bluuebunny/crossref_metadata_2025 huggingface dataset:
Prediction of Evaluation Result of E-learning Success Based on Student Activity Logs With Selection of Neural Network Attributes Base on PSO
Authors: Saputra Elin Panca , Supriatiningsih  , Indriyanti  , Sugiono
Published: November 2020

Evaluation of learning systems based on e-learning is very important to determine learning success. The purpose of this study is to obtain predictive results from evaluating students who follow e-learning based learning systems. The data used is the result of logs of student learning activities taken from the LMS. The data used in this study were 641 user logs of student activity. In predicting the evaluation results based on the learning system on e-learning we use a neural network method based on swarm particle optimization. Neural Network has a problem in optimizing very large data so using swarm particle optimization can solve this problem. From the data testing we have done, the results obtained by the Neural Network method get an accuracy value of 95.47%, and the results of the AUC value of 97.90%. The observation of variables C, ∊ and population of Neural Network and particle swarm optimization use the K-Fold Cross Validation method. Then the researchers tested several choices on the attributes used. By using the Neural Network method based on the swarm particle optimization attribute, there are 9 predictor variables so that as many as 6 attributes are used, namely sports, chat, discussion, messages, Quiz exercises and total logs. The results show an accuracy rate higher than 97.50%, and an AUC value of 98.20%. So the accuracy value increased by 2.03% and the AUC increased by 0.3%. With accuracy and AUC values, the Artificial Neural Network algorithm based on particle optimization is very well categorized.
Identification and Detection Odontoglossum ringspot virus on Native Orchids Collection of Nurserys in Java, Indonesia
Authors: Mahfut
Published: November 2020

Nature orchid are one of original floral in Indonesia. Virus infection is one of the limiting factor in the cultivation of orchid. Infection Odontoglossum ringspot virus (ORSV) was reported infets native orchids collection in Indonesia. The purpose of this study was to identification and ORSV that infects native orchid nurserys collection in Java, Indonesia. Symptomatic samples were collected from 5 nurserys collections, i.e. Rumah Bunga Rizal (Bandung), Bali Tanaman Hias (Cianjur), Borobudur Orchids Center (Magelang), Kebun Anggrek Bungarinte (Yogyakarta), and Titi Orchids (Yogyakarta). Detection and identification was conducted by serological test using ORSV specific antisera, RT-PCR and DNA sequencing. The serological test using ORSV antisera showed that 3 of 11 sampels reacted positively against ORSV antiserum i.e Phalaenopsis amabilis (Cianjur.1, Cianjur.2, and Magelang). RT-PCR of the 3 samples using specific primer of ORSV coat protein (CP) gene amplified a DNA with size ± 474 bp. Homology analysis of those 3 Indonesian isolates showed highest index similiarity (IS) was 99.8% with corresponding sequences from 10 other ORSV isolates. Phylogenetic analysis showed that ORSV Cianjur.1 and Cianjur.2 isolates clustered in separated group far from ORSV isolates in other countries.
Comparation of K-Nearest Neighboor (K-NN) and Naive Bayes Algorithm for the Classification of the Poor in Recipients of Social Assistance
Authors: Firasari Elly , Khasanah Nurul , Khultsum Umi , Kholifah Desiana Nur , Komarudin Rachman , Widyastuty Wiwiek
Published: November 2020

Poverty is a problems faced by developing countries, as well as Indonesia. According to data from the Central Statistics Agency in 2018, more than half the distribution of the poor population in Indonesia is in Java, which is 13,340.15 million people. Somokerto village is one of the villages in the district of Magelang, Central Java, which receives government assistance in an effort to reduce poverty. But in the process of classifying citizens who are entitled to receive assistance is still done manually. Manual classification is considered inaccurate in obtaining the results of social assistance recipients. In overcoming this problem, we need a systematic calculation to get accurate results. In this case, the researcher uses data mining classification calculation by comparing 2 calculation methods, namely K-NN and Naïve Bayes. The reseachers use Rapidminer tools. The research stages are identification of problems, data collection, implementation K-NN, Implementation Naive bayes, data testing process to produce accuracy and compare the result. The results obtained are the accuracy of Naïve Bayes higher than K-NN, namely Naïve Bayes 89.04% and K-NN 87.67%. This figure is classified in the category of good classification. From the results of the study it can be concluded the Naïve Bayes algorithm is suitable to be applied in the calculation of recipients of social assistance.
The Influence of “Check The Risk of Contracting Coronavirus” Application Quality from Alodokter on The Benefits Gained by Users, to get COVID-19 Early Detection
Authors: Firizkiansah A , Kriswantara B , Riana D , Widayanto A , Akbar F , Budi E S
Published: November 2020

This research uses the IS Success Model DeLone and McLean approach to finding the relationship that occurs between the quality of the Alodokter application to check the risk of contracting coronavirus. This research also tries to figure out how much benefit users have gained in conducting early detection of COVID-19. This Model uses six interrelated variables, including system quality, information quality, service quality, usage, user satisfaction, and clean benefits. With 200 respondents, data analysis uses the partial least square structural equation model (PLS-SEM) method with SmartPLS 3.0 software. This research gives the results that the better the quality of information and services of the Alodokter application, the more benefits gained by the user. However, the system quality factor from the Alodokter application does not affect how much benefits a user gains while they use the app.
E-Wallet Sentiment Analysis Using Naïve Bayes and Support Vector Machine Algorithm
Authors: Kristiyanti Dinar Ajeng , Putri Dwi Andini , Indrayuni Elly , Nurhadi Acmad , Umam Akhmad Hairul
Published: November 2020

Nowadays most of consumers in urban areas are accustomed to using digital wallets. The habit of transaction in cashless has been widely applied to the transportation system, restaurants and shops in the mall or supermarket. Apart of the ease of conducting transactions, various promotions in the form of points and cashback offered from various digital wallet application developers or e-wallets have become very attractive to users. One of the most widely used e-wallets by the public is OVO and DANA. This phenomena encourages researchers to do a research and make it as an object of study due to both are widely discussed by various groups, especially in the capital of Jakarta lately. As it is used, many customers write product and service reviews based on their experience on the Google Play store. Sentiment analysis is a technique that can find the right solution in creating a system that can automatically analyse these reviews and extract information that is most relevant to users. Researchers collected OVO and DANA review data on the Google Play store with a total of 2000 datasets. In this study, researchers compared the two algorithms namely Naïve Bayes and Support Vector Machine (SVM). The stages carried out in this study are data collection, initial data processing, modelling with the chosen method, experimental & model testing as well as evaluation and validation of result. Evaluation is carried out using 10 Fold Cross Validation. The result showed that OVO is the most popular e-wallet application by the public with an accuracy measurement using the Confusion Matrix reaching 91.00% for the SVM algorithm. The ROC curve showed the best AUC result of 0.986 (Excellent Classification).
The Classification Of Monster And Williams Pear Varieties Using K-Means Clustering And K-Nearest Neighbor (KNN) Algorithm
Authors: Indarti  , Indriyani Novita , Budi Arief Setya , Laraswati Dewi , Yusnaeni Wina , Hidayat Arief
Published: November 2020

Pear is a kind of fruits which has a lot of varieties. One of the way to differ pear varieties is by looking at the color, size and shape. This research is aimed at giving assistance to classify two varieties of pear i.e. Monster Pear and Williams Pear. In order to get the purpose, pear image processing is done to ease the classification process of pear varieties. Research method used consists of RGB Color Room to l*a*b, image segmentation, characteristics extraction with K-Means Clustering. Besides, K-Nearest Neighbor (KNN) is used to know the distribution and the classification. The use of practice data will increase the accuracy of pear varieties classification. The data used here are 88 pear images which cover 44 image practice data of Monster pear and 44 image practice data of Williams pear. Meanwhile, the test data taken are 10 images of each variety. The result of this research shows that the pear classification accuracy level is 95% which is very good.
Implementation Face Recognition Attendance Monitoring System for Lab Surveillance with Hash Encryption
Authors: Hamami F , Dahlan I A , Prakosa S W , Somantri K F
Published: November 2020

Face recognition (FR) is becoming popular to identify people. In fact, using the FR scheme, surveillance tasks can be built by recognizing people from their faces. This paper presents the implementation of face recognition as a biometric method for smart attendance as well as we also proposed the integrated scheme from capturing data from edge devices (CCTVs), streaming data to the dedicated server, then presenting the real-time data through android mobile devices. In this scheme, we proposed to employ deep learning algorithms based on the Convolutional Neural Network (CNN). Through the CCTV data streaming, faces are captured and matched with the database. Therefore, it is considered as their logging attendance. Furthermore, it is marked and stored into the database. This system prototype is developed by big data technology to tackle this complexity of data. The recognized faces can be monitored in real time monitoring. Eventually, real time reports are delivered through the web and android device with API after the data transmission is secured with hash encryption.
Performance Comparison and Optimized Algorithm Classification
Authors: Saputra Dedi , Dharmawan Weishky Steven , Wahyudi Mochamad , Irmayani Windi , Sidauruk Juniato , Martias
Published: November 2020

The current development of technology is quite rapidly not disengaged in a large data processor covering of all areas such as information technology, computer science, medicine, finance and other. This brings a large computing effect in identifying the processing of data. In data analysis for very large data, data processing is very much needed, in this study the authors propose data mining method as a solution to a large data processing problem, data mining is divided into several techniques including classification method techniques that aims to classify large amounts of data to be relevant data information. In this study the authors compared 5 algorithms in the classification method to get better performance in classification problems. Researchers analyze and test 5 Algorithm classifications with 4 different datasets as a tool in the problem of large data classification. The results of this research show the method SVM is much better to be used 4 comparison methods in calculating the value of AUC by using 4 datasets of UCI Repository. The LSVT Dataset shows the highest AUC value with 0973, Ionsphere 0887, Sonar 0897, Heartstatlog 0868.
Sentiment Analysis of Online Transportation Service using the Naïve Bayes Methods
Authors: Tika Adilah M , Supendar Hendra , Ningsih Rahayu , Muryani Sri , Solecha Kusmayanti
Published: November 2020

Sentiment analysis is a computational study of opinions and emotions expressed textually. Sentiment analysis will group text in sentences or documents to find out the opinions expressed in the sentence or document, whether negative or positive. This sentiment analysis research was conducted on the online taxi transportation service. Gojek uses a lot of social media to communicate with its customers, one of the social media used is Instagram. This research takes 1000 comments from the Instagram of the Gojek page which is used to see the public opinion of the online Gojek transportation services. Comments from the page are processed by doing text preprocessing and then classified using the Naive Bayes Classifier (NBC) method to obtain the value of the public value for online transportation services. The results of this study using the Naive Bayes method resulted in an accuracy value of 81.00%, which means that from all the comments on the Instagram page, the subject of the NBC method could be accurately classified by 81.00% whether the comments were negative or positive comments.
Expert System Of Syzygium Aqueum Disease Diagnose Using Bayes Method
Authors: Junaidi Agus , Dewi Nurmala , Baidawi Taufik , Agustiani Sarifah , Arifin Yoseph Tajul , Sihotang Hengki Tamando
Published: November 2020

This research examines how to design an application of an expert system to diagnose Syzygium aqueum (burm.f.alston) plant diseases so that it is easier to detect diseases in the Syzygium aqueum (burm.f.alston) plants. This study uses the Bayes method to be more precise because it is based on the probability value of disease symptoms that arise in Syzygium aqueum (burm.f.alston) plants. In this research, the authors used a web-based application with the Bayes method. The results or outputs from this application provide the probability value of disease certainty and then the hypothesis is chosen with the largest value.
Comparative Analysis on Dimension Reduction Algorithm of Principal Component Analysis and Singular Value Decomposition for Clustering
Authors: Muningsih Elly , Nur Hidayat Muhammad , Dwi Imaniawan Fabriyan Fandi , Saifudin  , Handayani Vembria Rose , Endiarto Feri
Published: November 2020

Clustering is a method of dividing datasets into several groups that have similarity or the same characteristics. High-dimensional Datasets will influence the effectiveness of the grouping process. This study compares two dimension reduction algorithms, namely Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) using K-Means clustering method to find out the best algorithm with the smallest Bouldin Davies Index evaluation. The dataset of this study involved public data from UCIMachine Learning which contains the number of weekly sales of a product. Data processing is performed by comparing the number of clusters from 3 to 10 and the dimension reduction from 2 to 10. From the data processing the RapidMiner tools, application with dimension reduction can provide better results than without dimension reduction. In particular, the PCA algorithm shows better results than the SVD, with which the best number of clusters is 5, and the number of dimensional reductions is 3 with a Bouldin Index of 0.376.
The Effect of Satisfaction and Loyalty Towards Digital Payment System Users Among Generation Z in Yogyakarta Special Region.
Authors: Pradiatiningtyas Diah , Dewa Chriswardana Bayu , Safitri Lina Ayu , Kiswati Sri
Published: November 2020

Two of the most popular digital wallet application companies in Indonesia are Go-Pay 80% and OVO 60%. To be able to survive in the midst of the intense competition in digital wallet applications, application companies must innovate marketing strategies that focus on consumers. Due to the use of digital wallets among the very large generation Z and increasingly intense competition in the business of digital payment applications, the authors examine the effect of user satisfaction and loyalty towards digital payments among generation Z ( case study on Go-Pay and OVO users in Yogyakarta). This research objective is to analyze the satisfaction and loyalty of digital payment system users among generation Z in Yogyakarta Special Region, so that the researchers can find out the influencing variable to empower the development of digital payment especially Ovo and Go Pay. The reseacrh methodology considered as descriptive research, in which the data collecting technique utilized Likert scale questionnaire and employed purposive sampling for selecting the sample. The data analysis applied multiple regressions. The result of the multiple regression testing shows that satisfaction and loyalty affect the Z-generation respondents using the digital payment with the indicators that the digital payment sounds more easy, cashless, and more benefit. Moreover, the usage of digital payment is easy to track small expenses.
Designing a conceptual framework of a smart city for sustainable development in Bangladesh
Authors: Sourav A I , Deborah Lynn N , Santoso A J
Published: November 2020

A smart city is a future solution to better management of the city and people. Recent researches have highlighted the necessity of smart city projects to improve urban lifestyle for the growing population. Bangladesh is one of the most densely populated countries in the world. Despite being a developing country, Bangladesh still lacks a smart city. A smart city framework is required to ameliorate urban lifestyle. The purpose of this study is to address a conceptual framework for a smart city project focusing on sustainable development in Bangladesh. The research approach follows an exhaustive literature review to collect suitable information to design the new smart city framework. Necessary information to design a smart city framework such as the core smart city dimensions and the sustainability indictors are identified through the thorough literature review. From the extracted information a new smart city framework is developed focusing on sustainable development. The findings of this study offer a clear overview of the smart city core dimensions and factors that influence sustainability in a smart city. The research presents a smart city framework that can be followed in a developing country like Bangladesh.
Present Situation, Challenge and Future of Health Management about Complex Equipment
Authors: Zhang Peng , Wu Man , Xiong Fei
Published: November 2020

Recently, the public has focused on the safety and reliability of complex equipment such as aerospace. Health management about complex equipment contains state detection, fault prognostics, fault diagnosis and so on, and is the guarantee for accomplishment of space missions and the long-term operation of in-orbit spacecraft. Under the background, the development of prognostics and health management (PHM) technology, aiming at improving equipment safety and reliability, which can eliminate faults in the embryonic state, has a profound significance for condition monitoring and in-orbit maintenance of aerospace equipment. By analyzing the research status and application of PHM technology both at home and abroad, the manuscript majorly addressed the PHM technical framework and key technologies about spacecraft. In the meanwhile, integrated with cloud service platform for equipment management, the development tendency of PHM was forecast. At last, the existing challenges of PHM technology was expounded and corresponding countermeasures were put forward.
Applications of GIS-based “one map” System in the Management of Certification Factory Inspection Process
Authors: Zhang Xiaoying , Yu Wei , Hu Qunming , Zhang Haochen , Jiang Hua
Published: November 2020

Certification factory inspection is one of the most important processes of risk control and cost management of certification bodies. And the information technologies based on IOT and GIS can be adopted to build an information management system, which helps to improve the efficiency, reduce costs and increase the capability of risk management for certification. In this article, authors try to identify risks of factory inspection process using process method and explain how GIS-based “one map” system is developed to minimize risks. Important system functions of the automatic task-assignment, the optimal route-planning and the personnel positioning and tracking is also introduced.
Visual Online Debugging and Diagnosis System for Substation Secondary Equipment
Authors: Wei Jieru , Tang Yixin , Zhou Shaoliang , Chen Li
Published: November 2020

Firstly, based on the demand of substation “operation and maintenance integration”, this paper describes the necessity of constructing an efficient, flexible and reliable remote operation and maintenance system. Then, the realization architecture, characteristics and key technologies of the visual online debugging and diagnosis system with flexible configuration are proposed. The system realizes the remote operation and maintenance services such as remote debugging of secondary equipment, intelligent diagnosis, health status assessment and status prediction by using container technology, video processing technology, virtual liquid crystal technology, etc. The application practice shows that the visual online debugging and diagnosis system improves the standardization management and visualization of secondary equipment and optimizes the remote operation and maintenance mode.
Visualization Analysis of Top Papers in the Subject Category of Computer Science, Information Systems Based on ESI
Authors: Yuan Baozhong , Sun Jie
Published: November 2020

All of 1,647 top papers for Computer Science, Information Systems from 2009 to 2019, which included 1,642 highly cited papers and 32 hot papers. All papers were written in English, from 69 countries/territories and listed in 115 journals. Top 5 core journals were IEEE Communications Surveys and Tutorials, Information Sciences, IEEE Access, IEEE Internet of Things Journal, MIS Quarterly. The research was separated 9 clusters.
Adaptive STDM-Based PDF Documents Watermarking Algorithm Robust to Fixed Gain Attack
Authors: Liu Jingcai , Liu Naixi , Li Shundong , Wang Daoshun
Published: November 2020

Spread Transform Dither Modulation (STDM) has good performance in robustness against Additive White Gaussian Noise (AWGN) and re-quantization, so it has been widely used in image watermarking algorithms. However, STDM cannot resist Fixed Gain Attack (FGA). In the paper, we introduce an adaptive function into the traditional STDM algorithm, so that it can automatically adjust the amount of distortion according to the text line spacing in the document. Then we give an improved adaptive function to make our algorithm robust to FGA. Analysis and experimental results show that the algorithm proposed in this paper has good invisibility and high robustness against AWGN and FGA.
A Study on the Quantitative Viscosity Detection of Automobile Lubricating Oils Based on Near-infrared Spectroscopy
Authors: Yang Xingxing , Hu Zhui , Xu Qiang , Cai Zihao , Zheng Xiao , Xiong Lieqiang
Published: November 2020

A quantitative viscosity prediction of automobile lubricating oils was performed. The near-infrared spectra were preprocessed by using three different techniques, namely, multiple scattering correction (MSC), moving average smoothing (MAS), and Savitzky–Golay smoothing (SG). The characteristic wavelength was extracted by using three different techniques, namely, successive projections algorithm (SPA), synergy interval partial least squares (siPLS), and interval partial least squares (iPLS). The initial data of the viscosity of lubricating oils collected by near-infrared (NIR) spectroscopy were preprocessed by using two different optimization methods, namely, particle swarm optimization (PSO) and genetic algorithm (PA). Finally, the quantitative viscosity detection model of the lubricating oils was built by combining with support vector regression (SVR). The optimal technical route for modeling was explored.
Researchment of Mainlobe Anti-jamming Algorithms
Authors: Luo Zhangkai , Pei Zhongmin , Li Yinan , Wang Xinmin , Zou Bo
Published: November 2020

Main lobe interferences usually cause the distortion and peak offset of main lobe and the heighten of side lobe level in adaptive beamformers. The block matrix algorithm utilized the direction vector which was determined by both the antenna arrays and the direction of interference to eliminate the main lobe interference. But this algorithms is sensitive to the error occurred in the interference arrive angle and would consume the array of degrees of freedom. In this paper a new algorithm is proposed which projects the receive signal into a space which is orthogonal to the signal subspace to achieve the goal. The space can get by three steps. Frist we obtain eigenvalues by the decomposition of the covariance matrix of the snapshots to evaluate the noise power. Then we get a new matrix by removing the noise from the covariance matrix of the snapshots. At last we get the target space by conducting the singular value decomposition to the new matrix. Performance is mainly assessed in terms of the beamforming gain and the signal to interference and noise (SINR) ratio. The computer simulation shows the effective of this method.
Hybrid Scheduling Technology of Time-triggered Ethernet Switches: A Review
Authors: Ma Xiaofang , Hamdulla Askar
Published: November 2020

Switches are one of the key devices in the time-triggered Ethernet. It is very necessary to design high-performance switches to achieve high real-time data transmission. This article sorts out relevant documents on converters, and first introduces the development of avionics system networks. Then, based on Crossbar’s switch structure and related scheduling algorithms, compare the performance of different algorithms. Compare the performance of different algorithms. Secondly, it introduces the current research methods of TTE switches. Finally, it summarizes the future development direction of time-triggered switching hybrid scheduling technology.
Research on the Wind-induced Dynamic Response Analysis Method of the High-voltage Transmission Tower-line System under Strong Wind
Authors: Zhang Junqiang , Feng Wentao , Sheng Fei , Wang Tao , Zhang Jingyu , Zhao Mingxi , He Bo
Published: November 2020

It has always been a research hotspot in the field of the transmission engineering to research on the stability of high-voltage transmission lines under strong wind and establish effective measures to prevent the wind disaster. Wind disasters such as the structure deformation, tower collapse and line disconnection affect the safety of transmission lines seriously, which trouble the technicians a lot. The long-span, high-flexibility and strong coupling characteristics of the tower-line system itself determine that it is extremely sensitive to external random loads. From the theoretical perspective, effective conclusion of the analysis on the wind-induced response of the tower-line system can be obtained only by following the inherent coupling characteristics of the tower-line system, the spatial distribution correlation of the strong wind in the large field and the probability characteristics of wind-induced response closely, which is also the focus of this paper. A specific 220kV transmission line was chosen as the research object in this paper. First of all, a refined finite element model of the high-voltage tower-line system was established which takes the tower-line coupling factors into account. Then, considering the non-Gaussian and spatial distribution correlation characteristics, a numerical simulation method of the strong wind in large field was provided. On this basis, the comparative analysis of the wind-induced response of the tower-line system was completed and the probability density function of the key mechanical parameters of the tower-line system was established and analyzed finally. The models and methods proposed in this paper are verified to be correct and more close to the actual working conditions, which can provide reference for the study of the transmission line operation, maintenance and wind disaster prevention.
A New Method for Potential Evaluation of Compound Flooding of Tertiary Oil Recovery
Authors: Liu Zhaoxia , Wang Qiang , Luo Wenli , Gao Ming , Cai Hongyan
Published: November 2020

Compound flooding and tertiary oil recovery is one of the leading methods to improve recovery efficiency in oil and gas development in China, Because there are certain requirements on the exploitation environment and technology in the process of petroleum exploitation, it is necessary to evaluate and analyze the reservoir potential in advance. Currently, our research team has integrated this method into the tertiary oil production potential evaluation software EORSYS3.0. In this paper, a new combined flooding potential prediction model is established to improve the accuracy of reservoir potential prediction results by changing the reservoir water cut of displacement front viscosity.
The Innovative Application of Computer Image Processing Technology in Web Design
Authors: He Ting
Published: November 2020

In recent years, with the rapid development of my country’s network technology, the content of computer network technology has become more and more diversified. As one of the contents of network technology, web design has also received more and more attention from the people, and its development market prospects are extensive. In order to make web design more competitive in the development of computers, the application of computer image processing technology in web design has become an unstoppable trend.This paper mainly studies the application of image processing technology in the design of computer web pages. It elaborates and analyzes the image processing technology in computer web design, and then combines with the emerging semiotics to promote the further development of computer web design.
Design of Four Rotor Unmanned Aerial Vehicle Based on Open CV
Authors: Tang Lili , Huang Wei , Jiang Hongyang , Zeng Teng
Published: November 2020

This design is based on the OpenCV technology of the four rotor UAV, mainly by the control module and image module of two parts. The control module contains a STM32 micro controller, it can generate multiple PWM to control the motor to produce basic movements, while the body is equipped with three axis acceleration of three axis gyroscope MPU-6050 module, electronic compass, barometer and wireless communication module, and use STM32 to read sensor data for gesture calculation and control motor action; The image module is equipped with a ARM Cortex-A53 processor, running the Python-OpenCV operating system in the embedded Linux environment, OpenCV implementation of the UAV aerial features, in addition, the onboard Wifi module can be accessed by SSH remote desktop to obtain detection images, which can be widely used in security monitoring, exploration engineering, home security and other fields.
Survey of Crystal-Oscillator Based Reference Frequency Quadrupler for Frequency Synthesizer
Authors: Hu Xueyou
Published: November 2020

Implementing low phase noise frequency synthesizers often utilizing a high frequency reference input. This paper analyses the reason why increasing the reference frequency can improve the phase noise performance of frequency synthesizers, describes three state of art crystal-oscillator based reference frequency quadruplers, and then summarizes their performance, advantages, and disadvantages.
The Integration of Digital Media Technology and Graphic Design in the Internet Era
Authors: Han Lu
Published: November 2020

With the rapid development of economy, computer software technology has been used in various fields, different design software has different design functions and software characteristics, graphic design has begun to use a variety of computer image processing software for processing. The combination of computer graphic design and related design software can optimize the effect of graphic design. Taking graphic design as an example, this paper briefly analyzes some applications of computer software technology in graphic design.
A Safety Analysis Method of Airborne Software Based on ARP4761
Authors: Sun Rui , Zhong Deming , Li Weigang , Lu Minyan , Ding Yuxin , Xu Zhi , Gong Haoyuan , Zha Yun
Published: November 2020

Most of the functions of civil aircraft are achieved through software at present. Therefore, the safety of airborne software has become an important part of safety analysis. However, to achieve complex functions, the onboard software becomes very complicated, and it is usually difficult to guarantee safety. ARP4761(Guidelines and Methods for Conducting the Safety Assessment Process on Civil Airborne Systems and Equipment) is an important safety assessment guideline for civil aircraft, but it believes that software development can satisfy safety as long as the software development process complies with the software development standards, instead of incorporating the software into the safety analysis process. To improve airborne software safety, this paper proposes a safety analysis method based on the ARP4761 process which is tailored to adapt to the software. In the software requirements and design, the method uses FHA(Functional Hazard Assessment) to analyze the hazards of the software and define the hazard levels, then use FTA(Fault Tree Analysis) to build a software fault tree, and carry out CCA(Common Cause Analysis) analysis based on the fault tree; after the software development is completed, the method uses FMEA(Failure Modes and Effects Analysis) and FMES(Failure Modes and Effects Summary) to summarize the failures of the software and return these failures to the fault tree to determine whether the software can meet the defined requirements. At the end of the article, an anti-icing software is used as an example to explain the process and effectiveness of the method.
Low Complexity Anti-Interference Transmission Model in Massive MIMO Systems
Authors: Cheng Tongtong , He Yigang , Ali Farhan
Published: November 2020

With the development of wireless communication, wireless communication technology plays an irreplaceable role in modern instrument measurement and communication. However, the complex electromagnetic been an important factor affecting wireless communication quality with the number of devices increasing. Especially in the industrial field and modern city, electromagnetic interference (EMI) is a serious issue in wireless data transmission. Besides, user interference will also reduce the communication performance with the increasing number of equipment users. In this paper, a low-complexity data transmission model is proposed. First, the direct sequence spread spectrum (DSSS) technology is utilized to oppose narrowband electromagnetic interference and noise interference. Nevertheless, the DSSS be affected by multi-user interference (MUI) due to the number of users increasing. Based on DSSS and ZF precoding, the DSSS-ZF structure is proposed to resist interference. The simulation platform utilized the Software Defined Radio (SDR) and channel emulators (CE) to measure our DSSS-ZF model. The results show that the DSSS-ZF structure model can effectively defeat interference and improve the reliability of communication in complicated electromagnetic environment.
Real-Time Vehicle Taillight Recognition Based on Siamese Recurrent Neural Network
Authors: Liu Ming , Liao Bingyan , Wang Chenye , Wang Yayun , Wang Yaonong
Published: November 2020

Vehicle taillight recognition has been well investigated recent years. However, few methods can be applied in practical because of the time consumption or less recognition accuracy. In this paper, we design a light-weight framework for vehicle taillight recognition in real-time, which is divided into detection and recognition stage. To achieve this purpose, Finer Detection Block and dense anchor regression are utilized to improve the robust of detection stage. In addition, a Siamese CNN-GRU network is proposed, which not only captures the short-time difference but also learns the long-time flash features. We conduct ablation experiments to verify our proposed method, which obtains a mean recall of 94.34% with 103 FPS on 1080Ti.
The Application of Elman Neural Network in Uninterrupted Maintenance of Power Grid
Authors: Cao Minggui , Xu Aidong , Jiang Yixin , Wang Qianru , Wang Zhen , Zhang Yunan , Wen Hong
Published: November 2020

Uninterrupted Maintenance of power grid is the development direction of power grid . Electricity load forecasting is the basis for non-stop maintenance and the key to power grid load migration. Therefore, this paper analyses the plans and requirements of non-stop maintenance and predicts the grid load based on the Elman algorithm, by which the load forecasts are given. A load migration model is proposed, which can select the optimal low-load area in all areas to meet the business needs of non-stop maintenance.
Identifying Influential Nodes in Social Networks Based on Social Strength
Authors: Lee Yanli , Fu Yan
Published: November 2020

Identifying influential nodes in social networks is significant in controlling the outbreak of epidemics, conducting advertisements for e-commercial products, predicting popular scientists or papers, and so on. Many methods have been proposed by paying much more attention to the role of nodes, while the social strength between nodes is rarely considered. Especially in unweighted networks, social strength is not explicit information. In this paper, we first characterize the social strength of unweighted networks, and then proposed a LOVital method based on the social strength. Numerical results on the relation network of Weibo dataset and the other datasets from various fields show that LOVital can much more accurately identify influential users.
Scheme design of Smart Platform for the Agricultural Science and Technology Park
Authors: Wang Yali , Sun Zhiguo , Wang Xiaoli , Yang Chenxue
Published: November 2020

With the rapid development of the global IoT and various information technologies, the construction of “smart city” has been gradually put on the agenda, and at the same time, the smart management of the National Agricultural Science and Technology Park will also become the key content of smart city construction. This paper proposes a scheme design of smart platform for the agricultural science and technology park, and lists the basic contents of the construction of the smart park. The proposed scheme design of smart platform applies 5G, AI, cloud computing, Internet of Things, mobile Internet and other new ICT technologies to solve the long-term pain points faced by traditional agricultural science and technology parks, including poor service experience, weak comprehensive security, low operational efficiency, high management costs, difficult business innovation, and so on. Because of the construction of the smart park has many contents and a long construction period, this paper discusses the specific design options of smart platform for the agricultural science and technology park. It can provide users with better service perception and help enterprises achieve innovative development through digital transformation. Based on the proposed smart platform, the business subsystem of the agricultural science and technology park is opened horizontally, through data fusion to business integration, to help customers more efficiently and agilely solve business problems that are difficult or even impossible to solve with traditional technical means. The future development and the impact on the life of people.
Development Mode Study of Jingdezhen Ceramic Vertical E-commerce Based on Cloud Computing
Authors: He Fubao , Ji Yanhua
Published: November 2020

As a new application service mode, cloud computing has become the focus of the integration between Internet and the traditional industries. It is infiltrating into e-commerce gradually and changing the development mode of e-commerce. This paper analyses the development status and the problems of the Jingdezhen ceramics e-commerce simply’ expounds the characteristics of cloud computing and the advantages of vertical e-commerce and the influence of cloud computing on the development mode of Jingdezhen ceramic e-commerce. On the basis, this paper tries to study the development mode of Jingdezhen ceramic vertical e-commerce based on cloud computing. And the overall architecture of Jingdezhen ceramic vertical e-commerce platform based on cloud computing is given in order to promote the further development of Jingdezhen ceramic e-commerce.
Low-Power Wide Area Network Construction Method Based on LoRa Technology for Agricultural Science and Technology Parks
Authors: Sun Zhiguo , Ji Zhiqiang , Wang Hongyan , Huang He , Wang Zheng , Ding Yongbo , Yu Tao , Yang Chenxue
Published: November 2020

The Internet of Things (IoT) requires different but complementary types of wireless network technologies, having their own merits from distance, power consumption, capacity and cost perspectives. This paper introduces a comparative study of LoRa wireless communication network technology for the long-distance and low-power wide-area network scenario deployment. In this paper, we propose a low-power wide-area network construction method based on LoRa technology for an agricultural science and technology park in Ningxia, Western China. Specifically, we design the optimal placement of LoRa sensors in the facility greenhouse by measuring the LoRa signal quality on the spot in the traditional brick wall sunlight greenhouse. In the traditional brick wall solar greenhouse, the success rate of data communication between LoRa terminal and LoRa gateway reaches 100%. Experiment shows that the coverage distance of the LoRa network reaches 3km in the most common conditions in agricultural science and technology park, i.e., the height of the LoRa gateway is 10 meters, and the maximum transmission power is 27dbm.
A Brief Analysis of the Construction of Private Gardens in Suzhou under the Theory of Spatial Narratology
Authors: Li Tianyingzi
Published: November 2020

At present, there are few researches on the exploration of Chinese classical private gardens from the perspective of spatial narration. This paper discusses the spatial narration of the classical private gardens in The Humble Administrator’s Garden of Suzhou. From the perspective of artistic conception creation, node setting, path arrangement, space combination and gardening techniques, the narrative scene in the garden is analyzed in all aspects. Thus it helps us to have a deeper understanding of Chinese classical gardens.
The Function and Enlightenment of Cloud Network Convergence in Fighting against the New Crown Epidemic
Authors: Wang Yin-chuan , Wang Lian-qing , Zhang Na
Published: November 2020

Firstly, this paper describes the challenge of the new coronary pneumonia epidemic to the ability of information service support. Secondly, it analyzes the main problems and related concepts of cloud network convergence, and points out the new problems faced by cloud network converge. Thirdly, this paper summarizes the current status and significant characteristics of the construction and development of cloud network convergence, and points out the important role and performance of cloud network convergence in fighting the new crown epidemic through specific examples; Finally, it analyzes the risks of cloud network convergence and the countermeasures was proposed.
Application of machine learning in optimizing b-value acquisition strategy of diffusion Magnetic Resonance Imaging
Authors: Ye Chen , Xu Daoyun , Wang Lihui , Wang Rongpin , Zhu Yuemin
Published: November 2020

The b-value acquisition strategy of diffusion Magnetic Resonance Imaging (dMRI) is very important for medical clinical application, especially the low b-value strategy. However, the choice of b-values is affected by several factors: for example, different tissue, different regions of tissue, the dependence of dMRI signals on b-values are different. Specifically, dMRI signals in areas with faster blood circulation may be more sensitive to low b-values (b<50 s/mm2); in addition, to obtain the diffusion or perfusion information from the diffusion-weighted (DW) signal, fitting methods are required, which also affected by low b-values. In this paper, Convolutional Neural Network (CNN), a machine learning based method is first used for learning the different characteristics of the DW signals in different regions of tissue and generated by different b-value acquisition strategy, and then analyse the dependence of DW signals on low b-values in different regions of the tissue. Finally, to study the dependence of the fitting methods on low b-values, which to determine the b-value acquisition strategy. The results show that the b-value acquisition strategy are different in different perfusion regions and using different fitting methods.
LiDAR target fusion and algorithm detection based on improved YOLO
Authors: Yu Zhuoquan , Liu Jingyao , Xu Wenyang , Liu Yiding , Lu Chenji
Published: November 2020

Abstract. In order to achieve safe driving behavior, the most important point of automatic driving is to detect the target. At present, the judgment of obstacles is based on single sensor, so it is difficult to detect some complex road environment, and it is easy to be missed. Therefore, this paper proposes another system device combined with color camera technology in lidar. This is another detection method proposed on the basis of Yolo, which improves the detection ability of small targets such as non motor vehicles and people. This is based on the Yolo algorithm, using images and other samples to obtain relevant useful data, and finally build the detection system model. Finally, the sensor is introduced to combine the color image and the deep image in order to improve the detection accuracy. Finally, the fusion of decision-making level is verified by test samples. The results show that the improved YOLO algorithm and decision-level fusion algorithm have higher target detection accuracy, can meet the real-time requirements, and can reduce the miss detection rate of small and weak targets such as non-motorized vehicles and pedestrians. Therefore, the method proposed in this paper has good performance and broad application prospects, while taking into account both accuracy and real-time.
Improved application of transfer learning in network traffic classification
Authors: Shang Fengjun , Li Saisai , He Jinlong
Published: November 2020

When using machine learning for traffic classification, there is such an assumption: the training data and the test data are independently and identically distributed. However, in reality, the assumption that the flow characteristics obey the same distribution may no longer hold because of conceptual drift or regional changes. Existing models will not be able to effectively classify new traffic. The transfer learning method TrAdaBoost has achieved great success in traffic classification and other aspects, but there are some problems, such as too much attention to the difficult-to-classify instances in the target domain, and failure to consider the wrong-classified instances in the source domain. In this study, the method of introducing weight correction factors in TrAdaBoost is used to make the iteration of weights more reasonable, and the effectiveness of this method is proved through theoretical analysis and experiments.