Abstract: This paper presents an application of Artificial Neural
Network (ANN) algorithm for improving power system voltage
stability. The training data is obtained by solving several normal and
abnormal conditions using the Linear Programming technique. The
selected objective function gives minimum deviation of the reactive
power control variables, which leads to the maximization of
minimum Eigen value of load flow Jacobian. The considered reactive
power control variables are switchable VAR compensators, OLTC
transformers and excitation of generators. The method has been
implemented on a modified IEEE 30-bus test system. The results
obtain from the test clearly show that the trained neural network is
capable of improving the voltage stability in power system with a
high level of precision and speed.
Abstract: This study found that most corporate personnel are
using social media to communicate with colleagues to make the
process of working more efficient. Complete satisfaction occurred on
the use of security within the University’s computer network. The
social network usage for communication, collaboration,
entertainment and demonstrating concerns accounted for fifty percent
of variance to predict interpersonal relationships of corporate
personnel. This evaluation on the effectiveness of social networking
involved 213 corporate personnel’s. The data was collected by
questionnaires. This data was analyzed by using percentage, mean,
and standard deviation.
The results from the analysis and the effectiveness of using online
social networks were derived from the attitude of private users and
safety data within the security system. The results showed that the
effectiveness on the use of an online social network for corporate
personnel of Suan Sunandha Rajabhat University was specifically at
a good level, and the overall effects of each aspect was (Ẋ=3.11).
Abstract: Implementation of advanced technologies requires
sophisticated instruments that deal with the operation, control,
restoration and protection of rapidly growing power system network
under normal and abnormal conditions. Presently, the applications of
Phasor Measurement Unit (PMU) are widely found in real time
operation, monitoring, controlling and analysis of power system
network as it eliminates the various limitations of supervisory control
and data acquisition system (SCADA) conventionally used in power
system. The use of PMU data is very rapidly increasing its
importance for online and offline analysis. Wide area measurement
system (WAMS) is developed as new technology by use of multiple
PMUs in power system. The present paper proposes a model of
Matlab based PMU using Discrete Fourier Transform (DFT)
algorithm and evaluation of its operation under different
contingencies. In this paper, PMU based two bus system having
WAMS network is presented as a case study.
Abstract: The new era of digital communication has brought up
many challenges that network operators need to overcome. The high
demand of mobile data rates require improved networks, which is a
challenge for the operators in terms of maintaining the quality of
experience (QoE) for their consumers. In live video transmission,
there is a sheer need for live surveillance of the videos in order to
maintain the quality of the network. For this purpose objective
algorithms are employed to monitor the quality of the videos that are
transmitted over a network. In order to test these objective algorithms,
subjective quality assessment of the streamed videos is required, as the
human eye is the best source of perceptual assessment. In this paper we
have conducted subjective evaluation of videos with varying spatial
and temporal impairments. These videos were impaired with frame
freezing distortions so that the impact of frame freezing on the quality
of experience could be studied. We present subjective Mean Opinion
Score (MOS) for these videos that can be used for fine tuning the
objective algorithms for video quality assessment.
Abstract: In this study, three robust predicting methods, namely artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) were used for computing the resonant frequency of A-shaped compact microstrip antennas (ACMAs) operating at UHF band. Firstly, the resonant frequencies of 144 ACMAs with various dimensions and electrical parameters were simulated with the help of IE3D™ based on method of moment (MoM). The ANN, ANFIS and SVM models for computing the resonant frequency were then built by considering the simulation data. 124 simulated ACMAs were utilized for training and the remaining 20 ACMAs were used for testing the ANN, ANFIS and SVM models. The performance of the ANN, ANFIS and SVM models are compared in the training and test process. The average percentage errors (APE) regarding the computed resonant frequencies for training of the ANN, ANFIS and SVM were obtained as 0.457%, 0.399% and 0.600%, respectively. The constructed models were then tested and APE values as 0.601% for ANN, 0.744% for ANFIS and 0.623% for SVM were achieved. The results obtained here show that ANN, ANFIS and SVM methods can be successfully applied to compute the resonant frequency of ACMAs, since they are useful and versatile methods that yield accurate results.
Abstract: This paper proposes a novel heuristic algorithm that aims to determine the best size and location of distributed generators in unbalanced distribution networks. The proposed heuristic algorithm can deal with the planning cases where power loss is to be optimized without violating the system practical constraints. The distributed generation units in the proposed algorithm is modeled as voltage controlled node with the flexibility to be converted to constant power factor node in case of reactive power limit violation. The proposed algorithm is implemented in MATLAB and tested on the IEEE 37 -node feeder. The results obtained show the effectiveness of the proposed algorithm.
Abstract: This paper focuses on a critical component of the
situational awareness (SA), the control of autonomous vertical flight
for vectored thrust aerial vehicle (VTAV). With the SA strategy, we
proposed a neural network motion control procedure to address the
dynamics variation and performance requirement difference of flight
trajectory for a VTAV. This control strategy with using of NARMAL2
neurocontroller for chosen model of VTAV has been verified by
simulation of take-off and forward maneuvers using software
package Simulink and demonstrated good performance for fast
stabilization of motors, consequently, fast SA with economy in
energy can be asserted during search-and-rescue operations.
Abstract: Designing cost-efficient, secure network protocols for
Wireless Sensor Networks (WSNs) is a challenging problem because
sensors are resource-limited wireless devices. Security services such
as authentication and improved pairwise key establishment are
critical to high efficient networks with sensor nodes. For sensor
nodes to correspond securely with each other efficiently, usage of
cryptographic techniques is necessary. In this paper, two key
predistribution schemes that enable a mobile sink to establish a
secure data-communication link, on the fly, with any sensor nodes.
The intermediate nodes along the path to the sink are able to verify
the authenticity and integrity of the incoming packets using a
predicted value of the key generated by the sender’s essential power.
The proposed schemes are based on the pairwise key with the mobile
sink, our analytical results clearly show that our schemes perform
better in terms of network resilience to node capture than existing
schemes if used in wireless sensor networks with mobile sinks.
Abstract: A sensory network consists of multiple detection
locations called sensor nodes, each of which is tiny, featherweight
and portable. A single path routing protocols in wireless sensor
network can lead to holes in the network, since only the nodes
present in the single path is used for the data transmission. Apart
from the advantages like reduced computation, complexity and
resource utilization, there are some drawbacks like throughput,
increased traffic load and delay in data delivery. Therefore, multipath
routing protocols are preferred for WSN. Distributing the traffic
among multiple paths increases the network lifetime. We propose a
scheme, for the data to be transmitted through a dominant path to
save energy. In order to obtain a high delivery ratio, a basic route
reconstruction protocol is utilized to reconstruct the path whenever a
failure is detected. A basic reconstruction routing (BRR) algorithm is
proposed, in which a node can leap over path failure by using the
already existing routing information from its neighbourhood while
the composed data is transmitted from the source to the sink. In order
to save the energy and attain high data delivery ratio, data is
transmitted along a multiple path, which is achieved by BRR
algorithm whenever a failure is detected. Further, the analysis of
how the proposed protocol overcomes the drawback of the existing
protocols is presented. The performance of our protocol is compared
to AOMDV and energy efficient node-disjoint multipath routing
protocol (EENDMRP). The system is implemented using NS-2.34.
The simulation results show that the proposed protocol has high
delivery ratio with low energy consumption.
Abstract: The venture capital becomes more and more advanced
and effective source of the innovation project financing, connected
with a high-risk level. In the developed countries, it plays a key role
in transforming innovation projects into successful businesses and
creating the prosperity of the modern economy. In Russia, there are
many necessary preconditions for creation of the effective venture
investment system: the network of the public institutes for innovation
financing operates; there is a significant number of the small and
medium-sized enterprises, capable to sell production with good
market potential. However, the current system does not confirm the
necessary level of efficiency in practice that can be substantially
explained by the absence of the accurate plan of action to form the
national venture model and by the lack of experience of successful
venture deals with profitable exits in Russian economy. This paper
studies the influence of various factors on the venture industry
development by the example of the IT-sector in Russia. The choice of
the sector is based on the fact, that this segment is the main driver of
the venture capital market growth in Russia, and the necessary set of
data exists. The size of investment of the second round is used as the
dependent variable. To analyse the influence of the previous round,
such determinant as the volume of the previous (first) round
investments is used. There is also used a dummy variable in
regression to examine that the participation of an investor with high
reputation and experience in the previous round can influence the size
of the next investment round. The regression analysis of short-term
interrelations between studied variables reveals prevailing influence
of the volume of the first round investments on the venture
investments volume of the second round. The most important
determinant of the value of the second-round investment is the value
of first–round investment, so it means that the most competitive on
the Russian market are the start-up teams that can attract more money
on the start, and the target market growth is not the factor of crucial
importance. This supports the point of view that VC in Russia is
driven by endogenous factors and not by exogenous ones that are
based on global market growth.
Abstract: Security can be defined as the degree of resistance to, or protection from harm. It applies to any vulnerable and valuable assets, such as persons, dwellings, communities, nations or organizations. Cybercrime is any crime committed or facilitated via the Internet. It is any criminal activity involving computers and networks. It can range from fraud to unsolicited emails (spam). It includes the distant theft of government or corporate secrets through criminal trespass into remote systems around the globe. Nigeria like any other nations of the world is currently having her own share of the menace that has been used even as tools by terrorists. This paper is an attempt at presenting cyber security as an issue that requires a coordinated national response. It also acknowledges and advocates the key roles to be played by stakeholders and the importance of forging strong partnerships to prevent and tackle cybercrime in Nigeria.
Abstract: To explore how the brain may recognise objects in its
general,accurate and energy-efficient manner, this paper proposes the
use of a neuromorphic hardware system formed from a Dynamic
Video Sensor (DVS) silicon retina in concert with the SpiNNaker
real-time Spiking Neural Network (SNN) simulator. As a first step
in the exploration on this platform a recognition system for dynamic
hand postures is developed, enabling the study of the methods used
in the visual pathways of the brain. Inspired by the behaviours of
the primary visual cortex, Convolutional Neural Networks (CNNs)
are modelled using both linear perceptrons and spiking Leaky
Integrate-and-Fire (LIF) neurons.
In this study’s largest configuration using these approaches, a
network of 74,210 neurons and 15,216,512 synapses is created and
operated in real-time using 290 SpiNNaker processor cores in parallel
and with 93.0% accuracy. A smaller network using only 1/10th of the
resources is also created, again operating in real-time, and it is able
to recognise the postures with an accuracy of around 86.4% - only
6.6% lower than the much larger system. The recognition rate of the
smaller network developed on this neuromorphic system is sufficient
for a successful hand posture recognition system, and demonstrates
a much improved cost to performance trade-off in its approach.
Abstract: Distributed applications deployed on LEO satellites
and ground stations require substantial communication between
different members in a constellation to overcome the earth
coverage barriers imposed by GEOs. Applications running on LEO
constellations suffer the earth line-of-sight blockage effect. They
need adequate lab testing before launching to space. We propose
a scalable cloud-based network simulation framework to simulate
problems created by the earth line-of-sight blockage. The framework
utilized cloud IaaS virtual machines to simulate LEO satellites
and ground stations distributed software. A factorial ANOVA
statistical analysis is conducted to measure simulator overhead on
overall communication performance. The results showed a very low
simulator communication overhead. Consequently, the simulation
framework is proposed as a candidate for testing LEO constellations
with distributed software in the lab before space launch.
Abstract: This article proposes a new method for application in
communication circuit systems that increase efficiency, PAE, output
power and gain in the circuit. The proposed method is based on a
combination of switching class-E and class-J and has been termed
class-EJ. This method was investigated using both theory and
simulation to confirm ∼72% PAE and output power of >39dBm. The
combination and design of the proposed power amplifier accrues gain
of over 15dB in the 2.9 to 3.5GHz frequency bandwidth. This circuit
was designed using MOSFET and high power transistors. The loadand
source-pull method achieved the best input and output networks
using lumped elements. The proposed technique was investigated for
fundamental and second harmonics having desirable amplitudes for
the output signal.
Abstract: The Trustworthy link failure recovery algorithm is
introduced in this paper, to provide the forwarding continuity even
with compound link failures. The ephemeral failures are common in
IP networks and it also has some proposals based on local rerouting.
To ensure forwarding continuity, we are introducing the compound
link failure recovery algorithm, even with compound link failures.
For forwarding the information, each packet carries a blacklist, which
is a min set of failed links encountered along its path, and the next
hop is chosen by excluding the blacklisted links. Our proposed
method describes how it can be applied to ensure forwarding to all
reachable destinations in case of any two or more link or node
failures in the network. After simulating with NS2 contains lot of
samples proved that the proposed protocol achieves exceptional
concert even under elevated node mobility using Trustworthy link
Failure Recovery Algorithm.
Abstract: The 5th generation of mobile networks is term used in
various research papers and projects to identify the next major phase
of mobile telecommunications standards. 5G wireless networks will
support higher peak data rate, lower latency and provide best
connections with QoS guarantees.
In this article, we discuss various promising technologies for 5G
wireless communication systems, such as IPv6 support, World Wide
Wireless Web (WWWW), Dynamic Adhoc Wireless Networks
(DAWN), BEAM DIVISION MULTIPLE ACCESS (BDMA), Cloud
Computing, cognitive radio technology and FBMC/OQAM.
This paper is organized as follows: First, we will give introduction
to 5G systems, present some goals and requirements of 5G. In the
next, basic differences between 4G and 5G are given, after we talk
about key technology innovations of 5G systems and finally we will
conclude in last Section.
Abstract: This paper presents an optimization method for
reducing the number of input channels and the complexity of the
feed-forward NARX neural network (NN) without compromising the
accuracy of the NN model. By utilizing the correlation analysis
method, the most significant regressors are selected to form the input
layer of the NN structure. An application of vehicle dynamic model
identification is also presented in this paper to demonstrate the
optimization technique and the optimal input layer structure and the
optimal number of neurons for the neural network is investigated.
Abstract: This paper examines the system protection for cyber-physical
systems (CPS). CPS are particularly characterized by their
networking system components. This means they are able to adapt to
the needs of their users and its environment. With this ability, CPS
have new, specific requirements on the protection against anti-counterfeiting,
know-how loss and manipulation. They increase the
requirements on system protection because piracy attacks can be
more diverse, for example because of an increasing number of
interfaces or through the networking abilities. The new requirements
were identified and in a next step matched with existing protective
measures. Due to the found gap the development of new protection
measures has to be forced to close this gap. Moreover a comparison
of the effectiveness between selected measures was realized and the
first results are presented in this paper.
Abstract: In this paper, a new concept of closed-loop design for a
product is presented. The closed-loop design model is developed by
integrating forward design and reverse design. Based on this new
concept, a closed-loop design model for sustainable manufacturing by
integrated evaluation of forward design, reverse design, and green
manufacturing using a fuzzy analytic network process is developed. In
the design stage of a product, with a given product requirement and
objective, there can be different ways to design the detailed
components and specifications. Therefore, there can be different
design cases to achieve the same product requirement and objective.
Subsequently, in the design evaluation stage, it is required to analyze
and evaluate the different design cases. The purpose of this research is
to develop a model for evaluating the design cases by integrated
evaluating the criteria in forward design, reverse design, and green
manufacturing. A fuzzy analytic network process method is presented
for integrated evaluation of the criteria in the three models. The
comparison matrices for evaluating the criteria in the three groups are
established. The total relational values among the three groups
represent the total relational effects. In applications, a super matrix
model is created and the total relational values can be used to evaluate
the design cases for decision-making to select the final design case. An
example product is demonstrated in this presentation. It shows that the
model is useful for integrated evaluation of forward design, reverse
design, and green manufacturing to achieve a closed-loop design for
sustainable manufacturing objective.
Abstract: Some plants of genus Schinus have been used in the
folk medicine as topical antiseptic, digestive, purgative, diuretic,
analgesic or antidepressant, and also for respiratory and urinary
infections. Chemical composition of essential oils of S. molle and S.
terebinthifolius had been evaluated and presented high variability
according with the part of the plant studied and with the geographic
and climatic regions. The pharmacological properties, namely
antimicrobial, anti-tumoural and anti-inflammatory activities are
conditioned by chemical composition of essential oils. Taking into
account the difficulty to infer the pharmacological properties of
Schinus essential oils without hard experimental approach, this work
will focus on the development of a decision support system, in terms
of its knowledge representation and reasoning procedures, under a
formal framework based on Logic Programming, complemented with
an approach to computing centered on Artificial Neural Networks
and the respective Degree-of-Confidence that one has on such an
occurrence.