Abstract: Directional over current relays (DOCR) are commonly used in power system protection as a primary protection in distribution and sub-transmission electrical systems and as a secondary protection in transmission systems. Coordination of protective relays is necessary to obtain selective tripping. In this paper, an approach for efficiency reduction of DOCRs nonlinear optimum coordination (OC) is proposed. This was achieved by modifying the objective function and relaxing several constraints depending on the four constraints classification, non-valid, redundant, pre-obtained and valid constraints. According to this classification, the far end fault effect on the objective function and constraints, and in consequently on relay operating time, was studied. The study was carried out, firstly by taking into account the near-end and far-end faults in DOCRs coordination problem formulation; and then faults very close to the primary relays (nearend faults). The optimal coordination (OC) was achieved by simultaneously optimizing all variables (TDS and Ip) in nonlinear environment by using of Genetic algorithm nonlinear programming techniques. The results application of the above two approaches on 6-bus and 26-bus system verify that the far-end faults consideration on OC problem formulation don-t lose the optimality.
Abstract: The evolution of technology and construction techniques has enabled the upgrading of transport networks. In particular, the high-speed rail networks allow convoys to peak at above 300 km/h. These structures, however, often significantly impact the surrounding environment. Among the effects of greater importance are the ones provoked by the soundwave connected to train transit. The wave propagation affects the quality of life in areas surrounding the tracks, often for several hundred metres. There are substantial damages to properties (buildings and land), in terms of market depreciation. The present study, integrating expertise in acoustics, computering and evaluation fields, outlines a useful model to select project paths so as to minimize the noise impact and reduce the causes of possible litigation. It also facilitates the rational selection of initiatives to contain the environmental damage to the already existing railway tracks. The research is developed with reference to the Italian regulatory framework (usually more stringent than European and international standards) and refers to a case study concerning the high speed network in Italy.
Abstract: In this paper, we use nonlinear system identification method to predict and detect process fault of a cement rotary kiln. After selecting proper inputs and output, an input-output model is identified for the plant. To identify the various operation points in the
kiln, Locally Linear Neuro-Fuzzy (LLNF) model is used. This model is trained by LOLIMOT algorithm which is an incremental treestructure
algorithm. Then, by using this method, we obtained 3
distinct models for the normal and faulty situations in the kiln. One of the models is for normal condition of the kiln with 15 minutes
prediction horizon. The other two models are for the two faulty situations in the kiln with 7 minutes prediction horizon are presented.
At the end, we detect these faults in validation data. The data collected from White Saveh Cement Company is used for in this study.
Abstract: The systematic manipulations of shapes and sizes of
inorganic compounds greatly benefit the various application fields
including optics, magnetic, electronics, catalysis and medicine.
However shape control has been much more difficult to achieve.
Hence exploration of novel method for the preparation of differently
shaped nanoparticles is challenging research area. II-VI group of
semiconductor cadmium sulphide (CdS) nanostructure with different
morphologies (such as, acicular like, mesoporous, spherical shapes)
and of crystallite sizes vary from 11 to 16 nm were successfully
synthesized by chemical aqueous precipitation of Cd2+ ions with
homogeneously released S2- ions from decomposition of cadmium
sulphate (CdSO4) and thioacetamide (CH3CSNH2) by annealing at
different radiations (microwave, ultrasonic and sunlight) with matter
and systematic research has been done for various factors affecting
the controlled growth rate of CdS nanoparticles. The obtained
nanomaterials have been characterized by X-ray Diffraction (XRD),
Fourier Transform Infrared Spectroscopy (FTIR),
Thermogravometric (DSC-TGA) analysis and Scanning Electron
Microscopy (SEM). The result indicates that on increasing the
reaction time particle size increases but on increasing the molar ratios
grain size decreases.
Abstract: Social media has led to paradigm shifts in ways
people work and do business, interact and socialize, learn and obtain
knowledge. So much so that social media has established itself as an
important spatial extension of this nation-s historicity and challenges.
Regardless of the enabling reputation and recommendation features
through social networks embedded in the social media system, the
overflow of broadcasted and publicized media contents turns the
table around from engendering trust to doubting the trust system.
When the trust is at doubt, the effects include deactivation of
accounts and creation of multiple profiles, which lead to the overflow
of 'ghost' contents (i.e. “the abundance of abandoned ships"). In
most literature, the study of trust can be related to culture; hence the
difference between Western-s “openness" and Eastern-s “blue-chip"
concepts in networking and relationships. From a survey on issues
and challenges among Malaysian social media users, 'authenticity'
emerges as one of the main factors that causes and is caused by other
factors. The other issue that has surfaced is credibility either in terms
of message/content and source. Another is the quality of the
knowledge that is shared. This paper explores the terrains of this
critical space which in recent years has been dominated increasingly
by, arguably, social networks embedded in the social media system,
the overflow of broadcasted and publicized media content.
Abstract: In this paper the combination of thermal oxidation and
electrochemical anodizing processes is used to produce titanium
oxide layers. The response of titanium alloy Ti6Al4V to oxidation
processes at various temperatures and electrochemical anodizing in
various voltages are investigated. Scanning electron microscopy
(SEM); X-Ray Diffraction (XRD) and porosity determination have
been used to characterize the oxide layer thickness, surface
morphology, oxide layer-substrate adhesion and porosity. In the first
experiment, samples modified by thermal oxidation process then
followed by electrochemical anodizing. Second experiment consists
of surfaces modified by electrochemical anodizing process and then
followed by thermal oxidation. The first method shows better
properties than other one. In second experiment, Surfaces modified
were achieved by thicker and more adherent thick oxide layers on
titanium surface. The existence of an electrochemical anodized oxide
layer did not improve the adhesion of thermal oxide layer. The high
temperature, thermal formation of an oxide layer leads to a coarse
oxide grain morphology and a complete oxidative particle. In
addition, in high temperature oxidation porosity content is increased.
The oxide layer of thermal oxidation and electrochemical anodizing
processes; on Ti–6Al–4V substrate was covered with different
colored oxide layers.
Abstract: Text categorization is the problem of classifying text documents into a set of predefined classes. After a preprocessing step the documents are typically represented as large sparse vectors. When training classifiers on large collections of documents, both the time and memory restrictions can be quite prohibitive. This justifies the application of features selection methods to reduce the dimensionality of the document-representation vector. Four feature selection methods are evaluated: Random Selection, Information Gain (IG), Support Vector Machine (called SVM_FS) and Genetic Algorithm with SVM (GA_FS). We showed that the best results were obtained with SVM_FS and GA_FS methods for a relatively small dimension of the features vector comparative with the IG method that involves longer vectors, for quite similar classification accuracies. Also we present a novel method to better correlate SVM kernel-s parameters (Polynomial or Gaussian kernel).
Abstract: Groundlessness of application probability-statistic methods are especially shown at an early stage of the aviation GTE technical condition diagnosing, when the volume of the information has property of the fuzzy, limitations, uncertainty and efficiency of application of new technology Soft computing at these diagnosing stages by using the fuzzy logic and neural networks methods. It is made training with high accuracy of multiple linear and nonlinear models (the regression equations) received on the statistical fuzzy data basis. At the information sufficiency it is offered to use recurrent algorithm of aviation GTE technical condition identification on measurements of input and output parameters of the multiple linear and nonlinear generalized models at presence of noise measured (the new recursive least squares method (LSM)). As application of the given technique the estimation of the new operating aviation engine D30KU-154 technical condition at height H=10600 m was made.
Abstract: A new method for color image segmentation using fuzzy logic is proposed in this paper. Our aim here is to automatically produce a fuzzy system for color classification and image segmentation with least number of rules and minimum error rate. Particle swarm optimization is a sub class of evolutionary algorithms that has been inspired from social behavior of fishes, bees, birds, etc, that live together in colonies. We use comprehensive learning particle swarm optimization (CLPSO) technique to find optimal fuzzy rules and membership functions because it discourages premature convergence. Here each particle of the swarm codes a set of fuzzy rules. During evolution, a population member tries to maximize a fitness criterion which is here high classification rate and small number of rules. Finally, particle with the highest fitness value is selected as the best set of fuzzy rules for image segmentation. Our results, using this method for soccer field image segmentation in Robocop contests shows 89% performance. Less computational load is needed when using this method compared with other methods like ANFIS, because it generates a smaller number of fuzzy rules. Large train dataset and its variety, makes the proposed method invariant to illumination noise
Abstract: The belief decision tree (BDT) approach is a decision
tree in an uncertain environment where the uncertainty is represented
through the Transferable Belief Model (TBM), one interpretation
of the belief function theory. The uncertainty can appear either in
the actual class of training objects or attribute values of objects to
classify. In this paper, we develop a post-pruning method of belief
decision trees in order to reduce size and improve classification
accuracy on unseen cases. The pruning of decision tree has a
considerable intention in the areas of machine learning.
Abstract: This paper considers the autonomous navigation
problem of multiple n-link nonholonomic mobile manipulators within
an obstacle-ridden environment. We present a set of nonlinear
acceleration controllers, derived from the Lyapunov-based control
scheme, which generates collision-free trajectories of the mobile
manipulators from initial configurations to final configurations in a
constrained environment cluttered with stationary solid objects of
different shapes and sizes. We demonstrate the efficiency of the
control scheme and the resulting acceleration controllers of the
mobile manipulators with results through computer simulations of an
interesting scenario.
Abstract: A neurofuzzy approach for a given set of input-output training data is proposed in two phases. Firstly, the data set is partitioned automatically into a set of clusters. Then a fuzzy if-then rule is extracted from each cluster to form a fuzzy rule base. Secondly, a fuzzy neural network is constructed accordingly and parameters are tuned to increase the precision of the fuzzy rule base. This network is able to learn and optimize the rule base of a Sugeno like Fuzzy inference system using Hybrid learning algorithm, which combines gradient descent, and least mean square algorithm. This proposed neurofuzzy system has the advantage of determining the number of rules automatically and also reduce the number of rules, decrease computational time, learns faster and consumes less memory. The authors also investigate that how neurofuzzy techniques can be applied in the area of control theory to design a fuzzy controller for linear and nonlinear dynamic systems modelling from a set of input/output data. The simulation analysis on a wide range of processes, to identify nonlinear components on-linely in a control system and a benchmark problem involving the prediction of a chaotic time series is carried out. Furthermore, the well-known examples of linear and nonlinear systems are also simulated under the Matlab/Simulink environment. The above combination is also illustrated in modeling the relationship between automobile trips and demographic factors.
Abstract: In this paper, Steam Assisted Gravity Drainage
(SAGD) is introduced and its advantages over ordinary steam
injection is demonstrated. A simple simulation model is built and
three scenarios of natural production, ordinary steam injection, and
SAGD are compared in terms of their cumulative oil production and
cumulative oil steam ratio. The results show that SAGD can
significantly enhance oil production in quite a short period of time.
However, since the distance between injection and production wells
is short, the oil to steam ratio decreases gradually through time.
Abstract: Load forecasting has become in recent years one of the major areas of research in electrical engineering. Most traditional forecasting models and artificial intelligence neural network techniques have been tried out in this task. Artificial neural networks (ANN) have lately received much attention, and a great number of papers have reported successful experiments and practical tests. This article presents the development of an ANN-based short-term load forecasting model with improved generalization technique for the Regional Power Control Center of Saudi Electricity Company, Western Operation Area (SEC-WOA). The proposed ANN is trained with weather-related data and historical electric load-related data using the data from the calendar years 2001, 2002, 2003, and 2004 for training. The model tested for one week at five different seasons, typically, winter, spring, summer, Ramadan and fall seasons, and the mean absolute average error for one hour-ahead load forecasting found 1.12%.
Abstract: An experiment was conducted with 80 unsexed
broilers of the Arbor Acress strain to determine the capability of a
carrot and fruit juice wastes mixture (carrot, apple, manggo, avocado,
orange, melon and Dutch egg plant) in the same proportion for
replacing corn in broiler diet. This study involved a completely
randomized design (CRD) with 5 treatments (0, 5, 10, 15, and 20% of
juice wastes mixture in diets) and 4 replicates per treatment. Diets
were isonitrogenous (22% crude protein) and isocaloric (3000 kcal/kg
diet). Measured variables were feed consumption, average daily
gain, feed conversion, as well as percentages of abdominal fat pad,
carcass, digestive organs (liver, pancreas and gizzard), and heart.
Data were analyzed by analysis of variance for CRD. Increasing
juice wastes mixture levels in diets increased feed consumption
(P
Abstract: Let G be a graph of order n, and let k 2 and m 0 be two integers. Let h : E(G) [0, 1] be a function. If e∋x h(e) = k holds for each x V (G), then we call G[Fh] a fractional k-factor of G with indicator function h where Fh = {e E(G) : h(e) > 0}. A graph G is called a fractional (k,m)-deleted graph if there exists a fractional k-factor G[Fh] of G with indicator function h such that h(e) = 0 for any e E(H), where H is any subgraph of G with m edges. In this paper, it is proved that G is a fractional (k,m)-deleted graph if (G) k + m + m k+1 , n 4k2 + 2k − 6 + (4k 2 +6k−2)m−2 k−1 and max{dG(x), dG(y)} n 2 for any vertices x and y of G with dG(x, y) = 2. Furthermore, it is shown that the result in this paper is best possible in some sense.
Abstract: As emails communications have no consistent
authentication procedure to ensure the authenticity, we present an
investigation analysis approach for detecting forged emails based on
Random Forests and Naïve Bays classifiers. Instead of investigating
the email headers, we use the body content to extract a unique writing
style for all the possible suspects. Our approach consists of four main
steps: (1) The cybercrime investigator extract different effective
features including structural, lexical, linguistic, and syntactic
evidence from previous emails for all the possible suspects, (2) The
extracted features vectors are normalized to increase the accuracy
rate. (3) The normalized features are then used to train the learning
engine, (4) upon receiving the anonymous email (M); we apply the
feature extraction process to produce a feature vector. Finally, using
the machine learning classifiers the email is assigned to one of the
suspects- whose writing style closely matches M. Experimental
results on real data sets show the improved performance of the
proposed method and the ability of identifying the authors with a
very limited number of features.
Abstract: This paper presents the visual control flow support of Visual Modeling and Transformation System (VMTS), which facilitates composing complex model transformations out of simple transformation steps and executing them. The VMTS Visual Control Flow Language (VCFL) uses stereotyped activity diagrams to specify control flow structures and OCL constraints to choose between different control flow branches. This work discusses the termination properties of VCFL and provides an algorithm to support the termination analysis of VCFL transformations.
Abstract: Drought is a phenomenon caused by
environmental and climatic changes. This phenomenon is
affected by shortage of rainfall and temperature. Dust is one
of important environmental problems caused by climate
change and drought. With recent multi-year drought, many
environmental crises caused by dust in Iran and Middle East.
Dust in the vast areas of the provinces occurs with high
frequency. By dust affecting many problems created in terms
of health, social and economic. In this study, we tried to study
the most important factors causing dust. In this way we have
used the satellite images and meteorological data. Finally,
strategies to deal with the dust will be mentioned.
Abstract: The scientific perspective, the practice area of physical education and sports activities improve power capacity in all its forms of expression, being a generator of the research topics. Today theories that strength training athletes and slow down development progress will affect the strength and flexibility are discredited. On the other hand there are sectors and / or samples whose results are sports of the way higher manifestation of power as a result of the composition of the force and velocity, being based in this respect on the systematic and continuous development of both bio-motric capacities said. Training of force for children was and is controversial. Teama de accidentări sau a stopării premature a procesului de creştere a făcut ca în trecut copiii să fie ţinuţi departe de lucrul cu diferite greutăţi.Fear of injury or premature stop the growth process in the past made the children to be kept away from working with different weights. Recent studies have shown that the risk of accidents is relatively small and the strength training can help prevent them. For example, most accidents occur at the level of athletics ligaments and tendons. From this point of view, it can be said that a progressive intervention of force training, optimal design, will help enhancing their process, such as athlete much better prepared to meet training requests and competitions. Preparation of force provides a solid basis for further phases in the highest performance.