Prony-Based Optimal Bayes Fault Classification of Overcurrent Protection

The development of deregulation and demand for high-quality electrical energy has lead to a new requirement in different fields of power systems. In the protection field, this means that high sensitivity and fast operation during the fault are required while maltripping of relay protection is not acceptable. One case that may lead to a maltrip of the high-sensitive overcurrent relay is the starting current of the induction motor or inrush current of the transformer. This transient current has the potential to affect the correct operation of protection relays close to the component being switched. In the case of switching events, such transients must not lead to overcurrent relay operation; therefore, a reliable and secure relay response becomes a critical matter. Meanwhile, proper techniques must be used to prevent maltripping of such relays, due to transient currents in the network. In this paper, the optimal Bayes classifier is utilized to develop a method for discriminating the fault from nonfault events. The proposed method has been designed based on extracting the modal parameters of the current waveform using the Prony method. By feeding the fundamental frequency damping and ratio of the 2nd harmonic amplitude over the fundamental harmonic amplitude to the classifier, the fault case is discriminated from the switching case. The suitable performance of this algorithm is demonstrated by simulation of different faults and switching conditions on a power system using PSCAD/EMTDC software.     

Comparison of PF4/heparin ELISA assay with the 14C-serotonin release assay in the diagnosis of heparin-induced thrombocytopenia

The diagnosis of heparin-induced thrombocytopenia (HIT) may be affirmed by demonstrating heparin-dependent anti-platelet antibodies using the 14C-serotonin release assay (SRA). In this study, results of the SRA was compared with the recently described platelet factor 4 (PF4)/heparin enzyme-linked immunosorbent assay (ELISA). Compared with the SRA, the sensitivity and specificity of a PF4/heparin ELISA was 87% and 92%, respectively, using an assay developed in our laboratory; and 90% and 98%, respectively, using a commercially developed kit (Diagnostica Stago, Asnieres, France). However, antibodies to PF4/heparin were also detected in up to 8% of patients whose plasma was negative by SRA, and 23% of patients receiving heparin who were not thrombocytopenic. These data indicate that results obtained with the PF4/heparin ELISA and the SRA are generally in accord in patients with a clinical diagnosis of HIT. However, discrepant results occur in approximately 20% of cases because of the greater sensitivity of ELISA and the possible involvement of other heparin-binding proteins. The fact that each assay contributes independent information in some cases must be considered in the sequence of test performance and in providing consultation to the practicing hematologist.     

Interpretation of way-finding healthcare symbols by a multicultural population: Navigation signage design for global health

The interpretation of way-finding symbols for healthcare facilities in a multicultural community was assessed in a cross-sectional study. One hundred participants recruited from Al Ain city in the United Arab Emirates were asked to interpret 28 healthcare symbols developed at Hablamos Juntos (such as vaccinations and laboratory) as well as 18 general-purpose symbols (such as elevators and restrooms). The mean age was 27.6 years (16–55 years) of whom 84 (84%) were females. Healthcare symbols were more difficult to comprehend than general-purpose signs. Symbols referring to abstract concepts were the most misinterpreted including oncology, diabetes education, outpatient clinic, interpretive services, pharmacy, internal medicine, registration, social services, obstetrics and gynecology, pediatrics and infectious diseases. Interpretation rates varied across cultural backgrounds and increased with higher education and younger age. Signage within healthcare facilities should be tested among older persons, those with limited literacy and across a wide range of cultures.     

Effect of expandable graphite and ammonium polyphosphate on the thermal degradation and weathering of intumescent fire-retardant coating

This research aims to study the effect of ammonium polyphosphate and expandable graphite on the intumescent coating formulations (ICF). The coating presented in this research article is based on carbon source expandable graphite (EG), blowing agent melamine, acid source ammonium polyphosphate (APP), epoxy resin as a binder with polyamide amine. The stability of the developed coating was verified at 950°C for 1-hour fire test. The results showed that the coating is stable and well bond with the steel substrate. The char was characterized by using FESEM, XRD, FTIR, DTA, TGA, XPS, Py-GCMS and Weathering Test. The morphology of the char was studied by SEM of the coating after furnace fire test. XRD and FTIR show the presence of graphite, borophosphate; boron oxide and sassolite in the residual char. TGA and DTG disclosed that EG improved the residual mass of coating. XPS analysis showed the char residue of IF5-APP-EG contains carbon and oxygen contents 47.50 and 40.70, respectively. Py-GCMS analysis described that the IF5-APP-EG released less gaseous compounds. The weathering test illustrated that's the char expansion of coatings samples was decreased due to the presence of a humid environment and UV light. The IF5-APP-EG showed the maximum char expansion, lower substrate temperature and high residual weight among the studied formulations.     

A secure image encryption scheme based on chaotic maps and affine transformation

Due to the interesting nonlinear dynamic properties of chaotic maps, recently chaos-based encryption algorithms have gained much attention in cryptographic communities. However, many encryption schemes do not fulfil the minimum key space requirement, which is an essential concern in many secure data applications. In this paper, an efficient chaos-based image encryption scheme with higher key space is presented. Even with a single round of encryption, a significantly larger key space can be achieved. The proposed scheme removes correlation among image pixels via random chaotic sequences, simply by XOR and addition operations. In order to resist against numerous attacks, we apply the affine transformation to get the final ciphertext image. The security of the proposed scheme is proved through histogram, contrast, PSNR, entropy, correlation, key space, key sensitivity and differential attack analysis. Many significant properties of chaotic maps, sensitivity to initial condition and control parameters, structure and attack complexity, make the anticipated scheme very reliable, practical and robust in various secure communication applications.     

Coordinated design of TCSC and PSS controllers using VURPSO and Genetic algorithms for multi-machine power system stability

Thyristor controlled series capacitor (TCSC) can regulate line impedance and therefore increase transferred power of the system. On the other hand power system stabilizer (PSS) increases dynamic stability of generator. To enhance the stability, combination of TCSC and PSS can be applied, and in such a case coordination of TCSC and PSS is essential. This paper applies this combined controller in order to enhance the stability of multi-machine system. Parameters of these controllers are optimized by velocity update relaxation particle swarm optimization (VURPSO) algorithm and Genetic algorithm (GA). The simulation results show that the combination of VURPSO algorithm and GA leads to a better design and stability.     

A multi-objective strategy in genetic algorithms for gene selection of gene expression data

A microarray machine offers the capacity to measure the expression levels of thousands of genes simultaneously. It is used to collect information from tissue and cell samples regarding gene expression differences that could be useful for cancer classification. However, the urgent problems in the use of gene expression data are the availability of a huge number of genes relative to the small number of available samples, and the fact that many of the genes are not relevant to the classification. It has been shown that selecting a small subset of genes can lead to improved accuracy in the classification. Hence, this paper proposes a solution to the problems by using a multiobjective strategy in a genetic algorithm. This approach was tried on two benchmark gene expression data sets. It obtained encouraging results on those data sets as compared with an approach that used a single-objective strategy in a genetic algorithm. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Selecting informative genes from microarray data by using hybrid methods for cancer classification

Gene expression technology, namely microarrays, offers the ability to measure the expression levels of thousands of genes simultaneously in biological organisms. Microarray data are expected to be of significant help in the development of an efficient cancer diagnosis and classification platform. A major problem in these data is that the number of genes greatly exceeds the number of tissue samples. These data also have noisy genes. It has been shown in literature reviews that selecting a small subset of informative genes can lead to improved classification accuracy. Therefore, this paper aims to select a small subset of informative genes that are most relevant for cancer classification. To achieve this aim, an approach using two hybrid methods has been proposed. This approach is assessed and evaluated on two well-known microarray data sets, showing competitive results. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Investigating the effect of varying block size on power and energy consumption of GPU kernels

The Journal of Supercomputing - Power consumption is likely to remain a significant concern for exascale performance in the foreseeable future. In addition, graphics processing units (GPUs) have...     

Evaluating Six Sigma failure rate for inverse Gaussian cycle times

Six Sigma is a quality philosophy and methodology that aims to achieve operational excellence and delighted customers. The cost of poor quality depends on the sigma quality level and its corresponding failure rate. Six Sigma provides a well-defined target of 3.4 defects per million. This failure rate is commonly evaluated under the assumption that the process is normally distributed and its specifications are two-sided. However, these assumptions may lead to implementation of quality-improvement strategies that are based on inaccurate evaluations of quality costs and profits. This paper defines the relationship between failure rate and sigma quality level for inverse Gaussian processes. The inverse Gaussian distribution has considerable applications in describing cycle times, product life, employee service times, and so on. We show that for these processes attaining Six Sigma target failure rate requires higher quality efforts than for normal processes. A generic model is presented to characterise cycle times in manufacturing systems. In this model, the asymptotic production is described by a drifted Brownian motion, and the cycle time is evaluated by using the first passage time theory of a Wiener process to a boundary. The proposed method estimates the right efforts required to reach Six Sigma goals.