Background In hepatic surgery, extensive skills and experience are indispensable and require time to master. However, there has been a recent global shortage of general surgeon candidates, who tend to have limited wor...
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Background In hepatic surgery, extensive skills and experience are indispensable and require time to master. However, there has been a recent global shortage of general surgeon candidates, who tend to have limited working hours and low surgical exposure. Learning about safe hepatectomy requires an easy understanding of the patient’s unique liver anatomy and conducting physical simulations. To solve this problem, we developed a new three-dimensional (3D) liver model that reproduces the conditions of the parenchyma with emulation capabilities, the Simulation Model Assisting Resection Technique for Liver (SMART Liver). This study evaluated the feasibility of using SMART Liver. Method Twenty-two professional surgeons out of 32 doctors in five institutes in Kitakyushu city were interviewed about the emulation function after watching an instructional video on the perspectives of professionals using the semantic differential method on a 5-point scale. Differences were evaluated using Wilcoxon signed -rank tests. Statistical analyses were performed using R software (R i386 3.5.1). A p -value < 0.05 was considered statistically significant. Results The SMART Liver had a median score of 4 on a 5-point scale. Specialists gave high levels of praise ( p < 0.05). Conclusions The SMART Liver is feasible for emulation functionality and physical simulation.
In this paper, we provide a compositional framework for the construction of infinite approximations of interconnected stochastic control systems. Our approach is based on a notion of so-called stochastic simulation fu...
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ISBN:
(纸本)9781479978878
In this paper, we provide a compositional framework for the construction of infinite approximations of interconnected stochastic control systems. Our approach is based on a notion of so-called stochastic simulation functions that are associated with interfaces. The stochastic simulation functions are used to quantify the approximation error while the interfaces are used to lift the controllers synthesized for the approximation to the controllers for the original stochastic system. In the first part of the paper, we analyze interconnected stochastic control systems which consist of several stochastic control subsystems. We derive sufficient conditions that facilitate the compositional construction of stochastic simulation functions together with the associated interfaces. Specifically, we show how to construct a stochastic simulation function with the corresponding interface for the interconnected stochastic control system from the simulation functions and interfaces of the individual stochastic control subsystems. In the second part of the paper, we focus on linear stochastic control systems. We extend a methodology, which is known for the non-probabilistic case, to construct infinite approximations of linear stochastic control systems together with their stochastic simulation functions and the corresponding interfaces. Finally, we illustrate the effectiveness of the proposed results on the interconnection of four linear stochastic control subsystems.
A new model for simulating elastic wave propagation using the open-source k-Wave MATLAB Toolbox is described. The model is based on two coupled first-order equations describing the stress and particle velocity within ...
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ISBN:
(纸本)9781479970490
A new model for simulating elastic wave propagation using the open-source k-Wave MATLAB Toolbox is described. The model is based on two coupled first-order equations describing the stress and particle velocity within an isotropic medium. For absorbing media, the Kelvin-Voigt model of viscoelasticity is used. The equations are discretised in 2D and 3D using an efficient time-stepping pseudospectral scheme. This uses the Fourier collocation spectral method to compute spatial derivatives and a leapfrog finite-difference scheme to integrate forwards in time. A multi-axial perfectly matched layer (M-PML) is implemented to allow free-field simulations using a finite-sized computational grid. Acceleration using a graphics processing unit (GPU) is supported via the MATLAB Parallel Computing Toolbox. An overview of the simulation functions and their theoretical and numerical foundations is described.
A Web-based, collaborative, computer-aided design software tool has been developed for sequential control system design. The software includes functions for specifying a sequential control system using icons for elect...
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A Web-based, collaborative, computer-aided design software tool has been developed for sequential control system design. The software includes functions for specifying a sequential control system using icons for electrical devices and circuits. With ...
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