Inspection process for dimensioning through images and fuzzy logic

This paper presents a hybrid methodology based on a type 1 fuzzy model in singleton version using a 2k factorial design that optimizes the model of the expert system and serves to perform in-line inspection. The factorial design method provides the required database for the creation of the rule base for the fuzzy model and also generates the database to train the expert system. The proposed method was validated in the process of verifying dimensional parameters by means of images compared with the ANFIS and RBFN models which show greater margins of error in the approximation of the function represented by the system compared with the proposed model. The results obtained show that the model has an excellent performance in the prediction and quality control of the industrial process studied when compared with similar expert system techniques as ANFIS and RBFN

NEW CLASS OF SOFT LINEAR ALGEBRAIC CODES AND THEIR PROPERTIES USING SOFT SETS

Algebraic codes play a signiÖcant role in the minimization of data corruption which caused by defects such as inference, noise channel, crosstalk, and packet lost. In this paper, we introduced soft codes (soft linear codes) through the application of soft sets which is an approximated collection of codes. We also discussed several types of soft codes such as type-1 soft codes, complete soft codes etc. The innovative idea of soft codes is advantageous, for it can simultaneously transmit n-distinct messages to n-set of receivers. Further this new technique makes use of bi-matrices or to be more general uses the concept of n-matrices. Certainly this notion will save both time and economy. Moreover, we develop two techniques for the decoding of soft codes. At the end, we present a soft communication process and develop a model for this soft communication process. The distinctions and comparison of soft linear codes and linear codes are also presented

An Image Segmentation by BFV and TLBO

This paper presents the establishing of a biconvex fuzzy variational (BFV) method with teaching learning based optimization (TLBO) for geometric image segmentation (GIS). Firstly, a biconvex object function is adopted to process GIS. Then, TLBO is introduced to maximally optimize the length penalty item (LPI), which will be changed under teaching and learner phase of TLBO, making the LPI closer to the target boundary. Afterward, the LPI can be adjusted based on fitness function, namely, the evaluation standards of image quality. Finally, the LP is combined item with the numerical order to get better results. Different GIS strategies are compared with various fitness functions in terms of accuracy. Simulations show that the presented method is more effective in this area

A new edge detection approach via neutrosophy based on maximum norm entropy

It is a quite important step to find object edges in applications such as object recognition, classification and segmentation. Therefore, the edge detection algorithm to be used directly influences the performance of these applications. In this study, a new edge detection method based on Neutrosophic Set (NS) structure via using maximum norm entropy (EDA-NMNE) is proposed. Many experts and intelligence systems, including the fuzzy system, do not satisfactorily succeed in resolving indeterminacies and deficiencies. However, in the NS approach, problems are solved by dividing them into True (T), False (F) and Indeterminacy (I) subsets. In addition, because the approach has a powerful algorithmic structure, NS’s conditions with indeterminate and missing situations can be solved successfully

Edge detection based on type-1 fuzzy logic and guided smoothening

Edge detection is an important phenomenon in computer vision. Edge detection is helpful in contour detection and thus helpful in obtaining the important information. Edge detection process heavily depends on chosen technique. Soft computing techniques are considered as powerful edge detection methods due to their adaptability. This paper presents a fuzzy logic based edge detection method where the quality of edges is controlled using sharpening guided filter and noise due to the sharpening is controlled using Gaussian filter. The accuracy of the method is judged using a variety of statistical measures. It has been found that by proper selecting the smoothening parameters a significant improvement in the detected edges can be obtained

A Novel Deep Belief Network Architecture with Interval Type-2 Fuzzy Set Based Uncertain Parameters Towards Enhanced Learning

This paper proposes a novel Deep Belief Network (DBN) architecture, the ‘Interval Type-2 Fuzzy DBN (IT2FDBN)’, which models the weights and biases with IT2 FSs. Thus, it introduces a novel algorithm for augmented deep leaning, which has the capability to address all the limitations of the classical DBN (CDBN) and T1 fuzzy DBN (T1FDBN). We comparatively evaluate the performance of the IT2FDBN by conducting experiments using the popular MNIST handwritten digit recognition datasets. Additionally, to demonstrate its robustness and generalization capabilities, we also conduct experiments taking two noisy variants of MNIST dataset, viz. the MNIST with AWGN (additive white Gaussian noise) and the MNIST with motion blur. We conduct extensive simulations by considering different combinations of nodes in the hidden layers of the DBN for better model selection. We thoroughly compare the results using well-known performance measures such as root mean square error (RMSE) and Error rate. We show that, in terms of RMSE values and error rates, the proposed IT2FDBN outperforms both T1FDBN and CDBN across all the three datasets. Further, we also provide the results of convergence, runtime-based comparison, and statistical analysis in support of our proposal.© 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed