Fuzzy controller for better tennis ball robot

This paper aims at designing a tennis ball robot as a training facility for tennis players. The robot is built with fuzzy controller which provides proper techniques for the players to gain practical experience as well as technical skills; thus, it can effectively serve the community and train athletes in the high-performance sport. It is found that it is more economically efficient by using the sensorless fuzzy control algorithm to replace the high-resolution optical encoders traditionally used in two main servo motors. From our simulation and practical experiment, the tennis ball robot can provide accurate speed and various directions as expected

The Abelian Manna model on two fractal lattices

We analyze the avalanche size distribution of the Abelian Manna model on two different fractal lattices with the same dimension d_g=ln(3)/ln(2), with the aim to probe for scaling behavior and to study the systematic dependence of the critical exponents on the dimension and structure of the lattices. We show that the scaling law D(2-tau)=d_w generalizes the corresponding scaling law on regular lattices, in particular hypercubes, where d_w=2. Furthermore, we observe that the lattice dimension d_g, the fractal dimension of the random walk on the lattice d_w, and the critical exponent D, form a plane in 3D parameter space, i.e. they obey the linear relationship D=0.632(3) d_g + 0.98(1) d_w - 0.49(3).Comment: 4 pages, 3 figures, 3 tables, submitted to PRE as a Brief Repor

Reinforcement Learning Approach for RF-Powered Cognitive Radio Network with Ambient Backscatter

© 2018 IEEE. For an RF-powered cognitive radio network with ambient backscattering capability, while the primary channel is busy, the RF-powered secondary user (RSU) can either backscatter the primary signal to transmit its own data or harvest energy from the primary signal (and store in its battery). The harvested energy then can be used to transmit data when the primary channel becomes idle. To maximize the throughput for the secondary system, it is critical for the RSU to decide when to backscatter and when to harvest energy. This optimal decision has to account for the dynamics of the primary channel, energy storage capability, and data to be sent. To tackle that problem, we propose a Markov decision process (MDP)-based framework to optimize RSU's decisions based on its current states, e.g., energy, data as well as the primary channel state. As the state information may not be readily available at the RSU, we then design a low-complexity online reinforcement learning algorithm that guides the RSU to find the optimal solution without requiring prior-and complete-information from the environment. The extensive simulation results then clearly show that the proposed solution achieves higher throughputs, i.e., up to 50%, than that of conventional methods

A Simple Linear Time Algorithm for Computing a 1-Median on Cactus Graphs

We address the problem of finding a 1-median on a cactus graph. The problem has already been solved in linear time by the algorithms of Burkard and Krarup (1998), and Lan and Wang (2000). These algorithms are complicated and need efforts. Hence, we develop in this paper a simpler algorithm. First, we construct a condition for a cycle that contains a 1-median or for a vertex that is indeed a 1-median of the cactus. Based on this condition, we localize the search for deriving a 1-median on the underlying cactus. Complexity analysis shows that the approach runs in linear time

Forecasting cryptocurrency returns and volume using search engines

In the context of the debate on the role of cryptocurrencies in the economy as well as their dynamics and forecasting, this brief study analyzes the predictability of Bitcoin volume and returns using Google search values. We employed a rich set of established empirical approaches, including a VAR framework, a copulas approach, and non-parametric drawings, to capture a dependence structure. Using a weekly dataset from 2013 to 2017, our key results suggest that the frequency of Google searches leads to positive returns and a surge in Bitcoin trading volume. Shocks to search values have a positive effect, which persisted for at least a week. Our findings contribute to the debate on cryptocurrencies/Bitcoins and have profound implications in terms of understanding their dynamics, which are of special interest to investors and economic policymakers

Damage assessment of concrete gravity dams using vibration characteristics

Vibration-based Structural Health Monitoring (VBSHM) has emerged as a feasible technique in long-term monitoring, structural performance evaluation and damage assessment of civil structures. As an important as-pect of the complete VBSHM system, over the last three decades, many vibration-based damage detection methods have been developed for buildings and bridges. However, the application of these techniques to con-crete gravity (CG) dams has been limited. In the present study, damage indices based on changes in modal flexibility and modal strain energy are suitably enhanced to be applicable for plane-strain structures. They are then used to investigate the feasibility of detecting and locating damage in a finite element CG dam model without noise effects. Results show that the enhanced damage indices can be promising for locating damage in the upstream part of CG dams by using only the first lateral mode of vibration. In addition, it is necessary to monitor both horizontal and vertical mode shape components and use these for structural damage diagnoses in CG dams