Search results for: prime and semiprime (generalized) bi-ideal

Authors: Parvinder S. Sandhu, Roma Jaswal, Sandeep Khimta, Shailendra Singh

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As the majority of faults are found in a few of its modules so there is a need to investigate the modules that are affected severely as compared to other modules and proper maintenance need to be done in time especially for the critical applications. As, Neural networks, which have been already applied in software engineering applications to build reliability growth models predict the gross change or reusability metrics. Neural networks are non-linear sophisticated modeling techniques that are able to model complex functions. Neural network techniques are used when exact nature of input and outputs is not known. A key feature is that they learn the relationship between input and output through training. In this present work, various Neural Network Based techniques are explored and comparative analysis is performed for the prediction of level of need of maintenance by predicting level severity of faults present in NASA-s public domain defect dataset. The comparison of different algorithms is made on the basis of Mean Absolute Error, Root Mean Square Error and Accuracy Values. It is concluded that Generalized Regression Networks is the best algorithm for classification of the software components into different level of severity of impact of the faults. The algorithm can be used to develop model that can be used for identifying modules that are heavily affected by the faults.

Keywords: Neural Network, Software faults, Software Metric.

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Authors: S. Anna Durai, E. Anna Saro

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Image Compression using Artificial Neural Networks is a topic where research is being carried out in various directions towards achieving a generalized and economical network. Feedforward Networks using Back propagation Algorithm adopting the method of steepest descent for error minimization is popular and widely adopted and is directly applied to image compression. Various research works are directed towards achieving quick convergence of the network without loss of quality of the restored image. In general the images used for compression are of different types like dark image, high intensity image etc. When these images are compressed using Back-propagation Network, it takes longer time to converge. The reason for this is, the given image may contain a number of distinct gray levels with narrow difference with their neighborhood pixels. If the gray levels of the pixels in an image and their neighbors are mapped in such a way that the difference in the gray levels of the neighbors with the pixel is minimum, then compression ratio as well as the convergence of the network can be improved. To achieve this, a Cumulative distribution function is estimated for the image and it is used to map the image pixels. When the mapped image pixels are used, the Back-propagation Neural Network yields high compression ratio as well as it converges quickly.

Keywords: Back-propagation Neural Network, Cumulative Distribution Function, Correlation, Convergence.

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Authors: Ivan C. Mustakerov, Daniela I. Borissova

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The increasing competitiveness in manufacturing industry is forcing manufacturers to seek effective processing schedules. The paper presents an optimization manufacture scheduling approach for dependent details processing with given processing sequences and times on multiple machines. By defining decision variables as start and end moments of details processing it is possible to use straightforward variables restrictions to satisfy different technological requirements and to formulate easy to understand and solve optimization tasks for multiple numbers of details and machines. A case study example is solved for seven base moldings for CNC metalworking machines processed on five different machines with given processing order among details and machines and known processing time-s duration. As a result of linear optimization task solution the optimal manufacturing schedule minimizing the overall processing time is obtained. The manufacturing schedule defines the moments of moldings delivery thus minimizing storage costs and provides mounting due-time satisfaction. The proposed optimization approach is based on real manufacturing plant problem. Different processing schedules variants for different technological restrictions were defined and implemented in the practice of Bulgarian company RAIS Ltd. The proposed approach could be generalized for other job shop scheduling problems for different applications.

Keywords: Optimal manufacturing scheduling, linear programming, metalworking machines production, dependant details processing.

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Authors: Baydaa H. Maula, Ling Zhang, Tang Liang, Gao Xia, Xu Peng-Ju, Zhang Yong-Qiang, Kang Jie, Su Lei

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Firstly, this study briefly presents the current situation that there exists a vast gap between current Chinese and Japanese seismic design specification for bridge pile foundation in liquefiable and liquefaction-induced lateral spreading ground; The Chinese and Japanese seismic design method and technical detail for bridge pile foundation in liquefying and lateral spreading ground are described and compared systematically and comprehensively, the methods of determining coefficient of subgrade reaction and its reduction factor as well as the computing mode of the applied force on pile foundation due to liquefaction-induced lateral spreading soil in Japanese design specification are especially introduced. Subsequently, the comparison indicates that the content of Chinese seismic design specification for bridge pile foundation in liquefiable and liquefaction-induced lateral spreading ground, just presenting some qualitative items, is too general and lacks systematicness and maneuverability. Finally, some defects of seismic design specification in China are summarized, so the improvement and revision of specification in the field turns out to be imperative for China, some key problems of current Chinese specifications are generalized and the corresponding improvement suggestions are proposed.

Keywords: liquefying soil, laterally spreading ground, seismic design specification for bridge pile foundation.

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Authors: Leila Vafajoo, Farhad Khorasheh, Mehrnoosh Hamzezadeh Nakhjavani, Moslem Fattahi

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In the present study, a procedure was developed to determine the optimum reaction rate constants in generalized Arrhenius form and optimized through the Nelder-Mead method. For this purpose, a comprehensive mathematical model of a fixed bed reactor for dehydrogenation of heavy paraffins over Pt–Sn/Al2O3 catalyst was developed. Utilizing appropriate kinetic rate expressions for the main dehydrogenation reaction as well as side reactions and catalyst deactivation, a detailed model for the radial flow reactor was obtained. The reactor model composed of a set of partial differential equations (PDE), ordinary differential equations (ODE) as well as algebraic equations all of which were solved numerically to determine variations in components- concentrations in term of mole percents as a function of time and reactor radius. It was demonstrated that most significant variations observed at the entrance of the bed and the initial olefin production obtained was rather high. The aforementioned method utilized a direct-search optimization algorithm along with the numerical solution of the governing differential equations. The usefulness and validity of the method was demonstrated by comparing the predicted values of the kinetic constants using the proposed method with a series of experimental values reported in the literature for different systems.

Keywords: Dehydrogenation, Pt-Sn/Al2O3 Catalyst, Modeling, Nelder-Mead, Optimization

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Authors: J. P. Dubois, Omar M. Abdul-Latif

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Support Vector Machine (SVM) is a statistical learning tool developed to a more complex concept of structural risk minimization (SRM). In this paper, SVM is applied to signal detection in communication systems in the presence of channel noise in various environments in the form of Rayleigh fading, additive white Gaussian background noise (AWGN), and interference noise generalized as additive color Gaussian noise (ACGN). The structure and performance of SVM in terms of the bit error rate (BER) metric is derived and simulated for these advanced stochastic noise models and the computational complexity of the implementation, in terms of average computational time per bit, is also presented. The performance of SVM is then compared to conventional binary signaling optimal model-based detector driven by binary phase shift keying (BPSK) modulation. We show that the SVM performance is superior to that of conventional matched filter-, innovation filter-, and Wiener filter-driven detectors, even in the presence of random Doppler carrier deviation, especially for low SNR (signal-to-noise ratio) ranges. For large SNR, the performance of the SVM was similar to that of the classical detectors. However, the convergence between SVM and maximum likelihood detection occurred at a higher SNR as the noise environment became more hostile.

Keywords: Colour noise, Doppler shift, innovation filter, least square-support vector machine, matched filter, Rayleigh fading, Wiener filter.

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Authors: Frank Emmert-Streib, Matthias Dehmer, Jing Liu, Max Muhlhauser

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The major objective of this paper is to introduce a new method to select genes from DNA microarray data. As criterion to select genes we suggest to measure the local changes in the correlation graph of each gene and to select those genes whose local changes are largest. More precisely, we calculate the correlation networks from DNA microarray data of cervical cancer whereas each network represents a tissue of a certain tumor stage and each node in the network represents a gene. From these networks we extract one tree for each gene by a local decomposition of the correlation network. The interpretation of a tree is that it represents the n-nearest neighbor genes on the n-th level of a tree, measured by the Dijkstra distance, and, hence, gives the local embedding of a gene within the correlation network. For the obtained trees we measure the pairwise similarity between trees rooted by the same gene from normal to cancerous tissues. This evaluates the modification of the tree topology due to tumor progression. Finally, we rank the obtained similarity values from all tissue comparisons and select the top ranked genes. For these genes the local neighborhood in the correlation networks changes most between normal and cancerous tissues. As a result we find that the top ranked genes are candidates suspected to be involved in tumor growth. This indicates that our method captures essential information from the underlying DNA microarray data of cervical cancer.

Keywords: Graph similarity, generalized trees, graph alignment, DNA microarray data, cervical cancer.

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Authors: Yongxin Yuan

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In this paper, we first give the representation of the general solution of the following inverse eigenvalue problem (IEP): Given X ∈ Rn×p and a diagonal matrix Λ ∈ Rp×p, find nontrivial real-valued symmetric arrow-head matrices A and B such that AXΛ = BX. We then consider an optimal approximation problem: Given real-valued symmetric arrow-head matrices A, ˜ B˜ ∈ Rn×n, find (A, ˆ Bˆ) ∈ SE such that Aˆ − A˜2 + Bˆ − B˜2 = min(A,B)∈SE (A−A˜2 +B −B˜2), where SE is the solution set of IEP. We show that the optimal approximation solution (A, ˆ Bˆ) is unique and derive an explicit formula for it.

Keywords: Partially prescribed spectral information, symmetric arrow-head matrix, inverse problem, optimal approximation.

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Authors: Angela–M. Paleologou, Aphrodite Dellaporta

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Erroneous computer entry problems [here: 'e'errors] in hospital labs threaten the patients-–health carers- relationship, undermining the health system credibility. Are e-errors random, and do lab professionals make them accidentally, or may they be traced through meaningful determinants? Theories on internal causality of mistakes compel to seek specific causal ascriptions of hospital lab eerrors instead of accepting some inescapability. Undeniably, 'To Err is Human'. But in view of rapid global health organizational changes, e-errors are too expensive to lack in-depth considerations. Yet, that efunction might supposedly be entrenched in the health carers- job description remains under dispute – at least for Hellenic labs, where e-use falls behind generalized(able) appreciation and application. In this study: i) an empirical basis of a truly high annual cost of e-errors at about €498,000.00 per rural Hellenic hospital was established, hence interest in exploring the issue was sufficiently substantiated; ii) a sample of 270 lab-expert nurses, technicians and doctors were assessed on several personality, burnout and e-error measures, and iii) the hypothesis that the Hardiness vs Alienation personality construct disposition explains resistance vs proclivity to e-errors was tested and verified: Hardiness operates as a resilience source in the encounter of high pressures experienced in the hospital lab, whereas its 'opposite', i.e., Alienation, functions as a predictor, not only of making e-errors, but also of leading to burn-out. Implications for apt interventions are discussed.

Keywords: Hospital lab, personality hardiness/alienation, e-errors' cost, burnout.

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Authors: Amira Abbes Kheriji, Faouzi Bouani, Mekki Ksouri, Mohamed Ben Ahmed

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Model Predictive Control (MPC) is increasingly being proposed for real time applications and embedded systems. However comparing to PID controller, the implementation of the MPC in miniaturized devices like Field Programmable Gate Arrays (FPGA) and microcontrollers has historically been very small scale due to its complexity in implementation and its computation time requirement. At the same time, such embedded technologies have become an enabler for future manufacturing enterprises as well as a transformer of organizations and markets. Recently, advances in microelectronics and software allow such technique to be implemented in embedded systems. In this work, we take advantage of these recent advances in this area in the deployment of one of the most studied and applied control technique in the industrial engineering. In fact in this paper, we propose an efficient framework for implementation of Generalized Predictive Control (GPC) in the performed STM32 microcontroller. The STM32 keil starter kit based on a JTAG interface and the STM32 board was used to implement the proposed GPC firmware. Besides the GPC, the PID anti windup algorithm was also implemented using Keil development tools designed for ARM processor-based microcontroller devices and working with C/Cµ langage. A performances comparison study was done between both firmwares. This performances study show good execution speed and low computational burden. These results encourage to develop simple predictive algorithms to be programmed in industrial standard hardware. The main features of the proposed framework are illustrated through two examples and compared with the anti windup PID controller.

Keywords: Embedded systems, Model Predictive Control, microcontroller, Keil tool.

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Authors: Karam Y. Maalawi

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This paper presents a generalized formulation for the problem of buckling optimization of anisotropic, radially graded, thin-walled, long cylinders subject to external hydrostatic pressure. The main structure to be analyzed is built of multi-angle fibrous laminated composite lay-ups having different volume fractions of the constituent materials within the individual plies. This yield to a piecewise grading of the material in the radial direction; that is the physical and mechanical properties of the composite material are allowed to vary radially. The objective function is measured by maximizing the critical buckling pressure while preserving the total structural mass at a constant value equals to that of a baseline reference design. In the selection of the significant optimization variables, the fiber volume fractions adjoin the standard design variables including fiber orientation angles and ply thicknesses. The mathematical formulation employs the classical lamination theory, where an analytical solution that accounts for the effective axial and flexural stiffness separately as well as the inclusion of the coupling stiffness terms is presented. The proposed model deals with dimensionless quantities in order to be valid for thin shells having arbitrary thickness-to-radius ratios. The critical buckling pressure level curves augmented with the mass equality constraint are given for several types of cylinders showing the functional dependence of the constrained objective function on the selected design variables. It was shown that material grading can have significant contribution to the whole optimization process in achieving the required structural designs with enhanced stability limits.

Keywords: Buckling instability, structural optimization, functionally graded material, laminated cylindrical shells, externalhydrostatic pressure.

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Authors: S. Sieniutycz

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This paper unifies power optimization approaches in various energy converters, such as: thermal, solar, chemical, and electrochemical engines, in particular fuel cells. Thermodynamics leads to converter-s efficiency and limiting power. Efficiency equations serve to solve problems of upgrading and downgrading of resources. While optimization of steady systems applies the differential calculus and Lagrange multipliers, dynamic optimization involves variational calculus and dynamic programming. In reacting systems chemical affinity constitutes a prevailing component of an overall efficiency, thus the power is analyzed in terms of an active part of chemical affinity. The main novelty of the present paper in the energy yield context consists in showing that the generalized heat flux Q (involving the traditional heat flux q plus the product of temperature and the sum products of partial entropies and fluxes of species) plays in complex cases (solar, chemical and electrochemical) the same role as the traditional heat q in pure heat engines. The presented methodology is also applied to power limits in fuel cells as to systems which are electrochemical flow engines propelled by chemical reactions. The performance of fuel cells is determined by magnitudes and directions of participating streams and mechanism of electric current generation. Voltage lowering below the reversible voltage is a proper measure of cells imperfection. The voltage losses, called polarization, include the contributions of three main sources: activation, ohmic and concentration. Examples show power maxima in fuel cells and prove the relevance of the extension of the thermal machine theory to chemical and electrochemical systems. The main novelty of the present paper in the FC context consists in introducing an effective or reduced Gibbs free energy change between products p and reactants s which take into account the decrease of voltage and power caused by the incomplete conversion of the overall reaction.

Keywords: Power yield, entropy production, chemical engines, fuel cells, exergy.

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Authors: Kaushik Chaudhuri

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High Performance Work Systems (HPWS) generally give rise to positive impacts on employees by increasing their commitments in workplaces. While some argued this actually have considerable negative impacts on employees with increasing possibilities of imposing strains caused by stress and intensity of such work places. Do stressful workplaces hamper employee commitment? The author has tried to find the answer by exploring linkages between HPWS practices and its impact on employees in Japanese organizations. How negative outcomes like job intensity and workplaces and job stressors can influence different forms of employees- commitments which can be a hindrance to their performance. Design: A close ended questionnaire survey was conducted amongst 16 large, medium and small sized Japanese companies from diverse industries around Chiba, Saitama, and Ibaraki Prefectures and in Tokyo from the month of October 2008 to February 2009. Questionnaires were aimed to the non managerial employees- perceptions of HPWS practices, their behavior, working life experiences in their work places. A total of 227 samples are used for analysis in the study. Methods: Correlations, MANCOVA, SEM Path analysis using AMOS software are used for data analysis in this study. Findings: Average non-managerial perception of HPWS adoption is significantly but negatively correlated to both work place Stressors and Continuous commitment, but positively correlated to job Intensity, Affective, Occupational and Normative commitments in different workplaces at Japan. The path analysis by SEM shows significant indirect relationship between Stressors and employee Affective organizational commitment and Normative organizational commitments. Intensity also has a significant indirect effect on Occupational commitments. HPWS has an additive effect on all the outcomes variables. Limitations: The sample size in this study cannot be a representative to the entire population of non-managerial employees in Japan. There were no respondents from automobile, pharmaceuticals, finance industries. The duration of the survey coincided in a period when Japan as most of the other countries is under going recession. Biases could not be ruled out completely. We must take cautions in interpreting the results of studies as they cannot be generalized. And the path analysis cannot provide the complete causality of the inter linkages between the variables used in the study. Originality: There have been limited studies on linkages in HPWS adoptions and their impacts on employees- behaviors and commitments in Japanese workplaces. This study may provide some ingredients for further research in the fields of HRM policies and practices and their linkages on different forms of employees- commitments.

Keywords: HPWS, Job Intensity, Job and workplace Stressors, Continuous commitment, Affective commitment, Occupational commitment, Japan.

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