An Overview of Some High Order and Multi-Level Finite Difference Schemes in Computational Aeroacoustics

In this paper, we have combined some spatial derivatives with the optimised time derivative proposed by Tam and Webb in order to approximate the linear advection equation which is given by = 0. Ôêé Ôêé + Ôêé Ôêé x f t u These spatial derivatives are as follows: a standard 7-point 6 th -order central difference scheme (ST7), a standard 9-point 8 th -order central difference scheme (ST9) and optimised schemes designed by Tam and Webb, Lockard et al., Zingg et al., Zhuang and Chen, Bogey and Bailly. Thus, these seven different spatial derivatives have been coupled with the optimised time derivative to obtain seven different finite-difference schemes to approximate the linear advection equation. We have analysed the variation of the modified wavenumber and group velocity, both with respect to the exact wavenumber for each spatial derivative. The problems considered are the 1-D propagation of a Boxcar function, propagation of an initial disturbance consisting of a sine and Gaussian function and the propagation of a Gaussian profile. It is known that the choice of the cfl number affects the quality of results in terms of dissipation and dispersion characteristics. Based on the numerical experiments solved and numerical methods used to approximate the linear advection equation, it is observed in this work, that the quality of results is dependent on the choice of the cfl number, even for optimised numerical methods. The errors from the numerical results have been quantified into dispersion and dissipation using a technique devised by Takacs. Also, the quantity, Exponential Error for Low Dispersion and Low Dissipation, eeldld has been computed from the numerical results. Moreover, based on this work, it has been found that when the quantity, eeldld can be used as a measure of the total error. In particular, the total error is a minimum when the eeldld is a minimum.

An Unstructured Finite-volume Technique for Shallow-water Flows with Wetting and Drying Fronts

An unstructured finite volume numerical model is presented here for simulating shallow-water flows with wetting and drying fronts. The model is based on the Green-s theorem in combination with Chorin-s projection method. A 2nd-order upwind scheme coupled with a Least Square technique is used to handle convection terms. An Wetting and drying treatment is used in the present model to ensures the total mass conservation. To test it-s capacity and reliability, the present model is used to solve the Parabolic Bowl problem. We compare our numerical solutions with the corresponding analytical and existing standard numerical results. Excellent agreements are found in all the cases.

Optimal Control of Viscoelastic Melt Spinning Processes

The optimal control problem for the viscoelastic melt spinning process has not been reported yet in the literature. In this study, an optimal control problem for a mathematical model of a viscoelastic melt spinning process is considered. Maxwell-Oldroyd model is used to describe the rheology of the polymeric material, the fiber is made of. The extrusion velocity of the polymer at the spinneret as well as the velocity and the temperature of the quench air and the fiber length serve as control variables. A constrained optimization problem is derived and the first–order optimality system is set up to obtain the adjoint equations. Numerical solutions are carried out using a steepest descent algorithm. A computer program in MATLAB is developed for simulations.

Cloning and Expression of D-Threonine Aldolase from Ensifer arboris NBRC100383

D-erythro-cyclohexylserine (D chiral unnatural β-hydroxy amino acid expected for the synthesis of drug for AIDS treatment. To develop a continuous bioconversion system with whole cell biocatalyst of D-threonine aldolase (D genes for the D-erythro-CHS production, D-threonine aldolase gene was amplified from Ensifer arboris 100383 by direct PCR amplication using two degenerated oligonucleotide primers designed based on genomic sequence of Shinorhizobium meliloti Sequence analysis of the cloned DNA fragment revealed one open-reading frame of 1059 bp and 386 amino acids. This putative D-TA gene was cloned into NdeI and EcoRI (pEnsi His-tag sequence or BamHI (pEnsi-DTA[2]) sequence of the pET21(a) vector. The expression level of the cloned gene was extremely overexpressed by E. coli BL21(DE3) transformed with pEnsi-DTA[1] compared to E. coli BL21(DE3) transformed with pEnsi-DTA[2]. When the cells expressing the wild used for D-TA enzyme activity, 12 mM glycine was successfully detected in HPLC analysis. Moreover, the whole cells harbouring the recombinant D-TA was able to synthesize D-erythro of 0.6 mg/ml in a batch reaction.

Optimal Measures in Production Developing an Universal Decision Supporter for Evaluating Measures in a Production

Due to the recovering global economy, enterprises are increasingly focusing on logistics. Investing in logistic measures for a production generates a large potential for achieving a good starting point within a competitive field. Unlike during the global economic crisis, enterprises are now challenged with investing available capital to maximize profits. In order to be able to create an informed and quantifiably comprehensible basis for a decision, enterprises need an adequate model for logistically and monetarily evaluating measures in production. The Collaborate Research Centre 489 (SFB 489) at the Institute for Production Systems (IFA) developed a Logistic Information System which provides support in making decisions and is designed specifically for the forging industry. The aim of a project that has been applied for is to now transfer this process in order to develop a universal approach to logistically and monetarily evaluate measures in production.

Weld Defect Detection in Industrial Radiography Based Digital Image Processing

Industrial radiography is a famous technique for the identification and evaluation of discontinuities, or defects, such as cracks, porosity and foreign inclusions found in welded joints. Although this technique has been well developed, improving both the inspection process and operating time, it does suffer from several drawbacks. The poor quality of radiographic images is due to the physical nature of radiography as well as small size of the defects and their poor orientation relatively to the size and thickness of the evaluated parts. Digital image processing techniques allow the interpretation of the image to be automated, avoiding the presence of human operators making the inspection system more reliable, reproducible and faster. This paper describes our attempt to develop and implement digital image processing algorithms for the purpose of automatic defect detection in radiographic images. Because of the complex nature of the considered images, and in order that the detected defect region represents the most accurately possible the real defect, the choice of global and local preprocessing and segmentation methods must be appropriated.

Atrial Fibrillation Analysis Based on Blind Source Separation in 12-lead ECG

Atrial Fibrillation is the most common sustained arrhythmia encountered by clinicians. Because of the invisible waveform of atrial fibrillation in atrial activation for human, it is necessary to develop an automatic diagnosis system. 12-Lead ECG now is available in hospital and is appropriate for using Independent Component Analysis to estimate the AA period. In this research, we also adopt a second-order blind identification approach to transform the sources extracted by ICA to more precise signal and then we use frequency domain algorithm to do the classification. In experiment, we gather a significant result of clinical data.

Application of Wavelet Neural Networks in Optimization of Skeletal Buildings under Frequency Constraints

The main goal of the present work is to decrease the computational burden for optimum design of steel frames with frequency constraints using a new type of neural networks called Wavelet Neural Network. It is contested to train a suitable neural network for frequency approximation work as the analysis program. The combination of wavelet theory and Neural Networks (NN) has lead to the development of wavelet neural networks. Wavelet neural networks are feed-forward networks using wavelet as activation function. Wavelets are mathematical functions within suitable inner parameters, which help them to approximate arbitrary functions. WNN was used to predict the frequency of the structures. In WNN a RAtional function with Second order Poles (RASP) wavelet was used as a transfer function. It is shown that the convergence speed was faster than other neural networks. Also comparisons of WNN with the embedded Artificial Neural Network (ANN) and with approximate techniques and also with analytical solutions are available in the literature.

Development of a Model for the Comprehensive Analysis and Evaluation of Service Productivity

Although services play a crucial role in economy, service did not gain as much importance as productivity management in manufacturing. This paper presents key findings from literature and practice. Based on an initial definition of complex services, seven productivity concepts are briefly presented and assessed by relevant, complex service specific criteria. Following the findings a complex service productivity model is proposed. The novel model comprises of all specific dimensions of service provision from both, the provider-s as well as costumer-s perspective. A clear assignment of identified value drivers and relationships between them is presented. In order to verify the conceptual service productivity model a case study from a project engineering department of a chemical plant development and construction company is presented.

The Link between Unemployment and Inflation Using Johansen’s Co-Integration Approach and Vector Error Correction Modelling

In this paper bi-annual time series data on unemployment rates (from the Labour Force Survey) are expanded to quarterly rates and linked to quarterly unemployment rates (from the Quarterly Labour Force Survey). The resultant linked series and the consumer price index (CPI) series are examined using Johansen’s cointegration approach and vector error correction modeling. The study finds that both the series are integrated of order one and are cointegrated. A statistically significant co-integrating relationship is found to exist between the time series of unemployment rates and the CPI. Given this significant relationship, the study models this relationship using Vector Error Correction Models (VECM), one with a restriction on the deterministic term and the other with no restriction. A formal statistical confirmation of the existence of a unique linear and lagged relationship between inflation and unemployment for the period between September 2000 and June 2011 is presented. For the given period, the CPI was found to be an unbiased predictor of the unemployment rate. This relationship can be explored further for the development of appropriate forecasting models incorporating other study variables.

Pipelines Monitoring System Using Bio-mimetic Robots

Recently there has been a growing interest in the field of bio-mimetic robots that resemble the behaviors of an insect or an aquatic animal, among many others. One of various bio-mimetic robot applications is to explore pipelines, spotting any troubled areas or malfunctions and reporting its data. Moreover, the robot is able to prepare for and react to any abnormal routes in the pipeline. Special types of mobile robots are necessary for the pipeline monitoring tasks. In order to move effectively along a pipeline, the robot-s movement will resemble that of insects or crawling animals. When situated in massive pipelines with complex routes, the robot places fixed sensors in several important spots in order to complete its monitoring. This monitoring task is to prevent a major system failure by preemptively recognizing any minor or partial malfunctions. Areas uncovered by fixed sensors are usually impossible to provide real-time observation and examination, and thus are dependent on periodical offline monitoring. This paper proposes a monitoring system that is able to monitor the entire area of pipelines–with and without fixed sensors–by using the bio-mimetic robot.

The Investigation of Green Roof and White Roof Cooling Potential on Single Storey Residential Building in the Malaysian Climate

The phenomenon of global warming or climate change has led to many environmental issues including higher atmospheric temperatures, intense precipitation, increased greenhouse gaseous emissions and increased indoor discomfort. Studies have shown that bringing nature to the roof such as constructing green roof and implementing high-reflective roof may give positive impact in mitigating the effects of global warming and in increasing thermal comfort sensation inside buildings. However, no study has been conducted to compare both types of passive roof treatments in Malaysia in order to increase thermal comfort in buildings. Therefore, this study is conducted to investigate the effect of green roof and white painted roof as passive roof treatment in improving indoor comfort of Malaysian homes. This study uses an experimental approach in which the measurements of temperatures are conducted on the case study building. The measurements of outdoor and indoor environments were conducted on the flat roof with two different types of roof treatment that are green roof and white roof. The measurement of existing black bare roof was also conducted to act as a control for this study.

A New Hardware Implementation of Manchester Line Decoder

In this paper, we present a simple circuit for Manchester decoding and without using any complicated or programmable devices. This circuit can decode 90kbps of transmitted encoded data; however, greater than this transmission rate can be decoded if high speed devices were used. We also present a new method for extracting the embedded clock from Manchester data in order to use it for serial-to-parallel conversion. All of our experimental measurements have been done using simulation.

Improved Closed Set Text-Independent Speaker Identification by Combining MFCC with Evidence from Flipped Filter Banks

A state of the art Speaker Identification (SI) system requires a robust feature extraction unit followed by a speaker modeling scheme for generalized representation of these features. Over the years, Mel-Frequency Cepstral Coefficients (MFCC) modeled on the human auditory system has been used as a standard acoustic feature set for SI applications. However, due to the structure of its filter bank, it captures vocal tract characteristics more effectively in the lower frequency regions. This paper proposes a new set of features using a complementary filter bank structure which improves distinguishability of speaker specific cues present in the higher frequency zone. Unlike high level features that are difficult to extract, the proposed feature set involves little computational burden during the extraction process. When combined with MFCC via a parallel implementation of speaker models, the proposed feature set outperforms baseline MFCC significantly. This proposition is validated by experiments conducted on two different kinds of public databases namely YOHO (microphone speech) and POLYCOST (telephone speech) with Gaussian Mixture Models (GMM) as a Classifier for various model orders.

Corporate Credit Rating using Multiclass Classification Models with order Information

Corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has been one of the attractive research topics in the literature. In recent years, multiclass classification models such as artificial neural network (ANN) or multiclass support vector machine (MSVM) have become a very appealing machine learning approaches due to their good performance. However, most of them have only focused on classifying samples into nominal categories, thus the unique characteristic of the credit rating - ordinality - has been seldom considered in their approaches. This study proposes new types of ANN and MSVM classifiers, which are named OMANN and OMSVM respectively. OMANN and OMSVM are designed to extend binary ANN or SVM classifiers by applying ordinal pairwise partitioning (OPP) strategy. These models can handle ordinal multiple classes efficiently and effectively. To validate the usefulness of these two models, we applied them to the real-world bond rating case. We compared the results of our models to those of conventional approaches. The experimental results showed that our proposed models improve classification accuracy in comparison to typical multiclass classification techniques with the reduced computation resource.

Selective Transverse Modes in a Diode End- Pumped Nd:Yag Pulsed Laser

The output beam quality of multi transverse modes of laser, are relatively poor. In order to obtain better beam quality, one may use an aperture inside the laser resonator. In this case, various transverse modes can be selected. We have selected various transverse modes both by simulation and doing experiment. By inserting a circular aperture inside the diode end-pumped Nd:YAG pulsed laser resonator, we have obtained 00 TEM , 01 TEM , 20 TEM and have studied which parameters, can change the mode shape. Then, we have determined the beam quality factor of TEM00 gaussian mode.

Qualification and Provisioning of xDSL Broadband Lines using a GIS Approach

In this paper is presented a Geographic Information System (GIS) approach in order to qualify and monitor the broadband lines in efficient way. The methodology used for interpolation is the Delaunay Triangular Irregular Network (TIN). This method is applied for a case study in ISP Greece monitoring 120,000 broadband lines.

Effect of Size of the Step in the Response Surface Methodology using Nonlinear Test Functions

The response surface methodology (RSM) is a collection of mathematical and statistical techniques useful in the modeling and analysis of problems in which the dependent variable receives the influence of several independent variables, in order to determine which are the conditions under which should operate these variables to optimize a production process. The RSM estimated a regression model of first order, and sets the search direction using the method of maximum / minimum slope up / down MMS U/D. However, this method selects the step size intuitively, which can affect the efficiency of the RSM. This paper assesses how the step size affects the efficiency of this methodology. The numerical examples are carried out through Monte Carlo experiments, evaluating three response variables: efficiency gain function, the optimum distance and the number of iterations. The results in the simulation experiments showed that in response variables efficiency and gain function at the optimum distance were not affected by the step size, while the number of iterations is found that the efficiency if it is affected by the size of the step and function type of test used.

Optimal Facility Layout Problem Solution Using Genetic Algorithm

Facility Layout Problem (FLP) is one of the essential problems of several types of manufacturing and service sector. It is an optimization problem on which the main objective is to obtain the efficient locations, arrangement and order of the facilities. In the literature, there are numerous facility layout problem research presented and have used meta-heuristic approaches to achieve optimal facility layout design. This paper presented genetic algorithm to solve facility layout problem; to minimize total cost function. The performance of the proposed approach was verified and compared using problems in the literature.