Abstract: The paper presents an investigation in to the effect of neural network predictive control of UPFC on the transient stability performance of a multimachine power system. The proposed controller consists of a neural network model of the test system. This model is used to predict the future control inputs using the damped Gauss-Newton method which employs ‘backtracking’ as the line search method for step selection. The benchmark 2 area, 4 machine system that mimics the behavior of large power systems is taken as the test system for the study and is subjected to three phase short circuit faults at different locations over a wide range of operating conditions. The simulation results clearly establish the robustness of the proposed controller to the fault location, an increase in the critical clearing time for the circuit breakers, and an improved damping of the power oscillations as compared to the conventional PI controller.
Abstract: Recently, grid computing has been widely focused on
the science, industry, and business fields, which are required a vast
amount of computing. Grid computing is to provide the environment
that many nodes (i.e., many computers) are connected with each
other through a local/global network and it is available for many
users. In the environment, to achieve data processing among nodes
for any applications, each node executes mutual authentication by
using certificates which published from the Certificate Authority
(for short, CA). However, if a failure or fault has occurred in the
CA, any new certificates cannot be published from the CA. As
a result, a new node cannot participate in the gird environment.
In this paper, an off-the-shelf scheme for dependable grid systems
using virtualization techniques is proposed and its implementation is
verified. The proposed approach using the virtualization techniques
is to restart an application, e.g., the CA, if it has failed. The system
can tolerate a failure or fault if it has occurred in the CA. Since
the proposed scheme is implemented at the application level easily,
the cost of its implementation by the system builder hardly takes
compared it with other methods. Simulation results show that the
CA in the system can recover from its failure or fault.
Abstract: Web-based cooperative learning focuses on (1) the interaction and the collaboration of community members, and (2) the sharing and the distribution of knowledge and expertise by network technology to enhance learning performance. Numerous research literatures related to web-based cooperative learning have demonstrated that cooperative scripts have a positive impact to specify, sequence, and assign cooperative learning activities. Besides, literatures have indicated that role-play in web-based cooperative learning environments enhances two or more students to work together toward the completion of a common goal. Since students generally do not know each other and they lack the face-to-face contact that is necessary for the negotiation of assigning group roles in web-based cooperative learning environments, this paper intends to further extend the application of genetic algorithm (GA) and propose a GA-based algorithm to tackle the problem of role assignment in web-based cooperative learning environments, which not only saves communication costs but also reduces conflict between group members in negotiating role assignments.
Abstract: In this paper an analysis of blackouts in electric power
transmission systems is implemented using a model and studied in
simple networks with a regular topology. The proposed model
describes load demand and network improvements evolving on a
slow timescale as well as the fast dynamics of cascading overloads
and outages.
Abstract: Financial forecasting is an example of signal processing problems. A number of ways to train/learn the network are available. We have used Levenberg-Marquardt algorithm for error back-propagation for weight adjustment. Pre-processing of data has reduced much of the variation at large scale to small scale, reducing the variation of training data.
Abstract: Ireland developed a National Strategy 2030 that
argued for the creation of a new form of higher education institution,
a Technological University. The research reported here reviews the
first stage of this partnership development. The study found that
national policy can create system capacity and change, but that
individual partners may have more to gain or lose in collaborating.
When presented as a zero-sum activity, fear among partners is high.
The level of knowledge and networking within the higher education
system possessed by each partner contributed to decisions to
participate or not in a joint proposal for collaboration. Greater
success resulted when there were gains for all partners. This research
concludes that policy mandates can provide motivation to
collaborate, but that the partnership needs to be built more on shared
values versus coercion by mandates.
Abstract: Wireless channels are characterized by more serious
bursty and location-dependent errors. Many packet scheduling
algorithms have been proposed for wireless networks to guarantee
fairness and delay bounds. However, most existing schemes do not
consider the difference of traffic natures among packet flows. This
will cause the delay-weight coupling problem. In particular, serious
queuing delays may be incurred for real-time flows. In this paper, it
is proposed a scheduling algorithm that takes traffic types of flows
into consideration when scheduling packets and also it is provided
scheduling flexibility by trading off video quality to meet the
playback deadline.
Abstract: In this article, we introduce a mechanism by which the same concept of differentiated services used in network transmission can be applied to provide quality of service levels to pervasive systems applications. The classical DiffServ model, including marking and classification, assured forwarding, and expedited forwarding, are all utilized to create quality of service guarantees for various pervasive applications requiring different levels of quality of service. Through a collection of various sensors, personal devices, and data sources, the transmission of contextsensitive data can automatically occur within a pervasive system with a given quality of service level. Triggers, initiators, sources, and receivers are four entities labeled in our mechanism. An explanation of the role of each is provided, and how quality of service is guaranteed.
Abstract: In this paper we address the issue of classifying the fluorescent intensity of a sample in Indirect Immuno-Fluorescence (IIF). Since IIF is a subjective, semi-quantitative test in its very nature, we discuss a strategy to reliably label the image data set by using the diagnoses performed by different physicians. Then, we discuss image pre-processing, feature extraction and selection. Finally, we propose two ANN-based classifiers that can separate intrinsically dubious samples and whose error tolerance can be flexibly set. Measured performance shows error rates less than 1%, which candidates the method to be used in daily medical practice either to perform pre-selection of cases to be examined, or to act as a second reader.
Abstract: We introduce an algorithm based on the
morphological shared-weight neural network. Being nonlinear and
translation-invariant, the MSNN can be used to create better
generalization during face recognition. Feature extraction is
performed on grayscale images using hit-miss transforms that are
independent of gray-level shifts. The output is then learned by
interacting with the classification process. The feature extraction and
classification networks are trained together, allowing the MSNN to
simultaneously learn feature extraction and classification for a face.
For evaluation, we test for robustness under variations in gray levels
and noise while varying the network-s configuration to optimize
recognition efficiency and processing time. Results show that the
MSNN performs better for grayscale image pattern classification
than ordinary neural networks.
Abstract: Nowadays, the increase of human population every
year results in increasing of water usage and demand. Saen Saep
canal is important canal in Bangkok. The main objective of this study
is using Artificial Neural Network (ANN) model to estimate the
Chemical Oxygen Demand (COD) on data from 11 sampling sites.
The data is obtained from the Department of Drainage and Sewerage,
Bangkok Metropolitan Administration, during 2007-2011. The
twelve parameters of water quality are used as the input of the
models. These water quality indices affect the COD. The
experimental results indicate that the ANN model provides a high
correlation coefficient (R=0.89).
Abstract: This report aims to utilize existing and future Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing Wireless Local Area Network (MIMO-OFDM WLAN) systems characteristics–such as multiple subcarriers, multiple antennas, and channel estimation characteristics–for indoor location estimation systems based on the Direction of Arrival (DOA) and Radio Signal Strength Indication (RSSI) methods. Hybrid of DOA-RSSI methods also evaluated. In the experimental data result, we show that location estimation accuracy performances can be increased by minimizing the multipath fading effect. This is done using multiple subcarrier frequencies over wideband frequencies to estimate one location. The proposed methods are analyzed in both a wide indoor environment and a typical room-sized office. In the experiments, WLAN terminal locations are estimated by measuring multiple subcarriers from arrays of three dipole antennas of access points (AP). This research demonstrates highly accurate, robust and hardware-free add-on software for indoor location estimations based on a MIMO-OFDM WLAN system.
Abstract: We have developed a distributed asynchronous Web
based training system. In order to improve the scalability and robustness
of this system, all contents and functions are realized on mobile
agents. These agents are distributed to computers, and they can use
a Peer to Peer network that modified Content-Addressable Network.
In the proposed system, only text data can be included in a exercise.
To make our proposed system more useful, the mechanism that it not
only adapts to multimedia data but also it doesn-t influence the user-s
learning even if the size of exercise becomes large is necessary.
Abstract: Ad hoc networks are characterized by multihop wireless connectivity, frequently changing network topology and the need for efficient dynamic routing protocols. We compare the performance of three routing protocols for mobile ad hoc networks: Dynamic Source Routing (DSR) , Ad Hoc On-Demand Distance Vector Routing (AODV), location-aided routing(LAR1).The performance differentials are analyzed using varying network load, mobility, and network size. We simulate protocols with GLOMOSIM simulator. Based on the observations, we make recommendations about when the performance of either protocol can be best.
Abstract: Co-integration models the long-term, equilibrium relationship of two or more related financial variables. Even if cointegration is found, in the short run, there may be deviations from the long run equilibrium relationship. The aim of this work is to forecast these deviations using neural networks and create a trading strategy based on them. A case study is used: co-integration residuals from Australian Bank Bill futures are forecast and traded using various exogenous input variables combined with neural networks. The choice of the optimal exogenous input variables chosen for each neural network, undertaken in previous work [1], is validated by comparing the forecasts and corresponding profitability of each, using a trading strategy.
Abstract: The scale, complexity and worldwide geographical
spread of the LHC computing and data analysis problems are
unprecedented in scientific research. The complexity of processing
and accessing this data is increased substantially by the size and
global span of the major experiments, combined with the limited
wide area network bandwidth available. We present the latest
generation of the MONARC (MOdels of Networked Analysis at
Regional Centers) simulation framework, as a design and modeling
tool for large scale distributed systems applied to HEP experiments.
We present simulation experiments designed to evaluate the
capabilities of the current real-world distributed infrastructure to
support existing physics analysis processes and the means by which
the experiments bands together to meet the technical challenges
posed by the storage, access and computing requirements of LHC
data analysis within the CMS experiment.
Abstract: In the past decade, artificial neural networks (ANNs)
have been regarded as an instrument for problem-solving and
decision-making; indeed, they have already done with a substantial
efficiency and effectiveness improvement in industries and businesses.
In this paper, the Back-Propagation neural Networks (BPNs) will be
modulated to demonstrate the performance of the collaborative
forecasting (CF) function of a Collaborative Planning, Forecasting and
Replenishment (CPFR®) system. CPFR functions the balance between
the sufficient product supply and the necessary customer demand in a
Supply and Demand Chain (SDC). Several classical standard BPN will
be grouped, collaborated and exploited for the easy implementation of
the proposed modular ANN framework based on the topology of a
SDC. Each individual BPN is applied as a modular tool to perform the
task of forecasting SKUs (Stock-Keeping Units) levels that are
managed and supervised at a POS (point of sale), a wholesaler, and a
manufacturer in an SDC. The proposed modular BPN-based CF
system will be exemplified and experimentally verified using lots of
datasets of the simulated SDC. The experimental results showed that a
complex CF problem can be divided into a group of simpler
sub-problems based on the single independent trading partners
distributed over SDC, and its SKU forecasting accuracy was satisfied
when the system forecasted values compared to the original simulated
SDC data. The primary task of implementing an autonomous CF
involves the study of supervised ANN learning methodology which
aims at making “knowledgeable" decision for the best SKU sales plan
and stocks management.
Abstract: The Ad Hoc on demand distance vector (AODV) routing protocol is designed for mobile ad hoc networks (MANETs). AODV offers quick adaptation to dynamic link conditions; it is characterized by low memory overhead and low network utilization. The security issues related to the protocol remain challenging for the wireless network designers. Numerous schemes have been proposed for establishing secure communication between end users, these schemes identify that the secure operation of AODV is a bi tier task (routing and secure exchange of information at separate levels). Our endeavor in this paper would focus on achieving the routing and secure data exchange in a single step. This will facilitate the user nodes to perform routing, mutual authentications, generation and secure exchange of session key in one step thus ensuring confidentiality, integrity and authentication of data exchange in a more suitable way.
Abstract: The wireless link can be unreliable in realistic wireless
sensor networks (WSNs). Energy efficient and reliable data
forwarding is important because each node has limited resources.
Therefore, we must suggest an optimal solution that considers using
the information of the node-s characteristics. Previous routing
protocols were unsuited to realistic asymmetric WSNs. In this paper,
we propose a Protocol that considers Both sides of Link-quality and
Energy (PBLE), an optimal routing protocol that balances modified
link-quality, distance and energy. Additionally, we propose a node
scheduling method. PBLE achieves a longer lifetime than previous
routing protocols and is more energy-efficient. PBLE uses energy,
local information and both sides of PRR in a 1-hop distance. We
explain how to send data packets to the destination node using the
node's information. Simulation shows PBLE improves delivery rate
and network lifetime compared to previous schemes. Moreover, we
show the improvement in various WSN environments.
Abstract: As in today's semiconductor industries test costs can make up to 50 percent of the total production costs, an efficient test error detection becomes more and more important. In this paper, we present a new machine learning approach to test error detection that should provide a faster recognition of test system faults as well as an improved test error recall. The key idea is to learn a classifier ensemble, detecting typical test error patterns in wafer test results immediately after finishing these tests. Since test error detection has not yet been discussed in the machine learning community, we define central problem-relevant terms and provide an analysis of important domain properties. Finally, we present comparative studies reflecting the failure detection performance of three individual classifiers and three ensemble methods based upon them. As base classifiers we chose a decision tree learner, a support vector machine and a Bayesian network, while the compared ensemble methods were simple and weighted majority vote as well as stacking. For the evaluation, we used cross validation and a specially designed practical simulation. By implementing our approach in a semiconductor test department for the observation of two products, we proofed its practical applicability.