Abstract: Recently, Electric Vehicles (EVs) have received extensive consideration since they offer a more sustainable and greener transportation alternative compared to fossil-fuel propelled vehicles. Lithium-Ion (Li-ion) batteries are increasingly being deployed in EVs because of their high energy density, high cell-level voltage, and low rate of self-discharge. Since Li-ion batteries represent the most expensive component in the EV powertrain, accurate monitoring and control strategies must be executed to ensure their prolonged lifespan. The Battery Management System (BMS) has to accurately estimate parameters such as the battery State-of-Charge (SOC), State-of-Health (SOH), and Remaining Useful Life (RUL). In order for the BMS to estimate these parameters, an accurate and control-oriented battery model has to work collaboratively with a robust state and parameter estimation strategy. Since battery physical parameters, such as the internal resistance and diffusion coefficient change depending on the battery state-of-life (SOL), the BMS has to be adaptive to accommodate for this change. In this paper, an extensive battery aging study has been conducted over 12-months period on 5.4 Ah, 3.7 V Lithium polymer cells. Instead of using fixed charging/discharging aging cycles at fixed C-rate, a set of real-world driving scenarios have been used to age the cells. The test has been interrupted every 5% capacity degradation by a set of reference performance tests to assess the battery degradation and track model parameters. As battery ages, the combined model parameters are optimized and tracked in an offline mode over the entire batteries lifespan. Based on the optimized model, a state and parameter estimation strategy based on the Extended Kalman Filter (EKF) and the relatively new Smooth Variable Structure Filter (SVSF) have been applied to estimate the SOC at various states of life.
Abstract: A challenge for laboratories is to provide bacterial identification and antibiotic sensitivity results within a short time. Hence, advancement in the required technology is desirable to improve timing, accuracy and quality. Even with the current advances in methods used for both phenotypic and genotypic identification of bacteria the need is there to develop method(s) that enhance the outcome of bacteriology laboratories in accuracy and time. The hypothesis introduced here is based on the assumption that the chromosome of any bacteria contains unique sequences that can be used for its identification and typing. The outcome of a pilot study designed to test this hypothesis is reported in this manuscript. Methods: The complete chromosome sequences of several bacterial species were downloaded to use as search targets for unique sequences. Visual basic and SQL server (2014) were used to generate a complete set of 18-base long primers, a process started with reverse translation of randomly chosen 6 amino acids to limit the number of the generated primers. In addition, the software used to scan the downloaded chromosomes using the generated primers for similarities was designed, and the resulting hits were classified according to the number of similar chromosomal sequences, i.e., unique or otherwise. Results: All primers that had identical/similar sequences in the selected genome sequence(s) were classified according to the number of hits in the chromosomes search. Those that were identical to a single site on a single bacterial chromosome were referred to as unique. On the other hand, most generated primers sequences were identical to multiple sites on a single or multiple chromosomes. Following scanning, the generated primers were classified based on ability to differentiate between medically important bacterial and the initial results looks promising. Conclusion: A simple strategy that started by generating primers was introduced; the primers were used to screen bacterial genomes for match. Primer(s) that were uniquely identical to specific DNA sequence on a specific bacterial chromosome were selected. The identified unique sequence can be used in different molecular diagnostic techniques, possibly to identify bacteria. In addition, a single primer that can identify multiple sites in a single chromosome can be exploited for region or genome identification. Although genomes sequences draft of isolates of organism DNA enable high throughput primer design using alignment strategy, and this enhances diagnostic performance in comparison to traditional molecular assays. In this method the generated primers can be used to identify an organism before the draft sequence is completed. In addition, the generated primers can be used to build a bank for easy access of the primers that can be used to identify bacteria.
Abstract: Available commercial applications of power system stabilizers assure optimal damping of synchronous generator’s oscillations only in a small part of operating range. Parameters of the power system stabilizer are usually tuned for the selected operating point. Extensive variations of the synchronous generator’s operation result in changed dynamic characteristics. This is the reason that the power system stabilizer tuned for the nominal operating point does not satisfy preferred damping in the overall operation area. The small-signal stability and the transient stability of the synchronous generators have represented an attractive problem for testing different concepts of the modern control theory. Of all the methods, the adaptive control has proved to be the most suitable for the design of the power system stabilizers. The adaptive control has been used in order to assure the optimal damping through the entire synchronous generator’s operating range. The use of the adaptive control is possible because the loading variations and consequently the variations of the synchronous generator’s dynamic characteristics are, in most cases, essentially slower than the adaptation mechanism. The paper shows the development and the application of the self-tuning power system stabilizer based on recursive least square identification method and linear quadratic regulator. Identification method is used to calculate the parameters of the Heffron-Phillips model of the synchronous generator. On the basis of the calculated parameters of the synchronous generator’s mathematical model, the synthesis of the linear quadratic regulator is carried-out. The identification and the synthesis are implemented on-line. In this way, the self-tuning power system stabilizer adapts to the different operating conditions. A purpose of this paper is to contribute to development of the more effective power system stabilizers, which would replace currently used linear stabilizers. The presented self-tuning power system stabilizer makes the tuning of the controller parameters easier and assures damping improvement in the complete operating range. The results of simulations and experiments show essential improvement of the synchronous generator’s damping and power system stability.
Abstract: Age determination of unknown dead bodies in forensic personal identification is a complicated process which involves the application of numerous methods and techniques. Skeletal remains are less exposed to influences of environmental factors. In order to enhance the accuracy of forensic age estimation additional properties of bones correlating with age are required to be revealed. Material and Methods: Dimensional examination of the sella turcica was carried out on cadavers with the cranium opened by a circular vibrating saw. The sample consisted of a total of 90 Russian subjects, ranging in age from two months and 87 years. Results: The tendency of dimensional variations throughout life was detected. There were no observed gender differences in the morphometry of the sella turcica. The shared use of the sella turcica depth and length values revealed the possibility to categorize an examined sample in a certain age period. Conclusions: Based on the results of existing methods of age determination, the morphometry of the sella turcica can be an additional characteristic, amplifying the received values, and accordingly, increasing the accuracy of forensic biological age diagnosis.
Abstract: Seismic inversion is a technique which has been in use for years and its main goal is to estimate and to model physical characteristics of rocks and fluids. Generally, it is a combination of seismic and well-log data. Seismic inversion can be carried out through different methods; we have conducted and compared post-stack and pre- stack seismic inversion methods on real data in one of the fields in the Persian Gulf. Pre-stack seismic inversion can transform seismic data to rock physics such as P-impedance, S-impedance and density. While post- stack seismic inversion can just estimate P-impedance. Then these parameters can be used in reservoir identification. Based on the results of inverting seismic data, a gas reservoir was detected in one of Hydrocarbon oil fields in south of Iran (Persian Gulf). By comparing post stack and pre-stack seismic inversion it can be concluded that the pre-stack seismic inversion provides a more reliable and detailed information for identification and prediction of hydrocarbon reservoirs.
Abstract: The paper describes the use of subspace based
identification methods for auto tuning of a state space control system.
The plant is an unstable but self balancing transport robot. Because
of the unstable character of the process it has to be identified
from closed loop input-output data. Based on the identified model
a state space controller combined with an observer is calculated. The
subspace identification algorithm and the controller design procedure
is combined to a auto tuning method. The capability of the approach
was verified in a simulation experiments under different process
conditions.
Abstract: This article is about first step of structural health monitoring by identifying structural system in the presence of unknown input. In the structural system identification, identification of structural parameters such as stiffness and damping are considered. In this study, the Kalman filter (KF) design for structural systems with unknown excitation is expressed. External excitations, such as earthquakes, wind or any other forces are not measured or not available. The purpose of this filter is its strengths to estimate the state variables of the system in the presence of unknown input. Also least squares estimation (LSE) method with unknown input is studied. Estimates of parameters have been adopted. Finally, using two examples advantages and drawbacks of both methods are studied.
Abstract: We present a family of data-reusing and affine
projection algorithms. For identification of a noisy linear finite
impulse response channel, a partial knowledge of a channel,
especially noise, can be used to improve the performance of
the adaptive filter. Motivated by this fact, the proposed scheme
incorporates an estimate of a knowledge of noise. A constraint, called
the adaptive noise constraint, estimates an unknown information of
noise. By imposing this constraint on a cost function of data-reusing
and affine projection algorithms, a cost function based on the adaptive
noise constraint and Lagrange multiplier is defined. Minimizing the
new cost function leads to the adaptive noise constrained (ANC)
data-reusing and affine projection algorithms. Experimental results
comparing the proposed schemes to standard data-reusing and affine
projection algorithms clearly indicate their superior performance.
Abstract: We propose two affine projection algorithms (APA)
with variable regularization parameter. The proposed algorithms
dynamically update the regularization parameter that is fixed in the
conventional regularized APA (R-APA) using a gradient descent
based approach. By introducing the normalized gradient, the proposed
algorithms give birth to an efficient and a robust update scheme for
the regularization parameter. Through experiments we demonstrate
that the proposed algorithms outperform conventional R-APA in
terms of the convergence rate and the misadjustment error.
Abstract: We present a new subband adaptive filter (R-SAF)
which is robust against impulsive noise in system identification. To
address the vulnerability of adaptive filters based on the L2-norm
optimization criterion against impulsive noise, the R-SAF comes from
the L1-norm optimization criterion with a constraint on the energy
of the weight update. Minimizing L1-norm of the a posteriori error
in each subband with a constraint on minimum disturbance gives
rise to the robustness against the impulsive noise and the capable
convergence performance. Experimental results clearly demonstrate
that the proposed R-SAF outperforms the classical adaptive filtering
algorithms when impulsive noise as well as background noise exist.
Abstract: Polycyclic Aromatic Hydrocarbons (PAHs) are
formed mainly because of incomplete combustion of organic
materials during industrial, domestic activities or natural occurrence.
Their toxicity and contamination of terrestrial and aquatic ecosystem
have been established. However, with limited validity index, previous
research has focused on PAHs isomer pair ratios of variable
physicochemical properties in source identification. The objective of
this investigation was to determine the empirical validity of Pearson
Correlation Coefficient (PCC) and Cluster Analysis (CA) in PAHs
source identification along soil samples of different land uses.
Therefore, 16 PAHs grouped, as Endocrine Disruption Substances
(EDSs) were determined in 10 sample stations in top and sub soils
seasonally. PAHs was determined the use of Varian 300 gas
chromatograph interfaced with flame ionization detector. Instruments
and reagents used are of standard and chromatographic grades
respectively. PCC and CA results showed that the classification of
PAHs along pyrolitic and petrogenic organics used in source
signature is about the predominance PAHs in environmental matrix.
Therefore, the distribution of PAHs in the studied stations revealed
the presence of trace quantities of the vast majority of the sixteen
PAHs, which may ultimately inhabit the actual source signature
authentication. Therefore, factors to be considered when evaluating
possible sources of PAHs could be; type and extent of bacterial
metabolism, transformation products/substrates, and environmental
factors such as salinity, pH, oxygen concentration, nutrients, light
intensity, temperature, co-substrates, and environmental medium are
hereby recommended as factors to be considered when evaluating
possible sources of PAHs.
Abstract: ESPRIT-TLS method appears a good choice for high
resolution fault detection in induction machines. It has a very high
effectiveness in the frequency and amplitude identification.
Contrariwise, it presents a high computation complexity which
affects its implementation in real time fault diagnosis. To avoid this
problem, a Fast-ESPRIT algorithm that combined the IIR band-pass
filtering technique, the decimation technique and the original
ESPRIT-TLS method was employed to enhance extracting accurately
frequencies and their magnitudes from the wind stator current with
less computation cost. The proposed algorithm has been applied to
verify the wind turbine machine need in the implementation of an online,
fast, and proactive condition monitoring. This type of remote
and periodic maintenance provides an acceptable machine lifetime,
minimize its downtimes and maximize its productivity. The
developed technique has evaluated by computer simulations under
many fault scenarios. Study results prove the performance of Fast-
ESPRIT offering rapid and high resolution harmonics recognizing
with minimum computation time and less memory cost.
Abstract: Iris codes contain bits with different entropy. This
work investigates different strategies to reduce the size of iris
code templates with the aim of reducing storage requirements and
computational demand in the matching process. Besides simple subsampling
schemes, also a binary multi-resolution representation as
used in the JBIG hierarchical coding mode is assessed. We find that
iris code template size can be reduced significantly while maintaining
recognition accuracy. Besides, we propose a two-stage identification
approach, using small-sized iris code templates in a pre-selection
stage, and full resolution templates for final identification, which
shows promising recognition behaviour.
Abstract: We present a solution to the Maxmin u/E parameters
estimation problem of possibility distributions in m-dimensional
case. Our method is based on geometrical approach, where minimal
area enclosing ellipsoid is constructed around the sample. Also we
demonstrate that one can improve results of well-known algorithms
in fuzzy model identification task using Maxmin u/E parameters
estimation.
Abstract: The paper deals with cross-gender and cross-linguistic comparison of pitch characteristics for Tuvinian with two other Turkic languages - Uzbek and Azerbaijani, based on the results of statistical analysis of pitch parameter values and intonation patterns used by male and female speakers.
The main goal of our work is to obtain the ranges of pitch parameter values typical for Tuvinian speakers for the purpose of automatic language identification. We also propose a cross-gender analysis of declarative intonation in the poorly studied Tuvinian language.
The ranges of pitch parameter values were obtained by means of specially developed software that deals with the distribution of pitch values and allows us to obtain statistical language-specific pitch intervals.
Abstract: A scheme integrated with deterministic–stochastic subspace system identification and the method of damage localization vector is proposed in this study for damage detection of structures based on seismic response data. A series of shaking table tests using a five-storey steel frame has been conducted in National Center for Research on Earthquake Engineering (NCREE), Taiwan. Damage condition is simulated by reducing the cross-sectional area of some of the columns at the bottom. Both single and combinations of multiple damage conditions at various locations have been considered. In the system identification analysis, either full or partial observation conditions have been taken into account. It has been shown that the damaged stories can be identified from global responses of the structure to earthquakes if sufficiently observed. In addition to detecting damage(s) with respect to the intact structure, identification of new or extended damages of the as-damaged (ill-conditioned) counterpart has also been studied. The proposed scheme proves to be effective.
Abstract: Standard Hammerstein-Wiener models consist of a linear subsystem sandwiched by two memoryless nonlinearities. The problem of identifying Hammerstein-Wiener systems is addressed in the presence of linear subsystem of structure totally unknown and polynomial input and output nonlinearities. Presently, the system nonlinearities are allowed to be noninvertible. The system identification problem is dealt by developing a two-stage frequency identification method. First, the parameters of system nonlinearities are identified. In the second stage, a frequency approach is designed to estimate the linear subsystem frequency gain. All involved estimators are proved to be consistent.
Abstract: The results of dissertation research "Preventing and
combating corruption in public procurement" are presented in this
publication. The study was conducted 2011 till 2013 in a Member
State of the European Union– in the Republic of Latvia.
Goal of the thesis is to explore corruption prevention and
combating issues in public procurement sphere, to identify the
prevalence rates, determinants and contributing factors and
prevention opportunities in Latvia.
In the first chapter the author analyzes theoretical aspects of
understanding corruption in public procurement, with particular
emphasis on corruption definition problem, its nature, causes and
consequences. A separate section is dedicated to the public
procurement concept, mechanism and legal framework. In the first
part of this work the author presents cognitive methodology of
corruption in public procurement field, based on which the author has
carried out an analysis of corruption situation in public procurement
in Republic of Latvia.
In the second chapter of the thesis, the author analyzes the
problem of corruption in public procurement, including its historical
aspects, typology and classification of corruption subjects involved,
corruption risk elements in public procurement and their
identification. During the development of the second chapter author's
practical experience in public procurements was widely used.
The third and fourth chapter deals with issues related to the
prevention and combating corruption in public procurement, namely
the operation of the concept, principles, methods and techniques,
subjects in Republic of Latvia, as well as an analysis of foreign
experience in preventing and combating corruption. The fifth chapter
is devoted to the corruption prevention and combating perspectives
and their assessment. In this chapter the author has made the
evaluation of corruption prevention and combating measures
efficiency in Republic of Latvia, assessment of anti-corruption
legislation development stage in public procurement field in Latvia.
Abstract: As internet continues to expand its usage with an
enormous number of applications, cyber-threats have significantly
increased accordingly. Thus, accurate detection of malicious traffic in
a timely manner is a critical concern in today’s Internet for security.
One approach for intrusion detection is to use Machine Learning (ML)
techniques. Several methods based on ML algorithms have been
introduced over the past years, but they are largely limited in terms of
detection accuracy and/or time and space complexity to run. In this
work, we present a novel method for intrusion detection that
incorporates a set of supervised learning algorithms. The proposed
technique provides high accuracy and outperforms existing techniques
that simply utilizes a single learning method. In addition, our
technique relies on partial flow information (rather than full
information) for detection, and thus, it is light-weight and desirable for
online operations with the property of early identification. With the
mid-Atlantic CCDC intrusion dataset publicly available, we show that
our proposed technique yields a high degree of detection rate over 99%
with a very low false alarm rate (0.4%).
Abstract: In the following article we begin from a multi-parameter unstable nonlinear model of a Quadrotor. We design a control to stabilize and assure the attitude of the device, starting off a linearized system at the equilibrium point of the null angles of Euler (hover),
which provides us a control with limited capacities at small angles of rotation of the vehicle in three dimensions. In order to clear this obstacle, we propose the identification of models in different angles by means of simulations and the design of a controller specifically implemented for the identification task, that in future works will allow the development of controllers according to fast and agile angles of Euler for Quadrotor.