Abstract: Developing a stable early warning system (EWS)
model that is capable to give an accurate prediction is a challenging
task. This paper introduces k-nearest neighbour (k-NN) method
which never been applied in predicting currency crisis before with the
aim of increasing the prediction accuracy. The proposed k-NN
performance depends on the choice of a distance that is used where in
our analysis; we take the Euclidean distance and the Manhattan as a
consideration. For the comparison, we employ three other methods
which are logistic regression analysis (logit), back-propagation neural
network (NN) and sequential minimal optimization (SMO). The
analysis using datasets from 8 countries and 13 macro-economic
indicators for each country shows that the proposed k-NN method
with k = 4 and Manhattan distance performs better than the other
methods.
Abstract: Skin color based tracking techniques often assume a
static skin color model obtained either from an offline set of library
images or the first few frames of a video stream. These models
can show a weak performance in presence of changing lighting or
imaging conditions. We propose an adaptive skin color model based
on the Gaussian mixture model to handle the changing conditions.
Initial estimation of the number and weights of skin color clusters
are obtained using a modified form of the general Expectation
maximization algorithm, The model adapts to changes in imaging
conditions and refines the model parameters dynamically using spatial
and temporal constraints. Experimental results show that the method
can be used in effectively tracking of hand and face regions.
Abstract: In this paper we propose the study of a centrifugal pump control system driven by a three-phase induction motor, which is supplied by a PhotoVoltaic PV generator. The system includes solar panel, a DC / DC converter equipped with its MPPT control, a voltage inverter to three-phase Pulse Width Modulation - PWM and a centrifugal pump driven by a three phase induction motor. In order to control the flow of the centrifugal pump, a Direct Torque Control - DTC of the induction machine is used. To illustrate the performances of the control, simulation results are carried out using Matlab/Simulink.
Abstract: In this paper, the influencing parameters of a novel
purely mechanical wireless in-mould injection moulding sensor
were investigated. The sensor is capable of detecting the melt
front at predefined locations inside the mould. The sensor comprises
a movable pin which acts as the sensor element generating
structure-borne sound triggered by the passing melt front. Due to
the sensor design, melt pressure is the driving force. For pressure
level measurement during pin movement a pressure transducer
located at the same position as the movable pin. By deriving
a mathematical model for the mechanical movement, dominant
process parameters could be investigated towards their impact
on the melt front detection characteristic. It was found that the
sensor is not affected by the investigated parameters enabling it
for reliable melt front detection. In addition, it could be proved
that the novel sensor is in comparable range to conventional melt
front detection sensors.
Abstract: Content-based music retrieval generally involves analyzing, searching and retrieving music based on low or high level features of a song which normally used to represent artists, songs or music genre. Identifying them would normally involve feature extraction and classification tasks. Theoretically the greater features analyzed, the better the classification accuracy can be achieved but with longer execution time. Technique to select significant features is important as it will reduce dimensions of feature used in classification and contributes to the accuracy. Artificial Immune System (AIS) approach will be investigated and applied in the classification task. Bio-inspired audio content-based retrieval framework (B-ACRF) is proposed at the end of this paper where it embraces issues that need further consideration in music retrieval performances.
Abstract: Partitioning is a critical area of VLSI CAD. In order to build complex digital logic circuits its often essential to sub-divide multi -million transistor design into manageable Pieces. This paper looks at the various partitioning techniques aspects of VLSI CAD, targeted at various applications. We proposed an evolutionary time-series model and a statistical glitch prediction system using a neural network with selection of global feature by making use of clustering method model, for partitioning a circuit. For evolutionary time-series model, we made use of genetic, memetic & neuro-memetic techniques. Our work focused in use of clustering methods - K-means & EM methodology. A comparative study is provided for all techniques to solve the problem of circuit partitioning pertaining to VLSI design. The performance of all approaches is compared using benchmark data provided by MCNC standard cell placement benchmark net lists. Analysis of the investigational results proved that the Neuro-memetic model achieves greater performance then other model in recognizing sub-circuits with minimum amount of interconnections between them.
Abstract: Today's business environment requires that companies have access to highly relevant information in a matter of seconds.
Modern Business Intelligence tools rely on data structured mostly in traditional dimensional database schemas, typically represented by
star schemas. Dimensional modeling is already recognized as a
leading industry standard in the field of data warehousing although
several drawbacks and pitfalls were reported. This paper focuses on
the analysis of another data warehouse modeling technique - the
anchor modeling, and its characteristics in context with the standardized dimensional modeling technique from a query performance perspective. The results of the analysis show
information about performance of queries executed on database
schemas structured according to principles of each database modeling
technique.
Abstract: One part of the total employee’s reward is apart from basic wages or salary, employee’s benefits and intangible remuneration also so called contingent (variable) pay. Contingent pay is connected to performance, contribution, cap competency or skills of individual employees, and to team’s or company-wide performance or to combination of few of the mentioned possibilities. Sometimes among the contingent pay is also incorporated the remuneration based on length of employment, when the financial reward is not connected to performance or skills, but to length of continuous employment either on one working position or in one level of remuneration scale. Main aim of this article is to define, based on available information, contingent pay, describe individual forms, its advantages and disadvantages and possibilities to utilization in practice; but also bring information not only about its extent and level of utilization of contingent pay by companies in one of the Czech Republic’s regions, but also mention their practical experience with this type of remuneration.
Abstract: In this paper, we have presented the effect of varying
time-delays on performance and stability in the single-channel multirate
sampled-data system in hard real-time (RT-Linux) environment.
The sampling task require response time that might exceed the
capacity of RT-Linux. So a straight implementation with RT-Linux is
not feasible, because of the latency of the systems and hence,
sampling period should be less to handle this task. The best sampling
rate is chosen for the sampled-data system, which is the slowest rate
meets all performance requirements. RT-Linux is consistent with its
specifications and the resolution of the real-time is considered 0.01
seconds to achieve an efficient result. The test results of our
laboratory experiment shows that the multi-rate control technique in
hard real-time operating system (RTOS) can improve the stability
problem caused by the random access delays and asynchronization.
Abstract: There are many real world problems in which
parameters like the arrival time of new jobs, failure of resources, and
completion time of jobs change continuously. This paper tackles the
problem of scheduling jobs with random due dates on multiple
identical machines in a stochastic environment. First to assign jobs to
different machine centers LPT scheduling methods have been used,
after that the particular sequence of jobs to be processed on the
machine have been found using simple stochastic techniques. The
performance parameter under consideration has been the maximum
lateness concerning the stochastic due dates which are independent
and exponentially distributed. At the end a relevant problem has been
solved using the techniques in the paper..
Abstract: The objective of this research is to study the technical
and economic performance of wind/diesel/battery (W/D/B) system
supplying a remote small gathering of six families using HOMER
software package. The electrical energy is to cater for the basic needs
for which the daily load pattern is estimated. Net Present Cost (NPC)
and Cost of Energy (COE) are used as economic criteria, while the measure of performance is % of power shortage. Technical and
economic parameters are defined to estimate the feasibility of the
system under study. Optimum system configurations are estimated for two sites. Using HOMER software, the simulation results showed that W/D/B systems are economical for the assumed community sites
as the price of generated electricity is about 0.308 $/kWh, without
taking external benefits into considerations. W/D/B systems are more
economical than W/B or diesel alone systems, as the COE is 0.86 $/kWh for W/B and 0.357 $/kWh for diesel alone.
Abstract: An Automated Rapid Maxillary Expander (ARME) is
a specially designed microcontroller-based orthodontic appliance to
overcome the shortcomings imposed by the traditional maxillary
expansion appliances. This new device is operates by automatically
widening the maxilla (upper jaw) by expanding the midpalatal suture
[1]. The ARME appliance that has been developed is a combination
of modified butterfly expander appliance, micro gear, micro motor,
and microcontroller to automatically produce light and continuous
pressure to expand the maxilla. For this study, the functionality of the
system is verified through laboratory tests by measure the forced
applied to the teeth each time the maxilla expands. The laboratory
test results show that the developed appliance meets the desired
performance specifications consistently.
Abstract: A novel nanofinishing process using improved ball
end magnetorheological (MR) finishing tool was developed for finishing of flat as well as 3D surfaces of ferromagnetic and non ferromagnetic workpieces. In this process a magnetically controlled
ball end of smart MR polishing fluid is generated at the tip surface of
the tool which is used as a finishing medium and it is guided to
follow the surface to be finished through computer controlled 3-axes
motion controller. The experiments were performed on ferromagnetic
workpiece surface in the developed MR finishing setup to study the effect of finishing time on final surface roughness. The performance
of present finishing process on final finished surface roughness was studied. The surface morphology was observed under scanning
electron microscopy and atomic force microscope. The final surface finish was obtained as low as 19.7 nm from the initial surface
roughness of 142.9 nm. The outcome of newly developed finishing process can be found useful in its applications in aerospace,
automotive, dies and molds manufacturing industries, semiconductor and optics machining etc.
Abstract: The empirical studies on High Performance Work Systems (HPWSs) and their impacts on firm performance have remarkably little in the developing countries. This paper reviews literatures on the HPWSs practices in different work settings, Western and Asian countries. A review on the empirical research leads to a conclusion that, country differences influence the Human Resource Management (HRM) practices. It is anticipated that there are similarities and differences in the extent of implementation of HPWSs practices by the Malaysian manufacturing firms due to the organizational contextual factors and, the HPWSs have a significant impact on firms- better performance amongst MNCs and local firms.
Abstract: In this paper, subtractive clustering based fuzzy inference system approach is used for early detection of faults in the function oriented software systems. This approach has been tested with real time defect datasets of NASA software projects named as PC1 and CM1. Both the code based model and joined model (combination of the requirement and code based metrics) of the datasets are used for training and testing of the proposed approach. The performance of the models is recorded in terms of Accuracy, MAE and RMSE values. The performance of the proposed approach is better in case of Joined Model. As evidenced from the results obtained it can be concluded that Clustering and fuzzy logic together provide a simple yet powerful means to model the earlier detection of faults in the function oriented software systems.
Abstract: An experimental study of anaerobic treatment was performed by hybrid upflow anaerobic sludge blanket (HUASB) reactor to treat produced water (PW) of an onshore crude oil terminal (COD: 1597 mg/L, NH3-N: 14.7 mg/L, phenol: 13.8 mg/L, BOD5: 862 mg/L, sodium: 6240 mg/L and chloride 9530 mg/L). The produced water with high salinity and other toxic substances will inhibit the methanogens performance if there is no adaptation on biomass before anaerobic digestion. COD removal from produced water was investigated at five different dilutions of produced water and tap water (TW) without any nutrient addition and pre-treatment. The dilution ratios were 1PW:4TW, 2PW:3TW, 3PW:2TW, 4PW:1TW and 5PW:0TW. The reactor was evaluated at mesophilic operating condition (35 ± 2 °C) at 5 days of HRT for 250 days continuous feed. The average COD removals for 1PW:4TW, 2PW:3TW, 3PW:2TW, 4PW:1TW and 5PW:0TW were found to be approximately 76.1%, 73.8%, 70.3%, 46.3% and 61.82% respectively, with final average effluent COD of 123.7 mg/L, 240 mg/L, 294 mg/L, 589 mg/L and 738 mg/L, respectively.
Abstract: In this paper a new approach is proposed for the
adaptation of the simulated annealing search in the field of the
Multi-Objective Optimization (MOO). This new approach is called
Multi-Case Multi-Objective Simulated Annealing (MC-MOSA). It
uses some basics of a well-known recent Multi-Objective Simulated
Annealing proposed by Ulungu et al., which is referred in the
literature as U-MOSA. However, some drawbacks of this algorithm
have been found, and are substituted by other ones, especially in
the acceptance decision criterion. The MC-MOSA has shown better
performance than the U-MOSA in the numerical experiments. This
performance is further improved by some other subvariants of the
MC-MOSA, such as Fast-annealing MC-MOSA, Re-annealing MCMOSA
and the Two-Stage annealing MC-MOSA.
Abstract: The γ-turns play important roles in protein folding and
molecular recognition. The prediction and analysis of γ-turn types are
important for both protein structure predictions and better
understanding the characteristics of different γ-turn types. This study
proposed a physicochemical property-based decision tree (PPDT)
method to interpretably predict γ-turn types. In addition to the good
prediction performance of PPDT, three simple and human
interpretable IF-THEN rules are extracted from the decision tree
constructed by PPDT. The identified informative physicochemical
properties and concise rules provide a simple way for discriminating
and understanding γ-turn types.
Abstract: This paper aims to provide a conceptual framework to examine competitive disadvantage of banks that suffer from poor performance. Banks generate revenues mainly from the interest rate spread on taking deposits and making loans while collecting fees in the process. To maximize firm value, banks seek loan growth and expense control while managing risk associated with loans with respect to non-performing borrowers or narrowing interest spread between assets and liabilities. Competitive disadvantage refers to the failure to access imitable resources and to build managing capabilities to gain sustainable return given appropriate risk management. This paper proposes a four-quadrant framework of organizational typology is subsequently proposed to examine the features of competitive disadvantage in the banking sector. A resource configuration model, which is extracted from CAMEL indicators to examine the underlying features of bank failures.
Abstract: The network traffic data provided for the design of
intrusion detection always are large with ineffective information and
enclose limited and ambiguous information about users- activities.
We study the problems and propose a two phases approach in our
intrusion detection design. In the first phase, we develop a
correlation-based feature selection algorithm to remove the worthless
information from the original high dimensional database. Next, we
design an intrusion detection method to solve the problems of
uncertainty caused by limited and ambiguous information. In the
experiments, we choose six UCI databases and DARPA KDD99
intrusion detection data set as our evaluation tools. Empirical studies
indicate that our feature selection algorithm is capable of reducing the
size of data set. Our intrusion detection method achieves a better
performance than those of participating intrusion detectors.