Real-time track cycling performance prediction using ANFIS system

2018 ◽  
Vol 18 (5) ◽  
pp. 806-822
Author(s):  
Sukhairi Sudin ◽  
Ali Yeon Md Shakaff ◽  
Ammar Zakaria ◽  
Ahmad Faizal Salleh ◽  
Latifah Munirah Kamarudin ◽  
...  
Author(s):  
Oliver Caddy ◽  
William Fitton ◽  
Digby Symons ◽  
Anthony Purnell ◽  
Dan Gordon

The aim of this research was to indicate improvements in 4-km cycling performance that may be gained as a function of reduced frontal surface area ( A) when Union Cycliste Internationale rule 1.3.013 is contravened. In 10 male cyclists age 26 ± 2 (mean ± standard deviation) years, height 180 ± 5 cm and body mass 71 ± 6 kg, entire cycling posture was rotated forward from where the nose of the saddle was 6 cm rearward of the bottom bracket spindle (P6) to 4, 2 and 0 cm (P4, P2 and P0); contravening Union Cycliste Internationale rule 1.3.013. Using computerised planimetry, A was estimated and a forward integration model was compiled to simulate 4-km track cycling end time ( T4km) when a fixed power profile was applied. At P2, there was a significant but non-meaningful reduction compared to P6 ( p < 0.05, d < 0.02). There were small but significant reductions in A and T4km between P6 and P0; −0.007 ± 0.004 m2 and −1.40 ± 0.73 s, respectively ( p < 0.001, d = −0.259). There were no significant differences between P4 and P6 for A and T4km. These results suggest that at the most forward position (P0), a small but significant increase in 4-km performance can be expected compared to the legal position (P6). Moreover, the mean difference in T4km between P6 and P0 is greater than the winning margin at the Union Cycliste Internationale 4-km pursuit world championships four times in the previous 10 years.


2014 ◽  
Author(s):  
Rita E. Okoroafor ◽  
Shahid Haq ◽  
Luca Ortenzi ◽  
Wentao Zhou ◽  
Akanimoh Nkanga ◽  
...  

2021 ◽  
Author(s):  
Sriramya P. ◽  
A.K. Reshmy ◽  
R. Subhashini ◽  
Korakod Tongkachok ◽  
Ajay Prakash Pasupulla ◽  
...  

Abstract Internet of things (IoT) has increased an importance for an area of interest in many devices. Then, the applications such as sensitive home sensors, medical devices, wireless sensors,and other devices are related to IoT network. The transmission of big data is subject to a possible attack that could cause network interruptions and problems with security. The security performance prediction is important for IoT networks to address complicated security issues in real-time which one of the attacks can freely threaten its global performance. Initially,investigate the safety performance of security intelligent prediction techniques is linking with deep learning algorithms into the IoT security risks. This contribution provides a CNN model that improves IoT security risk assessment (SRA) performance. Then, the access control techniques are changed with IoT-like dynamic systems with the number of items spread all over the place. Therefore, dynamic access control models are necessary. Thesedesign not individual use strategies of access but incorporate environmental and real-time data to predict the decision on access. The risk-based access control approach is one of those dynamic models. To decide the access decision, this model assesses the security risk value associated with the access request. This assessment of the model proposed results from the performance and accuracy of IoT networks.


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