Multi-objective reactive power market clearing in competitive electricity market using HFMOEA

This paper presents an application of a hybrid fuzzy multi-objective evolutionary algorithm (HFMOEA) for solving a highly constraint, mixed integer type, complex multi-objective reactive power market clearing (RPMC) problem for the competitive electricity market environment. In HFMOEA based multi-objective optimization approach, based on the output of a fuzzy logic controller crossover and mutation probabilities are varied dynamically. It enhances stochastic search capabilities of HFMOEA. In multi-objective RPMC optimization framework, two objective functions namely the total payment function (TPF) for reactive power support from generators and synchronous condensers and the total real transmission loss (TRTL) are minimized simultaneously for clearing the reactive power market. The proposed HFMOEA based multi-objective RPMC scheme is tested on a standard IEEE 24 bus reliability test system and its performance is compared with five other multi-objective evolutionary techniques such as MOPBIL, NSGA-II, UPS-EMOA and SPEA-2 and a new extended form of NSGA (ENSGA-II). Applying all these six evolutionary techniques, a detailed statistical analysis using T-test and boxplots is carried out on three performance metrics (spacing, spread and hypervolume) data for RPMC problem. The obtained simulation results confirm the overall superiority of HFMOEA to generate better Pareto-optimal solutions with higher convergence rate as compared to above mentioned algorithms. Further, TPF and TRTL values corresponding to the best compromise solutions are obtained using said multi-objective evolutionary techniques. These values are compared with one another to take better market clearing decisions in competitive electricity environment.     

Self-adaptive differential evolution for feature selection in hyperspectral image data

Hyperspectral images are captured from hundreds of narrow and contiguous bands from the visible to infrared regions of electromagnetic spectrum. Each pixel of an image is represented by a vector where the components of the vector constitute the reflectance value of the surface for each of the bands. The length of the vector is equal to the number of bands. Due to the presence of large number of bands, classification of hyperspectral images becomes computation intensive. Moreover, higher correlation among neighboring bands increases the redundancy among them. As a result, feature selection becomes very essential for reducing the dimensionality. In the proposed work, an attempt has been made to develop a supervised feature selection technique guided by evolutionary algorithms. Self-adaptive differential evolution (SADE) is used for feature subset generation. Generated subsets are evaluated using a wrapper model where fuzzy k-nearest neighbor classifier is taken into consideration. Our proposed method also uses a feature ranking technique, ReliefF algorithm, for removing duplicate features. To demonstrate the effectiveness of the proposed method, investigation is carried out on three sets of data and the results are compared with four other evolutionary based state-of-the-art feature selection techniques. The proposed method shows promising results compared to others in terms of overall classification accuracy and Kappa coefficient.     

A compact dual band short ended metamaterial antenna with extended bandwidth

A compact dual‐band CPW–fed metamaterial inspired antenna using Composite Right/Left Handed (CRLH) resonant approach is presented in this article. The antenna is designed such that it can be operated in series resonant mode where resonance behavior is characterized by series LC parameters. Proposed antenna comprises two annular ring resonators connected with the signal patch intended to excite the higher order modes. This results extension of second band from 51.4% (f_c = 6.92 GHz) to 69.2% (f_c = 7.35 GHz). In addition to that proposed antenna shows compact nature with an electrical size of 0.14 λ₀ × 0.21 λ0 × 0.01 λ₀ at f₀ = 2.18 GHz. The antenna is operating over 2.14–2.23 GHz, 4.81–9.90 GHz with simulated peak gain of 0.66 and 4.44 dB, respectively. Simulated radiation efficiencies of proposed antenna are 69.8 and 94.1% throughout first and second band, respectively. To examine the resonance and radiation characteristics prototype is fabricated and measured. Observed experimental results are in good agreement with those simulated one. These characteristics makes this antenna is a good candidate for modern wireless communication systems such as Bluetooth, WLAN/Wi‐Fi band. © 2016 Wiley Periodicals, Inc. Int J RF and Microwave CAE 26:435–441, 2016.     

Multiresolution approach for multiple human detection using moments and local binary patterns

Human detection is a central problem in development of any surveillance application. In this study, we present a simple and efficient, multi-resolution gray scale invariant approach for multiple human detection. The multiresolution is important for objects of different size and gray scale invariance is important due to uneven illumination and within-class variability. The proposed method is based on integration of central moments upon multi-resolution gray scale invariant local binary patterns operator. Since, the local binary patterns operator is invariant against different resolutions of space scale and monotonic change in gray scale, therefore the proposed method is robust in terms of variations in space scale as well as gray scale. Another advantage is high computational accuracy of the method due to use of moment operator which enhances the efficiency of the proposed method. Moreover, the proposed method is simple, as these operations can be performed within a few steps in a small neighborhood and a lookup table. The proposed method is tested on multiple human images and experimentally found appropriate for multiple human detection. The proposed method has been evaluated over two datasets, one is our own created dataset and the other is standard INRIA human detection dataset. Experimental results obtained from the proposed method demonstrate that better discrimination can be achieved for human and non-human objects in real scenes.     

A cohesive modeling technique for theoretical and experimental estimation of damping in serial robots with rigid and flexible links

Dynamic model incorporating damping characteristics, namely joint damping and structural damping in flexible links, of the serial robots with rigid and flexible links is presented. A novel procedure, based on the unified approach of theoretical formulation and analysis of experimental data, is proposed for the estimation of damping coefficients. First, the dynamic model of a robotic system with rigid and flexible links is presented. Next, the modifications in the dynamic model due to the considerations of damping characteristics of joints and structural damping characteristics of the flexible links are presented. A systematic methodology based on analysis of data obtained from experiments is presented for estimation and determination of damping coefficients of rigid-flexible links. The determination of joint damping coefficients, is based on the logarithmic decay of the amplitude of the oscillations of robotic links, while the structural damping coefficients are estimated mainly using the modal analysis and the method of evolving spectra. The method of evolving spectra, based on the Fast Fourier Transform of the decay of the amplitude in structural vibrations of the robot links in progressive windows is used to estimate the structural damping ratios while the critical structural coefficients are determined using the modal analysis. The methodology is illustrated through a series of simple experiments on simple robotic systems. The experimental results are then compared with the simulation results incorporating the damping coefficients determined using the proposed procedure. The comparisons leads to the validation of the proposed dynamic modeling technique, modeling of the damping characteristics, and the method proposed for estimation of damping coefficients for rigid-flexible link robotic systems.     

A bi-objective task scheduling approach in fog computing using hybrid fireworks algorithm

The Journal of Supercomputing - With the rapid increase in the functionality of IoT applications, the services provided by edge/IoT devices have surged in the recent past. Fog computing is gaining...     

Classifying and Summarizing Information from Microblogs During Epidemics

During a new disease outbreak, frustration and uncertainties among affected and vulnerable population increase. Affected communities look for known symptoms, prevention measures, and treatment strategies. On the other hand, health organizations try to get situational updates to assess the severity of the outbreak, known affected cases, and other details. Recent emergence of social media platforms such as Twitter provide convenient ways and fast access to disseminate and consume information to/from a wider audience. Research studies have shown potential of this online information to address information needs of concerned authorities during outbreaks, epidemics, and pandemics. In this work, we target three types of end-users (i) vulnerable population—people who are not yet affected and are looking for prevention related information (ii) affected population—people who are affected and looking for treatment related information, and (iii) health organizations—like WHO, who are interested in gaining situational awareness to make timely decisions. We use Twitter data from two recent outbreaks (Ebola and MERS) to build an automatic classification approach useful to categorize tweets into different disease related categories. Moreover, the classified messages are used to generate different kinds of summaries useful for affected and vulnerable communities as well as health organizations. Results obtained from extensive experimentation show the effectiveness of the proposed approach.     

Robust object tracking with crow search optimized multi-cue particle filter

Pattern Analysis and Applications - Particle filter is used extensively for estimation of target nonlinear and non-Gaussian state. However, its performance suffers due to its inherent problem of...