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Prediction and estimation model of energy demand of the AMR with cobot for the designed path in automated logistics systems

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Procedia CIRP 2021年 99卷 116-121页

作者： Khurshid Aliev Emiliano Traini Mansur Asranov Ahmed Awouda Paolo Chiabert Politecnico di Torino Corso Duca degli Abruzzi 24 10129 Torino Italy Turin polytechnic university in Tashkent Kichik Halqa Yuli 17 100095 Tashkent Uzbekistan

The ecosystem of the Industry 4.0 involves many new technologies, such as autonomous mobile robots (AMR) and cobots (collaborative robots), these are characterized with higher flexibility and cost effectiveness which makes them more suitable for automated internal logistics systems. The evaluation of energy consumption of AMRs for a designed path in a real case scenario using analytical tools are challenging. This paper proposes a method of evaluation of the sustainability of new technologies of Industry 4.0 in internal logistics. The proposed framework demonstrates data management technique of the industrial robots. Since, the AMR with manipulator perform different tasks as a single system in logistics there is big demand to develop model of cyber physical system. During task execution measured robots’ physical parameters used as input data to perform analytics. Moreover, acquired data from different condition use cases have been used to monitor the battery behaviour of the AMR and preliminary results of the linear regression model is presented.

关键词： Cobot AMR Battery prediction linear regression model Industry 4.0

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The Impact of Economic, Energy, and Environmental Factors on the Development of the Hydrogen Economy

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ENERGIES 2021年第16期14卷 4811页

作者： Cader, Justyna Koneczna, Renata Olczak, Piotr Polish Acad Sci Mineral & Energy Econ Res Inst PL-31261 Krakow Poland

This article attempts to model interdependencies between socio-economic, energy, and environmental factors with selected data characterizing the development of the hydrogen economy. The study applies Spearman's correlation and a linear regression model to estimate the influence of gross domestic product, population, final energy consumption, renewable energy, and CO2 emission on chosen hydrogen indicators-production, patents, energy technology research, development, and demonstration budgets. The study was conducted in nine countries selected for their actions towards a hydrogen economy based on analyses of national strategies, policies, research and development programs, and roadmaps. The results confirm the statistically significant impact of the chosen indicators, which are the drivers for the development of the hydrogen economy from 2008 to 2018. Moreover, the empirical results show that different characteristics in each country contribute to the development of the hydrogen economy vision.

关键词： linear regression model Spearman's correlation national hydrogen strategies hydrogen economy implementation

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linear regression model in Estimating Solar Radiation in Perlis

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Energy Procedia 2012年 18卷 1402-1412页

作者： S. Ibrahim I. Daut Y.M. Irwan M. Irwanto N. Gomesh Z. Farhana Institute of Mathematical Science Faculty of Science University of Malaya 50603 Kuala Lumpur Malaysia Cluster Power Electronic and Machine Design School of Electrical System Engineering University Malaysia Perlis (UniMAP) Malaysia

Statistical models for predicting the solar radiation have been developed. In any prediction of the solar radiation, an understanding of its characteristics is of fundamental importance. This study presents an investigation of a relationship between solar radiation and temperature in Perlis, Northern Malaysia for the year of 2006. To achieve this, the data are presented in daily averaged maximum and minimum air temperature, and daily averaged solar radiation. Since the scatter plots represent the straight line, the linear regression model was selected to estimate the solar radiation. It was found that the linear correlation coefficient value is 0.7473 shows that a strong linear relationship between solar radiation and temperature. The analysis of variance R 2 is 0.5585 that is; about 56 percent of the variability in temperature is accounted for by the straight-line fit to solar radiation. Based on the results, the fitted model is adequate to represent the estimation of solar radiation.

关键词： Solar radiation Temperature linear regression model Statistical analysis

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A Lossless Data Compression Algorithm for Wireless Sensor Networks Based on linear regression model

A Lossless Data Compression Algorithm for Wireless Sensor Ne...

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7th International Conference on MEMS, NANO and Smart Systems (ICMENS 2011)

作者： Lu Hongzhi Ren Xuejun Engn Coll Chinese Armed Police Force Xian Peoples R China

ISBN: (纸本)9783037853122

According to the theory of simple linear regression model, this paper designed a lossless sensor data compression algorithm based on one-dimensional linear regression model. The algorithm computes the linear fitting values of sensor data's differences and fitting residuals, which are input to a normal distribution entropy encoder to perform compression. Compared with two typical lossless compression algorithms, the proposed algorithm indicated better compression ratios.

关键词： Wireless Sensor Networks Data Compression lossless linear regression model

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An iteratively reweighted least-squares approach to adaptive robust adjustment of parameters in linear regression models with autoregressive and t-distributed deviations

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JOURNAL OF GEODESY 2018年第3期92卷 271-297页

作者： Kargoll, Boris Omidalizarandi, Mohammad Loth, Ina Paffenholz, Jens-Andre Alkhatib, Hamza Leibniz Univ Hannover Geodet Inst Nienburger Str 1 D-30167 Hannover Germany Rhein Friedrich Wilhelms Univ Bonn Inst Geodesy & Geoinformat Nussallee 17 D-53115 Bonn Germany

In this paper, we investigate a linear regression time series model of possibly outlier-afflicted observations and autocorrelated random deviations. This colored noise is represented by a covariance-stationary autoregressive (AR) process, in which the independent error components follow a scaled (Student's) t-distribution. This error model allows for the stochastic modeling of multiple outliers and for an adaptive robust maximum likelihood (ML) estimation of the unknown regression and AR coefficients, the scale parameter, and the degree of freedom of the t-distribution. This approach is meant to be an extension of known estimators, which tend to focus only on the regression model, or on the AR error model, or on normally distributed errors. For the purpose of ML estimation, we derive an expectation conditional maximization either algorithm, which leads to an easy-to-implement version of iteratively reweighted least squares. The estimation performance of the algorithm is evaluated via Monte Carlo simulations for a Fourier as well as a spline model in connection with AR colored noise models of different orders and with three different sampling distributions generating the white noise components. We apply the algorithm to a vibration dataset recorded by a high-accuracy, single-axis accelerometer, focusing on the evaluation of the estimated AR colored noise model.

关键词： linear regression model Autoregressive process Scaled t-distribution Adaptive robust estimation Expectation maximization (EM) algorithm Iteratively reweighted least squares

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Deriving Tests of the Semi-linear regression model Using the Density Function of a Maximal Invariant

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JOURNAL OF STATISTICAL THEORY AND PRACTICE 2012年第2期6卷 251-259页

作者： Bhowmik, Jahar L. King, Maxwell L. Swinburne Univ Technol Fac Life & Social Sci Melbourne Vic 3122 Australia Monash Univ Dept Econometr & Business Stat Melbourne Vic Australia

In the context of a general regression model in which some regression coefficients are of interest and others are purely nuisance parameters, we define the density function of a maximal invariant statistic with the aim of testing for the inclusion of regressors (either linear or non-linear) in linear or semi-linear models. This allows the construction of the locally best invariant test, which in two important cases is equivalent to the one-sided t test for a regression coefficient in an artificial linear regression model. We consider a specific semi-linear model to apply the constructed test.

关键词： Invariance linear regression model Locally best invariant test Nonlinear regression model Nuisance parameters t-test

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Optimal integration of recompression supercritical CO2 Brayton cycle with main compression intercooling in solar power tower system based on exergoeconomic approach

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APPLIED ENERGY 2019年 242卷 1134-1154页

作者： Ma, Yuegeng Morozyuk, Tatiana Liu, Ming Yan, Junjie Liu, Jiping Xi An Jiao Tong Univ MOE Key Lab Thermal Fluid Sci & Engn Xianning West Rd 28 Xian 710049 Shaanxi Peoples R China Tech Univ Berlin Inst Energy Engn Marchstr 18 D-10587 Berlin Germany Xi An Jiao Tong Univ State Key Lab Multiphase Flow Power Engn Xianning West Rd 28 Xian 710049 Shaanxi Peoples R China

Despite the emerging interest in applying the supercritical CO2 (S-CO2) Brayton cycle to solar power towers (SPTs), its unique characteristics necessitates a specific thermoeconomic consideration in the integration of this cycle in SPT plants to obtain a competitive electricity generation cost. In this work, the exergoeconomic approach is utilized to address the optimal integration of the recompression S-CO2 Brayton cycle with main compression intercooling in the SPT plant. Firstly, exergoeconomic optimization using a genetic algorithm is performed on six crucial variables of S-CO2 Brayton cycle to minimize the total unit exergy cost of the SPT system (c(p,tot)). The results are then compared with those obtained by thermodynamic optimization aiming at maximal SPT energetic efficiency. Secondly, a sensitivity analysis model is established, the effects of the cost and design conditions of solar components on the optimal S-CO2 cycle integration are investigated with this model. Finally, linear regression models are established to predict the optimal c(p,tot) under various conditions of solar component capital cost and design with a deviation less than 2%. Results indicate that the optimal cp,tot is reduced by 8.94% according to the exergoeconomic optimization relative to the conventional thermodynamic optimization. The integration of reheating is not justified for the cycle due to the significant decreased temperature change across the primary heat exchanger and the consequent reduction in the exergoeconomic performance of the SPT plant. Sensitivity analysis highlights the effects of cost and design conditions of solar components on the optimal integration of the S-CO2 cycle, and indicates that the optimal cycle layout may degrade from the recompression cycle to the simple recuperating cycle under certain cost and design conditions of solar components.

关键词： Concentrated solar power plant Supercritical carbon dioxide cycle Exergoeconomic analysis Global parameters optimization linear regression model

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The Time Between the Onset of COVID-19 and the Death

The Time Between the Onset of COVID-19 and the Death

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作者： Minhao Liang Feiyan Song Jiaqi Li Hongyi Xu College and Graduate School of Arts and Sciences University of Virginia Suzhou Foreign Language School University of Connecticut

The main pandemic this year around the world can be regarded as COVID-19,an extremely serious virus that negatively affect many *** order to better deal with the virus to save more lives,it is necessary to discover the relationship between the cases and deaths of COVID-19 to promote the plan to prevent and treat people in a desirable *** this work,the research mainly focusses on the length of the apparent lag between cases and deaths to help governments and organizations in prevention and *** the state with most cases and deaths,the study of the United States is deemed to be typical for the observation whole *** work focuses on 4 representative states in the America(Michigan,Arizona,Hawaii,Connecticut) to conclude that the time between the onsite of COVID-19 to death is about one to two months through multiple data analysis techniques including linear regression model,time lag analysis to assist the formulation of the plan in prevention and treatment.

关键词： COVID-19 linear regression model relationship of case and death time lag

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Factors Affecting Development of Blockchain

Factors Affecting Development of Blockchain

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作者： Jiyin Shen School of Finance Shanghai University of International Business and Economics

Blockchain is a digital record of transactions made with *** is a newly occurred concept,whose appearance just started twelve years ago symbolized by the anonymous publication of the paper,Bitcoin:A Peer-to-Peer Electronic Cash *** is limited research on this buoyant technology,thus this paper hopes to identify the factors that might have influence on the future development of blockchain to provide an explicit depicting of *** realize this goal,this paper identified three variables that may affect the future development of blockchain,including population acceptance,government policy,and technique support,and then constructed a model to describe development of blockchain.

关键词： Blockchain Development linear regression model

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Soil moisture content increases root length and decreases soil detachment capacity based on an optimal threshold

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Rhizosphere 2025年 34卷

作者： Parhizkar, Misagh Rice Research Institute of Iran Agricultural Research Education and Extension Organization (AREEO) Rasht Iran

Soil moisture can have a direct and indirect influence on soil detachment capacity (Dc). However, the appropriate threshold of soil moisture for increasing total root length and decreasing Dc is still unclear. This pa... 详细信息

Soil moisture can have a direct and indirect influence on soil detachment capacity (D_c). However, the appropriate threshold of soil moisture for increasing total root length and decreasing D_c is still unclear. This paper presents a quantitative assessment of impacts of soil moisture classes on total root length and D_c. The values of soil moisture content were classified into five classes, 10–15 %, 20–25 %, 30–35 %, 40–45 % and 50–55 %, and soil samples along with root prepared for D_c measurements. The results showed that the decrease in D_c as a function of total root length could be related to soil moisture class of 30–35 %. A modelling approach has shown that linear regression models between D_c and shear stress for soil moisture classes were accurate (r² = 0.71–83) for predicting rill erodibility (K_r). © 2025 Elsevier B.V.

关键词： Different classes of soil moisture content linear regression model Root system

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