Abstract:In this paper, a computer-aided polymer design (CAPD) framework with a stochastic optimization model is proposed for the design of perfluorinated sulfonic acid proton exchange membrane (PFSA-PEM) with desired properties. First, the requirements and target characteristics are identified and converted to property constraints. Then, the polymer design model is formulated as a stochastic optimization problem in which the operation temperature is treated as a random variable. Subsequently, the formulated stochastic… Show more
“…The aided design algorithm described in this study is also contrasted with the classic aided design algorithm. In Reference [ 25 ], the design similarity index is used to calculate the measurement index. Figure 5 depicts a comparison diagram of the two options.…”
Section: Simulation Resultsmentioning
confidence: 99%
“…Scene visual understanding is one of the most widely utilized computer technologies in the field of the art design, and it is one of the most widely integrated computer technologies in the field of art design [ 24 , 25 ]. Aiming to replace the human eye and brain with computers, scene vision understanding is the process of using computers to simulate the visual function of the human eye and brain to perceive, recognize, and understand three-dimensional scenes and objects in the objective world, as well as to analyze complex objects in scene images by integrating with natural language.…”
With the acceleration of economic development, people put forward higher requirements for clothing style. In this context, the application of traditional patterns has good artistic effects, can show a unique style, and can also express the artistic beauty of clothing through deformation, color distribution, and other forms, and occupies a relatively large position in the design process of dyeing and weaving art. Aiming at the problem of inaccurate extraction of image information from known visual scenes in the original art-aided design, resulting in unclear output images, this paper proposes a computer-aided design algorithm for dyeing and weaving graphics in the field of public art by color segmentation of the known visual scene images according to the set threshold, morphological processing of the segmented images, reducing noise and fractures affecting the acquired connected areas, and formulating extraction rules to screen candidate areas. Furthermore, dense sampling form is used to extract more scale invariant feature transform (SIFT) target features in the candidate area, match the feature points, integrate the coordinate system of known image information into a unified coordinate system, output the design image, and complete the auxiliary design of dyeing and weaving graphics. The results of simulation experiments show that the computer-aided design algorithm of dyeing and weaving graphics in the public art field is more accurate than the original method in extracting information from known images, which helps to solve the problem of clear output dyeing and weaving images, and improves the quality of dyeing and weaving images.
“…The aided design algorithm described in this study is also contrasted with the classic aided design algorithm. In Reference [ 25 ], the design similarity index is used to calculate the measurement index. Figure 5 depicts a comparison diagram of the two options.…”
Section: Simulation Resultsmentioning
confidence: 99%
“…Scene visual understanding is one of the most widely utilized computer technologies in the field of the art design, and it is one of the most widely integrated computer technologies in the field of art design [ 24 , 25 ]. Aiming to replace the human eye and brain with computers, scene vision understanding is the process of using computers to simulate the visual function of the human eye and brain to perceive, recognize, and understand three-dimensional scenes and objects in the objective world, as well as to analyze complex objects in scene images by integrating with natural language.…”
With the acceleration of economic development, people put forward higher requirements for clothing style. In this context, the application of traditional patterns has good artistic effects, can show a unique style, and can also express the artistic beauty of clothing through deformation, color distribution, and other forms, and occupies a relatively large position in the design process of dyeing and weaving art. Aiming at the problem of inaccurate extraction of image information from known visual scenes in the original art-aided design, resulting in unclear output images, this paper proposes a computer-aided design algorithm for dyeing and weaving graphics in the field of public art by color segmentation of the known visual scene images according to the set threshold, morphological processing of the segmented images, reducing noise and fractures affecting the acquired connected areas, and formulating extraction rules to screen candidate areas. Furthermore, dense sampling form is used to extract more scale invariant feature transform (SIFT) target features in the candidate area, match the feature points, integrate the coordinate system of known image information into a unified coordinate system, output the design image, and complete the auxiliary design of dyeing and weaving graphics. The results of simulation experiments show that the computer-aided design algorithm of dyeing and weaving graphics in the public art field is more accurate than the original method in extracting information from known images, which helps to solve the problem of clear output dyeing and weaving images, and improves the quality of dyeing and weaving images.
“…Liang et al [5] proposed a computer-aided polymer design (CAPD) framework for the design of rubber tires. Guo et al [6] developed a CAPD framework with stochastic optimization algorithm, which is used for the design of perfluoro sulfonic acid proton exchange membranes (PEM). However, only the effects of repeating unit structure, molecular weight and side chain structure distribution of polymer on the properties were considered in previous work, the effect of crosslinking was ignored.…”
The formation of crosslinking network between polymer chains has significant influence on polymer properties. In particular, the crosslinked structure of ionic networks like proton exchange membrane affects the conductivity performance. To further develop in this area, a framework for polymer membrane design based on the developed quantitative prediction model of the properties of crosslinked polymer is proposed. First, polymers with different crosslinking degrees are constructed by a crosslinking algorithm. Next, molecular dynamics is used to calculate the properties of crosslinked polymers. Then, the quantitative relationship between crosslinked polymer structures and macroscopical properties is developed. Subsequently, computer-aided polymer design method is integrated with the developed quantitative predict model. The crosslinked polymer design problem is expressed as an optimization problem to obtain the optimal crosslinking degree. Bayesian optimization strategy is used to solve the established optimization model. Finally, two case studies of perfluoro sulfonic acid and perfluoro imide acid design are given to illustrate the application of the proposed polymer design framework.
“…Recent research has demonstrated that it is possible to target specific properties and design compound, which will produce a membrane with those properties using CAMD [11]. Artificial neural networks (ANNs), which define a branch of machine learning, are capable of discovering complex patterns from large datasets.…”
Section: Introductionmentioning
confidence: 99%
“…Artificial neural networks (ANNs), which define a branch of machine learning, are capable of discovering complex patterns from large datasets. Applying the predictive power of ANNs to CAMD, specifically to predicting the properties of membrane polymers, provides reasonable results at a fraction of the computational cost of rigorous simulations [11,12], and avoids the costly and unfocused guess-and-experiment approach. This makes it possible to consider new membrane polymers on a scale which is orders of magnitude larger than either rigorous simulations or guess-and-experiment approaches would allow.…”
Membrane polymers are a promising technology for use in many challenging gas separation applications. The techniques of computer-aided molecular design can be used to search through the massive molecular space of heteropolymers and develop a set of likely candidate repeat units matching specific physical property targets. However, reasonably accurate property prediction algorithms are needed, but these algorithms must be very fast in order to be combined with an optimization framework. Artificial neural networks (ANNs), a branch of machine learning, are applied in this work to predict the physical properties of polymers. All of the physical properties investigated were found to be predicted by ANNs with R 2 scores exceeding 0.82.
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