bluuebunny/crossref_metadata_2025_split huggingface dataset:
Computer Intelligent Evaluation Model of Jining Water Quality by Fuzzy Mathematics and Intelligent Computing
Authors: Huang Xiao , Jiang Linlong , Jin Caixing 
Published: December 2021

Abstract
This study uses fuzzy mathematics comprehensive evaluation method to evaluate the water environment quality of Jining City and summarize the spatial distribution of water pollutants. The results of the research show: (1) The pollution of surface waters in Jining City is relatively serious. The water quality of rivers is mostly Grade V. The pollution of lakes is relatively low. The overall pollution of reservoirs is relatively light. (2) The concentration distribution area of each pollutant is roughly in a “V” shape. The most polluted areas mainly occur in the Beijing-Hangzhou Canal, Old Canal and other places. The water environment quality in the eastern mountainous and hilly area is better.
Comparison of various algorithms based on TSP solving
Authors: Zhang JianChen 
Published: December 2021

Abstract
The traveling salesman problem (TSP) is a classic combinatorial optimization problem. As a hot research problem, it has been applied in many fields. Since there is currently no algorithm with good performance that can perfectly solve this problem, for the solution of the traveling salesman problem, this article first establishes a mathematical model of the traveling salesman problem, and uses four solving methods for the same case: the greedy method, Branch and bound method, dynamic programming method and genetic algorithm, analyze the applicability and accuracy of different algorithms in the same case.
Forecast of Waterway Cargo Turnover Volume Based on Genetic Algorithm to Optimize Neural Network Parameters
Authors: Ma Rong 
Published: December 2021

Abstract
The traditional BP neural network is difficult to achieve the target effect in the prediction of waterway cargo turnover. In order to improve the accuracy of waterway cargo turnover forecast, a waterway cargo turnover forecast model was created based on genetic algorithm to optimize neural network parameters. The genetic algorithm overcomes the trap that the general iterative method easily falls into, that is, the “endless loop” phenomenon that occurs when the local minimum is small, and the calculation time is small, and the robustness is high. Using genetic algorithm optimized BP neural network to predict waterway cargo turnover, and the empirical analysis of the waterway cargo turnover forecast is carried out. The results obtained show that the neural network waterway optimized by genetic algorithm has a higher accuracy than the traditional BP neural network for predicting waterway cargo turnover, and the optimization model can long-term analysis of the characteristics of waterway cargo turnover changes shows that the prediction effect is far better than traditional neural networks.
Container throughput prediction of some ports in the Yangtze River Delta Based on GM (1,1)
Authors: Liu Xinna 
Published: December 2021

Abstract
The MATLAB implementation of grey neural network prediction modeling has been widely used.[1] Firstly, this paper briefly introduces the prediction process of grey GM (1,1) model, constructs the data of container throughput of some main ports in the Yangtze River Delta from 2016 to 2020, uses the powerful scientific calculation function of MATLAB, first accumulates the correlation coefficients a and B, and obtains the corresponding prediction model; Secondly, verify the original data with residuals and relative errors, and then predict the container throughput from 2021 to 2025. According to the prediction results, it provides suggestions for the logistics development of Shanghai port, which can provide a certain theoretical basis for the government departments to formulate the future development plan of the port to a certain extent.
Research on Logistics Distribution Path Planning Based on Genetic Algorithm
Authors: Liu Shaokun 
Published: December 2021

Abstract
In this paper, SF express company Jinzhou Guta District Pinganli business point as an example, to investigate its distribution, statistical analysis of the survey results, summed up the problems in logistics and distribution. Through the systematic study of the problem, a planning model with time window and with the objective of minimizing the total cost of distribution is established. At the same time, an intelligent algorithm for distribution path optimization - Genetic Algorithm (GA) is designed. Genetic algorithm is used to design chromosome coding methods and genetic operators for solving the planning model with the objective of minimizing the total cost of distribution. Finally, the simulation experiment is carried out. MATLAB software is used to solve the distribution route and the total driving distance of vehicles, and the distribution route with the goal of minimizing the total distribution cost is obtained.
Trend Prediction of DC Measuring System Based on LSTM
Authors: Pei Ying , Niu Lin , Li Haifeng , Li Yajin , Yu Dayang 
Published: December 2021

Abstract
The accuracy of DC measurement system directly affects the reliable operation of DC control and protection system. In order to improve the estimation and prediction of DC measurement system op-eration state, a trend prediction algorithm based on multi-dimensional analysis and long-term memory network is proposed. Based on the analysis of DC measurement principle, the DC measurement status is diagnosed and abnormal is identified by time series trend analysis and anomaly detection. The LSTM is used to construct a multi factor driving current prediction model, and the model is trained and an-alyzed based on the actual operation data. Compared with the traditional time series prediction model, the results indicate that the proposed method is more accurate, simple and effective, and can be applied to the prediction of driving current.
Research and application of 3D GIS in the visualization and information management of power grid construction projects
Authors: Zhang Le 
Published: December 2021

Abstract
The research direction and application scope of transmission line 3D visualization aided design system include design data retrieval of transmission line project, preliminary path selection, feasibility study, and preliminary design. Actively explore the in-depth application of line 3D construction drawing design and digital transfer. Based on the relevant work experience of the author engaged in the application of GIS technology for many years, and taking the application of 3D GIS technology in power grid planning and design as the research object, this paper designs and implements a 3D GIS visualization system for power grid planning and design, decomposes the system function with a case, and analyzes the operation flow of the system in detail, which is believed to be beneficial to colleagues engaged in related work.
Facial expression recognition based on improved VGG convolutional neural network
Authors: Dong Cui , Wang Rongfu , Hang Yuanqin 
Published: December 2021

Abstract
With the development of artificial intelligence, facial expression recognition based on deep learning has become a current research hotspot. The article analyzes and improves the VGG16 network. First, the three fully connected layers of the original network are changed to two convolutional layers and one fully connected layer, which reduces the complexity of the network; Then change the maximum pooling in the network to local-based adaptive pooling to help the network select feature information that is more conducive to facial expression recognition, so that the network can be used on the facial expression datasets RAF-DB and SFEW. The recognition rate increased by 4.7% and 7% respectively.
A GNSS anti-spoofing algorithm based on attention scheme in synchronization network
Authors: Lv Bo , Miao Xinyu , Liu Zishan 
Published: December 2021

Abstract
A GNSS anti-spoofing algorithm based on attention scheme jointly with label smooth loss was proposed and verified under designed GNSS dataset to detect potential and complex spoofing threat in synchronization network. Moreover the experimental results verified that the proposed algorithm showed some better performance of machine learning metrics comparing to conventional ways.
Urban Dynamic Expansion Computer Simulation of RF-NH-CA Model Considering Neighborhood Heterogeneity
Authors: Li Junyi , Liu Minghao , Liu Tianlin , Jing Lei 
Published: December 2021

Abstract
“Neighborhood” as the principle of “the closer the distance, the more relevant the attributes”, is often used as a key driving factor for the urban dynamic modeling of cellular automata; however, the current implementation of the “neighborhood” idea is mostly adopted Mean probability method. This method affects the accuracy of urban dynamic simulation to a certain extent because it ignores the spatial heterogeneity of neighboring cells. Based on the random forest method to evaluate the suitability probability of land use, this study uses the intensity gradient change characteristics of the luminous data to endow the traditional neighborhood cell heterogeneity characteristics, and builds a random forest neighborhood heterogeneity CA model (Random forest Neighborhood Heterogeneity Cellular Automata, RF-NH-CA), and verified the effectiveness of the model by simulating the changes in urban land use in the 21 districts of Chongqing’s main city from 2010 to 2017 through a multi-scheme comparative experiment. The results showed that the overall simulation accuracy of the RF-NH-CA model reached 97.59%, and the Kappa coefficient reached 0.7434; compared with the traditional models RF-CA, ANN-CA and Logistic-CA, FoM increased by 0.0274,0.0383,0.0579, respectively. The Kappa coefficient increased by 0.0162,0.0229,0.0351 respectively. Studies have shown that giving the neighborhood cell heterogeneity through luminous data has played a role in improving the accuracy of land use simulation, which is more in line with the real urban expansion.
Campus Computer Network Construction Simulation and Cyber Security Technology
Authors: Yu Bohan 
Published: December 2021

Abstract
With the rapid development of the network age, the network has become an indispensable part of our daily life and work. Computer network is also an indispensable part in campus. How to build a suitable computer network according to the campus environment has become an important problem. Then, while improving the level of network security management, it helps the school carry out its work smoothly. Therefore, this paper expounds the construction scheme of campus computer network, so as to help the campus computer network more perfect.
Research on Computer Modeling of Fractional Differential Equation Applied Mathematics
Authors: Su Linlin 
Published: December 2021

Abstract
This paper qualitatively analyzes the stability of the equilibrium solution of a class of fractional chaotic financial systems and the conditions for the occurrence of Hopf bifurcation, and uses the Adams-Bashford-Melton predictive-correction finite difference method to pass the analysis Bifurcation diagrams, phase diagrams, and time series diagrams are used to simulate the complex evolution behavior of the system.
Application of SAS Algorithm in Responsive Submarine-searching Path Planning
Authors: Han Qiang , Li Lin , Lv Xinyi , Hao Dongsheng 
Published: December 2021

Abstract
Whether the planned path is optimal or not is an important condition for the completion of the responsive submarine-searching task, which depends on the applicability of the planning algorithm. According to the requirements of responsive submarine-searching mission of the anti-submarine aircraft, SAS algorithm used in responsive submarine-searching path planning is analysed in this paper, the relevant constraints are given, and the simulation calculation is carried out by using SAS algorithm. The results show that SAS algorithm has excellent performance in operation speed and planning effect, and it can better meet the training requirements.
Research on Stereo Library Based on Homogeneous Markov Chain
Authors: Sun Shunkai , Li Jie , Xu Qi , Lu Haihua , Cui Haobo , Shen Xue , Chi Yingxiao , Liu Jingjie 
Published: December 2021

Abstract
The intelligent inventory has not yet been implemented in the automated three-dimensional library. The warehouse basically relies on the accounts of the warehouse. It is really necessary to check the pallets manually with the help of a stacker, or venture into the aisle to check on the spot, or install radio frequency identification (RFID) on the pallet. The radio frequency recognition device on the stacker’s loading platform obtains the information of a specific pallet to achieve a physical inventory. After investigation, the results that can pass the three-dimensional library system automatic library are still blank. For the three-dimensional library, the intelligent inventory is of great significance to the actual production. It can automatically identify whether the goods in the designated cargo space are consistent with the storage according to the needs of the warehouse manager, so the demand for the intelligent inventory is very urgent.
Study of BP neural network based on improved particle swarm algorithm
Authors: Ma Xiaoqian , Li Liyuan 
Published: December 2021

Abstract
This paper uses first-order difference to transform non-smooth data into smooth time series data, determines the p and q parameters in the model by judging the trailing and truncated nature of ACF, PACF, and finally establishes the ARIMA model after ACI, BCI detection. According to the parameters of the neural network randomly selected similar to the initial spatial position of the particles in the particle swarm algorithm, the improved particle swarm algorithm is used instead of the gradient correction method to precisely adjust the parameters and establish the BP neural network, which improves the robustness and accuracy of the prediction model.
Design and Research of Non-contact Temperature Measurement and Identity Recognition System
Authors: Zhou Zhaowei , Ruan Taiqi , Wang Tuyang 
Published: December 2021

Abstract
With the rapid spread of the new crown epidemic, prevention of the epidemic has become an important task in daily life. As of now, the cumulative number of confirmed cases in the world has exceeded 150 million, and the epidemic prevention situation is very serious. In the face of the spread of the epidemic, effective temperature measurement, mask recognition, and face recognition are necessary for mobile personnel. Traditional methods such as the deployment of Raspberry Pi, windows platform, etc. can better meet the above requirements, but there are disadvantages such as low number of recognition frames, low accuracy, and bulky equipment. Due to the large market demand, the cost of the chip has also risen. If the above-mentioned functions are deployed according to the traditional method, the cost is relatively high, which increases the economic pressure on small and medium-sized enterprises. The main work of this research is to solve related functions through a processor Kendryte K210 based on the open source instruction set RISC-V. So as to realize a portable and low-cost non-contact temperature measuring device. The paper introduces a low-cost solution based on K210 on the basis of comprehensive discussion of multi-party implementation methods. Based on this processor, the face recognition and mask recognition functions based on YOLOv2 training are deployed through its machine vision and machine hearing capabilities. The temperature is measured by an external MLX90614 sensor; the serial port screen and the voice module JQ8400 are used for user interaction, which meets the guidance of users to the greatest extent. In the design of the hardware circuit, a lithium battery charging and boosting mobile power board based on MH-CD42 is designed. The user can supply power through an external lithium battery to realize the portability of the system.
Research and analysis of music development based on k-means and PCA algorithm
Authors: Cai Zimo , Fu Luqi , Li Wenchao 
Published: December 2021

Abstract
The purpose of this article is to establish an algorithm model that can measure the influence of music, capture the evaluation index reflecting the influence of music, and extend the model to other fields such as politics, culture, and society. We have established a music influence-oriented network algorithm model based on influencers and followers, where each artist is a node, and each follower is a connection between artists. We define relative interaction strength indicators to help understand the entire network algorithm. In addition, we also used time, genre and other scales to further optimize the network algorithm. We first use the PCA algorithm to determine indicators that reflect music similarity, such as vitality, activity, popularity, overall loudness, etc. On this basis, an evaluation algorithm model based on cosine similarity is established to calculate music similarity values of different genres. In addition, we use the K-MEANS algorithm to normalize each feature index and sum its variance. Finally, we noticed that the similarity of artists within genres is higher than the similarity of artists between genres. We further analyzed the differences and influences within and between genres. Taking time as a distinction, a relative heat map of the interactive influence of genres is drawn. It is understood that certain genres will obviously have a certain influence over time. We summarize this model as an impact correlation analysis model. First, we choose a representative influencer. Then, based on the cosine similarity, we obtained the music similarity with the fans in batches, thus more intuitively concluded that the Internet celebrities did affect the respective artists. In addition, we combined the calculation of SPSS variance and selected different indicators to visualize the radar chart to understand the attractiveness differences of certain music features. We first select the musical characteristics with obvious changing trends, then locate the position of the changer in the music evolution process through the time distribution diagram of the corresponding work, and finally select the representative changer. We analyzed the change history of each indicator in the selected genre over time, and finally got the global directed network diagram. Based on the network algorithm model established in the previous question, we analyzed the background of the times and found that there is an interaction between music and the cultural environment. Finally, we also analyzed the advantages and disadvantages of the algorithm model, and discussed the application of the method in other fields.
Numerical Simulated and Mechanism Research on Fine Particle of Pigsty Removal in the Coupling Effection of Acoustic Wave and Electric Field
Authors: Hu Jie , Huang Yangyang , Li Bo 
Published: December 2021

Abstract
For improving the collection efficiency of electrostatic precipitator (EP) in the pigsty, MATLAB & Finite element method (FEM) is applied to calculate RNG k-ε equations, corona equation and acoustic radiation equations to analyze sound field, electric field and particle trajectory in the wire-plate EP. The results show that fine particle agglomeration can be induced, so as to promote the enlargement of fine particles and improve the capture efficiency of fine particles at acoustic node area. Moreover, the particle distribution can be controlled according to the above V-η curve in the pigsty. In addition, η initially increases, and then decreases in the range of f from 1000 Hz to 2000 Hz. Therefore, the linear variation law of V– η & SPL – η & f – η provide scientific control method for the construction of green and efficient removal technology of particulate pollutants in the piggery.
Modified Chirp Scaling Algorithm for Ultra-High Resolution Spaceborne-SAR
Authors: He Tao , Wang Pengbo , Ma Jixiang , Zhou Xinkai , Xue Lingling 
Published: December 2021

Abstract
The hyperbolic range equation model (HREM) and equivalent squint range model (ESRM) are applied in traditional chirp scaling algorithm (CSA). However, these range models cannot describe the satellite range history in the high-resolution case accurately because of the long azimuth integration time. The non-negligible phase error caused by this will lead the targets distort. In this paper, a modified chirp scaling algorithm (MCSA) is proposed by introducing a novel high-precision range model. A more accurate signal spectrum is calculated through it. Then, the modified chirp scaling factor, range compression filter, range cell migration correction (RCMC) filter and azimuth compression filter can be derived based on this signal spectrum, and the focused target obtained at last. Finally, the experimental results, to validate the proposed algorithm, adopted by the sliding spotlight synthetic aperture radar (SAR) simulation are provided.
Azimuth Ambiguity Suppression in SAR Images Based on VS-KSVD Dictionary Learning and Compressive Sensing
Authors: Hu Xinchang , Wang Pengbo , Guo Yanan , Han Qian , Zhou Xinkai 
Published: December 2021

Abstract
The azimuth ambiguities appear widely in Synthetic Aperture Radar (SAR) images, which causes a large number of false targets and seriously affect the quality of image interpretation. Due to under-sampling in Doppler domain, ambiguous energy is mixed with energy from the main zone in the time and frequency domains. In order to effectively suppress the ambiguous energy in SAR images without loss of resolution, this paper presents a novel method of KSVD dictionary learning based on variance statistics (VS-KSVD) and compressed sensing (CS) reconstruction. According to the statistical characteristics of distributed targets, the dictionary that represents the ambiguities is selected and suppressed by coefficient weighting, in which local window filtering is carried out to remove the block effect and optimize the edge information. Finally, the high resolution images with low-ambiguity can be reconstructed by CS. With the proposed approach, the feasibility and effectiveness of the proposed approach is validated by using satellite data and simulation in suppressing azimuth ambiguity.
A 3-D SAR Imaging Method Based on Back Projection Algorithm
Authors: Han Qian , Wang Pengbo , Zhou Xinkai , Hu Xinchang , Guo Yanan 
Published: December 2021

Abstract
3D back projection (BP) algorithm is an imaging algorithm based on time domain echo data, which effectively solves the overlapping mask problem existing in 2D SAR. It can complete the imaging processing of echo signal under any geometry configuration, and has the advantages of high target focusing accuracy and high phase preservation. However, the high complexity and low efficiency of 3D BP imaging algorithm limit its application and development. In this paper, a 3d imaging method based on improved back projection algorithm is proposed. Aiming at the problem that existing imaging algorithms need 2D imaging first and then 3D imaging, an improved 3D BP algorithm is proposed to directly 3D imaging, which avoids 2d imaging processing. The proposed method simplifies the steps of the traditional 3D BP algorithm and improves the efficiency of the algorithm. The validity and effectiveness of the proposed method are verified by the 3d imaging results of simulated lattice targets.