An Electromagnetic Detector of Insulator Surface Discharge

Over the past years many attempts have been made to develop means of monitoring possible leakage currents on polluted insulator surfaces in high-voltage power systems and warning against prospective flashovers. The literature offers a number of those detectors whose operation is based on measuring one of the following parameters: the maximum leakage current amplitude, the frequency of leakage current surges, and the conductance of the contaminating layer (usually assessed after deenergizing the system). A new principle of pollution assessment is introduced whereby the electromagnetic emission from the leakage current on the insulator surface is received, amplified, processed, and then used to activate an automatic alarm system. The advantages and limitations of the new detector are presented. Laboratory results of the detector output under various conditions of applied voltage and contaminants are given. A correlation between leakage current amplitudes and detected EM signals is established which allows a comparative evaluation of the detector.     

Phase boundary between Na–Si clathrates of structures I and II at high pressures and high temperatures

Understanding of the covalent clathrate formation is a crucial point for the design of new superhard materials with intrinsic coupling of superhardness and metallic conductivity. It has been found that silicon clathrates have the archetype structures, which can serve an existent model compounds for superhard clathrate frameworks Si–B, Si–C, B–C and C with intercalated atoms (e.g., alkali metals or even halogens) that can assure the metallic properties. Here we report our in situ and ex situ studies of high-pressure formation and stability of clathrates Na₈Si₄₆ (structure I) and Na_24+x Si₁₃₆ (structure II). Experiments have been performed using standard Paris–Edinburgh cells (opposite anvils) up to 6 GPa and 1500 K. We have established that chemical interactions in the Na–Si system and transition between two structures of clathrates occur at temperatures below silicon melting. The strong sensitivity of crystallization products to the sodium concentration has been observed. A tentative diagram of clathrate transformations has been proposed. At least up to ~6 GPa, Na_24+x Si₁₃₆ (structure II) is stable at lower temperatures as compared to Na₈Si₄₆ (structure I).     

Robust Fault Tolerant Control Design for Nonlinear Systems not Satisfying Matching and Minimum Phase Conditions

International Journal of Control, Automation and Systems - This paper proposes an adaptive sliding mode fault tolerant control design for lipschitz nonlinear system subject to simultaneous actuator...     

Low frequency dispersion in GaSe thin films

Frequency domain measurements on GaSe thin films have been carried out in the frequency range 10⁻² to 10⁴ Hz and temperature range 300 to 480 K. The measured samples with different contacts have shown low frequency dispersion (LFD) with activation energies 0.66±0.03 eV, 0.72±0.03 eV and 0.65±0.03 eV, respectively. Complex capacitance C(ω) behaviour of GaSe thin film with different contacts suggests possible contribution of diffused contact material.     

Crystal structure, thermal analysis and IR spectroscopic investigation of (C6H9N2)H2XO4 (X = As, P)

Crystals of 2-amino-4-methylpyridinium dihydrogenmonoarsenate (C₆H₉N₂)H₂AsO₄ and 2-amino-4-methylpyridinium dihydrogenmonophosphate (C₆H₉N₂)H₂PO₄ have been prepared and grown at room temperature. These materials are isotypic with the following unit cell dimensions (C₆H₉N₂)H₂AsO₄: a = 12.4415(5) Å, b = 6.8224(3) Å, c = 11.3524(5) Å, Z = 4, V = 963.60(6) Å³; (C₆H₉N₂)H₂PO₄: a = 12.4410(9) Å, b = 6.7165(3) Å, c = 11.3417(5) Å, Z = 4, V = 925.09(10) Å³. The common space group is Pnma. The structure of these compounds has been determined by X-ray data collection on single crystals of (C₆H₉N₂)H₂AsO₄ and (C₆H₉N₂)H₂PO₄. Due to the strong hydrogen-bond network connecting the H₂XO₄ groups, the anionic arrangement must be described as a linear organization. The chains composed by the macroanion spread along the b-direction, approximately centered by x = 0 and 1/2. All atoms of the structure, except one oxygen atom, are located in the mirror planes situated at y = 1/4 and 3/4, imparting an internal mirror symmetry to the anionic and the cationic entities. The linear macroanions are crossed by organic cations lying in mirrors perpendicular to the b-direction; this atomic arrangement is then described by a three-dimensional network of hydrogen bonds, built up by two types, O–HO bonds inside the chains and N–HO bonds linking adjacent chains. The thermal properties of both compounds are investigated as well as the IR properties supported by group theoretical analyses.     

Open writer identification from offline handwritten signatures by jointing the one-class symbolic data analysis classifier and feature-dissimilarities

Usually, a large number of reference signatures are required for building the writing style model from offline handwritten signatures (OHSs). Moreover, the existing writer identification systems from OHSs are generally closed systems that require a retraining process when a new writer is added. This paper proposes an open writer identification system from OHSs, based on a new scheme of the one-class symbolic data analysis (OC-SDA) classifier, using few reference signatures. For generating more data, intra-class feature-dissimilarities, generated from curvelet transform, are introduced for building the symbolic representation model (SRM) associated with each writer. Feature-dissimilarities allow capturing more efficiently the intra-personnel variability produced naturally by a writer and, thus, increase the inter-personnel variability. Instead of using the mean and the standard deviation for building the OC-SDA model, intra-class feature-dissimilarities generated for each writer are modeled through a new weighted membership function, inspired from the real probability distribution of training intra-class feature-dissimilarities. The comparative analysis against the state-of-the-art works shows that the proposed OC-SDA classifier outperforms the existing classifiers on three public signature datasets GPDS-300, CEDAR-55 and MCYT-75, using only five reference signatures, achieving 98.31%, 98.06% and 99.89%, respectively, even when a combination of multiple classifiers is performed or even using learned features. Moreover, the evaluation of the proposed writer identification system in front of skilled forgeries shows its ability to detect also possible forged signatures in addition to the genuine ones.

     

Service selection in stochastic environments: a learning-automaton based solution

In this paper, we propose a novel solution to the problem of identifying services of high quality. The reported solutions to this problem have, in one way or the other, resorted to using so-called “Reputation Systems” (RSs). Although these systems can offer generic recommendations by aggregating user-provided opinions about the quality of the services under consideration, they are, understandably, prone to “ballot stuffing” and “badmouthing” in a competitive marketplace. In general, unfair ratings may degrade the trustworthiness of RSs, and additionally, changes in the quality of service, over time, can render previous ratings unreliable. As opposed to the reported solutions, in this paper, we propose to solve the problem using tools provided by Learning Automata (LA), which have proven properties capable of learning the optimal action when operating in unknown stochastic environments. Furthermore, they combine rapid and accurate convergence with low computational complexity. In addition to its computational simplicity, unlike most reported approaches, our scheme does not require prior knowledge of the degree of any of the above mentioned problems associated with RSs. Instead, it gradually learns the identity and characteristics of the users which provide fair ratings, and of those who provide unfair ratings, even when these are a consequence of them making unintentional mistakes.     

AO4BPEL: An Aspect-oriented Extension to BPEL

Process-oriented composition languages such as BPEL allow Web Services to be composed into more sophisticated services using a workflow process. However, such languages exhibit some limitations with respect to modularity and flexibility. They do not provide means for a well-modularized specification of crosscutting concerns such as logging, persistence, auditing, and security. They also do not support the dynamic adaptation of composition at runtime. In this paper, we advocate an aspect-oriented approach to Web Service composition and present the design and implementation of AO4BPEL, an aspect-oriented extension to BPEL. We illustrate through examples how AO4BPEL makes the composition specification more modular and the composition itself more flexible and adaptable.     

FER-net: facial expression recognition using deep neural net

Neural Computing and Applications - Automatic facial expression recognition (FER) is one of the most challenging tasks in computer vision. FER admits a wide range of applications in...