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The coronavirus disease 2019 (COVID-19) outbreak has increased mortality and morbidity world-wide. Oropharyngeal swabbing is a well-known and commonly used sampling technique for COVID-19 diagnose around the world. We developed a robot to assist with COVID-19 oropharyngeal swabbing to prevent frontline clinical staff from being infected. The robot integrates a UR5 manipulator, rigid-flexible coupling (RFC) manipulator, force-sensing and control subsystem, visual subsystem and haptic device. The robot has strength in intrinsically safe and high repeat positioning accuracy. In addition, we also achieve one-dimensional constant force control in the automatic scheme (AS). Compared with the rigid sampling robot, the developed robot can perform the oropharyngeal swabbing procedure more safely and gently, reducing risk. Alternatively, a novel robot control schemes called collaborative manipulation scheme (CMS) which combines a automatic phase and teleoperation phase is proposed. At last, comparative experiments of three schemes were conducted, including CMS, AS, and teleoperation scheme (TS). The experimental results shows that CMS obtained the highest score according to the evaluation equation. CMS has the excellent performance in quality, experience and adaption. Therefore, the proposal of CMS is meaningful which is more suitable for robot-sampling.

The commercial solar energy market is dominated by non-concentrating photovoltaics and concentrating solar thermal systems. With the development of high efficiency thermoelectric materials, the solar thermoelectric generator is becoming to be a competitive alternative solar energy technology in certain applications. This paper proposes a simulation model and a design methodology for solar thermoelectric generator. Geometry parameters, namely the height, the fill-ratio, the ratio of the cross-section area of n-type material over p-type material of thermoelectric module, solar concentration ratio, were analyzed under different operating conditions. In order to perform the analysis, an L27 (35) orthogonal array was employed to assess all of the design parameters returning the maximum output power. By the analysis of variance, the effect of each design parameter on the output power performance and the interaction between design parameters are identified. The optimal design parameter set is also obtained and it is found that the optimal design performs can potentially achieve 5.47 W output power compared with the output power of 1.95 W from the original design in most operating conditions.

In this paper, we present a novel approach for video-anomaly detection in crowded and complicated scenes. The proposed approach detects anomalies based on a hierarchical activity-pattern discovery framework, comprehensively considering both global and local spatio-temporal contexts. The discovery is a coarse-to-fine learning process with unsupervised methods for automatically constructing normal activity patterns at different levels. A unified anomaly energy function is designed based on these discovered activity patterns to identify the abnormal level of an input motion pattern. We demonstrate the effectiveness of the proposed method on the UCSD anomaly-detection datasets and compare the performance with existing work.