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Adaptive parameter estimation of linear time invariant generators in finite time with relaxed excitation conditions

IFAC-PapersOnLine 2022年第12期55卷 97-102页

作者： Nguyen K. Tung Mikhail M. Yachmenkov Sergey M. Vlasov Anton A. Pyrkin Department of Control Systems and Robotics ITMO University Kronversky Pr. 49 St. Petersburg 197101 Russia

The problem of identifying the constant parameters of LTI generators in finite time are considered. The signal is represented as the output of a linear generator, the parameters of the state matrix and initial conditions are unknown. The main idea is to parameterize the signal using delays and obtain a linear or nonlinear regression model. The performance of the algorithms considered in the article is illustrated by computer modeling.

关键词： identification Jordan form matrix linear regression model generator

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Predictor Variables Influencing Visibility Prediction Based on Elevation and Its Range for Improving Traffic Operations and Safety

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Journal of Transportation Technologies 2022年第3期12卷 439-452页

作者： Ajinkya Sadashiv Mane Srinivas Subrahmanyam Pulugurtha Venkata Ramana Duddu Christopher Michael Godfrey IDEAS Center Civil & Environmental Engineering Department The University of North Carolina at Charlotte Charlotte USA Atmospheric Sciences Department The University of North Carolina at Asheville Asheville USA

Low visibility condition hinders both air traffic and road traffic operations. Accurate forecasting of visibility condition helps aircraft operators and travelers to make better decisions and improve their safety. It is, therefore, essential to investigate and identify the predictor variables that could influence and help predict visibility. The objective of this study is to identify the predictor variables that influence visibility. Four years of surface weather observations, from January 2011 to December 2014, were collected from the weather stations located in and around the state of North Carolina, USA for the model development. Ordinary least squares (OLS) and weighted least squares (WLS) regression models were developed for different visibility and elevation ranges. The results indicate that elevation, cloud cover, and precipitation are negatively associated with the visibility in visibility less than 15,000 m model. The elevation, cloud cover and the presence of water bodies within the vicinity play an important role in the visibility less than 2000 m model. The chances of low visibility condition are higher between six to twelve hours after the rainfall when compared to the first six hours after the rainfall. The results from this study help to understand the influence of predictor variables that should be dealt with to improve the traffic operations and safety concerning the visibility near the airports/road transportation network.

关键词： Visibility Prediction Weather Station linear regression model Elevation

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Research on the Employment of College Graduates in China based on regression model

Frontiers in Economics and Management

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Frontiers in Economics and Management 2022年第12期3卷 217-227页

作者： Changjin Qian Huilin Jiang Shiyu Pan Zejiong Zhou

With the increasing number of students in colleges and universities, the employment rate of graduates has been attached great importance. Because of the impact of various external factors, such as social environment and epidemic situation, the employment opportunities of graduates have greatly increased, but the salary level of graduates has also changed due to the different internal and external conditions of graduates. This paper analyzes the influence of various factors on the employment salary of college graduates according to the attached data and the graduates' employment situation on the Internet, and gives suggestions for specific situations, so as to provide practical employment advice for the college students who are about to graduate.

关键词： Data Analysis and Visualization Pearson Correlation Coefficient model Stochastic Forest model Weighted Evaluation linear regression model

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Analysis of GNSS clock prediction performance with different interrupt intervals and application to real-time kinematic precise point positioning

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ADVANCES IN SPACE RESEARCH 2020年第3期65卷 978-996页

作者： Yan, Chao Wang, Qing Zhang, Yong Ke, Fuyang Gao, Wang Yang, Yuan Southeast Univ Sch Instrument Sci & Engn Nanjing 210096 Peoples R China Nanjing Univ Informat Sci & Technol Sch Remote Sensing & Geomat Engn Nanjing 210044 Peoples R China

Continuous and timely real-time satellite orbit and clock products are mandatory for real-time precise point positioning (RT-PPP). Real-time high-precision satellite orbit and clock products should be predicted within a short time in case of communication delay or connection breakdown in practical applications. For prediction, historical data describing the characteristics of the real-time orbit and clock can be used as the basis for performing the prediction. When historical data are scarce, it is difficult for many existing models to perform precise predictions. In this paper, a linear regression model is used to predict clock products. Seven-day GeoForschungsZen-trum (GFZ) final clock products sampled at 30 s are used to analyze the characteristics of GNSS clocks. It is shown that the linear regression model can be used as the prediction model for the satellite clock products. In addition, the accuracy of the clock prediction for different satellites are analyzed using historical data with different periods (such as 2 and 10 epochs). Experimental results show that the accuracy of the clock with the linear regression prediction model using historical data with 10 epochs is 1.0 ns within 900 s. This is higher accuracy than that achieved using historical data of 2 epochs. Finally, the performance analysis for real-time kinematic precise point positioning (PPP) is provided using GFZ final clock prediction results and state space representation (SSR) clock prediction results when communication delay or connection breakdown occur. Experimental results show that the positioning accuracy without prediction is better than that with prediction in general, whether using the final clock product or the SSR clock product. For the final clock product, the positioning accuracy in the north (N), east (E), and up (U) directions is better than 10.0 cm with all visible GNSS satellites with prediction. In comparison, the 3D positioning accuracy of N, E, and U directions with visible

关键词： GNSS Real-time precise point positioning (RT-PPP) Real-time clock prediction linear regression model Real-time

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A Harmonic Impedance Identification Method of Traction Network Based on Data Evolution Mechanism

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ENERGIES 2020年第8期13卷 1904页

作者： Yang, Ruixuan Zhou, Fulin Zhong, Kai Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China

In railway electrification systems, the harmonic impedance of the traction network is of great value for avoiding harmonic resonance and electrical matching of impedance parameters between trains and traction networks. Therefore, harmonic impedance identification is beneficial to suppress harmonics and improve the power quality of the traction network. As a result of the coupling characteristics of the traction power supply system, the identification results of harmonic impedance may be inaccurate and controversial. In this context, an identification method based on a data evolution mechanism is proposed. At first, a harmonic impedance model is established and the equivalent circuit of the traction network is established. According to the harmonic impedance model, the proposed method eliminates the outliers of the measured data from trains by the Grubbs criterion and calculates the harmonic impedance by partial least squares regression. Then, the data evolution mechanism based on the sample coefficient of determination is introduced to estimate the reliability of the identification results and to divide results into several reliability levels. Furthermore, in the data evolution mechanism through adding new harmonic data, the low-reliability results can be replaced by the new results with high reliability and, finally, the high-reliability results can cover all frequencies. Moreover, the identification results based on the simulation data show the higher reliability results are more accurate than the lower reliability results. The measured data verify that the the data evolution mechanism can improve accuracy and reliability, and their results prove the feasibility and validation of the proposed method.

关键词： harmonic impedance traction network harmonic impedance identification linear regression model data evolution mechanism

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A Comprehensive Approach to Measuring Financial Vulnerability and Literacy: Unveiling Connections

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SAGE OPEN 2023年第4期13卷

作者： Fernandez-Lopez, Sara Alvarez-Espino, Marcos Rey-Ares, Lucia Univ Santiago De Compostela Santiago De Compostela Spain Fac Econ & Business Ave Burgo Nacions S-N Santiago De Compostela 15782 Spain

In the aftermath of the 2007-08 financial crisis, the worsening financial conditions of households increased concerns about their financial vulnerability (FV). In this context, policymakers embraced the notion of financial knowledge to foster sound financial behaviors among individuals and households, aiming to mitigate the detrimental effects of FV on households' financial wellbeing and the overall economy. However, the relationship between FV and financial literacy remains inconclusive. This lack of definitive findings may stem from limitations in measuring FV and narrow focus on specific dimensions of financial literacy. This paper analyzes the relationship between financial literacy and FV by creating a comprehensive measure of (the level of) FV and considering different dimensions of financial literacy. Using a sample of 8,554 individuals in Spain obtained from the 2016-17 Survey of Financial Competences, we construct a continuous measure of FV by using Nonlinear Principal Components Analysis (NLPCA). Then, we employ OLS and ordered probit regressions to examine the potential association between different dimensions of financial literacy and FV. The findings indicate that the level of FV is negatively related to self-perceived financial knowledge, while no statistically significant relationship is found regarding objective financial knowledge. Evidence also reveals that "highly financially included" individuals are more likely to exhibit financial resilience. These findings highlight the need for the development of financial education initiatives that are action-oriented.

关键词： financial knowledge financial capability Nonlinear Principal Components Analysis (NLPCA) linear regression model ordered probit regression model

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HARGREAVES ET0 EQUATION regression CORRECTION AND PARAMETER SPATIALIZATION FOR MAINLAND CHINA

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APPLIED ENGINEERING IN AGRICULTURE 2020年第5期36卷 673-686页

作者： Xia, X. Zhu, X. Pan, Y. Zhang, J. Qinghai Normal Univ Acad Plateau Sci & Sustainabil Xining Peoples R China Beijing Normal Univ State Key Lab Earth Surface Proc & Resource Ecol Beijing Peoples R China Chinese Acad Sci State Key Lab Remote Sensing Sci Beijing Peoples R China Beijing Normal Univ Fac Geog Sci State Key Lab Earth Surface Proc & Resource Ecol Beijing Peoples R China

Reference crop evapofranspiration (ET0) is an important parameter employed in irrigation engineering. The Hargreaves ET0 equation (HE) is a simplified calculation method recommended by the Food and Agriculture Organization (FAO) of the United Nations and is used to determine ET0. However, the HE is empirical, and thus, the FAO suggests that linear regression correction be conducted when applying the HE in new areas. To discuss the validity of applying the HE linear regression correction model combined with spatial interpolation methods to the Chinese mainland, this study used meteorological data released by the China National Meteorological Data Service Center and considered the ET0-PM calculated by the Penman Monteith equation to be the true value. With respect to conducting a linear regression correction with the HE, a regression analysis of the multi year average of mean monthly daily ET0-PM and ET0-HE calculated by the HE showed that the values were significantly correlated, and correlation coefficients and R-2 of 95% of stations exceeded 0.9 and 0.8, respectively. This indicates that the HE linear regression correction model recommended by the FAO is applicable for use in mainland China. The accuracy of ET0-HE corrected by the linear regression correction coefficient combined with simple tension spline interpolation was the optimal and stable at validation stations, whereas the accuracy of ET0-HE corrected by the linear regression correction coefficient combined with simple ordinary kriging interpolation contrasts with the accuracy from existing studies. This study can be used as a reference in large-scale correction research using ET0-HE, and it lays the foundation for research and development relating to spatiotemporal ET0 data products.

关键词： Coefficient modification linear regression model Reference crop evapofranspiration Spatial interpolation

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Netizens' risk perception in new coronary pneumonia public health events: an analysis of spatiotemporal distribution and influencing factors

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BMC PUBLIC HEALTH 2022年第1期22卷 1445-1445页

作者： Li, Yanling Wu, Xiancong Wang, Jihong Hunan Agr Univ Sch Publ Adm & Law 1 Nongda Rd Changsha 410128 Hunan Peoples R China

Background Internet search volume reflects the level of Internet users' risk perception during public health events. The Internet search volume index model, an algorithm of concentration of Internet users, and statistical analysis of popular topics on Weibo are used to analyze the effects of time, space, and space-time interaction. We conducted in-depth research on the characteristics of the spatial and temporal distribution of Internet users' risk perceptions of public health events and the associated influential factors. Methods We analyzed the spatiotemporal distribution characteristics of Internet users' risk perception after the Wuhan "city closing" order during the coronavirus disease 2019 (COVID-19) pandemic. We established five linear regression models according to different time periods and analyzed factors influencing Internet users' risk perception by employing a Poisson and spatial distribution and topic modeling analysis. Results Economy, education, health, and the degree of information disclosure affect Internet users' risk perception significantly. Internet users' risk perception conforms to the exponential distribution law in time and has periodic characteristics and stability trends. Additionally, Internet users' average arrival rate dropped from week 1 to week 8 after the "city closing." Internet users' risk perception has a uniform distribution in space, economic and social development level distribution consistency, spatial agglomeration, and other characteristics. The results of the time-space interaction show that after 8 weeks of COVID-19, Internet search hot topics have become more stable, and Internet users' information demand structure has become more rational. Conclusions The Internet search cycle of the COVID-19 event is synchronized with the evolution cycle of the epidemic. The physical risk of Internet users is at the top of the risk structure, focusing on the strong concern about the government's ability to control COVID-19 and its fut

关键词： Risk perception Internet search Space-time distribution linear regression model COVID-19

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A simple model to predict the spatiotemporally vegetation dynamics in terms of precipitation and temperature.

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ENVIRONMENTAL DEVELOPMENT 2022年 44卷

作者： Nzabarinda, Vincent Bao, Anming Xu, Wenqiang Uwamahoro, Solange Xiaoran, Huang Habiyakare, Telesphore Sindikubwabo, Celestin Habumugisha, Jules Maurice Itangishaka, Auguste Cesar Chinese Acad Sci Xinjiang Inst Ecol & Geog State Key Lab Desert & Oasis Ecol Urumqi 830011 Peoples R China Key Lab GIS & RS Applicat Xinjiang Uygur Autonomou Urumqi 830011 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Climate factors like precipitation and temperature fulfil a significant role in assessing the status of the vegetation. Vegetation prediction in terms of climatic conditions represents a critical issue because it helps monitor and maintain the ecosystem's well-being by maintaining track of changes in vegetation disturbance. It is challenging to locate large-scale current vegetation data for use in a variety of applications. In this study, temperature and precipitation were applied to develop a simple linear regression model that predict Normalized Difference Vegetation Index (NDVI) over the African continent. For training data, the third-general Normalized Difference Vegetation Index (NDVI3g) generated from Global Inventory modeling and Mapping Studies (GIMMS) at a 1/12 degrees resolution was the main input for simulation from 1982 to 2005. From 2006 to 2015, the third-general Normalized Difference Vegetation Index was used to validate the model outputs. The model's accuracy in the arid climate zone (poor vegetation areas) is stronger than in the other four climate zones of Africa, with an R2 of 74%, the lowest p-value of 0.001, the lowest root mean square error (RMSE) of 0.027 and the lowest mean absolute error (MAE) of 0.022. Based on future vegetation condition projections, this approach will assist a more effective management of the future food reservations for the battle against hunger. It will also help with the design, management, and execution of ecological restoration initiatives, research data availability as well as understanding the relationship between vegetation variation and climate variability.

关键词： Vegetation prediction Precipitation and temperature NDVI3g Climatic factors linear regression model

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Brine-Oil Interfacial Tension modeling: Assessment of Machine Learning Techniques Combined with Molecular Dynamics

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ACS APPLIED MATERIALS & INTERFACES 2020年第13期12卷 15837-15843页

作者： Kirch, Alexsandro Celaschi, Yuri M. de Almeida, James M. Miranda, Caetano R. Univ Sao Paulo Inst Fis DFMT BR-05315970 Sao Paulo SP Brazil Univ Fed ABC Ctr Ciencias Nat & Humanas BR-09210580 Santo Andre SP Brazil

The physical chemistry mechanisms behind the oil-brine interface phenomena are not yet fully clarified. The knowledge of the relation between brine composition and concentration for a given oil may lead to the ionic tuning of the injected solution on geochemical and enhanced oil recovery processes. Thus, it is worth examining the parameters influencing the interfacial properties. In this context, we have combined machine learning (ML) techniques with classical molecular dynamics simulations (MD) to predict oil/brine interfacial tensions (IFT) effectively and compared this process to a linear regression (LR) method. To diversify our data set, we have introduced a new atomistic crude oil model (medium) with 36 different types of hydrocarbon molecules. The MD simulations were performed for mono- and multicomponent (toluene, heptane, Heptol, light, and medium) oil systems interfaced with sulfate and chloride brines with varying cations (Na2+, K+, Ca2+, and Mg2+) and salinity concentration. Thus, a consistent IFT data set was built for the ML training and LR fitting at room temperature and pressure conditions, over the feature space considering oil density, oil composition, salinity, and ionic concentrations. On the basis of gradient boosted (GB) algorithms, we have observed that the dominant quantities affecting the IFT are related to the oil attributes and the salinity concentration, and no specific ion dominates the IFT changes. When the obtained LR model was validated against MD and experimental data from the literature, the error varied up to 2% and 9%, respectively, showing a robust and consistent transferability. The combination of MD simulations and ML techniques may provide a fast and cost-effective IFT determination over multiple and complex fluid-fluid and fluid-solid interfaces.

关键词： machine learning molecular dynamics simulations oil/brine interface medium oil model linear regression model feature importance analysis

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