Abstract: In the paper, two fault location algorithms are presented for transmission lines which use the line parameters to estimate the distance to the fault. The first algorithm uses only the measurements from one end of the line and the positive and zero sequence parameters of the line, while the second one uses the measurements from both ends of the line and only the positive sequence parameters of the line. The algorithms were tested using a transmission grid transposed in MATLAB. In a first stage it was established a fault location base line, where the algorithms mentioned above estimate the fault locations using the exact line parameters. After that, the positive and zero sequence resistance and reactance of the line were calculated again for different ground resistivity values and then the fault locations were estimated again in order to compare the results with the base line results. The results show that the algorithm which uses the zero sequence impedance of the line is the most sensitive to the line parameters modifications. The other algorithm is less sensitive to the line parameters modification.
Abstract: This paper deals with advanced state estimation algorithms for estimation of biomass concentration and specific growth rate in a typical fed-batch biotechnological process. This biotechnological process was represented by a nonlinear mass-balance based process model. Extended Kalman Filter (EKF) and Particle Filter (PF) was used to estimate the unmeasured state variables from oxygen uptake rate (OUR) and base consumption (BC) measurements. To obtain more general results, a simplified process model was involved in EKF and PF estimation algorithms. This model doesn’t require any special growth kinetic equations and could be applied for state estimation in various bioprocesses. The focus of this investigation was concentrated on the comparison of the estimation quality of the EKF and PF estimators by applying different measurement noises. The simulation results show that Particle Filter algorithm requires significantly more computation time for state estimation but gives lower estimation errors both for biomass concentration and specific growth rate. Also the tuning procedure for Particle Filter is simpler than for EKF. Consequently, Particle Filter should be preferred in real applications, especially for monitoring of industrial bioprocesses where the simplified implementation procedures are always desirable.
Abstract: Indoor wireless localization systems have played an
important role to enhance context-aware services. Determining the
position of mobile objects in complex indoor environments, such as
those in multi-floor buildings, is very challenging problems. This
paper presents an effective floor estimation algorithm, which can
accurately determine the floor where mobile objects located. The
proposed algorithm is based on the confidence interval of the
summation of online Received Signal Strength (RSS) obtained from
the IEEE 802.15.4 Wireless Sensor Networks (WSN).We compare
the performance of the proposed algorithm with those of other floor
estimation algorithms in literature by conducting a real
implementation of WSN in our facility. The experimental results and
analysis showed that the proposed floor estimation algorithm
outperformed the other algorithms and provided highest percentage
of floor accuracy up to 100% with 95-percent confidence interval.
Abstract: In this paper, we propose an easily computable proximity index for predicting voltage collapse of a load bus using only measured values of the bus voltage and power; Using these measurements a polynomial of fourth order is obtained by using LES estimation algorithms. The sum of the absolute values of the polynomial coefficient gives an idea of the critical bus. We demonstrate the applicability of our proposed method on 6 bus test system. The results obtained verify its applicability, as well as its accuracy and the simplicity. From this indicator, it is allowed to predict the voltage instability or the proximity of a collapse. Results obtained by the PV curve are compared with corresponding values by QV curves and are observed to be in close agreement.
Abstract: In this paper we propose new method for
simultaneous generating multiple quantiles corresponding to given
probability levels from data streams and massive data sets. This
method provides a basis for development of single-pass low-storage
quantile estimation algorithms, which differ in complexity, storage
requirement and accuracy. We demonstrate that such algorithms may
perform well even for heavy-tailed data.
Abstract: This paper includes two novel techniques for skew
estimation of binary document images. These algorithms are based on
connected component analysis and Hough transform. Both these
methods focus on reducing the amount of input data provided to
Hough transform. In the first method, referred as word centroid
approach, the centroids of selected words are used for skew detection.
In the second method, referred as dilate & thin approach, the selected
characters are blocked and dilated to get word blocks and later
thinning is applied. The final image fed to Hough transform has the
thinned coordinates of word blocks in the image. The methods have
been successful in reducing the computational complexity of Hough
transform based skew estimation algorithms. Promising experimental
results are also provided to prove the effectiveness of the proposed
methods.
Abstract: In this paper we consider the problem of distributed adaptive estimation in wireless sensor networks for two different observation noise conditions. In the first case, we assume that there are some sensors with high observation noise variance (noisy sensors) in the network. In the second case, different variance for observation noise is assumed among the sensors which is more close to real scenario. In both cases, an initial estimate of each sensor-s observation noise is obtained. For the first case, we show that when there are such sensors in the network, the performance of conventional distributed adaptive estimation algorithms such as incremental distributed least mean square (IDLMS) algorithm drastically decreases. In addition, detecting and ignoring these sensors leads to a better performance in a sense of estimation. In the next step, we propose a simple algorithm to detect theses noisy sensors and modify the IDLMS algorithm to deal with noisy sensors. For the second case, we propose a new algorithm in which the step-size parameter is adjusted for each sensor according to its observation noise variance. As the simulation results show, the proposed methods outperforms the IDLMS algorithm in the same condition.
Abstract: In 3D-wavelet video coding framework temporal
filtering is done along the trajectory of motion using Motion
Compensated Temporal Filtering (MCTF). Hence computationally
efficient motion estimation technique is the need of MCTF. In this
paper a predictive technique is proposed in order to reduce the
computational complexity of the MCTF framework, by exploiting
the high correlation among the frames in a Group Of Picture (GOP).
The proposed technique applies coarse and fine searches of any fast
block based motion estimation, only to the first pair of frames in a
GOP. The generated motion vectors are supplied to the next
consecutive frames, even to subsequent temporal levels and only fine
search is carried out around those predicted motion vectors. Hence
coarse search is skipped for all the motion estimation in a GOP
except for the first pair of frames. The technique has been tested for
different fast block based motion estimation algorithms over different
standard test sequences using MC-EZBC, a state-of-the-art scalable
video coder. The simulation result reveals substantial reduction (i.e.
20.75% to 38.24%) in the number of search points during motion
estimation, without compromising the quality of the reconstructed
video compared to non-predictive techniques. Since the motion
vectors of all the pair of frames in a GOP except the first pair will
have value ±1 around the motion vectors of the previous pair of
frames, the number of bits required for motion vectors is also
reduced by 50%.
Abstract: On-line (near infrared) spectroscopy is widely used to support the operation of complex process systems. Information extracted from spectral database can be used to estimate unmeasured product properties and monitor the operation of the process. These techniques are based on looking for similar spectra by nearest neighborhood algorithms and distance based searching methods. Search for nearest neighbors in the spectral space is an NP-hard problem, the computational complexity increases by the number of points in the discrete spectrum and the number of samples in the database. To reduce the calculation time some kind of indexing could be used. The main idea presented in this paper is to combine indexing and visualization techniques to reduce the computational requirement of estimation algorithms by providing a two dimensional indexing that can also be used to visualize the structure of the spectral database. This 2D visualization of spectral database does not only support application of distance and similarity based techniques but enables the utilization of advanced clustering and prediction algorithms based on the Delaunay tessellation of the mapped spectral space. This means the prediction has not to use the high dimension space but can be based on the mapped space too. The results illustrate that the proposed method is able to segment (cluster) spectral databases and detect outliers that are not suitable for instance based learning algorithms.
Abstract: In the recent years multimedia traffic and in particular
VoIP services are growing dramatically. We present a new algorithm
to control the resource utilization and to optimize the voice codec
selection during SIP call setup on behalf of the traffic condition
estimated on the network path.
The most suitable methodologies and the tools that perform realtime
evaluation of the available bandwidth on a network path have
been integrated with our proposed algorithm: this selects the best
codec for a VoIP call in function of the instantaneous available
bandwidth on the path. The algorithm does not require any explicit
feedback from the network, and this makes it easily deployable over
the Internet. We have also performed intensive tests on real network
scenarios with a software prototype, verifying the algorithm
efficiency with different network topologies and traffic patterns
between two SIP PBXs.
The promising results obtained during the experimental validation
of the algorithm are now the basis for the extension towards a larger
set of multimedia services and the integration of our methodology
with existing PBX appliances.
Abstract: Motion estimation is the most computationally
intensive part in video processing. Many fast motion estimation
algorithms have been proposed to decrease the computational
complexity by reducing the number of candidate motion vectors.
However, these studies are for fast search algorithms themselves while
almost image and video compressions are operated with software
based. Therefore, the timing constraints for running these motion
estimation algorithms not only challenge for the video codec but also
overwhelm for some of processors. In this paper, the performance of
motion estimation is enhanced by using Intel's Streaming SIMD
Extension 2 (SSE2) technology with Intel Pentium 4 processor.