Want to get experience? Want to get any ideas to create new things in your life? Read methods of numerical integration now! By reading this book as soon as possible, you can renew the situation to get the inspirations. Yeah, this way will lead you to always think more and more. In this case, this book will be always right for you. When you can observe more about the book, you will know why you need this.

Methods of Numerical Integration

Limited resources and other factors pose major challenges for engineering, technology, and science educators ability to provide adequate laboratory experience for students. An Internet accessible remote laboratory, which is an arrangement that allows laboratory equipment to be controlled remotely, addresses these difficulties and allows more efficient laboratory management.Internet Accessible Remote Laboratories: Scalable E-Learning Tools for Engineering and Science Disciplines collects current developments in the multidisciplinary creation of Internet accessible remote laboratories. This book offers perspectives on teaching with online laboratories, pedagogical design, system architectures for remote laboratories, future trends, and policy issues in the use of remote laboratories. It is useful resource for graduate and undergraduate students in electrical and computer engineering and computer science programs, as well as researchers who are interested in learning more about the current status of the field, as well as various approaches to remote laboratory design.

Internet Accessible Remote Laboratories: Scalable E-Learning Tools for Engineering and Science Disciplines

The Internet, the World Wide Web, and Globalization are continuing to induce new paradigms of enterprise business models and product realization processes. Fierce competition, unprecedented rates of technological discovery and development, instantaneous delivery of information via Internet communication technology, immeasurable amounts of data, and ever-increasing complexity of engineered systems and their designs coupled with an unquenchable thirst to minimize time-to-market while maximizing quality-of-product. These are just a few factors driving an evolution in the globalized product supply-demand ecosystem and their associated industries and enterprises. It is now common for people from all walks of life from around the globe to work together seamlessly using Web-based technologies and its associated communication frameworks such as, social networking, online/on-demand multipoint video tele-collaboration, Voice-Over-Internet-Protocol (VoIP), instant messaging, digital communities, virtual worlds, and many more. These technologies have facilitated game-changing paradigms such as globalization, mass collaboration, mass customization, as well as new technologies such as cloud computing and virtualization. These technologies provide capabilities for real-time communication using devices ranging from smart-phones and personal digital assistants to laptops and desktops. The Internet-based information and communication technologies (ICT) listed above have also provided mechanisms for radically new methods of innovation, engineering, design, and manufacturing that could not have even been imagined just a few years ago. In particular, the ideas of mass collaboration, distributed design, and distributed manufacturing fueled by the Internet are now realizable methodologies for the next generation of product design and manufacture. The methodology, which we refer to as Collaborative and Distributed Design and Manufacturing (CDDM), can encompass the entire spectrum of product lifecycle that revolves around the product manufacturing process. This product lifecycle spectrum includes product conception, design, comprehension, analysis, synthesis, simulation, prototyping, and physical manufacturing. This is not to say that the “Internet” and the “Web” can actually perform these processes. Instead, the Internet and the Web provide the ICT infrastructures and technologies, which bind together the necessary components of the underlying manufacturing lifecycle infrastructure components needed for the processes. CDDM provides significant economies of scale, reduced time to market, and many other features that can significantly benefit organizations involved in the manufacturing process.

/pdf/distributed-collaborative-design-and-manufacture-in-the-vp3aa3nwgs.pdf

Distributed collaborative design and manufacture in the cloud — motivation, infrastructure, and education

In this paper, we present an educational approach to facilitate Learning how to Learn, that is, to equip our students withcompetencies needed to become lifelong learners and succeed in the job market of the near tomorrow. Our approach is anchored in educational and instructional theory and closely tied to current professional practice. The approach is implemented in a graduatelevel engineering design course that is offered in a distributed collaborative distance learning setting.

/pdf/educating-engineers-for-the-near-tomorrow-25qcu610hl.pdf

Educating Engineers for the Near Tomorrow

One of the claims the OER movement makes is that availability of (open) digital learning materials improves the quality of education. The promise is the ability to offer educational programs that take into account specific demands of the learner. The question is how to reach a situation where a customized demand can be met using OER with acceptable quality against acceptable costs. This situation resembles mass customization as is common in industry for several decades now. Techniques from an industry where an end product is assembled with the demands of the customer as a starting point can be translated to the field of education where courses and learning paths through a curriculum are assembled using a mixture of open and closed learning materials and learning services offered by an institution. Advanced IT support for both the modeling of the learning materials and services and a configurator to be used by a learner are necessary conditions for this approach.

/pdf/mass-customization-of-education-by-an-institution-of-he-what-39p367t3md.pdf

Mass customization of education by an institution of HE: What can we learn from industry?

The rapid progress of globalization has lead to many unprecedented changes in the world in which our students will practice. New product development paradigms such as mass collaboration are redefining the way in which products are realized. The authors believe that in the light of these changes, new approaches to educating the next generation of engineers are needed. Towards this goal, the authors present a pedagogical approach to allow students to experience mass collaboration and to improve their understanding of emerging trends in product development. The approach is designed to foster collective learning and to apply mass customization in education. It is presented in the context of a graduate engineering design courseÐ`Designing Open Engineering Systems'. Two of the main features of the approach are: (1) providing the students with the opportunity to define their own learning goals, and (2) posing a broad question to which the students are required to develop an answer by the end of the semester. All activities of the course are geared towards answering this questionÐboth individually and collectively. Collective learning is facilitated through semester-long continuous development of a collaborative answer to the Q4S by the entire class. Mass customization of education is achieved by having students define their personal semester goals as well as personalizing their answer to the Q4S. A web-based collaborative learning framework is developed for this course using social networking tools to facilitate communication, and to simulate a mass collaborative environment. The authors believe that such pedagogical approaches are essential for developing a foundation for next generation educational environments.

/pdf/fostering-collaborative-learning-and-educational-mass-3my9nu3bnd.pdf

Fostering Collaborative Learning and Educational Mass Customization in a Graduate Level Engineering Design Course

In the early stages of an engineering design process it is necessary to explore the design space to find a feasible range that satisfies design requirements. When robustness of the system is among the requirements, the robust concept exploration method can be used. In this method, a global metamodel, such as a global response surface of the design space, is used to evaluate robustness. However, for large design spaces, this is computationally expensive and may be relatively inaccurate for some local regions. In this article, a method is developed for successively generating local response models at points of interest as the design space is explored. This approach is based on the probabilistic collocation method. Although the focus of this article is on the method, it is demonstrated using an artificial performance function and a linear cellular alloy heat exchanger. For these problems, this approach substantially reduces computation time while maintaining accuracy.

Probabilistic collocation for simulation-based robust concept exploration

When applying robust design methods, designers prefer to find solutions with a performance that is insensitive towards uncertainty and noise. It is common to use the variance of the performance as a measure for robustness. In many robust design methods the Taylor series expansion of the model is used as an inexpensive way to estimate the variance. In this paper, it is demonstrated that this can lead to fatal errors, especially for nonlinear and multimodal design spaces. Following the demonstration of this problem, three alternative methods for variance estimation are proposed. Their accuracy is assessed within the Robust Concept Exploration Method (RCEM) and recommendations for their application are given.

Alternatives to Taylor Series Approximation for the Variance Estimation in Robust Design

In early stages of the engineering design process it is necessary to explore the design space to find a feasible range or point that satisfies the design requirements. When robustness of the system is among the requirements, the Robust Concept Exploration Method (RCEM) can be used. In RCEM a metamodel such as a global response surface of the entire design space is used. Based on this surrogate model the robustness of the system is evaluated. In nonlinear or multimodal design spaces a very detailed metamodel such as a very high order response surface might be required to reflect accurately the characteristics of the model. For large design spaces this is computationally very expensive. In this paper, using the Probabilistic Collocation Method (PCM) for generating local response models at the points of interest, a Simulation-Based RCEM is proposed as a very efficient and flexible robust concept exploration method. We believe that using the PCM with other design exploration methods would be equally effective.Copyright © 2010 by ASME

A simulation-based robust concept exploration method

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Markus Rippel

Author Tools

Chat about Author

Papers

Fostering Collaborative Learning and Educational Mass Customization in a Graduate Level Engineering Design Course

Probabilistic collocation for simulation-based robust concept exploration

Alternatives to Taylor Series Approximation for the Variance Estimation in Robust Design

A simulation-based robust concept exploration method