PLIF: piezo light intelligent flea-new micro-robots controlled by self-learning techniques

2002 ◽  
Author(s):  
F. De Ambroggi ◽  
L. Fortuna ◽  
G. Muscato
Keyword(s):  
Learning Techniques ◽  
Self Learning

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.

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.

Evaluation of new self-learning techniques for the generation of criteria for differentiation of wide-QRS tachycardia in supraventricular tachycardia and ventricular tachycardia

1995 ◽  
Vol 18 (2) ◽  
pp. 103-108 ◽  
Author(s):  
Willem R.M. Dassen ◽  
Rob G.A. Mulleneers ◽  
Joep L.R.M. Smeets ◽  
Hein J.J. Wellens ◽  
Vincent L.J. Karthaus ◽  
...  
Keyword(s):  
Ventricular Tachycardia ◽  
Supraventricular Tachycardia ◽  
Learning Techniques ◽  
Self Learning

Identifying 3-vessel and main stem disease during pain at rest using self-learning techniques

2002 ◽  
Author(s):  
W.R.M. Dassen ◽  
A.P.M. Gorgels ◽  
R.G.A. Mulleneers ◽  
V.L.J. Karthaus ◽  
H. van Els ◽  
...  
Keyword(s):  
Main Stem ◽  
Learning Techniques ◽  
Pain At Rest ◽  
Self Learning

Machine Learning in Wireless Communication

2019 ◽  
pp. 141-161
Author(s):  
Neha Vaishnavi Sharma ◽  
Narendra Singh Yadav
Keyword(s):  
Machine Learning ◽  
Resource Allocation ◽  
Ad Hoc ◽  
Ad Hoc Wireless Networks ◽  
Machine Learning Techniques ◽  
Energy Aware ◽  
Learning Techniques ◽  
Optimal Node ◽  
Self Learning ◽  
Direct Applicability

As the circumstances are changing, mankind has turned out to be more inclined to snappy and speedier correspondence and access to information. The correspondence happens in numerous structures (e.g., presently, this correspondence is all the more a virtual substance than a physical one). So as to keep up fast correspondence, the coming age will depend on exceptionally tried and true, canny and self-learning/self-modifying correspondence organizers. In this context, this chapter reviews the most important machine learning techniques with the direct applicability in wireless ad-hoc systems. A guide of machine learning methods and their relevance is also provided. Different applications of ad-hoc wireless networks are discussed in terms of energy-aware communications, optimal node deployment and localization, resource allocation, and scheduling.

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