A Study on the Evaluation of Aerial Threats Based on Fuzzy Theory - Focusing on the Patriot System -

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.

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

The Open Mechanical Engineering Journal ◽

10.2174/1874155x01408010916 ◽

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.

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

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189833 ◽

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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The Application of Fuzzy Theory in the Evaluation of Visual Images of Smartphone Rear Cameras

Applied Sciences ◽

10.3390/app11083555 ◽

2021 ◽

Vol 11 (8) ◽

pp. 3555

Author(s):

Chien-Hsiung Chen ◽

Zhongzhen Lin

Keyword(s):

Factor Analysis ◽

Fuzzy Numbers ◽

Fuzzy Theory ◽

Consumer Perceptions ◽

Visual Images ◽

Triangular Fuzzy Numbers ◽

Potential Consumer ◽

Comprehensive Comparison ◽

Different Shapes ◽

The Relationship

In the present era, technology is developing rapidly. Smartphones play a significant part in people’s lives. However, the research on smartphones mainly focuses on the area of technological realization. The purposes of this study were to examine the relationship between the various rear cameras in smartphones and consumer perceptions, and to understand consumers’ purchasing intentions and preferences. Through the methods of multidimensional scaling (MDS), factor analysis and triangular fuzzy numbers, the visual images of the smartphone rear cameras were analyzed and discussed. The results indicate that the visual images taken by different shapes of rear camera are quite distinct in the categories of innovative and fashionable, and simple and pure, but less distinct in the categories of harmonious and ordered, premium and technical, and superior and valuable. Through a comprehensive comparison, four groups whose images were similar were created. The outcome effectively reflects the potential consumer demands for smartphone rear camera patterns, providing insights for design practices in the smartphone industry.

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A partner selection problem for complex product of manufacturing enterprises in supply chain

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-219000 ◽

2021 ◽

pp. 1-13

Author(s):

Congdong Li ◽

Yinyun Yu ◽

Wei Xu ◽

Jianzhu Sun

Keyword(s):

Decision Making ◽

Supply Chain ◽

Fuzzy Theory ◽

Partner Selection ◽

Manufacturing Companies ◽

Complex Product ◽

Manufacturing Enterprises ◽

Collaborative Manufacturing ◽

Matching Degree ◽

Decision Making Model

In order to better meet customer needs and respond to market demands more quickly, mounting number of manufacturing companies have begun to bid farewell to the traditional unitary manufacturing model. The collaborative manufacturing model has become a widely adopted manufacturing model for manufacturing companies. Aiming at the problem of partner selection for collaborative manufacturing of complex products in a collaborative supply chain environment, this paper proposes a multi-objective decision-making model that comprehensively considers the maximization of the matching degree of manufacturing capacity and the profits of supply chain, and gives the modeling process and application steps in detail. The method first uses fuzzy theory to evaluate the manufacturing capabilities of candidate collaborative manufacturing partners. Secondly, Vector Space Model (VSM) is used to calculate the matching degree of manufacturing capacity and manufacturing demand. Then, the paper studied the profit of the supply chain under the “non-cooperative” mechanism and the “revenue sharing” mechanism. Furthermore, the decision-making model is established. Finally, a simulation was carried out by taking complex product manufacturing of Gree enterprise as an example. The research results show the feasibility and effectiveness of the method.

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Risk early warning management and intelligent real-time system of financial enterprises based on fuzzy theory

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189441 ◽

2020 ◽

pp. 1-11

Author(s):

Qiaoying Ding

Keyword(s):

Real Time ◽

Early Warning ◽

Financial Risk ◽

Evaluation Method ◽

Comprehensive Evaluation ◽

Fuzzy Theory ◽

Fuzzy Comprehensive Evaluation ◽

Real Time System ◽

Enterprise Risk ◽

Early Warning Management

The financial market is changing rapidly. Since joining the WTO, our country’s financial companies have faced pressure from dual competition at domestic and abroad. The complex internal and external environment has forced financial enterprise managers to improve risk prevention awareness, early warning and monitoring, so as to responding to emergencies and challenges in the financial market. However, traditional forecasting and analysis methods have problems such as large workload, low efficiency, and low accuracy. Therefore, this article applies intelligent computing to the forecast of financial markets, using related concepts of fuzzy theory and Internet intelligent technology, and proposes to establish a model system for financial enterprise risk early warning management and intelligent real-time monitoring based on fuzzy theory. This article first collected a large amount of data through the literature investigation method, and made a systematic and complete introduction to the related theoretical concepts of fuzzy theory and financial risk early-warning management, has laid a sufficient theoretical foundation for the subsequent exploration of the application of fuzzy theory in financial enterprise risk early warning management and intelligent real-time systems; Then a fuzzy comprehensive evaluation method that combines the analytic hierarchy process and fuzzy evaluation method is proposed, taking a listed company mainly engaged in automobile sales in our province as a case, the company’s financial risk management and modeling experiment of the intelligent real-time system; Finally quoted specific cases again, used the fuzzy comprehensive evaluation method to carry out risk warning and evaluation on the PPP projects of private enterprises in our province, and concluded that the project risk score is between 20-60, which is meet the severe-medium range in the risk level. Research shows that the use of fuzzy theory and modern network technology can make more accurate warnings and assessments of potential and apparent risks of financial enterprises, greatly improving the safety of financial enterprise management and reducing the losses caused by various risks.

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Multiple performance characteristic index optimization of dimensional accuracy and geometrical shape angle in turn-mill multitasking machining

Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture ◽

10.1177/09544054211028532 ◽

2021 ◽

pp. 095440542110285

Author(s):

Wei-Tai Huang ◽

Shih-Cheng Yang ◽

Wen-Hsien Ho ◽

Jinn-Tsong Tsai

Keyword(s):

Performance Characteristic ◽

Fuzzy Theory ◽

Dimensional Accuracy ◽

Processing Parameters ◽

Geometrical Shape ◽

Characteristic Index ◽

Control Factors ◽

Experimental Sequence ◽

Gray Correlation ◽

Robust Process

Multiple performance objectives in turn-mill multitasking machining are investigated using the Taguchi method combined with the fuzzy theory. Using these two methods, optimized processing parameters can be rapidly identified to obtain optimized dimensional accuracy and geometrical shape angle, thus reducing machining cost and time. Herein, control factors for determining the single objective optimization parameter using the Taguchi robust process L9(34) orthogonal table were spindle speed (rpm), feed (mm/min), C-axis brake pressure (kg/cm2), axial cutting depth (mm), with dimensional accuracy and geometrical shape angle as objective characteristics. Then, signal-to-noise ratios of different groups were generated by gray correlation according to the experimental sequence to obtain the gray correlation coefficient for the calculation of the multiple performance characteristic index (MPCI). The MPCI results demonstrated that optimized dimensional accuracy was 0.005 mm and optimized geometrical shape angle was 0.004°. The optimized MPCI parameters were A3 (4000 rpm), B3 (250 mm/min), C3 (30 kg/cm2), and D3 (1.5 mm). It can reduce the processing for burr elimination and tool wear reduction by MPCI optimized process parameters.

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Time‐varying FMEA method based on interval‐valued spherical fuzzy theory

Quality and Reliability Engineering International ◽

10.1002/qre.2943 ◽

2021 ◽

Author(s):

Xinda Zhou ◽

Chuanhai Chen ◽

Hailong Tian ◽

Liding Wang ◽

Zhaojun Yang ◽

...

Keyword(s):

Fuzzy Theory ◽

Time Varying ◽

Fmea Method ◽

Interval Valued

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