A fuzzy theory-based MRI segmentation model

2018 ◽  
Author(s):  
Ying Shen ◽  
Weihua Zhu
Keyword(s):  
Fuzzy Theory ◽  
Mri Segmentation

Accurate MRI Segmentation of Skull Base Meningiomas

2016 ◽  
Vol 77 (S 02) ◽  
Author(s):  
Francesco Latini ◽  
Elna-Marie Larsson ◽  
Mats Ryttlefors
Keyword(s):  
Skull Base ◽  
Mri Segmentation ◽  
Skull Base Meningiomas

A Novel Fuzzy Theory-Based Differential Protection Scheme for Transmission Lines

2020 ◽  
Vol 12 (8) ◽  
Author(s):  
Mahyar Abasi ◽  
◽  
Ahmad Torabi Farsani ◽  
Arash Rohani ◽  
Arsalan Beigzadeh ◽  
...  
Keyword(s):  
Transmission Lines ◽  
Fuzzy Theory ◽  
Differential Protection ◽  
Protection Scheme

Statistical Analysis of Operation Data and Automatic Control on Fluidized Bed Incineration Plant with Dryer

1993 ◽  
Vol 28 (11-12) ◽  
pp. 341-345
Author(s):  
Shigeki Minami ◽  
Hidekazu Nagasawa ◽  
Yoshinori Saito ◽  
Motoharu Yamagishi ◽  
Masakatsu Hiraoka ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Fluidized Bed ◽  
Fuzzy Theory ◽  
Exhaust Gas ◽  
Furnace Temperature ◽  
Incineration Plant ◽  
Commercial Plant ◽  
Dried Sludge ◽  
Fluidized Bed Incineration ◽  
Plant Control

Continuous operation data were obtained on a fluidized bed incineration plant with dryers, and two autoregressive models were then prepared through statistical analysis of the data. Based on the results, an automatic plant control system using fuzzy theory was designed. An incinerator system of this type is characterized by energy efficiency, for which optimum and stable moisture control of the dried sludge is important. The large difference in time constants between incinerator and dryers makes energy saving difficult. Based on these analyses and design, control operations at a commercial plant with a capacity of 150 wet-tons/day were studied. It was confirmed that reduction of auxiliary fuel consumption and reduction of CO and NOx in the exhaust gas were optimized, while the moisture content of dried sludge and the furnace temperature were kept stable.

Recent advancements in Fuzzy C-means based techniques for brain MRI Segmentation

2021 ◽  
Vol 16 ◽  
Author(s):  
Ghazanfar Latif ◽  
Jaafar Alghazo ◽  
Fadi N. Sibai ◽  
D.N.F. Awang Iskandar ◽  
Adil H. Khan
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Brain Mri ◽  
Tumor Segmentation ◽  
Brain Anatomy ◽  
Mri Segmentation ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Low Contrast ◽  
Fuzzy C Means

Background: Variations of image segmentation techniques, particularly those used for Brain MRI segmentation, vary in complexity from basic standard Fuzzy C-means (FCM) to more complex and enhanced FCM techniques. Objective: In this paper, a comprehensive review is presented on all thirteen variations of FCM segmentation techniques. In the review process, the concentration is on the use of FCM segmentation techniques for brain tumors. Brain tumor segmentation is a vital step in the process of automatically diagnosing brain tumors. Unlike segmentation of other types of images, brain tumor segmentation is a very challenging task due to the variations in brain anatomy. The low contrast of brain images further complicates this process. Early diagnosis of brain tumors is indeed beneficial to patients, doctors, and medical providers. Results: FCM segmentation works on images obtained from magnetic resonance imaging (MRI) scanners, requiring minor modifications to hospital operations to early diagnose tumors as most, if not all, hospitals rely on MRI machines for brain imaging. In this paper, we critically review and summarize FCM based techniques for brain MRI segmentation.

Research of Fault Diagnosis of Belt Conveyor Based on Fuzzy Neural Network

2014 ◽  
Vol 8 (1) ◽  
pp. 916-921
Author(s):  
Yuan Yuan ◽  
Wenjun Meng ◽  
Xiaoxia Sun
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Fault Location ◽  
Fuzzy Neural Network ◽  
Fuzzy Theory ◽  
Belt Conveyor ◽  
Fuzzy Neural ◽  
Neural Network Algorithm ◽  
Network Fault

To address deficiencies in the process of fault diagnosis of belt conveyor, this study uses a BP neural network algorithm combined with fuzzy theory to provide an intelligent fault diagnosis method for belt conveyor and to establish a BP neural network fault diagnosis model with a predictive function. Matlab is used to simulate the fuzzy BP neural network fault diagnosis of the belt conveyor. Results show that the fuzzy neural network can filter out unnecessary information; save time and space; and improve the fault diagnosis recognition, classification, and fault location capabilities of belt conveyor. The proposed model has high practical value for engineering.

Research on visual recognition intelligent system of city brand image based on fuzzy theory and regional culture

2021 ◽  
pp. 1-9
Author(s):  
Lin Han ◽  
Lu Han
Keyword(s):  
Neural Network ◽  
Visual Recognition ◽  
Intelligent System ◽  
Brand Image ◽  
Fuzzy Theory ◽  
Recognition System ◽  
Market Competitiveness ◽  
Visual Identity ◽  
Fuzzy Neural ◽  
Intelligent Recognition

With the rapid development of China’s market economy, brand image is becoming more and more important for an enterprise to enhance its market competitiveness and occupy a favorable market share. However, the brand image of many established companies gradually loses with the development of society and the improvement of people’s aesthetic pursuit. This has forced it to change its corporate brand image and regain the favor of the market. Based on this, this article combines the related knowledge and concepts of fuzzy theory, from the perspective of visual identity design, explores the development of corporate brand image visual identity intelligent system, and aims to design a set of visual identity system that is different from competitors in order to shape the enterprise. Distinctive brand image and improve its market competitiveness. This article first collected a large amount of information through the literature investigation method, and made a systematic and comprehensive introduction to fuzzy theory, visual recognition technology and related theoretical concepts of brand image, which laid a sufficient theoretical foundation for the later discussion of the application of fuzzy theory in the design of brand image visual recognition intelligent system; then the fuzzy theory algorithm is described in detail, a fuzzy neural network is proposed and applied to the design of the brand image visual recognition intelligent system, and the design experiment of the intelligent recognition system is carried out; finally, through the use of the specific case of KFC brand logo, the designed intelligent recognition system was tested, and it was found that the visual recognition intelligent system had an overall accuracy rate of 96.08% for the KFC brand logo. Among them, the accuracy rate of color recognition was the highest, 96.62%; comparing the changes in the output value of the training sample and the test sample, the output convergence effect of the color network is the best; through the comparison test of the BP neural network, the recognition effect of the fuzzy neural network is better.

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