bluuebunny/crossref_metadata_2025 huggingface dataset:
The Influence of Pikobar Application in Suppressing the Rate of Coronavirus Spread
Authors: Wicaksono Bagus Dwi , Indrawan Dwin , Riana Dwiza , Taufik Andi , Nuryamin Yamin , Wasesha Dian Ambar
Published: November 2020

Coronavirus has developed massively in Indonesia, in the face of Coronavirus, Indonesian people are required to be able to survive the outbreak, by not interacting directly between people. Pikobar is one of the applications on a smartphone (android), which can provide up-to-date information about the development of the coronavirus in Indonesia (especially in West Java), which allows people to receive that information without having to do physical interaction between communities. To find out the level of customer satisfaction in using the pikobar application, the TAM method is used. the data collected was obtained from the results of a survey of the Indonesian population. The survey results were processed using smartPLS. From the results of the study showed if perceived ease of use of the application greatly influenced the community to use the pikobar application, so that the public could use the application in accordance with the application’s purpose, namely to reduce the rate of coronavirus spread.
Implementation Of The Lab Rotation Model In Blended Learning Based On Student Perspectives
Authors: Adiwisastra Miftah Farid , Mulyani Yani Sri , Alawiyah Tuti , Wibisono Taufik , Iskandar Iqbal Dzulfikar , Purnia Dini Silvi
Published: November 2020

The use of digital technology requires the world of education to transform digital so that students are increasingly in demand. One of them is Islamic Boarding School education which metamorphoses by providing education that is not only oriented towards religious knowledge but broader in the mission of improving the quality of human resources to be able to face a wider real life in accordance with challenges of the times. Various learning methods are applied in order to be able to balance and develop. One of them is the use of the Blended learning method, which is a learning method that combines technology-based learning and information with classroom or face-to-face learning. Combining learning or combining face-to-face, distance learning, and e learning. To implement the method, a study was conducted at the Darul Muta’allimin Islamic Boarding School Middle School to see students’ perceptions. The blended learning model used is the lab rotation model. The results of research students’ perceptions of learning using the blended learning method get a positive response with a value of 65% agree because it provides motivation for students to study hard and is very helpful in learning and the material provided is easy to understand.
Approval of Sharia Cooperative Customer Financing Using PSO-Based SVM Classification Algorithm
Authors: Nurajijah  , Amsury Fachri , Saputra Irwansyah , Frieyadie  , Sulistyowati Daning Nur , Rifai Bakhtiar
Published: November 2020

Credit financing approval is an important task for financial institutions. The accuracy of decision making in accepting or rejecting credit applications must be precise and accurate. Data mining techniques can help credit approval with a smaller risk of error. Previous research has been carried out to classify customer loan history data with the highest accuracy obtained by the Support Vector Machine (SVM) algorithm. This study aims to improve the accuracy of previous research in classifying loan history data based on smooth and stuck credit labels to predict credible prospective customers. The algorithm used in managing the data is Support Vector Machine with Particle Swarm Optimization (PSO). The dataset used was 869 records from January 2015 to September 2018, labeled 291 records and 578 records smoothly. The results of the study proved that the Support Vector Machine algorithm based on Particle Swarm Optimization produced the best performance with an accuracy of 90.91% compared to SVM without PSO of 89.86%.
Measuring The Quality of Website Services covid19.kalbarprov.go.id Using The Webqual 4.0 Method
Authors: Handayani Kartika , Juningsih Eka Herdit , Riana Dwiza , Hadianti Sri , Rifai Achmad , Serli Rosi Kusuma
Published: November 2020

Website covid19.kalbarprov.go.id is a website that serves as a medium for spreading information about the spread of the coronavirus in West Kalimantan. Currently, the number of covid19.kalbarprov.go.id website users is very large because many people want to know the latest information about the coronavirus in the West Kalimantan region. Therefore it is necessary to measure the level of user satisfaction on the site covid19.kalbarprov.go.id. The purpose of this study is to determine the level of user satisfaction with website services using the Webqual 4.0 method. Webqual is one method of measuring website quality based on the perception of end-users. Webqual variables are usability, information quality, service interaction quality, and user satisfaction. In processing research data using the smartPLS application to determine the level of validity and reliability of the data obtained. The results showed that usability, information quality, and service interaction had a positive effect on User Satisfaction. So it can be concluded that the quality of usability, quality of information, and quality of service interaction affect the satisfaction of website users covid19.kalbarprov.go.id.
Utilization of Information System in Electrical Panel Project Management to Provide Various Facility in Project Implementation
Authors: Soleh Oleh , Rosdiana  , Dewi Meta Amalya , Ningsih Yosi Fitria
Published: November 2020

The development of technology, especially in the field of information and communication from year to year plays an important role in human life. At present many projects specifically deal with work in the field of information technology. Project management is a scientific discipline in terms of planning, organizing, managing, to be able to achieve project objectives. For a small company that is just running a business, a system that can do the processing of a project is needed. Usually the data processing and project control processes still use manual paperwork and result in not requiring a lot of time so that it impacts on the preparation of project reports. The existence of a project management system can make it easier to make reports on the results of ongoing projects. The research method used consisted of; data collection methods, namely: interviews, observation, and literature study. For analysis using the BSC (Balanced Scorecard) analysis method. This system is made using PHP programming language and Mysql database. The system design uses the design of the UML (Unified Modeling Language) model. The testing method used is the Black box Testing method. The results of this study are a project management application system that helps companies in terms of data management, starting from the tender project, material data, until the reports generated.
Identification of Diabetic Retinopathy with Retinal Fundus Imagery Using Probabilistic Neural Network
Authors: Elveny M , Anjulina T , Siregar B , Syah R
Published: November 2020

Diabetic retinopathy is a microvascular complication of diabetes mellitus that attacks blood vessels in the retina. The main characteristics of diabetic retinopathy are microaneurysm, retinal haemorrhages, exudates, and neovascularization. One of the methods used to diagnose diabetic retinopathy is by examining the retinal fundus image. The examination is still done manually by an ophthalmologist. Manual examination requires a high level or concentration and misidentification may occur because some diabetic retinopathy characteristics are difficult to see directly, so it is needed a method that can facilitate the ophthalmologist in making decisions to identify diabetic retinopathy. The method proposed in this research is Probabilistic Neural Network to identify diabetes retinopathy. Before the identification stage is carried out, the retinal image will go through the pre-processing stage in the form of resize, green channel, contrast stretching and feature extraction using the Gray Level Co-Occurrence Matrix. After testing in this research, it was concluded that the proposed method was able to identify diabetes retinopathy with an accuracy of 86.8%.
Classification of Road Surface Quality Based on SVM Method
Authors: Afenika Adhelinia , Gunawan P. H. , Tarwidi D.
Published: November 2020

Damaged and potholes roads can occur due to rain puddles, too many heavy vehicles, poor asphalt quality, or maybe long road life. Damaged roads can hamper activities and endanger the safety of road users. It is necessary to monitor road quality periodically which is conducted by government, so that roads improvement can be done quick as possible. The aim of this study is to build a system that can classify roads surface quality. Support Vector Machine (SVM) classifications method is used to classify roads based on roadworthiness. In this study, 300 road surface data which contains good/smooth and damage quality of road are used. The simulation results show that SVM model can classify road surface data into two classes with average accuracy of 93%. The results can be a recommendation for government to prioritize which roads need to be improved.
A New Framework of Feature Selection Approach for Sentiment Analysis
Authors: Wahyudi Mochmad , Zarlis Muhammad , Mawengkang Herman , Efendi Syahril
Published: November 2020

Undoubtedly that the huge business data could make data analysis becomes more complicated such that the decision-making process would be out of reach. This condition happens. In the fields of consumer buying behavior, A well-known method called sentiment analysis can help in extracting information about the up-to-date trends and is able to increase market value of product through improving its quality. One of the approaches in solving the sentiment analysis is feature selection technique. However, this technique contains a combinatorial behavior and the analysis of the huge data can experience uncertainty parameter. This paper describes a framework for solving the sentiment analysis based on feature selection approach using a stochastic combinatorial programming.
Association Rule Implementation Using Algorithm Apriori To Analize Fishing Pattern In Indonesia
Authors: Kristiana Titin , Putri Sukmawati Anggraeni , Nurmalasari  , Handayani Rani Irma , Merlina Nita , Yunita Norma
Published: November 2020

There are more than 80 species of fish caught by fishermen in the sea of Indonesia. To find out what kinds of fish mostly caught, it is necessary to analyse the data pattern of fish being caught. The activities of searching and associating the data pattern are closely related to data mining technique that being used to discover the rules of association of items. In this associative rule method, there are two process can be used: the process of generating frequent itemset and finding associative rules. The Frequent Itemset Generation is a process to get the connection of the itemset and the value of the association based on the value of support and confidence. The algorithm used to generate the frequent itemset is Apriori Algorithm. The Apriori Algorithm has a weakness in the extraction of the appropriate feature of the used attributes. This condition causes the rules formed in large number. This research applies Apriori Algorithm based on principal component analysis to obtain more optimal rules. After the experiments using the apriori algorithm applied with the magnitude ˚ = 30, minimum Support 80% and Confidence 80%, the result of the rule formed are totally 82 rules.
Implementation of Data Mining in Grouping Percentage of Blind Letters Age 15+ By Province Using K-Means Algorithm
Authors: Saifullah  , Hidayati Nani , Solikhun
Published: November 2020

Data Mining is a method that is often needed in large-scale data processing, so data mining has important access to the fields of life including industry, finance, weather, science and technology. In data mining techniques there are methods that can be used, namely classification, clustering, regression, variable selection, and market basket analysis. Illiteracy is one of the factors that hinder the quality of human resources. One of the fundamental things that must be fulfilled to improve the quality of human resources is the eradication of illiteracy among the community. The purpose of this research is to determine the clustering of illiterate communities. The results of the study are illiterate data clustering according to the age proportion of 15+, namely 1 node of the high group, the low group has 23 nodes, and the medium group of 10 nodes. The results of this study become input for the government to determine illiteracy eradication policies in Indonesia based on provinces
Comparison of Text Mining Classification Algorithms in Interbank Money Transfer Application
Authors: Masripah Siti , Utami Lila Dini , Amalia Hilda , Nurlaela Dini , Ryansyah Muhamad , Yusuf Lestari
Published: November 2020

Funds transfer is a series of orders from the sender whose purpose is to move money from the sender to the recipient. The high interbank transaction fees imposed on each bank makes people use an interbank money transfer application, interbank money transfer transactions such as the Flip application are much in demand by the public because there are no administrative fees imposed on users. Opinion of the users of the application is processed using a text mining classification algorithm, namely the Naïve Bayes Algorithm and k-NN, the two algorithms are compared to produce which algorithm has high accuracy in processing the opinion of the flip money transfer application. Based on this matter, researchers conducted a sentiment analysis of the Flip Application, K-Nearest Neighbor (k-NN). After conducting research on sentiment analysis of Flip Applications, the Naïve Bayes classification algorithm has an accuracy of 91.25% and an ROC curve with an AUC value of 0.500. Whereas K-Nearest Neighbor has an accuracy of 85.25% and an ROC curve with an AUC value of 0.937. The Naïve Bayes algorithm can be said to be ”good classification” and the public can make the decision to use the Flip Application.
Optimization Of The Decision Tree Algorithm Used Particle Swarm Optimization In The Selection Of Digital Payments
Authors: Ariyati I , Rosyida S , Ramanda K , Riyanto V , Faizah S , Ridwansyah
Published: November 2020

One of the developments of information technology in Indonesia was the developments in Fintech (financial technology) that made it easy for people to access financial products, facilitated online transactions and also increased financial literacy. The development of fintech occurs when the use of cash was reduced to cashless when making payment transactions so that transactions would be more practical, easy, safe and comfortable. The purpose of this study to improve the quality of decision tree modeling and accuracy in the selection of digital payments. This research was focused on determining the fintech applications that were widely used by the people of Indonesia, namely Gopay or Ovo by comparing the advantages of applications and features of each fintech application. Optimization method of Decision Tree Algorithm (C4.5) and Particle Swarm Optimization by selecting several attributes including the level of ease of use, data security, application trust and convenience, maximum balance increase, discounts, cashback, ease of top-ups, range of existing merchants, return the money and customer complaint services. The results of the development of a decision tree algorithm based on particle swarm optimization provide a good classification and increase the validation value in the selection of digital payments.
Expert System For Detection Glaucoma Disease Using Certainty Factor Method
Authors: Hardi SM , Surbakti F P , Elviwani
Published: November 2020

Glaucoma is an eye disease that attacks nerves of human eye and it’s often not realized by sufferer until further damage to the eye nerve occurs. Glaucoma is second most common cause of permanent blindness according to Indonesian Ministry of Health. Permanent blindness caused by glaucoma can be avoided by early detection of disease and appropriate regular treatment. Early detection needed in an effort to prevent glaucoma can be done using an expert system. Expert system is an artificial intelligence program that can make reasoning and thinking like an expert in a particular field. Expert systems with certainty factor methods can describe level of certainty of expert expressions such as possible, most likely, and almost certain. Thus everyone can use an expert system to do glaucoma examination. In this research, diagnosis results will be obtained in the form of a percentage of a person’s CF value for glaucoma which is calculated using the certainty factor method based on the CF value of the symptoms entered by the user. Based on results of tests conducted, there are 8 out of 9 case data that are in accordance with the results of expert system diagnosis using certainty factor method that has been built. In other words, expert system has 90% accuracy in diagnosing glaucoma.
Support Aggregate Analytic Window Function over Large Data with Spilled State
Authors: Shi Xing , Wang Chao
Published: November 2020

Analytic function, also called window function, is to query the aggregation of data over a sliding window. For example, a simple query over the online stock platform is to return the average price of a stock of the last three days. These functions are commonly used features in SQL databases. They are supported in most of the commercial databases. With the increasing usage of cloud data infra and machine learning technology, the frequency of queries with analytic window functions rises. Some analytic functions only require const space in memory to store the state, such as SUM, AVG, while others require linear space, such as MIN, MAX. When the window is extremely large, the memory space to store the state may be too large. In this case, we need to spill the state to disk, which is a heavy operation. In this paper, we proposed an algorithm to manipulate the state data in the disk to reduce the disk I/O to make spill available and efficiency. We analyze the complexity of the algorithm with different data distribution.
Real-Time Scheduling Parallel Tasks on Multicore Platforms
Authors: Lei Zhenyang , Lei Xiangdong , Long Jun
Published: November 2020

Shared resources on the multicore chip, such as main memory, are increasingly becoming a point of contention. Traditional real-time task scheduling strategies focus on solely on the CPU, and do not take in account memory access and cache effects. In this paper, we propose real-time parallel scheduling (PRTTS) strategy on multicore platforms. Each task is represented as a directed acyclic graph (DAG). Tasks priorities are assigned according to task periods. In PRTTS scheduling strategy priorities of tasks which access memory are promoted over priorities of tasks not accessing memory. Tasks which read/write data in cache dynamically have their priority increased above all tasks. The results of simulation experiment show that proposed new scheduling strategy offers better performance in terms of core utilization and schedulability rate of tasks.
Research on Enterprise Service Governance Based on Service Mesh
Authors: Tian Yuchuan , Hao Haitian , Xu Xiaojian , Liang Bing
Published: November 2020

With the technology development, enterprise applications are rapidly changing from the traditional centralized application architecture to the distributed, microservices architecture (MSA). However, with the further development of MSA, enterprises are facing new challenges while enjoying the convenience brought by microservices, e.g. how to manage a large number of services, how to quickly locate and troubleshoot these services, how to avoid the performance issues, etc. As the next generation of MSA, service mesh is attracting more and more attentions and can be used to solve above problems. On top of service mesh, this paper proposes one mechanism that largely customizes service mesh implementation technologies. Furthermore, we implement this mechanism in a real project of one local large financial company. The final result illustrates that the mechanism is not only feasible but also effective to solve aforementioned service governance problems.
Community Management System Based on Embedded WEB Server Data Transmission Method
Authors: Yang Faquan , Su Huana , Huang Mei , Cai Zihong , Lan Di
Published: November 2020

in this paper, we design a data transmission method based on embedded WEB server of the community management system, the system through the embedded WEB server hang community intelligent systems, intelligent community system within the family in all the various functions of subsystems such as uploaded information fusion and data processing, implementation of the intelligent management of community operating.
The Method to Add Sky Background in 3D Virtual Engine
Authors: Chu Weijun , Liu Qing
Published: November 2020

In GTENG engine, scene manager is used to establish the corresponding mesh nodes of skybox or sky hemisphere, and sky texture files are set up in the order of the built-in texture name. These sky texture files are placed in the directory that can be searched by state manager, and they can be called when using to generate sky background. This paper gives the general process of creating skybox and sky hemisphere. By classifying and defining the outer, inner and cloud layer of skybox, the composition of their texture files is explained. Further, the general procedure of making skybox texture by using Terragen software is discussed. The methods of making outer and inner sky texture are illustrated, including setting and adjusting atmosphere, light, water, camera, coordinate, rendering and texture block. The application shows that it is a very effective and connvenient method to add sky background in GTENG engine cooperating with Terragen software to create skybox texture, and the effect of sky background in the application of 3D visual simulation is realistic.
Filling Algorithm for the Missing Value of Network Stability Monitoring Results Based on Big Data
Authors: Lyu Pinpin
Published: November 2020

In the research field of network stability monitoring based on big data, there are some disadvantages. For example, the processing method of the missing value of results influences execution effect, interfere the final filling effect and can only be applied in small data set. Therefore, a missing value filling algorithm for network stability monitoring results based on big data is proposed. The evaluation index of network stability monitoring based on big data is selected, and data missing mode and missing value filling mechanism are analyzed. The filling algorithm for the missing value of results based on big data is designed, the missing value filling parameter framework is constructed, the missing value filling parameters are processed, and the filling of the missing value of network stability detection results is completed. Compared with the traditional algorithm, the experimental results show that the efficiency of the designed missing value filling algorithm is 70.3% higher than that of the traditional filling algorithm, and the accuracy rate is far higher than the traditional method.
Driving Assistance System Based on Deep Learning and Traditional Vision
Authors: Bian Zhenwei , Yu Tao , Zhang Xin , Gong-Ye Xiaoyan
Published: November 2020

Relevant technologies such as computer vision and artificial intelligence are cheaper and easier to implement than detection technologies implemented by hardware such as lidar and radar. Cars are equipped with advanced intelligent driving assistance systems to prevent or reduce traffic accidents. In this context, this paper will identify and analyze the most important traffic lights, vehicles, and lane lines in traffic. Based on ImageNet pre-training, SqueezeNet builds fine-tuned network recognition traffic lights. Aims to achieve an assisted driving system that integrates deep learning and traditional vision. The final model size is only 7.84MB, the recognition accuracy is as high as 94.95%, and the processing speed is 12.4ms / frame. The single-frame processing speed of recognizer of YOLO v3 trained vehicle and classifier of B-CNN trained vehicle is up to 24.47ms. Using computer vision and mathematical operations, image perspective transformation, and polynomial fitting to analyze lane lines has the advantage of reducing cost.
Intelligent Analysis of Big Data for the Preventing of External Force Destruction on High-voltage Transmission Lines
Authors: Wu Peng
Published: November 2020

By constructing an intelligent analysis network, we can analyze autonomously and at the same time interact with the data. It makes the analysis more accurate, and it can construct a systematic model of the results. It can share to each smart terminal through the collation and analysis of the cloud computing library. The process will be transmitted to the background. Through the integration of big data the background collates a model of analysis and share to all the terminals. This will upgrade the ability of thinking on the terminals.
Detecting the Overlapping Community by Using Extract Method
Authors: Ma Yuejiao , Liu Yongjun , Tao Ji
Published: November 2020

The power overlapping communities are getting more and more attention in the complex network. In many networks, communities are not disjoint. It is very difficulty to get complete information of the network, such as the WWW, the local community detection has been studied by researcher. In this paper,we applied the local community detection to detecting the overlapping communities. Based on the algorithm in [11]. In this paper, we make an improvement to let the source node selected in each time is fixed, and then the algorithm has a certain stability.Even though the experimental results have no great improvement, but our improvement makes the algorithm has a certain stability.
Higher Education Online Courses Personalized Recommendation Algorithm Based on Score and Attributes
Authors: Wu Wenbin , Wang Bixi , Zheng Wenfeng , Liu Yan , Yin Lirong
Published: November 2020

Education through the online platform has gained popularity. While the online course keeps increasing, its benefit does not increase in a proportional way. Personalized recommendation systems can well solve this problem by mining users’ interests and preferences. Aiming at the cold start problem, this paper improves the collaborative filtering algorithm by combining the user score and project attribute characteristics of the online course platform. The network communication technology is used to obtain the user ratings and project attribute data to verify the feasibility of the recommendation algorithm, adjust the parameters in the model, and compare the accuracy of the algorithm. The designed algorithm can provide accurate and rapid personalized recommendation services, which is convenient for users and conducive to the development of the platform. The improved algorithm solves the cold start problem compared with the traditional algorithm with a significantly improved prediction accuracy. The scheme can also be modified according to the changes in user preferences and can achieve good real-time recommendation effect.
Bootstrap Estimating the Long Memory Parameter of Long Memory Time Series
Authors: Xing Yuhong
Published: November 2020

This paper evaluates the finite sample performances of three bootstrap methods, sieve AR bootstrap (SARB), fractional differencing sieve bootstrap (FDSB) and fractional differencing block bootstrap (FDBB), inestimating the long memory parameter in long memory time series. Extensive simulations show that the FDSB method outperforms others when estimating long memory parameter, and has stable estimated results in most cases.
Mode Control System of Intelligent Lighting Scene
Authors: Yang Faquan , Liang Porong , Cai Zihong , Lan Di
Published: November 2020

In this paper, through the wireless data communication technology to digital control of lighting, reach the purpose of reference light intelligent control, through the single chip microcomputer technology for home lighting more in terms of intensity, color, steering gear (each shift 6 or above) setting, a variety of tie-in, in order to meet the people under the condition of different patterns of lighting requirements of light.
Object Detection in Remote Sensing Images with Mask R-CNN
Authors: Gan Yuhang , You Shucheng , Luo Zhengyu , Liu Ke , Zhang Tao , Du Lei
Published: November 2020

With the wide applications of remote sensing technology in engineering, the demands of efficient object detection algorithms for remote sensing images have also been significantly increased in recent years. Traditional detection methods have the shortcomings of low accuracy and poor robustness, which is difficult to be applied to remote sensing images with complex background and varieties of objects. Recently, the deep convolutional neural networks have already shown great advances in object detection and outperformed many traditional methods. In this work, we study the performance of a region proposal-based method, Mask R-CNN, for detecting airplane and ship in remote sensing images. Specifically, we add an FPN module to improve the accuracy of small objects detection, and a mask branch is used to describe the shape of objects. In addition, we used a series of data augmentation strategies during training for meeting the CNN’s requirement of training samples diversity. The experimental results show that our model has superior performance in object detection of remote sensing images.
Analysis of Aflatoxin B1 in Peanut Oil by near Infrared Spectroscopy
Authors: Hu Zhui , Cai Zihao , Yang Xingxing , Xu Qiang , Zheng Xiao , Ye Jianping , Yang Junsheng
Published: November 2020

Aflatoxin is natural highly toxic substances. Aflatoxin B1 is the most common one, and is usually detected in moldy peanut, corn and other foods. In this paper, for the detection of aflatoxin, near-infrared spectroscopy technology was applied, and three preprocessing methods, two characteristic wavelength extraction algorithms and three parameter optimization methods were adopted to establish a variety of models for the quantitative prediction of aflatoxin B1 content in peanut oil. The influences of different spectral preprocessing methods, characteristic wavelength extraction methods, and parameter optimization algorithms on the prediction performances of these models were studied. Additionally, the ways to find an optimal quantitative prediction model and qualitative identification model were explored.
Research on Artificial Intelligence Ethics in the Field of Art Design
Authors: Li Yueen , Gu Jin , Wang Liyang
Published: November 2020

Through the analysis of the ethical dilemmas encountered by artificial intelligence in the field of art design, the method of systematic analysis is used to provide the methods and models of art ethics research under the condition of artificial intelligence, and the implementation of artificial intelligence ethical standards is adjusted to study new ethical principles., And strive to maximize the practical effectiveness of artificial intelligence ethics; establish a reliable art creation norm under artificial intelligence conditions. On the basis of emphasizing technical research, the largest possible research on humanities investigation. And from the research and top-level design of artificial intelligence ethics, promote the improvement of people’s livelihood and welfare, promote the healthy development of the industry, and grasp the initiative of a new round of technological revolution. Seize the period of strategic development opportunities for artificial intelligence, accelerate the pace of ethical research and innovation, build a competitive advantage in the development of artificial intelligence, build an intelligent society as soon as possible, and use technology to benefit the people.
Analysis on Reasonable Operation Mode of Newly Built Sediment Laden Reservoir
Authors: Liu Yanhong , Meng Jinjie , Wang Wenting , Jin Zhiwei
Published: November 2020

The operation mode of the reservoir should be selected according to the task, Reservoir type, Reservoir capacity, reservoir runoff and sediment of the reservoir. For the river with much sediment, the different Operation mode of reservoir will affect the Sediment deposition pattern and sedimentation rate of the reservoir, and have a great impact on the difference of the reservoir. The selection of operation mode of newly-built reservoir with sediment is directly related to the operation period and operation benefit of the reservoir. How to select the operation mode of the reservoir to maximize the benefit of the reservoir has been discussed and studied by water conservancy workers in recent years. Taking Gahaishan reservoir in Kulun banner of Inner Mongolia as an example, the paper uses one-dimensional reservoir sediment mathematical model, combined with suspended load unbalanced sediment transport equation, flow sediment carrying capacity formula and resistance formula suitable for sediment laden rivers, and uses existing sedimentation data of reservoirs in similar basins for verification. This paper briefly describes how to calculate the sediment deposition for the newly-built sediment laden reservoir and how to reasonably select the appropriate operation mode.
Research on the Construction of Teaching Quality Evaluation System
Authors: Liu Hongcheng , Zhu Yang
Published: November 2020

With the increasing application of information management methods to teaching management activities in Colleges and universities, it is imperative to use information system to realize the intelligent, automatic and accurate evaluation of teaching quality. This paper takes the evaluation system of teaching quality as the research object, focusing on the design and implementation of the evaluation system of teaching quality, so that we can evaluate the teaching performance of teachers fairly, reasonably and objectively from different perspectives such as students, leaders, supervision experts and colleagues, and fully mobilize the enthusiasm of teachers to complete the teaching work through the application of the system.
Research on Cloud Service Reliability Evaluation Model from the Perspective of Equal Protection 2.0
Authors: Li Zhijie , Suo Hui
Published: November 2020

In the era of equal assurance 2.0, network evaluation has brought great challenges to enterprises, institutions and evaluation institutions. The evaluation framework of cloud service reliability is proposed, which is divided into four characteristics: accessibility, continuity, recoverability and data backup, and further divided into sub characteristics of mutual correlation and combination of subjective and objective. It provides a measurement method for each sub feature The evaluation results are obtained by processing and calculating.
Neural Network Analysis Model of Air Environmental Factors for Urticaria Skin Disease Susceptibility
Authors: Wang Weihua
Published: November 2020

There is a certain correlation between the occurrence of skin diseases and external inducing factors, among which air quality is one of the important factors. Based on the analysis of outpatient medical records in a certain period of time, taking urticaria as a representative, the degree of skin disease susceptibility in outpatient medical records was divided. Combined with the changes of air quality indicators in the same period, the prediction model for analyzing the correlation between urticaria skin diseases and air quality indicators was established by using artificial neural network method, and a new way of skin disease risk prediction was discussed.
Research on the Integrated Development of Ice and Snow Industry and Health Industry——Take Genhe City and Tieli City as examples
Authors: Li Miaomiao , An Yonggang , Huo Pengfei , Zhang Lisheng
Published: November 2020

With the rapid development of modern society and economy, people’s pursuit of health is becoming more and more comprehensive, involving all aspects of material and spirituality. However, the ice and snow industry and the health industry are closely related social and economic phenomena, and the integration of the two is gradually increasing.[1]In recent years, various tourism activities such as ice and snow tourism, health tourism, and adventure tourism have become a necessary choice for people to return to nature, get close to nature, and pursue a healthy life. This article takes the integrated development of ice and snow industry and health industry in Tieli City and Genhe City as an example. Discuss the necessity and basic conditions for the integrated development of the ice and snow industry and the health industry and initially designed the “ice and snow + health” product system.
Fault diagnosis of photovoltaic array based on SE-ResNet
Authors: Fu Shaofeng , Cai Fenghuang , Wang Wu
Published: November 2020

In recent decades, fault diagnosis of photovoltaic (PV) arrays has become more and more important for the power generation of PV systems. Many traditional artificial intelligence methods have been successfully applied to use fault data samples to build fault diagnosis models, but most rely on manual feature extraction or expert knowledge to build diagnosis models, which is inefficient and may ignore some potentially useful features. Therefore, this paper proposes an SE-ResNet PV array fault diagnosis algorithm based on Residual Network (ResNet) and Squeeze-and-Excitation Network (SENet), and uses Bayesian Optimization (BO) optimize the parameters. In order to verify the effectiveness of the proposed fault diagnosis algorithm, a comprehensive fault experiment was carried out on the PV platform. Experimental results show that the model has achieved high overall performance in terms of accuracy, generalization performance, reliability and training efficiency, and has high practicability.
The Comparative Study on Underlying Features of PainingTang-ka Images and Natural Images
Authors: Zhang Weijiang , Dang Hongshe
Published: November 2020

Our purpose was to find the difference between the color features and texture features of natural images and painted Tang-ka images. Taking natural images as a reference, painted one were analyzed in HSV color space. The natural image and painted Thang-ga texture features were extracted, using the Tamura’s method. The data showed that the natural image’s coarse was larger than the painted Tang-ka, and painted Tang-ka were more saturated than natural images. Compared with natural images, the painted Tang-ka’s directonality has higher value. By contrast, the red Tang-ka had a higher value than the golden one.
Long-distanceinfrared video pedestrian detection using deep learning and backgroundsubtraction
Authors: Zhu Yaling , Yang Jungang , Xieg Xiaokai , Wang Zhihui , Deng Xinpu
Published: November 2020

Infrared video-based pedestrian detectionplays an important role in surveillance and automatic driving. Compared with vision cameras, infrared cameras have the characteristics of all-day and all-weather availability. The current infrared video detection methods generally follow the traditional paradigm. However, when objectsare at a long distance, it is challenging for existing methods to achieve accurate detection. Meanwhile, deep learning-based methods have been widely used in visible light object detection. In this paper, we introduce an algorithm which combines deep learning and background subtraction methods to achieve infrared pedestrian detection. Firstly, background subtraction method is performed to provide the inter-frame information for the deep learning module. Secondly, the RefineDet equipped with an attention module is used to improve the detection accuracy for small pedestrian. Moreover, we develop a novel infrared video dataset which includes long-distance pedestrian for performance evaluation. Experiments demonstrated that our method can achieve a superior performance as compared to RefinDetand SSD methods.
Z-Net: A Novel Way of Lane Detection
Authors: Zhang Ziyuan
Published: November 2020

In this paper we proposed a new neural network Z-net for lane detection. The novelty of this network is not the network itself but the detection method. After comprehensive investigation and analysis of the application of several used networks in lane detection, we analyzed the advantages and disadvantages of various networks and modified the existing network to get the Z-net. Under the influence of rapid development of lane detection, it is necessary to propose a neural network with fast detection speed and great accuracy. We found out that Z-netsatisfies these two requirements very well.
Study on academic Early Warning System for Information Engineering Vocational students
Authors: Luo Jing
Published: November 2020

Due to the expansion of higher vocational college enrollment, the different quality of students and the high quality requirements of enterprise talents, this paper proposes an academic early warning system for higher vocational college students, which can be used for the early warning of students’ performance and improve the teaching quality. Firstly, the clustering algorithm is analyzed, and the parallel K-means algorithm is adopted in the general algorithm. During the startup and running of the program, the processing time of the algorithm is short and the total consumption of the system is small. Compared with the existing data mining technology, parallel K-means algorithm has good practicability and flexibility.
Real TimeDetection and Tracking Method of Pilot’sHeadPositionBased on MTCNN-DeepSORT
Authors: Zhang Limin , Jiang Jie , Fang Wei , Liu Kai
Published: November 2020

In view of the problems of the traditional head detection and tracking technology in pilot cabin simulation training, such as low precision, poor real-time performance and easy to be interfered by the outside world, we propose a head detection and tracking method based on MTCNN-DeepSORT combined with deep learning method. Firstly, the continuous video images in the cockpit are preprocessed by computer vision, and then the processed images are extracted by convolution neural network to detect the face. At the same time, the facial feature points are marked. Through the spatial coordinate calculation of the obtained facial feature points, the pilot’s head posture angle is obtained, and then combined with DeepSORT tracking algorithm can track the pilot’s head continuously and in real time, and finally complete the detection and tracking of pilot’s head position. The experimental results show that the method based on MTCNN-DeepSORT has higher detection accuracy, better real-time performance and stronger robustness than the traditional methods.
Research Summary on Light Field Display Technology Based on Projection
Authors: HaiyangYu  , XiaoyuJiang  , XingpengYan  , Yan Zhiqiang , Wang Chenqing , Yan Zhan , Wang Fenghao
Published: November 2020

In recent years, three-dimensional display technology has developed rapidly, among which light field display technology is a type of technology with broad application prospects that can realize naked-eye stereo display. With the enhancement of digital image processing capabilities and the development of projection technology, projection-based light field display technology has also made great progress. This article analyzes the principle and development of light field display technology based on high-speed projection and projection arrays, briefly introduces the relevant experimental results of the research team at home and abroad, and finally discusses the application prospects of different technical directions.
A TSP algorithm based on link degree
Authors: Yu Guyang , Shi Huajun
Published: November 2020

The fundamentality of the traveling salesman problem (TSP) is the choice of an edge in the next step. This paper proposes a concept of link degree, which can display the potentiality of an edge to belong to the shortest Hamiltonian cycle in a more effectively manner and, on this basis, it presents a greedy algorithm for the TSP. Meanwhile, some relevant theorems and conjectures as well as some problems triggered are discussed as well.
Short-term Traffic Flow Prediction Based on Harmony Search Algorithm Optimized Wavelet Neural Network
Authors: Shen Guifang
Published: November 2020

ln order to improve the prediction accuracy of urban road short-term traffic flow, this paper proposes a short-term traffic flow prediction model of wavelet neural networkoptimized by harmony search algorithm(HS-WNN),to solve the problem of slow convergence speed and local optimization when the traditional wavelet neural network one-way gradient descent method is used for parameter optimization. The harmony search algorithm is used to optimize the parameters of the wavelet neural network, and the obtained optimal solution is used to optimize the initial value of the wavelet neural network model, and to predict the short-term traffic flow. And through the simulation experiment of the measured traffic flow data, it is verified that the prediction error of the HS-WNN in this paper is smaller than that of the WNN,HS-WNNhas a higher accuracy.
Design and implementation of remote experimental control platform
Authors: Wang Chenyang , Lin Zijian , Li Ruikai , Chen Chen
Published: November 2020

Based on the Internet of Things technology, our team has developed a remote wireless control experimental device based on the Internet of Things, which combines traditional experiments with Internet of Things technology. In the example experiment in this paper, the rotation angle of the corresponding motor on the experimental device is changed by means of buttons in the intelligent terminal APP. At the same time, the physical clamping and pushing control function of the control hub is exerted by mechanical push rod, bayonet and other workpieces, an d then the pushing force and rotating force are provided by the motor for the push rod. Finally, the camera is used to monitor the instrument panel and transmit the image data information via the wireless communication module to achieve remote control.
The improved equilibrium optimization algorithm with multiple updating discipline
Authors: Gao Zheng-Ming , Zhao Juan , Li Su-Ruo , Hu Yu-Rong
Published: November 2020

In this paper, a hybridization algorithm of the equilibrium optimization (EO) algorithm and the slime mould (SM) algorithm was proposed. The better performance of the SM algorithm in optimization both benchmark functions and real engineering problems were all encouraging. And consequently, the multiple updating discipline for individuals in swarms was introduced to the EO algorithm. Simulation experiments were carried out and the faster convergence, lower residual errors, higher accuracy verified the improved EO algorithm would perform better than the original one.
A Classification Method of Arrhythmia Based on Adaboost Algorithm
Authors: Zhang Bing , Wen Jingye , Ren Huihui
Published: November 2020

An arrhythmia classification model based on an adaptive boosting algorithm is proposed in this paper. According to the AAMI standard, 15 kinds of abnormal cardiac rhythms are grouped and the datasets are segmented by the non-crossover method. The electrocardiogram (ECG) signals are denoised by the filter method, and then divided into fixed-length ECG beats, and five features are extracted from time-domain and frequency-domain. Then, the base classifier of the algorithm and its optimal algorithm parameters is selected to realize the multi-classification of cardiac anomalies, aiming at mining hidden knowledge from human physiological data to detect human health status, making the diagnosis process more automatic, efficient, and intelligent.