Electromagnetic Wave Propagation Equations in 2D by Finite Difference Method
In this paper, the techniques to solve time dependent electromagnetic wave propagation equations based on the Finite Difference Method (FDM) are proposed by comparing the results with Finite Element Method (FEM) in 2D while discussing some special simulation examples. Here, 2D dynamical wave equations for lossy media, even with a constant source, are discussed for establishing symbolic manipulation of wave propagation problems. The main objective of this contribution is to introduce a comparative study of two suitable numerical methods and to show that both methods can be applied effectively and efficiently to all types of wave propagation problems, both linear and nonlinear cases, by using symbolic computation. However, the results show that the FDM is more appropriate for solving the nonlinear cases in the symbolic solution. Furthermore, some specific complex domain examples of the comparison of electromagnetic waves equations are considered. Calculations are performed through Mathematica software by making some useful contribution to the programme and leveraging symbolic evaluations of FEM and FDM.
An IM-COH Algorithm Neural Network Optimization with Cuckoo Search Algorithm for Time Series Samples
Back propagation algorithm (BP) is a widely used
technique in artificial neural network and has been used as a tool
for solving the time series problems, such as decreasing training
time, maximizing the ability to fall into local minima, and optimizing
sensitivity of the initial weights and bias. This paper proposes an
improvement of a BP technique which is called IM-COH algorithm
(IM-COH). By combining IM-COH algorithm with cuckoo search
algorithm (CS), the result is cuckoo search improved control output
hidden layer algorithm (CS-IM-COH). This new algorithm has a
better ability in optimizing sensitivity of the initial weights and bias
than the original BP algorithm. In this research, the algorithm of
CS-IM-COH is compared with the original BP, the IM-COH, and the
original BP with CS (CS-BP). Furthermore, the selected benchmarks,
four time series samples, are shown in this research for illustration.
The research shows that the CS-IM-COH algorithm give the best
forecasting results compared with the selected samples.
The Influence of Disturbances Generated by Arc Furnaces on the Power Quality
The paper presents the impact of work on the electric arc furnace. Arc equipment is one of the largest receivers powered by the power system. Electric arc disturbances arising during melting process occurring in these furnaces are the cause of an abrupt change of the passive power of furnaces. Currents drawn by these devices undergo an abrupt change, which in turn cause voltage fluctuations and light flicker. The quantitative evaluation of the voltage fluctuations is now the basic criterion of assessment of an influence of unquiet receiver on the supplying net. The paper presents the method of determination of range of voltage fluctuations and light flicker at parallel operation of arc devices. The results of measurements of voltage fluctuations and light flicker indicators recorded in power supply networks of steelworks were presented, with different number of parallel arc devices. Measurements of energy quality parameters were aimed at verifying the proposed method in practice. It was also analyzed changes in other parameters of electricity: the content of higher harmonics, asymmetry, voltage dips.
Development and Range Testing of a LoRaWAN System in an Urban Environment
This paper describes the construction and operation of an experimental LoRaWAN network surrounding the University of Southampton in the United Kingdom. Following successful installation, an experimental node design is built and characterised, with particular emphasis on radio range. Several configurations are investigated, including different data rates, and varying heights of node. It is concluded that although range can be great (over 8 km in this case), environmental topology is critical. However, shorter range implementations, up to about 2 km in an urban environment, are relatively insensitive although care is still needed. The example node and the relatively simple base station reported demonstrate that LoraWan can be a very low cost and practical solution to Internet of Things type applications for distributed monitoring systems with sensors spread over distances of several km.
Ontology-Based Backpropagation Neural Network Classification and Reasoning Strategy for NoSQL and SQL Databases
Big data applications have become an imperative for many fields. Many researchers have been devoted into increasing correct rates and reducing time complexities. Hence, the study designs and proposes an Ontology-based backpropagation neural network classification and reasoning strategy for NoSQL big data applications, which is called ON4NoSQL. ON4NoSQL is responsible for enhancing the performances of classifications in NoSQL and SQL databases to build up mass behavior models. Mass behavior models are made by MapReduce techniques and Hadoop distributed file system based on Hadoop service platform. The reference engine of ON4NoSQL is the ontology-based backpropagation neural network classification and reasoning strategy. Simulation results indicate that ON4NoSQL can efficiently achieve to construct a high performance environment for data storing, searching, and retrieving.
Long Wavelength Coherent Pulse of Sound Propagating in Granular Media
A mechanical wave or vibration propagating through
granular media exhibits a specific signature in time. A coherent
pulse or wavefront arrives first with multiply scattered waves (coda)
arriving later. The coherent pulse is micro-structure independent i.e.
it depends only on the bulk properties of the disordered granular
sample, the sound wave velocity of the granular sample and hence
bulk and shear moduli. The coherent wavefront attenuates (decreases
in amplitude) and broadens with distance from its source. The
pulse attenuation and broadening effects are affected by disorder
(polydispersity; contrast in size of the granules) and have often been
attributed to dispersion and scattering. To study the effect of disorder
and initial amplitude (non-linearity) of the pulse imparted to the
system on the coherent wavefront, numerical simulations have been
carried out on one-dimensional sets of particles (granular chains).
The interaction force between the particles is given by a Hertzian
contact model. The sizes of particles have been selected randomly
from a Gaussian distribution, where the standard deviation of this
distribution is the relevant parameter that quantifies the effect of
disorder on the coherent wavefront. Since, the coherent wavefront is
system configuration independent, ensemble averaging has been used
for improving the signal quality of the coherent pulse and removing
the multiply scattered waves. The results concerning the width of the
coherent wavefront have been formulated in terms of scaling laws. An
experimental set-up of photoelastic particles constituting a granular
chain is proposed to validate the numerical results.
A Distributed Mobile Agent Based on Intrusion Detection System for MANET
This study is about an algorithmic dependence of Artificial Neural Network on Multilayer Perceptron (MPL) pertaining to the classification and clustering presentations for Mobile Adhoc Network vulnerabilities. Moreover, mobile ad hoc network (MANET) is ubiquitous intelligent internetworking devices in which it has the ability to detect their environment using an autonomous system of mobile nodes that are connected via wireless links. Security affairs are the most important subject in MANET due to the easy penetrative scenarios occurred in such an auto configuration network. One of the powerful techniques used for inspecting the network packets is Intrusion Detection System (IDS); in this article, we are going to show the effectiveness of artificial neural networks used as a machine learning along with stochastic approach (information gain) to classify the malicious behaviors in simulated network with respect to different IDS techniques. The monitoring agent is responsible for detection inference engine, the audit data is collected from collecting agent by simulating the node attack and contrasted outputs with normal behaviors of the framework, whenever. In the event that there is any deviation from the ordinary behaviors then the monitoring agent is considered this event as an attack , in this article we are going to demonstrate the signature-based IDS approach in a MANET by implementing the back propagation algorithm over ensemble-based Traffic Table (TT), thus the signature of malicious behaviors or undesirable activities are often significantly prognosticated and efficiently figured out, by increasing the parametric set-up of Back propagation algorithm during the experimental results which empirically shown its effectiveness for the ratio of detection index up to 98.6 percentage. Consequently it is proved in empirical results in this article, the performance matrices are also being included in this article with Xgraph screen show by different through puts like Packet Delivery Ratio (PDR), Through Put(TP), and Average Delay(AD).
Analysis of Joint Source Channel LDPC Coding for Correlated Sources Transmission over Noisy Channels
In this paper, a Joint Source Channel coding scheme
based on LDPC codes is investigated. We consider two concatenated
LDPC codes, one allows to compress a correlated source and the
second to protect it against channel degradations. The original
information can be reconstructed at the receiver by a joint decoder,
where the source decoder and the channel decoder run in parallel by
transferring extrinsic information. We investigate the performance of
the JSC LDPC code in terms of Bit-Error Rate (BER) in the case
of transmission over an Additive White Gaussian Noise (AWGN)
channel, and for different source and channel rate parameters.
We emphasize how JSC LDPC presents a performance tradeoff
depending on the channel state and on the source correlation. We
show that, the JSC LDPC is an efficient solution for a relatively
low Signal-to-Noise Ratio (SNR) channel, especially with highly
correlated sources. Finally, a source-channel rate optimization has
to be applied to guarantee the best JSC LDPC system performance
for a given channel.
Nuclear Fuel Safety Threshold Determined by Logistic Regression Plus Uncertainty
Analysis of the uncertainty quantification related to nuclear safety margins applied to the nuclear reactor is an important concept to prevent future radioactive accidents. The nuclear fuel performance code may involve the tolerance level determined by traditional deterministic models producing acceptable results at burn cycles under 62 GWd/MTU. The behavior of nuclear fuel can simulate applying a series of material properties under irradiation and physics models to calculate the safety limits. In this study, theoretical predictions of nuclear fuel failure under transient conditions investigate extended radiation cycles at 75 GWd/MTU, considering the behavior of fuel rods in light-water reactors under reactivity accident conditions. The fuel pellet can melt due to the quick increase of reactivity during a transient. Large power excursions in the reactor are the subject of interest bringing to a treatment that is known as the Fuchs-Hansen model. The point kinetic neutron equations show similar characteristics of non-linear differential equations. In this investigation, the multivariate logistic regression is employed to a probabilistic forecast of fuel failure. A comparison of computational simulation and experimental results was acceptable. The experiments carried out use the pre-irradiated fuels rods subjected to a rapid energy pulse which exhibits the same behavior during a nuclear accident. The propagation of uncertainty utilizes the Wilk's formulation. The variables chosen as essential to failure prediction were the fuel burnup, the applied peak power, the pulse width, the oxidation layer thickness, and the cladding type.
Influence of Maximum Fatigue Load on Probabilistic Aspect of Fatigue Crack Propagation Life at Specified Grown Crack in Magnesium Alloys
The principal purpose of this paper is to find the influence of maximum fatigue load on the probabilistic aspect of fatigue crack propagation life at a specified grown crack in magnesium alloys. The experiments of fatigue crack propagation are carried out in laboratory air under different conditions of the maximum fatigue loads to obtain the fatigue crack propagation data for the statistical analysis. In order to analyze the probabilistic aspect of fatigue crack propagation life, the goodness-of fit test for probability distribution of the fatigue crack propagation life at a specified grown crack is implemented through Anderson-Darling test. The good probability distribution of the fatigue crack propagation life is also verified under the conditions of the maximum fatigue loads.
Reliable Line-of-Sight and Non-Line-of-Sight Propagation Channel Identification in Ultra-Wideband Wireless Networks
The paper addresses the problem of line-of-sight (LOS) vs. non-line-of-sight (NLOS) propagation link identification in ultra-wideband (UWB) wireless networks, which is necessary for improving the accuracy of radiolocation and positioning applications. A LOS/NLOS likelihood hypothesis testing approach is applied based on exploiting distinctive statistical features of the channel impulse response (CIR) using parameters related to the “skewness” of the CIR and its root mean square (RMS) delay spread. A log-normal fit is presented for the probability densities of the CIR parameters. Simulation results show that different environments (residential, office, outdoor, etc.) have measurable differences in their CIR parameters’ statistics, which is then exploited in determining the nature of the propagation channels. Correct LOS/NLOS channel identification rates exceeding 90% are shown to be achievable for most types of environments. Additional improvement is also obtained by combining both CIR skewness and RMS delay statistics.
Wave Interaction with Defects in Pressurized Composite Structures
A wave finite element (WFE) and finite element
(FE) based computational method is presented by which the
dispersion properties as well as the wave interaction coefficients for
one-dimensional structural system can be predicted. The structural
system is discretized as a system comprising a number of waveguides
connected by a coupling joint. Uniform nodes are ensured at the
interfaces of the coupling element with each waveguide. Then,
equilibrium and continuity conditions are enforced at the interfaces.
Wave propagation properties of each waveguide are calculated using
the WFE method and the coupling element is modelled using the
FE method. The scattering of waves through the coupling element,
on which damage is modelled, is determined by coupling the FE and
WFE models. Furthermore, the central aim is to evaluate the effect of
pressurization on the wave dispersion and scattering characteristics
of the prestressed structural system compared to that which is not
prestressed. Numerical case studies are exhibited for two waveguides
coupled through a coupling joint.
Thermal Effect on Wave Interaction in Composite Structures
There exist a wide range of failure modes in composite
structures due to the increased usage of the structures especially in
aerospace industry. Moreover, temperature dependent wave response
of composite and layered structures have been continuously studied,
though still limited, in the last decade mainly due to the broad
operating temperature range of aerospace structures. A wave finite
element (WFE) and finite element (FE) based computational method
is presented by which the temperature dependent wave dispersion
characteristics and interaction phenomenon in composite structures
can be predicted. Initially, the temperature dependent mechanical
properties of the panel in the range of -100 ◦C to 150 ◦C are
measured experimentally using the Thermal Mechanical Analysis
(TMA). Temperature dependent wave dispersion characteristics of
each waveguide of the structural system, which is discretized as a
system of a number of waveguides coupled by a coupling element, is
calculated using the WFE approach. The wave scattering properties,
as a function of temperature, is determined by coupling the WFE
wave characteristics models of the waveguides with the full FE
modelling of the coupling element on which defect is included.
Numerical case studies are exhibited for two waveguides coupled
through a coupling element.
Improving Utilization of Sugarcane by Replacing Ordinary Propagation Material with Small Chips of Sugarcane Planted in Paper Pots
Sugarcane is an important resource for bioenergy. Fields are usually established by using 15-20 cm pieces of sugarcane stalks as propagation material. An alternative method is to use small chips with nodes from sugarcane stalks. Plants from nodes are often established in plastic pots, but plastic pots could be replaced with biodegradable paper pots. This would be a more sustainable solution, reducing labor costs and avoiding pollution with plastic. We compared the establishment of plants from nodes taken from three different part of the sugarcane plant. The nodes were planted in plastic and paper pots. There was no significant difference between plants established in the two pot types. Nodes from different part of the stalk had different sprouting capacity. Nodes from the top parts sprouted significantly better than nodes taken from the middle or nodes taken closed to the ground in two experiments. Nodes with a length of 3 cm performed better than nodes with a length of 2 cm.
Equations of Pulse Propagation in Three-Layer Structure of As2S3 Chalcogenide Plasmonic Nano-Waveguides
This research aims at obtaining the equations of pulse propagation in nonlinear plasmonic waveguides created with As2S3 chalcogenide materials. Via utilizing Helmholtz equation and first-order perturbation theory, two components of electric field are determined within frequency domain. Afterwards, the equations are formulated in time domain. The obtained equations include two coupled differential equations that considers nonlinear dispersion.
Ultra-High Frequency Passive Radar Coverage for Cars Detection in Semi-Urban Scenarios
A study of achievable coverages using passive radar systems in terrestrial traffic monitoring applications is presented. The study includes the estimation of the bistatic radar cross section of different commercial vehicle models that provide challenging low values which make detection really difficult. A semi-urban scenario is selected to evaluate the impact of excess propagation losses generated by an irregular relief. A bistatic passive radar exploiting UHF frequencies radiated by digital video broadcasting transmitters is assumed. A general method of coverage estimation using electromagnetic simulators in combination with estimated car average bistatic radar cross section is applied. In order to reduce the computational cost, hybrid solution is implemented, assuming free space for the target-receiver path but estimating the excess propagation losses for the transmitter-target one.
Investigation of Flame and Soot Propagation in Non-Air Conditioned Railway Locomotives
Propagation of fire through a non-air conditioned
railway compartment is studied by virtue of numerical simulations.
Simultaneous computational fire dynamics equations, such as
Navier-Stokes, lumped species continuity, overall mass and energy
conservation, and heat transfer are solved using finite volume based
(for radiation) and finite difference based (for all other equations)
solver, Fire Dynamics Simulator (FDS). A single coupe with an eight
berth occupancy is used to establish the numerical model, followed
by the selection of a three coupe system as the fundamental unit
of the locomotive compartment. Heat Release Rate Per Unit Area
(HRRPUA) of the initial fire is varied to consider a wide range of
compartmental fires. Parameters, such as air inlet velocity relative
to the locomotive at the windows, the level of interaction with the
ambiance and closure of middle berth are studied through a wide
range of numerical simulations. Almost all the loss of lives and
properties due to fire breakout can be attributed to the direct or
indirect exposure to flames or to the inhalation of toxic gases and
resultant suffocation due to smoke and soot. Therefore, the temporal
stature of fire and smoke are reported for each of the considered
cases which can be used in the present or extended form to develop
guidelines to be followed in case of a fire breakout.
Numerical Simulations of Fire in Typical Air Conditioned Railway Coach
Railways in India remain primary mode of transport
having one of the largest networks in the world and catering to
billions of transits yearly. Catastrophic economic damage and loss
to life is encountered over the past few decades due to fire to
locomotives. Study of fire dynamics and fire propagation plays an
important role in evacuation planning and reducing losses. Simulation
based study of propagation of fire and soot inside an air conditioned
coach of Indian locomotive is done in this paper. Finite difference
based solver, Fire Dynamic Simulator (FDS) version 6 has been
used for analysis. A single air conditioned 3 tier coupe closed to
ambient surroundings by glass windows having occupancy for 8
people is the basic unit of the domain. A system of three such
coupes combined is taken to be fundamental unit for the entire
study to resemble effect to an entire coach. Analysis of flame and
soot contours and concentrations is done corresponding to variations
in heat release rate per unit volume (HRRPUA) of fire source,
variations in conditioned air velocity being circulated inside coupes
by vents and an alternate fire initiation and propagation mechanism
via ducts. Quantitative results of fractional area in top and front
view of the three coupes under fire and smoke are obtained using
MATLAB (IMT). Present simulations and its findings will be useful
for organizations like Commission of Railway Safety and others in
designing and implementing safety and evacuation measures.
Study of Fire Propagation and Soot Flow in a Pantry Car of Railway Locomotive
Fire accidents in trains bring huge disaster to human
life and property. Evacuation becomes a major challenge in such
incidents owing to confined spaces, large passenger density and
trains moving at high speeds. The pantry car in Indian Railways
trains carry inflammable materials like cooking fuel and LPG and
electrical fittings. The pantry car is therefore highly susceptible to
fire accidents. Numerical simulations have been done in a pantry car
of Indian locomotive train using computational fluid dynamics based
software. Different scenarios of a fire outbreak have been explored
by varying Heat Release Rate per Unit Area (HRRPUA) of the fire
source, introduction of exhaust in the cooking area, and taking a
case of an air conditioned pantry car. Temporal statures of flame and
soot have been obtained for each scenario and differences have been
studied and reported. Inputs from this study can be used to assess
casualties in fire accidents in locomotive trains and development of
smoke control/detection systems in Indian trains.
Air Quality Forecast Based on Principal Component Analysis-Genetic Algorithm and Back Propagation Model
Under the circumstance of environment deterioration, people are increasingly concerned about the quality of the environment, especially air quality. As a result, it is of great value to give accurate and timely forecast of AQI (air quality index). In order to simplify influencing factors of air quality in a city, and forecast the city’s AQI tomorrow, this study used MATLAB software and adopted the method of constructing a mathematic model of PCA-GABP to provide a solution. To be specific, this study firstly made principal component analysis (PCA) of influencing factors of AQI tomorrow including aspects of weather, industry waste gas and IAQI data today. Then, we used the back propagation neural network model (BP), which is optimized by genetic algorithm (GA), to give forecast of AQI tomorrow. In order to verify validity and accuracy of PCA-GABP model’s forecast capability. The study uses two statistical indices to evaluate AQI forecast results (normalized mean square error and fractional bias). Eventually, this study reduces mean square error by optimizing individual gene structure in genetic algorithm and adjusting the parameters of back propagation model. To conclude, the performance of the model to forecast AQI is comparatively convincing and the model is expected to take positive effect in AQI forecast in the future.
Comparative Analysis of Sigmoidal Feedforward Artificial Neural Networks and Radial Basis Function Networks Approach for Localization in Wireless Sensor Networks
With the increasing use and application of Wireless Sensor Networks (WSN), need has arisen to explore them in more effective and efficient manner. An important area which can bring efficiency to WSNs is the localization process, which refers to the estimation of the position of wireless sensor nodes in an ad hoc network setting, in reference to a coordinate system that may be internal or external to the network. In this paper, we have done comparison and analysed Sigmoidal Feedforward Artificial Neural Networks (SFFANNs) and Radial Basis Function (RBF) networks for developing localization framework in WSNs. The presented work utilizes the Received Signal Strength Indicator (RSSI), measured by static node on 100 x 100 m2 grid from three anchor nodes. The comprehensive evaluation of these approaches is done using MATLAB software. The simulation results effectively demonstrate that FFANNs based sensor motes will show better localization accuracy as compared to RBF.
Development of a Complete Single Jet Common Rail Injection System Gas Dynamic Model for Hydrogen Fueled Engine with Port Injection Feeding System
Modeling of hydrogen fueled engine (H2ICE) injection system is a very important tool that can be used for explaining or predicting the effect of advanced injection strategies on combustion and emissions. In this paper, a common rail injection system (CRIS) is proposed for 4-strokes 4-cylinders hydrogen fueled engine with port injection feeding system (PIH2ICE). For this system, a numerical one-dimensional gas dynamic model is developed considering single injection event for each injector per a cycle. One-dimensional flow equations in conservation form are used to simulate wave propagation phenomenon throughout the CR (accumulator). Using this model, the effect of common rail on the injection system characteristics is clarified. These characteristics include: rail pressure, sound velocity, rail mass flow rate, injected mass flow rate and pressure drop across injectors. The interaction effects of operational conditions (engine speed and rail pressure) and geometrical features (injector hole diameter) are illustrated; and the required compromised solutions are highlighted. The CRIS is shown to be a promising enhancement for PIH2ICE.
The Cracks Propagation Monitoring of a Cantilever Beam Using Modal Analysis
Cantilever beam is a simplified sample of a lot of mechanical components used in a wide range of applications, including many industries such as gas turbine blade. Due to the nature of the operating conditions, beams are subject to variety of damages especially crack propagates. Crack propagation may lead to catastrophic failure during operation. Therefore, online detection of crack presence and its propagation is very important and may reduce possible significant cost of the whole system failure. This paper aims to investigate the effect of cracks presence and crack propagation on one end fixed beam`s vibration. A finite element model will be developed for the blade in which the modal response of the structure with and without crack will be studied.
Aspects Concerning Flame Propagation of Various Fuels in Combustion Chamber of Four Valve Engines
In this paper, results concerning flame propagation of various fuels in a particular combustion chamber with four tilted valves were elucidated. Flame propagation was represented by the evolution of spatial distribution of temperature in various cut-planes within combustion chamber while the flame front location was determined by dint of zones with maximum temperature gradient. The results presented are only a small part of broader on-going scrutinizing activity in the field of multidimensional modeling of reactive flows in combustion chambers with complicated geometries encompassing various models of turbulence, different fuels and combustion models. In the case of turbulence two different models were applied i.e. standard k-ε model of turbulence and k-ξ-f model of turbulence. In this paper flame propagation results were analyzed and presented for two different hydrocarbon fuels, such as CH4 and C8H18. In the case of combustion all differences ensuing from different turbulence models, obvious for non-reactive flows are annihilated entirely. Namely the interplay between fluid flow pattern and flame propagation is invariant as regards turbulence models and fuels applied. Namely the interplay between fluid flow pattern and flame propagation is entirely invariant as regards fuel variation indicating that the flame propagation through unburned mixture of CH4 and C8H18 fuels is not chemically controlled.
Application of Artificial Intelligence to Schedule Operability of Waterfront Facilities in Macro Tide Dominated Wide Estuarine Harbour
Mumbai, being traditionally the epicenter of India's
trade and commerce, the existing major ports such as Mumbai and
Jawaharlal Nehru Ports (JN) situated in Thane estuary are also
developing its waterfront facilities. Various developments over the
passage of decades in this region have changed the tidal flux
entering/leaving the estuary. The intake at Pir-Pau is facing the
problem of shortage of water in view of advancement of shoreline,
while jetty near Ulwe faces the problem of ship scheduling due to
existence of shallower depths between JN Port and Ulwe Bunder. In
order to solve these problems, it is inevitable to have information
about tide levels over a long duration by field measurements.
However, field measurement is a tedious and costly affair;
application of artificial intelligence was used to predict water levels
by training the network for the measured tide data for one lunar tidal
cycle. The application of two layered feed forward Artificial Neural
Network (ANN) with back-propagation training algorithms such as
Gradient Descent (GD) and Levenberg-Marquardt (LM) was used to
predict the yearly tide levels at waterfront structures namely at Ulwe
Bunder and Pir-Pau. The tide data collected at Apollo Bunder, Ulwe,
and Vashi for a period of lunar tidal cycle (2013) was used to train,
validate and test the neural networks. These trained networks having
high co-relation coefficients (R= 0.998) were used to predict the tide
at Ulwe, and Vashi for its verification with the measured tide for the
year 2000 & 2013. The results indicate that the predicted tide levels
by ANN give reasonably accurate estimation of tide. Hence, the
trained network is used to predict the yearly tide data (2015) for
Ulwe. Subsequently, the yearly tide data (2015) at Pir-Pau was
predicted by using the neural network which was trained with the
help of measured tide data (2000) of Apollo and Pir-Pau. The analysis of measured data and study reveals that: The
measured tidal data at Pir-Pau, Vashi and Ulwe indicate that there is
maximum amplification of tide by about 10-20 cm with a phase lag
of 10-20 minutes with reference to the tide at Apollo Bunder
(Mumbai). LM training algorithm is faster than GD and with increase
in number of neurons in hidden layer and the performance of the
network increases. The predicted tide levels by ANN at Pir-Pau and
Ulwe provides valuable information about the occurrence of high and
low water levels to plan the operation of pumping at Pir-Pau and
improve ship schedule at Ulwe.
Anomaly Detection with ANN and SVM for Telemedicine Networks
In recent years, a wide variety of applications are developed with Support Vector Machines -SVM- methods and Artificial Neural Networks -ANN-. In general, these methods depend on intrusion knowledge databases such as KDD99, ISCX, and CAIDA among others. New classes of detectors are generated by machine learning techniques, trained and tested over network databases. Thereafter, detectors are employed to detect anomalies in network communication scenarios according to user’s connections behavior. The first detector based on training dataset is deployed in different real-world networks with mobile and non-mobile devices to analyze the performance and accuracy over static detection. The vulnerabilities are based on previous work in telemedicine apps that were developed on the research group. This paper presents the differences on detections results between some network scenarios by applying traditional detectors deployed with artificial neural networks and support vector machines.
Propagation of Cos-Gaussian Beam in Photorefractive Crystal
A physical model for guiding the wave in
photorefractive media is studied. Propagation of cos-Gaussian beam
as the special cases of sinusoidal-Gaussian beams in photorefractive
crystal is simulated numerically by the Crank-Nicolson method in
one dimension. Results show that the beam profile deforms as the
energy transfers from the center to the tails under propagation. This
simulation approach is of significant interest for application in optical
telecommunication. The results are presented graphically and
Effect of Load Ratio on Probability Distribution of Fatigue Crack Propagation Life in Magnesium Alloys
It is necessary to predict a fatigue crack propagation
life for estimation of structural integrity. Because of an uncertainty
and a randomness of a structural behavior, it is also required to
analyze stochastic characteristics of the fatigue crack propagation life
at a specified fatigue crack size. The essential purpose of this study is to find the effect of load ratio
on probability distribution of the fatigue crack propagation life at a
specified grown crack size and to confirm the good probability
distribution in magnesium alloys under various fatigue load ratio
conditions. To investigate a stochastic crack growth behavior, fatigue
crack propagation experiments are performed in laboratory air under
several conditions of fatigue load ratio using AZ31. By Anderson-Darling test, a goodness-of-fit test for probability
distribution of the fatigue crack propagation life is performed. The
effect of load ratio on variability of fatigue crack propagation life is
Liquid Temperature Effect on Sound Propagation in Polymeric Solution with Gas Bubbles
Acoustic properties of polymeric liquids are high
sensitive to free gas traces in the form of fine bubbles. Their presence
is typical for such liquids because of chemical reactions, small
wettability of solid boundaries, trapping of air in technological
operations, etc. Liquid temperature influences essentially its
rheological properties, which may have an impact on the bubble
pulsations and sound propagation in the system. The target of the
paper is modeling of the liquid temperature effect on single bubble
dynamics and sound dispersion and attenuation in polymeric solution
with spherical gas bubbles. The basic sources of attenuation (heat
exchange between gas in microbubbles and surrounding liquid,
rheological and acoustic losses) are taken into account. It is supposed
that in the studied temperature range the interface mass transfer has a
minor effect on bubble dynamics. The results of the study indicate
that temperature raise yields enhancement of bubble pulsations and
increase in sound attenuation in the near-resonance range and may
have a strong impact on sound dispersion in the liquid-bubble
mixture at frequencies close to the resonance frequency of bubbles.
Forecast Based on an Empirical Probability Function with an Adjusted Error Using Propagation of Error
This paper addresses a cutting edge method of
business demand forecasting, based on an empirical probability
function when the historical behavior of the data is random.
Additionally, it presents error determination based on the numerical
method technique ‘propagation of errors.’ The methodology was
conducted characterization and process diagnostics demand planning
as part of the production management, then new ways to predict its
value through techniques of probability and to calculate their mistake
investigated, it was tools used numerical methods. All this based on
the behavior of the data. This analysis was determined considering
the specific business circumstances of a company in the sector of
communications, located in the city of Bogota, Colombia. In
conclusion, using this application it was possible to obtain the
adequate stock of the products required by the company to provide its
services, helping the company reduce its service time, increase the
client satisfaction rate, reduce stock which has not been in rotation
for a long time, code its inventory, and plan reorder points for the
replenishment of stock.