Develop e-learning platform for reinforcement learning on temperature sensor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper presents the development of e-learning platform for reinforcement learning on temperature sensor experimental module based on the graphical monitoring and control system. The e-learning platform in its current configuration aims at enabling experience of students in temperature sensor. It is integration graphical monitoring and control with four different temperature sensor devices. In this platform, the students can realize not only the characteristics, function and application of different temperature sensors but also learn to use the graphical monitoring and control system. In advance, the students can design e-learning platform to monitor and control the sensor module. The students learn, verify and realize lecture concepts by performing different sensor experiments in the laboratory via e-learning platform. They work in teams, design and implement their sensor systems and demonstrate their experimental results in orally present.

Original languageEnglish
Title of host publicationKnowledge-Based Intelligent Information and Engineering Systems
Subtitle of host publicationKES 2007 - WIRN 2007 - 11th International Conference, KES 2007, XVII Italian Workshop on Neural Networks, Proceedings
Pages82-89
Number of pages8
EditionPART 2
DOIs
Publication statusPublished - 2007 Dec 1
Event11th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2007, and 17th Italian Workshop on Neural Networks, WIRN 2007 - Vietri sul Mare, Italy
Duration: 2007 Sep 122007 Sep 14

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume4693 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other11th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2007, and 17th Italian Workshop on Neural Networks, WIRN 2007
CountryItaly
CityVietri sul Mare
Period07-09-1207-09-14

Fingerprint

Temperature Sensor
Reinforcement learning
Temperature sensors
Electronic Learning
Reinforcement Learning
Learning
Students
Temperature
Monitoring System
Sensor
Monitoring
Sensors
Control System
Control systems
Module
Characteristic Function
Monitor
Verify
Configuration
Reinforcement (Psychology)

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Shyr, W-J. (2007). Develop e-learning platform for reinforcement learning on temperature sensor. In Knowledge-Based Intelligent Information and Engineering Systems: KES 2007 - WIRN 2007 - 11th International Conference, KES 2007, XVII Italian Workshop on Neural Networks, Proceedings (PART 2 ed., pp. 82-89). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4693 LNAI, No. PART 2). https://doi.org/10.1007/978-3-540-74827-4_11
Shyr, Wen-Jye. / Develop e-learning platform for reinforcement learning on temperature sensor. Knowledge-Based Intelligent Information and Engineering Systems: KES 2007 - WIRN 2007 - 11th International Conference, KES 2007, XVII Italian Workshop on Neural Networks, Proceedings. PART 2. ed. 2007. pp. 82-89 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).
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abstract = "This paper presents the development of e-learning platform for reinforcement learning on temperature sensor experimental module based on the graphical monitoring and control system. The e-learning platform in its current configuration aims at enabling experience of students in temperature sensor. It is integration graphical monitoring and control with four different temperature sensor devices. In this platform, the students can realize not only the characteristics, function and application of different temperature sensors but also learn to use the graphical monitoring and control system. In advance, the students can design e-learning platform to monitor and control the sensor module. The students learn, verify and realize lecture concepts by performing different sensor experiments in the laboratory via e-learning platform. They work in teams, design and implement their sensor systems and demonstrate their experimental results in orally present.",
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Shyr, W-J 2007, Develop e-learning platform for reinforcement learning on temperature sensor. in Knowledge-Based Intelligent Information and Engineering Systems: KES 2007 - WIRN 2007 - 11th International Conference, KES 2007, XVII Italian Workshop on Neural Networks, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 4693 LNAI, pp. 82-89, 11th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2007, and 17th Italian Workshop on Neural Networks, WIRN 2007, Vietri sul Mare, Italy, 07-09-12. https://doi.org/10.1007/978-3-540-74827-4_11

Develop e-learning platform for reinforcement learning on temperature sensor. / Shyr, Wen-Jye.

Knowledge-Based Intelligent Information and Engineering Systems: KES 2007 - WIRN 2007 - 11th International Conference, KES 2007, XVII Italian Workshop on Neural Networks, Proceedings. PART 2. ed. 2007. p. 82-89 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4693 LNAI, No. PART 2).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Shyr W-J. Develop e-learning platform for reinforcement learning on temperature sensor. In Knowledge-Based Intelligent Information and Engineering Systems: KES 2007 - WIRN 2007 - 11th International Conference, KES 2007, XVII Italian Workshop on Neural Networks, Proceedings. PART 2 ed. 2007. p. 82-89. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). https://doi.org/10.1007/978-3-540-74827-4_11