Abstract: In this paper, we present a system for content-based
retrieval of large database of classified satellite images, based on
user's relevance feedback (RF).Through our proposed system, we
divide each satellite image scene into small subimages, which stored
in the database. The modified radial basis functions neural network
has important role in clustering the subimages of database according
to the Euclidean distance between the query feature vector and the
other subimages feature vectors. The advantage of using RF
technique in such queries is demonstrated by analyzing the database
retrieval results.
Abstract: This paper presents a new sufficient condition for the
existence, uniqueness and global asymptotic stability of the equilibrium point for Cohen-Grossberg neural networks with multiple time delays. The results establish a relationship between the network parameters
of the neural system independently of the delay parameters. The results are also compared with the previously reported results in
the literature.
Abstract: In this paper the development of neural network based fuzzy inference system for electricity consumption prediction is considered. The electricity consumption depends on number of factors, such as number of customers, seasons, type-s of customers, number of plants, etc. It is nonlinear process and can be described by chaotic time-series. The structure and algorithms of neuro-fuzzy system for predicting future values of electricity consumption is described. To determine the unknown coefficients of the system, the supervised learning algorithm is used. As a result of learning, the rules of neuro-fuzzy system are formed. The developed system is applied for predicting future values of electricity consumption of Northern Cyprus. The simulation of neuro-fuzzy system has been performed.
Abstract: Multi-loop (De-centralized) Proportional-Integral-
Derivative (PID) controllers have been used extensively in process
industries due to their simple structure for control of multivariable
processes. The objective of this work is to design multiple-model
adaptive multi-loop PID strategy (Multiple Model Adaptive-PID)
and neural network based multi-loop PID strategy (Neural Net
Adaptive-PID) for the control of multivariable system. The first
method combines the output of multiple linear PID controllers,
each describing process dynamics at a specific level of operation.
The global output is an interpolation of the individual multi-loop
PID controller outputs weighted based on the current value of the
measured process variable. In the second method, neural network
is used to calculate the PID controller parameters based on the
scheduling variable that corresponds to major shift in the process
dynamics. The proposed control schemes are simple in structure with
less computational complexity. The effectiveness of the proposed
control schemes have been demonstrated on the CSTR process,
which exhibits dynamic non-linearity.
Abstract: Perhaps no single issue has been cited as either the root
cause and / or the greatest challenge to the restructured power system then the lack of adequate reliable transmission. Probabilistic transmission planning has become increasingly necessary and important in recent
years. The transmission planning analysis carried out by the authors,
spans a 10-year horizon, taking into consideration a value of 2 % load
increase / year at each consumer. Taking into consideration this increased
load, a probabilistic power flow was carried out, all the system components
being regarded from probabilistic point of view. Several contingencies
have been generated, for assessing the security of the power system. The results have been analyzed and several important conclusions were pointed. The objective is to achieve a network that works without limit violations for all (or most of) scenario realizations. The case study is represented by the IEEE 14 buses test power system.
Abstract: The Bayesian Optimization Algorithm (BOA) is an algorithm based on the estimation of distributions. It uses techniques from modeling data by Bayesian networks to estimating the joint distribution of promising solutions. To obtain the structure of Bayesian network, different search algorithms can be used. The key point that BOA addresses is whether the constructed Bayesian network could generate new and useful solutions (strings), which could lead the algorithm in the right direction to solve the problem. Undoubtedly, this ability is a crucial factor of the efficiency of BOA. Varied search algorithms can be used in BOA, but their performances are different. For choosing better ones, certain suitable method to present their ability difference is needed. In this paper, a greedy search algorithm and a stochastic search algorithm are used in BOA to solve certain optimization problem. A method using Kullback-Leibler (KL) Divergence to reflect their difference is described.
Abstract: Malware is software which was invented and meant for doing harms on computers. Malware is becoming a significant threat in computer network nowadays. Malware attack is not just only involving financial lost but it can also cause fatal errors which may cost lives in some cases. As new Internet Protocol version 6 (IPv6) emerged, many people believe this protocol could solve most malware propagation issues due to its broader addressing scheme. As IPv6 is still new compares to native IPv4, some transition mechanisms have been introduced to promote smoother migration. Unfortunately, these transition mechanisms allow some malwares to propagate its attack from IPv4 to IPv6 network environment. In this paper, a proof of concept shall be presented in order to show that some existing IPv4 malware detection technique need to be improvised in order to detect malware attack in dual-stack network more efficiently. A testbed of dual-stack network environment has been deployed and some genuine malware have been released to observe their behaviors. The results between these different scenarios will be analyzed and discussed further in term of their behaviors and propagation methods. The results show that malware behave differently on IPv6 from the IPv4 network protocol on the dual-stack network environment. A new detection technique is called for in order to cater this problem in the near future.
Abstract: Orthogonal frequency division multiplexing (OFDM)
has developed into a popular scheme for wideband digital
communications used in consumer applications such as digital broadcasting, wireless networking and broadband internet access. In
the OFDM system, carrier frequency offset (CFO) causes intercarrier
interference (ICI) which significantly degrades the system error performance. In this paper we provide an exact evaluation method for error performance analysis of arbitrary 2-D modulation OFDM systems with CFO, and analyze the effect of CFO on error performance.
Abstract: Social learning network analysis has drawn attention
for most researcher on e-learning research domain. This is due to the
fact that it has the capability to identify the behavior of student
during their social interaction inside e-learning. Normally, the social
network analysis (SNA) is treating the students' interaction merely as
node and edge with less meaning. This paper focuses on providing an
ontology structure of e-learning Moodle that can enrich the
relationships among students, as well as between the students and the
teacher. This ontology structure brings great benefit to the future
development of e-learning system.
Abstract: This paper proposes a novel architecture for At-
Home medical care which enables senior citizens, patients
with chronic ailments and patients requiring post- operative
care to be remotely monitored in the comfort of their homes.
This architecture is implemented using sensors and wireless
networking for transmitting patient data to the hospitals,
health- care centers for monitoring by medical professionals.
Patients are equipped with sensors to measure their
physiological parameters, like blood pressure, pulse rate etc.
and a Wearable Data Acquisition Unit is used to transmit the
patient sensor data. Medical professionals can be alerted to
any abnormal variations in these values for diagnosis and
suitable treatment. Security threats and challenges inherent to
wireless communication and sensor network have been
discussed and a security mechanism to ensure data
confidentiality and source authentication has been proposed.
Symmetric key algorithm AES has been used for encrypting
the data and a patent-free, two-pass block cipher mode CCFB
has been used for implementing semantic security.
Abstract: This paper uses the radial basis function neural
network (RBFNN) for system identification of nonlinear systems.
Five nonlinear systems are used to examine the activity of RBFNN in
system modeling of nonlinear systems; the five nonlinear systems are
dual tank system, single tank system, DC motor system, and two
academic models. The feed forward method is considered in this
work for modelling the non-linear dynamic models, where the KMeans
clustering algorithm used in this paper to select the centers of
radial basis function network, because it is reliable, offers fast
convergence and can handle large data sets. The least mean square
method is used to adjust the weights to the output layer, and
Euclidean distance method used to measure the width of the Gaussian
function.
Abstract: In this paper, the processing of sonar signals has been
carried out using Minimal Resource Allocation Network (MRAN)
and a Probabilistic Neural Network (PNN) in differentiation of
commonly encountered features in indoor environments. The
stability-plasticity behaviors of both networks have been
investigated. The experimental result shows that MRAN possesses
lower network complexity but experiences higher plasticity than
PNN. An enhanced version called parallel MRAN (pMRAN) is
proposed to solve this problem and is proven to be stable in
prediction and also outperformed the original MRAN.
Abstract: In the present paper, we-ll explore how social media tools provide an opportunity for new developments of the e-Learning in the context of managing personal knowledge. There will be a discussion how social media tools provide a possibility for helping knowledge workersand students to gather, organize and manage their personal information as a part of the e-learning process. At the centre of this social software driven approach to e-learning environments are the challenges of personalization and collaboration. We-ll share concepts of how organizations are using social media for e-Learning and believe that integration of these tools into traditional e-Learning is probably not a choice, but inevitability. Students- Survey of use of web technologies and social networking tools is presented. Newly developed framework for semantic blogging capable of organizing results relevant to user requirements is implemented at Varna Free University (VFU) to provide more effective navigation and search.
Abstract: This paper presents an exploration into the structure of the corporate governance network and interlocking directorates in the Czech Republic. First a literature overview and a basic terminology of the network theory is presented. Further in the text, statistics and other calculations relevant to corporate governance networks are presented. For this purpose an empirical data set consisting of 2 906 joint stock companies in the Czech Republic was examined. Industries with the highest average number of interlocks per company were healthcare, and energy and utilities. There is no observable link between the financial performance of the company and the number of its interlocks. Also interlocks with financial companies are very rare.
Abstract: Radio frequency identification (RFID) applications have grown rapidly in many industries, especially in indoor location identification. The advantage of using received signal strength indicator (RSSI) values as an indoor location measurement method is a cost-effective approach without installing extra hardware. Because the accuracy of many positioning schemes using RSSI values is limited by interference factors and the environment, thus it is challenging to use RFID location techniques based on integrating positioning algorithm design. This study proposes the location estimation approach and analyzes a scheme relying on RSSI values to minimize location errors. In addition, this paper examines different factors that affect location accuracy by integrating the backpropagation neural network (BPN) with the LANDMARC algorithm in a training phase and an online phase. First, the training phase computes coordinates obtained from the LANDMARC algorithm, which uses RSSI values and the real coordinates of reference tags as training data for constructing an appropriate BPN architecture and training length. Second, in the online phase, the LANDMARC algorithm calculates the coordinates of tracking tags, which are then used as BPN inputs to obtain location estimates. The results show that the proposed scheme can estimate locations more accurately compared to LANDMARC without extra devices.
Abstract: The purpose of this research is to develop a security model for voice eavesdropping protection over digital networks. The proposed model provides an encryption scheme and a personal secret key exchange between communicating parties, a so-called voice data transformation system, resulting in a real-privacy conversation. The operation of this system comprises two main steps as follows: The first one is the personal secret key exchange for using the keys in the data encryption process during conversation. The key owner could freely make his/her choice in key selection, so it is recommended that one should exchange a different key for a different conversational party, and record the key for each case into the memory provided in the client device. The next step is to set and record another personal option of encryption, either taking all frames or just partial frames, so-called the figure of 1:M. Using different personal secret keys and different sets of 1:M to different parties without the intervention of the service operator, would result in posing quite a big problem for any eavesdroppers who attempt to discover the key used during the conversation, especially in a short period of time. Thus, it is quite safe and effective to protect the case of voice eavesdropping. The results of the implementation indicate that the system can perform its function accurately as designed. In this regard, the proposed system is suitable for effective use in voice eavesdropping protection over digital networks, without any requirements to change presently existing network systems, mobile phone network and VoIP, for instance.
Abstract: In this paper, a Neural Network based predictive
DTC algorithm is proposed .This approach is used as an
alternative to classical approaches .An appropriate riate Feed -
forward network is chosen and based on its value of
derivative electromagnetic torque ; optimal stator voltage
vector is determined to be applied to the induction motor (by
inverter). Moreover, an appropriate torque and flux observer
is proposed.
Abstract: It is estimated that the total cost of abnormal
conditions to US process industries is around $20 billion dollars in
annual losses. The hydrotreatment (HDT) of diesel fuel in petroleum
refineries is a conversion process that leads to high profitable
economical returns. However, this is a difficult process to control
because it is operated continuously, with high hydrogen pressures
and it is also subject to disturbances in feed properties and catalyst
performance. So, the automatic detection of fault and diagnosis plays
an important role in this context. In this work, a hybrid approach
based on neural networks together with a pos-processing
classification algorithm is used to detect faults in a simulated HDT
unit. Nine classes (8 faults and the normal operation) were correctly
classified using the proposed approach in a maximum time of 5
minutes, based on on-line data process measurements.
Abstract: Optical burst switching(OBS) is considered as one of
preferable network technologies for the next generation Internet. The
Internet has two traffic classes, i.e. real-time bursts and reliable bursts.
It is an important subject for OBS to achieve cooperated operation of
real-time bursts and reliable bursts. In this paper, we proposes a new
effective traffic control method named Separate TB+LB (Token
Bucket + Leaky Bucket : TB+LB) method. The proposed method
presents a new Token Bucket scheme for real-time bursts called as
RBO-TB (Real-time Bursts Oriented Token Bucket). The method also
applies the LB method to reliable bursts for obtaining better
performance. This paper verifies the effectiveness of the Separate
TB+LB method through the performance evaluation.
Abstract: Here, a new idea to speed up the operation of
complex valued time delay neural networks is presented. The whole
data are collected together in a long vector and then tested as a one
input pattern. The proposed fast complex valued time delay neural
networks uses cross correlation in the frequency domain between the
tested data and the input weights of neural networks. It is proved
mathematically that the number of computation steps required for
the presented fast complex valued time delay neural networks is less
than that needed by classical time delay neural networks. Simulation
results using MATLAB confirm the theoretical computations.