Quality is one of the important criteria to determine the success of product. So quality control is required for all stages of production to ensure a good final product with lowest possible losses. Control charts are the most important means used to monitor the quality and its accuracy is measured by quickly detecting unusual changes in the quality to maintain the product and reduce the costs and losses that may result from the defective items. There are different types of quality control charts and new types appeases involving the concept of fuzziness named multinomial fuzzy quality control chart (FM) , dividing the product to accepted and not may not be accurate therefore adding fuzziness concept to quality charts confirm and add a new perspective. In this study, we will compare between the sensitivity of traditional quality control chart (fraction defective chart – P chart ) and multinomial fuzzy quality control chart (FM) to monitor the yarn gauge applied study using data (101 ) samples from Nineveh textile factory, sample size (20 ), we notice that (FM) control chart was more sensitive to detect the changes in the quality because it take in the consideration all the levels of the product not only accepted or not .So we have to take in consideration these kind of control charts to monitor the quality but dividing the product to accurate levels depending on experts.
The problem of research was the lack of research that dealt with issue of the organizational environment, job design approach that is more suitable for knowledge work, therefore, the research aims to determine the impact of quality of working life and job enrichment on knowledge capital, starting from the hypothesis that there significant impact of quality of working life and job enrichment on knowledge capital, to achieve this goal the researcher from the theoretical literature and related studies conclude to the construction of the scheme shows the hypothetical relationship between the variables, which was adopted quality of working life and job enrichment as independent variable while knowl
... Show MoreProducing pseudo-random numbers (PRN) with high performance is one of the important issues that attract many researchers today. This paper suggests pseudo-random number generator models that integrate Hopfield Neural Network (HNN) with fuzzy logic system to improve the randomness of the Hopfield Pseudo-random generator. The fuzzy logic system has been introduced to control the update of HNN parameters. The proposed model is compared with three state-ofthe-art baselines the results analysis using National Institute of Standards and Technology (NIST) statistical test and ENT test shows that the projected model is statistically significant in comparison to the baselines and this demonstrates the competency of neuro-fuzzy based model to produce
... Show MoreRecently, Image enhancement techniques can be represented as one of the most significant topics in the field of digital image processing. The basic problem in the enhancement method is how to remove noise or improve digital image details. In the current research a method for digital image de-noising and its detail sharpening/highlighted was proposed. The proposed approach uses fuzzy logic technique to process each pixel inside entire image, and then take the decision if it is noisy or need more processing for highlighting. This issue is performed by examining the degree of association with neighboring elements based on fuzzy algorithm. The proposed de-noising approach was evaluated by some standard images after corrupting them with impulse
... Show MoreSome Results on Fuzzy Zariski
Topology on Spec(J.L)
We present the notion of bipolar fuzzy k-ideals with thresholds (
In this paper, the human robotic leg which can be represented mathematically by single input-single output (SISO) nonlinear differential model with one degree of freedom, is analyzed and then a simple hybrid neural fuzzy controller is designed to improve the performance of this human robotic leg model. This controller consists from SISO fuzzy proportional derivative (FPD) controller with nine rules summing with single node neural integral derivative (NID) controller with nonlinear function. The Matlab simulation results for nonlinear robotic leg model with the suggested controller showed that the efficiency of this controller when compared with the results of the leg model that is controlled by PI+2D, PD+NID, and F
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