The aim of this study is to develop and assess the peg transfer training module face, content and construct validation use of the box, virtual reality (VR), cognitive virtual reality (CVR), augmented reality (AR), and mixed reality (MR) trainer, thereby to compare advantages and disadvantages of these simulators. Training system (VatsSim-XR) design includes customized haptic-enabled thoracoscopic instruments, virtual reality helmet set, endoscope kit with navigation, and the patient-specific corresponding training environment. A cohort of 32 trainees comprising 24 novices and 8 experts underwent the real and virtual simulators that were conducted in the department of thoracic surgery of Yunnan First People's Hospital. Both subjective and objective evaluations have been developed to explore the visual and haptic potential promotions in peg transfer education. Experiments and evaluation results conducted by both professional and novice thoracic surgeons show that the surgery skills from experts are better than novices overall, AR trainer is able to provide a more balanced training environments on visuohaptic fidelity and accuracy, box trainer and MR trainer demonstrated the best realism 3D perception and surgical immersive performance, respectively, and CVR trainer shows a better clinic effect that the traditional VR trainer. Combining these in a systematic approach, tuned with specific fidelity requirements, medical simulation systems would be able to provide a more immersive and effective training environment.
With the integration of photovoltaic (PV) power into an electrical network, the complexity of the grid management is increasing because of intermittent and fluctuation nature of solar energy. Solar irradiance forecasting is essential to facilitate planning and managing electricity generation and distribution in smart grid cyber-physical system (CPS). The performance of existing short-term forecasting methods is far from satisfactory due to a lack of reliable and fast time-frequency model for continuous-time solar irradiance data. To address this problem, this paper proposes a new method, Elman Neural Network (ENN) driven Wavelet Transform (WT-ENN), for hourly solar irradiance forecasting. Firstly, the solar irradiance series was decomposed into a set of constitutive series using wavelet transform. Secondly, the new wavelet coefficients were predicted by ENNs in every sub-series with the best network structure and parameters. Thirdly, Wavelet reconstruction will predict next hour solar irradiance through the aggregation of outputs of the ensemble of ENNs. Finally, the forecasting performance was evaluated using two large real-world solar irradiance datasets. Experiment results show that the new WT-ENN model outperforms a large number of alternative methods and an average forecast skill of 0.7590 over the persistence model. Thus, it is concluded that the proposed approach can significantly improve the forecasting accuracy and reliability.
Background: Neurosurgery has exceptionally high requirements for minimally invasive and safety. This survey attempts to analyse the practical application of AR in neurosurgical navigation. Also, this survey describes future trends in augmented reality neurosurgical navigation systems.
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