The required operating pressure of the Wolsong nuclear power plant is currently controlled by a proportional integral (PI) controller. The PI controller has a simple structure and was designed to meet requirements through gain setting. However, these control requirements can be difficult to meet without properly adjusting the gain when certain parameters change, such as the wear and tear in the valves or pipes. To solve these problems, it is important to dynamically change the PI controller gain or compensate for the PI controller output. The purpose of this study is to help design a controller that is capable of providing stable control in order to reduce errors regardless of parameter changes. The proposed PI neural network (PINN) control technique involves a PI controller and a neural network controller combined in parallel. The neural network component which is designed to be robust compensates the output of the controller for changes in the above-mentioned parameters. Because assessing the controller performance straightforwardly in real-time processes can be difficult, a simulator model was developed based on real-time processes, and it showed changes in the parameters involved. The results confirmed that the proposed PINN controller reduced the instability of the fuel supply machine and, hence the aforementioned problem could be properly controlled.
Motivation The investigation of DNA methylation can shed light on the processes underlying human well-being and help determine overall human health. However, insufficient coverage makes it challenging to implement single-stranded DNA methylation sequencing technologies, highlighting the need for an efficient prediction model. Models are required to create an understanding of the underlying biological systems and to project single-cell (methylated) data accurately. Results In this study, we developed positional features for predicting CpG sites. Positional characteristics of the sequence are derived using data from CpG regions and the separation between nearby CpG sites. Multiple optimized classifiers and different ensemble learning approaches are evaluated. The OPTUNA framework is used to optimize the algorithms. The CatBoost algorithm followed by the stacking algorithm outperformed existing DNA methylation identifiers. Availability The data and methodologies used in this study are openly accessible to the research community. Researchers can access the positional features and algorithms used for predicting CpG site methylation patterns. Implementation To achieve superior performance, we employed the CatBoost algorithm followed by the stacking algorithm, which outperformed existing DNA methylation identifiers. The proposed iCpG-Pos approach utilizes only positional features, resulting in a substantial reduction in computational complexity compared to other known approaches for detecting CpG site methylation patterns. In conclusion, our study introduces a novel approach, iCpG-Pos, for predicting CpG site methylation patterns. By focusing on positional features, our model offers both accuracy and efficiency, making it a promising tool for advancing DNA methylation research and its applications in human health and well-being. Supplementary information Supplementary data are available at Bioinformatics online.
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