An Improved Method to Transform Triangular Fuzzy Number Into Basic Belief Assignment in Evidence Theory

Aimed at the one-to-one certification problem of unsteady state iris at different shooting times, a multi-algorithm parallel integration general model structure is proposed in this paper. The iris in the lightweight constrained state affected by defocusing, deflection, and illumination is taken as the research object, the existing algorithms are combined into the model structure effectively, and a one-to-one certification algorithm for lightweight constrained state unsteady iris was designed based on multi-algorithm integration and maximum trusted decision. In this algorithm, a sufficient number of iris internal feature points from the unstable state texture were extracted as effective iris information through the image processing layer composed of various filtering processing algorithms, thereby eliminating defocused interference. In the feature recognition layer, iris deflection interference was excluded by the improved methods of Gabor and Hamming and Haar and BP for the stable features extracted by the image processing layer, and two certification results were obtained by means of parallel recognition. The correct number of certifications for an algorithm under a certain lighting condition were counted. The method with the most correct number was set as the maximum trusted method under this lighting condition, and the results of the maximum trusted method were taken as the final decision, thereby eliminating the effect of illumination. Experiments using the JLU and CASIA iris libraries under the prerequisites in this paper show that the correct recognition rate of the algorithm can reach a high level of 98% or more, indicating that the algorithm can effectively improve the accuracy of the one-to-one certification of lightweight constrained state unsteady iris. Compared with the latest architecture algorithms, such as CNN and deep learning, the proposed algorithm is more suitable for the prerequisites presented in this paper, which has good environmental inclusiveness and can better improve existing traditional algorithms’ effectiveness through the design of a parallel integration model structure.

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“…3. Evidence theory certification by clustering method [48], compared with certification model under clustering feature;…”

Section: Existing Methods Comparison Experimentsmentioning

confidence: 99%

Unsteady State Lightweight Iris Certification Based on Multi-Algorithm Parallel Integration

Liu

Zhu

et al. 2019

Algorithms

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“…According to evidence theory [46][47][48], m alternatives of intelligent manufacturing project in the sustainability assessment problem are treated as the elements in the discernment frame. Therefore, the discernment frame is defined as follows.…”

Section: Two-stage Evidence Combinationmentioning

confidence: 99%

“…(3) Object evaluation. Dempster Shafer theory (D-S theory) [46][47][48] can directly express uncertainty, which provides an effective method for the expression and synthesis of uncertain information. This paper creatively applies D-S theory to assessment problem.…”

Section: Introductionmentioning

confidence: 99%

Sustainability Assessment of Intelligent Manufacturing Supported by Digital Twin

et al. 2020

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As a major challenge and opportunity for traditional manufacturing, intelligent manufacturing is facing the needs of sustainable development in future. Sustainability assessment undoubtedly plays a pivotal role for future development of intelligent manufacturing. Aiming at this, the paper presents the digital twin driven information architecture of sustainability assessment oriented for dynamic evolution under the whole life cycle based on the classic digital twin mapping system. The sustainability assessment method segment of the architecture includes indicator system building, indicator value determination, indicator importance degree determination and intelligent manufacturing project assessing. A novel approach for treating the ambiguity of expert' judgment in indicator value determination by introducing trapezoidal fuzzy number into analytic hierarchy process is proposed, while the complexity of the influence relationship among the indicators is processed by the integration of complex networks modeling and PROMETHEE II for the indicator importance degree determination. A two-stage evidence combination model based on evidence theory is built for intelligent manufacturing project assessing lastly. The presented digital-twin-driven information architecture and the sustainability assessment method is tested and validated on a study of sustainability assessment of 8 intelligent manufacturing projects of an air conditioning enterprise. The results of the presented method were validated by comparing them with the results of the fuzzy and rough extension of the PROMETHEE II, TOPSIS and VIKOR methods, indicator importance degree determining method by entropy and indicator value determining method by accurate expert scoring.

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“…In practice, decision makers often choose a certain response mode or combine several ways to deal with risk in the process of green innovation according to the actual situation, including the possibility of risk occurrence, severity of consequences, risk tolerance, and risk preference. As the main body of green innovation, the manufacturing industry directly bears the risk of green innovation [82]. As the risk faced by different manufacturing industries are different, and the risk tolerance and risk preference of the manufacturing industry are different.…”

Section: Response Strategy To Green Innovation Riskmentioning

confidence: 99%

Measuring and Integrating Risk Management into Green Innovation Practices for Green Manufacturing under the Global Value Chain

Sun

Yin

2020

Sustainability

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How to solve the contradiction between economic growth and ecological environmental protection is a practical problem that should be solved urgently at present. The development of green technology in the manufacturing industry must rely on technology innovation. However, the process of implementing green innovation in the manufacturing industry is full of high uncertainty and risk. First, the green innovation risks were divided into global green R&D risk, global green manufacturing risk, global green marketing risk, and global green service risk from the perspective of the process. Then, this study established a management criteria system of green innovation risk identification in the manufacturing industry under the global value chain (GVC). Furthermore, three methods were applied to identify the green innovation risk of the manufacturing industry under the GVC. Finally, this paper put forward the countermeasures to the green innovation risk of the manufacturing industry under the GVC. The empirical research results of this paper are as follows: From the perspective of the green innovation process, four risks are classified in this study, namely, global green R&D risk, global green manufacturing risk, global green marketing risk, and global green service risk. Among the four stages of green innovation risk, green marketing risk is the highest, followed by green service risk, and green R&D risk and green manufacturing risk are the least. Global green service risk and green R&D risk can be reduced mainly through risk diversification and risk reduction. Global green manufacturing risk and green marketing risk can be reduced mainly through risk diversification and secondary through risk reduction.

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An Improved Method to Transform Triangular Fuzzy Number Into Basic Belief Assignment in Evidence Theory

Cited by 30 publications

References 71 publications

Unsteady State Lightweight Iris Certification Based on Multi-Algorithm Parallel Integration

Unsteady State Lightweight Iris Certification Based on Multi-Algorithm Parallel Integration

Sustainability Assessment of Intelligent Manufacturing Supported by Digital Twin

Measuring and Integrating Risk Management into Green Innovation Practices for Green Manufacturing under the Global Value Chain

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