Improved Similarity Method Research Articles

An improved similarity method for stiffened plates subjected to combined loads of longitudinal compression and lateral pressure

Structures’ ultimate strength has been increasingly valued in the past few decades. The model test is an important and reliable way to estimate the ultimate strength. For large and complex structures, like ship hull structures, the test subject is usually scale models due to the limitation of loading capacity and space of laboratories. Therefore, the similarity method and the design of scale models are of great importance. The stiffened plate is the minimum unit that makes up thin-walled structures. The similarity method of the ultimate strength of stiffened plates is the base of the similarity method of large and complex structures. This paper is a further study of an existing similarity method proposed in our previous work. A stiffened plate from a real ship is taken as the subject. 22 combinations of scale ratios are used to design scale models. The 23 models (including the original model) are subjected to a combined load of longitudinal compression and lateral pressure. The non-linear finite element method is used to analyze these models. The results revealed that the previous method is only suitable for scale models with small degrees of geometrical distortion. It didn't consider the effect of geometrical distortions. An improved similarity method and scale model design method are proposed to solve these problems. The improved methods consider the geometrical distortion of scale models and are applicable to more geometrically distorted scale models. More numerical examples are performed to verify the correctness and advantage of the improved methods.

A Hybrid Intuitionistic Fuzzy Group Decision Framework and Its Application in Urban Rail Transit System Selection

The selection of an urban rail transit system from the perspective of green and low carbon can not only promote the construction of an urban rail transit system but also have a positive impact on urban green development. Considering the uncertainty caused by different conflict criteria and the fuzziness of decision-making experts’ cognition in the selection process of a rail transit system, this paper proposes a hybrid intuitionistic fuzzy MCGDM framework to determine the priority of a rail transit system. To begin with, the weights of experts are determined based on the improved similarity method. Secondly, the subjective weight and objective weight of the criterion are calculated, respectively, according to the DEMATEL and CRITIC methods, and the comprehensive weight is calculated by the linear integration method. Thirdly, considering the regret degree and risk preference of experts, the COPRAS method based on regret theory is propounded to determine the prioritization of urban rail transit system ranking. Finally, urban rail transit system selection of City N is selected for the case study to illustrate the feasibility and effectiveness of the developed method. The results show that a metro system (P1) is the most suitable urban rail transit system for the construction of city N, followed by a municipal railway system (P7). Sensitivity analysis is conducted to illustrate the stability and robustness of the designed decision framework. Comparative analysis is also utilized to validate the efficacy, feasibility and practicability of the propounded methodology.

A Hybrid Recommender Algorithm Based on an Improved Similarity Method

Recommendation systems have achieved widespread success in E-commerce nowadays. There are several evaluation metrics for recommender systems, such as accuracy, diversity, computational efficiency and coverage. Accuracy is one of the most important measurement criteria. In this paper, to improve accuracy, we proposed a hybrid recommender algorithm by an improved similarity method (ISM), combining demographic recommendation techniques and user-based collaborative filtering (CF) algorithms. Experiments were performed to compare the present approach with the other classical similarity measures based on the MovieLens dataset. The Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) values show the superiority of the proposed algorithm.

Online functional testing with rapid prototypes: a novel empirical similarity method

Functional testing with rapid prototypes is confined to certain areas due to a number of issues: the lack of a reliable similarity method that can solve distorted similarity problems; limited material choices; range of prototype sizes; and distinct material structures between prototypes and actual products. Methods are thus needed to expand the application of functional testing with rapid prototypes, and thus potentially impact the performance and cycle times of current product development processes. In this context, an improved similarity method that utilizes a geometrically simple specimen pair is developed in this paper. A realistic numerical simulation and an experimental mold design example (using a selective laser sintering prototype) demonstrate the validity and impact of the new method.