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  • Open Access

    ARTICLE

    Low-Carbon Dispatch of an Integrated Energy System Considering Confidence Intervals for Renewable Energy Generation

    Yan Shi1, Wenjie Li1, Gongbo Fan2,*, Luxi Zhang1, Fengjiu Yang1

    Energy Engineering, Vol.121, No.2, pp. 461-482, 2024, DOI:10.32604/ee.2023.043835

    Abstract Addressing the insufficiency in down-regulation leeway within integrated energy systems stemming from the erratic and volatile nature of wind and solar renewable energy generation, this study focuses on formulating a coordinated strategy involving the carbon capture unit of the integrated energy system and the resources on the load storage side. A scheduling model is devised that takes into account the confidence interval associated with renewable energy generation, with the overarching goal of optimizing the system for low-carbon operation. To begin with, an in-depth analysis is conducted on the temporal energy-shifting attributes and the low-carbon modulation mechanisms exhibited by the source-side… More >

  • Open Access

    ARTICLE

    Prediction of Uncertainty Estimation and Confidence Calibration Using Fully Convolutional Neural Network

    Karim Gasmi1,*, Lassaad Ben Ammar2,, Hmoud Elshammari4, Fadwa Yahya2

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2557-2573, 2023, DOI:10.32604/cmc.2023.033270

    Abstract Convolution neural networks (CNNs) have proven to be effective clinical imaging methods. This study highlighted some of the key issues within these systems. It is difficult to train these systems in a limited clinical image databases, and many publications present strategies including such learning algorithm. Furthermore, these patterns are known for making a highly reliable prognosis. In addition, normalization of volume and losses of dice have been used effectively to accelerate and stabilize the training. Furthermore, these systems are improperly regulated, resulting in more confident ratings for correct and incorrect classification, which are inaccurate and difficult to understand. This study… More >

  • Open Access

    ARTICLE

    Bayesian Computation for the Parameters of a Zero-Inflated Cosine Geometric Distribution with Application to COVID-19 Pandemic Data

    Sunisa Junnumtuam, Sa-Aat Niwitpong*, Suparat Niwitpong

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1229-1254, 2023, DOI:10.32604/cmes.2022.022098

    Abstract A new three-parameter discrete distribution called the zero-inflated cosine geometric (ZICG) distribution is proposed for the first time herein. It can be used to analyze over-dispersed count data with excess zeros. The basic statistical properties of the new distribution, such as the moment generating function, mean, and variance are presented. Furthermore, confidence intervals are constructed by using the Wald, Bayesian, and highest posterior density (HPD) methods to estimate the true confidence intervals for the parameters of the ZICG distribution. Their efficacies were investigated by using both simulation and real-world data comprising the number of daily COVID-19 positive cases at the… More > Graphic Abstract

    Bayesian Computation for the Parameters of a Zero-Inflated Cosine Geometric Distribution with Application to COVID-19 Pandemic Data

  • Open Access

    ARTICLE

    CARM: Context Based Association Rule Mining for Conventional Data

    Muhammad Shaheen1,*, Umair Abdullah2

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3305-3322, 2021, DOI:10.32604/cmc.2021.016766

    Abstract This paper is aimed to develop an algorithm for extracting association rules, called Context-Based Association Rule Mining algorithm (CARM), which can be regarded as an extension of the Context-Based Positive and Negative Association Rule Mining algorithm (CBPNARM). CBPNARM was developed to extract positive and negative association rules from Spatio-temporal (space-time) data only, while the proposed algorithm can be applied to both spatial and non-spatial data. The proposed algorithm is applied to the energy dataset to classify a country’s energy development by uncovering the enthralling interdependencies between the set of variables to get positive and negative associations. Many association rules related… More >

  • Open Access

    ARTICLE

    Bayesian Analysis in Partially Accelerated Life Tests for Weighted Lomax Distribution

    Rashad Bantan1, Amal S. Hassan2, Ehab Almetwally3, M. Elgarhy4, Farrukh Jamal5, Christophe Chesneau6, Mahmoud Elsehetry7,*

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2859-2875, 2021, DOI:10.32604/cmc.2021.015422

    Abstract Accelerated life testing has been widely used in product life testing experiments because it can quickly provide information on the lifetime distributions by testing products or materials at higher than basic conditional levels of stress, such as pressure, temperature, vibration, voltage, or load to induce early failures. In this paper, a step stress partially accelerated life test (SS-PALT) is regarded under the progressive type-II censored data with random removals. The removals from the test are considered to have the binomial distribution. The life times of the testing items are assumed to follow length-biased weighted Lomax distribution. The maximum likelihood method… More >

  • Open Access

    ARTICLE

    An Adaptive Vision Navigation Algorithm in Agricultural IoT System for Smart Agricultural Robots

    Zhibin Zhang1,2,*, Ping Li1,3, Shuailing Zhao1,2, Zhimin Lv1,2, Fang Du1,2, Yajian An1,2

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 1043-1056, 2021, DOI:10.32604/cmc.2020.012517

    Abstract As the agricultural internet of things (IoT) technology has evolved, smart agricultural robots needs to have both flexibility and adaptability when moving in complex field environments. In this paper, we propose the concept of a vision-based navigation system for the agricultural IoT and a binocular vision navigation algorithm for smart agricultural robots, which can fuse the edge contour and the height information of rows of crop in images to extract the navigation parameters. First, the speeded-up robust feature (SURF) extracting and matching algorithm is used to obtain featuring point pairs from the green crop row images observed by the binocular… More >

  • Open Access

    ARTICLE

    Topp-Leone Odd Fréchet Generated Family of Distributions with Applications to COVID-19 Data Sets

    Sanaa Al-Marzouki1, Farrukh Jamal2, Christophe Chesneau3,*, Mohammed Elgarhy4

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 437-458, 2020, DOI:10.32604/cmes.2020.011521

    Abstract Recent studies have pointed out the potential of the odd Fréchet family (or class) of continuous distributions in fitting data of all kinds. In this article, we propose an extension of this family through the so-called “Topp-Leone strategy”, aiming to improve its overall flexibility by adding a shape parameter. The main objective is to offer original distributions with modifiable properties, from which adaptive and pliant statistical models can be derived. For the new family, these aspects are illustrated by the means of comprehensive mathematical and numerical results. In particular, we emphasize a special distribution with three parameters based on the… More >

  • Open Access

    ARTICLE

    Self-Organizing Gaussian Mixture Map Based on Adaptive Recursive Bayesian Estimation

    He Ni1,*, Yongqiao Wang1, Buyun Xu2

    Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 227-236, 2020, DOI:10.31209/2019.100000068

    Abstract The paper presents a probabilistic clustering approach based on self-organizing learning algorithm and recursive Bayesian estimation. The model is built upon the principle that the market data space is multimodal and can be described by a mixture of Gaussian distributions. The model parameters are approximated by a stochastic recursive Bayesian learning: searches for the maximum a posterior solution at each step, stochastically updates model parameters using a “dualneighbourhood” function with adaptive simulated annealing, and applies profile likelihood confidence interval to avoid prolonged learning. The proposed model is based on a number of pioneer works, such as Mixture Gaussian Autoregressive Model,… More >

  • Open Access

    ARTICLE

    Analysis of Naval Ship Evacuation Using Stochastic Simulation Models and Experimental Data Sets

    Roberto Bellas1, *, Javier Martínez1, Ignacio Rivera2, Ramón Touza2, Miguel Gómez1, Rafael Carreño1

    CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.3, pp. 971-995, 2020, DOI:10.32604/cmes.2020.07530

    Abstract The study of emergency evacuation in public spaces, buildings and large ships may present parallel characteristic in terms of complexity of the layout but there are also significant differences that can hindering passengers to reach muster stations or the lifeboats. There are many hazards on a ship that can cause an emergency evacuation, the most severe result in loss of lives. Providing safe and effective evacuation of passengers from ships in an emergency situation becomes critical. Recently, computer simulation has become an indispensable technology in various fields, among them, the evacuation models that recently evolved incorporating human behavioral factors. In… More >

  • Open Access

    ARTICLE

    A Combined Sensitive Matrix Method and Maximum Likelihood Method for Uncertainty Inverse Problems

    W. Zhang1, X. Han1,2, J. Liu1, Z. H. Tan1

    CMC-Computers, Materials & Continua, Vol.26, No.3, pp. 201-226, 2011, DOI:10.3970/cmc.2011.026.201

    Abstract The uncertainty inverse problems with insufficiency and imprecision in the input and/or output parameters are widely existing and unsolved in the practical engineering. The insufficiency refers to the partly known parameters in the input and/or output, and the imprecision refers to the measurement errors of these ones. In this paper, a combined method is proposed to deal with such problems. In this method, the imprecision of these known parameters can be described by probability distribution with a certain mean value and variance. Sensitive matrix method is first used to transform the insufficient formulation in the input and/or output to a… More >

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