Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 2
Search results for: Kazumitsu Nawata
2 Smartphone-Based Human Activity Recognition by Machine Learning Methods
Authors: Yanting Cao, Kazumitsu Nawata
Abstract:
As smartphones upgrading, their software and hardware are getting smarter, so the smartphone-based human activity recognition will be described as more refined, complex, and detailed. In this context, we analyzed a set of experimental data obtained by observing and measuring 30 volunteers with six activities of daily living (ADL). Due to the large sample size, especially a 561-feature vector with time and frequency domain variables, cleaning these intractable features and training a proper model becomes extremely challenging. After a series of feature selection and parameters adjustment, a well-performed SVM classifier has been trained.Keywords: smart sensors, human activity recognition, artificial intelligence, SVM
Procedia PDF Downloads 1121 Design of an Augmented Automatic Choosing Control with Constrained Input by Lyapunov Functions Using Gradient Optimization Automatic Choosing Functions
Authors: Toshinori Nawata
Abstract:
In this paper a nonlinear feedback control called augmented automatic choosing control (AACC) for a class of nonlinear systems with constrained input is presented. When designing the control, a constant term which arises from linearization of a given nonlinear system is treated as a coefficient of a stable zero dynamics. Parameters of the control are suboptimally selected by maximizing the stable region in the sense of Lyapunov with the aid of a genetic algorithm. This approach is applied to a field excitation control problem of power system to demonstrate the splendidness of the AACC. Simulation results show that the new controller can improve performance remarkably well.Keywords: augmented automatic choosing control, nonlinear control, genetic algorithm, zero dynamics
Procedia PDF Downloads 445