Abstract: Recently global concerns for the energy security have
steadily been on the increase and are expected to become a major
issue over the next few decades. Energy security refers to a resilient
energy system. This resilient system would be capable of
withstanding threats through a combination of active, direct security
measures and passive or more indirect measures such as redundancy,
duplication of critical equipment, diversity in fuel, other sources of
energy, and reliance on less vulnerable infrastructure. Threats and
disruptions (disturbances) to one part of the energy system affect
another. The paper presents methodology in theoretical background
about energy system as an interconnected network and energy supply
disturbances impact to the network. The proposed methodology uses
a network flow approach to develop mathematical model of the
energy system network as the system of nodes and arcs with energy
flowing from node to node along paths in the network.
Abstract: Support Vector Machine (SVM) is a statistical learning tool that was initially developed by Vapnik in 1979 and later developed to a more complex concept of structural risk minimization (SRM). SVM is playing an increasing role in applications to detection problems in various engineering problems, notably in statistical signal processing, pattern recognition, image analysis, and communication systems. In this paper, SVM was applied to the detection of medical ultrasound images in the presence of partially developed speckle noise. The simulation was done for single look and multi-look speckle models to give a complete overlook and insight to the new proposed model of the SVM-based detector. The structure of the SVM was derived and applied to clinical ultrasound images and its performance in terms of the mean square error (MSE) metric was calculated. We showed that the SVM-detected ultrasound images have a very low MSE and are of good quality. The quality of the processed speckled images improved for the multi-look model. Furthermore, the contrast of the SVM detected images was higher than that of the original non-noisy images, indicating that the SVM approach increased the distance between the pixel reflectivity levels (detection hypotheses) in the original images.
Abstract: Importance of software quality is increasing leading to development of new sophisticated techniques, which can be used in constructing models for predicting quality attributes. One such technique is Artificial Neural Network (ANN). This paper examined the application of ANN for software quality prediction using Object- Oriented (OO) metrics. Quality estimation includes estimating maintainability of software. The dependent variable in our study was maintenance effort. The independent variables were principal components of eight OO metrics. The results showed that the Mean Absolute Relative Error (MARE) was 0.265 of ANN model. Thus we found that ANN method was useful in constructing software quality model.
Abstract: The paradigm of mobile agent provides a promising technology for the development of distributed and open applications. However, one of the main obstacles to widespread adoption of the mobile agent paradigm seems to be security. This paper treats the security of the mobile agent against malicious host attacks. It describes generic mobile agent protection architecture. The proposed approach is based on the dynamic adaptability and adopts the reflexivity as a model of conception and implantation. In order to protect it against behaviour analysis attempts, the suggested approach supplies the mobile agent with a flexibility faculty allowing it to present an unexpected behaviour. Furthermore, some classical protective mechanisms are used to reinforce the level of security.
Abstract: In this paper, to optimize the “Characteristic Straight Line Method" which is used in the soil displacement analysis, a “best estimate" of the geodetic leveling observations has been achieved by taking in account the concept of 'Height systems'. This concept has been discussed in detail and consequently the concept of “height". In landslides dynamic analysis, the soil is considered as a mosaic of rigid blocks. The soil displacement has been monitored and analyzed by using the “Characteristic Straight Line Method". Its characteristic components have been defined constructed from a “best estimate" of the topometric observations. In the measurement of elevation differences, we have used the most modern leveling equipment available. Observational procedures have also been designed to provide the most effective method to acquire data. In addition systematic errors which cannot be sufficiently controlled by instrumentation or observational techniques are minimized by applying appropriate corrections to the observed data: the level collimation correction minimizes the error caused by nonhorizontality of the leveling instrument's line of sight for unequal sight lengths, the refraction correction is modeled to minimize the refraction error caused by temperature (density) variation of air strata, the rod temperature correction accounts for variation in the length of the leveling rod' s Invar/LO-VAR® strip which results from temperature changes, the rod scale correction ensures a uniform scale which conforms to the international length standard and the introduction of the concept of the 'Height systems' where all types of height (orthometric, dynamic, normal, gravity correction, and equipotential surface) have been investigated. The “Characteristic Straight Line Method" is slightly more convenient than the “Characteristic Circle Method". It permits to evaluate a displacement of very small magnitude even when the displacement is of an infinitesimal quantity. The inclination of the landslide is given by the inverse of the distance reference point O to the “Characteristic Straight Line". Its direction is given by the bearing of the normal directed from point O to the Characteristic Straight Line (Fig..6). A “best estimate" of the topometric observations was used to measure the elevation of points carefully selected, before and after the deformation. Gross errors have been eliminated by statistical analyses and by comparing the heights within local neighborhoods. The results of a test using an area where very interesting land surface deformation occurs are reported. Monitoring with different options and qualitative comparison of results based on a sufficient number of check points are presented.
Abstract: The problem of estimating time-varying regression is
inevitably concerned with the necessity to choose the appropriate
level of model volatility - ranging from the full stationarity of instant
regression models to their absolute independence of each other. In the
stationary case the number of regression coefficients to be estimated
equals that of regressors, whereas the absence of any smoothness
assumptions augments the dimension of the unknown vector by the
factor of the time-series length. The Akaike Information Criterion
is a commonly adopted means of adjusting a model to the given
data set within a succession of nested parametric model classes,
but its crucial restriction is that the classes are rigidly defined by
the growing integer-valued dimension of the unknown vector. To
make the Kullback information maximization principle underlying the
classical AIC applicable to the problem of time-varying regression
estimation, we extend it onto a wider class of data models in which
the dimension of the parameter is fixed, but the freedom of its values
is softly constrained by a family of continuously nested a priori
probability distributions.
Abstract: The purpose of this paper is to perform a multidisciplinary design and analysis (MDA) of honeycomb panels used in the satellites structural design. All the analysis is based on clamped-free boundary conditions. In the present work, detailed finite element models for honeycomb panels are developed and analysed. Experimental tests were carried out on a honeycomb specimen of which the goal is to compare the previous modal analysis made by the finite element method as well as the existing equivalent approaches. The obtained results show a good agreement between the finite element analysis, equivalent and tests results; the difference in the first two frequencies is less than 4% and less than 10% for the third frequency. The results of the equivalent model presented in this analysis are obtained with a good accuracy. Moreover, investigations carried out in this research relate to the honeycomb plate modal analysis under several aspects including the structural geometrical variation by studying the various influences of the dimension parameters on the modal frequency, the variation of core and skin material of the honeycomb. The various results obtained in this paper are promising and show that the geometry parameters and the type of material have an effect on the value of the honeycomb plate modal frequency.
Abstract: The paper discusses a computationally efficient
method for the design of prototype filters required for the
implementation of an M-band cosine modulated filter bank. The
prototype filter is formulated as an optimum interpolated FIR filter.
The optimum interpolation factor requiring minimum number of
multipliers is used. The model filter as well as the image suppressor
will be designed using the Kaiser window. The method will seek to
optimize a single parameter namely cutoff frequency to minimize the
distortion in the overlapping passband.
Abstract: Several models have been introduced so far for single
electron box, SEB, which all of them were restricted to DC response
and or low temperature limit. In this paper we introduce a new time
dependent, high temperature analytical model for SEB for the first
time. DC behavior of the introduced model will be verified against
SIMON software and its time behavior will be verified against a
newly published paper regarding step response of SEB.
Abstract: In this paper the design of maximally flat linear phase
finite impulse response (FIR) filters is considered. The problem is
handled with totally two different approaches. The first one is
completely deterministic numerical approach where the problem is
formulated as a Linear Complementarity Problem (LCP). The other
one is based on a combination of Markov Random Fields (MRF's)
approach with messy genetic algorithm (MGA). Markov Random
Fields (MRFs) are a class of probabilistic models that have been
applied for many years to the analysis of visual patterns or textures.
Our objective is to establish MRFs as an interesting approach to
modeling messy genetic algorithms. We establish a theoretical result
that every genetic algorithm problem can be characterized in terms of
a MRF model. This allows us to construct an explicit probabilistic
model of the MGA fitness function and introduce the Ising MGA.
Experimentations done with Ising MGA are less costly than those
done with standard MGA since much less computations are involved.
The least computations of all is for the LCP. Results of the LCP,
random search, random seeded search, MGA, and Ising MGA are
discussed.
Abstract: In this paper, a methodology of a model based on
predicting the tool forces oblique machining are introduced by
adopting the orthogonal technique. The applied analytical calculation
is mostly based on Devries model and some parts of the methodology
are employed from Amareggo-Brown model. Model validation is
performed by comparing experimental data with the prediction results
on machining titanium alloy (Ti-6Al-4V) based on micro-cutting tool
perspective. Good agreements with the experiments are observed. A
detailed friction form that affected the tool forces also been examined
with reasonable results obtained.
Abstract: Gas hydrates can agglomerate and block multiphase oil and gas pipelines when water is present at hydrate forming conditions. Using "Cold Flow Technology", the aim is to condition gas hydrates so that they can be transported as a slurry mixture without a risk of agglomeration. During the pipeline shut down however, hydrate particles may settle in bends and build hydrate plugs. An experimental setup has been designed and constructed to study the flow of such plugs at start up operations. Experiments have been performed using model fluid and model hydrate particles. The propagations of initial plugs in a bend were recorded with impedance probes along the pipe. The experimental results show a dispersion of the plug front. A peak in pressure drop was also recorded when the plugs were passing the bend. The evolutions of the plugs have been simulated by numerical integration of the incompressible mass balance equations, with an imposed mixture velocity. The slip between particles and carrier fluid has been calculated using a drag relation together with a particle-fluid force balance.
Abstract: In this paper, we probe into the traffic assignment problem by the chromosome-learning-based path finding method in simulation, which is to model the driver' behavior in the with-in-a-day process. By simply making a combination and a change of the traffic route chromosomes, the driver at the intersection chooses his next route. The various crossover and mutation rules are proposed with extensive examples.
Abstract: The first generation of Mobile Agents based Intrusion
Detection System just had two components namely data collection
and single centralized analyzer. The disadvantage of this type of
intrusion detection is if connection to the analyzer fails, the entire
system will become useless. In this work, we propose novel hybrid
model for Mobile Agent based Distributed Intrusion Detection
System to overcome the current problem. The proposed model has
new features such as robustness, capability of detecting intrusion
against the IDS itself and capability of updating itself to detect new
pattern of intrusions. In addition, our proposed model is also capable
of tackling some of the weaknesses of centralized Intrusion Detection
System models.
Abstract: A reliability, availability and maintainability (RAM) model has been built for acid gas removal plant for system analysis that will play an important role in any process modifications, if required, for achieving its optimum performance. Due to the complexity of the plant, the model was based on a Reliability Block Diagram (RBD) with a Monte Carlo simulation engine. The model has been validated against actual plant data as well as local expert opinions, resulting in an acceptable simulation model. The results from the model showed that the operation and maintenance can be further improved, resulting in reduction of the annual production loss.
Abstract: In this paper, the requirement for Coke quality
prediction, its role in Blast furnaces, and the model output is
explained. By applying method of Artificial Neural Networking
(ANN) using back propagation (BP) algorithm, prediction model has
been developed to predict CSR. Important blast furnace functions
such as permeability, heat exchanging, melting, and reducing
capacity are mostly connected to coke quality. Coke quality is further
dependent upon coal characterization and coke making process
parameters. The ANN model developed is a useful tool for process
experts to adjust the control parameters in case of coke quality
deviations. The model also makes it possible to predict CSR for new
coal blends which are yet to be used in Coke Plant. Input data to the
model was structured into 3 modules, for tenure of past 2 years and
the incremental models thus developed assists in identifying the
group causing the deviation of CSR.
Abstract: This paper is a survey of current component-based
software technologies and the description of promotion and
inhibition factors in CBSE. The features that software components
inherit are also discussed. Quality Assurance issues in componentbased
software are also catered to. The feat research on the quality
model of component based system starts with the study of what the
components are, CBSE, its development life cycle and the pro &
cons of CBSE. Various attributes are studied and compared keeping
in view the study of various existing models for general systems and
CBS. When illustrating the quality of a software component an apt
set of quality attributes for the description of the system (or
components) should be selected. Finally, the research issues that can
be extended are tabularized.
Abstract: A novel sponge submerged membrane bioreactor
(SSMBR) was developed to effectively remove organics and
nutrients from wastewater. Sponge is introduced within the SSMBR
as a medium for the attached growth of biomass. This paper evaluates
the effects of new and acclimatized sponges for dissolved organic
carbon (DOC) removal from wastewater at different mixed liquor
suspended solids- (MLSS) concentration of the sludge. It was
observed in a series of experimental studies that the acclimatized
sponge performed better than the new sponge whilst the optimum
DOC removal could be achieved at 10g/L of MLSS with the
acclimatized sponge. Moreover, the paper analyses the relationships
between the MLSSsponge/MLSSsludge and the DOC removal efficiency
of SSMBR. The results showed a non-linear relationship between the
biomass parameters of the sponge and the sludge, and the DOC
removal efficiency of SSMBR. A second-order polynomial function
could reasonably represent these relationships.
Abstract: Validation of an automation system is an important issue. The goal is to check if the system under investigation, modeled by a Petri net, never enters the undesired states. Usually, tools dedicated to Petri nets such as DESIGN/CPN are used to make reachability analysis. The biggest problem with this approach is that it is impossible to generate the full occurence graph of the system because it is too large. In this paper, we show how computational methods such as temporal logic model checking and Groebner bases can be used to verify the correctness of the design of an automation system. We report our experimental results with two automation systems: the Automated Guided Vehicle (AGV) system and the traffic light system. Validation of these two systems ranged from 10 to 30 seconds on a PC depending on the optimizing parameters.