Abstract: A numerical analysis of wave and hydrodynamic models
is used to investigate the influence of WAve and Storm Surge
(WASS) in the regional and coastal zones. The numerical analyzed
system consists of the WAve Model Cycle 4 (WAMC4) and the
Princeton Ocean Model (POM) which used to solve the energy
balance and primitive equations respectively. The results of both
models presented the incorporated surface wave in the regional
zone affected the coastal storm surge zone. Specifically, the results
indicated that the WASS generally under the approximation is not
only the peak surge but also the coastal water level drop which
can also cause substantial impact on the coastal environment. The
wave–induced surface stress affected the storm surge can significantly
improve storm surge prediction. Finally, the calibration of wave
module according to the minimum error of the significant wave height
(Hs) is not necessarily result in the optimum wave module in the
WASS analyzed system for the WASS prediction.
Abstract: Microwave energy is a superior alternative to several other thermal treatments. Extraction techniques are widely employed for the isolation of bioactive compounds and vegetable oils from oil seeds. Among the different and new available techniques, microwave pretreatment of seeds is a simple and desirable method for production of high quality vegetable oils. Microwave pretreatment for oil extraction has many advantages as follow: improving oil extraction yield and quality, direct extraction capability, lower energy consumption, faster processing time and reduced solvent levels compared with conventional methods. It allows also for better retention and availability of desirable nutraceuticals, such as phytosterols and tocopherols, canolol and phenolic compounds in the extracted oil such as rapeseed oil. This can be a new step to produce nutritional vegetable oils with improved shelf life because of high antioxidant content.
Abstract: Support Vector Machine (SVM) is a recent class of statistical classification and regression techniques 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 is applied to an infrared (IR) binary communication system with different types of channel models including Ricean multipath fading and partially developed scattering channel with additive white Gaussian noise (AWGN) at the receiver. The structure and performance of SVM in terms of the bit error rate (BER) metric is derived and simulated for these channel stochastic models and the computational complexity of the implementation, in terms of average computational time per bit, is also presented. The performance of SVM is then compared to classical binary signal maximum likelihood detection using a matched filter driven by On-Off keying (OOK) modulation. We found that the performance of SVM is superior to that of the traditional optimal detection schemes used in statistical communication, especially for very low signal-to-noise ratio (SNR) ranges. For large SNR, the performance of the SVM is similar to that of the classical detectors. The implication of these results is that SVM can prove very beneficial to IR communication systems that notoriously suffer from low SNR at the cost of increased computational complexity.
Abstract: Residues are produced in all stages of human activities
in terms of composition and volume which vary according to
consumption practices and to production methods. Forms of
significant harm to the environment are associated to volume of
generated material as well as to improper disposal of solid wastes,
whose negative effects are noticed more frequently in the long term.
The solution to this problem constitutes a challenge to the
government, industry and society, because they involve economic,
social, environmental and, especially, awareness of the population in
general. The main concerns are focused on the impact it can have on
human health and on the environment (soil, water, air and sights).
The hazardous waste produced mainly by industry, are particularly
worrisome because, when improperly managed, they become a
serious threat to the environment. In view of this issue, this study
aimed to evaluate the management system of solid waste of a coprocessing
industrial waste company, to propose improvements to the
rejects generation management in a specific step of the Blending
production process.
Abstract: The paper introduces and discusses definitions and concepts from the supplier relationship management area. This review has the goal to provide readers with the basic conditions to understand the market mechanisms and the technological developments of the SRM market. Further on, the work gives a picture of the actual business environment in which the SRM vendors are in, and the main trends in the field, based on the main SRM functionalities i.e. e-Procurement, e-Sourcing and Supplier Enablement, which indicates users and software providers the future technological developments and practises that will take place in this area in the next couple of years.
Abstract: In this paper, the noise maps for the area encircled by
the Second Ring Road in Riyadh city are developed based on real
measured data. Sound level meters, GPS receivers to determine
measurement position, a database program to manage the measured
data, and a program to develop the maps are used. A baseline noise
level has been established at each short-term site so subsequent
monitoring may be conducted to describe changes in Riyadh-s noise
environment. Short-term sites are used to show typical daytime and
nighttime noise levels at specific locations by short duration grab
sampling.
Abstract: SoftBoost is a recently presented boosting algorithm,
which trades off the size of achieved classification margin and
generalization performance. This paper presents a performance
evaluation of SoftBoost algorithm on the generic object recognition
problem. An appearance-based generic object recognition
model is used. The evaluation experiments are performed using
a difficult object recognition benchmark. An assessment with respect
to different degrees of label noise as well as a comparison to
the well known AdaBoost algorithm is performed. The obtained
results reveal that SoftBoost is encouraged to be used in cases
when the training data is known to have a high degree of noise.
Otherwise, using Adaboost can achieve better performance.
Abstract: The presence of harmonic in power system is a major
concerned to power engineers for many years. With the increasing
usage of nonlinear loads in power systems, the harmonic pollution
becomes more serious. One of the widely used computation
algorithm for harmonic analysis is fast Fourier transform (FFT). In
this paper, a harmonic analyzer using FFT was implemented on
TMS320C6713 DSK. The supply voltage of 240 V 59 Hz is stepped
down to 5V using a voltage divider in order to match the power
rating of the DSK input. The output from the DSK was displayed on
oscilloscope and Code Composer Studio™ software. This work has
demonstrated the possibility of analyzing the 240V power supply
harmonic content using the DSK board.
Abstract: The development and extension of large cities induced
a need for shallow tunnel in soft ground of building areas. Estimation
of ground settlement caused by the tunnel excavation is important
engineering point. In this paper, prediction of surface subsidence
caused by tunneling in one section of seventh line of Tehran subway
is considered. On the basis of studied geotechnical conditions of the
region, tunnel with the length of 26.9km has been excavated applying
a mechanized method using an EPB-TBM with a diameter of 9.14m.
In this regard, settlement is estimated utilizing both analytical and
numerical finite element method. The numerical method shows that
the value of settlement in this section is 5cm. Besides, the analytical
consequences (Bobet and Loganathan-Polous) are 5.29 and 12.36cm,
respectively. According to results of this study, due tosaturation of
this section, there are good agreement between Bobet and numerical
methods. Therefore, tunneling processes in this section needs a
special consolidation measurement and support system before the
passage of tunnel boring machine.
Abstract: Speed estimation is one of the important and practical tasks in machine vision, Robotic and Mechatronic. the availability of high quality and inexpensive video cameras, and the increasing need for automated video analysis has generated a great deal of interest in machine vision algorithms. Numerous approaches for speed estimation have been proposed. So classification and survey of the proposed methods can be very useful. The goal of this paper is first to review and verify these methods. Then we will propose a novel algorithm to estimate the speed of moving object by using fuzzy concept. There is a direct relation between motion blur parameters and object speed. In our new approach we will use Radon transform to find direction of blurred image, and Fuzzy sets to estimate motion blur length. The most benefit of this algorithm is its robustness and precision in noisy images. Our method was tested on many images with different range of SNR and is satisfiable.
Abstract: This paper introduces a new variable step-size APA with decorrelation of AR input process is based on the MSD analysis. To achieve a fast convergence rate and a small steady-state estimation error, he proposed algorithm uses variable step size that is determined by minimising the MSD. In addition, experimental results show that the proposed algorithm is achieved better performance than the other algorithms.
Abstract: This paper investigates the issue of building decision
trees from data with imprecise class values where imprecision is
encoded in the form of possibility distributions. The Information
Affinity similarity measure is introduced into the well-known gain
ratio criterion in order to assess the homogeneity of a set of
possibility distributions representing instances-s classes belonging to
a given training partition. For the experimental study, we proposed an
information affinity based performance criterion which we have used
in order to show the performance of the approach on well-known
benchmarks.
Abstract: In this paper, we have proposed a low cost optimized solution for the movement of a three-arm manipulator using Genetic Algorithm (GA) and Analytical Hierarchy Process (AHP). A scheme is given for optimizing the movement of robotic arm with the help of Genetic Algorithm so that the minimum energy consumption criteria can be achieved. As compared to Direct Kinematics, Inverse Kinematics evolved two solutions out of which the best-fit solution is selected with the help of Genetic Algorithm and is kept in search space for future use. The Inverse Kinematics, Fitness Value evaluation and Binary Encoding like tasks are simulated and tested. Although, three factors viz. Movement, Friction and Least Settling Time (or Min. Vibration) are used for finding the Fitness Function / Fitness Values, however some more factors can also be considered.
Abstract: This paper presents a design and prototype
implementation of new home automation system that uses WiFi
technology as a network infrastructure connecting its parts. The
proposed system consists of two main components; the first part is
the server (web server), which presents system core that manages,
controls, and monitors users- home. Users and system administrator
can locally (LAN) or remotely (internet) manage and control system
code. Second part is hardware interface module, which provides
appropriate interface to sensors and actuator of home automation
system. Unlike most of available home automation system in the
market the proposed system is scalable that one server can manage
many hardware interface modules as long as it exists on WiFi
network coverage. System supports a wide range of home
automation devices like power management components, and
security components. The proposed system is better from the
scalability and flexibility point of view than the commercially
available home automation systems.
Abstract: Plastic waste is a big issue in Thailand, but the amount of recycled plastic in Thailand is still low due to the high investment and operating cost. Hence, the rest of plastic waste are burnt to destroy or sent to the landfills. In order to be financial viable, an effective reverse logistics infrastructure is required to support the product recovery activities. However, there is a conflict between reducing the cost and raising environmental protection level. The purpose of this study is to build a goal programming (GP) so that it can be used to help analyze the proper planning of the Thailand-s plastic recycling system that involves multiple objectives. This study considers three objectives; reducing total cost, increasing the amount of plastic recovery, and raising the desired plastic materials in recycling process. The results from two priority structures show that it is necessary to raise the total cost budget in order to achieve targets on amount of recycled plastic and desired plastic materials.
Abstract: Training neural networks to capture an intrinsic
property of a large volume of high dimensional data is a difficult
task, as the training process is computationally expensive. Input
attributes should be carefully selected to keep the dimensionality of
input vectors relatively small.
Technical indexes commonly used for stock market prediction
using neural networks are investigated to determine its effectiveness
as inputs. The feed forward neural network of Levenberg-Marquardt
algorithm is applied to perform one step ahead forecasting of
NASDAQ and Dow stock prices.
Abstract: In this paper, the decomposition-aggregation method
is used to carry out connective stability criteria for general linear
composite system via aggregation. The large scale system is
decomposed into a number of subsystems. By associating directed
graphs with dynamic systems in an essential way, we define the
relation between system structure and stability in the sense of
Lyapunov. The stability criteria is then associated with the stability
and system matrices of subsystems as well as those interconnected
terms among subsystems using the concepts of vector differential
inequalities and vector Lyapunov functions. Then, we show that the
stability of each subsystem and stability of the aggregate model
imply connective stability of the overall system. An example is
reported, showing the efficiency of the proposed technique.
Abstract: This paper introduces an approach to construct a set of criteria for evaluating alternative options. Content analysis was used to collet criterion elements. Then the elements were classified and organized yielding to hierarchic structure. The reliability of the constructed criteria was evaluated in an experiment. Finally the criteria were used to evaluate alternative options indecision-making.
Abstract: Society has grown to rely on Internet services, and the
number of Internet users increases every day. As more and more
users become connected to the network, the window of opportunity
for malicious users to do their damage becomes very great and
lucrative. The objective of this paper is to incorporate different
techniques into classier system to detect and classify intrusion from
normal network packet. Among several techniques, Steady State
Genetic-based Machine Leaning Algorithm (SSGBML) will be used
to detect intrusions. Where Steady State Genetic Algorithm (SSGA),
Simple Genetic Algorithm (SGA), Modified Genetic Algorithm and
Zeroth Level Classifier system are investigated in this research.
SSGA is used as a discovery mechanism instead of SGA. SGA
replaces all old rules with new produced rule preventing old good
rules from participating in the next rule generation. Zeroth Level
Classifier System is used to play the role of detector by matching
incoming environment message with classifiers to determine whether
the current message is normal or intrusion and receiving feedback
from environment. Finally, in order to attain the best results,
Modified SSGA will enhance our discovery engine by using Fuzzy
Logic to optimize crossover and mutation probability. The
experiments and evaluations of the proposed method were performed
with the KDD 99 intrusion detection dataset.
Abstract: The rapid urbanization of cities has a bane in the form
road accidents that cause extensive damage to life and limbs. A
number of location based factors are enablers of road accidents in the
city. The speed of travel of vehicles is non-uniform among locations
within a city. In this study, the perception of vehicle users is captured
on a 10-point rating scale regarding the degree of variation in speed
of travel at chosen locations in the city. The average rating is used to
cluster locations using fuzzy c-means clustering and classify them as
low, moderate and high speed of travel locations. The high speed of
travel locations can be classified proactively to ensure that accidents
do not occur due to the speeding of vehicles at such locations. The
advantage of fuzzy c-means clustering is that a location may be a
part of more than one cluster to a varying degree and this gives a
better picture about the location with respect to the characteristic
(speed of travel) being studied.