Abstract: The ability of UML to handle the modeling process of complex industrial software applications has increased its popularity to the extent of becoming the de-facto language in serving the design purpose. Although, its rich graphical notation naturally oriented towards the object-oriented concept, facilitates the understandability, it hardly successes to report all domainspecific aspects in a satisfactory way. OCL, as the standard language for expressing additional constraints on UML models, has great potential to help improve expressiveness. Unfortunately, it suffers from a weak formalism due to its poor semantic resulting in many obstacles towards the build of tools support and thus its application in the industry field. For this reason, many researches were established to formalize OCL expressions using a more rigorous approach. Our contribution join this work in a complementary way since it focuses specifically on OCL predefined properties which constitute an important part in the construction of OCL expressions. Using formal methods, we mainly succeed in expressing rigorously OCL predefined functions.
Abstract: Interpretation of aerial images is an important task in
various applications. Image segmentation can be viewed as the essential
step for extracting information from aerial images. Among many
developed segmentation methods, the technique of clustering has been
extensively investigated and used. However, determining the number
of clusters in an image is inherently a difficult problem, especially
when a priori information on the aerial image is unavailable. This
study proposes a support vector machine approach for clustering
aerial images. Three cluster validity indices, distance-based index,
Davies-Bouldin index, and Xie-Beni index, are utilized as quantitative
measures of the quality of clustering results. Comparisons on the
effectiveness of these indices and various parameters settings on the
proposed methods are conducted. Experimental results are provided
to illustrate the feasibility of the proposed approach.
Abstract: The number of features required to represent an image
can be very huge. Using all available features to recognize objects
can suffer from curse dimensionality. Feature selection and
extraction is the pre-processing step of image mining. Main issues in
analyzing images is the effective identification of features and
another one is extracting them. The mining problem that has been
focused is the grouping of features for different shapes. Experiments
have been conducted by using shape outline as the features. Shape
outline readings are put through normalization and dimensionality
reduction process using an eigenvector based method to produce a
new set of readings. After this pre-processing step data will be
grouped through their shapes. Through statistical analysis, these
readings together with peak measures a robust classification and
recognition process is achieved. Tests showed that the suggested
methods are able to automatically recognize objects through their
shapes. Finally, experiments also demonstrate the system invariance
to rotation, translation, scale, reflection and to a small degree of
distortion.
Abstract: The Comparison analysis of the Wald-s and Bayestype sequential methods for testing hypotheses is offered. The merits of the new sequential test are: universality which consists in optimality (with given criteria) and uniformity of decision-making regions for any number of hypotheses; simplicity, convenience and uniformity of the algorithms of their realization; reliability of the obtained results and an opportunity of providing the errors probabilities of desirable values. There are given the Computation results of concrete examples which confirm the above-stated characteristics of the new method and characterize the considered methods in regard to each other.
Abstract: Computerized alarm systems have been applied
increasingly to nuclear power plants. For existing plants, an add-on
computer alarm system is often installed to the control rooms. Alarm
avalanches during the plant transients are major problems with the
alarm systems in nuclear power plants. Computerized alarm systems
can process alarms to reduce the number of alarms during the plant
transients. This paper describes various alarm processing methods, an
alarm cause tracking function, and various alarm presentation schemes
to show alarm information to the operators effectively which are
considered during the development of several computerized alarm
systems for Korean nuclear power plants and are found to be helpful to
the operators.
Abstract: Not only is municipal pattern the institution basement of urban management, but it also determines the forms of the management results. There-s a considerable possibility of bankruptcy for China-s current municipal pattern as it-s an overdraft of land deal in fact. Based on the analysis of China-s current municipal pattern, the passage proposed an assumption of a new pattern verified legitimacy by conceptual as well as econometric models. Conclusion is: the added supernumerary value of investment in public goods was not included in China-s current municipal pattern, but hidden in the rising housing prices; we should set housing tax or municipal tax to optimize the municipal pattern, to correct the behavior of local governments and to ensure the regular development of China-s urbanization.
Abstract: A model of vortex wake is suggested to determine the
induced power during animal hovering flight. The wake is modeled
by a series of equi-spaced rigid rectangular vortex plates, positioned
horizontally and moving vertically downwards with identical speeds;
each plate is generated during powering of the functionally wing
stroke. The vortex representation of the wake considered in the
current theory allows a considerable loss of momentum to occur. The
current approach accords well with the nature of the wingbeat since it
considers the unsteadiness in the wake as an important fluid
dynamical characteristic. Induced power in hovering is calculated as
the aerodynamic power required to generate the vortex wake system.
Specific mean induced power to mean wing tip velocity ratio is
determined by solely the normal spacing parameter (f) for a given
wing stroke amplitude. The current theory gives much higher specific
induced power estimate than anticipated by classical methods.
Abstract: This paper presents kinematic and dynamic analysis of a novel 8-DOF hybrid robot manipulator. The hybrid robot manipulator under consideration consists of a parallel robot which
is followed by a serial mechanism. The parallel mechanism has three translational DOF, and the serial mechanism has five DOF so that the overall degree of freedom is eight. The introduced
manipulator has a wide workspace and a high capability to reduce
the actuating energy. The inverse and forward kinematic solutions are described in closed form. The theoretical results are verified by
a numerical example. Inverse dynamic analysis of the robot is presented by utilizing the Iterative Newton-Euler and Lagrange dynamic formulation methods. Finally, for performing a multi-step
arc welding process, results have indicated that the introduced manipulator is highly capable of reducing the actuating energy.
Abstract: As the increase of intraoral acidity due to ingestion of sweet foods and acidic beverages usually bring forth a dental caries and a erosion, the measurement of intraoral pH is essential in the study of oral environment. The indwelling intraoral pH telemetry for lasting longer than 24 hours in the mouth was developed to overcome the limits of conventional wire electrode method previously used for salivary and plaque pH measurement, and to assess its effectiveness.
Abstract: This paper reviews the objectives, methods and results of previous studies on biodrying of solid waste in several countries. Biodrying of solid waste is a novel technology in developing countries such as in Malaysia where high moisture content in organic waste makes the segregation process for recycling purposes complicated and diminishes the calorific value for the use of fuel source. In addition, the high moisture content also encourages the breeding of vectors and disease-bearing animals. From the laboratory results, the average moisture content of organic waste, paper, plastics and metals are 58.17%, 37.93%, 29.79% and 1.03% respectively for UKM campus. Biodrying of solid waste is a simple method of waste treatment as well as a cost-efficient technology to dry the solid waste. The process depends on temperature monitoring and air flow control along with the natural biodegradable process of organic waste. This review shows that the biodrying of solid waste method has high potential in treatment and recycling of solid waste, be useful for biodrying study and implementation in Malaysia.
Abstract: Economically transformers constitute one of the largest investments in a Power system. For this reason, transformer condition assessment and management is a high priority task. If a transformer fails, it would have a significant negative impact on revenue and service reliability. Monitoring the state of health of power transformers has traditionally been carried out using laboratory Dissolved Gas Analysis (DGA) tests performed at periodic intervals on the oil sample, collected from the transformers. DGA of transformer oil is the single best indicator of a transformer-s overall condition and is a universal practice today, which started somewhere in the 1960s. Failure can occur in a transformer due to different reasons. Some failures can be limited or prevented by maintenance. Oil filtration is one of the methods to remove the dissolve gases and prevent the deterioration of the oil. In this paper we analysis the DGA data by regression method and predict the gas concentration in the oil in the future. We bring about a comparative study of different traditional methods of regression and the errors generated out of their predictions. With the help of these data we can deduce the health of the transformer by finding the type of fault if it has occurred or will occur in future. Additional in this paper effect of filtration on the transformer health is highlight by calculating the probability of failure of a transformer with and without oil filtrating.
Abstract: Studies were carried out to determine the in vitro
susceptibility of the typhoid pathogens to combined action of Euphorbia hirta, Euphorbia heterophylla and Phyllanthus niruri. Clinical isolates of the typhoid bacilli were subjected to susceptibility testing using agar diffusion technique and the minimum inhibitory
concentration (MIC) determined with tube dilution technique. These
isolates, when challenged with doses of the extracts from the three
medicinal plants showed zones of inhibition as wide as 26±0.2mm, 22±0.1mm and 18±0.0mm respectively. The minimum inhibitory concentration (MIC) revealed organisms inhibited at varying
concentrations of extracts: E. hirta (S. typhi 0.250mg/ml, S. paratyphi A 0.125mg/ml, S. paratyphi B 0.185mg/ml and S. paratyphi C 0.225mg/ml), E. heterophylla (S. typhi 0.280mg/ml, S. paratyphi A
0.150mg/ml, S. paratyphi B 0.200mg/ml and S. paratyphi C 0.250mg/ml) and P. niruri (S. typhi 0.150mg/ml, S. paratyphi A 0.100mg/ml, S. paratyphi B 0.115mg/ml and S. paratyphi C 0.125mg/ml). The results of the synergy between the three plants in
the ration of 1:1:1 showed very low MICs for the test pathogens as follows S. typhi 0.025mg/ml, S. paratyphi A 0.080mg/ml, S. paratyphi B 0.015mg/ml and S. paratyphi C 0.10mg/ml with the
diameter zone of inhibition (DZI) ranging from 35±0.2mm,
28±0.4mm, 20±0.1mm and 32±0.3mm respectively. The secondary
metabolites were identified using simple methods and HPLC. Organic components such as anthroquinones, different alkaloids,
tannins, 6-ethoxy-1,2,3,4-tetrahydro-2,2,4-trimethyl and steroids were identified. The prevalence of Salmonellae, a deadly infectious disease, is still very high in parts of Nigeria. The synergistic action of these three plants is very high. It is concluded that pharmaceutical companies should take advantage of these findings to develop new
anti-typhoid drugs from these plants.
Abstract: The approach of subset selection in polynomial
regression model building assumes that the chosen fixed full set of
predefined basis functions contains a subset that is sufficient to
describe the target relation sufficiently well. However, in most cases
the necessary set of basis functions is not known and needs to be
guessed – a potentially non-trivial (and long) trial and error process.
In our research we consider a potentially more efficient approach –
Adaptive Basis Function Construction (ABFC). It lets the model
building method itself construct the basis functions necessary for
creating a model of arbitrary complexity with adequate predictive
performance. However, there are two issues that to some extent
plague the methods of both the subset selection and the ABFC,
especially when working with relatively small data samples: the
selection bias and the selection instability. We try to correct these
issues by model post-evaluation using Cross-Validation and model
ensembling. To evaluate the proposed method, we empirically
compare it to ABFC methods without ensembling, to a widely used
method of subset selection, as well as to some other well-known
regression modeling methods, using publicly available data sets.
Abstract: Particle boards were prepared from Maize cob (MC) and urea-formaldehyde resin (UFR) on compression moulding machine. The amount of MC was varied from 50-120g while 30g of UFR was kept constant. Some mechanical properties of the particle boards were tested using the standard ASM methods. The results show that as the MC content increased from 50- 120g in 30g UFR, the hardness increased from about 6.89 x 102 to7.51 x 102MPa. Impact strength decreased from 3.3x 10-2 to 0.45 x 10-2J/M2, while tensile strength initially increased from 2.63 x 102 to 3.14 x 102 MPa as the MC increased from 50 to 60g in 30g UFR, thereafter, it decreased to about 1.35 x 102MPa at 120g in 30g content.
Abstract: This frame work describes a computationally more
efficient and adaptive threshold estimation method for image
denoising in the wavelet domain based on Generalized Gaussian
Distribution (GGD) modeling of subband coefficients. In this
proposed method, the choice of the threshold estimation is carried out
by analysing the statistical parameters of the wavelet subband
coefficients like standard deviation, arithmetic mean and geometrical
mean. The noisy image is first decomposed into many levels to
obtain different frequency bands. Then soft thresholding method is
used to remove the noisy coefficients, by fixing the optimum
thresholding value by the proposed method. Experimental results on
several test images by using this method show that this method yields
significantly superior image quality and better Peak Signal to Noise
Ratio (PSNR). Here, to prove the efficiency of this method in image
denoising, we have compared this with various denoising methods
like wiener filter, Average filter, VisuShrink and BayesShrink.
Abstract: This paper presents application artificial intelligent (AI) techniques, namely artificial neural network (ANN), adaptive neuro fuzzy interface system (ANFIS), to estimate the real power transfer between generators and loads. Since these AI techniques adopt supervised learning, it first uses modified nodal equation method (MNE) to determine real power contribution from each generator to loads. Then the results of MNE method and load flow information are utilized to estimate the power transfer using AI techniques. The 25-bus equivalent system of south Malaysia is utilized as a test system to illustrate the effectiveness of both AI methods compared to that of the MNE method. The mean squared error of the estimate of ANN and ANFIS power transfer allocation methods are 1.19E-05 and 2.97E-05, respectively. Furthermore, when compared to MNE method, ANN and ANFIS methods computes generator contribution to loads within 20.99 and 39.37msec respectively whereas the MNE method took 360msec for the calculation of same real power transfer allocation.
Abstract: This policy participation action research explores the
roles of Thai government units during its 2010 fiscal year on how to
create value added to recycling business in the central part of
Thailand. The research aims to a) study how the government plays a
role to support the business, and its problems and obstacles on
supporting the business, b) to design a strategic action – short,
medium, and long term plans -- to create value added to the recycling
business, particularly in local full-loop companies/organizations
licensed by Wongpanit Waste Separation Plant as well as those
licensed by the Department of Provincial Administration. Mixed
method research design, i.e., a combination of quantitative and
qualitative methods is utilized in the present study in both data
collection and analysis procedures. Quantitative data was analyzed
by frequency, percent value, mean scores, and standard deviation,
and aimed to note trend and generalizations. Qualitative data was
collected via semi-structured interviews/focus group interviews to
explore in-depth views of the operators. The sampling included 1,079
operators in eight provinces in the central part of Thailand.
Abstract: Evaluation and survey of curriculum quality as one of the most important components of universities system is necessary for different levels in higher education. The main purpose of this study was to survey of the curriculum quality of Actuarial science field. Case: University of SHahid Beheshti and Higher education institute of Eco insurance (according to viewpoint of students, alumni, employers and faculty members). Descriptive statistics (mean, tables, percentage, and frequency distribution) and inferential statistics (CHI SQUARE) were used to analyze the data. Six criteria considered for the Quality of curriculum: objectives, content, teaching and learning methods, space and facilities, Time, assessment of learning. Content, teaching and learning methods, space and facilities, assessment of learning criteria were relatively desirable level, objectives and time criterions were desirable level. The quality of curriculum of Actuarial Science field was relatively desirable level.
Abstract: Spatial trends are one of the valuable patterns in geo
databases. They play an important role in data analysis and
knowledge discovery from spatial data. A spatial trend is a regular
change of one or more non spatial attributes when spatially moving
away from a start object. Spatial trend detection is a graph search
problem therefore heuristic methods can be good solution. Artificial
immune system (AIS) is a special method for searching and
optimizing. AIS is a novel evolutionary paradigm inspired by the
biological immune system. The models based on immune system
principles, such as the clonal selection theory, the immune network
model or the negative selection algorithm, have been finding
increasing applications in fields of science and engineering.
In this paper, we develop a novel immunological algorithm based
on clonal selection algorithm (CSA) for spatial trend detection. We
are created neighborhood graph and neighborhood path, then select
spatial trends that their affinity is high for antibody. In an
evolutionary process with artificial immune algorithm, affinity of
low trends is increased with mutation until stop condition is satisfied.
Abstract: Particle Swarm Optimization (PSO) with elite PSO
parameters has been developed for power flow analysis under
practical constrained situations. Multiple solutions of the power flow
problem are useful in voltage stability assessment of power system.
A method of determination of multiple power flow solutions is
presented using a hybrid of Particle Swarm Optimization (PSO) and
local search technique. The unique and innovative learning factors of
the PSO algorithm are formulated depending upon the node power
mismatch values to be highly adaptive with the power flow problems.
The local search is applied on the pbest solution obtained by the PSO
algorithm in each iteration. The proposed algorithm performs reliably
and provides multiple solutions when applied on standard and illconditioned
systems. The test results show that the performances of
the proposed algorithm under critical conditions are better than the
conventional methods.