China
Africa
Explore chapters and articles related to this topic
Modelica as a Platform for Real-Time Simulation
Published in Katalin Popovici, Pieter J. Mosterman, Real-Time Simulation Technologies, 2017
John J. Batteh, Michael M. Tiller, Dietmar Winkler
Another opportunity that arises from the open platform is that of accessibility. Since the Modelica specification is open and freely available, any interested party is welcome to create a parser and even a compiler to interpret Modelica source code. With source code for many solvers already available, it is certainly feasible to create custom tools for Modelica model development and simulation. For example, the Modelica Software Development Kit (SDK) [12] provides an API and Modelica compiler to allow Modelica code to be embedded into existing software and tools. The open platform also supports innovation from small companies and universities. Several open-source, Modelica-based projects have been initiated. OpenModelica [13] is an open-source Modelica modeling and simulation environment. JModelica.org [14] is an open-source Modelica-based platform for simulation and optimization. Scicos [15] developed at INRIA is a modeling and simulation environment that includes partial support for Modelica. While these offerings may not be as comprehensive in their support of the Modelica language as existing commercial tools, they certainly illustrate potential for innovative offerings based on the open Modelica platform.
Standards Convergence in Mobile Multimedia Broadcasting
Published in Amitabh Kumar, Mobile Broadcasting with WiMAX: Principles, Technology, and Applications, 2014
Any discussion on the OMA must also mention the role of the Open Handset Alliance, which is a recently created association of industry players committed towards developing applications, services, and handsets which are based on open standards. One of the initiatives of the alliance is the release of Android, which is the first complete, open, and free mobile platform. The new platform being released in mid-2008 is complete, and it covers the operating system, middleware, and mobile applications. The open platform helps go beyond “walled gardens” created by applications that need to work on proprietary platforms or restrict users in installing open applications. The Open Handset Alliance is backed by Google and has Sprint as one of the members. The nationwide rollout of WiMAX by Sprint, the biggest WiMAX event of 2008, makes the developments in this arena especially interesting.
Smart door lock and automatic lighting device with bluetooth connection using android Arduino
Published in Aria Hendrawan, Rifi Wijayanti Dual Arifin, Engineering, Information and Agricultural Technology in the Global Digital Revolution, 2020
April Firman Daru, Susanto, Aria Hendrawan, Atmoko Nugroho
Science and technology, especially in the field of information technology, have developed very quickly in recent years. Along with the technological development of the times, communication problems are increasingly becoming very complex. Communication devices such as the Android smartphone can be used not only for periodic communication but also for utilizing newly developed technological features. Android is an operating system for cell phones based on Linux. Android provides an open platform for developers to create their applications for use by various mobile devices, and it is commonly used on smartphones and tablet personal computers (PCs). It functions the same as the Symbian operating system on Nokia, iOS on Apple, and BlackBerry OS (Safaat, 2015). One of the advantages of an Android smartphone is that it is easy to do programming on it, and it can be connected to a microcontroller such as Arduino. A microcontroller is a computer device that contains a chip used to control electronic equipment per preprogrammed instructions. It can be called a “small controller” where an electronic system that previously needed many supporting components such as TTL and CMOS ICs can be reduced and finally centralized and controlled by this microcontroller (Syahwil, 2013). Arduino is a microcontroller family name board created by the Smart Projects company, one of the creators of which is Massimo Banzi. Arduino is an “open-source” hardware that can be made by anyone. Arduino programming is done through computers using a software called Arduino Integrated Development Environment (Arduino IDE) (Abdul Kadir, 2013). Integrated Development Environment uses Java for writing programs, compiling binary code, and uploading the code into the microcontroller’s memory. The Arduino IDE window consists of three main parts. The top part, the toolbar, comprises a menu file, edit and sketch tools, and a help function. The middle part contains the program code. The bottom part is a message window or consul that provides status and error messages (Wicaksono, 2017).
Towards Adoption of Generative AI in Organizational Settings
Published in Journal of Computer Information Systems, 2023
Previous research has discovered that the adoption of innovation is promoted by the size of an organization.24,32,33 Larger organizations typically possess more resources to experiment with newly introduced innovations, thus enabling them to better handle the risks and expenses associated with implementing them.34 In order to expedite client projects with secure and contextual generative AI solutions, firms must rely not only on various language tools offered by different providers, but also develop their own proprietary solutions. To foster innovation and maximize value creation, organizations are increasingly leveraging multiple models rather than relying on a single model. However, the costs associated with training and managing these models must be carefully considered. Factors such as cost, effort, data privacy, intellectual property (IP), and security should be considered when making decisions. Moreover, organizations should embrace an open platform approach that allows them to benefit from available models in the open-source community. By adopting this approach, they can access a wider range of models and tap into external expertise. As these models will be deployed across hybrid cloud environments, organizations will require a platform that seamlessly integrates and deploys these models for their specific use cases. This integrated platform will enable organizations to derive value from their models, ensuring efficient utilization and enhancing overall organizational performance. Currently, due to the significant cost associated with generative AI systems, only organizations with substantial financial resources can invest in installing such technology. Therefore, the following hypothesis is suggested.
Integrating optimal process and supplier selection in personalised product architecture design
Published in International Journal of Production Research, 2022
Changbai Tan, Kira Barton, S. Jack Hu, Theodor Freiheit
The critical research endeavours required to enable the co-creation process of personalisation mainly include: (1) open product architecture (Peng et al. 2018; Zheng et al. 2019) and adaptable interfaces (Zhang et al. 2017) that facilitates the deployment of personalised functions in a product and cost-effectively support their physical embodiment, (2) on-demand manufacturing equipment (Lu and Xu 2019), processes (El Aita, Breitkreutz, and Quodbach 2019), and cloud services (Hof and Wüthrich 2018) that are flexible and agile to produce highly diversified personalised products, and (3) cyber-physical systems integrating big data (Xu and Duan 2019), virtual reality (Lin et al. 2017), internet of things (Xu et al. 2018), and other industry 4.0 technologies (Wang et al. 2017; Aheleroff et al. 2019) that engage customers in product design, manufacturing, and supply processes, as well as supporting collaboration and data sharing among customers, manufacturers, and suppliers. Koren et al. (2013) refers to the concept of open-architecture products as a class of products with an open platform that permit product modules from a variety of sources (e.g. manufactures and suppliers) to be integrated for product innovation. An open, modular product architecture that assembles common, customised, and personalised modules by standardised interfaces is regarded as a key enabler for personalisation (Hu 2013). This assembly architecture allows the creation of a personalised product family by integrating a bounded set of user-designed features at near mass production efficiency, and facilitates the participation of smaller companies in the product value creation process. This study focuses on the integrated design of an open product architecture and its manufacturing processes and supply chain for personalised products.
Autonomous materials discovery and manufacturing (AMDM): A review and perspectives
Published in IISE Transactions, 2023
Perspectives: Although the aforementioned systems are important steps towards autonomous experimentation, they fall short of the needs of autonomous materials discovery, and materials-on-demand manufacturing. The complexity and costs in running experiments, learning the forward and inverse mappings, as well as conducting the MDS search increase drastically, as researchers move from wet-chemistry to materials synthesis, and ultimately to their bulk-scale manufacturing. Even the successful prototypes, including of a robotic chemist, are still far distant from making autonomous discovery and materials-on-demand manufacturing a reality, because of the following reasons. First, many of them are at best semi-automatic. Their human-in-the-loop framework runs counter to what an autonomous system should be, since humans are not adept at navigating complex, multi-dimensional manufacturing process spaces. Second, they use methods that are rather mature and well-evaluated in the data science communities. However, they are known to be easily entrapped in local optima when searching in a design space that embeds a complicated manifold. Also, none of the previous systems can adaptively expand and learn MDS by fusing measurements, computational models, and experiential knowledge innate to manufacturing practice. Despite these limitations, these early experimentation platform implementations offer significant scope to create truly autonomous platforms for materials discovery and manufacturing. Another, more practical issue needs to be considered while leveraging manufacturing machine tools and other automated systems for materials synthesis. The architectures of most of the controllers employed in materials synthesis and manufacturing platforms are often closed. In effect, they may not lend themselves to autonomously receive, let alone execute the instructions and recipes from the “brain.” Although workarounds exist in the form of open platform communication modes, synthesis platforms with an open architecture controller or a more ground up-developed synthesis platforms should be preferred for AMDM.