Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Adaptive resonance theory (ART) (1)
- Domain knowledge (1)
- Line-of-sight (1)
- Monitoring (1)
- Nonline-of-sight (1)
-
- Packet collisions (1)
- Radio interference (1)
- Reinforcement learning (RL) (1)
- Self-organizing neural networks (1)
- Structural design (1)
- Structural health monitoring (1)
- Suspension bridges (1)
- Thermal effects (1)
- Time-of-arrival classification (1)
- Underwater acoustic localization (UWAL) (1)
- Wireless sensor network (1)
- Wireless sensor networks (1)
Articles 1 - 4 of 4
Full-Text Articles in Physical Sciences and Mathematics
Cama: Efficient Modeling Of The Capture Effect For Low Power Wireless Networks, Behnam Dezfouli, Marjan Radi, Kamin Whitehouse, Shukor Abd Razak, Hwee-Pink Tan
Cama: Efficient Modeling Of The Capture Effect For Low Power Wireless Networks, Behnam Dezfouli, Marjan Radi, Kamin Whitehouse, Shukor Abd Razak, Hwee-Pink Tan
Research Collection School Of Computing and Information Systems
Network simulation is an essential tool for the design and evaluation of wireless network protocols, and realistic channel modeling is essential for meaningful analysis. Recently, several network protocols have demonstrated substantial network performance improvements by exploiting the capture effect, but existing models of the capture effect are still not adequate for protocol simulation and analysis. Physical-level models that calculate the signal-to-interference-plus-noise ratio (SINR) for every incoming bit are too slow to be used for large-scale or long-term networking experiments, and link-level models such as those currently used by the NS2 simulator do not accurately predict protocol performance. In this article, …
Self-Organizing Neural Networks Integrating Domain Knowledge And Reinforcement Learning, Teck-Hou Teng, Ah-Hwee Tan, Jacek M. Zurada
Self-Organizing Neural Networks Integrating Domain Knowledge And Reinforcement Learning, Teck-Hou Teng, Ah-Hwee Tan, Jacek M. Zurada
Research Collection School Of Computing and Information Systems
The use of domain knowledge in learning systems is expected to improve learning efficiency and reduce model complexity. However, due to the incompatibility with knowledge structure of the learning systems and real-time exploratory nature of reinforcement learning (RL), domain knowledge cannot be inserted directly. In this paper, we show how self-organizing neural networks designed for online and incremental adaptation can integrate domain knowledge and RL. Specifically, symbol-based domain knowledge is translated into numeric patterns before inserting into the self-organizing neural networks. To ensure effective use of domain knowledge, we present an analysis of how the inserted knowledge is used by …
Los And Nlos Classification For Underwater Acoustic Localization, Roee Diamant, Hwee-Pink Tan, Lutz Lampe
Los And Nlos Classification For Underwater Acoustic Localization, Roee Diamant, Hwee-Pink Tan, Lutz Lampe
Research Collection School Of Computing and Information Systems
The low sound speed in water makes propagation delay (PD)-based range estimation attractive for underwater acoustic localization (UWAL). However, due to the long channel impulse response and the existence of reflectors, PD-based UWAL suffers from significant degradation when PD measurements of nonline-of-sight (NLOS) communication links are falsely identified as line-of-sight (LOS). In this paper, we utilize expected variation of PD measurements due to mobility of nodes and present an algorithm to classify the former into LOS and NLOS links. First, by comparing signal strength-based and PD-based range measurements, we identify object-related NLOS (ONLOS) links, where signals are reflected from objects …
Measuring And Modelling The Thermal Performance Of The Tamar Suspension Bridge Using A Wireless Sensor Network, Nicholas De Battista, James M. W. Brownjohn, Hwee-Pink Tan, Ki Young Koo
Measuring And Modelling The Thermal Performance Of The Tamar Suspension Bridge Using A Wireless Sensor Network, Nicholas De Battista, James M. W. Brownjohn, Hwee-Pink Tan, Ki Young Koo
Research Collection School Of Computing and Information Systems
A study on the thermal performance of the Tamar Suspension Bridge deck in Plymouth, UK, is presented in this paper. Ambient air, suspension cable, deck and truss temperatures were acquired using a wired sensor system. Deck extension data were acquired using a two-hop wireless sensor network. Empirical models relating the deck extension to various combinations of temperatures were derived and compared. The most accurate model, which used all the four temperature variables, predicted the deck extension with an accuracy of 99.4%. Time delays ranging from 10 to 66 min were identified between the daily cycles of the air temperature and …