bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dynamic Path Planning of Underwater Vehicle Based on Pulse Lidar Profiler
Authors: Chen Xinghong , Tao Li 
Published: February 2021

Abstract
In the underwater environment, the speed of light is much faster than that of sonar, so it can be used to sample and detect more intensively in space and high frequency in time. A better strategy is to integrate sonar and lidar into an integrated device. In military affairs, lidar sonar system is used for identification of underwater artificial works and fixed-point blasting. In the civil field, aquaculture, scientific research and sampling, and underwater infrastructure exploration are the main areas. This paper analyzes the application of pulse lidar profiler system integrated with multiple detection methods in the dynamic path planning of underwater vehicle. Three colors of “red, orange, red sauce” were used as the warm color light source group, and three colors of “blue, green, purple” were used as the cold color light source group. Through matrix operation, underwater objects with almost all colors can be quickly reached and superimposed. The speed of sound wave is slow, but the bypass performance is very good, which fully supplements the blind area of light source.
An Improved K Nearest Neighbor Classifier for High-Dimensional and Mixture Data
Authors: Tuerhong Gulanbaier , Wushouer Mairidan , Zhang DongMei 
Published: February 2021

Abstract
Conventional K nearest neighbor classifier is challenged by data with high-dimensional and mixture of continuous and categorical variable characteristics. To address this issue, we propose an improved K nearest neighbor classifier by using principal component mixed also called PCAmix. First, we employed the PCAmix to preprocess high-dimensional and mixture data. Then, the KNN classifier is used to classify objects characterized by high-dimensional mixture of continuous and categorical variables. We have evaluated our method using five mixture datasets that are available from the UCI machine learning repository and compared our results with the base line approaches. The experimental results showed that the proposed method had better classification performance for high-dimensional mixture data.
Data Analytics for the 3-extra Diagnosability of Bubble-sort Star Graphs
Authors: Zhao Jutao , Wang Shiying 
Published: February 2021

Abstract
The underlying topology of a large processor system is usually represented by a graph. The g -extra diagnosability of graph G requires that each fault-free component has at least (g + 1) fault-free vertices. It is difficult to prove this problem. The n -dimensional bubble-sort star graph BS has many excellent properties. We show that the 3 -extra diagnosability of BSn
is 8n − 20 under PMC and MM model for n ≥ 5 and n ≥12, respectively.
An Automatic Method of Sequential Image Processing
Authors: Kong Liangliang , Chen Lin , Wang Man 
Published: February 2021

Abstract
As the visual storage of objective things, images are usually used everywhere in the field of electronic information. Images of objects are collected or drawn to make electronic image files, which are stored into image files, and may be classified into different folders. Image users, such as accounting, HR and so on, often need to reprocess the images and save them into a word file when they use the images. Such manual processing of images is time costing and labor costing. In this paper, a method of automatically inserting and processing images is proposed and implemented. In the method, the sequence of inserting files is recorded in an excel file. Only the name of image file and the excel file need to be maintained. It is convenient to use the method to process images, and the method can be implemented automatically. So it is an effective way to help staffs to edit and insert images.
Screening of Candidate Key Genes Associated with Congenital Heart Disease Using Bioinformatics Data Analysis
Authors: Zhang Jingshu , An Xuyao , Sun Xiaohan , Yu Kun , Gong Tianxing 
Published: February 2021

Abstract
Congenital heart disease (CHD) is one of the most dangerous diseases seen in daily life. Aim of this study is to find the deep causes of congenital heart disease. The GSE35776 chip data was extracted from the Gene Expression Synthesis Database (GEO). Analyzing above data was using the R language. The enrichment pathways of differentially expressed genes were processed using the Kyoto Encyclopedia of Genes and Genomics (KEGG) and gene ontology (GO) database. Then this study uses Cytoscape and GCBI to structure protein-protein interaction (PPI) networks, gene regulation networks. 257 differentially expressed genes (DEGs) were found out, mainly focusing on cell cycle, oocyte meiosis, p53 signaling pathway and progesterone mediated oocyte maturation. By constructing gene regulation network, 12 hub genes were screened, including NUF2, BUB1, CENPI, CCNB2, SGO1, SMC4, NCAPD2, TUBB and NCAPH. We hypothesized that these 12 genes may be key factors in CHD.
Analysis and Research Based on Method of Questionnaire Survey and the Algorithm of Big Data Analysis
Authors: Xiao Fuliu , Song Bei , Liu Kun , Qu Chiwen 
Published: February 2021

Abstract
During the COVID-19 epidemic, the whole country suspended classes in schools without stopping studying. Under this circumstance, online teaching experienced a breakthrough development. This paper uses the method of questionnaire survey and the algorithm of big data analysis, and this paper explores the general situation of online teaching in local colleges and universities in southwest Guangxi which face challenges in online learning due to the relative shortage of educational resources and remote geographical location. This paper analyzes the supporting conditions, problems and shortcomings of online teaching in local colleges and universities in southwest Guangxi. Suggestions are also proposed on four aspects to improve online teaching and learning experiences, which are establishing the basis of online teaching conditions, strengthening the basic rules of online classroom management, improving teachers and students’ adaptation to online environment, and guiding students to improve independent learning ability.
Word Frequency Data Analysis in Virtual Reality Technology Industrialization
Authors: Liu Jinyu , Yang Teng 
Published: February 2021

Abstract
Virtual reality technology is increasingly in demand for economic, social and management applications, with Moore’s Law gradually failing, it’s difficult to balance the relationship between realistic needs and advanced technology by relying solely on technological progress, and even falling into the “IT black hole”, hindering the industrialisation of virtual reality technology. This paper uses word frequency analysis to extract the four most frequently occurring keywords from current policies, literature and other secondary sources, and abstracts them into four social support concepts: conceptual awareness, education, regulation and finance, to explore the inner connection between virtual reality technology and social support systems through comparison, and to realise the socialisation of virtual reality from the perspective of virtual reality. By comparing and exploring the intrinsic links between virtual reality technology and social support systems, the socialisation of virtual reality technology is realised from the perspective of virtual reality, thus realising the socialisation of technology.
Simplification Algorithm and Analysis of Random Sequence Test Packets based on Filtering Ability
Authors: Wang Zekun , Zhang Fuxi 
Published: February 2021

Abstract
A method was presented for simplifying the random sequences by means of introducing the concept of “the ability of filtering”. On the basis of explication of the relationship between each test and In-depth analysis of the role and contribution of individual tests to the overall test, the algorithm and implementation plan of simplified test package were proposed. Three procedures for filtering descending permutation tables for simplifying links and the algorithm for simulation results were discussed.
A Fuzzy Clustering Based Anomaly Node Detection Method for Publish/Subscribe Distributed Systems
Authors: Wang Defeng , Shen Zhuowei , Wu Wenjun 
Published: February 2021

Abstract
Timely and accurate understanding of the node running status can effectively control the propagation of faults in publish/subscribe distributed systems, which is of great significance to ensure the reliable operation of applications. A method of anomaly node detection based on fuzzy k-means clustering algorithm is proposed. Compared with the traditional K-means algorithm, this algorithm introduces fuzzy membership matrix to realize fuzzy clustering, and uses the idea of local reachability density to improve the selection method of cluster centers. Experimental results show that this method can effectively detect anomaly node in publish / subscribe distributed systems with higher accuracy, recall and F-measure than traditional K-means algorithm. The precision of publish anomaly detection and network anomaly detection is improved by 10.53% and 38.6% respectively.
Ship Target Image Segmentation Algorithm Based on Fuzzy Level Set
Authors: Zhang Yu , Li Yuntao , Guo Yonghui , Zhou Zheshuai 
Published: February 2021

Abstract
The effective recognition of ship target is very important for the protection of ocean rights, and the image segmentation is the precondition of the ship target recognition. To solve the two problems existing in the traditional level set graph, namely the increase of image segmentation and the increase of computation, the Fuzzy C-Means (FCM) and level set are introduced. A new algorithm for image segmentation based on fuzzy level set is proposed in this paper. The algorithm can use spatial fuzzy clustering to realize the parameter estimation and evolutionary adjustment. The algorithm is applied to optical remote sensing ship target image segmentation in complex sea environment, and the effectiveness of the proposed algorithm is verified by experiment results.
The Research of Quantitative Evaluation Algorithm of Process Auto Alarm Analysis Based on Chem-HRA
Authors: Jiang Xue , Dang Wenyi 
Published: February 2021

Abstract
For the process alarm management of chemical process safety analysis, its reliability mainly depends on the design of the safety limit and the optimization of the technical process, but there are few studies on the overall reliability level of process alarm based on the alarm disposal rate under the influence of human factor reliability. By focusing on independent layer analysis technique in the alarm and response, we combined the human reliability analysis (HRA) technology and Bayes theory, to form Human Reliability Analysis in chemical process safety(Chem-HRA), an optimized quantitative evaluation algorithm for chemical alarm failure frequency. The algorithm was applied to a risk event on tank farm’s cut over, ensuring the reliability and accuracy of petrochemical process analysis and disposal of the process alarm.
Application of Text Dimensionality Reduction Method in Information Filtering on Railway Transit Cloud Platform
Authors: Qiu Ye , Li Gang , An Junfeng 
Published: February 2021

Abstract
This paper focuses on the useful information data extraction of urban rail transportation’s urban rail cloud platform. Through the correlation and redundancy between feature items in the text vector space of a large amount of information data, a method of clustering text vector space dimensionality reduction is proposed and applied to the information data extraction of urban rail cloud platform. The vector aggregation theory and feature selection are applied to reduce the dimensionality of the feature space, so that the reduced dimensional feature items are more representative of the category. Through test experiments, it is proved that this text dimensionality reduction method algorithm is applied to the information data filtering of urban rail cloud platform, which effectively reduces the dimensionality of vector space and improves the accuracy and speed of text classification in urban rail cloud platform information data.
The Simulation Study of Multi-User Spread-Spectrum Wireless Communication System
Authors: Zhang Ling 
Published: February 2021

Abstract
Spread-spectrum technology is a promising and effective is an effective technique using for multiple accessing, combating multi-user interference and rejecting unwanted radio jamming. In this paper, we explore the effect of Direct Sequence spread-spectrum (DSSS) which base on π/4-DPSK modulation and demodulation technique, we provide a multi-user spread-spectrum wireless communication system and the system performance was studied from simulation and comparison. Four users information are transmitting over Rayleigh fading channel to model the 5G communication environments. The performances of the system are expressed through comparisons of the bit-error rate (BER) and signal to noise ratio (SNR) for both original signals at transmitter and recovered signals at receiver. From the contrast results, the anti-multi-user interference ability of the system is provided.
High Frequency Edge Information-based Sampling Algorithm for Multi-scale Block Compressive Sensing
Authors: Zhao Faqiang , Song Zejun , Li Jiayi , Zhu Binbin 
Published: February 2021

Abstract
The same sampling rate is applied to all subblocks of the same layer, which results in the deviation of the information contained in the sampling observation in the multi-scale block compressive sensing algorithm from the real information of the original image, thus affecting the accuracy of image reconstruction. For this reason, the multi-scale block compressive sensing adaptive sampling method (HEM) using HF edge information is proposed. In this method, the edge information is extracted by using the high frequency coefficients of wavelet transform, and then the edge information of the subblock of wavelet decomposition graph is calculated, and then converted into the adaptive sampling rate of each subblock. The influence of different decomposition layers and block sizes on HEM method is discussed through testing in 6 standard images. The experimental results show that the proposed algorithm can improve the PSNR of the reconstructed image at the same sampling rate on standard test images and cable trench inspection images. Among them, Stanwick image(S=0.1 and S=0.2), Russland image(S=0.3) and Stomach CT image(S=0.4) are partial exceptions.
Research on the Influence of Information Technology on Industrial Competitiveness
Authors: Wang Dong 
Published: February 2021

Abstract
Information technology, also known as information and communication technology, is mainly used to manage and process information. Information technology not only improves the operation and management efficiency of enterprises, but also gives birth to a new business model of the industry. Also, it has a profound impact on the competitiveness of national and regional industries. By analyzing the influence of information technology on four determinants and two external forces of Porter’s diamond model, this paper reveals the influence of information technology on industrial competitiveness.
The Prediction of New Medical Resources in China during COVID-19 Epidemic Period Based on Artificial Neural Network Model Optimized by Genetic Algorithm
Authors: Sun Fumin , Su Jie , Hu Yong , Zhou Ling 
Published: February 2021

Abstract
In this paper, a neural network model is constructed to predict the number of new infections, and on the basis of this model, the demand for medical resources of new infections is further predicted. Firstly, the traditional Back Propagation neural network is optimized by genetic algorithm, which solves the problem that Back Propagation neural network is easy to fall into the local optimal solution in the prediction. Taking the number of severe patients, the number of cured patients, the number of deaths, and the number of suspected patients on the first day of the new crown period as the input variables, and the number of newly diagnosed patients on the second day as the output variable, a new Back Propagation neural network is constructed Novel coronavirus pneumonia model is constructed. Secondly, according to the official documents on the new crown pneumonia research and the guide for the use of medical resources, the linear function of the infection proportion and the normal distribution function of each symptom duration are constructed. Finally, combined with the above two contents, a complete process is designed to achieve the medical treatment for the newly infected patients. Prediction of the demand for medical resources.
A Smoothness Regularized Low-Rank Completion Method for Power Grid Middle Platform Data
Authors: Wang Jijun , Xu Mingsheng , Zhou Situ 
Published: February 2021

Abstract
Currently, the construction of power middle platform plays a vitally important role in the evolution of the smart grid. However, due to sensor failures or network delays, the sampled power middle platform data is often inevitably missing. To address this challenge, in this paper, we propose a smoothness regularized low-rank completion method for power middle platform missing data. Technically, the acquired middle platform data are formed to a time-series data matrix. Then, the low-rank matrix recovery model is applied to complete the missing data. Since the middle platform data is time-continuous, we adopt a total variation term to use this piece-wise smoothness. Finally, the proposed model is efficiently solved by the distributed alternating direction method of multipliers. Experimental evaluations on a real middle platform dataset validate the performance of the proposed method.
A Typical Failure Mode and Effects Analysis for EMA Used for UAV and Guided Missile
Authors: Wu Jiufeng , Yang Tao , Cao Xinlei 
Published: February 2021

Abstract
Scout and strike UAV (Unmanned Aerial Vehicle) is one of the important products of latest UAV technology, which is becoming prevalent in modern war. The scout and strike UAV can complete scout tasks efficiently and hit the target with the guided missile accurately. And also the military use of scout and strike UAV brings low casualty. It attracts more and more attention. EMA is the actuator of the UAV and the guided missile. It controls the control surface, rotation of which determines the flight attitude, exerting heavy influence on the flight safety. Failure mode and effects analysis is an important tool for EMA (Electronic Mechanical Actuator) design, for the simple reason that it helps to assess EMA reliability, maintainability and safety. In this paper, a typical failure mode and effects analysis is conducted with EMA as the research object. EMA system definition and FMEA (Failure Mode and Effects Analysis) basic principle are presented. And then the detailed analysis is given based on some certain rules and the FMEA table illustrates the analysis. To summarize, the failure mode and effects analysis offers reference for EMA design. With it, the weak part in the design can be found and some potential risks can be eliminated in early stages of EMA design.
Research and Implementation of a Humanoid Robot Control Method Based on Action Mapping
Authors: Li Xuezhen , Zhang Yuanwen , Yang Rui , Zhang Jiayun 
Published: February 2021

Abstract
In order to reduce programming difficulty and let humanoid robot act more humanlike, we designed a robotics control method for humanoid robots. Firstly, based on human body movements collected by Kinect camera, we calculated 3D data of joint point with pinhole model, and established space model of robot and skeleton model of human. Then, we transformed 3D coordinates of human joints into joint values by space vector method, mapping to the joints of the robot and guides the robot to move. Finally, with semi-autonomous control experiment, it shows that humanoid robot action mapping strategy is feasible and effective. Compared with the existing similar research, this paper implements the action guidance control of humanoid robot, enhances the intelligent degree of robot and expands the application scene and scope of humanoid robot. Depending on the human-like appearance, humanoid robot with action mapping could provide a much more intelligent interaction method.
The Role of Colour Perception in Visual Sustainability: a Survey of Senate Building Facades in Selected Universities in Southwest, Nigeria
Authors: Oludare Obaleye J. , Adeboye Albert. B. , Ezema Isidore. C. 
Published: February 2021

Abstract
Colour perception is essential in the appreciation of the physical world. The inclusion of colour in the consideration of sustainable development of buildings is an important environmental and social factor which calls for distinguishing different colours for diverse building typology. This paper was aimed at identifying public colour preference for senate building facade in selected universities in southwest Nigeria, with a view to establish distinct colour scheme for office buildings. A survey research design was adopted with the stratified random sampling technique used in selecting respondents. Primary data were obtained through the administration of questionnaire to 577 users from ten selected universities. Photographs of 10 selected university senate building façade were attached to the questionnaire to serve as bases for assessing the buildings. Data was analysed using frequencies, percentages and mean ranking. The study revealed that façade colour is an important architectural element for visual sustainability and lighter shades of different colours were observed to be most preferred by the respondents in the study area. The study recommended the need for equipping the design professionals on the fundamentals of colour choices as an architectural design element for building façade attractiveness.
Determination of cross-sectional void fraction in a two-phase water flow through a PVC pipe
Authors: Ajesi O.H. , Latif M.B. , Gbenu S.T. , Onumejor C. A. , Fasasi M. K. , Akinpelu A. , Arijaje T. E. , Oyeyemi K. D. 
Published: February 2021

Abstract
Gamma ray transmission method for void faction measurement studies is in rare use in Nigeria, especially in industries and research. The study used this method to determine the cross-sectional void fraction of two-phase water flow through a Polyvinyl chloride (PVC) pipechannel. The gamma-ray counts transmitted through the water pipe were recorded and used to calculate the void fraction using a semi-empirical approach. Void ratio values varying from 0.171 – 0.036 were obtained for water flow rates ranging between 0.5gal/min - 4.0gal/min at estimated constant air flow rate. Conclusively, the experimental results compared well with Chisholm theoretical model predictions of void ratio applicable to horizontal flow. The use of two or more theoretical model predictions is recommended for further comparative study with experimental process. Also, more use of gamma ray transmission method should be encouraged.
Simulation and Techno-economic Analysis of a CO2 Capture Scheme for Combustion Processes
Authors: Babatunde Damilola E. , Anozie Ambrose N. , Omoleye James A. 
Published: February 2021

Abstract
Literature search has revealed that most post-combustion CO2 capture processes available to power plants are hinged on sorption technologies (chemical absorption and desorption) which are cost and energy-intensive. The colossal cost of solvents used for the absorption of CO2, the high energy requirements for the absorption & solvent recovery processes, plant design & control related complications, and environmental concerns about the solvents are some of the challenges associated with this option of post-combustion CO2 capture approach which is widely being adopted to mitigate CO2 emissions from power plants. In this study, the capture of CO2 from flue gas by exploring the concept of change of state of matter, which is essentially a physical approach to CO2capture, is presented as an alternative to the typical methods in the industry.