Multi-objective evolutionary algorithm for SSSC-based controller design

In this paper, an evolutionary multi-objective optimization approach is employed to design a static synchronous series compensator (SSSC)-based controller. The design objective is to improve the transient performance of a power system subjected to a severe disturbance by damping the multi-modal oscillations namely; local mode, inter-area mode and inter-plant mode. A genetic algorithm (GA)-based solution technique is applied to generate a Pareto set of global optimal solutions to the given multi-objective optimization problem. Further, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto solution set. Simulation results are presented and compared with a PI controller under various disturbances namely; three-phase fault, line outage, loss of load and unbalanced faults to show the effectiveness and robustness of the proposed approach.     

Computer-aided design of chiral ligands. Part I. Database search methods to identify chiral ligand types for asymmetric reactions

The utility of database searching to identify chiral ligand motifs is outlined. The key elements of three known chiral ligands have been described as bond vectors. The CAVEAT program was then used to screen the Cambridge Structural Database (CSD), portions of the Chemical Abstracts Services three-dimensional database (CAS-3D), and the TRIAD tricyclic structure database for scaffolds containing these elements. Scaffolds corresponding to the known starting points were identified indicating that this method can be used to identify chiral ligand structural motifs. In addition, alternate structural motifs were found that suggested alternative possible ligands.     

Effect of conventional and microwave sintering on the properties of yttria alumina garnet-dispersed austenitic stainless steel

The current study examines the effect of heating mode, temperature, and varying yttria alumina garnet (YAG) addition (5 and 10 wt pct) on the densification and properties of austenitic (316L) stainless steel. The straight 316L stainless steel and 316L-YAG composites were heated in a radiatively heated (conventional) and 2.45 GHz microwave sintering furnace. The compacts were consolidated through solid state as well as supersolidus sintering at 1200 °C and 1400 °C, respectively. Both 316L and 316L-YAG compacts couple with microwaves and heat to the sintering temperature rapidly (∼45 °C/min). The overall processing time was reduced by about 90 pct through microwave sintering. As compared to conventional sintering, compacts sintered in microwaves exhibit higher densification and finer microstructure but no corresponding improvement in mechanical properties and wear resistance. This has been correlated to elongated, irregular pore structure in microwave-sintered compacts.     

High Performance RDMA-Based MPI Implementation over InfiniBand

Although InfiniBand Architecture is relatively new in the high performance computing area, it offers many features which help us to improve the performance of communication subsystems. One of these features is Remote Direct Memory Access (RDMA) operations. In this paper, we propose a new design of MPI over InfiniBand which brings the benefit of RDMA to not only large messages, but also small and control messages. We also achieve better scalability by exploiting application communication pattern and combining send/receive operations with RDMA operations. Our RDMA-based MPI implementation achieves a latency of 6.8 sec for small messages and a peak bandwidth of 871 million bytes/sec. Performance evaluation shows that for small messages, our RDMA-based design can reduce the latency by 24%, increase the bandwidth by over 104%, and reduce the host overhead by up to 22% compared with the original design. For large data transfers, we improve performance by reducing the time for transferring control messages. We have also shown that our new design is beneficial to MPI collective communication and NAS Parallel Benchmarks.     

Green synthesis and antimicrobial activity of silver nanoparticles using wild medicinal mushroom Ganoderma applanatum (Pers.) Pat. from Similipal Biosphere Reserve,Odisha, India

In the present study, green synthesis and cost effective approach of silver nanoparticles using wild medicinal mushroom Ganoderma applanatum (Pers.) Pat. from Similipal Biosphere Reserve, Odisha, India is reported. The biosynthesised AgNPs were characterised using UV‐visible spectroscopy, particle analyser and scanning electron microscopy studies. It was found by dynamic light scattering analysis, that the average size and charges of the AgNPs were 133.0 ± 0.361 nm and −6.01 ± 5.30 mV, respectively. Moreover, the Fourier transform infrared study was also conducted to identify the biomolecules or functional groups responsible for the reduction of Ag and stabilisation of the AgNPs. The potential biomedical application with reference to antimicrobial activity of the synthesised AgNPs was investigated against some pathogenic microorganisms viz. Escherichia coli, Bacillus subtilis, Staphylococcus epidermidis, Vibrio cholerae, Staphylococcus aureus and Shigella flexneri.Inspec keywords: antibacterial activity, biomedical materials, nanomedicine, nanofabrication, materials preparation, ultraviolet spectra, visible spectra, scanning electron microscopy, Fourier transform infrared spectra, molecular biophysics, reduction (chemical), biochemistry, microorganisms, silver, nanoparticlesOther keywords: green synthesis, antimicrobial activity, silver nanoparticles, wild medicinal mushroom, Ganoderma applanatum Pat, Similipal Biosphere Reserve, Odisha, UV‐visible spectroscopy, particle analyser, scanning electron microscopy, dynamic light scattering analysis, Fourier transform infrared study, biomolecules, functional groups, silver reduction, AgNP stabilisation, biomedical application, pathogenic microorganisms, Escherichia coli, Bacillus subtilis, Staphylococcus epidermidis, Vibrio cholerae, Staphylococcus aureus, Shigella flexneri, Ag

Abbreviations

AgNPs

‐ Silver Nanoparticles

SPR

‐Surface plasmon resonance

AgNO3

‐Silver nitrate

nm

‐ Nanometer

mm

‐Milimetre

     

Automatic clustering algorithm based on multi-objective Immunized PSO to classify actions of 3D human models

Multi-objective clustering algorithms are preferred over its conventional single objective counterparts as they incorporate additional knowledge on properties of data in the from of objectives to extract the underlying clusters present in many datasets. Researchers have recently proposed some standardized multi-objective evolutionary clustering algorithms based on genetic operations, particle swarm optimization, clonal selection principles, differential evolution and simulated annealing, etc. In many cases it is observed that hybrid evolutionary algorithms provide improved performance compared to that of individual algorithm. In this paper an automatic clustering algorithm MOIMPSO (Multi-objective Immunized Particle Swarm Optimization) is proposed, which is based on a recently developed hybrid evolutionary algorithm Immunized PSO. The proposed algorithm provides suitable Pareto optimal archive for unsupervised problems by automatically evolving the cluster centers and simultaneously optimizing two objective functions. In addition the algorithm provides a single best solution from the Pareto optimal archive which mostly satisfy the users' requirement. Rigorous simulation studies on 11 benchmark datasets demonstrate the superior performance of the proposed algorithm compared to that of the standardized automatic clustering algorithms such as MOCK, MOPSO and MOCLONAL. An interesting application of the proposed algorithm has also been demonstrated to classify the normal and aggressive actions of 3D human models.     

Development and performance evaluation of an improved complex valued radar pulse compressor

Pulse compression is an important and burning issue in radar signal processing. In the recent past, many adaptive and neural network based methods have been proposed to achieve effective pulse compression performance for real coded transmitted waveforms. Even though the radar signal is complex, it is mostly processed as real-valued in-phase and quadrature components. Hence it is desirable that for processing complex radar signal for pulse compression both the structure as well as the learning algorithm associated with it need to be complex in nature. Accordingly in this paper a novel adaptive method is proposed by employing a complex valued fully connected cascaded (CFCC) neural network. For training this network, a new complex Levenberg–Marquardt (CLM) algorithm is derived and used for imparting effective training of its weights. The new CLM based CFCC (CFCC-CLM) model offers superior convergence performance with the least residual mean squared error during training phase compared to those provided by the multilayer perceptron (MLP) trained with complex domain backpropagation (CDBP) and CLM based methods. Further the comparison of peak signal-to-sidelobe ratio (PSR) under noisy and Doppler shift conditions of the proposed method exhibits best performance compared to those offered by the MLP-CDBP, MLP-CLM and the matched filter (MF) based methods.     

Fault detection and diagnosis in synchronous motors using hidden Markov model-based semi-nonparametric approach

Early detection and diagnosis of faults in industrial machines would reduce the maintenance cost and also increase the overall equipment effectiveness by increasing the availability of the machinery systems. In this paper, a semi-nonparametric approach based on hidden Markov model is introduced for fault detection and diagnosis in synchronous motors. In this approach, after training the hidden Markov model classifiers (parametric stage), two matrices named probabilistic transition frequency profile and average probabilistic emission are computed based on the hidden Markov models for each signature (nonparametric stage) using probabilistic inference. These matrices are later used in forming a similarity scoring function, which is the basis of the classification in this approach. Moreover, a preprocessing method, named squeezing and stretching is proposed which rectifies the difficulty of dealing with various operating speeds in the classification process. Finally, the experimental results are provided and compared. Further investigations are carried out, providing sensitivity analysis on the length of signatures, the number of hidden state values, as well as statistical performance evaluation and comparison with conventional hidden Markov model-based fault diagnosis approach. Results indicate that implementation of the proposed preprocessing, which unifies the signatures from various operating speeds, increases the classification accuracy by nearly 21% and moreover utilization of the proposed semi-nonparametric approach improves the accuracy further by nearly 6%.     

Optimal pricing and lot-sizing for perishable inventory with price and time dependent ramp-type demand

Product perishability is an important aspect of inventory control. To minimise the effect of deterioration, retailers in supermarkets, departmental store managers, etc. always want higher inventory depletion rate. In this article, we propose a dynamic pre- and post-deterioration cumulative discount policy to enhance inventory depletion rate resulting low volume of deterioration cost, holding cost and hence higher profit. It is assumed that demand is a price and time dependent ramp-type function and the product starts to deteriorate after certain amount of time. Unlike the conventional inventory models with pricing strategies, which are restricted to a fixed number of price changes and to a fixed cycle length, we allow the number of price changes before as well as after the start of deterioration and the replenishment cycle length to be the decision variables. Before start of deterioration, discounts on unit selling price are provided cumulatively in successive pricing cycles. After the start of deterioration, discounts on reduced unit selling price are also provided in a cumulative way. A mathematical model is developed and the existence of the optimal solution is verified. A numerical example is presented, which indicates that under the cumulative effect of price discounting, dynamic pricing policy outperforms static pricing strategy. Sensitivity analysis of the model is carried out.