Throughput-Oriented Full-Duplex Cognitive Radio Network Parameter Optimization

Full-duplex cooperative spectrum sensing (FD-CSS) is an important research field in the field of spectrum sensing. In the FD-CSS network, the secondary user (SU) senses the usage status of the authorized spectrum by the primary user (PU) through the sensing channel and then reports the perceived data to the fusion center (FC) through the reporting channel. The FC makes a comprehensive judgment after summarizing the data through the fusion algorithm. In the secondary network with SU, throughput is an important index to measure the performance of the network. Taking throughput as the optimization goal, this paper theoretically deduces and verifies the optimal data fusion algorithm in cooperative spectrum sensing (CSS), the threshold of optimal energy detection, and the optimal transmission power of SU in the secondary network. The simulation results show the correctness of the results in this paper.

Evaluation of Parameter Settings for Training Neural Networks Using Backpropagation Algorithms

Artificial neural networks (ANN) are widely used for classification, and the training algorithm commonly used is the backpropagation (BP) algorithm. The major bottleneck faced in the backpropagation neural network training is in fixing the appropriate values for network parameters. The network parameters are initial weights, biases, activation function, number of hidden layers and the number of neurons per hidden layer, number of training epochs, learning rate, minimum error, and momentum term for the classification task. The objective of this work is to investigate the performance of 12 different BP algorithms with the impact of variations in network parameter values for the neural network training. The algorithms were evaluated with different training and testing samples taken from the three benchmark clinical datasets, namely, Pima Indian Diabetes (PID), Hepatitis, and Wisconsin Breast Cancer (WBC) dataset obtained from the University of California Irvine (UCI) machine learning repository.

A Personalized Eccentric Cyber-Physical System Architecture for Smart Healthcare

The development and technological advancement of wireless sensor networks in different fields has been a revolution for mankind. To meet the high-end requirements, the support of the cloud that provides the resources for the application is very much essential. This paper presents an architecture called cloud sense to connect cyber and physical spaces for wireless body area networks with varying high-end workflow at different perspectives. The scalability issue in collecting patient data and processing the data is established using ganglia that is a scalable, distributed monitoring system to support high-performance computing in clusters for the set of input events such as electrocardiogram (ECG), blood pressure (BP), saturation of peripheral oxygen (SPO2), temperature, and skin conductance of the kind of human body parameters. Various parameter metrics have been analyzed based on the equivalent creation of instances. The connectivity mechanism behind the proposed cyber-physical system is unique of its kind; it is exhibited through wireless Internet on a small scale of three remote locations; the system works well with specific network parameter metrics; and the results proved that availability and scalability issues were addressed with numerical analysis.

Intelligent Speed Regulation Control in Switched Reluctance Motor of Electric Vehicle Based on Neural Network Parameter Identification

This paper studies the intelligent speed regulation control of switched reluctance motor of electric vehicle based on neural network parameter identification. Starting with the analysis of the performance of switched reluctance motor, the nonlinear flux linkage characteristic inversion model and torque characteristic model of switched reluctance motor are established based on BP neural network. This paper studies and improves the fast self configuration algorithm of BP neural network. Finally, the nonlinear simulation model of switched reluctance motor is established under Matlab/Simulink. The model can be used for further control research. In this paper, the integrated control method of instantaneous torque control based on torque observation and three-step commutation control is studied, and the simulation analysis is carried out. The results show that this method can effectively reduce the torque ripple of switched reluctance motor and improve the performance of its drive system.

Intelligent Speed Regulation Control of Switched Reluctance Motor of Electric Vehicle Based on Neural Network Parameter Identification

Based on the analysis of the related theories of switched reluctance motor, this paper designs and implements the intelligent speed regulation system of switched reluctance motor for electric vehicle. The speed regulation system has the characteristics of high efficiency and energy saving, wide speed regulation range and small starting current. The system also has the advantages of large starting torque, frequent start and stop, forward and reverse switching, high torque / inertia ratio and four quadrant operation. The system can provide a good solution for variable speed drive. The digital controller of the system is composed of TMS320F2812 DSP and EPM7128S CPLD, which can realize the starting, electric and braking functions of electric vehicles. Experiments show that the switched reluctance motor drive system for electric vehicle has good control performance and strong fault tolerance. The system can meet the requirements of various working conditions of electric vehicles and has broad development prospects.

Distinguishing level-1 phylogenetic networks on the basis of data generated by Markov processes

Phylogenetic networks can represent evolutionary events that cannot be described by phylogenetic trees. These networks are able to incorporate reticulate evolutionary events such as hybridization, introgression, and lateral gene transfer. Recently, network-based Markov models of DNA sequence evolution have been introduced along with model-based methods for reconstructing phylogenetic networks. For these methods to be consistent, the network parameter needs to be identifiable from data generated under the model. Here, we show that the semi-directed network parameter of a triangle-free, level-1 network model with any fixed number of reticulation vertices is generically identifiable under the Jukes–Cantor, Kimura 2-parameter, or Kimura 3-parameter constraints.

A Network Parameter Database False Data Injection Correction Physics-Based Model: A Machine Learning Synthetic Measurement-Based Approach

Concerning power systems, real-time monitoring of cyber–physical security, false data injection attacks on wide-area measurements are of major concern. However, the database of the network parameters is just as crucial to the state estimation process. Maintaining the accuracy of the system model is the other part of the equation, since almost all applications in power systems heavily depend on the state estimator outputs. While much effort has been given to measurements of false data injection attacks, seldom reported work is found on the broad theme of false data injection on the database of network parameters. State-of-the-art physics-based model solutions correct false data injection on network parameter database considering only available wide-area measurements. In addition, deterministic models are used for correction. In this paper, an overdetermined physics-based parameter false data injection correction model is presented. The overdetermined model uses a parameter database correction Jacobian matrix and a Taylor series expansion approximation. The method further applies the concept of synthetic measurements, which refers to measurements that do not exist in the real-life system. A machine learning linear regression-based model for measurement prediction is integrated in the framework through deriving weights for synthetic measurements creation. Validation of the presented model is performed on the IEEE 118-bus system. Numerical results show that the approximation error is lower than the state-of-the-art, while providing robustness to the correction process. Easy-to-implement model on the classical weighted-least-squares solution, highlights real-life implementation potential aspects.