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Determining node duty cycle using Q-learning and linear regression for WSN

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Frontiers of Computer Science 2021年第1期15卷 17-23页

作者： Han Yao HUANG Kyung Tae KIM Hee Yong YOUN College of Software Sungkyunkwan UniversitySuwon 440-746Korea

Wireless sensor network(WSN)is effective for monitoring the target environment,which consists of a large number of sensor nodes of limited *** efficient medium access control(MAC)protocol is thus imperative to maximize the energy efficiency and performance of *** most existing MAC protocols are based on the scheduling of sleep and active period of the nodes,and do not consider the relationship between the load condition and *** this paper a novel scheme is proposed to properly determine the duty cycle of the WSN nodes according to the load,which employs the Q-leaming technique and function approximation with linear *** allows low-latency energy-efficient scheduling for a wide range of traffic conditions,and effectively overcomes the limitation of Q-learning with the problem of continuous state-action ***3 simulation reveals that the proposed scheme significantly improves the throughput,latency,and energy efficiency compared to the existing fully active scheme and S-MAC.

关键词： wireless sensor network media access control duty-cycle scheduling Q-leaming linear regression

A class of estimators of the mean survival time from interval censored data with application to linear regression

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Applied Mathematics(A Journal of Chinese Universities) 2008年第4期23卷 377-390页

作者： ZHENG Zu-kang Dept. of Statist. School of Management Fudan Univ. Shanghai 200433 China

A class of estimators of the mean survival time with interval censored data are studied by unbiased transformation method. The estimators are constructed based on the observations to ensure unbiasedness in the sense that the estimators in a certain class have the same expectation as the mean survival time. The estimators have good properties such as strong consistency （with the rate of O（n^-1/1 （log log n）^1/2）） and asymptotic normality. The application to linear regression is considered and the simulation reports are given.

关键词： interval censored data linear regression

Experimental Analysis of Methods Used to Solve linear regression Models

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Computers, Materials & Continua 2022年第9期72卷 5699-5712页

作者： Mua’ad Abu-Faraj Abeer Al-Hyari Ziad Alqadi Computer Information Systems Department The University of JordanAqaba77110Jordan Electrical Engineering Department Al-Balqa Applied UniversityAs Salt19117Jordan Computers and Networks Engineering Department Amman15008Jordan

Predicting the value of one or more variables using the values of other variables is a very important process in the various engineering experiments that include large data that are difficult to obtain using different measurement *** is one of the most important types of supervised machine learning,in which labeled data is used to build a prediction model,regression can be classified into three different categories:linear,polynomial,and *** this research paper,different methods will be implemented to solve the linear regression problem,where there is a linear relationship between the target and the predicted *** methods for linear regression will be analyzed using the calculated Mean Square Error(MSE)between the target values and the predicted outputs.A huge set of regression samples will be used to construct the training dataset with selected sizes.A detailed comparison will be performed between three methods,including least-square fit;Feed-Forward Artificial Neural Network(FFANN),and Cascade Feed-Forward Artificial Neural Network(CFFANN),and recommendations will be *** proposed method has been tested in this research on random data samples,and the results were compared with the results of the most common method,which is the linear multiple regression *** should be noted here that the procedures for building and testing the neural network will remain constant even if another sample of data is used.

关键词： linear regression ANN CFFANN FFANN MSE training cycle training set

Online measurement of alkalinity in anaerobic co-digestion using linear regression method

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International Journal of Agricultural and Biological Engineering 2017年第1期10卷 176-183页

作者： Bai Xue Li Zifu Wang Xuemei He Xi Cheng Shikun Bai Xiaofeng Gao Ruiling School of Energy and Environmental Engineering University of Science and Technology BeijingBeijing 100083China Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants Beijing 100083China International Science and Technology Cooperation Base for Environmental and Energy Technology of MOST Beijing 100083China

Alkalinity is a reliable indicator of process stability in anaerobic digestion *** alkalinity(TA)and partial alkalinity(PA)are usually monitored offline as indicators for the status of anaerobic digestion *** order to online monitor TA and PA,the linear regression method was used as estimator to predict alkalinity via software sensor ***,namely,pH,oxidation and reduction potential(ORP),and electrical conductivity(EC),were used as input *** was the most significant parameter with TA and *** linear regression(MLR)models and simple linear regression models with EC were constructed to predict TA and PA in anaerobic co-digestion *** the basis of the evaluation of prediction accuracy,the applications of linear regression models were better for monitoring PA than *** models provided higher accuracy for alkalinity prediction than simple linear regression *** two MLR models based on single-phase anaerobic digestion system were also feasible to predict TA in anaerobic co-digestion ***,the accuracy of these models should be improved by calibrating for broad applications of linear regression method in online alkalinity measurement.

关键词： anaerobic digestion alkalinity online measurement model linear regression

IMPACTS OF BUILDING FUNCTION ON NORMALIZED-STEAM CONSUMPTION:ANALYSIS OF FLOOR AREA NORMALIZATION VERSUS linear regression ON HEATING DEGREE-DAYS IN A HEATING-DOMINATED CLIMATE

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Journal of Green Building 2021年第3期16卷 73-85页

作者： Mahsa Farid Mohajer Ajla Aksamija Department of Environmental Conservation University of Massachusetts Amherst School of Architecture University of Utah375 South 1530 EastSuite 235Salt Lake CityUT 84112

linear regression analysis is one the most common methods for weather-normalizing energy data,where energy versus degree-days is plotted,quantifying the impacts of outside temperature on buildings’energy ***,this approach solely con-siders dry-bulb temperature,while other climate variables are *** addition,depending on buildings’internal loads,weather impact can be less influential,making the linear regression method not applicable for energy data normalization in inter-nally driven buildings(such as research laboratory buildings,healthcare facilities,etc.).In this study,several existing buildings from different categories,all located on the University of Massachusetts Amherst campus and exposed to the same weather conditions in a heating-dominated climate,were *** all cases,regression of monthly steam use on heating degree-days and floor-area normalized steam data were used,investigating applicability of the former when the latter *** was found that internal loads can skew steam consumption,depending on the build-ing functionality,making the effect of degree-days *** laboratory-type buildings,besides heating and domestic hot water production,steam is also used for scientific ***,daily occupancy percentage,even during weekends and holidays,was higher than that of other buildings,indicating the intensity of scientific experiments *** significantly impacted steam consumption,resulting in higher floor-area-normalized steam *** these cases,steam use did not provide an outstanding correlation to heating ***,for cases with other func-tionality-types and lower floor-area normalized steam,coefficients of determination in regressions were *** study concludes that even for buildings located in the same climate,depending on how building functionality and occupancy schedule influ-ence floor-area normalized steam use,multivariate linear regression can provide more accurate analysis,rathe

关键词： linear regression weather normalization heating degree-days heating loads internal loads steam consumption energy usage

A fault recognition method based on clustering linear regression

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High Technology Letters 2023年第4期29卷 406-415页

作者：陈雷 SHI Jiaqi ZHANG Ting Computer School Beijing Information Science and Technology UniversityBeijing 100101P.R.China Faculty of Information Technology Beijing University of TechnologyBeijing 100124P.R.China

Aiming at the problems of low accuracy,long time consumption,and failure to obtain quantita-tive fault identification results of existing automatic fault identification technic,a fault recognition method based on clustering linear regression is ***,Hough transform is used to detect the line segment of the enhanced image obtained by the coherence cube ***,the endpoint of the line segment detected by Hough transform is taken as the key point,and the adaptive clustering linear regression algorithm is used to cluster the key points adaptively according to the lin-ear relationship between ***,a fault is generated from each category of key points based on least squares curve fitting method to realize fault *** verify the feasibility and pro-gressiveness of the proposed method,it is compared with the traditional method and the latest meth-od on the actual seismic data through experiments,and the effectiveness of the proposed method is verified by the experimental results on the actual seismic data.

关键词： fault recognition clustering linear regression curve fitting seismic interpreta-tion

Finite Element Analysis and linear regression of Maximum Temperature for Inner Wall of Chimney Foundation

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Journal of China University of Mining and Technology 2005年第3期15卷 234-240页

作者： ZHANG Yu-mei SONG Yu-pu ZHAO Yun-fei ZHANG Ye-min The State Key Laboratory of Coastal and Offshore Engineering Dalian University of Technology Dalian Liaoning Jinzhou Shihua Building Limited Company Jinzhou Liaoning 121001 China The Civil Engineering Department Liaoning Institute of Technology Jinzhou Liaoning 121001China

The uniform design method was adopted and the twenty-four groups of different geometric and physical pa-rameters were chosen. The finite element model was built. Comparisons between the simulation results and the test re-sults prove that the simulation results are correct. The distribution of the temperature field of the chimney foundationwas analyzed. The multivariate linear regression of the hightest tomperature was performed on the inner wall of thechimney foundation by the numerical calculated results. The fitting property of the highest temperature with six influ-ence factors was obtained. A simple method for the calculation of the temperature field of the chimney foundation wasprovided.

关键词： chimney foundation temperature field nonlinear finite element analysis linear regression

Calculations of rock matrix modulus based on a linear regression relation

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Applied Geophysics 2011年第3期8卷 155-162,239页

作者：贺锡雷贺振华汪瑞良王绪本蒋炼油气藏地质及开发工程国家重点实验室成都理工大学 610059 中海石油(中国)有限公司深圳分公司 518067

The rock matrix bulk modulus or its inverse, the compressive coefficient, is an important input parameter for fluid substitution by the Biot-Gassmann equation in reservoir prediction. However, it is not easy to accurately estimate the bulk modulus by using conventional methods. In this paper, we present a new linear regression equation for calculating the parameter. In order to get this equation, we first derive a simplified Gassmann equation by using a reasonable assumption in which the compressive coefficient of the saturated pore fluid is much greater than the rock matrix, and, second, we use the Eshelby- Walsh relation to replace the equivalent modulus of a dry rock in the Gassmann equation. Results from the rock physics analysis of rock sample from a carbonate area show that rock matrix compressive coefficients calculated with water-saturated and dry rock samples using the linear regression method are very close （their error is less than 1%）. This means the new method is accurate and reliable.

关键词： Bulk modulus rock matrix fluid substitution rock physics linear regression

An Intelligent Early Warning Method of Press-Assembly Quality Based on Outlier Data Detection and linear regression

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Transactions of Nanjing University of Aeronautics and Astronautics 2020年第4期37卷 597-606页

作者： XUE Shanliang LI Chen College of Computer Science and Technology/College of Artificial Intelligence Nanjing University of Aeronautics and AstronauticsNanjing 211106P.R.China

Focusing on controlling the press-assembly quality of high-precision servo mechanism,an intelligent early warning method based on outlier data detection and linear regression is *** regression is used to deal with the relationship between assembly quality and press-assembly process,then the mathematical model of displacement-force in press-assembly process is established and a qualified press-assembly force range is defined for assembly quality *** preprocess the raw dataset of displacement-force in the press-assembly process,an improved local outlier factor based on area density and P weight(LAOPW)is designed to eliminate the outliers which will result in inaccuracy of the mathematical model.A weighted distance based on information entropy is used to measure distance,and the reachable distance is replaced with P *** show that the detection efficiency of the algorithm is improved by 5.6 ms compared with the traditional local outlier factor(LOF)algorithm,and the detection accuracy is improved by about 2%compared with the local outlier factor based on area density(LAOF)*** application of LAOPW algorithm and the linear regression model shows that it can effectively carry out intelligent early warning of press-assembly quality of high precision servo mechanism.

关键词： quality early warning outlier data detection linear regression local outlier factor based on area density and P weight(LAOPW) information entropy P weight