Computational models are commonly used in engineering design and scientific discovery activities for simulating
complex physical systems in disciplines such as fluid mechanics, structural dynamics, heat transfer, nonlinear
structural mechanics, shock physics, and many others. These simulators can be an enormous aid to engineers who
want to develop an understanding and/or predictive capability for complex behaviors typically observed in the
corresponding physical systems. Simulators often serve as virtual prototypes, where a set of predefined system
parameters, such as size or location dimensions and material properties, are adjusted to improve the performance
of a system, as defined by one or more system performance objectives. Such optimization or tuning of the
virtual prototype requires executing the simulator, evaluating performance objective(s), and adjusting the system
parameters in an iterative, automated, and directed way. System performance objectives can be formulated, for
example, to minimize weight, cost, or defects; to limit a critical temperature, stress, or vibration response; or
to maximize performance, reliability, throughput, agility, or design robustness. In addition, one would often
like to design computer experiments, run parameter studies, or perform uncertainty quantification (UQ). These
approaches reveal how system performance changes as a design or uncertain input variable changes. Sampling
methods are often used in uncertainty quantification to calculate a distribution on system performance measures,
and to understand which uncertain inputs contribute most to the variance of the outputs.
A primary goal for Dakota development is to provide engineers and other disciplinary scientists with a systematic
and rapid means to obtain improved or optimal designs or understand sensitivity or uncertainty using simulationbased
models. These capabilities generally lead to improved designs and system performance in earlier design
stages, alleviating dependence on physical prototypes and testing, shortening design cycles, and reducing product
development costs. In addition to providing this practical environment for answering system performance questions,
the Dakota toolkit provides an extensible platform for the research and rapid prototyping of customized
methods and meta-algorithms
標簽：
Optimization and Uncertainty Quantification
上傳時間：
2016-04-08
上傳用戶：huhu123456
Over the past few decades there has been an exponential growth in service robots
and smart home technologies, which has led to the development of exciting new
products in our daily lives. Service robots can be used to provide domestic aid for
the elderly and disabled, serving various functions ranging from cleaning to enter-
tainment. Service robots are divided by functions, such as personal robots, field
robots, security robots, healthcare robots, medical robots, rehabilitation robots and
entertainment robots. A smart home appears “intelligent” because its embedded
computers can monitor so many aspects of the daily lives of householders. For
example, the refrigerator may be able to monitor its contents, suggest healthy alter-
natives and order groceries. Also, the smart home system may be able to clean the
house and water the plants.
標簽：
Robotics
Service
Digital
within
Home
the
上傳時間：
2020-06-06
上傳用戶：shancjb
以單片機控制A/D轉換器TLC549為例,對A/D轉換器的主要技術指標進行了分析研究,在Proteus平臺下,完成了A/D轉換電路的構建,采用器件工作時序方式進行程序編寫,借助仿真圖表、虛擬儀器等工具對A/D轉換的數據進行測量并對失調誤差、增益誤差、微分非線性、積分非線性和轉換時間等重要參數進行了詳細分析。結果表明:使用Proteus軟件可對A/D轉換過程進行定性分析,將抽象的A/D轉換器技術指標直觀化、形象化展現出來,有助于學生更好地理解A/D轉換過程。The main technical indicators of A/D converter were analyzed and studied with an example from A/D converter TLC2543 which is controlled by using SCM.It was completed the construction of the A/D converter circuit under the Proteus software.The programming based on the operation sequence of the chip is put forward.With the aid of the simulation tools such as virtual instrument,simulation charts provided by Proteus,the important parameters of circuit such as offset error,gain error,differential nonlinearity(DNL),integral nonlinearity (INL) and conversion time are analyzed detailedly.Simulation results show that the A/D conversion process can be qualitatively analyzed and visualized the abstract indicators of A/D.The system can help students better to understand the SCM conversion process.
標簽：
proteus
單片機
模數轉換
上傳時間：
2022-04-04
上傳用戶：
