Ultrasonic motors are available for the construction of precise positioning servo systems because of having high holding torque. However, deriving their dynamical models are generally difficult due to the complicated physical structures and a specific driving principle. Especially, their transient states in driving at high velocities need higher-order system models that are generally make the problems more difficult. In this paper, we provide a second-order linear system model for a ultrasonic motor by taking nonlinearity of steady-states into account. The modeling errors of transient states are compensated by a finite-time stabilizer based on homogeneous control Lyapunov functions. Then,after tuning the control parameters via computer simulation, we show the effectiveness of our servo system by comparing simulation and experimental results.

This study considers a data collection system using optical space communication technology. The transmitters of the system asynchronously send their data to a base station using optical CDMA strategy. Because of different propagation characteristics, the intensity of the optical signals may not be the same at the base station. In this paper, we introduce a hard limiter to reduce the influence of this variation of signal intensity to the communication performance. The numerical results derived by computer simulation show the performance improvement by the introduction of a hard limiter,but for the improvement, it is required the threshold of the HL is optimized.

Motivated by power balancing and scheduling in energy management systems, a variety of distributed optimization algorithms have been investigated recently. This paper formulates a scheduling problem of not only electric appliances with fixed load profiles but also those without, and presents a distributed optimization method based on an average consensus algorithm and a primal-dual perturbation method. Moreover, this paper provides a scheduling framework to consider request changes of appliance operations by introducing a model predictive control scheme. The effectiveness of the proposed method is verified through simulation results.

In critically ill patients suffering from hyperglycemia it has been recently shown that mortality and morbidity can be reduced by keeping glycemia within 80-110 mg/dL (4.4-6.1 mmol/L). However, maintaining blood glucose (BG) levels within such range is difficult because of the time variability in insulin sensitivity through the patient recovery. In this study, we introduce a nonlinear model predictive control algorithm using a time-invariant model of glucose-insulin metabolism for the eventual development of a control system that regulates BG levels of critically ill patients. Simulation results using virtual patients with time-varying insulin sensitivity show that BG levels can be kept 58.3% within the 80-110 mg/dL range with only 1.4% of BG levels decreasing below 80 mg/dL (4.4 mmol/L), which demonstrates the safety of the present control algorithm.

In this paper, we propose an automated GrowCut method for building extraction from scenery images. GrowCut achieves a high-performance for image segmentation to a wide variety of images,but its quality of segmentation depends largely on accuracy of user-specified seed pixels. Therefore, if seed pixels are specified automatically and accurately, GrowCut can be employed as a promising method for automatic building extraction. The proposed method uses a priori knowledge that target is a building, in order to specify seed pixels automatically and accurately. Moreover, it also achieves a higher repeatability in building extraction than the conventional method by introducing multi-level seed strength in GrowCut.

In this paper, we consider the simultaneous improvement of a controller and a model for an integral type servo system with a minimal-order state observer. The authors have already studied this issue for the full-state observer. However, in the case in which it is preferable to implement a controller as simply as possible and it is possible to observe a part of state variables directly, utilization of the minimal-state observer is desirable. Thus, this paper extends the previous result on the full state observer to the minimal order state observer case. By employing Fictitious Reference Iterative Tuning (FRIT), we give an effective method of a parameter tuning with only one-shot experimental data for the improvement of a controller that achieves more desired tracking property than the initial controller and a model that reflects the dynamics more accurately than the nominal model, respectively. Finally, we give an illustrative example to show the validity of the proposed method.