Abstract: This article is an extension and a practical application
approach of Wheeler-s NEBIC theory (Net Enabled Business
Innovation Cycle). NEBIC theory is a new approach in IS research
and can be used for dynamic environment related to new technology.
Firms can follow the market changes rapidly with support of the IT
resources. Flexible firms adapt their market strategies, and respond
more quickly to customers changing behaviors. When every leading
firm in an industry has access to the same IT resources, the way that
these IT resources are managed will determine the competitive
advantages or disadvantages of firm. From Dynamic Capabilities
Perspective and from newly introduced NEBIC theory by Wheeler,
we know that only IT resources cannot deliver customer value but
good configuration of those resources can guarantee customer value
by choosing the right emerging technology, grasping the economic
opportunities through business innovation and growth. We found
evidences in literature that SOA (Service Oriented Architecture) is a
promising emerging technology which can deliver the desired
economic opportunity through modularity, flexibility and loosecoupling.
SOA can also help firms to connect in network which can
open a new window of opportunity to collaborate in innovation and
right kind of outsourcing
Abstract: In this paper, a class of recurrent neural networks (RNNs) with variable delays are studied on almost periodic time scales, some sufficient conditions are established for the existence and global exponential stability of the almost periodic solution. These results have important leading significance in designs and applications of RNNs. Finally, two examples and numerical simulations are presented to illustrate the feasibility and effectiveness of the results.
Abstract: In this paper optical code-division multiple-access (OCDMA) packet network is considered, which offers inherent security in the access networks. Two types of random access protocols are proposed for packet transmission. In protocol 1, all distinct codes and in protocol 2, distinct codes as well as shifted versions of all these codes are used. O-CDMA network performance using optical orthogonal codes (OOCs) 1-D and two-dimensional (2-D) wavelength/time single-pulse-per-row (W/T SPR) codes are analyzed. The main advantage of using 2-D codes instead of onedimensional (1-D) codes is to reduce the errors due to multiple access interference among different users. In this paper, correlation receiver is considered in the analysis. Using analytical model, we compute and compare packet-success probability for 1-D and 2-D codes in an O-CDMA network and the analysis shows improved performance with 2-D codes as compared to 1-D codes.
Abstract: In this work a new platform for mobile-health systems is
presented. System target application is providing decision support to
rescue corps or military medical personnel in combat areas. Software
architecture relies on a distributed client-server system that manages a
wireless ad-hoc networks hierarchy in which several different types of
client operate. Each client is characterized for different hardware and
software requirements. Lower hierarchy levels rely in a network of
completely custom devices that store clinical information and patient
status and are designed to form an ad-hoc network operating in the
2.4 GHz ISM band and complying with the IEEE 802.15.4 standard
(ZigBee). Medical personnel may interact with such devices, that are
called MICs (Medical Information Carriers), by means of a PDA
(Personal Digital Assistant) or a MDA (Medical Digital Assistant),
and transmit the information stored in their local databases as well as
issue a service request to the upper hierarchy levels by using IEEE
802.11 a/b/g standard (WiFi). The server acts as a repository that
stores both medical evacuation forms and associated events (e.g., a
teleconsulting request). All the actors participating in the diagnostic
or evacuation process may access asynchronously to such repository
and update its content or generate new events. The designed system
pretends to optimise and improve information spreading and flow
among all the system components with the aim of improving both
diagnostic quality and evacuation process.
Abstract: Recently, there have been considerable efforts towards the convergence between P2P and Grid computing in order to reach a solution that takes the best of both worlds by exploiting the advantages that each offers. Augmenting the peer-to-peer model to the services of the Grid promises to eliminate bottlenecks and ensure greater scalability, availability, and fault-tolerance. The Grid Information Service (GIS) directly influences quality of service for grid platforms. Most of the proposed solutions for decentralizing the GIS are based on completely flat overlays. The main contributions for this paper are: the investigation of a novel resource discovery framework for Grid implementations based on a hierarchy of structured peer-to-peer overlay networks, and introducing a discovery algorithm utilizing the proposed framework. Validation of the framework-s performance is done via simulation. Experimental results show that the proposed organization has the advantage of being scalable while providing fault-isolation, effective bandwidth utilization, and hierarchical access control. In addition, it will lead to a reliable, guaranteed sub-linear search which returns results within a bounded interval of time and with a smaller amount of generated traffic within each domain.
Abstract: The Sensor Network consists of densely deployed
sensor nodes. Energy optimization is one of the most important
aspects of sensor application design. Data acquisition and aggregation
techniques for processing data in-network should be energy efficient.
Due to the cross-layer design, resource-limited and noisy nature
of Wireless Sensor Networks(WSNs), it is challenging to study
the performance of these systems in a realistic setting. In this
paper, we propose optimizing queries by aggregation of data and
data redundancy to reduce energy consumption without requiring
all sensed data and directed diffusion communication paradigm to
achieve power savings, robust communication and processing data
in-network. To estimate the per-node power consumption POWERTossim
mica2 energy model is used, which provides scalable and
accurate results. The performance analysis shows that the proposed
methods overcomes the existing methods in the aspects of energy
consumption in wireless sensor networks.
Abstract: Active Power Filters (APFs) are today the most
widely used systems to eliminate harmonics compensate power
factor and correct unbalanced problems in industrial power plants.
We propose to improve the performances of conventional APFs by
using artificial neural networks (ANNs) for harmonics estimation.
This new method combines both the strategies for extracting the
three-phase reference currents for active power filters and DC link
voltage control method. The ANNs learning capabilities to
adaptively choose the power system parameters for both to compute
the reference currents and to recharge the capacitor value requested
by VDC voltage in order to ensure suitable transit of powers to
supply the inverter. To investigate the performance of this
identification method, the study has been accomplished using
simulation with the MATLAB Simulink Power System Toolbox. The
simulation study results of the new (SAPF) identification technique
compared to other similar methods are found quite satisfactory by
assuring good filtering characteristics and high system stability.
Abstract: A subjectively influenced router for vehicles in a fourjunction
traffic system is presented. The router is based on a 3-layer
Backpropagation Neural Network (BPNN) and a greedy routing
procedure. The BPNN detects priorities of vehicles based on the
subjective criteria. The subjective criteria and the routing procedure
depend on the routing plan towards vehicles depending on the user.
The routing procedure selects vehicles from their junctions based on
their priorities and route them concurrently to the traffic system. That
is, when the router is provided with a desired vehicles selection
criteria and routing procedure, it routes vehicles with a reasonable
junction clearing time. The cost evaluation of the router determines
its efficiency. In the case of a routing conflict, the router will route
the vehicles in a consecutive order and quarantine faulty vehicles.
The simulations presented indicate that the presented approach is an
effective strategy of structuring a subjective vehicle router.
Abstract: In this contribution a newly developed e-learning environment is presented, which incorporates Intelligent Agents and Computational Intelligence Techniques. The new e-learning environment is constituted by three parts, the E-learning platform Front-End, the Student Questioner Reasoning and the Student Model Agent. These parts are distributed geographically in dispersed computer servers, with main focus on the design and development of these subsystems through the use of new and emerging technologies. These parts are interconnected in an interoperable way, using web services for the integration of the subsystems, in order to enhance the user modelling procedure and achieve the goals of the learning process.
Abstract: Today, transport and logistic systems are often tightly
integrated in the production. Lean production and just-in-time delivering create multiple constraints that have to be fulfilled. As transport networks often have evolved over time they are very
expensive to change. This paper describes a discrete-event-simulation
system which simulates transportation models using real time
resource routing and collision avoidance. It allows for the
specification of own control algorithms and validation of new
strategies. The simulation is integrated into a virtual reality (VR)
environment and can be displayed in 3-D to show the progress.
Simulation elements can be selected through VR metaphors. All data
gathered during the simulation can be presented as a detailed summary afterwards. The included cost-benefit calculation can help to optimize the financial outcome. The operation of this approach is shown by the example of a timber harvest simulation.
Abstract: This paper presents a novel control method based on radial basis function networks (RBFNs) for chaotic dynamical systems. The proposed method first identifies the nonlinear part of the chaotic system off-line and then constructs a model-following controller using only the estimated system parameters. Simulation results show the effectiveness of the proposed control scheme.
Abstract: In this paper, the issue of pth moment stability of a class of stochastic neural networks with mixed delays is investigated. By establishing two integro-differential inequalities, some new sufficient conditions ensuring pth moment exponential stability are obtained. Compared with some previous publications, our results generalize some earlier works reported in the literature, and remove some strict constraints of time delays and kernel functions. Two numerical examples are presented to illustrate the validity of the main results.
Abstract: Design, as an area of knowledge, is subject to changes that affect it through different approaches, both theoretical and practical; its include matters related with responsibility, environment, social worries, and things alike. Commensurately, such contemporary aspects open room for social initiatives. This scenario begins to be looked at, especially in creative communities. Such proposal for a systemic approach of design is seen as a way to involve the stakeholders in the processes of investigation and of social innovation, which can decisively contribute for the development of traditional local communities. As a theoretical basis for the research, this paper outlines some especial features of design and social innovation, in their particular and in their complementary aspects, as well as in the way they relate with each other.
Abstract: This paper is concerned with the establishment of relationships among knowledge management (KM) criteria that will ensure an essential foundation to evaluate KM outcomes. The major issue under investigation is to assess the popularity of criteria within organizations and to establish a structure of criteria for measuring KM results. An empirical survey was conducted among Malaysian organizations to investigate KM criteria for measuring success of KM initiatives. Therefore, knowledge workers as the respondents were targeted to establish a structure of criteria for evaluating KM outcomes. An established structure of criteria based on the Interpretive Structural Modeling (ISM) is used to map criteria relationships inside organizations. This structure is portrayed to identify that how these set of criteria are related. This network schema should be investigated and implemented to promote innovation and improve enterprise performance. To the researchers, this survey has significant insights into relationship between KM programs and business success.
Abstract: New generation mobile communication networks have
the ability of supporting triple play. In order that, Orthogonal
Frequency Division Multiplexing (OFDM) access techniques have
been chosen to enlarge the system ability for high data rates
networks. Many of cross-layer modeling and optimization schemes
for Quality of Service (QoS) and capacity of downlink multiuser
OFDM system were proposed. In this paper, the Maximum Weighted
Capacity (MWC) based resource allocation at the Physical (PHY)
layer is used. This resource allocation scheme provides a much better
QoS than the previous resource allocation schemes, while
maintaining the highest or nearly highest capacity and costing similar
complexity. In addition, the Delay Satisfaction (DS) scheduling at the
Medium Access Control (MAC) layer, which allows more than one
connection to be served in each slot is used. This scheduling
technique is more efficient than conventional scheduling to
investigate both of the number of users as well as the number of
subcarriers against system capacity. The system will be optimized for
different operational environments: the outdoor deployment scenarios
as well as the indoor deployment scenarios are investigated and also
for different channel models. In addition, effective capacity approach
[1] is used not only for providing QoS for different mobile users, but
also to increase the total wireless network's throughput.
Abstract: In this work, a Modified Functional Link Artificial
Neural Network (M-FLANN) is proposed which is simpler than a
Multilayer Perceptron (MLP) and improves upon the universal
approximation capability of Functional Link Artificial Neural
Network (FLANN). MLP and its variants: Direct Linear Feedthrough
Artificial Neural Network (DLFANN), FLANN and
M-FLANN have been implemented to model a simulated Water Bath
System and a Continually Stirred Tank Heater (CSTH). Their
convergence speed and generalization ability have been compared.
The networks have been tested for their interpolation and
extrapolation capability using noise-free and noisy data. The results
show that M-FLANN which is computationally cheap, performs
better and has greater generalization ability than other networks
considered in the work.
Abstract: A healthcare monitoring system is presented in this
paper. This system is based on ultra-low power sensor nodes and a
personal server, which is based on hardware and software extensions
to a Personal Digital Assistant (PDA)/Smartphone. The sensor node
collects data from the body of a patient and sends it to the personal
server where the data is processed, displayed and made ready to be
sent to a healthcare network, if necessary. The personal server
consists of a compact low power receiver module and equipped with
a Smartphone software. The receiver module takes less than 30 × 30
mm board size and consumes approximately 25 mA in active mode.
Abstract: Hybrid algorithm is the hot issue in Computational
Intelligence (CI) study. From in-depth discussion on Simulation
Mechanism Based (SMB) classification method and composite patterns,
this paper presents the Mamdani model based Adaptive Neural
Fuzzy Inference System (M-ANFIS) and weight updating formula in
consideration with qualitative representation of inference consequent
parts in fuzzy neural networks. M-ANFIS model adopts Mamdani
fuzzy inference system which has advantages in consequent part.
Experiment results of applying M-ANFIS to evaluate traffic Level
of service show that M-ANFIS, as a new hybrid algorithm in computational
intelligence, has great advantages in non-linear modeling,
membership functions in consequent parts, scale of training data and
amount of adjusted parameters.
Abstract: Nowadays, power systems, energy generation by wind
has been very important. Noting that the production of electrical
energy by wind turbines on site to several factors (such as wind speed
and profile site for the turbines, especially off the wind input speed,
wind rated speed and wind output speed disconnect) is dependent. On
the other hand, several different types of turbines in the market there.
Therefore, selecting a turbine that its capacity could also answer the
need for electric consumers the efficiency is high something is
important and necessary. In this context, calculating the amount of
wind power to help optimize overall network, system operation, in
determining the parameters of wind power is very important.
In this article, to help calculate the amount of wind power plant,
connected to the national network in the region Manjil wind,
selecting the best type of turbine and power delivery profile
appropriate to the network using Monte Carlo method has been.
In this paper, wind speed data from the wind site in Manjil, as minute
and during the year has been. Necessary simulations based on
Random Numbers Simulation method and repeat, using the software
MATLAB and Excel has been done.
Abstract: The tagging data of (users, tags and resources) constitutes a folksonomy that is the user-driven and bottom-up approach to organizing and classifying information on the Web. Tagging data stored in the folksonomy include a lot of very useful information and knowledge. However, appropriate approach for analyzing tagging data and discovering hidden knowledge from them still remains one of the main problems on the folksonomy mining researches. In this paper, we have proposed a folksonomy data mining approach based on FCA for discovering hidden knowledge easily from folksonomy. Also we have demonstrated how our proposed approach can be applied in the collaborative tagging system through our experiment. Our proposed approach can be applied to some interesting areas such as social network analysis, semantic web mining and so on.