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The main objective of this paper is to improve the identification efficiency of non-linear systems using the Kalman filter (KF), which is optimised with the Artificial Electric Field (AEF) algorithm. The conventional KF suffers from the proper tuning of its parameters, which leads to a divergence problem. This issue has been solved to a great extent by the meta-heuristic AEF algorithm assisted Kalman filter (AEF-KF). This paper proposes three steps for the identification of the systems while solving the problem as mentioned above. Firstly, it converts the identification model to a measurement problem. Next, the AEF algorithm optimises the KF parameters by considering the fitness function with the KF equations. The third step is to identify the model using conventional KF algorithm with the optimised KF parameters. To evaluate the performance of the proposed method, parameter estimation error, mean squared error (MSE), fitness (FIT) percentage, statistical information and percentage improvement are considered as the performance metrics. To validate the performance of the proposed method, five distinct non-linear models are identified with the Volterra model using KF and the AEF-KF techniques under various noisy input conditions. Besides, the practical applicability of the proposed approach is also tested on two non-linear benchmark systems using experimental data sets. The obtained simulation results confirm the efficacy and robustness of the proposed identification method in terms of the convergence speed, computational time and various performance metrics as compared to KF, Kalman smoother (KS) which is optimised using different state-of-the-art evolutionary algorithms and also other existing recently reported similar types of stochastic algorithms based approaches. • The proposed AEF-KF method resolves the proper tuning problem of Kalman filter. • The role of AEF algorithm is to obtain the optimised set of all the KF parameters. • The proposed method is insensitive to various additive noise input levels. • Simulations carried out on five Volterra models along with two benchmark systems. • AEF-KF outperforms KF and all other reported results with higher convergence speed. [ABSTRACT FROM AUTHOR]

This paper presents an accurate approach for Concentric Circular Antenna Array (CCAA) synthesis to improve the far-field radiation characteristics of the antenna using a meta-heuristic optimization technique called Moth Flame Optimization (MFO). The far-field radiation pattern improves with lower Side Lobe Level (SLL) which is essential for the reduction of interference in the entire side lobe regions. MFO algorithm is a recently proposed evolutionary algorithm which is applied to determine the optimum sets of current excitation weights and to find the optimal inter-element spacing between the array elements in the 3-rings structure of CCAA design. In this context, the design examples of two 3-ring CCAAs, one having the set of 4, 6, 8 elements and the other having the set of 8, 10, 12 elements, with and without centre element, are presented by optimizing the array parameters. The results obtained by using MFO algorithm based approach show a considerable improvement of SLL with respect to that of the uniform array pattern and those of the approaches reported in the recent literature. [ABSTRACT FROM AUTHOR]

This paper presents an efficient approach to identify different stable and practically useful Hammerstein models as well as unstable nonlinear process along with its stable closed loop counterpart with the help of an evolutionary algorithm as Colliding Bodies Optimization (CBO) optimization algorithm. The performance measures of the CBO based optimization approach such as precision, accuracy are justified with the minimum output mean square value (MSE) which signifies that the amount of bias and variance in the output domain are also the least. It is also observed that the optimization of output MSE in the presence of outliers has resulted in a very close estimation of the output parameters consistently, which also justifies the effective general applicability of the CBO algorithm towards the system identification problem and also establishes the practical usefulness of the applied approach. Optimum values of the MSEs, computational times and statistical information of the MSEs are all found to be the superior as compared with those of the other existing similar types of stochastic algorithms based approaches reported in different recent literature, which establish the robustness and efficiency of the applied CBO based identification scheme. [ABSTRACT FROM AUTHOR]

We studied the roles of vitamin D and its receptor, VDR, in the progression of leprosy. The majority of individuals with leprosy from Kolkata, India, with a type 1 or type 2 reaction have low levels of vitamin D 3 in serum samples. Interestingly, individuals with a type 2 reaction associated with neuritis/erythema nodosum leprosum had very low VDR mRNA expression levels, ranging from 5% to 10%, compared to that of healthy control subjects; these patients also had a high bacilli index, ranging from 3+ to 5+. This is the first report to indicate that VDR expression levels may determine the complexity and severity of the progression of leprosy. [ABSTRACT FROM AUTHOR]

A shaped beam synthesis from a concentric ring array has been presented. Two different cases are considered. In the first case, a flat-top beam with desired specification has been generated and in the second case, a cosec 2 beam pattern from the same array configuration is generated. The desired beam pattern in each of the individual case is obtained by finding optimum excitations of the array elements using Differential Evolution (DE) algorithm. Dynamic range ratio (DRR) of the excitation amplitudes are improved by eliminating the weakly excited array elements from the optimized array without distorting the obtained pattern. To illustrate the effectiveness of DE, the two beam-patterns are computed from the same array using Genetic Algorithm (GA) by finding out optimum excitations among the elements. Results clearly show the superiority of DE over GA to handle the proposed problem. [ABSTRACT FROM AUTHOR]

Hydrodynamic properties of different particles in unary liquid–solid fluidized bed were well studied and are reported in literature. However, till date the same in liquid–solid binary packed fluidized bed has not been studied and no information is available in literature. Presently, it is well known that particles behave differently in a gas–solid packed fluidized bed; than in a unary gas–solid fluidized bed. A series of experiments were conducted to investigate the hydrodynamic characteristics of particles in liquid–solid packed fluidized bed as a function of particle size, fraction of interstitial void volume filled by particles and bed wall temperature. It was found that, like in gas–solid packed fluidized bed; the minimum fluidization velocity of particles in liquid–solid packed fluidized bed is lower than that of particles in conventional unary liquid–solid fluidized bed, but unlike gas–solid system, the minimum fluidization velocity increases with an increase in bed wall temperature. Experimental details and results are discussed in this paper. [ABSTRACT FROM AUTHOR]

This paper presents a global heuristic search optimization technique, which is a hybridized version of the Gravitational Search Algorithm (GSA) and Wavelet Mutation (WM) strategy. Thus, the Gravitational Search Algorithm with Wavelet Mutation (GSAWM) was adopted for the design of an 8th-order infinite impulse response (IIR) filter. GSA is based on the interaction of masses situated in a small isolated world guided by the approximation of Newtonian’s laws of gravity and motion. Each mass is represented by four parameters, namely, position, active, passive and inertia mass. The position of the heaviest mass gives the near optimal solution. For better exploitation in multidimensional search spaces, the WM strategy is applied to randomly selected particles that enhance the capability of GSA for finding better near optimal solutions. An extensive simulation study of low-pass (LP), high-pass (HP), band-pass (BP) and band-stop (BS) IIR filters unleashes the potential of GSAWM in achieving better cut-off frequency sharpness, smaller pass band and stop band ripples, smaller transition width and higher stop band attenuation with assured stability. [ABSTRACT FROM AUTHOR]

• An optimal adaptive filter (AF) is implemented to enhance the EEG signals. • The MPA is applied for coefficient estimation of AF. • The MPA-assisted AF outperformed in various performance metrics. • The consistency of the proposed algorithm is verified by the Wilcoxon hypothesis test. Electro-encephalogram (EEG) signals are corrupted with numerous noises while recording itself which changes the attributes of physiological data. In this research, a swarm intelligence-based method for the enhancement of adaptive filters/noise cancellers (AFs/ANCs) is proposed and designed for denoising of electro-encephalogram/event-related potential (EEG/ERP) signals. The results have been evaluated both qualitatively and quantitatively using marine predators algorithm (MPA), teaching learning-based optimization (TLBO) and firefly algorithm (FA) used to compute and accommodate the coefficients of the adaptive filter, which is very much suitable to monitor unconstrained changes of uneven waveforms. The performance of the proposed method is tested on simulated visual evoked potentials (SVEP) and real sensorimotor evoked potentials (SEP). A comprehensive comparative study and analysis of performance have been performed among the previously reported gradient techniques and evolutionary-based techniques, which reveals that the proposed ANC based on MPA is an enhanced competitor compared to other futuristic techniques with fewer evaluation parameters. The performance superiority of the proposed method has been justified through the Wilcoxon signed-rank test. The proposed ANC provides a lesser mean of normalized root mean square error (NRMSE), lesser mean square error (MSE) and an improved mean SNR in contrast to different state-of-the-art techniques as well as considered benchmark algorithms-based ANCs. Furthermore, the ANC based on MPA advances the resemblance between the clean and filtered EEG/ERP signals. [ABSTRACT FROM AUTHOR]

Abstract: Packed fluidization is a novel technique, in which small particles are allowed to fluidize in the interstices of relatively large and stationary packing to enhance heat transfer rates of a unary packed bed of same size pebbles. In the present study, heat transfer in unary packed bed and binary packed fluidized bed were investigated and compared in terms of the effective thermal conductivity. In the experimental works, large pebbles (size: 3–10mm) of two different materials viz. lithium titanate and alumina and small particles (size: 231–780μm), also of two different materials viz., lithium titanate and silica were used. It was found that the heat transfer in unary packed bed is enhanced due to the packed fluidization and in terms of the effective thermal conductivity; the enhancement was up to 260%. It was found that the volume fraction of small particles, operating gas velocity, particle to pebble size ratio and the type of materials have a significant effect in enhancing the effective thermal conductivity and heat transfer rates. It was also found that 60% (v/v) of small particles in the interstitial voids of packing pebbles can give much improved effective thermal conductivity keeping all other operating parameters same. Possible mechanism for heat transfer enhancement due to packed fluidization has been proposed. Based on the results for different particle and pebble sizes, materials and process variables viz. particles to pebble size ratios, operating gas velocity ratios, volume fraction of small particles in the interstices and bed wall temperatures, we could arrive at optimum conditions for determining effective thermal conductivity and a correlation to estimate the same. [Copyright &y& Elsevier]

Abstract: In the first generation fusion reactors the fusion of deuterium (D) and tritium (T) is considered to produce energy to meet the future energy demand. Deuterium is available in nature whereas, tritium is not. Lithium-6 (Li6) isotope has the ability to produce tritium in the n, α nuclear reaction with neutrons. Thus lithium-based ceramics enriched by Li6 isotope are considered for the tritium generation for its use in future fusion reactors. Lithium titanate is one such Li-based ceramic material being considered for its some attractive properties viz., high thermal and chemical stability, high thermal conductivity, and low tritium solubility. It is reported in the literature, that the burn up of these pebbles in the fusion reactor will be limited to only 15–17 atomic percentage. At the end of life, the pebbles will contain more than 45% unused Li6 isotope. Due to the high cost of enriched Li6 and the waste disposal considerations, it is necessary to recover the unused Li from the spent lithium titanate pebbles. Till date, only the feasibilities of different processes are reported, but no process details are available. Experiments were carried out for the recovery of Li from simulated Li2TiO3 pebbles and to reuse of lithium in lithium titanate pebble fabrication. The details of the experiments and results are discussed in this paper. [Copyright &y& Elsevier]