Current Issue : January - March Volume : 2020 Issue Number : 1 Articles : 5 Articles
With the further research in communication systems, especially in wireless communication systems, a statistical model called\nNakagami-m distribution appears to have better performance than other distributions, including Rice and Rayleigh, in explaining\nreceived faded envelopes. Therefore, the Nakagami-m quantile function plays an important role in numerical calculations and\ntheoretical analyses for wireless communication systems. However, it is quite difficult to operate numerical calculations and\ntheoretical analyses because Nakagami-m quantile function has no exact closed-form expression. In order to obtain the closedform\nexpression that is able to fit the curve of Nakagami-m quantile function as well as possible, we adopt the method of curve\nfitting in this paper. An efficient expression for approximating the Nakagami-m quantile function is proposed first and then a\nnovel heuristic optimization algorithm-generalized opposition-based quantum salp swarm algorithm (GO-QSSA)â??which\ncontains quantum computation, intelligence inspired by salp swarm and generalized opposition-based learning strategy in\nquantum space, to compute the coefficients of the proposed expression. Meanwhile, we compare GO-QSSA with three swarm\nintelligence algorithms: artificial bee colony algorithm (ABC), particle swarm optimization algorithm (PSO), and salp swarm\nalgorithm (SSA). The comparing simulation results reveal that GO-QSSA owns faster convergence speed than PSO, ABC, and\nSSA. Moreover, GO-QSSA is capable of computing more accurately than traditional algorithms. In addition, the simulation results\nshow that compared with existing curve-fitting-based methods, the proposed expression decreases the fitting error by roughly one\norder of magnitude in most cases and even higher in some cases. Our approximation is proved to be simple and efficient....
Due to the poor working conditions of an engine, its control system is prone to failure.\nIf these faults cannot be treated in time, it will cause great loss of life and property. In order to\nimprove the safety and reliability of an aero-engine, fault diagnosis, and optimization method of\nengine control system based on probabilistic neural network (PNN) and support vector machine\n(SVM) is proposed. Firstly, using the German 3Wpiston engine as a control object, the fault diagnosis\nscheme is designed and introduced briefly. Then, the fault injection is performed to produce faults,\nand the data sample for engine fault diagnosis is established. Finally, the important parameters of\nPNN and SVM are optimized by particle swarm optimization (PSO), and the results are analyzed and\ncompared. It shows that the engine fault diagnosis method based on PNN and SVM can effectively\ndiagnose the common faults. Under the optimization of PSO, the accuracy of PNN and SVM results\nare significantly improved, the classification accuracy of PNN is up to 96.4%, and the accuracy of SVM\nis up to 98.8%, which improves the application of them in fault diagnosis technology of aero-piston\nengine control system....
It has been widely known that 3D shape models are comprehensively parameterized using point cloud and meshes. The point\ncloud particularly is much simpler to handle compared with meshes, and it also contains the shape information of a 3D model. In\nthis paper, we would like to introduce our new method to generating the 3D point cloud from a set of crucial measurements and\nshapes of importance positions. In order to find the correspondence between shapes and measurements, we introduced a method\nof representing 3D data called slice structure. A Neural Networks-based hierarchical learning model is presented to be compatible\nwith the data representation. Primary slices are generated by matching the measurements set before the whole point cloud tuned\nby Convolutional Neural Network. We conducted the experiment on a 3D human dataset which contains 1706 examples. Our\nresults demonstrate the effectiveness of the proposed framework with the average error 7.72% and fine visualization. This study\nindicates that paying more attention to local features is worthwhile when dealing with 3D shapes....
To establish and consummate the electric power network, the construction and investment scale of power substation projects is\nexpanding every year. As a capital-technology-intensive project, it has high requirements for power substation project management.\nAccurate cost forecasting can help to reduce the project cost, improve the investment efficiency, and optimize project\nmanagement. However, affected by many factors, the construction cost of a power substation project usually presents strong\nnonlinearity and uncertainty, which make it difficult to accurately forecast the cost. This paper presents a new hybrid substation\nproject cost forecasting method called PCA-PSO-SVM model, which is a support vector machine (SVM) model optimized by a\nparticle swarm optimization (PSO) algorithm with principal component analysis (PCA). In this intelligent prediction model, the\nPCA method is introduced to reduce the data dimension. Furthermore, the PSO algorithm is used to optimize the model\nparameters. In the example, 65 sets of substation construction data are input into PCA-PSO-SVM model for construction cost\nprediction, and the prediction results are compared with other prediction methods to verify the forecasting accuracy. The results\nshow that the MAPE and RMSE of the PCA-PSO-SVM model is 6.21% and 3.62, respectively. And, the prediction accuracy of this\nmodel is better than that of other models, which can provide a reliable basis for investment decision-making of substation projects....
The antlion optimizer (ALO) is a new swarm-based metaheuristic algorithm for optimization, which mimics the hunting\nmechanism of antlions in nature. Aiming at the shortcoming that ALO has unbalanced exploration and development capability\nfor some complex optimization problems, inspired by the particle swarm optimization (PSO), the updated position of antlions in\nelitismoperator of ALO is improved, and thus the improved ALO (IALO) is obtained.The proposed IALO is compared against sine\ncosine algorithm (SCA), PSO,Moth-flame optimization algorithm (MFO),multi-verse optimizer (MVO), and ALO by performing\non 23 classic benchmark functions.The experimental results show that the proposed IALO outperforms SCA, PSO, MFO, MVO,\nand ALO according to the average values and the convergence speeds. And the proposed IALO is tested to optimize the parameters\nof BP neural network for predicting the Chinese influenza and the predictedmodel is built, written as IALO-BPNN,which is against\nthe models: BPNN, SCA-BPNN, PSO-BPNN, MFO-BPNN,MVO-BPNN, and ALO-BPNN. It is shown that the predicted model\nIALO-BPNN has smaller errors than other six predicted models, which illustrates that the IALO has potentiality to optimize the\nweights and basis of BP neural network for predicting the Chinese influenza effectively. Therefore, the proposed IALO is an effective\nand efficient algorithm suitable for optimization problems....
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