Due to the powerful state awareness and optimization decision-making capabilities of Deep Reinforcement Learning (DRL), which avoid the traditional processes of physical modeling and formula solving, DRL has become on...
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computer simulation is the main method to predict and control the micro-geometry and surface roughness of the machined surface. This paper takes the ball end milling cutter as the research object. Through the establis...
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computer technology and financial management are effectively combined to build a computer financial management system. With the in-depth application of computer technology in financial management, the financial manage...
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modeling efficient energy in the design of buildings is an important step for sustainability. Current methodologies are unable to accurately predict the energy requirements. The paper introduces a novel approach '...
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Decentralized finance, or DeFi, has become the dominating application category on public blockchains like Ethereum. DeFi allows users to lend, borrow, collateralize, and exchange using smart contracts for a cheap cost...
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This paper presents a novel approach for evaluating the selection of overseas warehouse sites by integrating an enhanced evidence theory framework with cloud modeling. Initially, a comprehensive evaluation index syste...
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This article presents a comparative analysis of two simulation environments for robots and robotic systems - Gazebo and CoppeliaSim. The study evaluates their functionality and effectiveness for teaching students robo...
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A group intruder detection system built on K-Nearest Neighbors, Decision Trees, Neural Networks, Support Vector Machines, and Random Forests is shown in this study. A thorough study on ablation shows how the algorithm...
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The paper analyzes dynamic models with a delay in describing the processes of epidemics. The introduction of a delay makes it possible to make models adequate for natural processes. SIR simulations of the lag model de...
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ISBN:
(数字)9798350350043
ISBN:
(纸本)9798350350050
The paper analyzes dynamic models with a delay in describing the processes of epidemics. The introduction of a delay makes it possible to make models adequate for natural processes. SIR simulations of the lag model describing the COVID-19 pandemic showed that the numerical results agree with the data. Utilizing reinforcement learning approaches to model virus spread presents a complex challenge due to numerous uncertainties and fluctuations. Nevertheless, some methodologies allow the application of reinforcement learning to accurately model disease dynamics, which have shown promise in forecasting the spread of COVID-19 and adapting public health policies.
The requirements for data privacy are changing, which affects energy Internet price plans. A novel strategy combines quantitative analysis, data modeling, and computer technologies to properly grasp this dynamic relat...
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