Considering both exploitation and exploration capabilities, a self-adaptive differential evolution algorithm is proposed in this paper. All populations are divided into two parts, and two mutation strategies are selected. The one sub-population use the DE/rand/1 approach, in which scale factor F is changed according to corresponding model of exponential declining function; The second sub-population adopt DE/best/2 method in which best fitness value's information is kept in each generation and scale factor is self-adapted according to distance between current and optimal individual. To strengthen information sharing between individuals, it is crucial that two groups are integrated into a population after mutation operator, Meanwhile, the adaptive crossover probability CR is to adaptively adjust the value by decreasing quadratic function. In the following benchmark function experiments, the proposed algorithm performs obviously better than the basic DE algorithm. Finally, it is found that the improved differential algorithm(IDE) could also be well applied in optimization design of pressure vessel.
The problem of fault detection and diagnosis (FDD) in dynamic systems has received considerable attention in last decades due to the growing complexity of modern engineering systems and ever increasing demand for fault tolerance, cost efficiency, and reliability (Willsky, 1976; Basseville, 1988). Existing FDD approaches can be roughly divided into two major categories including model-based and knowledge-based approaches (Venkatasubramanian et al., 2003a; Venkatasubramanian et al., 2003b). Model-based approaches make use of the quantitative analytical model of a physical system. Knowledgebased approaches do not need full analytical modeling and allow one to use qualitative models based on the available information and knowledge of a physical system. Whenever the mathematical models describing the system are available, analytical model-based methods are preferred because they are more amenable to performance analysis. Generally, there are two steps in the procedure of model-based FDD. First, on the basis of the available observations and a mathematical model of the system, the state variable x and test statistics are required to be obtained. Then, based on the generated test statistics, it is required to decide on the potential occurrence of a fault. For linear and Gaussian systems, the Kalman filter (KF) is known to be optimal and employed for state estimation. The innovations from the KF are used as the test statistics, based on which hypothesis tests can be carried out for fault detection (Belcastro & Weinstein, 2002). In reality, however, the models representing the evolution of the system and the noise in observations typically exhibit complex nonlinearity and non-Gaussian distributions, thus precluding analytical solution. One popular strategy for estimating the state of such a system as a set of observations becomes available online is to use sequential Monte-Carlo (SMC) methods, also known as particle filters (PFs) (Doucet et al., 2001). These methods allow for a complete representation of the posterior probability distribution function (PDF) of the states by particles (Guo & Wang, 2004; Li & Kadirkamanathan, 2001). The aforementioned FDD strategies are single-model-based. However, a single-model-based FDD approach is not adequate to handle complex failure scenarios. One way to treat this
To solve the problem of speed-sensorless control for induction motor with unknown rotor resistance,the voltage model was adopted to estimate the rotor flux;then injected AC(altering current) signal to rotor flux reference signal to estimate the rotor resistance.The reason for using the injection signal was analyzed using the static frame model of induction motor.The rotor speed expression was derived,and substituted the estimated rotor resistance into the expression to estimate the rotor speed.The simulation results indicate that the estimation of the rotor resistance,rotor flux and the rotor speed is accurate and the estimated rotor resistance can trace the changing rotor resistance.
A nonlinear PID parameters tuning for some big delay time plants with divisional intelligent control strategy is discussed in this paper.Division has been made according to system error and error speed.Some intrinsic mechanism is analyzed and some control rules are extracted according to human intelligence function.The parameters are tuned adaptively based on absolute error value in division.Simulation comparison has been done between the method discussed in this paper and the methods in paper .The simulation results show that the performance obtained following the discussed method is much better than those from the paperin tracing speed and stability of step response.
The Stability of power quality combined compensator and the steady state error performance are analyzed.Firstly,based on equivalent circuit diagram the structural diagram of power quality combined compensator is educed to attain corresponding transfer function of the system,and then according to Routh criterion the necessary and sufficient conditions for stability of the system are given to prove this system is structurally stable;secondly,the steady state error performance of applying generalized integral iteration control algorithm in harmonic current suppression is analyzed,and it is proved by mathematical derivation that the generalized integral controller can perfectly trace harmonic currents,thus the error performance of the traditional PI controller is surmounted.PSIM simulation results show that the sturcture of the proposed combined system is feasible,and the stability of the proposed system as well as the correctness of the analysis on its steady state error performance are verified.
Background: This study aimed to determine the potential roles of RNA N6-methyladenosine (m6A) modification of tumor microenvironment (TME) cell infiltration in breast cancer (BRCA).Methods: We evaluated the m6A modification patterns of 1204 samples in The Cancer Genome Atlas (TCGA) database, including 1091 BRCA samples and 113 normal samples based on 23 m6A regulators, and correlated their modification patterns with TME cell-infiltrating characteristics. A m6A regulator score model was established based on TCGA-BRCA RNA sequencing data of m6A regulators. By using weighted coexpression network analysis (WGCNA), we identified m6A-related lncRNAs and mRNAs. Models of m6A-related mRNA or lncRNA scores were also established and compared. The m6A regulator score model was validated across GSE20685 datasets.Results: The expression patterns of seven m6A regulators (KIAA1429, ELAVL1, YTHDC1, YTHDF2, WTAP, YTHDF1, and YTHDF3) were found to influence the survival rates of patients. Two distinct m6A modification patterns were identified. The TME cell-infiltrating characteristics and m6A regulator expression differed under these two patterns, especially in regulatory T cells. m6A regulator, m6A-related mRNA, and m6A-related lncRNA scores could predict the prognoses of patients with BRCA, and their accuracies in predicting 5-year survival were 0.65, 0.73, and 0.61, respectively. High scores indicated a lower survival probability at early stages. However, compared with the other two systems, the m6A regulator score seemed easier to manipulate and was more stable.Conclusions: m6A regulators and their associated lncRNA and mRNA contribute to the individualized prediction of BRCA prognosis, and these RNAs can be used as potential biomarkers of BRCA that specifically target m6A modification.Funding Information: This research was supported by National Natural Science Foundation of China [81871943 to JC]; Guangdong Provinvial Clinical Research Center for Digestive Diseases [2020B1111170004]; Guangzhou High-level Key Clinical Specialty Construction Project[No.9];The Project of Key Medical Discipline in Guangzhou [2021-2023].Declaration of Interests: The authors have no conflicts of interest to declare.Ethics Approval Statement: N/a.
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