Experimental analysis of power harvesting on vehicle vibration using smart piezoelectric materials

In this paper the experimental analysis for power harvesting from mechanical vibration on a vehicle has been studied by using QuickPack smart materials with piezoelectric effect. The finite element ANSYS method (ANSYS FEM) was applied to explore the required mechanical structure, modal and harmonic analysis, and electrical feature, i.e., output voltage, admittance. The experimental platform consists of a shocker and a lever, which simulated a periodical oscillation on vehicle vibration, for evaluating conversion efficiency from mechanical energy to electrical energy. During loading experiments of power generation, the electromechanical coupling characteristics of smart materials were investigated via a proposed testing circuit. Also, various electrical output loadings were specified within resistance of 5~3000 kΩ. Through the experiment analysis, the power harvesting test with a buck converter at the output terminal was processed to obtain the spectrum analysis of output voltage within the vibrating frequencies below 200 Hz, controlled by the electromagnetic shaker. Based on the comparison between ANSYS FEM and spectrum analysis, the optimal results of mechanical oscillating quantities have been verified by the maximum output voltage for the QuickPack NQ45N material. Hence, the optimum power harvesting of the smart material has the maximum output power of 0.18 mW at 26-Hz-vibration on a vehicle

Fuzzy Environment Mapping for Robot Navigation Based on Grid Computing

In order to navigate autonomously, a mobile robot needs to build an environment map where the robot is navigating. Currently, the sensors are mounted on the robot to detect if the obstacles exist and then the map immediate surrounding of the robot is built to help for navigation path planning. The map created by this method is a local map that may cause global navigation problem which a global coverage map is needed to solve such a problem. In this study, a sensor network is deployed for building global environment map. All the sensor locations are assumed known. The navigation space is divided into grids and a grid is to be detected if obstacles exist by one or a number of sensors. Fuzzy set concept is used to introduce a tool useful for sensor perception. Those sensors work as a team to explore all the space and then the global fuzzy map is constructed. The experiments show that the fuzzy map is more practical and helps the path planning problem to be solved more efficiently

A Deep Learning Approach to Radar-based QPE

In this study, we propose a volume-to-point framework for quantitative precipitation estimation (QPE) based on the Quantitative Precipitation Estimation and Segregation Using Multiple Sensor (QPESUMS) Mosaic Radar data set. With a data volume consisting of the time series of gridded radar reflectivities over the Taiwan area, we used machine learning algorithms to establish a statistical model for QPE in weather stations. The model extracts spatial and temporal features from the input data volume and then associates these features with the location-specific precipitations. In contrast to QPE methods based on the Z-R relation, we leverage the machine learning algorithms to automatically detect the evolution and movement of weather systems and associate these patterns to a location with specific topographic attributes. Specifically, we evaluated this framework with the hourly precipitation data of 45 weather stations in Taipei during 2013-2016. In comparison to the operational QPE scheme used by the Central Weather Bureau, the volume-to-point framework performed comparably well in general cases and excelled in detecting heavy-rainfall events. By using the current results as the reference benchmark, the proposed method can integrate the heterogeneous data sources and potentially improve the forecast in extreme precipitation scenarios.Comment: 22 pages, 11 figures. Published in Earth and Space Scienc

Association of age-adjusted shock index with mortality in children with trauma: a single-center study in Korea

Purpose This study was performed to investigate the association of high age-adjusted shock index (AASI) with mortality in Korean children with trauma. Methods The data of children (aged < 15 years) with trauma who visited a university hospital in Korea from 2010 through 2018 were reviewed. High AASI was defined by age groups as follows: < 12 months, ≥ 2.7; 12-23 months, ≥ 2.1; 2-4 years, ≥ 1.9; 5-11 years, ≥ 1.5; and 12-14 years, ≥ 1.1. Age, sex, transfer status, injury mechanism, hypotension, tachycardia, base deficit, hemoglobin concentration, trauma scores, hemorrhage-related procedures (transfusion and surgical interventions), and severe traumatic brain injury were compared according to high AASI and in-hospital mortality. The association of high AASI with the mortality was analyzed using logistic regression. Results Of the 363 enrolled children, 29 (8.0%) had high AASI and 24 (6.6%) died. The children with high AASI showed worse trauma scores and underwent hemorrhage-related procedures more frequently, without a difference in the rate of the traumatic brain injury. High AASI was associated with in-hospital mortality (survivors, 6.5% vs. non-survivors, 29.2%; P = 0.001). This association remained significant after adjustment (adjusted odds ratio, 6.42; 95% confidence interval, 1.38-29.82). The other predictors were Glasgow Coma Scale (for increment of 1 point; 0.62; 0.53-0.72) and age (for increment of 1 year; 0.84; 0.73-0.97). High AASI showed a 29.2% sensitivity and 93.5% specificity for the mortality. Conclusion High AASI is associated with mortality, and have a high specificity but low sensitivity in Korean children with trauma. This predictor of mortality can be used prior to obtaining the results of laboratory markers of shock