A fog-based security framework for intelligent traffic light control system

Multimedia Tools and Applications - The world is facing many problems including that of traffic congestion. To highlight the issue of traffic congestion worldwide specially in urban areas and to...     

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.     

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     

High responsivity silicon MOS phototransistors

We describe a metal-oxide silicon (MOS) phototransistor that relies on a novel lateral doping scheme that creates a p-i-n junction configuration for light detection. This is essentially a hybrid device with the horizontal structure of a p-i-n diode and the vertical structure of a MOS field-effect transistor. The lateral p-i-n diode detects light whereas the gate can be used to change the current flowing through the device; making it appear as a MOSFET. This feature makes it easy to integrate it with other conventional MOSFETs on a CMOS process flow. The device shows high optical responsivities that persist to wavelengths in the near-ultraviolet region. The fabrication of the device as well as its electrical and optical characteristics is described.     

Auto-recognition and part model complexity quantification of regular-freeform revolved surfaces through delta volume generations

Engineering with Computers - Vast research works implementing feature-based technology have successfully been devoted. However, work on recognition of revolved regular-freeform surfaces is still...     

Effect of cooling methods on residual compressive strength and cracking behavior of fly ash concretes exposed at elevated temperatures

This paper presents the effects of cooling methods on residual compressive strength and cracking behavior of concretes containing four different class F fly ash contents of 10%, 20%, 30% and 40% as partial replacement of cement at various elevated temperatures. The residual compressive strength of the aforementioned fly ash concretes is measured after being exposed to 200, 400, 600 and 800 °C temperatures and two different cooling methods, for example, slow cooling and rapid water cooling. Results show that the residual compressive strengths of all fly ash concretes decrease with increase in temperatures irrespective of cooling regimes, which is similar to that of ordinary concrete. Generally, control ordinary concrete and all fly ash concretes exhibited between 10% and 35% more reduction in residual compressive strength because of rapid cooling than slow cooling except few cases. Cracks are observed over concrete specimens after being exposed to temperatures ranging from 400 to 800 °C. Samples that are slowly cooled developed smaller cracks than those rapidly cooled. At 800 °C, all fly ash concretes that are exposed to rapid cooling showed the most severe cracking. X‐ray diffraction analysis shows reduction of Ca(OH)₂ peak and formation of new calcium silicate peak in concretes containing 20% and 40% fly ash when subjected to 800 °C in both cooling methods. Thermo gravimetric analysis and differential thermal analysis results show increase in thermal stability of concrete with increase in fly ash contents. The existing Eurocode also predicted the compressive strength of fly ash concretes with reasonable accuracy when subjected to the aforementioned elevated temperatures and cooling methods. Copyright © 2014 John Wiley & Sons, Ltd.     

Sign Language to Sentence Formation: A Real Time Solution for Deaf People

Communication is a basic need of every human being to exchange thoughts and interact with the society. Acute peoples usually confab through different spoken languages, whereas deaf people cannot do so. Therefore, the Sign Language (SL) is the communication medium of such people for their conversation and interaction with the society. The SL is expressed in terms of specific gesture for every word and a gesture is consisted in a sequence of performed signs. The acute people normally observe these signs to understand the difference between single and multiple gestures for singular and plural words respectively. The signs for singular words such as I, eat, drink, home are unalike the plural words as school, cars, players. A special training is required to gain the sufficient knowledge and practice so that people can differentiate and understand every gesture/sign appropriately. Innumerable researches have been performed to articulate the computer-based solution to understand the single gesture with the help of a single hand enumeration. The complete understanding of such communications are possible only with the help of this differentiation of gestures in computer-based solution of SL to cope with the real world environment. Hence, there is still a demand for specific environment to automate such a communication solution to interact with such type of special people. This research focuses on facilitating the deaf community by capturing the gestures in video format and then mapping and differentiating as single or multiple gestures used in words. Finally, these are converted into the respective words/sentences within a reasonable time. This provide a real time solution for the deaf people to communicate and interact with the society.