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Copyright of Petroleum Geology & Recovery Efficiency is the property of Petroleum Geology & Recovery Efficiency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, the reliability for a repairable system under G-mixed redundancy strategy is investigated. The system consists of four components and a repairman, in which the multiple vacation policy of the repairman is adopted so that the human resources can be fully utilized. The phase-type (PH) distribution is used to describe various random variables in the model. A system reliability model with better commonality is developed, and some reliability indexes of the system are obtained in both transient and stationary regimes by employing matrix-analytic method. Finally, the applicability of the model is verified by numerical examples, and the system reliability under G-mixed redundancy strategy is compared with that under K-mixed redundancy strategy. The results show that the proposed reliability model under G-mixed redundancy strategy is significantly higher than that under the K-mixed redundancy strategy. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to analyze the influence of the uncertainty of hydrogeological parameters on the output of the groundwater numerical simulation model, this paper studies a hypothetical example. First, a groundwater numerical simulation model is established. Then sensitivity analysis is used to screen out the parameters that have a greater impact on the output of the simulation model. As a random variable, in order to reduce the computational load caused by repeatedly calling the simulation model, the Kriging method and the BP neural network method are used to establish the replacement model of the simulation model and the accuracy levels of the two are compared, and the BP neural network replacement model with higher accuracy is selected to perform Monte Carlo simulation. Among them, when the BP neural network method is used to establish the replacement model, the third-point algorithm is used to quickly determine the number of hidden layer nodes that minimize the error of the replacement model. Finally, the results of the random simulation are statistically analyzed and the interval estimation and evaluation of the risk of groundwater pollution are compared. The results show that when the confidence level is 90%, the confidence intervals for the concentration of the three observation wells are 367.48~415.67, 205.12~230.33 and 118.85~132.82 mg/L. Combined with the risk assessment, it is calculated that the risk of groundwater pollution in observation wells No.1, No.2 and No.3 in the study area are 0.66, 0.60, and 0.58, which provides a scientific basis for groundwater pollution prevention and control. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Learning causal structure from observed data has important applications. An existing method for learning causal structure is to learn causal structure by testing the independence between noise and causal variables under the functional causal model assumption. However, such methods often involve highly computationally complex in process of testing independence, which affects the practicability and robustness of the structure learning algorithm. To this end, this paper proposed a causal structure learning algorithm that used higher-order cumulants as independence scores under a linear non-Gaussian model. The algorithm was mainly divided into two steps. The first step was to use the method based on conditional independence constraints to learn the Markov equivalence class of the causal structure. The second step was to define a score based on high-order cumulants, the score could determine the independence of two random variables so that the causal structure of the best independence score could be searched from the Markov equivalence class as the output of the algorithm. The advantages of this algorithm were: a)Compared with the independence test based on the kernel method, the method had lower computational complexity. b)The method based on score search could always obtain a model that best matches the data generation process, which improved the robustness of the learning method. Experimental results show that the high-order cumulant-based causal structure learning method improves the F1 score by 5% in synthetic data and learns more causal directions in real data. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the situation of mixed heterogeneous information in the evaluation of multi-attribute decision-making problems, this paper proposed a Modular Random Measurement Alternatives and Ranking According to Compromise Solution（Mo-RMARCOS). This method does not need to unify information and reduces the losses in information processing. Considering the case where the attribute value is a random variable, decompose the random variable into a random vector, and determine the probability distribution of the overall random variable from the probability distribution of the different stages of the random vector. For other qualitative information of uncertainty, use intuitionistic fuzzy numbers to express and process. Using the improved dispersion maximization method to determine the weights of different attributes. Using the simple and effective advantages of MARCOS to sort the schemes; finally, taking the selection of a lithium battery supplier for a new energy vehicle as an example to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

To address the uncertainty of surgery duration, this paper investigates surgery planning scheduling problem with multiple servers, which proposes chance constraints of operating rooms overtime to guarantee the surgery durations of patients is no more than the time limit of operating rooms with a high probability. A stochastic chance-constrained program is proposed to determine which operating rooms to operate, and surgeries to operating rooms allocation. Based on a finite support set of the surgery duration, this paper introduces 0-1 variables to formulate the chance constraints, and derives 0-1 integer linear program counterpart. To improve the efficiency of the model, this paper presents two classes of valid inequalities and uses the longest path algorithm to separate the second class of valid inequalities, which are implemented in a branch-and-cut framework. Computational experiments based on real-life data from hospital in Beijing are conducted to verify the algorithm performance and determine the optimal planning scheme, so as to take full utilization of healthcare resources, i.e. operating rooms. [ABSTRACT FROM AUTHOR]

Iterative learning control problem is studied for a class of discrete nonlinear partial difference systems where the trial lengths vary randomly in this paper. The considered systems are composed of parabolic partial difference equations. Firstly, for the trial lengths vary randomly in systems, a stochastic variable satisfying Bernoulli distribution is introduced. Then, according to the properties of partial difference systems and P-type distributed update schemes, iterative learning controller is designed. Through rigorous theoretical analysis, the convergence sufficient condition of output error under λ norm and contractive mapping principle is established to ensure the convergence of tracking error in the sense of norm. The convergence speed of the algorithm is faster than the classical iterative learning algorithm. Finally, the effectiveness of the algorithm is verified by the numerical simulation. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Computational Mechanics / Jisuan Lixue Xuebao is the property of Chinese Journal of Computational Mechanics Editorial Office, Dalian University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)