Non-invasive identification of turbogenerator parameters from actual transient network data

Synchronous machines are the most widely used form of generators in electrical power systems. Identifying the parameters of these generators in a non-invasive way is very challenging because of the inherent non-linearity of power station performance. This study proposes a parameter identification method using a stochastic optimisation algorithm that is capable of identifying generator, exciter and turbine parameters using actual network data. An eighth order generator/turbine model is used in conjunction with the measured data to develop the objective function for optimisation. The effectiveness of the proposed method for the identification of turbo-generator parameters is demonstrated using data from a recorded network transient on a 178 MVA steam turbine generator connected to the UK's national grid

Kinect based Intelligent Wheelchair navigation with potential fields

Increasing elderly people population and people with disabilities constitute a huge demand for wheelchairs. Wheelchairs have an important role on improving the lives and mobilization of people with disabilities. Moreover, autonomous wheelchairs constitute a suitable research platform for academic and industrial researchers. In this study, Finite state machine (FSM) based high-level controller and Kinect based navigation algorithm have been developed for ATEKS (Intelligent Wheelchair) which has high-tech control mechanisms, low-cost sensors and open source software (ROS, GAZEBO, ANDROID). © 2014 IEEE

The psychological science accelerator’s COVID-19 rapid-response dataset

In response to the COVID-19 pandemic, the Psychological Science Accelerator coordinated three large-scale psychological studies to examine the effects of loss-gain framing, cognitive reappraisals, and autonomy framing manipulations on behavioral intentions and affective measures. The data collected (April to October 2020) included specific measures for each experimental study, a general questionnaire examining health prevention behaviors and COVID-19 experience, geographical and cultural context characterization, and demographic information for each participant. Each participant started the study with the same general questions and then was randomized to complete either one longer experiment or two shorter experiments. Data were provided by 73,223 participants with varying completion rates. Participants completed the survey from 111 geopolitical regions in 44 unique languages/dialects. The anonymized dataset described here is provided in both raw and processed formats to facilitate re-use and further analyses. The dataset offers secondary analytic opportunities to explore coping, framing, and self-determination across a diverse, global sample obtained at the onset of the COVID-19 pandemic, which can be merged with other time-sampled or geographic data

The performance and design of alternating current machines

xxi+664hlm.;23c

Alternating current machines/ Say

xi, 591 hal.: ill.; 23 cm

Direct current machines

x, 380 p.; 21 cm