Search results for: Optical Network Unit

Authors: Edgar Ochoa, G. Torres-Villasenor

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Superplastic deformation is shown by materials with a fine grain size, usually less than 10 μm, when they are deformed within the strain rate range 10-5 10-1 s-1 at temperatures greater than 0.5Tm, where Tm is the melting point in Kelvin. According to the constitutive equation for superplastic flow, refinement of the grain size would be expected to increase the optimum strain rate and decrease the temperature required for superplastic flow. Ribbons of eutectic Ag-Cu and Bi-Cd alloys were manufactured by using a single roller melt-spinning technique to obtain a fine grain structure for later test in superplastic regime. The eutectics ribbons were examined by scanning electron microscopy and X-Ray diffraction, and the grain size was determined using the image analysis software ImageJ. The average grain size was less than 1 μm. Tensile tests were carried out from 10-4 to 10-1 s-1, at room temperature, to evaluate the superplastic behavior. The largest deformation was shown by the Bi-Cd eutectic ribbons, Ɛ=140 %, despite that these ribbons have a hexagonal unit cell. On the other hand, Ag-Cu eutectic ribbons have a minor grain size and cube unit cell, however they showed a lower deformation in tensile test under the same conditions than Bi-Cd ribbons. This is because the Ag-Cu grew in a strong cube-cube orientation relationship.

Keywords: eutectic ribbon, fine grain, superplastic deformation, cube-cube orientation

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Authors: Amin Hamrahi, Niloofar Moghaddam

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Denial of Service is for denial-of-service attack, a type of attack on a network that is designed to bring the network to its knees by flooding it with useless traffic. Denial of Service (DoS) attacks have become a major threat to current computer networks. Many recent DoS attacks were launched via a large number of distributed attacking hosts in the Internet. These attacks are called distributed denial of service (DDoS) attacks. To have a better understanding on DoS attacks, this article provides an overview on existing DoS and DDoS attacks and major defense technologies in the Internet.

Keywords: denial of service, distributed denial of service, traffic, flooding

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Authors: Nateepat Srivarom, Weng Jingnong, Serm Chinnarat

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Precise Point Positioning (PPP) technique is use to improve accuracy by using precise satellite orbit and clock correction data, but this technique is complicated methods and high costs. Currently, there are several online processing service providers which offer simplified calculation. In the first part of this research, we compare the efficiency and precision of four software. There are three popular online processing service providers: Australian Online GPS Processing Service (AUSPOS), CSRS-Precise Point Positioning and CenterPoint RTX post processing by Trimble and 1 offline software, RTKLIB, which collected data from 10 the International GNSS Service (IGS) stations for 10 days. The results indicated that AUSPOS has the least distance root mean square (DRMS) value of 0.0029 which is good enough to be calculated for monitoring the movement of tectonic plates. The second, we use AUSPOS to process the data of geodetic network of Thailand. In December 26, 2004, the earthquake occurred a 9.3 MW at the north of Sumatra that highly affected all nearby countries, including Thailand. Earthquake effects have led to errors of the coordinate system of Thailand. The Royal Thai Survey Department (RTSD) is primarily responsible for monitoring of the crustal movement of the country. The difference of the geodetic network movement is not the same network and relatively large. This result is needed for survey to continue to improve GPS coordinates system in every year. Therefore, in this research we chose the AUSPOS to calculate the magnitude and direction of movement, to improve coordinates adjustment of the geodetic network consisting of 19 pins in Thailand during October 2013 to November 2017. Finally, results are displayed on the simulation map by using the ArcMap program with the Inverse Distance Weighting (IDW) method. The pin with the maximum movement is pin no. 3239 (Tak) in the northern part of Thailand. This pin moved in the south-western direction to 11.04 cm. Meanwhile, the directional movement of the other pins in the south gradually changed from south-west to south-east, i.e., in the direction noticed before the earthquake. The magnitude of the movement is in the range of 4 - 7 cm, implying small impact of the earthquake. However, the GPS network should be continuously surveyed in order to secure accuracy of the geodetic network of Thailand.

Keywords: precise point positioning, online processing service, geodetic network, inverse distance weighting

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Authors: Goodness Onwuka, Khaled Abou-El-Hossein

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The increase in the demand for precision optics, coupled with the absence of much research output in the ultra high precision grinding of precision optics as compared to the ultrahigh precision diamond turning of optical metals has fostered the need for more research in the ultra high precision grinding of an optical lens. Furthermore, the increase in the stringent demands for nanometric surface finishes through lapping, polishing and grinding processes necessary for the use of borosilicate-crown glass in the automotive and optics industries has created the demand to effectively monitor the surface roughness during the production process. Acoustic emission phenomenon has been proven as useful monitoring technique in several manufacturing processes ranging from monitoring of bearing production to tool wear estimation. This paper introduces a rare and unique approach with the application of acoustic emission technique to monitor the surface roughness of borosilicate-crown glass during an ultra high precision grinding process. This research was carried out on a 4-axes Nanoform 250 ultrahigh precision lathe machine using an ultra high precision grinding spindle to machine the flat surface of the borosilicate-crown glass with the tip of the grinding wheel. A careful selection of parameters and design of experiment was implemented using Box-Behnken method to vary the wheel speed, feed rate and depth of cut at three levels with a 3-center point design. Furthermore, the average surface roughness was measured using Taylor Hobson PGI Dimension XL optical profilometer, and an acoustic emission data acquisition device from National Instruments was utilized to acquire the signals while the data acquisition codes were designed with National Instrument LabVIEW software for acquisition at a sampling rate of 2 million samples per second. The results show that the raw and root mean square amplitude values of the acoustic signals increased with a corresponding increase in the measured average surface roughness values for the different parameter combinations. Therefore, this research concludes that acoustic emission monitoring technique is a potential technique for monitoring the surface roughness in the ultra high precision grinding of borosilicate-crown glass.

Keywords: acoustic emission, borosilicate-crown glass, surface roughness, ultra high precision grinding

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Authors: Zakir Husain, Neem Sagar, Neeraj Gupta

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Due to the increased penetration of renewable energy resources in the distribution system, the system is no longer passive in nature. In this paper, a steady state analysis of the distribution system has been done with the inclusion of wind generation. The modeling of wind turbine generator system and wind generator has been made to obtain the average active and the reactive power injection into the system. The study has been conducted on a IEEE-33 bus system with two wind generators. The present research work is useful not only to utilities but also to customers.

Keywords: distributed generation, distribution network, radial network, wind turbine generating system

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Authors: Ha-Guk Jeong, Jong-Beom Lee

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Cladding process is very typical technology for manufacturing composite materials by the hydrostatic extrusion. Because there is no friction between the metal and the container, it can be easily obtained in uniform flow during the deformation. The general manufacturing process for a metal-matrix composite in the solid state, mixing metal powders and ceramic powders with a suited volume ratio, prior to be compressed or extruded at the cold or hot condition in a can. Since through a plurality of unit processing steps of dispersing the materials having a large difference in their characteristics and physical mixing, the process is complicated and leads to non-uniform dispersion of ceramics. It is difficult and hard to reach a uniform ideal property in the coherence problems at the interface between the metal and the ceramic reinforcements. Metal hybrid composites, which presented in this report, are manufactured through the traditional plastic deformation processes like hydrostatic extrusion, caliber-rolling, and drawing. By the previous process, the realization of uniform macro and microstructure is surely possible. In this study, as a constituent material, aluminum, copper, and titanium have been used, according to the component ratio, excellent characteristics of each material were possible to produce a metal hybrid composite that appears to maximize. MgB₂ superconductor wire also fabricated via the same process. It will be introduced to their unique artistic and thermal characteristics.

Keywords: cladding process, metal-hybrid composites, hydrostatic extrusion, electronic/thermal characteristics

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Authors: Alexander Rodríguez-Hernández, Arnulfo Rojas-Perez, Liz Diaz-Vazquez

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The rise in consumerism over the past century has resulted in the creation of higher amounts of plasticizers, personal care products and other chemical substances, which enter and accumulate in water systems. Other sources of pollutants in Neotropical regions experience large inputs of nutrients with these pollutants resulting in eutrophication of water which consume large quantities of oxygen, resulting in high fish mortality. This dilemma has created a need for the development of targeted detection in complex matrices and remediation of emerging contaminants. We have synthesized carbon nanoparticles from macro algae (Ulva fasciata) by oxidizing the graphitic carbon network under extreme acidic conditions. The resulting material was characterized by STEM, yielding a spherical 12 nm average diameter nanoparticles, which can be fixed into a polysaccharide aerogel synthesized from the same macro algae. Spectrophotometer analyses show a pH dependent fluorescent behavior varying from 450-620 nm in aqueous media. Heavily oxidized edges provide for easy functionalization with enzymes for a more targeted analysis and remediation technique. Given the optical properties of the carbon base nanoparticles and the numerous possibilities of functionalization, we have developed a selective and robust targeted bio-detection and bioremediation technique for the treatment of emerging contaminants in complex matrices like estuarine embayment.

Keywords: aerogels, carbon nanoparticles, fluorescent, targeted analysis

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Authors: Jainendra Singh, Zaheeruddin

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A typical wireless sensor networks (WSNs) consists of several tiny and low-power sensors which use radio frequency to perform distributed sensing tasks. The longevity of wireless sensor networks (WSNs) is a major issue that impacts the application of such networks. While routing protocols are striving to save energy by acting on sensor nodes, recent studies show that network lifetime can be enhanced by further involving sink mobility. A common approach for energy efficiency is partitioning the network into clusters with correlated data, where the representative nodes simply transmit or average measurements inside the cluster. In this paper, we propose an energy- efficient homogenous clustering (EHC) technique. In this technique, the decision of each sensor is based on their residual energy and an estimate of how many of its neighboring cluster heads (CHs) will benefit from it being a CH. We, also explore the routing algorithm in clustered WSNs. We show that the proposed schemes significantly outperform current approaches in terms of packet delay, hop count and energy consumption of WSNs.

Keywords: wireless sensor network, energy efficiency, clustering, routing

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Authors: Behnaz Moradijamei, Michael Higgins

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Identifying community structure is a critical task in analyzing social media data sets often modeled by networks. Statistical models such as the stochastic block model have proven to explain the structure of communities in real-world network data. In this work, we develop a goodness-of-fit test to examine community structure's existence by using a distinguishing property in networks: cyclic structures are more prevalent within communities than across them. To better understand how communities are shaped by the cyclic structure of the network rather than just the number of edges, we introduce a novel method for deciding on the existence of communities. We utilize these structures by using renewal non-backtracking random walk (RNBRW) to the existing goodness-of-fit test. RNBRW is an important variant of random walk in which the walk is prohibited from returning back to a node in exactly two steps and terminates and restarts once it completes a cycle. We investigate the use of RNBRW to improve the performance of existing goodness-of-fit tests for community detection algorithms based on the spectral properties of the adjacency matrix. Our proposed test on community structure is based on the probability distribution of eigenvalues of the normalized retracing probability matrix derived by RNBRW. We attempt to make the best use of asymptotic results on such a distribution when there is no community structure, i.e., asymptotic distribution under the null hypothesis. Moreover, we provide a theoretical foundation for our statistic by obtaining the true mean and a tight lower bound for RNBRW edge weights variance.

Keywords: hypothesis testing, RNBRW, network inference, community structure

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Authors: Manverpreet Kaur, Amarpreet Singh

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The demand of Vehicular ad hoc networks is increasing day by day, due to offering the various applications and marvelous benefits to VANET users. Clustering in VANETs is most important to overcome the connectivity problems of VANETs. In this paper, we proposed a new clustering technique Enhanced cluster based connectivity maintenance in vehicular ad hoc network. Our objective is to form long living clusters. The proposed approach is grouping the vehicles, on the basis of the longest list of neighbors to form clusters. The cluster formation and cluster head selection process done by the RSU that may results it reduces the chances of overhead on to the network. The cluster head selection procedure is the vehicle which has closest speed to average speed will elect as a cluster Head by the RSU and if two vehicles have same speed which is closest to average speed then they will be calculate by one of the new parameter i.e. distance to their respective destination. The vehicle which has largest distance to their destination will be choosing as a cluster Head by the RSU. Our simulation outcomes show that our technique performs better than the existing technique.

Keywords: VANETs, clustering, connectivity, cluster head, intelligent transportation system (ITS)

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Authors: Nesrine Toubaline, Djamel Bennouar, Ali Mahdoum

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3D technology can lead to a significant reduction in power and average hop-count in Networks on Chip (NoCs). It offers short and fast vertical links which copes with the long wire problem in 2D NoCs. This work proposes heuristic-based method to optimize number and placement of vertical links to achieve specified performance goals. Experiments show that significant improvement can be achieved by using a specific number of vertical interconnect.

Keywords: interconnect optimization, monolithic inter-tier vias, network on chip, system on chip, through silicon vias, three dimensional integration circuits

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Authors: Prateeksha Mahamallik, Anjali Pal

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In the present study, Co(II)-adsolubilized surfactant modified alumina (SMA) was prepared, and methylene blue (MB) degradation was carried out on Co-SMA surface by visible light photo-Fenton process. The entire reaction proceeded on solid surface as MB was embedded on Co-SMA surface. The reaction followed zero order kinetics. Response surface methodology (RSM) and artificial neural network (ANN) were used for modeling the decolorization of MB by photo-Fenton process as a function of dose of Co-SMA (10, 20 and 30 g/L), initial concentration of MB (10, 20 and 30 mg/L), concentration of H2O2 (174.4, 348.8 and 523.2 mM) and reaction time (30, 45 and 60 min). The prediction capabilities of both the methodologies (RSM and ANN) were compared on the basis of correlation coefficient (R2), root mean square error (RMSE), standard error of prediction (SEP), relative percent deviation (RPD). Due to lower value of RMSE (1.27), SEP (2.06) and RPD (1.17) and higher value of R2 (0.9966), ANN was proved to be more accurate than RSM in order to predict decolorization efficiency.

Keywords: adsolubilization, artificial neural network, methylene blue, photo-fenton process, response surface methodology

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Authors: Celeste Lawson

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Learning to embrace and value teamwork assessment at a university level is critical for students, as graduates enter a real-world working environment where teamwork is likely to occur virtually. Student disdain for teamwork exercises is an area often overlooked or disregarded by academics. This research explored the implementation of an online teamwork assessment approach at a regional Australian university with a significant cohort of Distance Education students. Students had disliked teamwork for three reasons: it was not relevant to their study, the grading was unfair amongst team members, and managing the task was challenging in a virtual environment. Teamwork assessment was modified so that the task was an authentic task that could occur in real-world practice; team selection was based on the task topic rather than randomly; grading was based on the individual’s contribution to the task, and students were provided virtual team management skills as part of a the assessment. In this way, management of the team became an output of the task itself. Data was gathered over three years from student satisfaction surveys, failure rates, attrition figures, and unsolicited student comments. In one unit where this approach was adopted (Advanced Public Relations), student satisfaction increased from 3.6 (out of 5) in 2012 to 4.6 in 2016, with positive comments made about the teamwork approach. The attrition rate for another unit (Public Relations and the Media) reduced from 20.7% in 2012 to 2.2% in 2015. In 2012, criticism of teamwork assessment made up 50% of negative student feedback in Public Relations and the Media. By 2015, following the successful implementation of the teamwork assessment approach, only 12.5% of negative comments on the student satisfaction survey were critical of teamwork, while 33% of positive comments related to a positive teamwork experience. In 2016, students explicitly nominated teamwork as the best part of this unit. The approach is transferable to other disciplines and was adopted by other academics within the institution with similar results.

Keywords: assessment, distance education, teamwork, virtual

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Authors: Thomas Weber, Nina Strobel, Thomas Kohne, Eberhard Abele

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In context of the energy transition in Germany, the importance of so-called virtual power plants in the energy supply continues to increase. The progressive dismantling of the large power plants and the ongoing construction of many new decentralized plants result in great potential for optimization through synergies between the individual plants. These potentials can be exploited by mathematical optimization algorithms to calculate the optimal application planning of decentralized power and heat generators and storage systems. This also includes linear or linear mixed integer optimization. In this paper, procedures for reducing the number of decision variables to be calculated are explained and validated. On the one hand, this includes combining n similar installation types into one aggregated unit. This aggregated unit is described by the same constraints and target function terms as a single plant. This reduces the number of decision variables per time step and the complexity of the problem to be solved by a factor of n. The exact operating mode of the individual plants can then be calculated in a second optimization in such a way that the output of the individual plants corresponds to the calculated output of the aggregated unit. Another way to reduce the number of decision variables in an optimization problem is to reduce the number of time steps to be calculated. This is useful if a high temporal resolution is not necessary for all time steps. For example, the volatility or the forecast quality of environmental parameters may justify a high or low temporal resolution of the optimization. Both approaches are examined for the resulting calculation time as well as for optimality. Several optimization models for virtual power plants (combined heat and power plants, heat storage, power storage, gas turbine) with different numbers of plants are used as a reference for the investigation of both processes with regard to calculation duration and optimality.

Keywords: CHP, Energy 4.0, energy storage, MILP, optimization, virtual power plant

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Authors: M. Monisha, Sudha D. Kamath

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We investigate the optical and photoluminescence properties from Dy³+ singly doped and Dy³+ co-doped with Ce³+alumino borosilicate glasses prepared using high temperature melt-quenching technique. The glass composition formula is 25SiO₂-(40-x-y)B2O₃-10Al₂O₃-15NaF-10ZnO-xDy₂O₃ yCe₂O₃ where, x = 0.5 mol% and y = 0, 0.1, and 0.5 mol%. The XRD study reveals the amorphous nature of both singly doped and co-doped glasses. Absorption study on Dy3+ singly doped glass shows nearly twelve absorption peaks arising from the ground level of Dy³+ ions (⁶H₁₅/₂) to various upper levels, and for Dy³+/Ce³+ co-doped glasses, few of the transitions in the visible region are suppressed. The absorption band edge is shifted towards the higher wavelength region on increasing Ce3+concentration. The decrease in indirect energy bandgap and increase in Urbach energy of the prepared glasses is observed due to codoping with Ce3+ ions. The photoluminescence studies on singly doped glass under 350 nm excitation showed three peaks at the blue (482 nm), yellow (575 nm), and red (663 nm) region. For codoped glasses, the emission peak at 403 nm is raised due to the 4d to 5f transition of Ce3+ ions. Lifetime values (ms) of co-doped glass is found to be higher than singly doped glass. Under 350 nm excitation, CIE coordinates of the co-doped glasses moved towards the bright white light region. The correlated color temperature (CCT) values were obtained in the range 4500 – 4700 K. Thus, the prepared glasses can be used for photonics device applications.

Keywords: absorption spectra, borosilicate glasses, Ce³+, Dy³+, photoluminescence

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Authors: Ismail Abubakar, Hamid Mehrabi, Reg Morton

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Currently, extensive signal analysis is performed in order to evaluate structural health of turbomachinery blades. This approach is affected by constraints of time and the availability of qualified personnel. Thus, new approaches to blade dynamics identification that provide faster and more accurate results are sought after. Generally, modal analysis is employed in acquiring dynamic properties of a vibrating turbomachinery blade and is widely adopted in condition monitoring of blades. The analysis provides useful information on the different modes of vibration and natural frequencies by exploring different shapes that can be taken up during vibration since all mode shapes have their corresponding natural frequencies. Experimental modal testing and finite element analysis are the traditional methods used to evaluate mode shapes with limited application to real live scenario to facilitate a robust condition monitoring scheme. For a real time mode shape evaluation, rapid evaluation and low computational cost is required and traditional techniques are unsuitable. In this study, artificial neural network is developed to evaluate the mode shape of a lab scale rotating blade assembly by using result from finite element modal analysis as training data. The network performance evaluation shows that artificial neural network (ANN) is capable of mapping the correlation between natural frequencies and mode shapes. This is achieved without the need of extensive signal analysis. The approach offers advantage from the perspective that the network is able to classify mode shapes and can be employed in real time including simplicity in implementation and accuracy of the prediction. The work paves the way for further development of robust condition monitoring system that incorporates real time mode shape evaluation.

Keywords: modal analysis, artificial neural network, mode shape, natural frequencies, pattern recognition

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Authors: Oladapo Tolulope Ibitoye

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Neural networks have become prominent and widely engaged in algorithmic-based machine learning networks. They are perfect in solving day-to-day issues to a certain extent. Neural networks are computing systems with several interconnected nodes. One of the numerous areas of application of neural networks is object detection. This is a prominent area due to the coronavirus disease pandemic and the post-pandemic phases. Wearing a face mask in public slows the spread of the virus, according to experts’ submission. This calls for the development of a reliable and effective model for detecting face masks on people's faces during compliance checks. The existing neural network models for facemask detection are characterized by their black-box nature and large dataset requirement. The highlighted challenges have compromised the performance of the existing models. The proposed model utilized Faster R-CNN Model on Inception V3 backbone to reduce system complexity and dataset requirement. The model was trained and validated with very few datasets and evaluation results shows an overall accuracy of 96% regardless of skin tone.

Keywords: convolutional neural network, face detection, face mask, masked faces

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Authors: B. Safi, B. Amrane, M. Saidi

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The main purpose of this study is to evaluate the reuse of refractory brick wastes (RBW) as a supplementary cementitious materials (by a total replacement of silica fume) to produce a high performance fiber-reinforced concrete (HPFRC). This work presents an experimental study on the formulation and physico-mechanical characterization of ultra high performance fiber reinforced concretes based on three types of refractory brick wastes. These have been retrieved from the manufacturing unit of float glass MFG (Mediterranean Float Glass) after their use in the oven basin (ie d. they are considered waste unit). Three compositions of concrete (HPFRC) were established based on three types of refractory brick wastes (finely crushed), with the dosage of each type of bricks is kept constant, similar the dosage of silica fume used for the control concrete. While all the other components and the water/binder ratio are maintained constant with the same quantity of the superplasticizer. The performance of HPFRC, were evaluated by determining the essential characteristics of fresh and hardened concrete.

Keywords: refractory bricks, concrete, fiber, fluidity, compressive strength, tensile strength

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Authors: Pavel Kotin, Sergey Dotofeev, Daniil Kozlov, Alexey Garshev

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We propose the investigation of CdSe quantum dots which were synthesized in the presence of silver halides. To understand a process of nanoparticle formation in more detail, we varied the silver halide amount in the synthesis and proposed a sampling during colloidal growth. The attempts were focused on the investigation of shape, structure and optical properties of nanoparticles. We used the colloidal method of synthesis. Cadmium oleate, tri-n-octylphosphine selenide (TOPSe) and AgHal in TOP were precursors of cadmium, selenium and silver halides correspondingly. The molar Ag/Cd ratio in synthesis was varied from 1/16 to 1/1. The sampling was basically realized in 20 sec, 5 min, and 30 min after the beginning of quantum dots nucleation. To investigate nanoparticles we used transmission electron microscopy (including high resolution one), X-ray diffraction, and optical spectroscopy. It was established that silver halides lead to obtaining tetrapods with different leg length and large ellipsoidal nanoparticles possessing an intensive near IR photoluminescence. The change of the amount of silver halide in synthesis and the selection of an optimal growth time allows controlling the shape and the share of tetrapods or ellipsoidal nanoparticles in the product. Our main attempts were focused on a detailed investigation of the quantum dots structure and shape evolution and, finally, on mechanisms of such nanoparticle formation.

Keywords: colloidal quantum dots, shape evolution, silver doping, tetrapods

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Authors: Beier Zhu, Rui Zhang, Qi Song

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Text contained within fixed-layout documents can be of great semantic value and so requires a high localization accuracy, such as ID cards, invoices, cheques, and passports. Recently, algorithms based on deep convolutional networks achieve high performance on text detection tasks. However, for text localization in fixed-layout documents, such algorithms detect word bounding boxes individually, which ignores the layout information. This paper presents a novel architecture built on convolutional neural networks (CNNs). A global text localization network and a regional bounding-box regression network are introduced to tackle the problem in a coarse-to-fine manner. The text localization network simultaneously locates word bounding points, which takes the layout information into account. The bounding-box regression network inputs the features pooled from arbitrarily sized RoIs and refine the localizations. These two networks share their convolutional features and are trained jointly. A typical type of fixed-layout documents: ID cards, is selected to evaluate the effectiveness of the proposed system. These networks are trained on data cropped from nature scene images, and synthetic data produced by a synthetic text generation engine. Experiments show that our approach locates high accuracy word bounding boxes and achieves state-of-the-art performance.

Keywords: bounding box regression, convolutional networks, fixed-layout documents, text localization

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Authors: Hamd Rezaeifar, Hamid Reza Sahriari

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Accidents are considered to be one of the challenges of modern life. Due to the fact that the victims of this problem and also internal transportations are getting increased day by day in Iran, studying effective factors of accidents and identifying suitable models and parameters about this issue are absolutely essential. The main purpose of this research has been studying the factors and spatial data affecting accidents of Mashhad during 2007- 2008. In this paper it has been attempted to – through matching spatial layers on each other and finally by elaborating them with the place of accident – at the first step by adding landmarks of the accident and through adding especial fields regarding the existence or non-existence of effective phenomenon on accident, existing information banks of the accidents be completed and in the next step by means of data mining tools and analyzing by neural network, the relationship between these data be evaluated and a logical model be designed for predicting accident-prone spots with minimum error. The model of this article has a very accurate prediction in low-accident spots; yet it has more errors in accident-prone regions due to lack of primary data.

Keywords: accident, data mining, neural network, GIS

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Authors: S. Nickolas, Shobha K.

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The treatment of incomplete data is an important step in the data pre-processing. Missing values creates a noisy environment in all applications and it is an unavoidable problem in big data management and analysis. Numerous techniques likes discarding rows with missing values, mean imputation, expectation maximization, neural networks with evolutionary algorithms or optimized techniques and hot deck imputation have been introduced by researchers for handling missing data. Among these, imputation techniques plays a positive role in filling missing values when it is necessary to use all records in the data and not to discard records with missing values. In this paper we propose a novel artificial neural network based clustering algorithm, Adaptive Resonance Theory-2(ART2) for imputation of missing values in mixed attribute data sets. The process of ART2 can recognize learned models fast and be adapted to new objects rapidly. It carries out model-based clustering by using competitive learning and self-steady mechanism in dynamic environment without supervision. The proposed approach not only imputes the missing values but also provides information about handling the outliers.

Keywords: ART2, data imputation, clustering, missing data, neural network, pre-processing

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Authors: Lauren Dhugga, Meena Parameswaran, David Blundell, Abbas Khushnood

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Context: A multidisciplinary weekly safety briefing was implemented at the Paediatric Cardiothoracic Unit at the Freeman Hospital in Newcastle-upon-Tyne. It is a tertiary referral centre with a quarternary cardiac paediatric intensive care unit and provides complexed care including heart and lung transplants, mechanical support and advanced heart failure assessment. Aim: The aim of this briefing is to provide a structured platform of communication, in an effort to improve efficiency, safety, and patient care. Problem: The paediatric cardiothoracic unit is made up of a vast multidisciplinary team including doctors, intensivists, anaesthetists, surgeons, specialist nurses, echocardiogram technicians, physiotherapists, psychologists, dentists, and dietitians. It provides care for children with congenital and acquired cardiac disease and is one of only two units in the UK to offer paediatric heart transplant. The complexity of cases means that there can be many teams involved in providing care to each patient, and frequent movement of children between ward, high dependency, and intensive care areas. Currently, there is no structured forum for communicating important information across the department, for example, staffing shortages, prescribing errors and significant events. Strategy: An initial survey questioning the need for better communication found 90% of respondents agreed that they could think of an incident that had occurred due to ineffective communication, and 85% felt that incident could have been avoided had there been a better form of communication. Lastly, 80% of respondents felt that a weekly 60 second safety briefing would be beneficial to improve communication within our multidisciplinary team. Based on those promising results, a weekly 60 second safety briefing was implemented to be conducted on a Monday morning. The safety briefing covered four key areas (SAFE): staffing, awareness, fix and events. This was to highlight any staffing gaps, any incident reports to be learned from, any issues that required fixing and any events including teachings for the week ahead. The teams were encouraged to email suggestions or issues to be raised for the week or to approach in person with information to add. The safety briefing was implemented using change theory. Effect: The safety briefing has been trialled over 6 weeks and has received a good buy in from staff across specialties. The aim is to embed this safety briefing into a weekly meeting using the PDSA cycle. There will be a second survey in one month to assess the efficacy of the safety briefing and to continue to improve the delivery of information. The project will be presented at the next clinical governance briefing to attract wider feedback and input from across the trust. Lessons: The briefing displays promise as a tool to improve vigilance and communication in a busy multi-disciplinary unit. We have learned about how to implement quality improvement and about the culture of our hospital - how hierarchy influences change. We demonstrate how to implement change through a grassroots process, using a junior led briefing to improve the efficiency, safety, and communication in the workplace.

Keywords: briefing, communication, safety, team

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Authors: Yaser Miaji, Mohammed Aloryani

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The rise of recent applications in everyday implementation like videoconferencing, online recreation and voice speech communication leads to pressing the need for novel mechanism and policy to serve this steep improvement within the application itself and users‟ wants. This diversity in web traffics needs some classification and prioritization of the traffics since some traffics merit abundant attention with less delay and loss, than others. This research is intended to reinforce the mechanism by analysing the performance in application according to the proposed mechanism implemented. The mechanism used is quite direct and analytical. The mechanism is implemented by modifying the queue limit in the algorithm.

Keywords: traffic classification, IPv6, internet, application categorization

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Authors: Ji-yang Yu, Dan Huang, Guo-ping Feng, Xin Li, Lu-yuan Wang

Abstract:

The navigation satellite network, especially the Beidou MEO Constellation, can relay data effectively with wide coverage and is applied in navigation, detection, and position widely. But the constellation has not been completed, and the amount of satellites on-board is not enough to cover the earth, which makes the data-relay disrupted or delayed in the transition process. The data-relay function needs to tolerant the delay or disruption in some extension, which make the Beidou MEO Constellation a delay/disruption-tolerant network (DTN). The traditional DTN designs mainly employ the relay table as the basic of data path schedule computing. But in practical application, especially in critical condition, such as the war-time or the infliction heavy losses on the constellation, parts of the nodes may become invalid, then the traditional DTN design could be useless. Furthermore, when transmitting the critical message in the navigation system, the maximum priority strategy is used, but the nodes still inquiry the relay table to design the path, which makes the delay more than minutes. Under this circumstances, it needs a function which could compute the optimum data path on-board in real-time according to the constellation states. The on-board critical message transmission design for navigation satellite delay/disruption-tolerant network (DTN) is proposed, according to the characteristics of navigation satellite network. With the real-time computation of parameters in the network link, the least-delay transition path is deduced to retransmit the critical message in urgent conditions. First, the DTN model for constellation is established based on the time-varying matrix (TVM) instead of the time-varying graph (TVG); then, the least transition delay data path is deduced with the parameters of the current node; at last, the critical message transits to the next best node. For the on-board real-time computing, the time delay and misjudges of constellation states in ground stations are eliminated, and the residual information channel for each node can be used flexibly. Compare with the minute’s delay of traditional DTN; the proposed transmits the critical message in seconds, which improves the re-transition efficiency. The hardware is implemented in FPGA based on the proposed model, and the tests prove the validity.

Keywords: critical message, DTN, navigation satellite, on-board, real-time

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Authors: Zyad Elkhadir, Khalid Chougdali, Mohammed Benattou

Abstract:

Most of the existing intrusion detection systems works on quantitative network traffic data with many irrelevant and redundant features, which makes detection process more time’s consuming and inaccurate. A several feature extraction methods, such as linear discriminant analysis (LDA), have been proposed. However, LDA suffers from the small sample size (SSS) problem which occurs when the number of the training samples is small compared with the samples dimension. Hence, classical LDA cannot be applied directly for high dimensional data such as network traffic data. In this paper, we propose two solutions to solve SSS problem for LDA and apply them to a network IDS. The first method, reduce the original dimension data using principal component analysis (PCA) and then apply LDA. In the second solution, we propose to use the pseudo inverse to avoid singularity of within-class scatter matrix due to SSS problem. After that, the KNN algorithm is used for classification process. We have chosen two known datasets KDDcup99 and NSLKDD for testing the proposed approaches. Results showed that the classification accuracy of (PCA+LDA) method outperforms clearly the pseudo inverse LDA method when we have large training data.

Keywords: LDA, Pseudoinverse, PCA, IDS, NSL-KDD, KDDcup99

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Authors: S. Neelima, P. S. Subramanyam

Abstract:

The major tool for distribution planning is load forecasting, which is the anticipation of the load in advance. Artificial neural networks have found wide applications in load forecasting to obtain an efficient strategy for planning and management. In this paper, the application of neural networks to study the design of short term load forecasting (STLF) Systems was explored. Our work presents a pragmatic methodology for short term load forecasting (STLF) using proposed two-stage model of wavelet transform (WT) and artificial neural network (ANN). It is a two-stage prediction system which involves wavelet decomposition of input data at the first stage and the decomposed data with another input is trained using a separate neural network to forecast the load. The forecasted load is obtained by reconstruction of the decomposed data. The hybrid model has been trained and validated using load data from Telangana State Electricity Board.

Keywords: electrical distribution systems, wavelet transform (WT), short term load forecasting (STLF), artificial neural network (ANN)

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Authors: Redouane Tlemsani, Abdelkader Benyettou

Abstract:

Work is in on line Arabic character recognition and the principal motivation is to study the Arab manuscript with on line technology. This system is a Markovian system, which one can see as like a Dynamic Bayesian Network (DBN). One of the major interests of these systems resides in the complete models training (topology and parameters) starting from training data. Our approach is based on the dynamic Bayesian Networks formalism. The DBNs theory is a Bayesians networks generalization to the dynamic processes. Among our objective, amounts finding better parameters, which represent the links (dependences) between dynamic network variables. In applications in pattern recognition, one will carry out the fixing of the structure, which obliges us to admit some strong assumptions (for example independence between some variables). Our application will relate to the Arabic isolated characters on line recognition using our laboratory database: NOUN. A neural tester proposed for DBN external optimization. The DBN scores and DBN mixed are respectively 70.24% and 62.50%, which lets predict their further development; other approaches taking account time were considered and implemented until obtaining a significant recognition rate 94.79%.

Keywords: Arabic on line character recognition, dynamic Bayesian network, pattern recognition, computer vision

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Authors: Agampodi Promoda Perera, Yong Shin, Mi Kyoung Park

Abstract:

The successful detection of pathogenic bacteria in blood provides important information for early detection, diagnosis and the prevention and treatment of infectious diseases. Silicon microring resonators are refractive-index-based optical biosensors that provide highly sensitive, label-free, real-time multiplexed detection of biomolecules. We demonstrate the technique of using GO (graphene oxide) to enhance the signal output of the silicon microring optical sensor. The activated carboxylic groups in GO molecules bind directly to single stranded DNA with an amino modified 5’ end. This conjugation amplifies the shift in resonant wavelength in a real-time manner. We designed a capture probe for strain Staphylococcus aureus of 21 bp and a longer complementary target sequence of 70 bp. The mismatched target sequence we used was of Streptococcus agalactiae of 70 bp. GO is added after the complementary binding of the probe and target. GO conjugates to the unbound single stranded segment of the target and increase the wavelength shift on the silicon microring resonator. Furthermore, our results show that GO could successfully differentiate between the mismatched DNA sequences from the complementary DNA sequence. Therefore, the proposed concept could effectively enhance sensitivity of pathogen detection sensors.

Keywords: label free biosensor, pathogenic bacteria, graphene oxide, diagnosis

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Authors: Sima Aznavi, Poria Fajri, Hanif Livani

Abstract:

Smart homes are small energy systems which may be equipped with renewable energy sources, storage devices, and loads. Energy management strategy plays a main role in the efficient operation of smart homes. Effective energy scheduling of the renewable energy sources and storage devices guarantees efficient energy management in households while reducing the energy imports from the grid. Nevertheless, despite such strategies, independently day ahead energy schedules for multiple households can cause undesired effects such as high power exchange with the grid at certain times of the day. Therefore, the interactions between multiple smart home day ahead energy projections is a challenging issue in a smart grid system and if not managed appropriately, the imported energy from the power network can impose additional burden on the distribution grid. In this paper, a central energy management strategy for a network consisting of multiple households each equipped with renewable energy sources, storage devices, and Plug-in Electric Vehicles (PEV) is proposed. The decision-making strategy alongside the smart home energy management system, minimizes the energy purchase cost of the end users, while at the same time reducing the stress on the utility grid. In this approach, the smart home energy management system determines different operating scenarios based on the forecasted household daily load and the components connected to the household with the objective of minimizing the end user overall cost. Then, selected projections for each household that are within the same cost range are sent to the central decision-making system. The central controller then organizes the schedules to reduce the overall peak to average ratio of the total imported energy from the grid. To validate this approach simulations are carried out for a network of five smart homes with different load requirements and the results confirm that by applying the proposed central energy management strategy, the overall power demand from the grid can be significantly flattened. This is an effective approach to alleviate the stress on the network by distributing its energy to a network of multiple households over a 24- hour period.

Keywords: energy management, renewable energy sources, smart grid, smart home

Procedia PDF Downloads 253