Abstract: Classification is one of the primary themes in
computational biology. The accuracy of classification strongly
depends on quality of a dataset, and we need some method to
evaluate this quality. In this paper, we propose a new graphical
analysis method using 'Membership-Deviation Graph (MDG)' for
analyzing quality of a dataset. MDG represents degree of
membership and deviations for instances of a class in the dataset. The
result of MDG analysis is used for understanding specific feature and
for selecting best feature for classification.
Abstract: This study considers the problem of determining
operation and maintenance schedules for a containership equipped
with components during its sailing according to a pre-determined
navigation schedule. The operation schedule, which specifies work
time of each component, determines the due-date of each maintenance
activity, and the maintenance schedule specifies the actual start
time of each maintenance activity. The main constraints are component
requirements, workforce availability, working time limitation,
and inter-maintenance time. To represent the problem mathematically,
a mixed integer programming model is developed. Then,
due to the problem complexity, we suggest a heuristic for the objective
of minimizing the sum of earliness and tardiness between the
due-date and the starting time of each maintenance activity. Computational
experiments were done on various test instances and the
results are reported.
Abstract: When the profile information of an existing road is
missing or not up-to-date and the parameters of the vertical
alignment are needed for engineering analysis, the engineer has to recreate
the geometric design features of the road alignment using
collected profile data. The profile data may be collected using
traditional surveying methods, global positioning systems, or digital
imagery. This paper develops a method that estimates the parameters
of the geometric features that best characterize the existing vertical
alignments in terms of tangents and the expressions of the curve, that
may be symmetrical, asymmetrical, reverse, and complex vertical
curves. The method is implemented using an Excel-based
optimization method that minimizes the differences between the
observed profile and the profiles estimated from the equations of the
vertical curve. The method uses a 'wireframe' representation of the
profile that makes the proposed method applicable to all types of
vertical curves. A secondary contribution of this paper is to introduce
the properties of the equal-arc asymmetrical curve that has been
recently developed in the highway geometric design field.
Abstract: In this paper in consideration of each available
techniques deficiencies for speech recognition, an advanced method
is presented that-s able to classify speech signals with the high
accuracy (98%) at the minimum time. In the presented method, first,
the recorded signal is preprocessed that this section includes
denoising with Mels Frequency Cepstral Analysis and feature
extraction using discrete wavelet transform (DWT) coefficients; Then
these features are fed to Multilayer Perceptron (MLP) network for
classification. Finally, after training of neural network effective
features are selected with UTA algorithm.
Abstract: Isobaric vapor-liquid equilibrium measurements are reported for the binary mixtures of Mesitylene + 1-Heptanol and Mesitylene + 1-Octanol at 97.3 kPa. The measurements have been performed using a vapor recirculating type (modified Othmer's) equilibrium still. Both the mixtures show positive deviation from ideality. The Mesitylene + 1-Heptanol mixture forms an azeotrope whereas Mesitylene + 1- Octanol form a non – azeotropic mixture. The activity coefficients have been calculated taking into consideration the vapor phase nonideality. The data satisfy the thermodynamic consistency tests of Herington, and Hirata. The activity coefficients have been satisfactorily correlated by means of the Margules, Redlich-Kister, Wilson, Black, and NRTL equations. The activity coefficient values have also been obtained by UNIFAC method.
Abstract: Rockfall is a kind of irregular geological disaster. Its
destruction time, space and movements are highly random. The impact
force is determined by the way and velocity rocks move. The
movement velocity of a rockfall depends on slope gradient of its
moving paths, height, slope surface roughness and rock shapes. For
effectively mitigate and prevent disasters brought by rockfalls, it is
required to precisely calculate the moving paths of a rockfall so as to
provide the best protective design. This paper applies Colorado
Rockfall Simulation Program (CRSP) as our study tool to discuss the
impact of slope shape and surface roughness on the moving paths of a
single rockfall. The analytical results showed that the slope, m=1:1,
acted as the threshold for rockfall bounce height on a monoclinal slight
slope. When JRC ´╝£ 1.2, movement velocity reduced and bounce
height increased as JCR increased. If slope fixed and JRC increased,
the bounce height of rocks increased gradually with reducing
movement velocity. Therefore, the analysis on the moving paths of
rockfalls with CRSP could simulate bouncing of falling rocks. By
analyzing moving paths, velocity, and bounce height of falling rocks,
we could effectively locate impact points of falling rocks on a slope.
Such analysis can be served as a reference for future disaster
prevention and control.
Abstract: In this work a novel approach for color image
segmentation using higher order entropy as a textural feature for
determination of thresholds over a two dimensional image histogram
is discussed. A similar approach is applied to achieve multi-level
thresholding in both grayscale and color images. The paper discusses
two methods of color image segmentation using RGB space as the
standard processing space. The threshold for segmentation is decided
by the maximization of conditional entropy in the two dimensional
histogram of the color image separated into three grayscale images of
R, G and B. The features are first developed independently for the
three ( R, G, B ) spaces, and combined to get different color
component segmentation. By considering local maxima instead of the
maximum of conditional entropy yields multiple thresholds for the
same image which forms the basis for multilevel thresholding.
Abstract: In this paper we will develop further the sequential life test approach presented in a previous article by [1] using an underlying two parameter Inverse Weibull sampling distribution. The location parameter or minimum life will be considered equal to zero. Once again we will provide rules for making one of the three possible decisions as each observation becomes available; that is: accept the null hypothesis H0; reject the null hypothesis H0; or obtain additional information by making another observation. The product being analyzed is a new electronic component. There is little information available about the possible values the parameters of the corresponding Inverse Weibull underlying sampling distribution could have.To estimate the shape and the scale parameters of the underlying Inverse Weibull model we will use a maximum likelihood approach for censored failure data. A new example will further develop the proposed sequential life testing approach.
Abstract: Samples of CoFe2-xCrxO4 where x varies from 0.0 to 0.5 were prepared by co-precipitation route. These samples were sintered at 750°C for 2 hours. These particles were characterized by X-ray diffraction (XRD) at room temperature. The FCC spinel structure was confirmed by XRD patterns of the samples. The crystallite sizes of these particles were calculated from the most intense peak by Scherrer formula. The crystallite sizes lie in the range of 37-60 nm. The lattice parameter was found decreasing upon substitution of Cr. DC electrical resistivity was measured as a function of temperature. The room temperature thermoelectric power was measured for the prepared samples. The magnitude of Seebeck coefficient depends on the composition and resistivity of the samples.
Abstract: The goal of this work is to improve the efficiency and the reliability of the automatic artifact rejection, in particular from the Electroencephalographic (EEG) recordings. Artifact rejection is a key topic in signal processing. The artifacts are unwelcome signals that may occur during the signal acquisition and that may alter the analysis of the signals themselves. A technique for the automatic artifact rejection, based on the Independent Component Analysis (ICA) for the artifact extraction and on some high order statistics such as kurtosis and Shannon-s entropy, was proposed some years ago in literature. In this paper we enhance this technique introducing the Renyi-s entropy. The performance of our method was tested exploiting the Independent Component scalp maps and it was compared to the performance of the method in literature and it showed to outperform it.
Abstract: In ubiqutious healthcare environment, user's health data are transfered to the remote healthcare server by the user's wearable system or mobile phone. These collected user's health data should be managed and analyzed in the healthcare server, so that care giver or user can monitor user's physiological state. In this paper, we designed and developed the intelligent Healthcare Server to manage the user's health data using CDSS and ontology. Our system can analyze user's health data semantically using CDSS and ontology, and report the result of user's physiological raw data to the user and care giver.
Abstract: Recently, a growing interest has emerged on the
development of new and efficient energy sources, due to the inevitable extinction of the nonrenewable energy reserves. One of
these alternative sources which has a great potential and sustainability to meet up the energy demand is biomass energy. This
significant energy source can be utilized with various energy
conversion technologies, one of which is biomass gasification in
supercritical water.
Water, being the most important solvent in nature, has very important characteristics as a reaction solvent under supercritical
circumstances. At temperatures above its critical point (374.8oC and
22.1 MPa), water becomes more acidic and its diffusivity increases.
Working with water at high temperatures increases the thermal
reaction rate, which in consequence leads to a better dissolving of the
organic matters and a fast reaction with oxygen. Hence, supercritical water offers a control mechanism depending on solubility, excellent
transport properties based on its high diffusion ability and new reaction possibilities for hydrolysis or oxidation.
In this study the gasification of a real biomass, namely olive mill
wastewater (OMW), in supercritical water is investigated with the
use of Pt/Al2O3 and Ni/Al2O3 catalysts. OMW is a by-product
obtained during olive oil production, which has a complex nature
characterized by a high content of organic compounds and
polyphenols. These properties impose OMW a significant pollution
potential, but at the same time, the high content of organics makes
OMW a desirable biomass candidate for energy production.
All of the catalytic gasification experiments were made with five
different reaction temperatures (400, 450, 500, 550 and 600°C),
under a constant pressure of 25 MPa. For the experiments conducted
with Ni/Al2O3 catalyst, the effect of five reaction times (30, 60, 90,
120 and 150 s) was investigated. However, procuring that similar
gasification efficiencies could be obtained at shorter times, the experiments were made by using different reaction times (10, 15, 20,
25 and 30 s) for the case of Pt/Al2O3 catalyst. Through these experiments, the effects of temperature, time and catalyst type on the
gasification yields and treatment efficiencies were investigated.
Abstract: The primary education system in Indonesia involved the community recognized as the school committee, to take a part in the process of achieving the quality of education via the school facility performance, the low level of school committee involvement in the education system has become the issue in the development of education and reflected to the quality of education. This paper will discuss the conceptual framework and methodology for the performance of school committees within the management of school facilities in Batubara district of Indonesia. The concepts of Community based Facility Management (CbFM) and Logometrix are used as a basis to measure the school committee performance in order to address the needs of quality school management. The data will be taken from questionnaires distributed for those who work and use school facilities spread over seven sub district of Batubara, Indonesia. The result of this study is expected to provide a guide for evaluating the performance of existing school committee in improving the quality of education in Indonesia.
Abstract: This paper proposes a technique to protect against
email bombing. The technique employs a statistical approach, Naïve
Bayes (NB), and Neural Networks to show that it is possible to
differentiate between good and bad traffic to protect against email
bombing attacks. Neural networks and Naïve Bayes can be trained
by utilizing many email messages that include both input and output
data for legitimate and non-legitimate emails. The input to the model
includes the contents of the body of the messages, the subject, and
the headers. This information will be used to determine if the email
is normal or an attack email. Preliminary tests suggest that Naïve
Bayes can be trained to produce an accurate response to confirm
which email represents an attack.
Abstract: In this work Artificial Intelligence (AI) techniques like Fuzzy logic, Genetic Algorithms and Particle Swarm Optimization have been used to improve the performance of the Automatic Generation Control (AGC) system. Instead of applying Genetic Algorithms and Particle swarm optimization independently for optimizing the parameters of the conventional AGC with PI controller, an intelligent tuned Fuzzy logic controller (acting as the secondary controller in the AGC system) has been designed. The controller gives an improved dynamic performance for both hydrothermal and thermal-thermal power systems under a variety of operating conditions.
Abstract: In this paper are illustrated the principal aspects
connected with the numerical evaluation of thermal stress induced by high gradient temperature in the concrete beam. The reinforced concrete beam has many advantages over steel
beam, such as high resistance to high temperature, high resistance to thermal shock, Better resistance to fatigue and buckling, strong
resistance against, fire, explosion, etc.
The main drawback of the reinforced concrete beam is its poor resistance to tensile stresses. In order to investigate the thermal
induced tensile stresses, a numerical model of a transient thermal
analysis is presented for the evaluation of thermo-mechanical
response of concrete beam to the high temperature, taking into account the temperature dependence of the thermo physical properties of the concrete like thermal conductivity and specific heat.
Abstract: Many medical devices are driven by motor pumps. Some researchers reported that the vibration mainly affected medical devices using a motor pump. The purpose of this study was to examine the effect of stiffness and damping coefficient in a 3-dimensional (3D) model of a motor pump and spring. In the present paper, experimental and mathematical tests for the moments of inertia of the 3D model and the material properties were investigated by an INSTRON machine. The response surfaces could be generated by using 3D multi-body analysis and the design of experiment method. It showed that differences in contours of the response surface were clearly found for the particular area. Displacement of the center of the motor pump was decreased at K≈2000 N/M, C≈12.5 N-sec/M. However, the frequency was increased at K≈2000 N/M, C≈15 N-sec/M. In this study, this study suggested experimental technique for vibration reduction for a motor pump in medical device. The combined method suggested in this study will greatly contribute to design of medical devices concerning vibration and noise intervention.
Abstract: In this study, we developed a model to predict the
temperature and the pressure variation in an internal combustion
engine operated in HCCI (Homogeneous charge compression ignition)
mode. HCCI operation begins from aspirating of homogeneous charge
mixture through intake valve like SI (Spark ignition) engine and the
premixed charge is compressed until temperature and pressure of
mixture reach autoignition point like diesel engine. Combustion phase
was described by double-Wiebe function. The single zone model
coupled with an double-Wiebe function were performed to simulated
pressure and temperature between the period of IVC (Inlet valve close)
and EVO (Exhaust valve open). Mixture gas properties were
implemented using STANJAN and transfer the results to main model.
The model has considered the engine geometry and enables varying in
fuelling, equivalence ratio, manifold temperature and pressure. The
results were compared with the experiment and showed good
correlation with respect to combustion phasing, pressure rise, peak
pressure and temperature. This model could be adapted and use to
control start of combustion for HCCI engine.
Abstract: Histogram plays an important statistical role in digital
image processing. However, the existing quantum image models are
deficient to do this kind of image statistical processing because
different gray scales are not distinguishable. In this paper, a novel
quantum image representation model is proposed firstly in which the
pixels with different gray scales can be distinguished and operated
simultaneously. Based on the new model, a fast quantum algorithm of
constructing histogram for quantum image is designed. Performance
comparison reveals that the new quantum algorithm could achieve an
approximately quadratic speedup than the classical counterpart. The
proposed quantum model and algorithm have significant meanings for
the future researches of quantum image processing.
Abstract: Biochemical Oxygen Demand (BOD) is a measure of
the oxygen used in bacteria mediated oxidation of organic substances
in water and wastewater. Theoretically an infinite time is required for
complete biochemical oxidation of organic matter, but the
measurement is made over 5-days at 20 0C or 3-days at 27 0C test
period with or without dilution. Researchers have worked to further
reduce the time of measurement.
The objective of this paper is to review advancement made in
BOD measurement primarily to minimize the time and negate the
measurement difficulties. Survey of literature review in four such
techniques namely BOD-BARTTM, Biosensors, Ferricyanidemediated
approach, luminous bacterial immobilized chip method.
Basic principle, method of determination, data validation and their
advantage and disadvantages have been incorporated of each of the
methods.
In the BOD-BARTTM method the time lag is calculated for the
system to change from oxidative to reductive state. BIOSENSORS
are the biological sensing element with a transducer which produces
a signal proportional to the analyte concentration. Microbial species
has its metabolic deficiencies. Co-immobilization of bacteria using
sol-gel biosensor increases the range of substrate. In ferricyanidemediated
approach, ferricyanide has been used as e-acceptor instead
of oxygen. In Luminous bacterial cells-immobilized chip method,
bacterial bioluminescence which is caused by lux genes was
observed. Physiological responses is measured and correlated to
BOD due to reduction or emission.
There is a scope to further probe into the rapid estimation of BOD.