Kongzhi yu Juece/Control and Decision (ISSN:1001-0920) is a monthly peer-reviewed scopus indexed journal originally founded in 1986. It is sponsored by the Ministry of Education, china and Northeastern University, china. Kongzhi yu Juece/Control and Decision (ISSN:1001-0920) has always adhered to the correct purpose of running the journal, and has been committed to gathering and disseminating excellent academic achievements, inspiring technological innovation, and promoting the development of disciplines in my country.Aiming at major national needs and international frontiers, this journal has published a large number of original and high-level research result. The journal was selected into the "China Science and Technology Journal Excellence Action Plan Project" in December 2019.In the future, it will strive to build an open innovation, collaborative integration.
Text similarity determination is one of the core technologies in the field of natural language processing. Its accuracy directly affects the accuracy of semantic similarity analysis and image, graphic and audio similarity. It is of great significance to conduct in-depth research on text similarity and mine efficient algorithms. The research goal of this paper is to improve the accuracy and efficiency of text similarity determination. This study summarize five traditional text similarity determination methods, namely Euclidean distance, cosine similarity, Manhattan distance, Jaccard similarity
This paper introduces an advanced design methodology of a 2.6 GHz CMOS LC Voltage-Controlled Oscillator (VCO) within the AMS CMOS 0.35 μm technology process. The primary objectives of this design approach are to achieve minimal power consumption, a high figure-of-merit (FOM), and low phase noise. In addressing the inherent design challenges, a metaheuristic Particle Swarm Optimization (PSO) is implemented to identify the most effective dimensions for the components of the LC-VCO. In particular, the PSO algorithm is applied to optimize the channel length and width of the MOS transistors, aimin
Accurate failure prediction in cloud computing is vital for maintaining system reliability and minimizing downtime. This paper presents an optimized approach for cloud failure prediction by integrating Long Short-Term Memory (LSTM) and Multi-Layer Perceptron (MLP) models, leveraging the Google Cloud Trace dataset. To enhance the predictive accuracy and efficiency of the models, Grey Wolf Optimization (GWO) algorithm is employed for feature selection, ensuring that the most relevant features are utilized in the prediction process. The integration of LSTM's temporal sequence learning capabilitie
The exploration and practice for the cultivation of the thinking ability of "data-based decision making" in teaching reform of chemical thermodynamicsThe exploration and practice for the cultivation of the thinking ability of "data-based decision making" in teaching reform of chemical thermodynamicsThe exploration and practice for the cultivation of the thinking ability of "data-based decision making" in teaching reform of chemical thermodynamicsThe exploration and practice for the cultivation of the thinking ability of "data-based decision making" in
Medical staff face issues when ventilating patients manually using the Bag-Valve-Mask (BVM) for long periods. As for ventilators in hospitals, medical specialists must check on patients frequently and adjust settings manually. This study aims to conduct machine learning (ML) study using data collected from the CHU Sainte-Justine database to provide suitable setting recommendations for patients using linear and Poisson regression. The response variables were the fraction of inspired oxygen (FiO2) setting, positive end-expiratory pressure (PEEP) setting and tidal volume (TV) setting. Linear regr