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Taiwan’s rowing athletes have performed well during the Asian Games, but their performance in the Olympics has not been adequate. In addition to their hard work and rigorous and effective training, the skill of the athletes is a key factor for achieving good results. In this study, an artificial intelligence (AI) evaluation algorithm is developed to help rowing athletes excel in the sporting events. The AI algorithm uses the analytic hierarchy process to invite experts and scholars in the rowing field to answer a questionnaire. The technique for order performance by similarity to ideal solution is then applied to calculate the ranking of selection indicators, to construct an evaluation model for rowing athletes. The key findings indicate that physicality (or the body structure) is the highest priority among the four main aspects of talent identification; this is followed, in descending order, by specialism, reaction, and psychological elements. The proposed AI strategy was established as the most beneficial decision model and can be used to identify talented rowers in the future.

Taiwan’s rowing has achieved well performance in the Asian Games, but it has not in the Olympics. To get good results, in addition to the hard work and solid and effective training on weekdays, the qualification of the players is one of the key factors. Therefore, this research attempts to construct an artificial intelligence (AI) evaluation algorithm for rowing athletes to obtain advantages in the sports field, and can start from the athletes. The AI algorithm involves the hierarchical analysis method (AHP) is adopted to invite experts and scholars in the rowing field to answer the expert questionnaire. The TOPSIS (technique for order performance by similarity to ideal solution) method is then applied to calculate the ranking of material selection indicators, aiming to construct an evaluation model for rowers. The main research result shows that physicality is considered as the highest priority among the four main criteria of talent identification, following in a descending order by specialism, reaction and psychological elements. The propose model established by the most beneficial decision model for identification of talented rowers in the future.

The purpose of this paper is to present the use of uniform design of experiments method in improving the permanent deformation and mass of an on-road bicycle frame which undergoes the drop-mass and drop-frame impact tests. Six system parameters of the bicycle frame are selected as the control factors to be improved. Uniform design of experiment is applied to create a set of simulation experiments. Using ANSYS/LS-DYNA software, the dynamic finite element modeling is investigated and the permanent deformations of bicycle frame under the drop-mass and drop-frame impact testing. From the numerical results, the best frame of all the experiments which causes the smaller permanent deformation and lighter mass of bicycle frame is selected as the improved version of design.

In this paper, the finite element method (FEM) is used to study the incremental sheet forming process of pyramidal shape. The material used is aluminium alloy 5052. The tool, a hemispherical ball-head with a diameter (d = 4 mm) made of HSS tool steel, is used to press down on the sheet metal causing locally plastic deformation. The comparison between spiral tool path, spiral-step tool path and z-level tool path is carried out. Moreover, the final thickness distribution is investigated. The results indicate that the minimal thickness can be found on the corner of wall angle in SPIF process. Under the same step over, spiral-step tool path can obtain the deepest depth for pyramidal shape. The maximum formability for successful forming of the pyramidal shape with depth 60 mm is wall angles 65 ∘ .