This paper proposes a new population-based hybrid particle swarm optimized-gravitational search algorithm (PSO-GSA) for tuning the parameters of the proportional-integral-derivative (PID) controller of a two-area interconnected dynamic power system with the presence of nonlinearities such as generator rate constraints (GRC) and governor dead-band (GDB). The tuning of controller parameters such as Kp, Ki, and Kd are obtained by minimizing the objective function formulated using the steady-state performance indices like Integral absolute error (IAE) of tie-line power and frequency deviation of interconnected system. To test the robustness of the propounded controller, the system is studied with system uncertainties, such as change in load demand, synchronizing power coefficient and inertia constant. The two-area interconnected power system (TAIPS) is modeled and simulated using Matlab/Simulink. The results exhibit that the steady-state and transient performance indices such as IAE, settling time, and control effort are impressively enhanced by an amount of 87.65%, 15.39%, and 91.17% in area-1 and 86.46%, 41.35%, and 91.04% in area-2, respectively, by the proposed method compared to other techniques presented. The minimum control effort of PSO-GSA-tuned PID controller depicts the robust performance of the controller compared to other non-meta-heuristic and meta-heuristic methods presented. The proffered method is also validated using the hardware-in-the-loop (HIL) real-time digital simulation to study the effectiveness of the controller.
Bio-inspired algorithms are the most powerful way to solve optimization problems. The objective of this paper is to use optimized network parameters for website classification and the efficiency of Neural Network is improved by optimized network parameters. The network parameters are optimized using two bio-inspired algorithms: Particle Swarm Optimization (PSO) and Cuckoo Search (CS) and the optimized parameter values are used with two neural network models, a standard Multi-Layer Feed Forward Network with Backpropagation (BPN) and Radial Basis Function (RBF) Network. Security is one of the major concerns in this digital era. There are numerous websites, which are potentially risky in spreading malicious files. It is difficult to detect such websites. In this work, Neural Network is used to classify the websites as benign and malicious. The proposed neural network models are tested with URL dataset. The experimental results are assessed in terms of Error reduction, training time and classification accuracy. The experimental result shows that the optimized network parameters have given good improvement in classification with faster convergence.
The analysis of functional magnetic resonance imaging (fMRI) time-series data can provide information on task-related activities, functional/effective connectivity among regions and the influences of behavioral/physiologic states on connectivity. This paper illustrates the importance of the neurobiological constraints involved in using statistical parametric mapping (SPM) through Matlab simulation and thus helping the radiologist to interpret the results better. This paper also presents the results and inferences from neuroimaging data of the lip movement experiment using statistical parametric mapping (SPM). The results match with the sensory/motor activation atlas by Penfield and Rasmussen (1950).
This article visualizes the three-dimensional view of t-statistics for the lip movement experiment. It also visualizes the t-contrast image. These three dimensional views help the radiologist to understand the volume of activations in the brain. The 3D view of the t-statistics of the subject under study looks like a piece of ginger and hence is called the "ginger effect of t-statistics".
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