Abstract: Artificial Neural Networks (ANN) trained using backpropagation
(BP) algorithm are commonly used for modeling
material behavior associated with non-linear, complex or unknown
interactions among the material constituents. Despite multidisciplinary
applications of back-propagation neural networks
(BPNN), the BP algorithm possesses the inherent drawback of
getting trapped in local minima and slowly converging to a global
optimum. The paper present a hybrid artificial neural networks and
genetic algorithm approach for modeling slump of ready mix
concrete based on its design mix constituents. Genetic algorithms
(GA) global search is employed for evolving the initial weights and
biases for training of neural networks, which are further fine tuned
using the BP algorithm. The study showed that, hybrid ANN-GA
model provided consistent predictions in comparison to commonly
used BPNN model. In comparison to BPNN model, the hybrid ANNGA
model was able to reach the desired performance goal quickly.
Apart from the modeling slump of ready mix concrete, the synaptic
weights of neural networks were harnessed for analyzing the relative
importance of concrete design mix constituents on the slump value.
The sand and water constituents of the concrete design mix were
found to exhibit maximum importance on the concrete slump value.
Abstract: Control of a semi-batch polymerization reactor using
an adaptive radial basis function (RBF) neural network method is
investigated in this paper. A neural network inverse model is used to
estimate the valve position of the reactor; this method can identify the
controlled system with the RBF neural network identifier. The
weights of the adaptive PID controller are timely adjusted based on
the identification of the plant and self-learning capability of RBFNN.
A PID controller is used in the feedback control to regulate the actual
temperature by compensating the neural network inverse model
output. Simulation results show that the proposed control has strong
adaptability, robustness and satisfactory control performance and the
nonlinear system is achieved.
Abstract: In this paper, the design problem of state estimator for
neural networks with the mixed time-varying delays are investigated
by constructing appropriate Lyapunov-Krasovskii functionals and
using some effective mathematical techniques. In order to derive
several conditions to guarantee the estimation error systems to be
globally exponential stable, we transform the considered systems
into the neural-type time-delay systems. Then with a set of linear
inequalities(LMIs), we can obtain the stable criteria. Finally, three
numerical examples are given to show the effectiveness and less
conservatism of the proposed criterion.
Abstract: Tsunami early detection and warning systems have proved to be of ultimate importance, especially after the destructive tsunami that hit Japan in March 2012. Such systems are crucial to inform the authorities of any risk of a tsunami and of the degree of its danger in order to make the right decision and notify the public of the actions they need to take to save their lives. The purpose of this research is to enhance existing tsunami detection and warning systems. We first propose an automated and miniaturized model of an early tsunami detection and warning system. The model for the operation of a tsunami warning system is simulated using the data acquisition toolbox of Matlab and measurements acquired from specified internet pages due to the lack of the required real-life sensors, both seismic and hydrologic, and building a graphical user interface for the system. In the second phase of this work, we implement various satellite image filtering schemes to enhance the acquired synthetic aperture radar images of the tsunami affected region that are masked by speckle noise. This enables us to conduct a post-tsunami damage extent study and calculate the percentage damage. We conclude by proposing improvements to the existing telecommunication infrastructure of existing warning tsunami systems using a migration to IP-based networks and fiber optics links.
Abstract: In this paper, we employ a directed hypergraph model
to investigate the extent to which environmental variability influences
the set of available biochemical reactions within a living cell.
Such an approach avoids the limitations of the usual complex
network formalism by allowing for the multilateral relationships (i.e.
connections involving more than two nodes) that naturally occur
within many biological processes. More specifically, we extend the
concept of network reciprocity to complex hyper-networks, thus
enabling us to characterise a network in terms of the existence
of mutual hyper-connections, which may be considered a proxy
for metabolic network complexity. To demonstrate these ideas, we
study 115 metabolic hyper-networks of bacteria, each of which
can be classified into one of 6 increasingly varied habitats.
In particular, we found that reciprocity increases significantly
with increased environmental variability, supporting the view that
organism adaptability leads to increased complexities in the resultant
biochemical networks.
Abstract: Generally, distributed generation units refer to small-scale electric power generators that produce electricity at a site close to the customer or an electric distribution system (in parallel mode). From the customers’ point of view, a potentially lower cost, higher service reliability, high power quality, increased energy efficiency, and energy independence can be the key points of a proper DG unit. Moreover, the use of renewable types of distributed generations such as wind, photovoltaic, geothermal or hydroelectric power can also provide significant environmental benefits. Therefore, it is of crucial importance to study their impacts on the distribution networks. A marked increase in Distributed Generation (DG), associated with medium voltage distribution networks, may be expected. Nowadays, distribution networks are planned for unidirectional power flows that are peculiar to passive systems, and voltage control is carried out exclusively by varying the tap position of the HV/MV transformer. This paper will compare different DG control methods and possible network reconfiguration aimed at assessing their effect on voltage profiles.
Abstract: Intrabody communication (IBC) is a new way of transferring data using human body as a medium. Minute current can travel though human body without any harm. IBC can remove electrical wires for human area network. IBC can be also a secure communication network system unlike wireless networks which can be accessed by anyone with bad intentions. One of the IBC systems is based on frequency shift keying modulation where individual data are transmitted to the external devices for the purpose of secure access such as digital door lock. It was found that the quality of IBC data transmission was heavily dependent on ground configurations of electronic circuits. Reliable IBC transmissions were not possible when both of the transmitter and receiver used batteries as circuit power source. Transmission was reliable when power supplies were used as power source for both transmitting and receiving sites because the common ground was established through the grounds of instruments such as power supply and oscilloscope. This was due to transmission dipole size and the ground effects of floor and AC power line. If one site used battery as power source and the other site used the AC power as circuit power source, transmission was possible.
Abstract: This paper deals with the problem of delay-dependent
stability for neural networks with distributed delays. Some new
sufficient condition are derived by constructing a novel
Lyapunov-Krasovskii functional approach. The criteria are
formulated in terms of a set of linear matrix inequalities, this is
convenient for numerically checking the system stability using the
powerful MATLAB LMI Toolbox. Moreover, in order to show the
stability condition in this paper gives much less conservative results
than those in the literature, numerical examples are considered.
Abstract: This paper investigates the effect of simultaneous placement of DGs and smart meters (SMs), on voltage profile improvement in active distribution networks (ADNs). A substantial center of attention has recently been on responsive loads initiated in power system problem studies such as distributed generations (DGs). Existence of responsive loads in active distribution networks (ADNs) would have undeniable effect on sizing and siting of DGs. For this reason, an optimal framework is proposed for sizing and siting of DGs and SMs in ADNs. SMs are taken into consideration for the sake of successful implementing of demand response programs (DRPs) such as direct load control (DLC) with end-side consumers. Looking for voltage profile improvement, the optimization procedure is solved by genetic algorithm (GA) and tested on IEEE 33-bus distribution test system. Different scenarios with variations in the number of DG units, individual or simultaneous placing of DGs and SMs, and adaptive power factor (APF) mode for DGs to support reactive power have been established. The obtained results confirm the significant effect of DRPs and APF mode in determining the optimal size and site of DGs to be connected in ADN resulting to the improvement of voltage profile as well.
Abstract: Social media refers to the means of interactions
among people in which they create share, exchange and comment
contents among themselves in virtual communities and networks.
Social media or "social networking" has almost become part of our
daily lives and being tossed around over the past few years. It is like
any other media such as newspaper, radio and television but it is far
more than just about sharing information and ideas. Social
networking tools like Twitter, Facebook, Flickr and Blogs have
facilitated creation and exchange of ideas so quickly and widely than
the conventional media. This paper shows the choices,
communication, feeling comfort, time saving and effects of social
media among the people.
Abstract: Traditional Wireless Sensor Networks (WSNs) generally use static sinks to collect data from the sensor nodes via multiple forwarding. Therefore, network suffers with some problems like long message relay time, bottle neck problem which reduces the performance of the network.
Many approaches have been proposed to prevent this problem with the help of mobile sink to collect the data from the sensor nodes, but these approaches still suffer from the buffer overflow problem due to limited memory size of sensor nodes. This paper proposes an energy efficient scheme for data gathering which overcomes the buffer overflow problem. The proposed scheme creates virtual grid structure of heterogeneous nodes. Scheme has been designed for sensor nodes having variable sensing rate. Every node finds out its buffer overflow time and on the basis of this cluster heads are elected. A controlled traversing approach is used by the proposed scheme in order to transmit data to sink. The effectiveness of the proposed scheme is verified by simulation.
Abstract: In this paper, we experimentally investigate the performance of an efficient high gain triple-pass L-band Erbium-Doped Fiber (EDF) amplifier structure with a single pump source. The amplifier gain and noise figure variation with EDF pump power, input signal power and wavelengths have been investigated. The generated backward Amplified Spontaneous Emission (ASE) noise of the first amplifier stage is suppressed by using a tunable band-pass filter. The amplifier achieves a signal gain of 55 dB with low noise figure of 3.8 dB at -50 dBm input signal power. The amplifier gain shows significant improvement of 12.8 dB compared to amplifier structure without ASE suppression.
Abstract: In this paper, we propose an optimization technique
that can be used to optimize the placements of reference nodes and
improve the location determination performance for the multi-floor
building. The proposed technique is based on Simulated Annealing
algorithm (SA) and is called MSMR-M. The performance study in
this work is based on simulation. We compare other node-placement
techniques found in the literature with the optimal node-placement
solutions obtained from our optimization. The results show that using
the optimal node-placement obtained by our proposed technique can
improve the positioning error distances up to 20% better than those of
the other techniques. The proposed technique can provide an average
error distance within 1.42 meters.
Abstract: In recent years, the adoption of mobile phones has been exceptionally rapid in many parts of the world, and Tanzania is not exceptional. We are witnessing a number of new mobile network operators being licensed from time to time by Tanzania Communications Regulatory Authority (TCRA). This makes competition in the telecommunications market very stiff. All mobile phone companies are struggling to earn more new customers into their networks. This trend courses a stiff competition. The various measures are being taken by different companies including, lowering tariff, and introducing free short messages within and out of their networks, and free calls during off-peak periods. This paper is aimed at investigating the influence of tariffs on students’ mobile customers in selecting their mobile network operators. About seventy seven students from high learning institutions in Dodoma Municipality, Tanzania, participated in responding to the prepared questionnaires. The sought information was aimed at determining if tariffs influenced students into selection of their current mobile operators. The results indicate that tariffs were the major driving factor in selection of mobile operators. However, female mobile customers were found to be more easily attracted into subscribing to a mobile operator due to low tariffs, a bigger number of free short messages or discounted call charges than their fellow male customers.
Abstract: This paper presents a comparative study between two
neural network models namely General Regression Neural Network
(GRNN) and Back Propagation Neural Network (BPNN) are used
to estimate radial overcut produced during Electrical Discharge
Machining (EDM). Four input parameters have been employed:
discharge current (Ip), pulse on time (Ton), Duty fraction (Tau) and
discharge voltage (V). Recently, artificial intelligence techniques, as
it is emerged as an effective tool that could be used to replace
time consuming procedures in various scientific or engineering
applications, explicitly in prediction and estimation of the complex
and nonlinear process. The both networks are trained, and the
prediction results are tested with the unseen validation set of the
experiment and analysed. It is found that the performance of both the
networks are found to be in good agreement with average percentage
error less than 11% and the correlation coefficient obtained for the
validation data set for GRNN and BPNN is more than 91%. However,
it is much faster to train GRNN network than a BPNN and GRNN is
often more accurate than BPNN. GRNN requires more memory space
to store the model, GRNN features fast learning that does not require
an iterative procedure, and highly parallel structure. GRNN networks
are slower than multilayer perceptron networks at classifying new
cases.
Abstract: This paper presents an intensive measurement studying of the network performance analysis when IEEE 802.11g Wireless Local Area Networks (WLAN) coexisting with IEEE 802.15.4 Wireless Personal Area Network (WPAN). The measurement results show that the coexistence between both networks could increase the Frame Error Rate (FER) of the IEEE 802.15.4 networks up to 60% and it could decrease the throughputs of the IEEE 802.11g networks up to 55%.
Abstract: Indoor wireless localization systems have played an
important role to enhance context-aware services. Determining the
position of mobile objects in complex indoor environments, such as
those in multi-floor buildings, is very challenging problems. This
paper presents an effective floor estimation algorithm, which can
accurately determine the floor where mobile objects located. The
proposed algorithm is based on the confidence interval of the
summation of online Received Signal Strength (RSS) obtained from
the IEEE 802.15.4 Wireless Sensor Networks (WSN).We compare
the performance of the proposed algorithm with those of other floor
estimation algorithms in literature by conducting a real
implementation of WSN in our facility. The experimental results and
analysis showed that the proposed floor estimation algorithm
outperformed the other algorithms and provided highest percentage
of floor accuracy up to 100% with 95-percent confidence interval.
Abstract: The rapid expansion of deserts in recent decades as a result of human actions combined with climatic changes has highlighted the necessity to understand biological processes in arid environments. Whereas physical processes and the biology of flora and fauna have been relatively well studied in marginally used arid areas, knowledge of desert soil micro-organisms remains fragmentary. The objective of this study is to conduct a diversity analysis of bacterial communities in unvegetated arid soils. Several biological phenomena in hot deserts related to microbial populations and the potential use of micro-organisms for restoring hot desert environments. Dry land ecosystems have a highly heterogeneous distribution of resources, with greater nutrient concentrations and microbial densities occurring in vegetated than in bare soils. In this work, we found it useful to use techniques of artificial intelligence in their treatment especially artificial neural networks (ANN). The use of the ANN model, demonstrate his capability for addressing the complex problems of uncertainty data.
Abstract: This paper presents an Artificial Neural Network based approach for short-term load forecasting and exactly for two days ahead. Two seasons have been discussed for Iraqi power system, namely summer and winter; the hourly load demand is the most important input variables for ANN based load forecasting. The recorded daily load profile with a lead time of 1-48 hours for July and December of the year 2012 was obtained from the operation and control center that belongs to the Ministry of Iraqi electricity.
The results of the comparison show that the neural network gives a good prediction for the load forecasting and for two days ahead.
Abstract: This paper deals with the assessment of faulted area around an industrial customer connected to a particular electric grid that will cause a certain sag magnitude on this customer. The faulted (critical or exposed) area’s length is calculated by adding all line lengths in the neighborhood of the critical node (customer). The applied method is the so-called Method of Critical Distances. By using advanced short-circuit analysis, the Critical Area can be accurately calculated for radial and meshed power networks due to all symmetrical and asymmetrical faults. For the demonstration of the effectiveness of the proposed methodology, a study case is used.