This paper investigates the convergence performance of activity-driven networks with a focus on the average consensus problem in multi-agent systems. In order to accurately represent real networks, temporal network models are crucial. To account for the sparsity observed in real networks, we propose a novel model of temporal networks called the sparse activity-driven network model. For the proposed network model, we then present an upper bound on the convergence rate of the average consensus protocol. We further extend our study to simplicial activity-driven networks. Our research highlights the challenges of using conventional methods to analyze the convergence performance in activity-driven networks, and provides valuable insights into how to improve the performance through the sparse and simplicial approaches.

Recently, there has been a need to optimize the flow of electric power by appropriately integrating distributed power sources and conventional power distribution equipment. In regions with heavy snowfall, the living environment is becoming a serious problem due to heavy snow accumulation. To tackle these issues, this paper considers a power distribution system including road heating for snow melting. We first describe a mathematical model of the system by combining the nonlinear ordinary differential equation (ODE) model with switching of heating cables, thermal diffusion equations, and equation for snow melting. As a main result, we propose a predictive switching control that achieves reduction of distribution loss, attenuation of voltage fluctuation, and efficient snow melting, simultaneously. We verify the efficiency of the proposed control through numerical simulation based on the actual time series data on active powers and weather conditions.

The electromyogram (EMG) signal generated by muscle contraction has been widely utilized for motion estimation of arms and fingers. To develop a myoelectric prosthetic hand that has high general versatility and safeness, a classifier that can consider complex forearm motions and motions that are not assumed during training, is required. However, hardware implementation of complex classifiers that has high classification performance is difficult. To tackle these problems, this paper proposed a novel probabilistic neural network that can be implemented in FPGA (Field Programmable Gate Array), and it was applied to an EMG-based human-machine interface system. The proposed neural network includes two types of probability density functions optimized for hardware implementation and enabled the execution of multi-class discrimination considering the unlearned class on the FPGA. Furthermore, by combining a forearm motion classifier and a hand motion classifier, the consideration of compound motions consisting of multiple hand gestures can be achieved. In experiments, the results showed that the proposed method can be implemented on FPGAs, and demonstrated that it can achieve highly accurate motion classification for compound motions and unlearned motions.

In this paper, we verify whether a fuzzy control system designed for a small autonomous under water vehicle (AUV) robot can be controlled by inputting state estimates obtained by using an Extended Kalman filter. The fuzzy controller determines a set of rules similar to those used by humans to steer the robot, and quantifies them in the form of a membership function to obtain a control value. The state estimator uses an EKF to reduce noise from noisy velocity measurements. The current position, which cannot be measured, is estimated and input to the controller for control simulation. As a result, the AUV succeeded in tracking the target value moving in three dimensions in time.

This paper uses the potential function to propose an obstacle avoidance method for a two-wheeled mobile robot with an artificial potential field and the PurePursuit. The proposed control is that the robot's path is planned by referring to its posture changes, escaping from a local minimum point. The efficiency of our proposed method is demonstrated in a numerical simulation and experiments by an experimental robot.