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Search Results (33)
  • Open Access

    ARTICLE

    Sensor Location Optimisation Design Based on IoT and Geostatistics in Greenhouse

    Yang Liu1,3, Xiaoyu Liu2,3, Xiu Dai1, Guanglian Xun1, Ni Ren1,*, Rui Kang1,4, Xiaojuan Mao1

    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1653-1663, 2022, DOI:10.32604/iasc.2022.017049

    Abstract Environmental parameters such as air temperature (T) and air relative humidity (RH) should be intensively monitored in a greenhouse in real time. In most cases, one set of sensors is installed in the centre of a greenhouse. However, as the microclimate of a greenhouse is always heterogeneous, the sensor installation location is crucial for practical cultivation. In this study, the T and RH monitoring performance of different sensors were compared. Two types of real-time environmental sensors (Air Temperature and Humidity sensor and Activity Monitoring sensor, referred as ATH and AM) were selected and calibrated by reliable non-real-time sensors (Honest Observer… More >

  • Open Access

    ARTICLE

    Skew t Distribution-Based Nonlinear Filter with Asymmetric Measurement Noise Using Variational Bayesian Inference

    Chen Xu1, Yawen Mao2, Hongtian Chen3,*, Hongfeng Tao1, Fei Liu1

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 349-364, 2022, DOI:10.32604/cmes.2021.019027

    Abstract This paper is focused on the state estimation problem for nonlinear systems with unknown statistics of measurement noise. Based on the cubature Kalman filter, we propose a new nonlinear filtering algorithm that employs a skew t distribution to characterize the asymmetry of the measurement noise. The system states and the statistics of skew t noise distribution, including the shape matrix, the scale matrix, and the degree of freedom (DOF) are estimated jointly by employing variational Bayesian (VB) inference. The proposed method is validated in a target tracking example. Results of the simulation indicate that the proposed nonlinear filter can perform… More >

  • Open Access

    ARTICLE

    Inferential Statistics and Machine Learning Models for Short-Term Wind Power Forecasting

    Ming Zhang, Hongbo Li, Xing Deng*

    Energy Engineering, Vol.119, No.1, pp. 237-252, 2022, DOI:10.32604/EE.2022.017916

    Abstract The inherent randomness, intermittence and volatility of wind power generation compromise the quality of the wind power system, resulting in uncertainty in the system's optimal scheduling. As a result, it's critical to improve power quality and assure real-time power grid scheduling and grid-connected wind farm operation. Inferred statistics are utilized in this research to infer general features based on the selected information, confirming that there are differences between two forecasting categories: Forecast Category 1 (0–11 h ahead) and Forecast Category 2 (12–23 h ahead). In z-tests, the null hypothesis provides the corresponding quantitative findings. To verify the final performance of… More >

  • Open Access

    ARTICLE

    Traffic Flow Statistics Method Based on Deep Learning and Multi-Feature Fusion

    Liang Mu, Hong Zhao*, Yan Li, Xiaotong Liu, Junzheng Qiu, Chuanlong Sun

    CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.2, pp. 465-483, 2021, DOI:10.32604/cmes.2021.017276

    Abstract Traffic flow statistics have become a particularly important part of intelligent transportation. To solve the problems of low real-time robustness and accuracy in traffic flow statistics. In the DeepSort tracking algorithm, the Kalman filter (KF), which is only suitable for linear problems, is replaced by the extended Kalman filter (EKF), which can effectively solve nonlinear problems and integrate the Histogram of Oriented Gradient (HOG) of the target. The multi-target tracking framework was constructed with YOLO V5 target detection algorithm. An efficient and long-running Traffic Flow Statistical framework (TFSF) is established based on the tracking framework. Virtual lines are set up… More >

  • Open Access

    ARTICLE

    On Relations for Moments of Generalized Order Statistics for Lindley–Weibull Distribution

    Faten A. Momen khan1, Saman Hanif Shahbaz2, Muhammad Qaiser Shahbaz2,*

    Computer Systems Science and Engineering, Vol.41, No.1, pp. 197-208, 2022, DOI:10.32604/csse.2022.020448

    Abstract Moments of generalized order statistics appear in several areas of science and engineering. These moments are useful in studying properties of the random variables which are arranged in increasing order of importance, for example, time to failure of a computer system. The computation of these moments is sometimes very tedious and hence some algorithms are required. One algorithm is to use a recursive method of computation of these moments and is very useful as it provides the basis to compute higher moments of generalized order statistics from the corresponding lower-order moments. Generalized order statistics provides several models of ordered data… More >

  • Open Access

    ARTICLE

    Quantile Version of Mathai-Haubold Entropy of Order Statistics

    Ibrahim M. Almanjahie1,2,*, Javid Gani Dar3, Amer Ibrahim Al-Omari4, Aijaz Mir5

    CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.3, pp. 907-925, 2021, DOI:10.32604/cmes.2021.014896

    Abstract Many researchers measure the uncertainty of a random variable using quantile-based entropy techniques. These techniques are useful in engineering applications and have some exceptional characteristics than their distribution function method. Considering order statistics, the key focus of this article is to propose new quantile-based Mathai-Haubold entropy and investigate its characteristics. The divergence measure of the Mathai-Haubold is also considered and some of its properties are established. Further, based on order statistics, we propose the residual entropy of the quantile-based Mathai-Haubold and some of its property results are proved. The performance of the proposed quantile-based Mathai-Haubold entropy is investigated by simulation… More >

  • Open Access

    ARTICLE

    Main Factor Selection Algorithm and Stability Analysis of Regional FDI Statistics

    Juan Huang1, Bifang Zhou1, Huajun Huang2,*, Dingwen Qing1, Neal N. Xiong3

    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 303-318, 2021, DOI:10.32604/iasc.2021.016953

    Abstract There are various influencing factors in regional FDI (foreign direct investment) and it is difficult to identify the main influencing factors. For this reason, a main factor selection algorithm is proposed in this article for the main factors affecting regional FDI statistics by analyzing the regional economic characteristics and the possible influencing factors in the regional FDI. Then, an example is used to illustrate its effectiveness and its stability. Firstly, the characteristics of regional economy and the regional FDI data are introduced to develop the main factor selection algorithm based on the adaptive Lasso problem for the regional FDI and… More >

  • Open Access

    ARTICLE

    An Adaptive Lasso Grey Model for Regional FDI Statistics Prediction

    Juan Huang1, Bifang Zhou1, Huajun Huang2,*, Jianjiang Liu1, Neal N. Xiong3

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2111-2121, 2021, DOI:10.32604/cmc.2021.016770

    Abstract To overcome the deficiency of traditional mathematical statistics methods, an adaptive Lasso grey model algorithm for regional FDI (foreign direct investment) prediction is proposed in this paper, and its validity is analyzed. Firstly, the characteristics of the FDI data in six provinces of Central China are generalized, and the mixture model's constituent variables of the Lasso grey problem as well as the grey model are defined. Next, based on the influencing factors of regional FDI statistics (mean values of regional FDI and median values of regional FDI), an adaptive Lasso grey model algorithm for regional FDI was established. Then, an… More >

  • Open Access

    ARTICLE

    Extended Rama Distribution: Properties and Applications

    Khaldoon M. Alhyasat1,*, Kamarulzaman Ibrahim1, Amer Al-Omari2, Mohd Aftar Abu Bakar1

    Computer Systems Science and Engineering, Vol.39, No.1, pp. 55-67, 2021, DOI:10.32604/csse.2021.014909

    Abstract In this paper, the Rama distribution (RD) is considered, and a new model called extended Rama distribution (ERD) is suggested. The new model involves the sum of two independent Rama distributed random variables. The probability density function (pdf) and cumulative distribution function (cdf) are obtained and analyzed. It is found that the new model is skewed to the right. Several mathematical and statistical properties are derived and proved. The properties studied include moments, coefficient of variation, coefficient of skewness, coefficient of kurtosis and moment generating function. Some simulations are undertaken to illustrate the behavior of these properties. In addition, the… More >

  • Open Access

    ARTICLE

    A New Modified Inverse Lomax Distribution: Properties, Estimation and Applications to Engineering and Medical Data

    Abdullah M. Almarashi*

    CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.2, pp. 621-643, 2021, DOI:10.32604/cmes.2021.014407

    Abstract In this paper, a modified form of the traditional inverse Lomax distribution is proposed and its characteristics are studied. The new distribution which called modified logarithmic transformed inverse Lomax distribution is generated by adding a new shape parameter based on logarithmic transformed method. It contains two shape and one scale parameters and has different shapes of probability density and hazard rate functions. The new shape parameter increases the flexibility of the statistical properties of the traditional inverse Lomax distribution including mean, variance, skewness and kurtosis. The moments, entropies, order statistics and other properties are discussed. Six methods of estimation are… More >

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