Cancer victims, particularly those with lung cancer, are more susceptible and at higher danger of COVID-19 and associated consequences as a result of their compromised immune systems, which makes them particularly sen...
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Cancer victims, particularly those with lung cancer, are more susceptible and at higher danger of COVID-19 and associated consequences as a result of their compromised immune systems, which makes them particularly sensitive. Because of a variety of circumstances, cancer patients' diagnosis, treatment, and aftercare are very complicated and time-consuming during an epidemic. In such circumstances, advances in artificial intelligence (AI) and machine learning algorithms (ML) offer the capacity to boost cancer sufferer diagnosis, therapy, and care via the use of cutting technologies. For example, using clinical and imaging data combined with machine learning methods, the researchers may be able to distinguish among lung alterations induced by corona virus and those produced by immunotherapy and radiation. During this epidemic, artificial intelligence (AI) may be utilized to guarantee that the appropriate individuals are recruited in cancer clinical trials more quickly and effectively than in the past, which was done in a conventional and complicated manner. In order to better care for cancer patients and find novel and more effective therapies, It is critical that we move beyond traditional research methods and use artificial intelligence (AI) and machine learning to update our research (ML). Artificial intelligence (AI) and machine learning (ML) are being utilised to help with several aspects of the COVID-19 epidemic, such as epidemiology, molecular research and medication development, medical diagnosis and treatment, and socioeconomics. The use of artificial intelligence (AI) and machine learning (ML) in the diagnosis and treatment of COVID-19 patients is also being investigated. The combination of artificial intelligence and machine learning in COVID-19 may help to identify positive patients more quickly. In order to understand the dynamics of an epidemic that is relevant to artificial intelligence, when used in different patient groups, AI-based algorithms can quic
This paper demonstrates model-based dynamic optimization through the coupling of two open source tools: OpenModelica, which is a Modelica-based modeling and simulation platform, and CasADi, a framework for numerical o...
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ISBN:
(纸本)9783902823434
This paper demonstrates model-based dynamic optimization through the coupling of two open source tools: OpenModelica, which is a Modelica-based modeling and simulation platform, and CasADi, a framework for numerical optimization. The coupling uses a standardized XML format for exchange of differential-algebraic equations (DAE) models. OpenModelica supports export of models written in Modelica and the optimization language extension using this XML format, while CasADi supports import of models represented in this format. This allows users to define optimal control problems (OCP) using Modelica and optimization language specification, and solve the underlying model formulation using a range of optimization methods, including direct collocation and direct multiple shooting. The proposed solution has been tested on several industrially relevant optimal control problems, including a dieselelectric power train.
The article is devoted to the development of means for recognition of the emotions of the speaker, based on the neural network analysis of fixed fragments of the voice signal. The possibility of improving recognition ...
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This paper describes a new solution method applied to the problem initializing DAEs using the Modelica language. Modelica is primarily an object- oriented equ-tion-based modeling language that allows specification of ...
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This paper describes a new solution method applied to the problem initializing DAEs using the Modelica language. Modelica is primarily an object- oriented equ-tion-based modeling language that allows specification of mathematical models of complex natural or man-made systems. Major features of Modelica are the multidomain modeling capability and the reusability of model components corresponding to physical objects, which allow to build and simulate highly complex systems. However, initializing such models has been quite cumbersome, since initial equations have to be pro-vided at the system level, where the user needs to know details on the underlying transformation and index-reduction algorithms, that in general are applied to simulate a Modelica model.
This paper demonstrates model-based dynamic optimization through the coupling of two open source tools: OpenModelica, which is a Modelica-based modeling and simulation platform, and CasADi, a framework for numerical o...
详细信息
This paper demonstrates model-based dynamic optimization through the coupling of two open source tools: OpenModelica, which is a Modelica-based modeling and simulation platform, and CasADi, a framework for numerical optimization. The coupling uses a standardized XML format for exchange of differential-algebraic equations (DAE) models. OpenModelica supports export of models written in Modelica and the optimization language extension using this XML format, while CasADi supports import of models represented in this format. This allows users to define optimal control problems (OCP) using Modelica and optimization language specification, and solve the underlying model formulation using a range of optimization methods, including direct collocation and direct multiple shooting. The proposed solution has been tested on several industrially relevant optimal control problems, including a diesel-electric power train.
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