Modeling, monitoring, and self-learning techniques for building an AI-driven digital twin optical system

2021 ◽  
Author(s):  
Qunbi Zhuge
Keyword(s):  
Optical System ◽  
Digital Twin ◽  
Learning Techniques ◽  
Self Learning

scholarly journals Digital Twin for Operation of Microgrid: Optimal Scheduling in Virtual Space of Digital Twin

Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5504
Author(s):  
Hyang-A Park ◽  
Gilsung Byeon ◽  
Wanbin Son ◽  
Hyung-Chul Jo ◽  
Jongyul Kim ◽  
...  
Keyword(s):  
Real Time ◽  
Industrial Revolution ◽  
Storage System ◽  
Research Field ◽  
Optimization Techniques ◽  
Virtual Space ◽  
Machine Learning Techniques ◽  
Time Data ◽  
Digital Twin ◽  
Learning Techniques

Due to the recent development of information and communication technology (ICT), various studies using real-time data are now being conducted. The microgrid research field is also evolving to enable intelligent operation of energy management through digitalization. Problems occur when operating the actual microgrid, causing issues such as difficulty in decision making and system abnormalities. Using digital twin technology, which is one of the technologies representing the fourth industrial revolution, it is possible to overcome these problems by changing the microgrid configuration and operating algorithms of virtual space in various ways and testing them in real time. In this study, we proposed an energy storage system (ESS) operation scheduling model to be applied to virtual space when constructing a microgrid using digital twin technology. An ESS optimal charging/discharging scheduling was established to minimize electricity bills and was implemented using supervised learning techniques such as the decision tree, NARX, and MARS models instead of existing optimization techniques. NARX and decision trees are machine learning techniques. MARS is a nonparametric regression model, and its application has been increasing. Its performance was analyzed by deriving performance evaluation indicators for each model. Using the proposed model, it was found in a case study that the amount of electricity bill savings when operating the ESS is greater than that incurred in the actual ESS operation. The suitability of the model was evaluated by a comparative analysis with the optimization-based ESS charging/discharging scheduling pattern.

scholarly journals Classification Tool Based on Interactivity Between Expertise and Self-Learning Techniques 1

2003 ◽  
Vol 36 (5) ◽  
pp. 675-680 ◽  
Author(s):  
Tatiana Kempowsky ◽  
Joseph Aguilar ◽  
Audine Subias ◽  
Marie-Veronique Le Lann
Keyword(s):  
Learning Techniques ◽  
Classification Tool ◽  
Self Learning

scholarly journals Effectiveness of Programmed Learning Material in Learning Cognitive Domain of B.Ed Students

2016 ◽  
Vol 3 (3) ◽  
Author(s):  
Jignasa H. Joshi
Keyword(s):  
Cognitive Domain ◽  
Teaching Methodology ◽  
Learning Material ◽  
Learning Method ◽  
Teaching Techniques ◽  
Programmed Learning ◽  
Learning Techniques ◽  
Effective Media ◽  
Programmed Learning Material ◽  
Self Learning

The present teaching techniques needs revised thinking to make learning more effective for students. In fact the teaching methodology should be such by which the students can be involved in reading, thinking, problem solving and then learning by their own efforts. It becomes more important at B.Ed. Level. For this purpose self-learning method is a very effective media. There are several Self Learning Techniques in which learner can learn by their own pace. Inamdar, J.A (1981), Suthar, K.S (1981), Debi Meena Kumari (1989), concluded that Programmed learning method was more effective. Can the learning of cognitive domain be made easier by using Programmed Learning Material? Is the Programmed Learning Method similarly effective for boys and girls? The investigator has thought about all such crucial questions for undertaking this research. Hence the topic “Effectiveness of Programmed Learning Material in Learning Cognitive Domain of B.Ed Students” is selected for the presentation.

scholarly journals HISTORICAL SENTIENT – BUILDING INFORMATION MODEL: A DIGITAL TWIN FOR THE MANAGEMENT OF MUSEUM COLLECTIONS IN HISTORICAL ARCHITECTURES

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 755-762
Author(s):  
F. M. La Russa ◽  
C. Santagati
Keyword(s):  
Machine Learning Techniques ◽  
The Novel ◽  
Museum Collections ◽  
Historical Building ◽  
Building Information Model ◽  
Digital Twin ◽  
Digital Twins ◽  
Learning Techniques ◽  
Building Information ◽  
Architectural Restoration

Abstract. This paper investigates the application of the Digital Twin approach to get a Sentient building able to acquire the ability to perceive external inputs and develop strategies to support its management and/or conservation. The experimentation foresees the integration of an H-BIM model with a Decision Support System based on Artificial Intelligence (in this case Machine Learning techniques) for the management of museum collections in historical architectures. The innovative aspect of this methodology resides in the change of paradigm regarding the relations between the historical building under consideration and the professional figures who deal with the management, conservation and architectural restoration. This work tries to contextualize the novel HS-BIM methodology within the theoretical discussion of the disciplines mentioned above and to participate in Digital Twin’s debate. HS-BIM can be seen as a possible path that leads to creating digital twins for cultural heritage. The reflection inspired by this experience aims to revise the concept of Digital Twin as a parallel/external digital model in favour of an artificial evolution of the real system augmented by a “cognitive” apparatus. In this vision, thanks to AI application, future buildings will be able to sense “comfort and pain” and learning from their own life-cycle experience but also from that one of elder sentient-buildings thanks to transfer learning already applied in AI’s fields.

Shipboard Mission Readiness Assessment

2008 ◽  
Author(s):  
Thomas C. Cook ◽  
Scott G. Valentine
Keyword(s):  
Gas Turbines ◽  
Health Indicators ◽  
Interactive Effects ◽  
Data Systems ◽  
Component Level ◽  
Readiness Assessment ◽  
Learning Techniques ◽  
Future System ◽  
Us Navy ◽  
Self Learning

Gas turbines are widely used, highly critical, components for generating electrical power and propulsion on modern US Navy ships. Increasing numbers of sensors and sophisticated health management systems are being integrated into shipboard systems to enhance monitoring, performance, diagnostic, and maintenance planning capabilities. Development of a decision support tool to fuse multiple independent system health indicators provides the US military with a readily deployable technology for enabling comprehensive assessment of overall system platform health and mission readiness. This technology leverages existing open source data systems and on-board diagnostic and prognostic modules to efficiently and seamlessly employ advanced reasoning and self-learning techniques to predict high level system health and readiness from component level inputs. This paper summarizes the work associated with development of a software application to provide real-time mission readiness assessment for US Navy ships. The application incorporates several novel approaches including use of a uniform gray-scale method for identifying system health and readiness; fusion of multiple independent low-level indicators to predict overall system health and readiness; methodologies to account for the interactive effects of interconnected subsystems on overall system health and readiness; and use of self learning techniques to provide continuous refinement to future system health and readiness predictions.

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