Abstract: Computer based geostatistical methods can offer effective data analysis possibilities for agricultural areas by using
vectorial data and their objective informations. These methods will help to detect the spatial changes on different locations of the large
agricultural lands, which will lead to effective fertilization for optimal yield with reduced environmental pollution. In this study, topsoil (0-20 cm) and subsoil (20-40 cm) samples were taken from a
sugar beet field by 20 x 20 m grids. Plant samples were also collected
from the same plots. Some physical and chemical analyses for these
samples were made by routine methods. According to derived variation coefficients, topsoil organic matter (OM) distribution was more than subsoil OM distribution. The highest C.V. value of
17.79% was found for topsoil OM. The data were analyzed
comparatively according to kriging methods which are also used
widely in geostatistic. Several interpolation methods (Ordinary,Simple and Universal) and semivariogram models (Spherical,
Exponential and Gaussian) were tested in order to choose the suitable
methods. Average standard deviations of values estimated by simple
kriging interpolation method were less than average standard
deviations (topsoil OM ± 0.48, N ± 0.37, subsoil OM ± 0.18) of measured values. The most suitable interpolation method was simple
kriging method and exponantial semivariogram model for topsoil,
whereas the best optimal interpolation method was simple kriging
method and spherical semivariogram model for subsoil. The results
also showed that these computer based geostatistical methods should
be tested and calibrated for different experimental conditions and semivariogram models.
Abstract: Content-Based Image Retrieval (CBIR) has been
one on the most vivid research areas in the field of computer vision
over the last 10 years. Many programs and tools have been
developed to formulate and execute queries based on the visual or
audio content and to help browsing large multimedia repositories.
Still, no general breakthrough has been achieved with respect to
large varied databases with documents of difering sorts and with
varying characteristics. Answers to many questions with respect to
speed, semantic descriptors or objective image interpretations are
still unanswered. In the medical field, images, and especially
digital images, are produced in ever increasing quantities and used
for diagnostics and therapy. In several articles, content based
access to medical images for supporting clinical decision making
has been proposed that would ease the management of clinical data
and scenarios for the integration of content-based access methods
into Picture Archiving and Communication Systems (PACS) have
been created. This paper gives an overview of soft computing
techniques. New research directions are being defined that can
prove to be useful. Still, there are very few systems that seem to be
used in clinical practice. It needs to be stated as well that the goal
is not, in general, to replace text based retrieval methods as they
exist at the moment.
Abstract: This paper presents a novel method that allows an
agent host to delegate its signing power to an anonymous mobile
agent in such away that the mobile agent does not reveal any information about its host-s identity and, at the same time, can be authenticated by the service host, hence, ensuring fairness of service
provision. The solution introduces a verification server to verify the
signature generated by the mobile agent in such a way that even if colluding with the service host, both parties will not get more information than what they already have. The solution incorporates
three methods: Agent Signature Key Generation method, Agent
Signature Generation method, Agent Signature Verification method.
The most notable feature of the solution is that, in addition to allowing secure and anonymous signature delegation, it enables
tracking of malicious mobile agents when a service host is attacked. The security properties of the proposed solution are analyzed, and the solution is compared with the most related work.
Abstract: In this paper, the transformers over-load problem of Shiraz substation in Fars Regional Electric Company (FREC) is investigated for a period of three years plan. So the suggestions for using phase shifting transformer (PST) and unified power flow controller (UPFC) in order to solve this problem are examined in details and finally, some economical and practical designs will be given in order to solve the related problems. Practical consideration and using the basic and fundamental concept of powers in transmission lines in order to find the economical design are the main advantages of this research. The simulation results of the integrated overall system with different designs compare them base on economical and practical aspects to solve the over-load and loss-reduction.
Abstract: The environmental impact caused by industries is an issue that, in the last 20 years, has become very important in terms of society, economics and politics in Colombia. Particularly, the tannery process is extremely polluting because of uneffective treatments and regulations given to the dumping process and atmospheric emissions. Considering that, this investigation is intended to propose a management model based on the integration of Lean Supply Chain, Green Supply Chain, Cleaner Production and ISO 14001-2004, that prioritizes the strategic components of the organizations. As a result, a management model will be obtained and it will provide a strategic perspective through a systemic approach to the tanning process. This will be achieved through the use of Multicriteria Decision tools, along with Quality Function Deployment and Fuzzy Logic. The strategic approach that embraces the management model using the alignment of Lean Supply Chain, Green Supply Chain, Cleaner Production and ISO 14001-2004, is an integrated perspective that allows a gradual frame of the tactical and operative elements through the correct setting of the information flow, improving the decision making process. In that way, Small Medium Enterprises (SMEs) could improve their productivity, competitiveness and as an added value, the minimization of the environmental impact. This improvement is expected to be controlled through a Dashboard that helps the Organization measure its performance along the implementation of the model in its productive process.
Abstract: Because nodes are usually battery-powered, the energy
presents a very scarce resource in wireless sensor networks. For this
reason, the design of medium access control had to take energy
efficiency as one of its hottest concerns. Accordingly, in order to
improve the energy performance of MAC schemes in wireless sensor
networks, several ways can be followed. In fact, some researchers try
to limit idle listening while others focus on mitigating overhearing
(i.e. a node can hear a packet which is destined to another node)
or reducing the number of the used control packets. We, in this
paper, propose a new hybrid MAC protocol termed ELE-MAC
(i.e. Energy Latency Efficient MAC). The ELE-MAC major design
goals are energy and latency efficiencies. It adopts less control
packets than SMAC in order to preserve energy. We carried out ns-
2 simulations to evaluate the performance of the proposed protocol.
Thus, our simulation-s results prove the ELE-MAC energy efficiency.
Additionally, our solution performs statistically the same or better
latency characteristic compared to adaptive SMAC.
Abstract: This paper aims to improve a fine lapping process of
hard disk drive (HDD) lapping machines by removing materials from
each slider together with controlling the strip height (SH) variation to
minimum value. The standard deviation is the key parameter to
evaluate the strip height variation, hence it is minimized. In this
paper, a design of experiment (DOE) with factorial analysis by twoway
analysis of variance (ANOVA) is adopted to obtain a
statistically information. The statistics results reveal that initial stripe
height patterns affect the final SH variation. Therefore, initial SH
classification using a radial basis function neural network is
implemented to achieve the proportional gain prediction.
Abstract: A novel adaptive fuzzy trajectory tracking algorithm of Stewart platform based motion platform is proposed to compensate path deviation and degradation of controller-s performance due to actuator torque limit. The algorithm can be divided into two parts: the real-time trajectory shaping part and the joint space adaptive fuzzy controller part. For a reference trajectory in task space whenever any of the actuators is saturated, the desired acceleration of the reference trajectory is modified on-line by using dynamic model of motion platform. Meanwhile an additional action with respect to the difference between the nominal and modified trajectories is utilized in the non-saturated region of actuators to reduce the path error. Using modified trajectory as input, the joint space controller incorporates compute torque controller, leg velocity observer and fuzzy disturbance observer with saturation compensation. It can ensure stability and tracking performance of controller in present of external disturbance and position only measurement. Simulation results verify the effectiveness of proposed control scheme.
Abstract: Environmental responsibility includes improvement of environmental performance in order to reduce environmental impact. This paper gives a short review of some important environmental objectives, targets and actions that modern shipping company should follow.
Abstract: From the importance of the conference and its
constructive role in the studies discussion, there must be a strong
organization that allows the exploitation of the discussions in opening
new horizons. The vast amount of information scattered across the
web, make it difficult to find experts, who can play a prominent role
in organizing conferences. In this paper we proposed a new approach
of extracting researchers- information from various Web resources
and correlating them in order to confirm their correctness. As a
validator of this approach, we propose a service that will be useful to
set up a conference. Its main objective is to find appropriate experts,
as well as the social events for a conference. For this application we
us Semantic Web technologies like RDF and ontology to represent
the confirmed information, which are linked to another ontology
(skills ontology) that are used to present and compute the expertise.
Abstract: In this study we propose a novel monitor hydraulic
automatic gauge control (HAGC) system based on fuzzy feedforward
controller. This is used in the development of cold rolling
mill automation system to improve the quality of cold strip. According to features/ properties of entry steel strip like its average
yield stress, width of strip, and desired exit thickness, this controller realizes the compensation for the exit thickness error. The traditional methods of adjusting the roller position, can-t tolerate the variance in
the entry steel strip. The proposed method uses a mathematical model
of the system together with the expert knowledge to perform this adjustment while minimizing the effect of the stated problem. In
order to improve the speed of the controller in rejecting disturbances
introduced by entry strip thickness variations, expert knowledge is added as a feed-forward term to the HAGC system. Simulation
results for the application of the proposed controller to a real cold mill show that the exit strip quality is highly improved.
Abstract: We report here, the results of molecular dynamics
simulation of p-doped (Ga-face)GaN over n-doped (Siface)(
0001)4H-SiC hetero-epitaxial material system with one-layer
each of Ga-flux and (Al-face)AlN, as the interface materials, in the
form of, the total Density of States (DOS). It is found that the total
DOS at the Fermi-level for the heavily p-doped (Ga-face)GaN and ndoped
(Si-face)4H-SiC hetero-epitaxial system, with one layer of
(Al-face)AlN as the interface material, is comparatively higher than
that of the various cases studied, indicating that there could be good
vertical conduction across the (Ga-face)GaN over (Si-face)(0001)4HSiC
hetero-epitaxial material system.
Abstract: While computers are known to facilitate lower levels of learning, such as rote memorization of facts, measurable through electronically administered and graded multiple-choice questions, yes/no, and true/false answers, the imparting and measurement of higher-level cognitive skills is more vexing. These require more open-ended delivery and answers, and may be more problematic in an entirely virtual environment, notwithstanding the advances in technologies such as wikis, blogs, discussion boards, etc. As with the integration of all technology, merit is based more on the instructional design of the course than on the technology employed in, and of, itself. With this in mind, this study examined the perceptions of online students in an introductory Computer Information Systems course regarding the fostering of various higher-order thinking and team-building skills as a result of the activities, resources and technologies (ART) used in the course.
Abstract: In present paper we proposed a simple and effective method to compress an image. Here we found success in size reduction of an image without much compromising with it-s quality. Here we used Haar Wavelet Transform to transform our original image and after quantization and thresholding of DWT coefficients Run length coding and Huffman coding schemes have been used to encode the image. DWT is base for quite populate JPEG 2000 technique.
Abstract: This paper examines the students’ self-concept among 16- and 17- year- old adolescents in Malaysian secondary schools. Previous studies have shown that positive self-concept played an important role in student adjustment and academic performance during schooling. This study attempts to investigate the factors influencing students’ perceptions toward their own self-concept. A total of 1168 students participated in the survey. This study utilized the CoPs (UM) instrument to measure self-concept. Principal Component Analysis (PCA) revealed three factors: academic selfconcept, physical self-concept and social self-concept. This study confirmed that students perceived certain internal context factors, and revealed that external context factor also have an impact on their self-concept.
Abstract: novel and simple method is introduced for rapid and
highly efficient water treatment by reverse osmosis (RO) method using
multi-walled carbon nanotubes (MWCNTs) / polyacrylonitrile (PAN)
polymer as a flexible, highly efficient, reusable and semi-permeable
mixed matrix membrane (MMM). For this purpose, MWCNTs were
directly synthesized and on-line purified by chemical vapor deposition
(CVD) process, followed by directing the MWCNT bundles towards an
ultrasonic bath, in which PAN polymer was simultaneously suspended
inside a solid porous silica support in water at temperature to ~70 οC.
Fabrication process of MMM was finally completed by hot isostatic
pressing (HIP) process. In accordance with the analytical figures of
merit, the efficiency of fabricated MMM was ~97%. The rate of water
treatment process was also evaluated to 6.35 L min-1. The results reveal
that, the CNT-based MMM is suitable for rapid treatment of different
forms of industrial, sea, drinking and well water samples.
Abstract: In this article, we propose a methodology for the
characterization of the suspended matter along Algiers-s bay. An
approach by multi layers perceptron (MLP) with training by back
propagation of the gradient optimized by the algorithm of Levenberg
Marquardt (LM) is used. The accent was put on the choice of the
components of the base of training where a comparative study made
for four methods: Random and three alternatives of classification by
K-Means. The samples are taken from suspended matter image,
obtained by analytical model based on polynomial regression by
taking account of in situ measurements. The mask which selects the
zone of interest (water in our case) was carried out by using a multi
spectral classification by ISODATA algorithm. To improve the
result of classification, a cleaning of this mask was carried out using
the tools of mathematical morphology. The results of this study
presented in the forms of curves, tables and of images show the
founded good of our methodology.
Abstract: The electrical potentials generated during eye movements and blinks are one of the main sources of artifacts in Electroencephalogram (EEG) recording and can propagate much across the scalp, masking and distorting brain signals. In recent times, signal separation algorithms are used widely for removing artifacts from the observed EEG data. In this paper, a recently introduced signal separation algorithm Mutual Information based Least dependent Component Analysis (MILCA) is employed to separate ocular artifacts from EEG. The aim of MILCA is to minimize the Mutual Information (MI) between the independent components (estimated sources) under a pure rotation. Performance of this algorithm is compared with eleven popular algorithms (Infomax, Extended Infomax, Fast ICA, SOBI, TDSEP, JADE, OGWE, MS-ICA, SHIBBS, Kernel-ICA, and RADICAL) for the actual independence and uniqueness of the estimated source components obtained for different sets of EEG data with ocular artifacts by using a reliable MI Estimator. Results show that MILCA is best in separating the ocular artifacts and EEG and is recommended for further analysis.
Abstract: We present a hybrid architecture of recurrent neural
networks (RNNs) inspired by hidden Markov models (HMMs). We
train the hybrid architecture using genetic algorithms to learn and
represent dynamical systems. We train the hybrid architecture on a
set of deterministic finite-state automata strings and observe the
generalization performance of the hybrid architecture when presented
with a new set of strings which were not present in the training data
set. In this way, we show that the hybrid system of HMM and RNN
can learn and represent deterministic finite-state automata. We ran
experiments with different sets of population sizes in the genetic
algorithm; we also ran experiments to find out which weight
initializations were best for training the hybrid architecture. The
results show that the hybrid architecture of recurrent neural networks
inspired by hidden Markov models can train and represent dynamical
systems. The best training and generalization performance is
achieved when the hybrid architecture is initialized with random real
weight values of range -15 to 15.
Abstract: In order to study the influence of different methods of controlling weeds such as mechanical weeding and mechanical weeder efficiency analysis in mechanical cultivation conditions, in farming year of 2011 an experiment was done in a farm in coupling and development of technology center in Haraz,Iran. The treatments consisted of (I) control treatment: where no weeding was done, (II) use of mechanical weeding without engine and (III) power mechanical weeding. Results showed that experimental treatments had significantly different effects (p=0.05) on yield traits and number of filled grains per panicle, while treatments had the significant effects on grain weight and dry weight of weeds in the first, second and third weeding methods at 1% of confidence level. Treatment (II) had its most significant effect on number of filled grains per panicle and yield performance standpoint, which was 3705.97 kg ha-1 in its highest peak. Treatment (III) was ranked as second influential with 3559.8 kg ha-1. In addition, under (I) treatments, 2364.73 kg ha-1 of yield produced. The minimum dry weights of weeds in all weeding methods were related to the treatment (II), (III) and (I), respectively. The correlation coefficient analysis showed that total yield had a significant positive correlation with the panicle grain yield per plant (r= 0.55*) and the number of grains per panicle-1 (r= 0.57*) and the number of filled grains (r= 0.63*). Total rice yield also had negative correlation of r= -0. 64* with weed dry weight at second weed sampling time (17 DAT). The weed dry weight at third and fourth sampling times (24 and 40 DAT) had negative correlations of -0.65** and r=-0.61* with rice yield, respectively.