LOD search engine: A semantic search over linked data

Journal of Intelligent Information Systems - In the last few years, there has been a significant growth in the amount of data published in RDF and adoption of Linked Data principles. Every day, a...     

Genetic algorithms in mesh optimization for visualization and finite element models

This paper investigates the use of a genetic algorithm (GA) to perform the large-scale triangular mesh optimization process. This optimization process consists of a combination of mesh reduction and mesh smoothing that will not only improve the speed for the computation of a 3D graphical or finite element model, but also improve the quality of its mesh. The GA is developed and implemented to replace the original mesh with a re-triangulation process. The GA features optimized initial population, constrained crossover operator, constrained mutation operator and multi-objective fitness evaluation function. While retaining features is important to both visualization models and finite element models, this algorithm also optimizes the shape of the triangular elements, improves the smoothness of the mesh and performs mesh reduction based on the needs of the user.     

Covering-based rough set classification system

Medical data classification is applied in intelligent medical decision support system to classify diseases into different categories. Several classification methods are commonly used in various healthcare settings. These techniques are fit for enhancing the nature of prediction, initial identification of sicknesses and disease classification. The categorization complexities in healthcare area are focused around the consequence of healthcare data investigation or depiction of medicine by the healthcare professions. This study concentrates on applying uncertainty (i.e. rough set)-based pattern classification techniques for UCI healthcare data for the diagnosis of diseases from different patients. In this study, covering-based rough set classification (i.e. proposed pattern classification approach) is applied for UCI healthcare data. Proposed CRS gives effective results than delicate pattern classifier model. The results of applying the CRS classification method to UCI healthcare data analysis are based upon a variety of disease diagnoses. The execution of the proposed covering-based rough set classification is contrasted with other approaches, such as rough set (RS)-based classification methods, Kth nearest neighbour, improved bijective soft set, support vector machine, modified soft rough set and back propagation neural network methodologies using different evaluating measures.

     

Prosody modification for speech recognition in emotionally mismatched conditions

A degradation in the performance of automatic speech recognition systems (ASR) is observed in mismatched training and testing conditions. One of the reasons for this degradation is due to the presence of emotions in the speech. The main objective of this work is to improve the performance of ASR in the presence of emotional conditions using prosody modification. The influence of different emotions on the prosody parameters is exploited in this work. Emotion conversion methods are employed to generate the word level non-uniform prosody modified speech. Modification factors for prosodic components such as pitch, duration and energy are used. The prosody modification is done in two ways. Firstly, emotion conversion is done at the testing stage to generate the neutral speech from the emotional speech. Secondly, the ASR is trained with the generated emotional speech from the neutral speech. In this work, the presence of emotions in speech is studied for the Telugu ASR systems. A new database of IIIT-H Telugu speech corpus is collected to build the large vocabulary neutral Telugu speech ASR system. The emotional speech samples from IITKGP-SESC Telugu corpus are used for testing it. The emotions of anger, happiness and compassion are considered during the evaluation. An improvement in the performance of ASR systems is observed in the prosody modified speech.     

Speaker recognition under stressed condition

This paper presents the feature analysis and design of compensators for speaker recognition under stressed speech conditions. Any condition that causes a speaker to vary his or her speech production from normal or neutral condition is called stressed speech condition. Stressed speech is induced by emotion, high workload, sleep deprivation, frustration and environmental noise. In stressed condition, the characteristics of speech signal are different from that of normal or neutral condition. Due to changes in speech signal characteristics, performance of the speaker recognition system may degrade under stressed speech conditions. Firstly, six speech features (mel-frequency cepstral coefficients (MFCC), linear prediction (LP) coefficients, linear prediction cepstral coefficients (LPCC), reflection coefficients (RC), arc-sin reflection coefficients (ARC) and log-area ratios (LAR)), which are widely used for speaker recognition, are analyzed for evaluation of their characteristics under stressed condition. Secondly, Vector Quantization (VQ) classifier and Gaussian Mixture Model (GMM) are used to evaluate speaker recognition results with different speech features. This analysis help select the best feature set for speaker recognition under stressed condition. Finally, four VQ based novel compensation techniques are proposed and evaluated for improvement of speaker recognition under stressed condition. The compensation techniques are speaker and stressed information based compensation (SSIC), compensation by removal of stressed vectors (CRSV), cepstral mean normalization (CMN) and combination of MFCC and sinusoidal amplitude (CMSA) features. Speech data from SUSAS database corresponding to four different stressed conditions, Angry, Lombard, Question and Neutral, are used for analysis of speaker recognition under stressed condition.     

Preparation and characterisation of carbon-supported palladium nanoparticles for oxygen reduction in low temperature PEM fuel cells

Pd nanoparticles have been synthesised using different reducing agents, including ethylene glycol (EG), formaldehyde and sodium borohydride and their activity for the oxygen reduction reaction (ORR) evaluated. The use of EG led to the best morphology for the ORR and this synthetic method was optimised by adjusting the system pH. Carbon-supported Pd nanoparticles of approximately 7 nm diameter were obtained when reduction took place in the alkaline region. Pd synthesised by EG reduction at pH 11 presented the highest mass activity 20 A g^?2 and active surface area 15 m² g^?1. These synthetic conditions were used in further synthesis. The effect of heat treatment in H₂ atmosphere was also studied; and increased size of the palladium nanoparticles was observed in every case. The Pd/C catalyst synthesised by reduction with EG at pH 11 was tested in a low temperature H₂/O₂ (air) PEMFC with a Nafion^® 112 membrane, at 20 and 40 °C. Current densities at 0.5 V, with O₂ fed to the cathode, at 40 °C were 1.40 A cm^?2 and peak power densities 0.79 W cm^?2, approximately; which compared with 1.74 A cm^?2 and 0.91 W cm^?2, respectively for a commercial Pt/C.     

Giant negative electrocaloric effect and energy storage response in 0.94(K0.5Na0.5)NbO3-0.06SrMnO3 nanocrystalline ceramics

We report the electrocaloric (EC) effect investigation on lead-free 0.94(K_0.5Na_0.5)NbO₃-0.06SrMnO₃ nanocrystalline ceramics by an indirect thermodynamic method using Maxwell's relations. The maximum value of the negative EC effect ($\mathrm{\Delta }{\mathrm{T}}_{\mathit{max}}=?1.66\mathrm{K}$) was observed at 459?K near the transition temperature at the field of 50?kV/cm. The corresponding EC responsivity was calculated to be $?3.32\times {10}^{?7}\mathrm{K}\mathrm{m}/\mathrm{V}$ under 50?kV/cm at 459?K whereas the coefficient of performance (COP) and recoverable energy density (${\mathrm{W}}_{\mathit{rec}}$) were found to be 1.03 and $0.17\mathrm{J}/{\mathrm{cm}}^3$, respectively under 50?kV/cm at 443?K. The observed values of negative EC effect, and EC responsivity are larger than any other lead-free ferroelectric ceramics with good COP and ${\mathrm{W}}_{\mathit{rec}}$ value. The results are interesting to improve the cooling efficiency and energy storage for device application.     

Recent Advances in Electrochemical Performances of Graphene Composite (Graphene-Polyaniline/Polypyrrole/Activated Carbon/Carbon Nanotube) Electrode Materials for Supercapacitor: A Review

The latest trend in the direction of miniaturized portable electronic devices has brought up necessitate for rechargeable energy sources. Among the various non conventional energy devices, the supercapacitor is the promising candidate for gleaning the energy. Supercapacitor, as a new energy device that colligates the gap between conventional capacitors and batteries, it has attracted more attention due to its high power density and long cycle life. Many researchers work on, synthesizing new electrode material for the development of supercapacitor. The electrode material possesses salient structure and electrochemical properties exhibit the efficient performance of the supercapacitor. Graphene has high carrier mobility, thermal conductivity, elasticity and stiffness and also has a theoretical specific capacitance of 2630 m²g^??1 corresponds to a specific capacitance of 550 Fg^??1. This article summarizes and reviews the electrochemical performance and applications of various graphene composite materials such as graphene/polyaniline, graphene/polypyrrole, graphene/metal oxide, graphene/activated carbon, graphene/carbon nanotube as an electrode materials towards highly efficient supercapacitors and also dealt with symmetric, asymmetric and hybrid nature of the graphene based supercapacitor.