bluuebunny/crossref_metadata_2025 huggingface dataset:
A mathematical model to study the effect of travel between two regions on the COVID-19 infections
Authors: Pratama M A A , Supriatna A K , Anggriani N
Published: January 2021

COVID-19 has attracted a lot of researchers’ attention since it has emerged in Wuhan, China in December 2019. Numerous model predictions on the COVID-19 epidemic have been created in case of Wuhan and the other regions. In this paper, a new COVID-19 epidemic model between two regions is proposed. The model differentiates asymptomatic infectious compartment and symptomatic infectious compartment. It is assumed that the symptomatic population cannot infect the susceptible population due to direct isolation, but the asymptomatic population can. The symptomatic population is also assumed to be unable to travel between regions. We analyse the stability of the model using Lyapunov Function. The Basic Reproduction Number for the model is presented. The numerical simulation and sensitivity analysis are explored to determine the significant parameter of the model.
Mitigation and models for determining premiums for natural disaster insurance due to excessive rainfall
Authors: Kalfin  , Sukono  , Supian S , Mamat M
Published: January 2021

Natural disasters due to excessive rainfall often cause huge losses in the form of impacts including casualties and economic losses. To minimize casualties, disaster mitigation is needed, while financing for disasters (emergency response) and financing after disasters (rehabilitation and reconstruction) can be carried out with insurance. This paper aims to determine mitigation efforts and insurance premiums for natural disasters. The method as a mitigation effort is studied based on several methods that are suitable for the conditions of the community in areas that are often affected by disasters. Meanwhile, regarding loss insurance, the premium is calculated based on the number of cases, the rate set by the insurance company and the amount insured (loss). Based on the results of natural disaster mitigation studies, it is necessary to adjust it to the type of disaster. In the case of landslides, housing buildings must be standardized, while in cases of flooding, river normalization is required. Mitigation in general, namely the installation of disaster sirens and disaster response training. Regarding loss insurance, it is found that the optimal premium for the government and insurance companies is obtained.
Comparison of SARIMA and SVM model for rainfall forecasting in Bogor city, Indonesia
Authors: Abdullah A S , Ruchjana B N , Jaya I G N M , Soemartini
Published: January 2021

Heavy rainfall and flash floods are still significant for several cities in Indonesia, especially Bogor City. Rainfall prediction is essential to develop early warning systems for early detection of massive and flash floods. The accurate forecasting method is needed to produce accurate predictions of rainfall. Several methods have been developed to get a precise rainfall forecast, such as the classical approach, such as seasonal autoregressive integrated moving average (SARIMA), and machine learning approach, for example, gradient boosting and support vector machine (SVM). In this study, we introduce for forecasting rainfall at Bogor city, Indonesia. The SVM provides an accurate result with a small MAPE.
Determinants of business performance of village-owned enterprises (V-OE) in Jembrana regency of Bali using SEM
Authors: Gandhiadi G K
Published: January 2021

One of the ideas offered is the establishment of economic institutions such as Village-Owned Enterprises (V-OE) which are supported by the role of the local government and are based on the wishes of the village community. The role of the government is needed in enhancing the culture of entrepreneurship in the managers and management of V-OE in addition to strengthening the capacity of social capital. SEM as a basis for multivariate analysis involving latent variables will be used to determine the factors affecting the business performance of V-OE. The survey through structured interviews was carried out on active V-OE involving 90 respondents from managers in Jembrana Regency of Bali. The results, are : (1) social capital and the role of government directly have a positive effect but not significant, only entrepreneurial orientation has a positive and significant effect on business performance; and (2) social capital and the role of the government in a total positive and significant effect, and require full mediation through entrepreneurial orientation to the business performance in Jembrana Regency. The results of this study are expected to be able to contribute to stakeholders in formulating rural economic development strategies through V-OE in Jembrana Regency of Bali.
Evaluation of the North American multi-model ensemble for monthly precipitation forecast
Authors: Faidah D Y , Kuswanto H , Suhartono
Published: January 2021

The North American multi-model ensemble (NMME) is a multi-model seasonal forecasting system consisting of a collection of models generated from several climate modelling centers. This research examined the monthly precipitation in North Maluku generated by five NMME models. The purpose of this research is to assess the performance of monthly precipitation prediction by using RMSE and Rank Histogram analysis. The NMME models are verified against observed precipitation. The analysis shows that they are biased and underdispersive. Among the five NMME models, the Center for Ocean-Land-Atmosphere Studies (COLA) exhibits the best predictive skill. The performances of the Canadian Meteorological Centre (CMC) are relatively worse than that of the other models. The COLA model shows relatively high skill when used to forecast May-November monthly precipitation. Meanwhile, the National Oceanic and Atmospheric Administration (NOAA)’s Geophysical Fluid Dynamics Laboratory (GFDL) model shows high skill in December-April periods. The ensemble forecast is calibrated with the BMA approach in order to obtain reliable forecasts.
Implementation of centrality measures in graph represented information spreads with hashtag #bersatulawancorona in Twitter
Authors: Carnia E , Fermadona B , Napitupulu H , Anggriani N , Supriatna A K
Published: January 2021

Twitter is a social network that is very popular among the people that allows the users to send and read text-based messages up to 140 characters, known as tweets. Since it was first launched until now, this social network has become one of the ten most visited sites on the internet. Because there are so many people use this social network, Twitter has become a source of information that can disseminate information in a short time. Dissemination of information on Twitter is very preferred by the accounts of Twitter users. The most important account in the dissemination of information on Twitter can be searched by calculating the centrality measurement. In this paper, four centrality measures are used to find the most important accounts, that are degree centrality, closeness centrality, betweenness centrality, and eigenvector centrality. Before calculating the centrality of the Twitter accounts, data must be collected first, wherein this paper the data to be used is tweeted data from Twitter using hashtag #BersatuLawanCorona obtained using Python with the help of Twitter API. In calculating this centrality value, we can get the most influential account in distributing information related to hashtag #BersatuLawanCorona. Based on the data, obtained one account that became the most influential account because it obtained the highest value for each centrality measurement, which is SapawargaSby account.
Covid-19 classification using X-Ray imaging with ensemble learning
Authors: Siswantining T , Parlindungan R
Published: January 2021

Coronavirus (Covid-19) first appeared in Wuhan, December 2019, and continues to spread rapidly to other countries. one of the countries infected with the Covid-19 virus is Indonesia. In Indonesia, the spread of this virus is very fast. Therefore, we need a detection system to detect people who are infected with this virus or not. Rapid detection of Covid-19 can contribute to control the spread of this disease. Chest x-ray images are one of the first imaging techniques to play an important role in the diagnosis of Covid-19. This research data uses chest x-ray images dataset in the Covid-19 cases. The data used in this study were 170 images data with 130 data for training data and 40 for testing data. In this study, the Neural Network, Support Vector Machine (SVM), and Convolutional Neural Network (CNN) methods were used, then applied to Stacking which is one of the methods of Ensemble Learning. The results of this study indicate that the best accuracy is obtained from the Stacking model with an accuracy of 95%.
Graphical exploration of robustness of support vector regression for bearing vibration acceleration
Authors: Darwis Sutawanir , Hajarisman Nusar , Suliadi  , Widodo Achmad
Published: January 2021

Bearing defect is a major factor in failure of rotating machinery. As a defect is detected the machinery is shut down as soon as possible to avoid catastrophic damage. Therefore, it is important to monitor the condition of bearings and to predict its remaining useful life. Six run to failure datasets are provided by pronostia laboratory to build prognostics models and to estimate the remaining useful life of eleven remaining bearings acceleration vibration signals have been gathered during the experiment. Vertical and horizontal acceleration was measured by two accelerometer mounted on the bearing housing. Support vector regression was proposed as a tool to develop the bearing remaining useful life. It is known that the method is affected by outliers because there are possibilities that outliers were selected as support vectors. Some robust support vector regression have been proposed. However, empirical study of robustness of support vector regression for bearing acceleration vibration has not yet been studied. This paper proposes a graphical exploration to study the robustness of support vector regression of bearing acceleration vibration. The results shows that the graphical approach can be used as a tool for preliminary study of robustness of support vector regression for bearing vibration acceleration.
Comparison of IDW and GP models with application to spatiotemporal interpolation of rainfall in Bali Province, Indonesia
Authors: Jaya I G N M , Ruchjana B N , Abdullah A S , Andriyana Y
Published: January 2021

Precipitation is a critical weather component in our daily life. Spatiotemporal variation in rainfall is very influential in agriculture, health, tourism, and many more. Low or large precipitation volume or intensity can be a problem. However, precipitation is not easy to control, so minimize the negative impact of rainfall is an essential thing that can be done. Precipitation volume or intensity in an area is generally reported based on meteorology stations at several location points, which are then used to describe the value of precipitation in the broader region. However, the number of weather observation stations is often minimal. So using this average of little station information can produce incorrect rainfall information. Various approaches have been developed to be able to provide accurate information based on several observation points. These approaches include Inverse Distance Weighted (IDW) and Gaussian Process (GP). This study aims to compare the accuracy of the IDW and GP methods in conducting rainfall interventions in Bali’s Indonesian province. Bali is one of the world’s tourist destinations where rainfall is very influential on tourist visits. We found IDW provides a good prediction for close location, and GP is much better for a distant place.
Causal impact of PSBB and new normal in West Java
Authors: Pontoh R S , Zahroh S , Sunengsih N
Published: January 2021

Indonesia is one of the countries affected by COVID-19. This disease has spread to all provinces of Indonesia, including West Java. West Java is one of the provinces in Indonesia with the most cases of COVID-19 and becomes one of the epicentres, therefore, the regional government has issued a policy to manage the transmission chain. The guidelines implemented by the government, including social distancing and the Large-Scale Social Restrictions (PSBB). In addition, the implementation of PSBB is expected to disrupt the transmission chain because it is more strictly with more sanctions. After the spread is expected to decrease, the government, then, introduced a New Normal system, in which activities will gradually return to normal, but with strict health protocols. Furthermore, this study will find out whether PSBB and New Normal have a significant impact on daily new cases in West Java. The causal impact analysis will be conducted in this study to showed that the implementation of the PSBB and New Normal system has a significant impact on the increase of daily new cases in West Java. The results obtained indicate that there are significant changes after the PSBB policy changed to the new normal. If the number of cases continues to increase based on the results of this study, it can be implemented, among others, that the West Java Provincial Government can enforce the PSBB in this region.
Covid-19 daily forecasting during ramadhan in countries with high muslim population
Authors: Darmawan G , Rosadi D , Ruchjana B N
Published: January 2021

For Muslims, the month of Ramadhan is a month of full ritual and non-ritual worship, such as Tarawih, Eid al-Fitr, and going home to hometown. These activities are very risky if carried out during the outbreak of Covid-19, as the spread of Covid-19 occurs within a short distance from one person to another. This study aims to look at the Covid-19 movement in Ramadhan for countries with the largest Muslim populations, such as Indonesia, Malaysia, Saudi Arabia, Egypt, Kuwait, Pakistan, Afghanistan, Oman, Qatar, Morocco, Bangladesh and India. Model the movement of Covid-19 using the Singular Spectrum Analysis Model. The method used is visual scanning and modelling using the Singular Spectrum Analysis Model. Daily forecasting is done twice as much as the first forecast for the weekly MAPE (Mean Absolute Percentage Error) from May 8th, 2020 to May 14th, while the second forecast (Data is not yet known) for the weekly MAPE from May 15th, 2020 to Eid. Based on the results of the graphical exploration, in general, the Muslim-majority countries rose in mining cases in Covid-19 during the month of Ramadhan. The states that declined in the case of Covid-19 were Oman, Qatar and Morocco. The Singular Spectrum Analysis Model with the Long Memory Time Series approach is more accurate than the Alexandrov version of the Auto grouping Model. The overall results of the MAPE.
The comparison between recurrent neural network and grey model to predict Indonesia tuberculosis morbidity rate
Authors: Harlianto D. , Mardiyati S. , Lestari D.
Published: January 2021

Many insurance companies offer health insurance products that cover the risk of tuberculosis (TB). The risk of disease is generally stated as the morbidity rate that is the ratio of the number of residents suffers from an illness to the total population. In 2018, the World Health Organization (WHO) reported that Indonesia was at rank two for the case numbers of TB, so Indonesia has a high risk of this disease. The recurrent neural network (RNN) and grey model are two models that can be employed to predict TB morbidity rates. In this research, the accuracy of these two models was compared. The results of this research may give the insurance company an assist to choose an appropriate mathematical model to provide a competitive and profitable premium. TB morbidity rate in a certain year had been predicted based on the past several year’s morbidity rates as model input. The size of past years data used as model input was made varied to observe how information availability influences the model accuracy measured by mean squared error (MSE) and mean absolute percentage error (MAPE). The results show that the grey model has better accuracy when the small data used as input. On the other hand, the accuracy of RNN is not affected significantly by the setting of the data input size used in this research.
Application of partitioning around medoids cluster for analysis of stunting in 100 priority regencies in Indonesia
Authors: Ramdani M A , Abdullah S
Published: January 2021

In 2018, Indonesian government set 100 districts as the priority to reduce stunting. In this study, we hypothesize that the 100 determined districts should not be treated on an equal policy, due to some underlying factors that might affect the stunting in those districts. Thus, it is necessary to identify and analyze the grouping of 100 priority districts for stunting interventions in 2018 based on the National Team’s indicators for the Acceleration of Poverty Reduction to see the severity of stunting. It is hoped that this clustering could be a reference for the government in determining priority regency groups to reduce stunting rates. Data on 100 districts, represented by eight numerical measurements and six categorical measurements were analyzed using Partitioning Around Medoids (PAM) method. Data similarity was measured using Gower distance, which can handle the clustering of mixed data types. We identified five priority district groups which provide meaningful insights. One of the groups has the worst stunting severity condition among other groups, for each indicator; implying the high priority to follow-up by the government. The majority of districts in Papua and East Nusa Tenggara Provinces are districts with poor stunting severity. We also found that poverty, proportion of the population with defecation in latrines, access to clean water and proper sanitation, number of integrated healthcare centers (posyandu) in a village, and number of doctors in each district are important factors that explain the stunting severity.
Audio fingerprint for automatic Balinese rindik music identification using gaussian mixture model
Authors: Widarsa A A R S , Darmawan I D M B A
Published: January 2021

Rindik is a traditional gamelan instrument that has been known for a long time in which is made from bamboo. Rindik music can be identified by just hearing a little bit snippets from the music that can only be done by professional rindik players. This research assumes doing some training in the system can make the system recognize the music by just using some rindik music snippets. This research focuses on comparing some features extraction method for automatic rindik music identification by using some rindik music snippet to find out which of these features perform the best to represent rindik music. This research will use an audio fingerprint method for song identification. MFCC (Mel Frequency Cepstral Coefficient) and Spectral Subband Centroid will be used and compared as the music’s feature, and Gaussian Mixture Model will be used to model the music’s fingerprint. The result of this research show both methods give excellent results. Both features only need 10-second duration data to get over 90% overall accuracies. Both Feature gives excellent performance with GMM for automatic rindik song identification task. However, Spectral subband centroid show better result with the highest accuracies is 99.3%.
Heterogeneity of problem-based learning outcomes for improving mathematical competence: A systematic literature review
Authors: Juandi D
Published: January 2021

This study aims to analyze the impact of the application of Problem Based Learning (PBL) on improving the mathematical competence of elementary, junior high and high school students. The method used is a Systematic Literature Review (SLR). The sample consisted of 117 experimental research results on the effect of PBL on improving mathematical understanding, problem-solving, creative thinking skills, or mathematical literacy. The research was conducted in all related classes. Samples were taken from indexed journals published in the 2010-2020 period. The key question of this research is how to describe the effect of PBL on students’ mathematical competence, in terms of year of study, study level, duration of experiment, and sample size. Through SLR method it was found that there was an increase in the number of studies over ten years. Based on the study level research on junior high school students was more dominant, based on the duration of experiment, more research was conducted in junior high schools, and based on the sample size research was mostly conducted in classes with 30 or more subjects.
Deep Attentional Factorization Machines Learning Approach for Driving Safety Risk Prediction
Authors: Zhang Jun , Wu Zhongcheng , Li Fang , Li Wenjing , Ren Tingting , Li Wei , Chen Jie
Published: January 2021

The data of Internet of Vehicles (IoV) can be used to evaluate the driving safety risk of auto insurance policyholder and provide technical means for Usage Based Insurance (UBI). There are many types of IoV data, such as continuous or ordinal, categorical, binary etc., which contain highly sparse and dimensional features after One-Hot processing, thus they learning the interaction between critical features and training predictive model difficult. Furthermore, some of the available data have been desensitized, so it is impossible to perform feature engineering based on experience. We propose an end-to-end deep learning framework named Deep Attentional Factorization Machine (DeepAFM), which combines the power of attentional factorization machine with deep learning for feature learning in a new neural network architecture. Compared with existing deep learning models, our approach can learn the weighted interactions between various features effectively by introducing the structure of feature fields without feature engineering. Experimental results showed that our model yields excellent results in real-world data.
Implicit-Discrete-Maximum-Principle-Based Production Optimization in Reservoir Development
Authors: Zeng Yijin , Nie Yandong , Ding Shidong
Published: January 2021

Cost-effective development of proved oil resources is the goal of oil and gas field development workers. One of the ways to achieve this is to make the reservoir development in an optimal state based on the existing production conditions, which is also one of the core contents of intelligent oilfield. This paper proposes a new production optimization control method, which aims to maximize the net present value of reservoir development and production. By solving the mathematical model of development and production, the input and output control parameters of the reservoir are optimized in real time to obtain the optimal production plan. The conventional maximum principle method requires two time series solution calculations. In this paper, the coefficient matrix of the full implicit simulator is used to directly obtain the required adjoint equations. The efficient combination of simulation calculation and gradient solution of discrete maximum principle greatly improves the solution efficiency of the model and saves the calculation time. The reservoir examples are analyzed through theoretical research. The results show that the optimal production scheme is in line with the actual situation of the oilfield, which provides theoretical and technical support for the intelligent oilfield system.
Influence of 80-mesh Traditional Chinese Medicine Particle Stack Thickness on Percolation Performance
Authors: Li Juan , Wu Jun , Wang Xiaochun
Published: January 2021

In order to research the influence of 80-mesh traditional Chinese medicine particle characteristics and particle stack thickness on percolation performance, wire screen was used to carry out filtration by the vibratory action of oscillator, screen No. and screen pore size were recorded and weight of screen residue was measured, so as to calculate average distribution density of particles, distribution function of particle size, form factor and specific surface area of particles. Percolation pressure drop was utilized as the index to evaluate percolation performance. Ethanol solution at 20°C with volumetric concentration of 47% was selected as percolation solution to measure and calculate the percolation pressure drop when ethanol solution flowed through 100mm, 150mm, 180mm, 250mm and 300mm 80-mesh particle layer. The results showed that percolation pressure drop is proportional to particle stack thickness, and it is high relatively due to the small size of 80-mesh particle, which shall result in high energy consumption of percolation system.
Assessment model for the Stability Situation of Crowd Evacuation based on Thermodynamic Map
Authors: Zhao Rongyong , Liu Qiong , Li Cuiling , Dong Daheng , Hu Qianshan , Ma Yunlong
Published: January 2021

To solve the shortcomings of the conventional assessment methods for crowd evacuation stability, such as over-reliance on the experience of the judges, obvious subjectivity and time-space limitations, this paper proposes an assessment model for judgment on the stability situation of crowd evacuation. Furthermore, the scenarios of large-scale crowd evacuation is diverse, and the compositions of crowd, the causes of crowd trampling are different. So it is particularly important to analyze specific problems for different scenarios. However, different data acquisition and poor real-time performance are main practical problems of judgment on the stability situation of large-scale crowd evacuation. With regard to shortcomings and problems above, the assessment model proposed in this paper uses big data technology to figure out a real-time crowd distribution thermodynamic map and obtain the crowd distribution density data. According to the criterion of crowd stability proposed by the Green Guide in Britain, the assessment model is further constructed to judge the stability situation of large-scale crowd evacuation in real-time.
A Technique of Time Synchronization in Pseudolite System Based on Single-difference Method
Authors: Liu Haiqing , Zhao Li , Huang Xing , Tang Qiaoxiao
Published: January 2021

Aiming at the time synchronization problem of pure pseudolite system in high precision positioning, a technique of time synchronization in pseudolite system based on single-difference method is proposed. The reference receiver with dual-antenna and the carrier phase observation equation are used and phase integer ambiguity is adjust-ed by the least-squares ambiguity decorrelation adjustment (LAMBDA) method. High precision synchronization of the system clock can be realized through the clock error broadcast or centralized processing. Both theoretical derivation and simulation show that the system is able to provide high accuracy time synchronization and the precision of synchronization can reach 0.02 ns.
Design and Implementation of Indoor Positioning Performance Test System for IoT Intelligent Communication Terminal
Authors: Xu Jie , Miao Yi , Zheng Xiaonan
Published: January 2021

Indoor positioning technique is the foundation of Internet of Things (IoT) and location big data, which provides IoT function and location data for retail, manufacturing, energy, medical, emergency, logistics, robotics and other industries. Indoor positioning performance is an important performance indicator of IoT intelligent communication terminals. In order to accurately evaluate the indoor positioning performance of IoT intelligent communication terminals, this paper constructs an indoor positioning performance test system for IoT intelligent communication terminals. The test results show that the uncertainty of horizontal positioning accuracy of Wi-Fi positioning and Bluetooth positioning can be limited within 0.32 m (k=2).
Research on Practical Wireless Sensor Network Query Processing Technology
Authors: Guo Changjian , Wu Xiaoxuan
Published: January 2021

WSN(Wireless Sensor Network) is intended for obtaining sensory data so that the query processing technology of high efficiency is the precondition of widely used WSN. Existing query processing technology assumes that the WSN deployed in two-dimensional or three-dimensional free space are not suitable for those deployed in such limited circumstances as roads, pipelines and indoor environments. This paper explores the problems of existing query processing technology, does research on perception data query processing method and technology, and seeks out the existing problem and its solution by means of using the WSN actually deployed in a building as an experimental platform.
Water quality evaluation of Xiaoshan water quality station in eastern Zhejiang Water Diversion Project Based on BP network
Authors: Xu Xiaofeng , Guo Huifang , Fan Jianfen
Published: January 2021

East Zhejiang water diversion project is a major water resources allocation project to ensure the sustainable economic and social development of xiaoshaoningzhou area in eastern Zhejiang. The project task is to introduce Qiantang River water to provide domestic, industrial and agricultural irrigation water to xiaoshaoning plain and Zhoushan City, and to improve water environment. Its head project is Xiaoshan hub project. In order to ensure the safety of water quality along the water diversion line, it is very important to strengthen the monitoring and evaluation of water quality at the source of eastern Zhejiang. In this paper, BP network algorithm based on error back propagation is used to evaluate the water quality station of Xiaoshan junction. The evaluation results show that most of the water quality in Xiaoshan water diversion project is class II water and class III water.
Research on optimization of distribution route for cold chain logistics cooperative distribution of fresh e-commerce based on price discount
Authors: Wang Xinxin , Cao Wujun
Published: January 2021

In order to solve the problem that the development of self-built logistics cannot meet the development of fresh e-commerce, a coordinated distribution strategy of self-built logistics and third-party logistics is proposed. The tasks assigned by third-party logistics are used to determine the price discount ratio. Third-party distribution outsourcing cost function, customer satisfaction function, and freshness function of fresh produce. Aiming at the goal of minimum delivery cost, maximum customer satisfaction, and maximum freshness of fresh produce, a collaborative delivery optimization model was established, and a corresponding genetic algorithm was designed with examples. Simulation results show that: collaborative distribution can effectively reduce the cost of distribution, when the proportion of price discount increases, the cost of collaborative distribution decreases. It has a certain reference value for the optimization of fresh e-commerce enterprises’ distribution mode.
Research on Innovation of Urban Combat Equipment Support Model Based on Smart City and Artificial Intelligence
Authors: Yang Lili , Liu Zichang , Zhang Suoliang , Yue Qiangbin , Li Feng
Published: January 2021

With the remarkable achievements of smart city construction and the evolution of war form to intelligence, the future urban operations will be intelligent operations supported by smart city. This paper analyses the requirements of artificial intelligence on urban operations equipment support, and clarifies the supporting condition provided by smart city construction on equipment support from the equipment status perception, equipment support command and control, equipment supply support and equipment maintenance support four aspects, focusing on winning urban operations under intelligent warfare. On this basis, a new support model of urban operations equipment support is proposed.
Risk assessment method for IoT software supply chain vulnerabilities
Authors: Zhu Zhicheng , Lan Kun , Rao Zhihong , Zhang Yuguang
Published: January 2021

With the development of IoT technology, the number of attacks against IoT software chain vulnerabilities is greater than ever, and a reasonable vulnerability assessment system needs to be established for research. The Common Vulnerability Scoring System (CVSS) is a free, public risk assessment system used by information security vendors to assess the severity of vulnerability threats. However, CVSS is insufficient because of the strong subjectivity in the selection of measurement standards and the allocation of evaluation index weights. Based on this reason, this paper designs and proposes a more objective risk assessment method for IoT software chain vulnerabilities, and verifies the feasibility and effectiveness of the method through experiments.
Design and Implementation of Software Bus Based on Object Model Operating System
Authors: Zhou RuiLiang , Tao Yongchao , Piao Songhao
Published: January 2021

At present, the commercial operating system VxWorks is the first choice of leading international intelligent equipment manufacturers. They implement their own equipment controller based on VxWorks. However, VxWorks is not specifically designed for the control field, so there are many deficiencies in the control field, such as: using a dynamic model, dynamically calling various resources at runtime, making the system less predictable and real-time. Aiming at the typical application scenarios of the object operating system, this paper gives the overall software architecture of the object bus. The designed object bus provides a transparent mechanism for communication between components. In view of the highly dynamic adjustment of the objects in the object bus, this article has designed a corresponding set of migration mechanisms for the object bus to solve the various negative effects of cache invalidation after dynamic adjustment. In the last part of the article, the functional verification of the implemented object bus is carried out, and the analysis and summary are carried out.
The design of embedded Operating System for vehicle Internet of Things
Authors: Cheng Luwei , Tao Yongchao , Piao Songhao
Published: January 2021

This article distinguishes the difference between dynamic operating system and static operating system from the design goal, and discusses the resulting difference in the technology used in system implementation. With this as the theoretical basis, according to the ISO17356 OS specification, using hardware abstraction layer design ideas, a simple and efficient static real-time operating system is implemented on the ARM platform. This paper adopts the idea of separating mechanism and strategy, and uses internal resources as a mechanism to implement different scheduling strategies; a task scheduling algorithm based on priority grouping is proposed, which achieves good performance in time and space.
Comparative analysis of three distribution entropy methods for chaos recognition
Authors: Zeng Xiangjian , Wan Li , Liu Hui
Published: January 2021

Distribution entropy (DistEn) is an effective index to measure the complexity of time series. In this paper, the moving distribution entropy (M-DistEn) method is proposed by combining the distribution entropy method with the moving window technology. The moving cut data-distribution entropy (MC-DistEn) method is proposed by combining the DistEn with the moving removal window. Based on the M-DistEn method, moving weighted distribution entropy (MW-DistEn) is proposed. These three distribution entropy methods are used to identify the chaotic state of mixed Logistic map sequences with different parameters. The results show that the M-DistEn method only recognizes the first three mixed states of the sequence, and it cannot accurately recognize the last two chaotic states of the sequence, so it has certain limitations. The MC-DistEn method can accurately identify the four different chaotic states of the sequence, but the DistEn value greatly fluctuates due to the influence of the size of the moving removal window, and the position judgment of sequence state change is not accurate enough. The MW-DistEn method not only accurately recognizes different chaotic states, but also is more accurate in judging the position of sequence state changes and more stable in DistEn value than the M-DistEn method and MC-DistEn method, thereby it has a good application prospect.
Research on main transformer defect detection methods based on Conditional Inference Tree and AdaBoost Algorithm
Authors: Zhang Chenmeng , Hu Can , Zhang Zongxi , Cao Shuping
Published: January 2021

With the development of data science, there are more and more ways to dig for patterns hidden in the data. If we can apply the advanced model of data science to the power data, we can mine the potential value of power data. In this paper, the monitoring and inspection data of 110kV main transformers and related basic environmental data are fused to establish conditional inference tree model and AdaBoost algorithm to evaluate whether the 110kV main transformers have defects and faults. The accuracy of the two algorithms is compared. Finally, we select the AdaBoost algorithm with higher accuracy for building the 110kV main transformer fault evaluating model. This paper provides a reference for the automatic detection of power grid faults, and provides new ideas for the application of power grid big data.
Comparative Study on Defects and Faults Detection of Main Transformer Based on Logistic Regression and Naive Bayes Algorithm
Authors: Hu Can , Zhang Chenmeng , Zhang Zongxi , Xie Shijun
Published: January 2021

The proper work of main transformers plays an important role in the stability of the power grid. The development of power data allows us to evaluate the instant status of the main transformer. If we use appropriate data classification algorithm, the laws and values hidden behind the mass power data can be discovered. This paper took basic power data, main transformers’ status data and oil chromatography data into consideration, and used Logistic regression and Naive Bayes algorithm to evaluate 110kV main transformers’ defects and faults from regression analysis and probability statistics seperately. The results of these two algorithms were obtained and compared. Finally, a more accurate algorithm of evaluating main transformer fault was selected, which provided a new reference for the application of power big data.
Charging path planning algorithm based on multi-parameters in underwater wireless rechargeable sensor network
Authors: Zhang Shaojuan , Huang Xianqing , Du Zehua
Published: January 2021

UWSN (Underwater wireless sensor network) is a significant part of marine environment stereo monitoring system, and the energy problem has always been an important constraint and challenge that limits its wide application. Due to the development of wireless charging technology and intelligent mobile nodes, the energy problem in UWSN has been solved. The underwater sensor network using wireless charging method is called UWRSN (underwater wireless rechargeable sensor network). In wireless rechargeable sensor networks, one of the core issues is the design of the charging planning. In this paper, a charging path planning algorithm based on multi-parameters is proposed for the on-demand charging system of UWRSN. The algorithm plans the charging path for the underwater MC (mobile charger) according to the parameters such as the remaining power, position, and power consumption speed of the underwater sensor node, so as to minimize the number of network dead nodes, improve network energy efficiency, and extend life of the network. Simulation results show that the algorithm has good performance in terms of charging efficiency, extending network life and other performance indicators.
Research on Real-time Scheduling Algorithm of Partition Operating System in Single Core Environment
Authors: Tao Yongchao , Song Qilong , Piao Songhao
Published: January 2021

In an embedded environment, applications have a strong demand for real-time scheduling, but it is difficult to ensure the real-time scheduling performance under a partitioned operating system, mainly because in a multi-operating system environment. It is difficult to determine the real-time schedulability within and between partitions. The research of this paper will focus on the real-time scheduling algorithm of partition operating system with simple task model in single-core environment.
Application of improved algorithm based on sphere OBB hybrid hierarchical bounding box in Teaching
Authors: Zhao Wei , Qu Huiyan
Published: January 2021

In this paper, on the premise of analyzing the theoretical knowledge of collision detection algorithm, the advantages and disadvantages of several commonly used hierarchical bounding box methods are compared and analyzed, and then the traditional collision detection algorithm is studied. This paper presents a new collision detection algorithm. After preprocessing, the first order collision detection based on the separation axis theorem is used to exclude a large number of disjoint spatial model objects through preprocessing. The purpose of adding primary collision detection is to further investigate disjoint object pairs, and prepare for the final detailed detection stage. The pre-determined OBB bounding box is used for fast collision detection, and its storage structure and mode are optimized quickly. After preprocessing, it effectively eliminates the obviously disjoint space object pairs, reduces a large number of operations, and improves the detection efficiency.
Machine Learning Regression Algorithm Predicts Multi-component Crystal Configuration Energy
Authors: Wang Peng , Mei Jinshuo , Lang Yingjie , Li Shu
Published: January 2021

Some machine learning algorithm tools, such as neural networks and Gaussian process regression, are increasingly being applied to the exploration of materials. Here, we have developed a form to use this nonlinear interpolation tool to describe properties that depend on the degrees of freedom in multi-component solids. A symmetrically adapted clustering function is used to distinguish different atomic order degrees. These features are used as the input of neural networks, Gaussian process regression and other algorithmic models, and some inherent properties of materials, such as formation energy, can be reproduced by the trained machine algorithm model. We use this technique to reproduce the expansion Hamiltonian of a synthetic cluster with multi-body interaction, and calculate the formation energy of ZrO based on first principles. The form proposed in this paper and the results shown that complex multi-body interactions can be approximated by nonlinear models involving smaller clusters. The training models used in this paper to predict energy include neural networks, Gaussian process regression, random forests, and support vectors regression, using MSE and coefficient of determination to evaluate the prediction results, and adding genetic algorithms in the feature selection process can remove some redundant features and improve the prediction efficiency and accuracy. The results show that the neural network is the best algorithm model which selected in this article, the prediction effect of support vector regression is relatively inferior.
Analysis and Evaluation of Pollutant Residues in Freshwater Fish at Jiamusi Section of Songhua River
Authors: Zhao Yufeng , Liu Jingyi , Liu Xu , Meng Qingqing , Wang Shuai , Jiang Tao , Bai Li , Yang Yang
Published: January 2021

Carp from Jiamusi section of Songhua River were collected to analyze and evaluate the residues of heavy metals, organochlorine pesticides, polycyclic aromatic hydrocarbons and volatile organic compounds in muscle tissue. The results showed that 100% of heavy metals, organochlorine pesticides and polycyclic aromatic hydrocarbons were up to standard, and a small amount of volatile organic compounds were detected. The structure and morphology of fish tissue samples were not abnormal, and the structure of nucleus and cytoplasm was normal. No pathological changes or morphological abnormalities were found. Fish were healthy.
Shaping Strategies of Resilience Landscapes in Urban Wetland Park
Authors: Zou Shuzhen , Sun Mengyu , Tao Biaohong , Xiong Cui
Published: January 2021

Environmental art design is a subject to deal with the relationship between artificial environment and natural environment, and also a subject to create a livable and ecological balanced living environment for human beings. Thus, it is of paramount of importance to study how artificial construction can adapt to the natural environment. To this end, the concept of physics “Resilience” is introduced into the landscape design of urban wetland park, and the strategies of shaping and utilizing resilience landscapes in urban wetland park is discussed in order to provide effective measures to deal with urban flood disaster, realize “sponge function”, promote regional ecological environment balance and sustainable development.
Analysis of the impact on the water quality of Cixi by areas along the north line of water diversion from Cao’e river
Authors: Fan Jianfen , Guo Huifang , Xu Xiaofeng
Published: January 2021

The north line of water diversion from Cao’e river is one of the six major water diversion projects in eastern Zhejiang Province. The project passes through three cities of YuYuCi. It mainly supplies water for Industry and agriculture, and takes into account the improvement of water environment. Cixi City is located at the end of the north line of water diversion from Cao’e river, and its water quality is affected by the nodes along the water diversion line. Using the least square method which can extract the main components of the independent variables on the dependent variables, the regression equations of the independent variables: Mushan sluice, Qitang sluice, Sitang sluice, Puqian sluice, xinsanjiang sluice and Luzhongwan pumping station are established, and the contribution values of each node to Luzhongwan pumping station under the indexes of permanganate, ammonia nitrogen, total phosphorus and dissolved oxygen are calculated. The results show that Sitang sluice has the greatest contribution to permanganate and total phosphorus of Luzhongwan pumping station, Puqian sluice has the greatest impact on ammonia nitrogen index of Luzhongwan pumping station, and xinsanjiang sluice has the greatest impact on dissolved oxygen index of Luzhongwan pumping station. Generally speaking, the stations which have the greatest impact on the water quality of Luzhongwan pumping station are Sitang sluice and Qitang sluice.
The spatial distribution of soil heavy metals and ecological restoration of Datong Abandoned Coal Mine in Huainan City
Authors: Sun Xianbin , Sun Liangping , Zhang Xing , Li Guangyao , Yu Maohui
Published: January 2021

Based on laboratory analysis of soil samples collected from abandoned coal mine in Datong area, content of heavy metals in the soils was monitored and the soils were evaluated for heavy metal pollution. The spatial distribution of heavy metals was study with means of Kriging method by under ArcGIS environment in the abandoned coal mine in Huainan area. The results show: (1) the content of 5 types of heavy metals( Hg, Cd, Cr, Pb, Cu) were all higher than Huainan area background value of soil elements, among of these heavy metals, the enrichment of Cd was higher and polluted quite seriously; the enrichment of Hg, Cr, Pb and Cu was lower, and polluted lightly comparatively. (2) The coal mine waste rock pile and abandoned chemical plant were the main pollution sources. (3) The heavy metal concentrations and space distribution were mainly affected coal mine waste rock pile, and abandoned chemical plant. (4) Positive correlation between concentrations of most heavy metals was significant, and there is a certain relationship between concentrations of heavy metals and organic matter, nitrogen, available phosphorus. On the basis of the above research, some countermeasures were put forward for ecological restoration in Datong abandoned coal mine.