bluuebunny/crossref_metadata_2025_split huggingface dataset:
Heterogeneity of problem-based learning outcomes for improving mathematical competence: A systematic literature review
Authors: Juandi D 
Published: January 2021

Abstract
This study aims to analyze the impact of the application of Problem Based Learning (PBL) on improving the mathematical competence of elementary, junior high and high school students. The method used is a Systematic Literature Review (SLR). The sample consisted of 117 experimental research results on the effect of PBL on improving mathematical understanding, problem-solving, creative thinking skills, or mathematical literacy. The research was conducted in all related classes. Samples were taken from indexed journals published in the 2010-2020 period. The key question of this research is how to describe the effect of PBL on students’ mathematical competence, in terms of year of study, study level, duration of experiment, and sample size. Through SLR method it was found that there was an increase in the number of studies over ten years. Based on the study level research on junior high school students was more dominant, based on the duration of experiment, more research was conducted in junior high schools, and based on the sample size research was mostly conducted in classes with 30 or more subjects.
Deep Attentional Factorization Machines Learning Approach for Driving Safety Risk Prediction
Authors: Zhang Jun , Wu Zhongcheng , Li Fang , Li Wenjing , Ren Tingting , Li Wei , Chen Jie 
Published: January 2021

Abstract
The data of Internet of Vehicles (IoV) can be used to evaluate the driving safety risk of auto insurance policyholder and provide technical means for Usage Based Insurance (UBI). There are many types of IoV data, such as continuous or ordinal, categorical, binary etc., which contain highly sparse and dimensional features after One-Hot processing, thus they learning the interaction between critical features and training predictive model difficult. Furthermore, some of the available data have been desensitized, so it is impossible to perform feature engineering based on experience. We propose an end-to-end deep learning framework named Deep Attentional Factorization Machine (DeepAFM), which combines the power of attentional factorization machine with deep learning for feature learning in a new neural network architecture. Compared with existing deep learning models, our approach can learn the weighted interactions between various features effectively by introducing the structure of feature fields without feature engineering. Experimental results showed that our model yields excellent results in real-world data.
Implicit-Discrete-Maximum-Principle-Based Production Optimization in Reservoir Development
Authors: Zeng Yijin , Nie Yandong , Ding Shidong 
Published: January 2021

Abstract
Cost-effective development of proved oil resources is the goal of oil and gas field development workers. One of the ways to achieve this is to make the reservoir development in an optimal state based on the existing production conditions, which is also one of the core contents of intelligent oilfield. This paper proposes a new production optimization control method, which aims to maximize the net present value of reservoir development and production. By solving the mathematical model of development and production, the input and output control parameters of the reservoir are optimized in real time to obtain the optimal production plan. The conventional maximum principle method requires two time series solution calculations. In this paper, the coefficient matrix of the full implicit simulator is used to directly obtain the required adjoint equations. The efficient combination of simulation calculation and gradient solution of discrete maximum principle greatly improves the solution efficiency of the model and saves the calculation time. The reservoir examples are analyzed through theoretical research. The results show that the optimal production scheme is in line with the actual situation of the oilfield, which provides theoretical and technical support for the intelligent oilfield system.
Influence of 80-mesh Traditional Chinese Medicine Particle Stack Thickness on Percolation Performance
Authors: Li Juan , Wu Jun , Wang Xiaochun 
Published: January 2021

Abstract
In order to research the influence of 80-mesh traditional Chinese medicine particle characteristics and particle stack thickness on percolation performance, wire screen was used to carry out filtration by the vibratory action of oscillator, screen No. and screen pore size were recorded and weight of screen residue was measured, so as to calculate average distribution density of particles, distribution function of particle size, form factor and specific surface area of particles. Percolation pressure drop was utilized as the index to evaluate percolation performance. Ethanol solution at 20°C with volumetric concentration of 47% was selected as percolation solution to measure and calculate the percolation pressure drop when ethanol solution flowed through 100mm, 150mm, 180mm, 250mm and 300mm 80-mesh particle layer. The results showed that percolation pressure drop is proportional to particle stack thickness, and it is high relatively due to the small size of 80-mesh particle, which shall result in high energy consumption of percolation system.
Assessment model for the Stability Situation of Crowd Evacuation based on Thermodynamic Map
Authors: Zhao Rongyong , Liu Qiong , Li Cuiling , Dong Daheng , Hu Qianshan , Ma Yunlong 
Published: January 2021

Abstract
To solve the shortcomings of the conventional assessment methods for crowd evacuation stability, such as over-reliance on the experience of the judges, obvious subjectivity and time-space limitations, this paper proposes an assessment model for judgment on the stability situation of crowd evacuation. Furthermore, the scenarios of large-scale crowd evacuation is diverse, and the compositions of crowd, the causes of crowd trampling are different. So it is particularly important to analyze specific problems for different scenarios. However, different data acquisition and poor real-time performance are main practical problems of judgment on the stability situation of large-scale crowd evacuation. With regard to shortcomings and problems above, the assessment model proposed in this paper uses big data technology to figure out a real-time crowd distribution thermodynamic map and obtain the crowd distribution density data. According to the criterion of crowd stability proposed by the Green Guide in Britain, the assessment model is further constructed to judge the stability situation of large-scale crowd evacuation in real-time.
A Technique of Time Synchronization in Pseudolite System Based on Single-difference Method
Authors: Liu Haiqing , Zhao Li , Huang Xing , Tang Qiaoxiao 
Published: January 2021

Abstract
Aiming at the time synchronization problem of pure pseudolite system in high precision positioning, a technique of time synchronization in pseudolite system based on single-difference method is proposed. The reference receiver with dual-antenna and the carrier phase observation equation are used and phase integer ambiguity is adjust-ed by the least-squares ambiguity decorrelation adjustment (LAMBDA) method. High precision synchronization of the system clock can be realized through the clock error broadcast or centralized processing. Both theoretical derivation and simulation show that the system is able to provide high accuracy time synchronization and the precision of synchronization can reach 0.02 ns.
Design and Implementation of Indoor Positioning Performance Test System for IoT Intelligent Communication Terminal
Authors: Xu Jie , Miao Yi , Zheng Xiaonan 
Published: January 2021

Abstract
Indoor positioning technique is the foundation of Internet of Things (IoT) and location big data, which provides IoT function and location data for retail, manufacturing, energy, medical, emergency, logistics, robotics and other industries. Indoor positioning performance is an important performance indicator of IoT intelligent communication terminals. In order to accurately evaluate the indoor positioning performance of IoT intelligent communication terminals, this paper constructs an indoor positioning performance test system for IoT intelligent communication terminals. The test results show that the uncertainty of horizontal positioning accuracy of Wi-Fi positioning and Bluetooth positioning can be limited within 0.32 m (k=2).
Research on Practical Wireless Sensor Network Query Processing Technology
Authors: Guo Changjian , Wu Xiaoxuan 
Published: January 2021

Abstract
WSN(Wireless Sensor Network) is intended for obtaining sensory data so that the query processing technology of high efficiency is the precondition of widely used WSN. Existing query processing technology assumes that the WSN deployed in two-dimensional or three-dimensional free space are not suitable for those deployed in such limited circumstances as roads, pipelines and indoor environments. This paper explores the problems of existing query processing technology, does research on perception data query processing method and technology, and seeks out the existing problem and its solution by means of using the WSN actually deployed in a building as an experimental platform.
Water quality evaluation of Xiaoshan water quality station in eastern Zhejiang Water Diversion Project Based on BP network
Authors: Xu Xiaofeng , Guo Huifang , Fan Jianfen 
Published: January 2021

Abstract
East Zhejiang water diversion project is a major water resources allocation project to ensure the sustainable economic and social development of xiaoshaoningzhou area in eastern Zhejiang. The project task is to introduce Qiantang River water to provide domestic, industrial and agricultural irrigation water to xiaoshaoning plain and Zhoushan City, and to improve water environment. Its head project is Xiaoshan hub project. In order to ensure the safety of water quality along the water diversion line, it is very important to strengthen the monitoring and evaluation of water quality at the source of eastern Zhejiang. In this paper, BP network algorithm based on error back propagation is used to evaluate the water quality station of Xiaoshan junction. The evaluation results show that most of the water quality in Xiaoshan water diversion project is class II water and class III water.
Research on optimization of distribution route for cold chain logistics cooperative distribution of fresh e-commerce based on price discount
Authors: Wang Xinxin , Cao Wujun 
Published: January 2021

Abstract
In order to solve the problem that the development of self-built logistics cannot meet the development of fresh e-commerce, a coordinated distribution strategy of self-built logistics and third-party logistics is proposed. The tasks assigned by third-party logistics are used to determine the price discount ratio. Third-party distribution outsourcing cost function, customer satisfaction function, and freshness function of fresh produce. Aiming at the goal of minimum delivery cost, maximum customer satisfaction, and maximum freshness of fresh produce, a collaborative delivery optimization model was established, and a corresponding genetic algorithm was designed with examples. Simulation results show that: collaborative distribution can effectively reduce the cost of distribution, when the proportion of price discount increases, the cost of collaborative distribution decreases. It has a certain reference value for the optimization of fresh e-commerce enterprises’ distribution mode.
Research on Innovation of Urban Combat Equipment Support Model Based on Smart City and Artificial Intelligence
Authors: Yang Lili , Liu Zichang , Zhang Suoliang , Yue Qiangbin , Li Feng 
Published: January 2021

Abstract
With the remarkable achievements of smart city construction and the evolution of war form to intelligence, the future urban operations will be intelligent operations supported by smart city. This paper analyses the requirements of artificial intelligence on urban operations equipment support, and clarifies the supporting condition provided by smart city construction on equipment support from the equipment status perception, equipment support command and control, equipment supply support and equipment maintenance support four aspects, focusing on winning urban operations under intelligent warfare. On this basis, a new support model of urban operations equipment support is proposed.
Risk assessment method for IoT software supply chain vulnerabilities
Authors: Zhu Zhicheng , Lan Kun , Rao Zhihong , Zhang Yuguang 
Published: January 2021

Abstract
With the development of IoT technology, the number of attacks against IoT software chain vulnerabilities is greater than ever, and a reasonable vulnerability assessment system needs to be established for research. The Common Vulnerability Scoring System (CVSS) is a free, public risk assessment system used by information security vendors to assess the severity of vulnerability threats. However, CVSS is insufficient because of the strong subjectivity in the selection of measurement standards and the allocation of evaluation index weights. Based on this reason, this paper designs and proposes a more objective risk assessment method for IoT software chain vulnerabilities, and verifies the feasibility and effectiveness of the method through experiments.
Design and Implementation of Software Bus Based on Object Model Operating System
Authors: Zhou RuiLiang , Tao Yongchao , Piao Songhao 
Published: January 2021

Abstract
At present, the commercial operating system VxWorks is the first choice of leading international intelligent equipment manufacturers. They implement their own equipment controller based on VxWorks. However, VxWorks is not specifically designed for the control field, so there are many deficiencies in the control field, such as: using a dynamic model, dynamically calling various resources at runtime, making the system less predictable and real-time. Aiming at the typical application scenarios of the object operating system, this paper gives the overall software architecture of the object bus. The designed object bus provides a transparent mechanism for communication between components. In view of the highly dynamic adjustment of the objects in the object bus, this article has designed a corresponding set of migration mechanisms for the object bus to solve the various negative effects of cache invalidation after dynamic adjustment. In the last part of the article, the functional verification of the implemented object bus is carried out, and the analysis and summary are carried out.
The design of embedded Operating System for vehicle Internet of Things
Authors: Cheng Luwei , Tao Yongchao , Piao Songhao 
Published: January 2021

Abstract
This article distinguishes the difference between dynamic operating system and static operating system from the design goal, and discusses the resulting difference in the technology used in system implementation. With this as the theoretical basis, according to the ISO17356 OS specification, using hardware abstraction layer design ideas, a simple and efficient static real-time operating system is implemented on the ARM platform. This paper adopts the idea of separating mechanism and strategy, and uses internal resources as a mechanism to implement different scheduling strategies; a task scheduling algorithm based on priority grouping is proposed, which achieves good performance in time and space.
Comparative analysis of three distribution entropy methods for chaos recognition
Authors: Zeng Xiangjian , Wan Li , Liu Hui 
Published: January 2021

Abstract
Distribution entropy (DistEn) is an effective index to measure the complexity of time series. In this paper, the moving distribution entropy (M-DistEn) method is proposed by combining the distribution entropy method with the moving window technology. The moving cut data-distribution entropy (MC-DistEn) method is proposed by combining the DistEn with the moving removal window. Based on the M-DistEn method, moving weighted distribution entropy (MW-DistEn) is proposed. These three distribution entropy methods are used to identify the chaotic state of mixed Logistic map sequences with different parameters. The results show that the M-DistEn method only recognizes the first three mixed states of the sequence, and it cannot accurately recognize the last two chaotic states of the sequence, so it has certain limitations. The MC-DistEn method can accurately identify the four different chaotic states of the sequence, but the DistEn value greatly fluctuates due to the influence of the size of the moving removal window, and the position judgment of sequence state change is not accurate enough. The MW-DistEn method not only accurately recognizes different chaotic states, but also is more accurate in judging the position of sequence state changes and more stable in DistEn value than the M-DistEn method and MC-DistEn method, thereby it has a good application prospect.
Research on main transformer defect detection methods based on Conditional Inference Tree and AdaBoost Algorithm
Authors: Zhang Chenmeng , Hu Can , Zhang Zongxi , Cao Shuping 
Published: January 2021

Abstract
With the development of data science, there are more and more ways to dig for patterns hidden in the data. If we can apply the advanced model of data science to the power data, we can mine the potential value of power data. In this paper, the monitoring and inspection data of 110kV main transformers and related basic environmental data are fused to establish conditional inference tree model and AdaBoost algorithm to evaluate whether the 110kV main transformers have defects and faults. The accuracy of the two algorithms is compared. Finally, we select the AdaBoost algorithm with higher accuracy for building the 110kV main transformer fault evaluating model. This paper provides a reference for the automatic detection of power grid faults, and provides new ideas for the application of power grid big data.
Comparative Study on Defects and Faults Detection of Main Transformer Based on Logistic Regression and Naive Bayes Algorithm
Authors: Hu Can , Zhang Chenmeng , Zhang Zongxi , Xie Shijun 
Published: January 2021

Abstract
The proper work of main transformers plays an important role in the stability of the power grid. The development of power data allows us to evaluate the instant status of the main transformer. If we use appropriate data classification algorithm, the laws and values hidden behind the mass power data can be discovered. This paper took basic power data, main transformers’ status data and oil chromatography data into consideration, and used Logistic regression and Naive Bayes algorithm to evaluate 110kV main transformers’ defects and faults from regression analysis and probability statistics seperately. The results of these two algorithms were obtained and compared. Finally, a more accurate algorithm of evaluating main transformer fault was selected, which provided a new reference for the application of power big data.
Charging path planning algorithm based on multi-parameters in underwater wireless rechargeable sensor network
Authors: Zhang Shaojuan , Huang Xianqing , Du Zehua 
Published: January 2021

Abstract
UWSN (Underwater wireless sensor network) is a significant part of marine environment stereo monitoring system, and the energy problem has always been an important constraint and challenge that limits its wide application. Due to the development of wireless charging technology and intelligent mobile nodes, the energy problem in UWSN has been solved. The underwater sensor network using wireless charging method is called UWRSN (underwater wireless rechargeable sensor network). In wireless rechargeable sensor networks, one of the core issues is the design of the charging planning. In this paper, a charging path planning algorithm based on multi-parameters is proposed for the on-demand charging system of UWRSN. The algorithm plans the charging path for the underwater MC (mobile charger) according to the parameters such as the remaining power, position, and power consumption speed of the underwater sensor node, so as to minimize the number of network dead nodes, improve network energy efficiency, and extend life of the network. Simulation results show that the algorithm has good performance in terms of charging efficiency, extending network life and other performance indicators.
Research on Real-time Scheduling Algorithm of Partition Operating System in Single Core Environment
Authors: Tao Yongchao , Song Qilong , Piao Songhao 
Published: January 2021

Abstract
In an embedded environment, applications have a strong demand for real-time scheduling, but it is difficult to ensure the real-time scheduling performance under a partitioned operating system, mainly because in a multi-operating system environment. It is difficult to determine the real-time schedulability within and between partitions. The research of this paper will focus on the real-time scheduling algorithm of partition operating system with simple task model in single-core environment.
Application of improved algorithm based on sphere OBB hybrid hierarchical bounding box in Teaching
Authors: Zhao Wei , Qu Huiyan 
Published: January 2021

Abstract
In this paper, on the premise of analyzing the theoretical knowledge of collision detection algorithm, the advantages and disadvantages of several commonly used hierarchical bounding box methods are compared and analyzed, and then the traditional collision detection algorithm is studied. This paper presents a new collision detection algorithm. After preprocessing, the first order collision detection based on the separation axis theorem is used to exclude a large number of disjoint spatial model objects through preprocessing. The purpose of adding primary collision detection is to further investigate disjoint object pairs, and prepare for the final detailed detection stage. The pre-determined OBB bounding box is used for fast collision detection, and its storage structure and mode are optimized quickly. After preprocessing, it effectively eliminates the obviously disjoint space object pairs, reduces a large number of operations, and improves the detection efficiency.
Machine Learning Regression Algorithm Predicts Multi-component Crystal Configuration Energy
Authors: Wang Peng , Mei Jinshuo , Lang Yingjie , Li Shu 
Published: January 2021

Abstract
Some machine learning algorithm tools, such as neural networks and Gaussian process regression, are increasingly being applied to the exploration of materials. Here, we have developed a form to use this nonlinear interpolation tool to describe properties that depend on the degrees of freedom in multi-component solids. A symmetrically adapted clustering function is used to distinguish different atomic order degrees. These features are used as the input of neural networks, Gaussian process regression and other algorithmic models, and some inherent properties of materials, such as formation energy, can be reproduced by the trained machine algorithm model. We use this technique to reproduce the expansion Hamiltonian of a synthetic cluster with multi-body interaction, and calculate the formation energy of ZrO based on first principles. The form proposed in this paper and the results shown that complex multi-body interactions can be approximated by nonlinear models involving smaller clusters. The training models used in this paper to predict energy include neural networks, Gaussian process regression, random forests, and support vectors regression, using MSE and coefficient of determination to evaluate the prediction results, and adding genetic algorithms in the feature selection process can remove some redundant features and improve the prediction efficiency and accuracy. The results show that the neural network is the best algorithm model which selected in this article, the prediction effect of support vector regression is relatively inferior.