to determine the center point; thirdly, we extract the image of the scale position and calculate the scale template array; finally, the pointer value of each input instrument image is calculated based on the scale template array. Experiments show that this method can effectively solve the shortcomings of poor applicability and poor intelligence of existing algorithms, and can identify the value of the pointer accurately.
Music Recommendation System based on Fusion Deep Learning Models
Authors: Dong Yuchen , Guo Xiaotong , Gu Yuchen 
Published: June 2020

Abstract
With the growth of user popularity of online music platforms, the amount of music data and the demands on accurate recommendation system for music also increases. Therefore, an accurate and efficient recommendation system is urgently needed. In this paper, a music recommendation system based on fusion deep learning models is proposed. Both of the audio information and lyrics data are originally exploited. A supervised training method is applied to train this model with annotated data. In this paper, the result of the test dataset shows that the proposed model can achieve more than 90% accuracy performance on average.
CapsNets algorithm
Authors: ZHang Ping , Wei Ping , Han SHuhuan 
Published: June 2020

Abstract
Capsule networks (CapsNets) is a commonly used neural network structure, especially in computer vision research., which may have a significant impact on deep learning. Convolutional neural networks (CNNs) have reached superhuman level in various computer classification tasks such as classification, parsing, object detection, semantic segmentation, and instance segmentation. Traditional deep learning tends to deteriorate when the relevance of detection objects changes. For this kind of situation, this paper improves a new network, namely the capsule network(CapsNets), the network can achieve a higher detection rate in the hostile environment.
New Mode of Distance Learning in Primary Schools in the Environment of Multimedia Computer Assisted Instruction
Authors: Gao Xiaoyan 
Published: June 2020

Abstract
with the Rapid Development of Science and Technology in China, Modern Information Technology Has Been Deepened and Popularized in China. At the Same Time, It Has Also Developed to a Certain Extent in the Classroom Teaching of Primary School Language. At the Same Time, the Education Reform Has Made Great Changes in the Teaching Methods of Education, and the Teaching Requirements for Each Stage Have Gradually Improved, Especially the Pupils Who Are Extremely Malleable. At the Same Time, Because of the Development of Science and Technology, Teachers Gradually Found Ways to Improve the Efficiency of Primary School Teaching: Traditional Education Combined with Modern Technology. the Development of Technology Has an Ever-Increasing Impact on Society. Therefore, in Primary School Teaching, Multimedia Technology and Traditional Media Teaching Are Combined.
Research on Path Planning of Robot Based on Artificial Intelligence Algorithm
Authors: Yan Cheng 
Published: June 2020

Abstract
the Task of Robot Path Planning Based on Artificial Intelligence Algorithm is to Determine an Optimal Strategy, with the Goal of Maximizing the Return. When the Robot is in a New State, It Needs to Select an Action from the Action Set and Execute It. in Order to Improve the Quality of the Algorithm, It is Necessary to Apply the Search Strategy to the Action Selection. in the Path Planning System of the Robot, One of the Most Critical Links is the Realization of Directional Reference Vehicle Scheduling. Generally, Artificial Intelligence Algorithm is the Most Widely Used in Robot Path Planning. in This Paper, an Artificial Intelligence Algorithm is Used to Optimize the Vehicle Scheduling Problem. the Path Variable of the Problem Can Be Defined Correspondingly, Which Originates from the Planning Node. the Sequence of Sending Points Reached by Completing Other Sending Requirements is Called the Legitimate Sub Path, While the Corresponding Traversal Path Does Not Include the Sequence of Several Legitimate Sub Paths of Repeated Planning Points. the Algorithm Can Significantly Improve the Efficiency of Multi-Robot System, Reduce the Number of Explorations and Speed Up the Process of Convergence.