bluuebunny/crossref_metadata_2025 huggingface dataset:
Improving The Effectiveness of Classification Using The Data Level Approach and Feature Selection Techniques in Online Shoppers Purchasing Intention Prediction
Authors: Kurniawan I , Abdussomad  , Akbar M F , Saepudin D F , Azis M S , Tabrani M
Published: November 2020

Online shopping is a form of trading using electronic devices that allows consumers to buy goods or services from sellers via the internet. Other names for these activities are: e-web-shop, e-shop, e-shop, internet shop, web-shop, web-store, online shop, and virtual shop. An online store generates purchases of products or services at retailers or shopping centers, which are referred to as business-to-consumer (B2C) online shopping. n another process where a business buys from another business, it is called business-to-business (B2B). Nowadays online shopping has become more sophisticated with trading via mobile phones (m-commerce). Cellular phones have been optimized with an application to buy from online sites. In this study, we proposed a data level approach and feature selection techniques as a solution for the classification of imbalanced data. The imbalance class classification is one of the classic problems in the field of artificial intelligence, especially for classification in machine learning. Imbalanced data have been proven to reduce the performance of machine learning algorithms, where imbalance data means that the total data from each class is significantly different. The proposed method is evaluated using a dataset from the UCI repository and area under the curve (AUC) as the main evaluation. The results have shown that the proposed method produces good performance. (AUC¿ 0.8). Overall the second experiment outperformed and was better than the first and third experiments because the main evaluation in the unbalanced class classification is AUC. Therefore, it can be concluded that the proposed method produces optimal performance both for large scale data sets. Overall the second experiment outperformed and better than the first and third experiments, because the main evaluation in the unbalanced class classification was AUC.
Comparison of Naive Bayes Algorithm and Support Vector Machine using PSO Feature Selection for Sentiment Analysis on E-Wallet Review
Authors: Putri Dwi Andini , Kristiyanti Dinar Ajeng , Indrayuni Elly , Nurhadi Acmad , Hadinata Denda Rinaldi
Published: November 2020

Cashless payment habits have been widely applied to the transportation system, restaurants and shops in the mall area. So, it is normal if the growth of mobile payment services is currently very rapid. The ease of doing transactions and promotional offers in the form of points and cashback in digital wallet applications (e-wallets) is very beneficial for its users. One of the most popular e-wallets is OVO. With so many reviews about OVO customer opinions on social media, there has also been a lot of public opinion. These opinions can produce negative or positive statements. Sentiment analysis is the mining of opinions or text to classify opinions or user reviews, of a brand reviews, product reviews, or service reviews into the category of positive or negative opinion. The methods used in this research are Naive Bayes and SVM. Both of these algorithms are the best algorithms widely used in text classification research. However, both of these algorithms have weaknesses in several parameters. So, in this study Feature Selection is used to improve its performance. The evaluation was carried out using 10-fold cross validation. Measurement accuracy is measured by confusion matrix and ROC curves. This study uses 500 positive reviews and 500 negative reviews as data training. The results of this study indicate that the use of PSO-based Naive Bayes algorithm produces an accuracy value of 93.10 percent with an AUC value of 0.750. While the results of research from the PSO-based SVM algorithm are 91.30 percent with an AUC value of 0.970. Based on these results the accuracy value generated by the Naive Bayes algorithm is classified as Fair Classification and SVM is classified as Excellent Classification. The AUC value generated by the Naive Bayes algorithm is also smaller than SVM. Therefore, in this study found that SVM is the best algorithm in classifying text.
Determination Wart Treatment Method Using Data Mining Technique
Authors: Amalia Hilda , Yunita  , Pohan Achmad Baroqah , Puspita Ari , Lestari Ade Fitria , Retnasari Tri
Published: November 2020

Wart disease is a benign tumor that can grow on all parts of human skin. Although this wart disease belongs to a type of benign tumor, this wart disease can interfere with physical appearance and disturb the psychological sufferers if the infected part of the skin is the area around the face. This wart disease will also be very dangerous if there is infection in vital parts of the body such as the genitals. For treatment or treatment of wart harm, two methods are used: immunotherapy and cryotherapy. However, until now it is still unknown which treatment is better for patients with this wart disease. It has long been known that data mining is a method that is widely used in the health world to obtain valuable knowledge from medical data sets. Data Mining is used to classify and detect early diseases and choose the optimal type of treatment for warts. Data mining is able to find patterns and predict the trends of events. In this study four data mining methods will be used, namely the neural network, naive Bayes, SVM and K-NN. from the results of the study obtained the best method for processing the wart dataset is neural network
Identification of monogeneans parasite using gray level co-occurrence matrix and artificial neural network
Authors: Hikmatulloh  , Riana Dwiza , Hudin Jamal Maulana , Susilawati  , Wintana Dede , Hadianti Sri
Published: November 2020

flatworms that are parasitic and attack the skin and gills of fish are called monogenean parasites. In this study 3 monogenic parasites were used, namely grandis, liewi, and johorensis. GLCM (Grey Level Co-occurrence Matrix) used in digital image processing to obtain feature extraction on images. ANN (Artificial Neural Network) used to classification mongenean parasites based on features extracted. This research resulted overall accuracy of 86,67%. Studying monogenean parasites is needed because it is related to the health of fish that we need in our daily lives, so that this research can ease and assist the work of ecologists in classifying monogenean parasites more quickly and efficiently.
An Analysis and Measurement of Website Quality Using WebQual 4.0 and Importance Performance Analysis (IPA) Method (A Case Study of Kemiriamba Village Brebes)
Authors: Faqih Husni , Warjiyono  , Kuhon Fiola , Aji Sopian , Ardiansyah Angga , Fandhilah
Published: November 2020

Based on the Law of the Republic of Indonesia number 6/2016 regarding villages, it is said that regional governments are obliged to develop village information systems in implementing e-government towards good governance. In Indonesia, e-government continues to increase in number, but it is not accompanied by quality, including the Kemiriamba Village website. This study was aimed to measure the quality of the Kemiriamba Village website using the Webqual 4.0 method and Importance Performance Analysis (IPA) with 4 (four) instruments, namely Usability Quality, Information Quality, Service Interaction Quality and Visual Quality. This study is descriptive with a quantitative approach. The questionnaire data were 132 and were processed with SPSS software. The results of the analysis and measurement stated that the suitability level of the Kemiriamba Village website was 96.63%, and the average value of the gap was negative, which was -0.11, which meant that the performance level of the Kemiriamba Village website still did not meet user satisfaction and expectations, especially in Service Interaction Quality. The main priority that needs to be corrected and improved immediately is the website attribute; it should have a good reputation. Moreover, the questions, suggestions and complaints need to be processed as promised. The results of this study suggested the Kemiriamba Village website to immediately make improvements and development in order to become a qualified website towards good governance.
Causal Loop Diagram (CLD) Model In Planning A Sustainable Smart Sharia Tourism
Authors: Husain  , Zarlis Muhammad , Mawengkang Herman , Efendi Syahril
Published: November 2020

The development of sustainable tourism such as dynamic and complex smart sharia tourism consists of many interrelated and diverse components of stakeholders, which each has different management objectives, which can trigger unexpected conflicts among stakeholders. Regarding the dynamic and complex challenges in the field of developing sustainable tourism, the ability of identidying is really needed. For this reason, it is necessary to see further what are the factors that influence the plan for implementing sharia tourism intelligently and the strategies needed and analyze the relationship of dynamic causes of these factors using approachment methods which use the Causal Loop Diagram system ( CLD). The purpose of this research is to get a solution to overcome conflicts among stakeholders through approachment system with the Causal Loop Diagram (CLD) model. The results of this CLD Model can identify the root causes of complex problems and the effects of a sustainable tourism development system so that it can be used to develop and test alternative management policies in making smart sharia tourism planning decisions.
Usability Evaluation of the Website Services Using the WEBUSE Method (A Case Study: covid19.go.id)
Authors: Aziz Faruq , Irmawati  , Riana Dwiza , Mulyanto Joko Dwi , Nurrahman Dede , Tabrani Muhamad
Published: November 2020

The purpose of this study is to analyze the site usability evaluation system using the WEBUSE method on one of the information sites about COVID-19 namely the website covid19.go.id. The basic analysis was carried out on 24 usability criteria classified into 5 usability categories, namely: content, organization, and readability, navigation and links, user interface design, performance and effectiveness, educational purpose. Each category deals with one aspect of usability. Data from respondents’ answers were analyzed using the WEBUSE method to get the usage point and usability level of the website that has been studied. This research produces a usability evaluation information system that provides an online evaluation questionnaire and this analysis is useful to present high-quality system information and user satisfaction aspects of the website covid19.go.id. The case study on the website covid19.go.id, shows that the level of usability and overall level of user satisfaction is good and acceptable for the general public from all walks of life. And in terms of satisfaction in the user interface design category, it is possible that some people are still uncomfortable with the look of the website.
E-Learning for English for Business-Based Podcast: One of Learning Solutions Amid the Pandemic of COVID-19.
Authors: Aditya Nalendra Aloysius Rangga , Rahayuningsih Retno , Rosalinah Yanti , Subroto Ibnu , Wibowo Ary Iswanto , Nelfianti Fera
Published: November 2020

This research used qualitative methods of descriptive where information was required directly and where time and resources were limited. It was designed to investigate whether e-learning podcast-based could be used in learning to listen in English for students of the University of Bina Sarana Informatika Management Study program. This research used samples that are 6 classes as a source of research data with the 2nd-semester students who came from Management study program. The number of 1 average class consisted of 20-25 students. Researchers used the Android-based Anchor Application as a podcast maker as well as Google Classroom as an E-Learning learning medium. To collect data, researchers used observations, interviews, and documentation. Interviews were conducted by giving 25 questions through online based on theoretical approaches related to this study with 90 sample data. Researchers used test instruments with pre-test and post-test as research instruments. This research was focused on the use of ICT in the learning process by way of e-learning Podcasts based. The novelty of this research is this study used a different target and numbers of the target from the previous studies, namely Management Program Students who only get Basic English teaching during lectures. As a result of this research was the use of podcasts more effective than e-learning without using the media podcast, characterized by a grade average result of 76.05 after Post tests were done.
Determination of the Real Contents of Olive Oil in Blend Oils by near Infrared Spectroscopy
Authors: Cai Zihao , Xu Qiang , Yang Xingxing , Hu Zhui , Zheng Xiao , Zhang Jiafan
Published: November 2020

Aiming at the determination of real contents of olive oil in blend oils, near infrared spectroscopy and support vector machine were combined to establish regression models for the determination of olive oil contents. The data were fused on the feature level with canonical correlation analysis to improve the prediction performance of the models. The results showed that the SVM regression models could effectively predict the real contents of olive oil in 135 samples containing 2%-20% olive oil. Among these models, the highest correlation coefficient R2 was 99.74%, and the root mean square error of the prediction set was 0.08. In addition, the prediction performances of some regression models were deteriorated after the curves were smoothed with the Savitzky-Golay method. But, the prediction performances were improved in the models established with data fused with the canonical correlation analysis approach.
Reliability Sensitivity Analysis of Vehicle CAN Bus Message Transmission Based on Second-moment Method
Authors: Liu Yu , Yin Yue
Published: November 2020

CAN bus, as one of the most successful on-board network systems, is widely used in modern automobiles. The main function of CAN bus is to realize timely and accurate message transmission, so higher and higher requirements are put forward for the reliability of message transmission. In this paper, a typical CAN bus message transmission model was selected for analysis, and the reliability sensitivity of the bus message transmission was analyzed and calculated based on the reliability second-moment theory. The results were consistent with the qualitative analysis, and the reliability sensitivity calculation of the second-moment method is verified. The reliability sensitivity analysis of CAN bus message transmission by this method will provide a reasonable theoretical support for the further study of message transmission reliability in CAN bus system.
Crank Call Detection Models Based on Call Data of Telephone Subscribers
Authors: Xie Wenbo , Liu Zhen , Fu Yan
Published: November 2020

Data explosion makes our life much more convenient than ever. For example, people always get timely news from Apps, and get interesting recommendations when shopping online. However, these conveniences come by probing people’s private data, such as account information, search history, and chat records. Thus, if the management of data is not as secure as it supposes to be, many problems, e.g., crank calls, will arise and cause big troubles in people’s lives. Although many security Apps have been developed to help mobile phone users stay away from crank calls, the users need to update Apps frequently due to their user-labels-based blacklist. These feedback-based systems depend on a large number of user actions that have significant lag. In this paper, we propose two crank call detection models to help operators mark crank callers based on the call data of telephone subscribers, in which the features are organized by different ways. One is a composited classification model, and the other is a convolutional neural network model based on the time-sliced data. The experiments show that the model applying convolutional neural network gets better accuracies in detecting crank calls.
Design and Implementation of Welding Procedure Qualification Expert System
Authors: Wang Ren , He Jianying , Ma Mingju
Published: November 2020

Welding process evaluation is an important link to ensure the quality of welding engineering and determine the welding process specification. It is also the main procedure to evaluate the welder’s examination results. In the traditional way of processing WPS documents, the test results are usually judged manually by experts based on experience. However, the evaluation of welding process is often very complex, involving the base material, sample type, process parameters and a large number of international standards. Therefore, how to utilize the software engineering method and experience, make the welding process evaluation program accurate and efficient information, make the welding process evaluation program easier and more efficient, is very important to the whole field of welding.
Resource Allocation Based on Chaotic Ant Colony Algorithm in Self-organizing Wireless Sensor Network
Authors: Tian Min , Bai Jinbin
Published: November 2020

A resource allocation scheme based on chaotic ant colony algorithm in self-organizing wireless sensor network is proposed, and corresponding chaotic sequence generator is designed in this paper. Simulations are conducted to compare the proposed method with conventional genetic algorithm and dynamic programming algorithm under self-organizing wireless sensor network environment. Results show that compared with the scheme based on dynamic allocation algorithm and conventional genetic algorithm, the scheme based on chaotic ant colony algorithm has higher target detection rate and lower power consumption, which also extends the networks lifetime.
Research on Spam Filtering Technology Based on New Mutual Information Feature Selection Algorithm
Authors: Wang Kunfu , Mao Wanfeng , Feng Wei , Wang Hui
Published: November 2020

Aiming at the deficiency of traditional mutual information algorithm in feature selection, this paper proposes a weighted naive Bayesian algorithm based on improved mutual information, called imi-wnb algorithm. In the feature selection stage, the word frequency factor and the difference factor between classes are introduced to improve the traditional mutual information algorithm to achieve feature dimension reduction. In the process of classification, the value of IMI is introduced to weight the attributes of naive Bayes algorithm, which partly eliminates the influence of conditional independence assumption of naive Bayes algorithm on classification, and improves the efficiency and stability of spam classification.
Recommendation Algorithm of Crowdfunding Platform Based on Collaborative Filtering
Authors: Wang Bixi , Wu Wenbin , Zheng Wenfeng , Liu Yan , Yin Lirong
Published: November 2020

With the continuous progress of Internet technology, the crowdfunding platform has become a new way of network financing. While the generated data keeps increasing, its benefit does not increase in a proportional way, resulting in the “information overload” phenomenon. The personalized recommendation system can solve this problem by mining users’ interests and preferences from a large amount of data. It has achieved success in many fields. This paper applies machine learning algorithm to build a recommendation system based on collaborative filtering. The designed personalized recommendation algorithm can provide accurate and rapid personalized recommendation services, which is convenient for users and conducive to the development of the crowdfunding platform. In addition, this paper uses the data from the crowdfunding platform in practice to complete the performance verification of the algorithm.
Distribution Network Planning Method Based on Hybrid Genetic Algorithm
Authors: Shao Yunfeng , Sun Yuanming , Wang Yajing , Ma Zhongjing , Liu Yongqiang , Zhao Yang
Published: November 2020

Facing the discrete, multi-constrained, non-linear, multi-objective combination optimization problem of distribution network grid planning, some traditional heuristic algorithms such as genetic algorithms sometimes fall into local optimum. This paper proposes a distribution network planning method based on hybrid genetic algorithm The algorithm consists of two stages. In the first stage, the genetic algorithm is used to obtain the initial planning scheme. In the second stage, the initial planning scheme obtained in the first stage is used to form the planned route set. The improved minimum spanning tree method is used to obtain the final planning scheme. In order to make full use of the effective information obtained in the first stage, this paper proposes a transmission line classification method to assess the importance of the transmission line, provide guidance for the second stage, and improve the search efficiency and accuracy. The algorithm solves the problem that heuristic algorithms such as genetic algorithm often fall into local optimization to a certain extent, and the problem of slow convergence when the minimum spanning tree algorithm has a large number of lines to be planned.
Application of Rank Likelihood Ratio Scanning Method in Multiple Mean Changes in Long Memory Time Series with Heavy Tail
Authors: Xu Qiongyao , Xi Li
Published: November 2020

In this paper, a rank-likelihood ratio scanning method for long memory time series with heavy tail is proposed to solve the problem that when likelihood ratio scanning method is used to estimate the mean change points in the long memory time series with heavy tail, the estimation accuracy decreases rapidly with the decrease of the heavy tail index. Numerical simulation and analysis of real data demonstrate the effectiveness and practicability of the rank-likelihood ratio scanning method.
A Real-time Face Tracking and Recognition System Based on SeetaFace
Authors: Yilihamu Dilizhati , Tuerxun Palidan , Dawut Abdusalam , Hamdulla Askar
Published: November 2020

A real-time face tracking and recognition system is constructed based on Seetaface, and it is proposed to use Gamma correction to reduce the impact of illumination changes on the detection and recognition results. First, the system has face detection function, then by using OpenCV to get the image in front of the camera, the system completes the function of real-time face tracking, users can also upload local images to make face comparison. If a stranger’s face is detected, the system can collect information and train it. The system achieves multiple face tracking as well as recognition. Through the analysis and evaluation on public datasets. The improved face tracking and recognition system has a satisfactory accuracy and performance.
Fingertip-Specific Mobile Interaction Through Camera-Based Fingertip Identification
Authors: Chen Liang , Chen Dongyi , Tan Zhizhong
Published: November 2020

In this paper, we propose to utilize camera-based fingertip identification to enable fingertip-specific mobile interaction without hand occlusion. The core idea of the proposed fingertip identification method is to utilize features, whether innate or got-up, on a fingertip to recognize its identity rather than utilize hand information to infer the fingertip’s identity. A fighter game prototype is presented to demonstrate the interaction idea. Possible ways to realize fingertip identification and how to utilize fingertip identification to enable richer interactions on mobile devices are also discussed.
Speedup and Password Recovery for Encrypted WinRAR3 without Encrypting Filename on GPUs
Authors: Ji Qingbing , Yin Hao
Published: November 2020

The encryption mode of WinRAR3 which does not encrypt the file name uses encryption and compression, the password recovery complexity is high. The existing cracking systems crack on a single CPU or GPU platform. Because the decryption algorithm is slow on the CPU platform, while the decompression algorithm is slow on the GPU platform, the overall performance of the cracking algorithm is not high. This paper studies the mode of CPU and GPU collaborative computing, and proposes an efficient cracking method of encrypted WinRAR3 without encrypting filename. By using the CPU + GPU pipeline cooperation method, the waiting time in the calculation is reduced, and the performance of the algorithm is improved; by using the magic number matching method of compressed files, the decompression calculation can be effectively reduced. The experimental results show that the speed of the cracking algorithm proposed by this paper for 8-digit passwords is 24423/s, which is 2.3 times as fast as before.
Identification of Friction Parameters of Robot Joints Based on Energy Consumption Model
Authors: Lv Ningbo , Zhu Xuejun , Ding Yunpeng , Meng Xiangxiu
Published: November 2020

In order to solve the problem that the joint friction torque value of industrial robots is difficult to be measured in practice, the joint friction identification method based on the energy model is used to indirectly obtain the friction model parameters by measuring the energy consumption value. According to the energy model and the measured robot energy value, the genetic algorithm is used to realize the parameter identification of the Stribeck friction model of joint friction. The results of the two identification methods based on friction torque identification and energy model identification are compared. The simulation results show that the error ratio of all the friction model parameters obtained by the two identification methods is less than 8%, and the error target value of the two identification results is less than 0.1%, which can effectively identify the relevant friction parameters. By designing an experimental scheme, the different speeds and corresponding energy consumption data of the IRB140 robot when joint 1 operates at a constant speed in a specified time are measured, and the Stribeck friction model parameters of the IRB140 robot’s joint 1 are identified by the proposed method.
Some Certiticateless Short Signature Schemes of Security Analysis and Simple Improvement
Authors: Hu Xiaoming , Chen Fangshu , Tan Wenan , Ma Chuang
Published: November 2020

Certificateless signature (CLS) scheme as an important filed of cryptography has a wide range of applications, such as information security, network security and so on. But, in the CLS scheme, the public key of the signer is generated directly by the signer itself without the verification of the trusted third part, so the public key easily is replaced by the malicious users which lead to the forgeability of the signature. In this paper, we show three CLS schemes proposed recently which have high efficient with short signature are not secure and the malicious user can forge a signature by replacing the public key of the signer. In the same, we also present three improved CLS schemes with simple modifying based on the original CLS schemes respectively. We also make a simple security analysis and efficiency analysis on the three improved CLS schemes. The analysis shows that the improved schemes not only can overcome the security problem but also almost have the same efficiency with the original CLS schemes.
Research on a New Attribute Encryption Anonymous Algorithm in Cloud Computing Environment
Authors: Zha Xin
Published: November 2020

The attribute encryption anonymous model is used to encrypt data and protect anonymity in cloud computing environment, so as to realize attribute encryption and privacy protection in cloud computing environment. In order to improve the self-adaptability and accurate control of data encryption in cloud computing environment, an anonymous data encryption algorithm based on hyperbolic cyclic mapping is proposed. The priority list control model of adaptive privacy protection for cloud data is constructed. The load balancing control and encryption key design of anonymous big data with hyperbolic cyclic mapping are implemented by using bilinear differential control protocol. The hyperbolic differential equation control and adaptive weighting method are used to construct the key function for anonymous encryption of cloud computing data. The time window and privacy protection protocol of attribute encryption anonymous control is optimized by slot allocation. The encryption anonymous algorithm is improved. Simulation results show that the adaptive equalization performance of attribute anonymous encryption in cloud computing environment is better, the ability of anti-attack is better, and the recall rate of data is better than the traditional method.
Research on Electric Vehicle Charging Scheduling Strategy Based on Graph Model
Authors: Wang Zhen , Xu Aidong , Zhang Yunan , Wang Qianru , Xu Xinchen , Jiang Yixin , Wen Hong
Published: November 2020

In recent years, electric vehicles (EVs) have gradually become the development trend of the future automotive industry due to its advantages of zero emission, low noise, and high efficiency. At this stage, a large number of EV freely charging without guidance will bring many negative effects on the operation of urban traffic and power distribution systems. Therefore, how to correctly guide the vehicle to charge efficiently has become an urgent problem to be solved. Starting from economic factors, time factors, and user characteristics, this paper proposes an electric vehicle charging recommendation optimization model based on graph calculation. The graph model is generated according to the geographic location of the charging station, and the shortest path algorithm and charging scheduling strategy, which is used to recommend EV to users. The charging station with the smallest comprehensive cost within a certain range of the car.
Terminal Security Configuration Optimization under Edge Computing
Authors: Wang Qianru , Xu Aidong , Zhang Yunan , Cao Minggui , Song Jianfang , Jiang Yixin , Wen Hong
Published: November 2020

It is an important task of system security management to configure terminal security policy reasonably in edge computing (EC) to implement necessary security protection for terminals and EC system. Therefore, this paper analyses the security goals and requirements of terminal access for multi-service EC systems under the power grid, conducts terminal security risk assessment based on the AHP algorithm, and proposes an edge computing platform terminal security configuration optimization strategy, creatively using the OS-ELM algorithm. A framework for cloud training and edge-side online learning to meet the real-time and lightweight communication needs of edge platforms.
Application of neural network to predict mutations in proteins from influenza A viruses - A review of our approaches with implication for predicting mutations in coronaviruses
Authors: Yan Shaomin , Wu Guang
Published: November 2020

The recent outbreak of COVID-19 pandemic is attributed to cross-species transmission of new coronavirus from bats to humans through unknown intermediate hosts, and the essence of the transmission is closely related to the mutations in coronaviruses. Furthermore, the effort to develop the vaccines against coronaviruses always faces the challenge of unexpected mutations in coronaviruses. In fact, it is very difficult to predict the mutations in any virus and bacterium, although mutations are a process of evolution. Over years, we have been applied the neural network to predict the mutations in proteins from influenza A viruses in comparison with the predictions using logistic regression. Our results are encouraging, but our approaches still need the improvements, for example, to upgrade to using machine learning and artificial intelligence instead of neural network. In this review, we summarize the rationales of neural network modelling, its strength and weakness, with the hope that we can apply the improved methods to predict the mutations in coronaviruses, thus to explore the origin of SARS-CoV-2, to find its intermediate host, and eventually to predict its mutations.
Research on Sponge Landscape Design of Primary School Campus Based on BIM Technology——Take Hefei Experimental School as an example
Authors: Tieqiao Xiao , Shaojie Zhang , Ziyu Zhao , Zijun Xia , Jianghui Guo , Chengyong Zhang , Yanuo Wang
Published: November 2020

With my country’s vigorous advancement of the construction of sponge cities, the research on the sponge landscape design of primary school campuses has become a hot spot. This paper takes Hefei Experimental School as an example, through the investigation of the current situation of the school landscape, using BIM technology to assist modelling, summarizes the shortcomings of the campus landscape and proposes a sponge landscape design strategy. With Revit and Vectorworks2019 as the main tools and platforms, under the support of the whole process of BIM technology, according to the current situation of the primary school campus and the behaviour characteristics of the primary school students, a design plan for the sponge landscape of Hefei Experimental School is proposed, showing that BIM technology is fully used in the sponge landscape design of the primary school campus, and put forward deficiencies and prospects.
Design of offline transfer management software base on QR code
Authors: Cong Lihu , He Weixin
Published: November 2020

With the development of our army’s informatization and actual combat, the transfer of ordnance has become more frequent, and higher requirements have been placed on the efficiency, accuracy and security of data recording during the transfer. At present, the data management of our military ordnance allocation still uses manual methods. This method has the disadvantages of inconvenient storage of transfer slips, handwriting errors, and information leakage during the flow. At the same time, staff query and statistical transfer data are inconvenient and inaccurate. This design research uses database technology to manage the transfer data to overcome the current shortcomings in the management of ordnance transfer data. The research uses QR code technology to realize the exchange of transfer data, and solves the problem of reading the transfer data and lowering the accuracy of entry. This system uses C # language for development and design in Visual Studio2015 software. The data is stored in the SQL server database. The QR code technology is used to develop and design a transfer management software based on two-dimensional bar code to exchange offline data. By operating the software, operators can realize the exchange of transfer data, query and statistics of historical transfer data, and at the same time can ensure efficiency, accuracy and security.
Research and Application on Smart Courtyard Monitoring System
Authors: Li Zhen-hua , Li He-he , Liu Bo-fang
Published: November 2020

Residential safety has always been a basic need for people’s lives. In the vast suburbs and villages of China, quantities of courtyard houses are distributed, and the level of security monitoring is related to the people’s security and happiness. This study takes the characteristics of the human body with a large aspect ratio into account. By improving the structure of the Faster-RCNN network, a method for enhancing human body features is designed to enhance the network’s ability to recognize the human body, and covers various human postures such as standing and lying down, and eventually improves the accuracy of the original human body recognition by 7%. In addition, this study designs and deploys a complete set of real-time monitoring system, which continuously saves images when detecting human intrusion, and sends alarm messages to the user’s mobile phone WeChat in real time, which meets the real-time and adds convenience. The actual deployment shows that the system can find the target within 120 milliseconds when the intruder enters the sight and send out alarm messages to meet the realtime design requirements.
Research on Application of Ant Colony Algorithm Based on Multi-sensor Image Fuzzy Fusion in Image Recognition
Authors: Liu Chenyu
Published: November 2020

In the current rapidly developing society, image recognition technology is currently an important means that can provide a full range of dynamic data. It plays an important role in economics, politics and other fields. Fuzzy image fusion algorithms have opened up to solve these problems in image recognition. Based on the current wide application of image recognition, this paper studies the research of multi-sensor image fuzzy fusion algorithm in the application of high-definition image recognition. Aiming at the characteristics of infrared and visible image fusion, an infrared-visible image fusion algorithm based on non-down sampling transformation and hybrid particle swarm algorithm is proposed. For low-frequency sub-images, an improved weighted average method based on area average is used for neighborhood fusion. For high-frequency sub-images, a hybrid particle swarm optimization algorithm is used to select the threshold, and the neighborhood algorithm based on average gradient selection is used for fusion. This algorithm can effectively improve the fusion effect of fused images. The experimental research results show that combining the characteristics of gradient intensity and phase consistency, the characteristic method of compressing the information while retaining important information, which is convenient for real-time processing, is used for related research on sensor image fusion.
Channel Mismatch Speaker Verification Based on Deep Learning and PLDA
Authors: Li Pengqi , Li Guanyu , Han Jiao , Zhi Tiankai , Wang Di
Published: November 2020

At present, speaker recognition technology is developing continuously, but in real world, existing speaker recognition algorithms are generally disturbed by channel factors. With the increase of current data volume, neural network has also become a key technology for speaker recognition. This paper introduction a speaker recognition method based on supervised training and a channel compensation algorithm. The experimental results show that this method combined with channel compensation algorithm can remove the influence of channel noise and has a good speaker recognition effect. The experimental results show that the combination of x-vector and PLDA channel compensation algorithm can obtain better recognition rate and ensure the robustness of the algorithm.
Research on multi vehicle tracking based on TLD optimization algorithm
Authors: Wang Chao , Huang Lei , Li Zhiyuan
Published: November 2020

With the increasing complexity of traffic conditions, the research on multi tracking technology of vehicle targets has become a hot topic. In this paper, a multi vehicle tracking algorithm based on TLD optimization algorithm is proposed, including TLD optimization algorithm of detection module and KCF algorithm of tracking module. The experimental results show that the proposed algorithm has small time delay in multi vehicle target tracking, and it can track multiple targets stably and meet the performance requirements.
DRNN: Deep Residual Neural Network for Heart Disease Prediction
Authors: Xiao Nianhao , Zou Yuanchen , Yin Yaguang , Liu Peishun , Tang Ruichun
Published: November 2020

Heart disease is one of the major diseases threatening human health. This paper proposed a novel deep neural network model to predict heart disease based on routine clinical data. We adapt the deep residual structure to discover a novel Deep Residual Neural Network (DRNN). In order to verify the effectiveness of DRNN, we performed experiments on Heart Disease UCI. The accuracy reached 95%, which is better than the traditional machine learning methods among Random Forest 83%, Decision Tree 68%, Logistic Regression 87%, KNN 60%, Native Bayes 80%.
Research on Computer Test Paper Generation Algorithm Based on Gene Expression Programming
Authors: Wenyan Yang
Published: November 2020

This article analyzes the common classifications of computer test paper algorithms. This article includes random method, backtracking method, genetic algorithm, random location search method, ant colony algorithm, gene expression algorithm, etc. The author obtains the expected calculated values by studying the technologies of setting control parameters, creating initial populations, calculating individual fitness, optimizing preservation strategies, performing genetic operations, and generating new populations. The author’s purpose is to improve the application effect of the computer test paper algorithm and the practicability of the information in the question bank.
Research on Underground Remote Control Detonation System
Authors: Hong-peng Chi
Published: November 2020

In order to meet the technical requirements for unmanned mining of underground metal mines and overcome the key technologies of unmanned blasting, a wireless network-based underground remote control detonation system was studied. The research content includes the software and hardware of the underground and surface initiation control unit and the initiation control process, the communication realization method of the underground and surface initiation control terminal, the remote wireless initiation network security encryption technology, and the system security design. Under the premise of ensuring safety, the requirements of underground remote control blasting are realized, which is of great significance to the development of underground unmanned blasting technology.
Analysis of spatial interaction vitality based on high-speed railway network and highway network
Authors: Jia Hui-di , Li Qian
Published: November 2020

This paper applies spatial interaction model theory to analyse the impact of transportation network infrastructure construction on the city. 36 cities in Chinese high-speed railways network and 72 cities in Chinese highway networks are selected, and the interactive vitality of cities is calculated and analysed from the perspective of the two networks. The spatial interaction vitality of some cities on two networks is different. Cities like Beijing, Shanghai and Zhengzhou perform well on both networks, while the remote areas such as Kuerle and Lasa has low values, indicating the urban spatial interaction vitality is related to the construction of transportation network infrastructure and is consistent with the actual situation.
Thermal behavior of C32 carbon nanoparticles in a nitrogen atmosphere
Authors: Barbin N M , Yakupova L V , Terent’ev D I , Kuanyshev V T
Published: November 2020

Using the method of computer thermodynamic modeling, the behavior of carbon nanoparticles is studied C32 when heated in nitrogen atmosphere at pressure 105 Pa. The modeling consisted in a complete thermodynamic analysis of the system using the TERRA software package. Experiment temperature range – from 273 to 3373 К. A graph of the carbon balance in the system is built C32-N2, the ongoing physical and chemical processes are described, divided into four classes. The equilibrium constants of reactions with the allocation of temperature ranges are calculated and graphs of the dependence of the equilibrium constants of reactions on temperature are given.
Effect of high-pressure torsion and subsequent aging on the structure, microhardness, and electrical conductivity of Cu-7% Cr and Cu-10% Fe alloys
Authors: Martynenko N S , Straumal P B , Bochvar N R , Aksenov D A , Raab G I , Dobatkin S V
Published: November 2020

The structure, microhardness, and electrical conductivity of Cu-7% Cr and Cu-10% Fe alloys are studied after high-pressure torsion (HPT) and subsequent aging. It is shown that the grain refinenment after the HPT to 230 ± 12 nm and 275 ± 11 nm for Cu-7%Cr and Cu-10%Fe alloys, respectively, leads to a significant increase in their microhardness. Additional aging leads to a simultaneous increase in the microhardness and electrical conductivity of hardened alloys. At the same time, heating both alloys after HPT improves their electrical conductivity, but negatively affects their microhardness due to an increase in the average grain size to 357 ± 23 nm and 411 ± 46 nm for Cu-7%Cr and Cu-10%Fe alloys, respectively. The best combination of microhardness and electrical conductivity was obtained for the Cu-7%Cr alloy after quenching and aging and is 1.66 ± 0.06 GPa and 76.6 ± 1.6 %IACS, respectively.
Behavior of magnetization in high magnetic fields of (Sm,Ho)2Fe17N2.4 powders obtained by mechanical grinding
Authors: Tereshina I , Ivanov L , Veselova S , Verbetsky V , Gorbunov D
Published: November 2020

Fundamental magnetic properties of the R2Fe17 compounds are highly sensitive to the atomic substitutions and interstitial absorption of light elements (nitrogen or hydrogen). In our work, both were combined, and the influence of the substitutions in the R-sublattice and nitrogen absorption on the magnetization behavior of Sm2Fe17 compound in magnetic fields up to 58 T was studied. (Sm,Ho)2Fe17N2.4 nitride was prepared. Magnetisation measurements were carried out on powder samples. Nanopowders of (Sm,Ho)2Fe17N2.4 were obtained by mechanical grinding. The grinding time was varied from 0 to 60 minutes. The strength of the inter-sublattice coupling in all samples is estimated by analyzing high-field magnetization data. In our work is shown that nitrogenation weakens the inter-sublattice exchange interaction in the compounds up to 45 - 50 %. Grinding time and, as a consequence, particle size affects the inter-sublattice exchange interaction. To achieve a ferromagnetic state, fields are needed well in excess of 58 T.
Development of methods for determination of impurity elements in zircon-alumo-ytterbium ceramic materials for medical purpose
Authors: Volchenkova V A , Kazenas E K , Andreeva N A , Fomina A A , Ovchinnikova O A , Penkina T N , Rodionova S K , Smirnova V B , Podzorova L I , Il’icheva A A
Published: November 2020

Determining the presence of impurities, carcinogenic and harmful elements in the synthesized materials is a necessary step to identify the field of application, especially for medicine. Methods have been developed for the atomic emission with inductively coupled plasma (AES-ICP) for simultaneous quantifying of impurities contents: Al, As, Ba, Be, Bi, Ca, Cd, Ce, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Si, Ti, Y, Yb, Hf, V, Zn in new zircon-alumo-ytterbium ceramic materials for medical use. Various schemes for dissolving samples were offered for different types of ceramic compounds. Elements were determinate with founded optimal analytical parameters. The influence and methods elimination of matrix elements (Zr, Al, Yb) were studied. It allowed to determine elements with good metrological characteristics in a wide range of concentration from 1·10-3 to n·10% without preliminary separation of the matrix and without using solid standard samples. The relative standard deviation (Sr) does not exceed 0.12.
Multi-surface phase demodulation based on the selective weighted multi-step algorithms
Authors: Chang Lin , Yu Yingjie , Zheng Weiwei , Xu Qulei , Wang Chen , Sun Tao , Liu Tingtui
Published: November 2020

The transparent parallel plate plays a practical role in the optical system. The accurate measurement of its surface topography is a great importance of quantifying and evaluating the performance of the transparent plate. Based on the phase shifting interferometry, a measurement method using triple iteration and selective phase demodulation algorithms is presented, which can realize multi-surface interferometry at arbitrary positions. Through the design of the three-dimensional data matrix used to store errors, its element pixels can be read out a wealth of key parameter information, thus guiding the design of the algorithm, realizing the unity of the actual use of the algorithm and the measurement scheme, achieving the multi-surface interference measurement with the minimum residual errors with arbitrary cavity length, and expanding the limitations of the traditional weighted multi-step algorithm.