bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dynamic encryption method of user privacy data based on association rules hiding
Authors: Li Zongrong , Li Nanfang , Yuan Xuebin , Zhao Lei , Ma Xuezhi , Li Shengchun 
Published: February 2021

Abstract
Because the traditional data encryption method remains unchanged after the key is generated, the encryption scheme is single, the encryption cost is high, and it is easy to be cracked. Aiming at the above problems, the dynamic encryption method of user privacy data based on association rules hiding is studied. Based on the DGHV encryption scheme, a dynamic encryption scheme is designed to hide the mined user privacy data association rules. After the key is processed by confounding method, the dynamic encryption process of data is completed. By comparing with the traditional methods, it is verified that the encryption efficiency of the studied method is increased by at least 26.2%, which has the advantage of low encryption cost.
Probabilistic model of wind power output considering temporal autocorrelation and cross-correlation with forecast output
Authors: Sun Yue , Zhou Jiahao , Huang Wei , Yan Jiahao , Tie Yingqi , Lai Weibin , Cheng Xin , Jiao Dan , Xie Kaigui 
Published: February 2021

Abstract
Wind power output possesses temporal autocorrelation and cross-correlation with the forecast output. However, these two types of correlations are quite different. Existing studies fail to model them simultaneously, which reduces the accuracy of the conditional probability distribution of wind power output and has a negative effect on the economic operation of wind power systems. Therefore, this paper proposes a method for modeling the conditional probability distribution of wind power output that can take both correlations into account. The copula function is used to separately model the dependency between wind power outputs and the uncertainty of actual/forecast output. Based on pair-copula theory, the modeling of the conditional probability distribution of wind power output with respect to the two correlations is broken down into the chain transfer process of the traditional two-dimensional copula function. The probability distribution function of wind power output can be obtained as long as enough historical data is available. No specific forecast techniques are required. Using the actual wind field output data in Belgium, the proposed method is compared with the methods that only consider one of the correlations to prove its effectiveness.
Variation of PM2.5 concentration at some typical districts in Chengdu
Authors: Tang Hong 
Published: February 2021

Abstract
Aerosol particles are one of the most variable components in the Earth’s atmosphere. The study of aerosol properties plays an important role in science and application. The PM2.5 concentration at different locations in Chengdu city is studied in this paper. In order to measure the PM2.5 concentration at different locations in Chengdu city, three areas including the commercial district, the university area and the overpass in the main road are choose to represent the typical locations. The variation of PM2.5 concentration with time at different locations and different dates are tested with the particle instrument. According to these results, it is obvious to understand that the PM2.5 concentration is different at different location even within the same time. If with the conditions of rainfall and wind, the PM2.5 concentration will be reduced rapidly.
Calculation method of line loss rate of photovoltaic station based on PCA-GRNN
Authors: Li Yuan , Liu Jing , Tan Huang , Li Yajie , Diao Xinping , Yao Li , Xu Zhiguang 
Published: February 2021

Abstract
Line loss rate of substation area is a comprehensive economic and technical index of power companies. With the continuous expansion of grid connected scale of distributed generation, accurate calculation of line loss rate of substation area with distributed generation is imminent. In this paper, considering the basic operation attributes and the grid connection attributes of distributed energy, a calculation method of line loss rate of substation area based on principal component analysis and generalized regression neural network is proposed. Firstly, the influence factors of line loss are extracted for the area containing photovoltaic distributed generation, and the feature dimension is reduced through principal component analysis; secondly, the reduced features are used as the feature input of generalized regression neural network for training, and the error between the training results and the real line loss is analyzed; finally, the ability of the proposed algorithm in the area identification of high line loss rate is tested. The results show that the generalized regression neural network trained in this paper has good generalization ability, the calculation error of online loss rate is small, and the identification of high loss area has high accuracy.
Design of control algorithm based on high voltage transformer
Authors: Shixi Su , Hui Luo , Zhikun Liao 
Published: February 2021

Abstract
Based on the application of transformers in switching power supplies, the article makes a kind of digital algorithm control. Through the 32 series’ chip to control the start and maintain state of the equipment, the node voltage is continuously collected and the output is corrected in real time to ensure that the equipment remains stable. The article provides a complete algorithm design process and ideas including the calculation of the timer parameters, the configuration process of ADC voltage acquisition. Finally, the experimental verification is carried out to ensure the feasibility of the algorithm.
Study of Fault Diagnosis Distribution Network Based on Rough Set and Artificial Intelligence
Authors: Zhou Hui , Wang Zhong , Shi Chunqing , Liu Chaoying , Zhao ShiQin , Zhang Ninghuan 
Published: February 2021

Abstract
The current real-time data collected by the power grid includes remote measurement, remote signaling, and other fault factors. The research in this paper is based on the fault diagnosis technology of rough set theory and self-learning theory, taking fault influencing factors as conditional attributes, fault type as decision-making attribute, and generating rough set rule table through self-learning reduction through a large number of fault history records, the influence of meteorological factors, environmental factors, equipment factors and other factors on the probability of equipment failure, which can realize the route Risk level prediction and fault location. Case analysis shows that the distribution network diagnosis technology based on rough set plays an extremely important role in predicting the risk level of each section of the line and positioning after a fault occurs.
Research and Application of Algorithm Based on Maximum Expectation and Collaborative Filtering In Recommended System
Authors: Fan Ying , Ma Huan , Chen Zhongyang , Shen Keli 
Published: February 2021

Abstract
The problem of sparse information is easily found in information searching for new users in recommended system, and it also has difficulty in recommending related modules. In view of the problem, the maximum expectation algorithm in demographics is adopted to cluster users for neighbouring users, and then it is regarded as an input of collaborative filtering algorithm. As the users’ scores on different projects represent their preference, certain demand relevance exists in similar users when evaluating the same project. And this kind of relevance degree varies with the change of individual demand. Therefore, a cooperative filtering algorithm based on the change of user’ demand is put forward by introducing a time weight function, which alleviates the shortness of traditional cooperative filtering recommendation algorithm. By tracking the needs of users, the scoring matrix can be predicted. According to experiments comparison, this algorithm can help solve the problem of sparseness of user’s scoring matrix and the recommendation quality is improved.
Design of topic Web crawler based on improved PageRank algorithm
Authors: Yu Linxuan , Li Yeli , Zeng Qingtao 
Published: February 2021

Abstract
With the continuous development of network information technology, the network is filled with a large number of all kinds of unstructured data called big data. However, this data is not easily stored in a local database. People realize that it is essential to get useful information from the Internet efficiently. The effort to gather information by human hands has led to the emergence of web crawler technology. However, the existing search engines still have shortcomings in topic similarity judgment and web page sorting algorithm. Therefore, this paper applies PageRank algorithm to topic crawler, constructs a vertical search engine, and introduces topic relevance factor to suppress "topic drift" according to the shortcomings of PageRank algorithm.
Method for Predicting Cutter Remaining Life Based on Multi-scale Cyclic Convolutional Network (MSRCNN)
Authors: Li Tao , Yu YinQuan , Yang JinWen , Zhang Long , Tu WenBin , Yong Hao 
Published: February 2021

Abstract
In the process of predicting the remaining cutter life (RUL), the deep learning method such as convolutional neural network (CNN) does not consider the time correlation of different degradation states, which directly affects the accuracy of the remaining cutter life prediction. To extract the features with time series information to predict the RUL more effectively, this paper proposes a new deep neural network, which is named the multi-scale cyclic convolutional neural network (MSRCNN). In the MSRCNN, a cyclic convolutional layer is constructed to simulate the time correlation of different degradation states for mining the timing characteristics of the data. Multi-scale features are extracted through multi-scale convolution, and the convergence of parameters is improved by layer-by-layer training and fine-tuning. Finally, the RUL is predicted based on the features. The comparison with the published prediction methods of CNN and recurrent neural network (RNN) models proves that our proposed method (MSRCNN) is effective and superior in improving the accuracy of RUL prediction.
A Real-Time Oocyte Polar Body Detection Method
Authors: Yongxing Zhang , Liqun Li , Juan Liu , Huiquan Wang , Dan Zhang 
Published: February 2021

Abstract
Based on the extensive application requirements of cell micromanipulation in bioengineering, this paper focuses on the intracytoplasmic sperm injection technology of assisted reproductive technology in the field of obstetrics and gynecology, and develops algorithms to automatically and accurately identify the position of the polar body of the oocyte, laying a foundation for the realization of sperm and oocyte automated injection. This paper proposes an algorithm that can accurately locate polar body of oocyte; this method uses a lightweight U-Net network to semantically segment oocyte images and extract their edges, and then search for polar body based on image edges. This method has been verified on 524 pictures of mice oocyte, which is real-time and robust.
Evaluation on non-visual effects of lighting based on both physiological parameters and subjective ratings
Authors: jiaqi JU , hong SUN , ke XU , ting XU , yan JIN 
Published: February 2021

Abstract
This article explores the influence of different colors of lights on human physiological parameters and at the same time the effects on human emotions, and tries to explore the inner connection of the changes. It helps us to better understand the color of lighting and apply it in daily life or commercial purpose, Then the experience of visual sensation will be enhanced. This article examines the changes in physiological parameters and the subjective emotional ratings triggered by the six lighting conditions such as red, green, blue, warm white, cold white and darkness. From the experiment results, the changes of physiological parameters and subjective emotions can be observed under different lighting conditions, which can be applied to guide lighting design and projects.
The PINNs method discovery to the solution of coupled Wave- Klein-Gordon equations
Authors: Wang Tianyi , Chi Xuebin 
Published: February 2021

Abstract
Recently, the research of PDEs is regarded as one of the most important disciplines. Almost all scientific problems can be described by a differential equation, especially, many physical phenomena can be described by the system of coupled Wave-Klein-Gordon equations, which plays an important role in high-performance computing, control engineering, and electronic power system. Consequently, in our work, we use the PINNs to solve the numerical solution of coupled Wave-Klein-Gordon equations, to help us better understand the nonlinear physical phenomena, and to promote the rapid development of various fields such as in high-performance computing, control engineering, and electronic power system.
Sentiment analysis of movie reviews based on deep learning
Authors: Zhang Fuqian , Zeng Qingtao , Lu Likun , Li Yeli 
Published: February 2021

Abstract
In recent years, with the rapid development of NLP (Natural Language Processing) and deep learning, public opinion and public opinion on the Internet have decreased a lot compared to the past. Many Internet users have changed from mere "bystanders" to disseminators of Internet information. Movie review sentiment analysis technology is an emerging category in the field of information mining. More and more people have joined the "review army". The quality of a movie is closely related to movie reviews! The manual screening method not only consumes a lot of manpower and material resources, but also is inefficient. Therefore, the use of deep learning-based sentiment analysis has become the current general trend. Based on the principle of word mosaic (word vector) and deep learning, this paper proposes a movie review sentiment analysis technology based on deep learning and machine learning word mosaic. Experiments show that the method used in this article has reached the correct rate of emotional classification of movie reviews. 83.13%, the experimental results prove the practicability and scalability of this method and the effectiveness of this method.
Multi-timescale Distributed Model Predictive Control for Large-Scale Systems and a Case Study
Authors: Zhao Xiaoming , Zhang Wenli , Zhang Weidong , Zhang Wenzhao 
Published: February 2021

AbstractTo solve the control problem effectively of complicated large scale system with obvious difference in the dynamic response at each channel, a strategy based on multi-timescale and distributed communication mode is presented. These systems can be regarded as combinations of fast system and slow system, the response speeds of which are in two-time scale. The algorithm takes into account the fast and slow characteristics and the coupling relationship of each subsystem, uses the Nash optimal idea and the multi-time standard information prediction method to realize the optimization control of the whole system. A simulation example is given to illustrate the effectiveness.
Concentration Effect of Ethanol Extract Pinus merkusii Leaves Litter on Zea mays L. Seed Germination
Authors: Retnoningrum Mita Dewi , Setiawan Mohammad Arfi 
Published: February 2021

Abstract
Human resources is one of the main factor in forest management, so it is important to develop a model of community-based forest management through agroforestry system. The agroforestry system in Trenggalek was implemented by combining a pine plant with Bisi-2 corn varieties. However, the presence of dry pine leaves litter suspected to contain allelopathy, thus make the percentage of germination of seed corn in a low. This study aimed to determine the concentration effect of ethanol extract of pine leaves litter (Pinus merkusii) on the germination of seed corn (Zea mays L.) Bisi-2 varieties. The research design that used Completely Randomized Design and four replications with concentration variations of 0% (control), 10%, 15%, 20% and 25%. Based on ANOVA and Duncan Multiple Range Test with significant level of 5% showed that there was the influence of the ethanol extract of pine leaves litter on the germination of seed corn Bisi-2 varieties. The higher concentration, make the germination of corn seeds more inhibited.
The Profile of Delay Erasure Time and Imaging Plate Sizes to Dark Noise Evaluation in Carestream Computed Radiography System
Authors: Setiawan Agung Nugroho , Onney Ney Da , Jannah Marichatul , Prastanti Agustina Dwi 
Published: February 2021

Abstract
Increasing delay time between erasure and image acquisition can lead to the appearance of dark noise on the Computed Radiography (CR) imaging plate (IP). The purpose of this study is to investigate the time delay of the last erasure that can be affected in dark noise appearance of four sizes Carestream CR’s imaging plate in Radiology Laboratory of Poltekkes Kemenkes Semarang. This study used a quantitative experimental method. Each size of IP was exposed with one of Hand, Cranium, Chest, and Abdomen anthropomorphic phantom, read on CR, and left for 0 hours, 1 hour, 3 hours, 6 hours, 9 hours, 12 hours and 24 hours to produce blank images. The blank images after post-processing were evaluated with qualitative and quantitative criteria. Qualitative criteria produced uniformity images, without ghost images artifact. While quantitative criteria were determined by the exposure index value, pixel value, and pixel value standard deviation calculated by ImageJ. The results of this study showed that all of the IPs did not pass the criteria. Dark noise appeared in 0 hours after the last erasure shown from un-uniformity images, ghost images artifact, PV, and PVSD were overpassed the standard values. Dark noise has increased the time delay between last erasure and subsequent use. The imaging plate must be erased before it acquires the next images.
Development of Macromedia Flash-Based Mathematics Learning for Elementary School Students
Authors: Mahendra Maulana Rais , Supriansyah  , Zulherman  
Published: February 2021

Abstract
One of the learning media that needs to be developed is Macromedia Flash-based learning media the purpose of this study was to develop mathematics learning media in the form of a Macromedia application in solid Geometry for fifth-grade students of SDN Paseban 01 Pagi. The development research method used is Research and Development (R&D), with the ADDIE development model. At the analysis stage, a problem analysis and needs analysis are carried out. At the design stage, an initial design is made which is then validated by material experts and media experts. At the implementation stage, the design of instructional media was tested on 24 students of SDN Paseban 01 Pagi. The evaluation stage aims to analyze expert validation data and student trials. The data collected using the observation method were qualitatively analyzed. The validation data were then evaluated qualitatively and quantitatively in summary. The results showed that Macromedia flashed on space building materials that had been developed based on expert media assessments in terms of display feasibility 85% with highly viable categories, 83% writing learning design with highly feasible categories, engineering and software aspects with highly feasible categories so that the overall assessment of 83% with categories was very feasible. Material expert assessment in terms of feasibility of displaying 90% material with very decent category, 93% learning media utilization aspect with very decent category, quiz aspect 90% with the category is very feasible so overall assessment 91% with the category is very feasible. The trial of the learners was obtained as a result of the 84% display feasibility aspect with a very decent category, the presentation aspect of the material 81% with a very decent category, the aspect to the engineering and software appeal 83% with the category is very feasible so the overall assessment of 83% with the category is very feasible. Based on the results of this acquisition, it can be concluded that the Macromedia Flash application on spatial geometry material is very suitable for use as a learning medium in mathematics subjects.
The Implementation of Fuzzy-C Means to Categorize Poverty Data in Riau Province
Authors: Mustakim  , Saputa Hadi Eka , Mutasir  , Nuryanti  , Salmiah  
Published: February 2021

Abstract
Riau Province was the 13th in line as the poverty contributor in Indonesia. It was because of the fund from government was not properly distributed. Therefore, an identification system to distribute the poverty funding was needed in order to reach the citizens in poverty. Based on the available parameter, it could accelerate the eradication of poverty. Poverty caused by various factors, i.e. educational background, living environment, job and health. One of the solutions to distribute the fund properly was by categorizing the poverty data. This research used Fuzzy Clustering Means (FCM) method because every area has the possibility to become part of each cluster with different membership level between score 0 and 1. The recommended parameter was w = 2, Max_iter = 100, ε = 0.1, P0 = 10.000.0000, t = 1, C = 3 with Partition Coefficient Index (PCI) validity score 0.47. The web built based-system uses programming language PHP with Framework YII 2 Advanced. All clustered data was analyzed based on poverty variable level that was dominant in range of regency and District.
The Classification Status of River Water Quality in Riau Province Using Modified K-Nearest Neighbor Algorithm with STORET Modeling and Water Pollution Index
Authors: Mustakim  , Rosdina  , Ramadhani Dian , Afdal M. , Rahmawita Medyantiwi 
Published: February 2021

AbstractThe Department of Environment and Forestry, Pollution and Environmental Damage Control Division, has an active role in monitoring water quality in Riau Province. The rivers that are still monitored and managed are Kampar River, Siak River and Indragiri River. Division of Environment Pollution calculates river quality status manually using Microsoft Excel, this is not maximally done since this important information should be processed quickly. Division of water pollution must determine the right calculation to get the results of the water quality status. Because of many calculation formulas set by the government, the commonly used method is the STORET method and the Pollution Index. So, in overcoming the problem of classification, the researcher proposes the use of learning methods that can predict or determine the status of water quality with classification techniques on data mining that is Modified K-Nearest Neighbor (MKNN) which is a modification of K-NN. The calculation of the MKNN algorithm produced the highest accuracy of 85.10% at K = 5 using STORET result data as training data. While, using the Pollution Index data results, the highest accuracy is 76.92% at K = 1. Based on the analysis with attribute analysis, the attributes that influence the determination of river water quality are BOD, COD, NH3, Fecal Coli and Total Coli. This result can be taken into consideration by the Division of Environmental Pollution in the process of overcoming and reducing pollutant overload that exceeds quality standards.