総説・解説等

The aerodynamic forces acting on a discus were measured by wind tunnel tests, and the optimization study on discus throw was carried out by using a genetic algorithm. It was found that the stalling angle is about 30°, and the higher the spin rate the smaller the stalling angle. From the optimization study, it was revealed that the spin rate on its transverse axis should be maximized, and the angle of attack should increase from the negative value to the positive value with increasing time. The absolute value of the angle of attack should be less than the stalling angle during the whole flight.

Multi-objective optimization of ski jumping flight was carried out, and then the trade-offs between the objective functions and the control variables could be visualized using Self-Organizing Maps. It was assumed that there are four objective functions and eighteen control variables in this case. Three of the four objective functions are concerned with the style point, and the other is the flight distance. It was found that that the longer the flight distance the smaller the style point. The forward leaning angle and the angular velocity in the nose-down direction should be almost 0 and 10 degrees at 0.3 seconds.

The purpose of this study was to propose a new methodology for the automatic identification and the classification of the swimmers' kinematical information during the interval trainig on competitive swimming. Forty-seven college swimmers attached the newly developed chest band sensor unit, which has a triple-axes accelerometer and then performed a controlled interval training set with four stroke styles. The author identified swimmer's states, such as the swimming/rest phases and the start, turn and goal touch instants by using the Ay acceleration. With the inductive inference based on the experimental results and the deductive inference based on the empirical rule on the interval training brought the estimation of the swimming time. As for the classification of the swimming strokes, extracted swimming phase signal, the mean, variance and skewness of each axis acceleration were calculated for each bout. The authors compared some of data mining algorithms for the stroke style classification with four descriptive statistics, such as Var(Ax), Skewness(Ay), Mean(Az), Skewness(Az) as the independent variables and stroke style as the depending variable. The accuracy of the stroke style classification by both the multi-layered neural network and the C4.5 decision tree were 91.1%.