Abstract: This paper illustrates the use of a combined neural
network model for classification of electrocardiogram (ECG) beats.
We present a trainable neural network ensemble approach to develop
customized electrocardiogram beat classifier in an effort to further
improve the performance of ECG processing and to offer
individualized health care.
We process a three stage technique for detection of premature
ventricular contraction (PVC) from normal beats and other heart
diseases. This method includes a denoising, a feature extraction and a
classification. At first we investigate the application of stationary
wavelet transform (SWT) for noise reduction of the
electrocardiogram (ECG) signals. Then feature extraction module
extracts 10 ECG morphological features and one timing interval
feature. Then a number of multilayer perceptrons (MLPs) neural
networks with different topologies are designed.
The performance of the different combination methods as well as
the efficiency of the whole system is presented. Among them,
Stacked Generalization as a proposed trainable combined neural
network model possesses the highest recognition rate of around 95%.
Therefore, this network proves to be a suitable candidate in ECG
signal diagnosis systems. ECG samples attributing to the different
ECG beat types were extracted from the MIT-BIH arrhythmia
database for the study.
Abstract: Daily production of information and importance of the sequence of produced data in forecasting future performance of market causes analysis of data behavior to become a problem of analyzing time series. But time series that are very complicated, usually are random and as a result their changes considered being unpredictable. While these series might be products of a deterministic dynamical and nonlinear process (chaotic) and as a result be predictable. Point of Chaotic theory view, complicated systems have only chaotically face and as a result they seem to be unregulated and random, but it is possible that they abide by a specified math formula. In this article, with regard to test of strange attractor and biggest Lyapunov exponent probability of chaos on several foreign exchange rates vs. IRR (Iranian Rial) has been investigated. Results show that data in this market have complex chaotic behavior with big degree of freedom.
Abstract: Image-based Rendering(IBR) techniques recently
reached in broad fields which leads to a critical challenge to build up
IBR-Driven visualization platform where meets requirement of high
performance, large bounds of distributed visualization resource
aggregation and concentration, multiple operators deploying and
CSCW design employing. This paper presents an unique IBR-based
visualization dataflow model refer to specific characters of IBR
techniques and then discusses prominent feature of IBR-Driven
distributed collaborative visualization (DCV) system before finally
proposing an novel prototype. The prototype provides a well-defined
three level modules especially work as Central Visualization Server,
Local Proxy Server and Visualization Aid Environment, by which
data and control for collaboration move through them followed the
previous dataflow model. With aid of this triple hierarchy architecture
of that, IBR oriented application construction turns to be easy. The
employed augmented collaboration strategy not only achieve
convenient multiple users synchronous control and stable processing
management, but also is extendable and scalable.
Abstract: A new algorithm called Character-Comparison to Character-Access (CCCA) is developed to test the effect of both: 1) converting character-comparison and number-comparison into character-access and 2) the starting point of checking on the performance of the checking operation in string searching. An experiment is performed using both English text and DNA text with different sizes. The results are compared with five algorithms, namely, Naive, BM, Inf_Suf_Pref, Raita, and Cycle. With the CCCA algorithm, the results suggest that the evaluation criteria of the average number of total comparisons are improved up to 35%. Furthermore, the results suggest that the clock time required by the other algorithms is improved in range from 22.13% to 42.33% by the new CCCA algorithm.
Abstract: Clustering is a very well known technique in data mining. One of the most widely used clustering techniques is the kmeans algorithm. Solutions obtained from this technique depend on the initialization of cluster centers and the final solution converges to local minima. In order to overcome K-means algorithm shortcomings, this paper proposes a hybrid evolutionary algorithm based on the combination of PSO, SA and K-means algorithms, called PSO-SA-K, which can find better cluster partition. The performance is evaluated through several benchmark data sets. The simulation results show that the proposed algorithm outperforms previous approaches, such as PSO, SA and K-means for partitional clustering problem.
Abstract: An appropriate method for fault identification and classification on extra high voltage transmission line using discrete wavelet transform is proposed in this paper. The sharp variations of the generated short circuit transient signals which are recorded at the sending end of the transmission line are adopted to identify the fault. The threshold values involve fault classification and these are done on the basis of the multiresolution analysis. A comparative study of the performance is also presented for Discrete Fourier Transform (DFT) based Artificial Neural Network (ANN) and Discrete Wavelet Transform (DWT). The results prove that the proposed method is an effective and efficient one in obtaining the accurate result within short duration of time by using Daubechies 4 and 9. Simulation of the power system is done using MATLAB.
Abstract: The Major Depressive Disorder has been a burden of
medical expense in Taiwan as well as the situation around the world.
Major Depressive Disorder can be defined into different categories by
previous human activities. According to machine learning, we can
classify emotion in correct textual language in advance. It can help
medical diagnosis to recognize the variance in Major Depressive
Disorder automatically. Association language incremental is the
characteristic and relationship that can discovery words in sentence.
There is an overlapping-category problem for classification. In this
paper, we would like to improve the performance in classification in
principle of no overlapping-category problems. We present an
approach that to discovery words in sentence and it can find in high
frequency in the same time and can-t overlap in each category, called
Association Language Features by its Category (ALFC).
Experimental results show that ALFC distinguish well in Major
Depressive Disorder and have better performance. We also compare
the approach with baseline and mutual information that use single
words alone or correlation measure.
Abstract: A DEA model can generally evaluate the performance
using multiple inputs and outputs for the same period. However, it is
hard to avoid the production lead time phenomenon some times, such
as long-term project or marketing activity. A couple of models have
been suggested to capture this time lag issue in the context of DEA.
This paper develops a dual-MPO model to deal with time lag effect in
evaluating efficiency. A numerical example is also given to show that
the proposed model can be used to get efficiency and reference set of
inefficient DMUs and to obtain projected target value of input
attributes for inefficient DMUs to be efficient.
Abstract: This study adopted previous fault patterns, results of
detection analysis, historical records and data, and experts-
experiences to establish fuzzy principles and estimate the failure
probability index of components of a power transformer. Considering
that actual parameters and limiting conditions of parameters may
differ, this study used the standard data of IEC, IEEE, and CIGRE as
condition parameters. According to the characteristics of each
condition parameter, relative degradation was introduced to reflect the
degree of influence of the factors on the transformer condition. The
method of fuzzy mathematics was adopted to determine the
subordinate function of the transformer condition. The calculation
used the Matlab Fuzzy Tool Box to select the condition parameters of
coil winding, iron core, bushing, OLTC, insulating oil and other
auxiliary components and factors (e.g., load records, performance
history, and maintenance records) of the transformer to establish the
fuzzy principles. Examples were presented to support the rationality
and effectiveness of the evaluation method of power transformer
performance conditions, as based on fuzzy comprehensive evaluation.
Abstract: How to effectively allocate system resource to process
the Client request by Gateway servers is a challenging problem. In
this paper, we propose an improved scheme for autonomous
performance of Gateway servers under highly dynamic traffic loads.
We devise a methodology to calculate Queue Length and Waiting
Time utilizing Gateway Server information to reduce response time
variance in presence of bursty traffic. The most widespread
contemplation is performance, because Gateway Servers must offer
cost-effective and high-availability services in the elongated period,
thus they have to be scaled to meet the expected load. Performance
measurements can be the base for performance modeling and
prediction. With the help of performance models, the performance
metrics (like buffer estimation, waiting time) can be determined at
the development process. This paper describes the possible queue
models those can be applied in the estimation of queue length to
estimate the final value of the memory size. Both simulation and
experimental studies using synthesized workloads and analysis of
real-world Gateway Servers demonstrate the effectiveness of the
proposed system.
Abstract: A number of routing algorithms based on learning
automata technique have been proposed for communication
networks. How ever, there has been little work on the effects of
variation of graph scarcity on the performance of these algorithms. In
this paper, a comprehensive study is launched to investigate the
performance of LASPA, the first learning automata based solution to
the dynamic shortest path routing, across different graph structures
with varying scarcities. The sensitivity of three main performance
parameters of the algorithm, being average number of processed
nodes, scanned edges and average time per update, to variation in
graph scarcity is reported. Simulation results indicate that the LASPA
algorithm can adapt well to the scarcity variation in graph structure
and gives much better outputs than the existing dynamic and fixed
algorithms in terms of performance criteria.
Abstract: A wireless sensor network with a large number of tiny sensor nodes can be used as an effective tool for gathering data in various situations. One of the major issues in wireless sensor networks is developing an energy-efficient routing protocol which has a significant impact on the overall lifetime of the sensor network. In this paper, we propose a novel hierarchical with static clustering routing protocol called Energy-Efficient Protocol with Static Clustering (EEPSC). EEPSC, partitions the network into static clusters, eliminates the overhead of dynamic clustering and utilizes temporary-cluster-heads to distribute the energy load among high-power sensor nodes; thus extends network lifetime. We have conducted simulation-based evaluations to compare the performance of EEPSC against Low-Energy Adaptive Clustering Hierarchy (LEACH). Our experiment results show that EEPSC outperforms LEACH in terms of network lifetime and power consumption minimization.
Abstract: Wind turbines with double output induction
generators can operate at variable speed permitting conversion
efficiency maximization over a wide range of wind velocities. This
paper presents the performance analysis of a wind driven double
output induction generator (DOIG) operating at varying shafts speed.
A periodic transient state analysis of DOIG equipped with two
converters is carried out using a hybrid induction machine model.
This paper simulates the harmonic content of waveforms in various
points of drive at different speeds, based on the hybrid model
(dqabc). Then the sinusoidal and trapezoidal pulse-width–modulation
control techniques are used in order to improve the power factor of
the machine and to weaken the injected low order harmonics to the
supply. Based on the frequency spectrum, total harmonics distortion,
distortion factor and power factor. Finally advantages of sinusoidal
and trapezoidal pulse width modulation techniques are compared.
Abstract: A multivariable discontinuous feedback linearization approach is proposed to position control of an electrically driven fast robot manipulator. A desired performance is achieved by selecting a useful controller and suitable sampling rate and considering saturation for actuators. There is a high flexibility to apply the proposed control approach on different electrically driven manipulators. The control approach can guarantee the stability and satisfactory tracking performance. A PUMA 560 robot driven by geared permanent magnet dc motors is simulated. The simulation results show a desired performance for control system under technical specifications.
Abstract: In this paper we present a general formalism for the
establishment of the family of selective regressor affine projection
algorithms (SR-APA). The SR-APA, the SR regularized APA (SR-RAPA),
the SR partial rank algorithm (SR-PRA), the SR binormalized
data reusing least mean squares (SR-BNDR-LMS), and the SR normalized
LMS with orthogonal correction factors (SR-NLMS-OCF)
algorithms are established by this general formalism. We demonstrate
the performance of the presented algorithms through simulations in
acoustic echo cancellation scenario.
Abstract: Currently, web usage make a huge data from a lot of
user attention. In general, proxy server is a system to support web
usage from user and can manage system by using hit rates. This
research tries to improve hit rates in proxy system by applying data
mining technique. The data set are collected from proxy servers in the
university and are investigated relationship based on several features.
The model is used to predict the future access websites. Association
rule technique is applied to get the relation among Date, Time, Main
Group web, Sub Group web, and Domain name for created model.
The results showed that this technique can predict web content for the
next day, moreover the future accesses of websites increased from
38.15% to 85.57 %.
This model can predict web page access which tends to increase
the efficient of proxy servers as a result. In additional, the
performance of internet access will be improved and help to reduce
traffic in networks.
Abstract: This research explorers the relationship between leadership style and continuous improvement (CI) teams. CI teams have several features that are not always found in other types of teams, including multi-functional members, short time period for performance, positive and actionable results, and exposure to senior leadership. There is no one best style of leadership for these teams. Instead, it is important to select the best leadership style for the situation. The leader must have the flexibility to change styles and the skill to use the chosen style effectively in order to ensure the team’s success.
Abstract: Concrete performance is strongly affected by the
particle packing degree since it determines the distribution of the
cementitious component and the interaction of mineral particles. By
using packing theory designers will be able to select optimal
aggregate materials for preparing concrete with low cement content,
which is beneficial from the point of cost. Optimum particle packing
implies minimizing porosity and thereby reducing the amount of
cement paste needed to fill the voids between the aggregate particles,
taking also the rheology of the concrete into consideration. For
reaching good fluidity superplasticizers are required. The results from
pilot tests at Luleå University of Technology (LTU) show various
forms of the proposed theoretical models, and the empirical approach
taken in the study seems to provide a safer basis for developing new,
improved packing models.
Abstract: In a wireless communication system, a
predistorter(PD) is often employed to alleviate nonlinear distortions
due to operating a power amplifier near saturation, thereby improving
the system performance and reducing the interference to adjacent
channels. This paper presents a new adaptive polynomial digital
predistorter(DPD). The proposed DPD uses Coordinate Rotation
Digital Computing(CORDIC) processors and PD process by pipelined
architecture. It is simpler and faster than conventional adaptive
polynomial DPD. The performance of the proposed DPD is proved by
MATLAB simulation.
Abstract: The selection of parents and breeding strategies for
the successful maize hybrid production will be facilitated by
heterotic groupings of parental lines and determination of combining
abilities of them. Fourteen maize inbred lines, used in maize breeding
programs in Iran, were crossed in a diallel mating design. The 91 F1
hybrids and the 14 parental lines were studied during two years at
four locations of Iran for investigation of combining ability of
gentypes for grain yield and to determine heterotic patterns among
germplasm sources, using both, the Griffing-s method and the biplot
approach for diallel analysis. The graphical representation offered by
biplot analysis allowed a rapid and effective overview of general
combining ability (GCA) and specific combining ability (SCA)
effects of the inbred lines, their performance in crosses, as well as
grouping patterns of similar genotypes. GCA and SCA effects were
significant for grain yield (GY). Based on significant positive GCA
effects, the lines derived from LSC could be used as parent in crosses
to increase GY. The maximum best- parent heterosis values and
highest SCA effects resulted from crosses B73 × MO17 and A679 ×
MO17 for GY. The best heterotic patterns were LSC × RYD, which
would be potentially useful in maize breeding programs to obtain
high-yielding hybrids in the same climate of Iran.