• Korean Journal of Exercise Nutrition
• /
• v.25 no.3
• /
• pp.23-27
• /
• 2021

[Purpose] Functional beverages are intended to support those who want to maintain optimal physical condition and improve their quality of life through the enhancement of heart health, immunity, and digestion. The purpose of this study was to investigate the performance of top-level athletes consuming immune-strengthening conditioning nutritional drinks. [Methods] A total of 107 top-level athletes (baseball (56 players), pro volleyball (17), athletics (16), cycling (8), golf (6), and fencing (6)) participated in the experiment. They consumed an immune-enhancing functional beverage once a day for 8 weeks and responded to a survey before, during, and after drinking the beverage. [Results] Three total aspect-based subfactors were drawn from 24 questions in the factor analysis: physical, satisfaction with mental stability, and activity in performance. The physical, mental stability and performance changes of athletes significantly increased in period 2 (4 weeks after intake) and period 3 (after 8 weeks of intake). [Conclusion] We evaluated the efficacy of a new conditioned beverage containing Lactobacillus B240 and protein in improving the performance and physiological utility of top athletes. This functional drink may gain popularity among those seeking health benefits and improved exercise performance.

• The KIPS Transactions:PartB
• /
• v.10B no.3
• /
• pp.281-286
• /
• 2003

A large number of features are collected for problem solving in real life, but to utilize ail the features collected would be difficult. It is not so easy to collect of correct data about all features. In case it takes advantage of all collected data to learn, complicated learning model is created and good performance result can't get. Also exist interrelationships or hierarchical relations among the features. We can reduce feature's number analyzing relation among the features using heuristic knowledge or statistical method. Heuristic technique refers to learning through repetitive trial and errors and experience. Experts can approach to relevant problem domain through opinion collection process by experience. These properties can be utilized to reduce the number of feature used in learning. Experts generate a new feature (highly abstract) using raw data. This paper describes machine learning model that reduce the number of features used in learning using heuristic function and use abstracted feature by neural network's input value. We have applied this model to the win/lose prediction in pro-baseball games. The result shows the model mixing two techniques not only reduces the complexity of the neural network model but also significantly improves the classification accuracy than when neural network and heuristic model are used separately.