bluuebunny/crossref_metadata_2025 huggingface dataset:
Characteristic polynomial and eigenvalues of antiadjacency matrix of directed unicyclic flower vase graph
Authors: Aji Bayu H. , Sugeng Kiki A. , Aminah Siti
Published: January 2021

This research discussed the characteristic polynomial and eigenvalues of antiadjacency matrix of directed unicyclic flower vase graph. The entries of the antiadjacency matrix of a directed graph represent the presence or the absence of a directed arc from one vertex to the others. If A is the adjacency matrix of a graph

G

→

, then the antiadjacency matrix B of graph

G

→

is B = J − A, where J is the square matrix with all entries equal to one. The general form of characteristic polynomial coefficients of the antiadjacency matrix of directed unicyclic flower vase graph can be obtained by calculating the sum of determinants of the antiadjacency matrices of all induced cyclic and acyclic subgraphs, while the eigenvalues were obtained by using polynomial factorization and Horner’s method. This paper gives the characteristic polynomial coefficients and eigenvalues of antiadjacency matrix of directed unicyclic flower vase graph. The characteristic polynomial can be considered as a function that depends on the number of vertices.
Determining the premium of paddy insurance using the extreme value theory method and the operational value at risk approach
Authors: Riaman  , Sukono  , Supian S , Ismail N
Published: January 2021

Paddy farming is a source of livelihood for most rural communities in Indonesia. Indonesia as an agrarian country with a tropical climate, where the sun shines all the time and farmers can grow crops throughout the season. However, changes in air temperature, weather, and annual rainfall which sometimes change uncertainly cause changes in cropping patterns, which are caused by minimal water supplies in the dry season and flooding in the rainy season. This uncertainty will certainly increase the risk of crop failure. In addition to the risk of weather, other disturbances from plant pests and plant pests also cause the risk of crop failure, which results in greater losses to farmers. One way to transfer this risk is through a rice agricultural insurance program. This study aims to calculate the premium price using the Extreme Value Theory (EVT) method with the Operational Value at Risk (OpVaR) approach based on weather, pests and plant diseases. In this study, the data used are weather data Pests and plant diseases disturbance in West Java in 2009-2019. The stages in this research, the first step is to determine the estimated threshold value to obtain extreme data, then estimate the parameters using the likelihood method. Data suitability test with Generalized Pareto Distribution (GPD) was performed using the QQplot test. Risk is determined by the Operational Value at Risk (OpVaR) approach, the results of which are used to calculate the appropriate premium for paddy agricultural insurance.
Centrality measures in transportation networks for Unpad campus route
Authors: Napitupulu H , Carnia E , Anggriani N , Supriatna A. K.
Published: January 2021

Centrality measurement still becomes an interesting research topic recently, including its relation to transportation’s effectiveness and efficiency. A transportation network can be expressed in the form of a graph so that even a level of centrality for each vertex of a complex transportation network can be simply analyzed. There have been various developments/modifications of methods on centrality measures in transportation networks. The offered methods show the different points of view regarding which vertices are most important in the transport network. In this research, the study of some centrality measures in the transportation network is discussed. A numerical experiment of some centrality measures implemented to Universitas Padjadjaran (Unpad) campus route as a network is given. The comparisons are done to explore the performance of each method. The calculation is done using Python software with NetworkX library. The results for Unpad campus route show that some vertices (locations) are more prior than another.
Application of GARCH model in the price inflation of foodstuff in West Java
Authors: Nahar J , Hertini E , Supriatna A K
Published: January 2021

The risk and uncertainty faced by consumers and producers due to fluctuations in food prices in recent years have continued to increase. One of them is the fluctuation in the price of foodstuffs which are basic human needs. The process of increasing prices in general and continuously related to the market mechanism which can be caused by various factors is called inflation. Inflation can be measured by calculating the change in the percentage rate of change in a price index. In this study, the index used is the Consumer Price Index (CPI), which is an index that measures the average price of certain goods purchased by consumers. The food price index tends to have high volatility, this can be seen from the sharp increase and decrease in the price of food commodities in Indonesia. Data that has high volatility will be very risky if you use the basic time series method which has stationary data and costly variance, for that we need a method to deal with data that has non-constant and changing variance, or is called heteroscedasticity. Therefore in this study using the GARCH model application to analyze data on foodstuff price inflation in West Java. The results showed that the best GARCH model for foodstuff price inflation data is the ARCH (1) model, which means that the variance of foodstuff price inflation in period t is determined by a constant (1.565) and the remaining square in the previous period with a proportion of 74.41. %.
Dynamic modelling of covid-19 and the use of “Merah Putih” vaccination and herbal medicine treatment as optimal control strategies in Semarang city Indonesia
Authors: Mahardika D , Tjahjana R H , Sunarsih HJ
Published: January 2021

The Corona virus is a disease that threatens human population on earth in early 2020, the first infection began in China, is thought to be a virus that host in mammals and then able transmits into human body, this present paper will describe dynamic system of Covid-19, analyzing direction of the equilibrium, Disease Free Equilibrium (DFE) and Endemic Equilibrium (EE), which can be done by determining dimensionless number of Basic Reproduction Number (R
0). And then we put vaccine and treatment as control variables to reduce the infected population, we use the vaccinations and treatments recommended by the Indonesian government. Furthermore, we use Pontryagin Minimum Principle to find optimal solution of the control. Optimal control method in this paper is fixed time and fixed end point, means that we are determined the duration of the control and set goal for reaching the final state point. This optimal control will aim to minimize the number of infected population and values (cost) of control measures. Numerical calculations also performed to illustrate the graph.
Comparative study of pitch detection algorithm to detect traditional Balinese music tones with various raw materials
Authors: Brata I P B W , Darmawan I D M B A
Published: January 2021

The characteristics of a tone are formed from the fundamental frequency values that make up the tone. A musical instrument’s tone has different fundamental values in building the timbre of the instrument’s tone. This study compares four PDA (Pitch Detection Algorithm) methods and tries to find the best algorithm for detecting Balinese instruments’ tone with different raw materials, namely leather, metal, and bamboo. The PDA method analyzed was the Autocorrelation, ZCR, HPS, and FFT methods. This study also explores the performance of each PDA (Pitch Detection Algorithm) approach in checking the pitch of traditional Balinese musical instruments and focuses on which of the four PDA (Pitch Detection Algorithm) methods detect Balinese musical instruments by comparing the results of the pitch analysis of each line with the original frequency. This study used 193 tone data consisting of Kendang to represent skin, Gangsa instruments to represent metal, Rindik, and Balinese flutes to represent bamboo material. Based on the test results, the PDA (Pitch Detection Algorithm) method that produces the closest value of the four musical instruments is HPS (Harmonic Product Spectrum) and FFT (Fast Fourier Transform) with an average difference of 4.81 Hz and 4.85 Hz.
Comprehensive survey on convex analysis in robust optimization
Authors: Chaerani D , Rusyaman E , Muslihin K R A
Published: January 2021

This paper presents a comprehensive survey on Convex Analysis (CA) in Robust Optimization (RO). Since RO is a class of continuous optimization problem which involved uncertain parameter thus convex analysis is needed to guarantee that the uncertain set is compact set, i.e., closed and bounded. Furthermore, in RO, to handle the uncertainty, the Robust Counterpart Methodology is employed to guarantee that the Robust Counterpart (RC) of the uncertain optimization problem will be ended up in one of the computationally tractable problems, whether it is a linear optimization, conic quadratic or semidefinite optimization problems. The importance of CA in RO lies in a fact that the robustness can be represented by a convex hull, i.e., the set which is the smallest convex set such that the uncertainty set is included in. This convex hull can replaces the uncertainty set. This can be done since determining the solution feasibility with respect to an uncertainty set is equivalent to taking the constraint left hand side supremum over the uncertainty set. If the uncertainty set can be replaced by a convex hull, this yields that the optimal solution remains the same. Thus, this paper discuss the recent researches, the most cited paper, the top journal and publishers also the bibliometric analysis on the topic of CA in RO.
Implementation of Ordinary Co-Kriging method for prediction of coal quality variable at unobserved locations
Authors: Falah A N , Hamid N , Rusyaman E , Abdullah A S , Ruchjana B N
Published: January 2021

A Co-Kriging method is a method that used to predict the value of the point at unobserved locations by sample points are known to be spatially interconnected by adding other variables that have a correlation with the main variable or can also be used to predict 2 or more variables simultaneously. In this research, the Lagrange Multiplier approach is used to produce the minimum variance of the Co-Kriging estimator. The case study is predicted on coal quality variable, Fixed Carbon as the main variable with Calorific Value as an additional variable. The process of prediction calculation by Co-Kriging method using package GStat on R software which produces a best theoretical model is Gaussian model as input in the prediction calculation at unobserved locations. The calculation result with Lagrange Multiplier approach using R Program is faster, precise and accurate which produces minimum prediction variance for Fixed Carbon and Calorific Value variables.
Homogeneous Poisson process in daily case of covid-19
Authors: Alawiyah M , Johar D A , Ruchjana B N
Published: January 2021

Since December 2019, an increasing number of new pneumonia cases have emerged in Wuhan, China. The rise of the spread of diseases caused by the Corona Virus Disease (covid-19) which has been established as a pandemic by WHO on March 12, 2020, gave rise to so much anxiety and speculation from various parties. The case of covid-19 positive patients Daily can be calculated by the homogeneous Poisson process. A Poisson process with a constant rate (λ) is called a homogeneous Poisson process. The average number of positive patients of Covid-19 from January 24, 2020, to April 16, 2020, is still very large. The chances of not having cases of covid-19 positive patients from January 24, 2020 to April 16, 2020 are very small so there will always be covid-19 cases every. Therefore, elements of society and government must consider handling and preventing the Covid-19 case.
Ordinary least square and maximum likelihood estimation of VAR(1) model’s parameters and it’s application on covid-19 in China 2020
Authors: Nalita Y , Rahani R , Tirayo E R , Toharudin T , Ruchjana B N
Published: January 2021

Vector Autoregressive (VAR) is a multivariate time series model for examining objects with two or more variables in which the variables affect each other under the stationarity assumption. This study aims to compare the parameter estimation procedure of VAR(1) model of Ordinary Least Square (OLS) and Maximum Likelihood Estimation (MLE) methods. The comparison is investigated by both theoretical and empirical approaches. This study uses daily data of the number of positive cases and the number of deaths caused by covid-19 in China. Result shows that theoretically, the parameter estimation of OLS and MLE give the same results. Empirically, it is proven that the parameter estimation done by both methods provide the same results either in the presence and absence of intercept. For the presence of intercept, the number of positive cases influenced by both the number of positive cases itself and the number of deaths from the preceding period. Meanwhile, the number of deaths is only explained by the number of deaths in the previous period. For the absence of intercept, there is a significant effect of the number of positive cases from the previous period toward the number of positive cases, but the effect of the number of deaths in the preceding period is not significant. Hence, there is no effect of interaction between the number of positive cases and the number of deaths cases and vice versa.
Geographically weighted logistic regression modeling on stunting cases in Indonesia
Authors: Alam F K , Widyaningsih Y , Nurrohmah S
Published: January 2021

Stunting is a condition of failure to thrive in children as a result of chronic malnutrition, so the child is too short at his/her age. Stunting harms children’s growth and affects the quality of human resources in the future. To reduce the prevalence of stunting in Indonesia, the government determined priority areas for handling stunting cases in Indonesia. This study aims to determine the variables that affect the status of priority areas for handling stunting in Indonesia. The model used in this study is Geographically Weighted Logistic Regression (GWLR) as a development of logistic regression model that considers spatial effect. This study used Maximum Likelihood Estimation (MLE) method to estimate the parameter model. The spatial weighting function used in this study is the Fixed Gaussian and Fixed Bisquare kernel weighting functions. The response and predictor variables in this study contain missing values, so Classification and Regression Tree (CART) method used to handle the missing values. The results showed that the best GWLR model on stunting cases modeling in Indonesia is the GWLR model with Fixed Bisquare kernel weighting function with AIC value of 622.806477 and model classification accuracy of 0.7257.
Review of problem-based learning trends in 2010-2020: A meta-analysis study of the effect of problem-based learning in enhancing mathematical problem-solving skills of Indonesian students
Authors: Suparman  , Juandi D , Tamur M
Published: January 2021

The present study analyzes various inconsistent research findings on the effect of problem-based learning (PBL) in enhancing students’ mathematical problem-solving skills (MPSS) in Indonesia. A meta-analysis of 50 primary studies that conform to the inclusion criteria was conducted to summarize, estimate, and evaluate findings of the effect of PBL by analyzing the heterogeneity of the studies. Heterogeneity analysis was conducted by analyzing the effect of moderator variables, namely the characteristics of sample and publication. The analysis tool used the Comprehensive Meta-Analysis (CMA) application by selecting the Hedge’s equation to determine its effect size. The “One study removed” tool in the CMA application was used to analyze the sensitivity of effect size data, and publication bias was assessed by the funnel plot, fill and trim test, and Rosenthal’s fail-safe N test. The findings of the study showed that the overall implementation of PBL had a larger positive effect significantly compared to conventional learning on the students’ MPSS in Indonesia based on a random effect model. The characteristics of the education level and research area significantly affected the heterogeneity of the effect of PBL on students’ MPSS. Thus, these findings suggest that mathematics teachers and lecturers should choose PBL as one of the best solutions in implementing Mathematics learning in the classroom to enhance MPSS. Furthermore, they should pay attention and consider the education level of students and the area where students learn in implementing PBL in enhancing students’ MPSS.
The effectiveness of the blended learning approach in algorithm and programming courses
Authors: Darmawan I D M B A , Linawati  , Wirastuti N M A E D , Nilakusmawati D P E , Raharja M A
Published: January 2021

The purpose of this research is to increase learning achievement by applying the Blended Learning Approach. The Algorithm and Programming course is a subject that requires more problem-solving practice. Learning is done by applying the Flipped Classroom. In face-to-face activities, students do cooperative learning. They discuss the solution of a given computational problem. Student motivation and engagement were measured using the MUSIC® Model of Academic Motivation. MUSIC® Model measures five components, namely Empowerment, Usefulness, Success, Interest, and Caring. Log activities are used to see the interaction between students and Moodle’s Learning Management System (LMS). Log activities show student contributions to discussion forums, number of hits on LMS, number of learning object accesses, and days to access learning objects. Face-to-face learning with cooperative learning trains the ability to work together in groups. The study showed that the Usefulness component had the highest value, with an average of 80.11, while success had the lowest score with a value of 70.16. The learning experience of each student using LMS was different. Students who actively use LMS have better abilities when learning face-to-face. Research shows there is a link between online learning activities and face-to-face learning on learning achievement.
Analysis of student interaction with learning objects on blended learning course applying cooperative learning together method on Moodle learning management system
Authors: Nilakusmawati D P E , Suprapti N W S , Darmawan I D M B A , Raharja M A
Published: January 2021

The purpose of this study is to determine the results of implementation of blended learning on Marketing Research Techniques course using cooperative learning together method, and how the interaction between students and learning objects on blended learning course using Learning Management System (LMS) of Moodle. Class activities using cooperative learning together method include group assignments in accordance with the topic set by lecturer, presentation of group work results, inter-group discussions, and continued with improvement of group assignments based on the results of discussion. Student activities in accessing learning objects in the form of URL viewed from student log activities on LMS, showed only 11.42% of students were enrolled from total of 35 students with an average access of 1.47 views. Student interactions with objects in the form of text, accessed by 29.10% of students with an average access of 2.33 views. Presentation slides is accessed by 12.85% of students and images/maps/charts is accessed by 6.66% of students. Unlike only the learning objects in the form of assignments and quizzes, accessed by almost all students with a high average number of accesses. The learning object in the form of quizzes is accessed by 99.44% and assignments accessed by 95.37% of students.
Passive control for a class of T-S fuzzy systems with memory controller
Authors: Wang Yanhua , Han Ying , He Xiqin
Published: January 2021

Taking the advantage of fuzzy systems, a class of T-S fuzzy models used to describe nonlinear systems. Firstly, by designing a memory state feedback controller, we draw the conclude of robust stability and the conditions of passive control. Secondly, using Lyapunov stability and linear matrix inequality technology in MATLAB, we can utilize the gain matrix about the controller of the memory state feedback. Finally, a simulation example is given to illustrate the effectiveness of the proposed method.
A software defect prediction method based on sampling and integration
Authors: Shen Peng , Ding Xiaoming , Mu Xuyun , Xu Jianing
Published: January 2021

This paper mainly analyzes the characteristics of software defect prediction from the perspective of machine learning, and proposes a semi-supervised software defect prediction method based on sampling and integration for the problem of class imbalance in software defect data and the incomplete classification of data sets. SISDP). SISDP firstly constructs a robust KNN marking model by taking a balanced sample of samples to mark a batch of unmarked data, and then iteratively adds the newly marked data to the original data set for the next marking model. , iterate until the data is marked. For the marked data set, the hybrid sampling algorithm is used to obtain the training set, and the integrated classification model composed of the multi-classification algorithm is classified and trained. SISDP not only reduces the interference of a few classes on the marking process, but also improves the generalization ability of the defect prediction model.
The Effect and Research of Environmental Analysis Software in Environmental Early Warning under the Background of Big Data
Authors: Liu Yun , Chen Xin , Jia Liming , Zhao Yue , Zhao Ran , Dong Yingli , Du Yingqiu , Wang Shuai , Jiang Tao , Bai Li
Published: January 2021

Enrichment of Pesticide Organic pollutants in Surface Water by solid Phase extraction, Analysis and characterization by Gas Chromatography-Mass Spectrometry and Total screening Software, semi-quantitative, At the same time, the semi-quantitative results were verified by SIM mode of gas chromatography-mass spectrometry. The results show that the recovery rate of acids, alcohols and ketones in water meets the requirements of monitoring index. The verification results show that, This screening method is based on the absence of external standard materials and targets. Can be used for early warning monitoring of most potential pesticide organic pollutants in water.
Application of Internet of Things Technology in Forest Fire Warning of Jizhou District
Authors: Han Junde
Published: January 2021

With the IoT technology as the entry point, this paper designs the general plan for the IoT-based forest fire warning & monitoring system of Jizhou District, through the analysis of the application of IoT technology in the forest fire prevention of Tianjin; builds the workflow of the IoT-based forest fire warning system; and put forward the suggestions and countermeasures for the development of forest fire warning system in Jizhou District, thus providing theoretical basis and realistic foundation for improving the efficiency of Tianjin forest disaster prevention and mitigation.
Digital protection and reflection on Tibetan ghee sculpture art of intangible cultural heritage in China
Authors: Lu Bin , Zheng Gang
Published: January 2021

China’s Tibetan ghee sculpture art is the first batch of works of art approved by the state council to be included in the national intangible cultural heritage list. This paper mainly studies the origin, digitalization and intelligent protection of the Tibetan butter sculpture art in China, and also explores and attempts to effectively protect the intangible cultural heritage with modern science and technology. We hope that more people will understand, inherit and protect the fine traditional culture of the Chinese nation, and that our cultural confidence will continue to advance to new levels.
A machine learning model for quantifying the effect of lifestyle interventions for patients with type 2 diabetes mellitus
Authors: Bi Suhuan , Ding Xiangqian , Yu Shusong , Guo Baoqi , Mu Liangliang , Wang Bin
Published: January 2021

Type 2 diabetes is the most common type of diabetes. The cornerstone of type 2 diabetes treatment is healthy lifestyle. This paper proposes a machine learning model for quantifying the effect of lifestyle interventions for patients. In the proposed incremental intervention model, the original physical indicators and the lifestyle interventions were taken as input vectors separately and transformed through different nonlinear functions. We evaluated our method with the dataset of 12,318 patients from a national funding project and compared with MLP and SVR. The experimental results (R

2
=0.85, RMSE= 0.51, MAE=0.35) indicated that the model outperformed those prediction models. Besides, the machine learning based method is cost-effective and time-saving. The proposed method provides new insights into prevention and treatment of chronic diseases.
Facilities Layout and Location Analysis based on Police Department Position in Dekalb, IL
Authors: Zhang Ouzheng , Ding Mengda
Published: January 2021

The officers of Dekalb Police Department were aware of the high crime rate and they’re considering whether set up a new office or relocate the existing department to shorten the distance, also, ensure to reach the crime scene in super speedy. To select a more reasonable location, an essential assumption is that the current office will no longer associated with. Besides the existed office, geo-policing is not a necessary factor neither. Therefore, the police officers will work from the new location that we inferred and answer the incoming calls there. First thing first is to model it as a single facility location problem, and then, calculate by the Minisum model with rectangular distances as well as the Minimax model with tchebychev distances.
Similarity study of clinical data
Authors: Yu Wenjun , Wang Kai , Hu Bin , Huang Yu
Published: January 2021

The classification of patients is very important for accurately determining the patient’s disease and subsequent drug use. More and more clinical data provide a clue for the classification of various diseases. We use related software to generate simulation data based on ICD10 and propose a specific algorithm to classify patients, and find patients belonging to other categories in the classified patient group, which provides a theoretical basis for the improvement of various classification algorithms for diseases.
Research of Recommendation System Based on Deep Interest Network
Authors: Wang Shan-Shan , Pan Yuan-Yuan , Yang Xiao
Published: January 2021

With the development of e-commerce, it provides users with many choices. But how to quickly and accurately recommend goods to users is an important topic in this field. Matrix factorization recommendation model based on scoring data is widely used, but data sparsity affects the recommendation quality of the model. In the paper, a product recommendation algorithm based on deep interest network is proposed. First, the user’s purchased goods and the user’s search are embedded coded, and the sparse features are transformed into low-dimensional dense features.Then, the feature vectors of the purchased goods and the search text vectors are joined together to input the deep interest network as the feature to predict the user’s interest.Finally, the effectiveness of the model is validated by using record data. The validity of the model is verified by comparing with other models.
Research on Named Entity Recognition Technology for Chinese Titles
Authors: Zhang Kong , Qian Gang
Published: January 2021

Aiming at the problem that the feature extraction of Chinese title data is insufficient in the process of named entity recognition and the marking data is difficult to obtain, this paper presents a CNN-BiLSTM-CRF model. The model trains large-scale corpus to generate word vectors with semantic information and trains the character vectors in words through CNN to generate character vectors containing character features, the word vector and the character vectors are merged as the input of BiLSTM network. Through the BiLSTM network, the front and back texts features are fully extracted, and CRF is used to restrict the relevance of tags. In addition, this paper proposes an algorithm based on active learning. This algorithm uses the proposed model construction committee to select the samples with large amount of information to mark, which effectively reduces the amount of data annotation.
Improvement of Winding Gait for Snake Robot
Authors: Xiao Wenyu , Wei Wu , Gao Yong
Published: January 2021

In order to solve the problem that the image information is difficult to handle due to the swing of the snake robot’s head during its serpentine locomotion, this paper improves the control function of the general winding gait for the orthogonal joint-connected snake-like robot, and the useful image information is obtained by reducing the swing of the camera-mounted snake’s head based on the improved function. Combining the Sigmoid function with the general function to control the rotation of the yaw joints and applying the Gaussian function to the pitch joints to lift the snake head off the ground, this new control function presented in this paper not only enables the joints to rotate smoothly, but also decreases the swing range of the snake head. Additionally, it can also fasten the robot’s movement. Experiments based on MATLAB and GAZEBO platform demonstrate the superiority and effectiveness of the improved winding gait.
A Study on Different Functionalities and Performances among Different Activation Functions across Different ANNs for Image Classification
Authors: Zhang Xia , Chang Di , Qi Weimin , Zhan Zhiming
Published: January 2021

Activation functions are very essential in artificial neural networks (ANNs), since they are non-linear functions and they have been proved necessary to implement deep learning. Recently, ReLU is one of the most well-known activation functions; however, several competitors – e.g. LReLU and SWISH – have nowadays been proposed or ‘discovered’. In this paper, the authors perform a detailed comparison of five activation functions over two image classification datasets. We found the overall performances of accuracy rates ranked from the best GELU, RELU, SWISH, SELU, down to Sigmoid. Such the observation would result in the improvement of future image classification via designing new state-of-the-art activation functions.
A Study on Machine-Learning-Based Prediction for Bitcoin’s Price via Using LSTM and SVR
Authors: Zhang Xia , Qi Weimin , Zhan Zhiming
Published: January 2021

Bitcoin plays an increasingly crucial role currently, whose of prices are challenged to be predicted when compared to the index of any traditional stock markets. With the accent of machine learning and deep learning, it is urgent to find out whether a machine learning model can or cannot see the way that the changes of Bitcoin’s price or volume interact over some period of time leads to a price increase or decrease within next few days. Testing results indicated that, the proposed two machine learning models built by LSTM and SVR were capable to capture longer-range dependencies and performed accurately by producing state-of-the-art results.
BeiDou Navigation Satellite System III Application in The Power Industry
Authors: Wang Zhiqiang , Yang Hongzhen , He Chen , Shao Weiping
Published: January 2021

As an important lifeline industry of the national economy, electric power has great demand for navigation and positioning, short message communication and timing of the BeiDou system. With the BeiDou III put into operation, the BeiDou system will be more widely used and deepened in power. This paper first introduces the main application of BeiDou system in China’s power system, and then introduces the construction situation and various performance indicators of BeiDou III in detail. From the three aspects of precise positioning, short message communication and timing function, the performance index of BeiDou III and its specific application in power system are expounded. In addition, the future application and development of BeiDou system in energy industry are prospected.
Gas sensor fault diagnosis for imbalanced data based on generative adversarial networks
Authors: Sun Yongyi , Liu Shuxia , Yu Ying , Zhao Tingting , Zou Zhihui , Zhang Jinghua , Zhang Shuang , Zhang Hongquan
Published: January 2021

Gas sensors play a key role in gas detection. Gas sensors are prone to failure, so they are of great significance to classify gas sensor fault. Traditional gas sensor fault diagnosis methods do not consider the condition of the imbalance of fault data. In this paper, the generative adversarial networks (GAN) method is used to generate small sample sensor fault data, balance various types of sensor fault data, and then the random forest is used for fault classification. Finally, the sensor fault data is obtained by self-made experimental system to verify the proposed method. The experimental results show that the proposed method could effectively improve the accuracy of gas sensor fault diagnosis.
RETRACTED: Intelligent traffic strategy based on 5G auto Autonomous driving
Authors: Cheng Bihui
Published: January 2021

In this paper, a complete mathematical model is established for the traffic efficiency of autonomous vehicles under high traffic light. Under the background of the mature automatic driving technology, a model to calculate the traffic efficiency is established to calculate the time consumed by passing a certain vehicle. In the case of setting traffic lights, we first block off intersections that interfere with the operation of fire trucks, and then make dynamic planning for the traffic lights after the fire trucks pass, so as to finally restore traffic order. Based on the tabu search algorithm scheduling strategy under traffic lights, the scheduling mechanism of special vehicles under general conditions is obtained.
Research for aircraft design process optimization based on design structure matrix
Authors: Jiang Yueqing , Yin Bo , Jia Dongning
Published: January 2021

The interaction of information during the structural design of aircraft is complex. In order to optimize the design process and improve the efficiency in research and development, this paper performs clustering calculation on the structural design matrix of a certain type of trainer and optimizes it through genetic algorithm. In addition, a specific method for calculating the amount of information through information connection weight is given. The simulation results show that the optimized matrix has strengthened the information interaction within the cluster, reduced the information interaction between clusters, and reduced the frequency of information feedback during the design process, which plays a role in improving the efficiency of research and development.
Fault Diagnosis of Rolling Bearing Based on Convolutional Neural Network of Convolutional Block Attention Module
Authors: Wang Chaoqun , Li Binbin , Jiao Bin
Published: January 2021

Aiming at the problem of insufficient comprehensive feature extraction in the fault diagnosis of rolling bearings, this paper proposes a fault diagnosis model of convolutional neural network (CNN) based on Convolutional Block Attention Module (CBAM). The model uses CBAM instead of pooling layer to join the CNN, and then exports to the full connected layer to realize the fault type identification and complete the fault diagnosis of rolling bearing. Experiments show that the accuracy of this method is 99.94%, and comparison with some other methods proves the effectiveness of this method.
Computer Vision-based Intelligent Bookshelf System
Authors: Yu Hao
Published: January 2021

This paper is devoted to the research of an intelligent bookshelf system. By introducing the current status of reading and purchasing books by people in various countries around the world, the value of the existence of the intelligent bookshelf is analyzed and proved. By introducing the main related technologies and algorithms used in the intelligent bookshelf system, this paper brings out the introduction to the principle and mode of operation, and an analysis of the system.
Application of Deep Reinforcement Learning in Control of Drawing Robots
Authors: Liu Cheng , Zhu Liucun , Ji Xinyu , Zheng Xiaodong
Published: January 2021

In this paper, the drawing robot studied is after the system extracts the edge of the image, convert its pixels to actual physical coordinates, then control the robot to track these coordinate values and realize image drawing. In many cases, the edges extracted by the edge detection algorithm have a lot of unnecessary details and edge lines of many branch points. Here, some techniques are used to remove these redundant and branch points. When the robot is tracking the trajectory, the quality of its control algorithm directly determines its performance. In this paper, the control system is designed using the deep deterministic strategy gradient (DDPG) algorithm in reinforcement learning, focusing on the setting of its reward function. Here we propose a reward function. After simulation testing, the reward function setting meets the system requirements, the controller can achieve high-precision and high-speed trajectory tracking effect, and it can also suppress chattering caused by external interference and model errors.
Ray tracing collision detection based on GPU pipeline reorganization
Authors: Cheng Sining , Qu Huiyan , Chen Xianjun
Published: January 2021

This paper proposes a parallel collision detection algorithm based on ray tracing to improve GPU performance. The algorithm uses parallel ray tracing pipeline technology to improve the performance of the graphics processing unit (GPU). When the GPU combines multiple ray tracing, it will cause the work group threads to diverge, resulting in too many idle threads, which will reduce the performance of the system. In order to avoid thread divergence, a new collision detection technology for the detailed detection stage of ray tracing using a pipeline structure is proposed. By optimizing the addition of buffers, the ray tracing is divided into three stages, and the pipeline is designed to integrate multiple rays tracking to avoid unnecessary synchronization issues between the CPU and GPU, thereby improving system performance. The comparison experiment results show that the proposed algorithm improves the system performance by 2.7 times.
A Novel Aircraft Refueling Behavior Detection Model based on Deep Learning
Authors: He Ran , Wang Jianxing , Zhang Zhengning
Published: January 2021

How to efficiently and accurately monitor the sparse aircraft Refueling behaviors from a large amount of video streams is of great significant for improving the level of management and refueling efficiency of aviation fuel stations. Due to the COVID-19 virus epidemic, the number of flights has dropped severely, the collection of image samples for refueling behaviors from large airport becomes difficult, which hinders the real-time detection of Refueling behaviors and reduces the efficiency of aviation fuel station. Therefore, automatically detecting the refueling behaviors of each station in time and accurately from a large number of aviation refuel stations still keeps challenging. To address this challenge, we propose a novel aircraft refueling behavior detection model based on deep learning, to quickly and accurately determine the refueling behaviors through analysing the video stream collected from the massive cameras deployed in the airport. Our proposed model adopts Inception v3 architecture of ImageNet to realize the model capability of transfer learning, the data augmentation to address the issue of overfitting, and the mAP (mean Average Precision) to test the performance. Our proposed model is also applied in the detection of refueling behaviors in China National Aviation Fuel Group, LTD (CNAF). The practical application results show better performance than other existing methods. Our work will promote the updating of related industry standard.
A parallel construction method based on linear hierarchical bounding box without stack
Authors: Wang Mingquan , Qu Huiyan , Liu Yi
Published: January 2021

The intersect operation of geometry is a common method of collision detection. To achieve a fast intersect operation, a hierarchical bounding box must be quickly constructed. This paper proposes an effective space division method for constructing high-quality linear hierarchical bounding boxes through ray tracing. This method can generate more regular axial bounding boxes into a complete binary tree. This structure can be completely parallel on the GPU, and effective parallelization can ensure the fastest tree-building time. The algorithm in this paper uses a non-stack ray tracing method to increase the frame rate of the 3D model rendering in the graphics processing unit and reduce the construction time of the bounding box. The experimental analysis of construction time, frame rate, average number of intersections and other performance indicators, the experimental results show that the use of stack-free midpoint splitting method and surface heuristic splitting method to ensure rapid construction time and efficient ray traversal performance, improve the object’s light traversal efficiency.
Visual Servo Control of Industrial Robot Based on Convolutional Neural Network
Authors: Li Quanfang , Zhu Liucun , Li Boquan , Zheng Xiaodong , Luo Junqi
Published: January 2021

Aiming at the problem of poor flexibility of traditional industrial robot, the vision system and industrial robot control system are combined to propose a visual servo control method based on Faster RCNN. The image information of the target object is collected through the camera, and the Convolutional Neural Network(CNN) is used to detect the entire picture, then the category and position of the object to be grasped are obtained, which improves the control performance of the robot system. Experiments show that the visual servo robot control system can quickly complete the position recognition of different objects. Mean average precision of this method can reach 0.867. The maximum error between the recognized grasping position and the actual position on the coordinate axis does not exceed 3.4 mm, the detection time of each picture in the GPU environment is 17.1ms, which has certain guiding significance in the visual servo control of industrial robots.
The Fault Diagnosis of Diesel Fuel Supply System Based on BP Neural Network Optimized by Genetic Algorithm
Authors: He Jiangjiang , Li Xiaoquan , Zhao Yuwei
Published: January 2021

As an important power source of diesel engine, the fuel supply system has the characteristics of high failure rate and being difficult to be detected, which is critical to detect and diagnose the fault for the reliable and stable operation of diesel engine. In the paper, principal component analysis (PCA) was used to process the initial data, back-propagation (BP) neural network was used to diagnose fault of diesel engine, BP diagnosis efficacy was analyzed, then the BP neural network fault diagnosis system was optimized by being introduced in the genetic algorithm , and the comparison results of training error and diagnostic analysis showed that: the fault diagnosis of BP neural network optimized by genetic algorithm is more accurate than that of traditional BP, which can effectively improve the fault diagnosis accuracy and reduce the training time.
Research on parallel algorithm based on AABB bounding box coordinate chain method
Authors: Qu Huiyan
Published: January 2021

With the rapid development of virtual reality technology and collision detection technology, virtual reality has become a lot of research fields in China. Collision detection is one of the most important technologies, and virtual surgery simulation has gradually become an important application of medicine in virtual reality technology. Virtual surgery can not only provide a skilled training platform for medical staff, but also analyze the feasibility and danger of surgical scheme, so that medical staff can have the same authenticity and strong immersion in virtual environment as in physical surgery. In this paper, a mixed hierarchical bounding box is constructed to detect the collision between the virtual human soft tissue and the rigid tissue of surgical instruments, and a parallel algorithm, fast bounding box coordinate chain calculation, compression storage and so on are introduced. For the deformation model of the software which is difficult to construct, the collision detection can be completed quickly and accurately, which can effectively save the collision detection time and improve the real-time performance of the collision.
Combine the AGA and L-M algorithms to predict the oil-water flow profile of production wells
Authors: Shi Hangyu , Song Hongwei , Guo Haimin
Published: January 2021

Production logging is the main means of dynamic monitoring of oil wells at present. The interpretation method of production logging fluid profiles is the key to the dynamic evaluation of oil wells and is of great significance. A joint optimization technique of a AGA and L-M is proposed to deal with the profile data of oil-water two-phase flow. The method is based on the energy conservation of the fluid in the production unit space. According to the nonlinear weighted least squares principle and the error theory, the objective function of the minimum value is established by the difference between the theoretical fluid temperature and the measured value of the thermometer. According to the fluid volume conversion, the flow rate of each phase in the well and the flow log value at the bottom of the control body are constrained. In order to improve the interpretation accuracy, the global search method (AGA) of genetic algorithm and the direct local search method of L-M algorithm are combined to solve the optimal solution. Firstly, the adaptive genetic algorithm is used for heuristic global search, and then as a result of the initial parameters, the L-M method is applied to approach the optimal solution. The interpretation results from the joint inversion are stable and reliable. This method is not only suitable for vertical wells but also for inclined wells.
Algorithm Study of New Association Rules and Classification Rules in Data Mining
Authors: Yang Xiaobo
Published: January 2021

In order to further improve the efficiency of data mining, it proposes a kind of data mining algorithm based on new association rules and classification rules. Specific research process is as follows. Firstly, MDML-PP algorithm is analyzed and applied to the multi-dimensional multi-level association rules in data mining, and then selects the test data sets for performance evaluation, meanwhile, it also studies the multi-support rate cut and classification rule mining algorithm, which is applied to multi-support rate classification rules in data mining, finally, using the compare experiment to verify the effectiveness of multi-support rate classification rules. The results show that the proposed algorithm in this paper can be better used in data mining with multi-dimensional and multi-layer association rules, which can improve the efficiency and quality of data mining.
The Research on Clustering Ensembles Selection Algorithm based on Semi-supervised K-means Clustering
Authors: Wu Xiaoxuan , Guo Changjian , Hu Tanglei
Published: January 2021

Selective clustering ensemble algorithm can eliminate the inferior quality clustering member’s influence and can achieve a better clustering solution relative to the clustering ensemble algorithm. For high dimensional data clustering, in this paper, a novel selective ensemble algorithm based on semi-supervised K-means clustering is proposed. In this paper, through a large number of experiments to verify the validity of the proposed algorithm for dealing with high dimensional data clustering. The new algorithm can achieve statistically significant performance improvement over other clustering algorithms.