Theproposed Method Research Articles

Numerical Solution for Initial and Boundary Value Problems of Fractional Order

Fractional calculus has been used in many fields, such as engineering, population, medicine, fluid mechanics and different fields of chemistry and physics. These fields were found to be best described using fractional differential equations (FDEs) to model their processes and equations. One of the well-known methods for solving fractional differential equations is the Shifted Legendre operational matrix (LOM) method. In this article, I proposed a numerical method based on Shifted Legendre polynomials for solving a class of fractional differential equations. A fractional order operational matrix of Legendre polynomials is also derived where the fractional derivatives are described by the Caputo derivative sense. By using the operational matrix, the initial and boundary equations are transformed into the products of several matrixes and by scattering the coefficients and the products of matrixes. I got a system of linear equations. Results obtained by using the proposed method (LOM) presented here show that the numerical method is very effective and appropriate for solving initial and boundary value problems of fractional ordinary differential equations. Moreover, some numerical examples are provided and the comparison is presented between the obtained results and those analytical results achieved that have proved the method’s validity.

A novel method for planning liver resections using deformable Bézier surfaces and distance maps

Background and Objective: For more than a decade, computer-assisted surgical systems have been helping surgeons to plan liver resections. The most widespread strategies to plan liver resections are: drawing traces in individual 2D slices, and using a 3D deformable plane. In this work, we propose a novel method which requires low level of user interaction while keeping high flexibility to specify resections. Methods: Our method is based on the use of Bézier surfaces, which can be deformed using a grid of control points, and distance maps as a base to compute and visualize resection margins (indicators of safety) in real-time. Projection of resections in 2D slices, as well as computation of resection volume statistics are also detailed. Results: The method was evaluated and compared with state-of-the-art methods by a group of surgeons (n=5, 5–31 years of experience). Our results show that theproposed method presents planning times as low as state-of-the-art methods (174 s median time) with high reproducibility of results in terms of resected volume. In addition, our method not only leads to smooth virtual resections easier to perform surgically compared to other state-of-the-art methods, but also shows superior preservation of resection margins. Conclusions: Our method provides clinicians with a robust and easy-to-use method for planning liver resections with high reproducibility, smoothness of resection and preservation of resection margin. Our results indicate the ability of the method to represent any type of resection and being integrated in real clinical work-flows.

Improved OCT Human Corneal segmentation Using Bayesian Residual Transform

The inherent poor signal to noise ratio of Optical Coherent Tomography(OCT) is considered as a main limitation of OCT segmentation,particularly because images are sampled quickly, at high resolutions,and in-vivo. Furthermore, speckle noise is generated bythe reflections of the OCT LASER limits the ability of automaticallysegmenting OCT images. This paper presents a novel method toautomatically segment human corneal OCT images. The proposedmethod uses Bayesian Residual Transform (BRT) to build a noiserobust external force map, that guides active contours model to thecorneal data in OCT images. Experimental results show that theproposed method outperforms the classical as well as the state-ofthe-art methods.

Determination of Some Cephalosporins in Pharmaceutical Formulations by a Simple and Sensitive Spectrofluorimetric Method

Background: Cephalosporins are among the safest and the most effective broad-spectrum bactericidal antimicrobial agents which have been prescribed by the clinician as antibiotics. Thus, the developing of simple, sensitive and rapid analytical methods for their determination can be attractive and desirable. Methods: A simple, rapid and sensitive spectrofluorimetric method was developed for the determination of cefixime, cefalexin and ceftriaxone in pharmaceutical formulations. Proposed method is based on the oxidation of these cephalosporins with cerium (IV) to produce cerium (III), and its fluorescence was monitored at 356 ± 3 nm after excitation at 254 ± 3 nm. Results: The variables effecting oxidation of each cephalosporin with cerum (IV) were studied and optimized. Under the experimental conditions used, the calibration graphs were linear over the range 0.1-4 µg/mL. The limit of detection and limit of quantification were in the range 0.031-0.054 and 0.102-0.172 µg/mL, respectively. Intra- and inter-day assay precisions, expressed as the relative standard deviation (RSD), were lower than 5.6 and 6.8%, respectively. Conclusion: The proposed method was applied to the determination of studied cephalosporins in pharmaceutical formulations by good recoveries in the range 91-110%.

Multibeam Pattern Synthesis With Adaptive Finite Excitation Amplitude Levels

An optimization approach based on alternating projection (AP) is proposed to generate low earth orbit (LEO)-satellite-borne multiple patterns with isoflux coverage. The purpose is to find a common amplitude distribution and different phases corresponding to the different patterns. The common amplitudes are with finite levels, which are calculated adaptively in the iteration. To demonstrate the good performance of the proposed method (TPM), synthesis results of multibeam patterns using TPM and modified AP (MAP) are compared in Section IV .

A Proposed Textual Graph Based Model for Arabic Multi-document Summarization

Text summarization task is still an active area of research in natural language preprocessing. Several methods that have been proposed in the literature to solve this task have presented mixed success. However, such methods developed in a multi-document Arabic text summarization are based on extractive summary and none of them is oriented to abstractive summary. This is due to the challenges of Arabic language and lack of resources. In this paper, we present a minimal languagedependent processing abstractive Arabic multi-document summarizer. The proposed model is based on textual graph to remove multi-document redundancy and generate coherent summary. Firstly, the original text, highly redundant and related multidocument, will be converted into textual graph. Next, graph traversal with structural rules will be applied to concatenate related sentences to single ones. Finally, unwanted and less weighted phrases will be removed from the summarized sentences to generate final summary. Preliminary results show that the proposed method has achieved promising results for multidocument summarization.

Metaheuristic Based Noise Identification and Image Denoising Using Adaptive Block Selection Based Filtering

Image denoising has become one of the major forms of image enhancement methods that form the basis of image processing. Due to the inconsistencies in the machinery producing these signals, medical images tend to require these techniques. In real time, images do not contain a single noise, and instead they contain multiple types of noise distributions in several indistinct regions. This paper presents an image denoising method that uses Metaheuristics to perform noise identification. Adaptive block selection is used to identify and correct the noise contained in these blocks. Though the system uses a block selection scheme, modifications are performed on pixel- to-pixel basis and not on the entire blocks; hence the image accuracy is preserved. PSO is used to identify the noise distribution, and appropriate noise correction techniques are applied to denoise the images. Experiments were conducted using salt and pepper noise, Gaussian noise and a combination of both the noise in the same image. It was observed that the proposed method performed effectively on noise levels up-to 0.5 and was able to produce results with PSNR values ranging from 20 to 30 in most of the cases. Excellent reduction rates were observed on salt and pepper noise and moderate reduction rates were observed on Gaussian noise. Experimental results show that our proposed system has a wide range of applicability in any domain specific image denoising scenario, such as medical imaging, mammogram etc.

Generalized Fuzzy Time Series Forecasting Model Enhanced with Particle Swarm Optimization

In the past two decades, many forecasting models based on the concepts of fuzzy time series have been proposed for dealing with various problem domains. In this paper, we present a novel model to forecast enrollments and the close prices of stock based on particle swarm optimization and generalized fuzzy logical relationships. After that some concepts of the generalized fuzzy logical relationships and an operation for combining the generalizedrelationships are introduced in the first part of this paper, we use particle swarm optimization to optimal represents for the given intervals in the universe of discourse to increase the forecasting accuracy. To test the effectiveness of the model, the proposed method is demonstrated on the procedure of forecasting enrollments as well as its experiment on forecasting the close price of Shanghai Stock Exchange Composite Index. Empirical analyses show that theproposed method gets a higher average forecasting accuracy rate than the existing methods.

THE METHOD OF PREDICTION OF SOFTWARE PROJECTS SUCCESS BASED ON THE ANALYSIS OF PREDICTED VALUES OF SOFTWARE CHARACTERISTICS

The task of development of methodological support for predicting of the software projects success is solved. The object of research is theprocess of predicting of software projects success at the early lifecycle stages. The subject of the research is a method of prediction of softwareprojects success based on the analysis of predicted values of software characteristics. The purpose of the work is to increase the accuracy ofselection of software projects and to increase the success of software projects. The intelligent method of prediction of software projects success is proposed. It provides to calculate the integrative indicators of success of various software projects based on the predicted (by artificial neural network) values of the characteristics of the software developed for the project. It also provides to calculate the probability of success ofsoftware projects. The method differs from the known methods, that provides to predict the success of software projects, compare softwareprojects on the main characteristics of the project and the software and on the predicted value of the probability of success (and not just onthe cost and duration, as is happening now), and perform the grounded choice of software project by customer and by developer for furtherrealization. Experiments on investigations of the method operability were performed The experimental results provide to recommend theproposed method for use in practice.

Robust Multi-View Spectral Clustering via Low-Rank and Sparse Decomposition

Multi-view clustering, which seeks a partition of the data inmultiple views that often provide complementary information to eachother, has received considerable attention in recent years. In reallife clustering problems, the data in each view may haveconsiderable noise. However, existing clustering methods blindlycombine the information from multi-view data with possiblyconsiderable noise, which often degrades their performance. In thispaper, we propose a novel Markov chain method for RobustMulti-view Spectral Clustering (RMSC). Our method has a flavor oflow-rank and sparse decomposition, where we firstly construct atransition probability matrix from each single view, and then usethese matrices to recover a shared low-rank transition probabilitymatrix as a crucial input to the standard Markov chain methodfor clustering. The optimization problem of RMSC has a low-rankconstraint on the transition probability matrix, and simultaneouslya probabilistic simplex constraint on each of its rows. To solvethis challenging optimization problem, we propose an optimization procedurebased on the Augmented Lagrangian Multiplier scheme. Experimentalresults on various real world datasets show that theproposed method has superior performance over severalstate-of-the-art methods for multi-view clustering.

An interactive fuzzy satisficing method for random fuzzy multiobjective linear programming problems through fractile criteria optimization with possibility

This paper considers multiobjective linear programming problems where each coefficient of the objective functions isexpressed by a random fuzzy variable. A new decision making model is proposed by incorporating the concept of fractilecriteria optimization into a possibilistic programming model. An interactive fuzzy satisficing method is presented forderiving a satisficing solution for a decision maker efficiently by updating the reference membership levels. In theproposed method, it is shown that the transformed deterministic problems for obtaining Pareto optimal solutions can besolved by using some convex programming techniques. An illustrative numerical example is provided to clarify theproposed method.

Facile preparation of monodisperse ZnO quantum dots with high quality photoluminescencecharacteristics

We report a new method that enables the facile synthesis of monodisperse zinc oxide (ZnO)quantum dots (QDs) with high quality photoluminescence (PL) characteristics. In theproposed method, ZnO QDs are grown on acid-treated multiwalled carbon nanotubes(a-MWCNTs) under remarkably mild reaction conditions (low temperature, shorttime, atmospheric pressure, and no need for subsequent thermal annealing). TheZnO QDs grown on the a-MWCNTs were monodisperse and highly crystalline,and had diameters of about 7 nm. Moreover, the a-MWCNT-grown ZnO QDsexhibited a definitive blue emission without defect-related blue–green emission,features that are indicative of definite quantum confinement effects and highquality.

Study on the Strength of Jointed Rock Mass

Based on the structure of the rock mass surrounding a highway tunnel under construction, a new methodology for defining the strength of jointed rock mass is proposed. The laboratory experiments and numerical simulations of rock samples, including both intact rock and rock with a single typical joint, are carried out to ensure that the mechanical parameters of the rock and the joint can be obtained. In addition, the strength of the rock mass surrounding the tunnel, without resorting to the difficult task of sampling and testing of large-scale rock mass, is evaluated by using the proposed method. It is shown that the strength reduction of rock mass is governed by the geometric configurations as well as the mechanical properties of the joints, and that the presence of joints results in the non-linearity of the pre-peak region.

Nanoscale displacement measurement by a digital nano-moiré method with wavelettransformation

A digital nano-moiré method with wavelet transformation is explored to measure nanoscalein-plane displacement fields. By applying e-beam lithography, a periodic PMMAnanostructure array is fabricated directly on the specimen and used as the specimengrating. Moiré patterns are generated by overlapping the images of the PMMA specimengrating obtained from AFM scanning and the virtual reference grating produced by adigital image generating process. Then, the overlapped images are filtered by the 2Dwavelet transformation (WT) to capture the target moiré patterns. Existing methods, byoverlapping the monitor-generated scanning lines with the image of the specimengrating, cause a mismatch problem. Previously, the carrier moiré method wasexplored with the aim of curing the mismatch problem. Unfortunately, the carriermoiré method, in addition to suffering from increased complexity of mathematicalcalculations, is incapable of directly obtaining the displacement field. Thus, themismatch problem will result in inconveniences and restrictions in the practicalapplication. Instead of using monitor-generated scanning lines, the proposed methodapplies the virtual reference grating, and thus puts the mismatch problem to rest.Nevertheless, the resultant moiré image suffers from low contrast which, if leftuntreated, might distort the measurement result. Therefore, the WT, known for itssharpened abilities of characteristic and edge detection, is used to capture the targetmoiré patterns and improve the measurement accuracy. The proposed method hasbeen carried out in the laboratory. Experimental results have shown that theproposed method is convenient and efficient for nanoscale displacement measurement.

Global Signal Classification of ULF Geomagnetic Field Variations Using Interstation Transfer Function

SUMMARY In order to study earthquake-related ULF geomag-netic field changes, it is important to discriminate the signalfrom the other noises such as magnetic pulsations origi-nated from solar activity and artificial noises from the DCdriven train and factories. For this goal, the interstationtransfer function method with wavelet transform has beenapplied to data observed in the ULF electromagnetic sensorarray at Boso Pe ninsula, Japan. The results indicate that theproposed method has the capacity to eliminate the globalsignal. © 2005 Wiley Periodicals, Inc. Electr Eng Jpn,151(2): 12–19, 2005; Published online in Wiley Inter-Science (www.interscience.wiley.com). DOI 10.1002/eej.20035 Key words: earthquake-related ULF electromag-netic field changes; interstation transfer function; waveletanalysis; geomagnetic pulsation; artificial noise. 1. Introduction Vari ous electromagnetic phenomena related to crus-tal activities such as earthquakes and volcanic eruptions arereported in the frequency range from HF to DC [1–3].Ground observations of natural electromagnetic field in theULF band (

An orthogonal moment-based method for automatic verification ofradiation field shape

The purpose of this paper is to develop a new method for automated on-lineverification of the treatment field shape during radiotherapy. The treatmentfield boundary is extracted from the digital portal image and is thenapproximated by a polygon. The proposed procedure used one of the approvedfield shapes as the reference boundary for automated comparison withsubsequent portal field boundaries. The orthogonal moment-based method wasapplied to align treatment field boundaries that include the translationalshifts, scaling factor and rotation angle. Firstly, the moments of order up toone were used to adjust the magnification and translation of the test fieldboundary related to the reference one; this step created a common coordinatesystem for the two images. Then a quadratic least-square objective functionbased on the orthogonal moments (e.g. Legendre moments) of the two fieldshapes was employed to perform rotational correction. Since momentcomputation by a straightforward method required a large number ofmultiplication and addition operations, a fast method for computing Legendremoments was also developed to decrease the calculation time. Application ofthe method to some simulated cases showed that our alignment procedure has anaccuracy of 0.5 mm in detecting translational shift, 0.004 in detectingmagnification and less than 0.3° in detecting rotation angle betweenthe test shape and the reference shape. The alignment procedure using theproposed method can be done within 2 s on a Pentium II personal computer.Therefore, our method is potentially useful for automated real-time treatmentfield shape verification.

A method for solving ship routing problemswith inventory constraints

We present an optimisation-based solution approach for a real ship planning problem,which is a combination of a variant of the multi-vehicle pickup and delivery problemwith time windows (m-PDPTW), and a multi-inventory model. This problem involves thedesign of a set of minimum cost routes for a fleet of heterogeneous ships servicing a set ofproduction and consumption harbours with a single product (ammonia). The production andinventory information at each harbour, together with the ship capacities and the location ofthe harbours, determine the number of possible arrivals at each harbour during the planningperiod, the time windows for start of service and the load quantity intervals at each arrival.We call this problem the inventory pickup and delivery problem with time windows -IPDPTW. In the mathematical programming model, we duplicate some of the variables anduse a Dantzig - Wolfe decomposition approach. Then the IPDPTW decomposes into a sub-problemfor each harbour and each ship. By synchronising the solutions from both types ofsubproblems, we get extra constraints in the master problem as compared to the masterproblem for the m-PDPTW discussed in the literature. The LP-relaxation of the masterproblem is solved by column generation, where the columns represent ship routes or harbourvisit sequences. Finally, this iterative solution process is embedded in a branch-and-boundsearch to make the solution integer optimal. Our computational results indicate that theproposed method works for the real planning problem.

Estimating Zones of Tolerance in Perceived Service Quality and Perceived Service Value

Focuses on proposing a new method for capturing the customer′s zone of tolerance for service quality. Interprets the zone of tolerance as a kind of inertia regarding behavioural responses to disconfirmation of expectations. Gives a predicted example in which adequate and desired expectations are operationalized by conjoint analysis. Explores the relationship between expectations, service quality, and service value. Gives examples of the kinds of result which one can obtain using the proposed method.