With the rapid development of high-precision positioning service applications, there is a growing demand for accurate and seamless positioning services in indoor and outdoor (I/O) scenarios. To address the problem of ...
详细信息
With the rapid development of high-precision positioning service applications, there is a growing demand for accurate and seamless positioning services in indoor and outdoor (I/O) scenarios. To address the problem of low localization accuracy in the I/O transition area and the difficulty of achieving fast and accurate I/O switching, a Kalman filter based on the salpswarmalgorithm (SSA) for seamless multi-source fusion positioning of global positioning system/inertial navigation system/smartphones (GPS/INS/smartphones) is proposed. First, an Android smartphone was used to collect sensor measurement data, such as light, magnetometer, and satellite signal-to-noise ratios in different environments;then, the change rules of the data were analyzed, and an I/O detection algorithm based on the SSA was used to identify the locations of users. Second, the proposed I/O detection service was used as an automatic switching mechanism, and a seamless indoor-outdoor localization scheme based on improved Kalman filtering with K-L divergence is proposed. The experimental results showed that the SSA-based I/O switching model was able to accurately recognize environmental differences, and the average accuracy of judgment reached 97.04%. The localization method achieved accurate and continuous seamless navigation and improved the average localization accuracy by 53.79% compared with a traditional GPS/INS system.
This paper deals with the problem regarding the optimal placement and sizing of distribution static compensators (D-STATCOMs) in radial and meshed distribution networks. These grids consider industrial, residential, a...
详细信息
This paper deals with the problem regarding the optimal placement and sizing of distribution static compensators (D-STATCOMs) in radial and meshed distribution networks. These grids consider industrial, residential, and commercial loads within a daily operation scenario. The optimal reactive power flow compensation problem is formulated through a mixed-integer nonlinear programming (MINLP) model. The objective function is associated with the minimization of the expected energy losses costs for a year of operation by considering the investment costs of D-STATCOMs. To solve the MINLP model, the application of a master-slave optimization approach is proposed, which combines the salpswarmalgorithm (SSA) in the master stage and the matricial backward/forward power flow method in the slave stage. The master stage is entrusted with defining the optimal nodal location and sizes of the D-STATCOMs, while the slave stage deals with the power flow solution to determine the expected annual energy losses costs for each combination of nodes and sizes for the D-STATCOMs as provided by the SSA. To validate the effectiveness of the proposed master-slave optimizer, the IEEE 33-bus grid was selected as a test feeder. Numerical comparisons were made against the exact solution of the MINLP model with different solvers in the general algebraic modeling system (GAMS) software. All the simulations of the master-slave approach were implemented in the MATLAB programming environment (version 2021b). Numerical results showed that the SSA can provide multiple possible solutions for the studied problem, with small variations in the final objective function, which makes the proposed approach an efficient tool for decision-making in distribution companies.
Data analysis in medicine is becoming more and more frequent to clarify diagnoses, refine research methods, and plan appropriate equipment supplies according to the importance of the pathologies that appear. Artificia...
详细信息
Data analysis in medicine is becoming more and more frequent to clarify diagnoses, refine research methods, and plan appropriate equipment supplies according to the importance of the pathologies that appear. Artificial intelligence offers software solutions that are required to analyze the present data for optimal prediction of results. A system model is capable of several data processing algorithms for the classification of heart disease. This research work is particularly interested in the category of data. The classification allows us to obtain a prediction model from training data and test data. These data are screened by a classification algorithm that produces a new model capable of detailed data, possibly having the same classes of data through the combination of mathematical tools and computer methods. To analyze the present data to predict optimal results, we need to use the optimization technique. This research work aims to design a framework for heart disease prediction by using major risk factors based on different classifier algorithms such as Naïve Bayes (NB), Bayesian Optimized Support Vector Machine (BO-SVM), K-Nearest Neighbors (KNN), and salpswarm Optimized Neural Network (SSA-NN). This research is carried out for the effective diagnosis of heart disease using the heart disease dataset available on the UCI Machine Repository. The highest performance was obtained using BO-SVM (accuracy = 93.3%, precision = 100%, sensitivity = 80%) followed by SSA-NN with (accuracy = 86.7%, precision = 100%, sensitivity = 60%) respectively. The results reveal that the proposed novel optimized algorithm can provide an effective healthcare monitoring system for the early prediction of heart disease.
暂无评论