scholarly journals Estimation and Categorization of Errors in Error Recovery Using Task Stratification and Error Classification

2017 ◽  
Vol 4 (2) ◽  
pp. 163
Author(s):  
Akira Nakamura ◽  
Kazuyuki Nagata ◽  
Kensuke Harada ◽  
Natsuki Yamanobe
Keyword(s):  
Error Recovery ◽  
Error Classification

scholarly journals Estimation and Categorization of Errors in Error Recovery Using Task Stratification and Error Classification

2017 ◽  
Vol 22 ◽  
pp. 217-220
Author(s):  
Akira Nakamura ◽  
Kazuyuki Nagata ◽  
Kensuke Harada ◽  
Natsuki Yamanobe
Keyword(s):  
Error Recovery ◽  
Error Classification

scholarly journals The Suitable Timing of Visual Sensing in Error Recovery Using Task Stratification and Error Classification

2017 ◽  
Vol 4 (2) ◽  
pp. 134
Author(s):  
Akira Nakamura ◽  
Kazuyuki Nagata ◽  
Kensuke Harada ◽  
Natsuki Yamanobe
Keyword(s):  
Error Recovery ◽  
Visual Sensing ◽  
Error Classification

scholarly journals Technique of Recovery Process and Application of AI in Error Recovery Using Task Stratification and Error Classification

2018 ◽  
Vol 5 (1) ◽  
pp. 56 ◽  
Author(s):  
Akira Nakamura ◽  
Kazuyuki Nagata ◽  
Kensuke Harada ◽  
Natsuki Yamanobe
Keyword(s):  
Recovery Process ◽  
Error Recovery ◽  
Error Classification

scholarly journals A Novel Strategy for Automatic Error Classification and Error Recovery for Robotic Assembly in Flexible Production

2020 ◽  
Vol 100 (3-4) ◽  
pp. 863-877
Author(s):  
Ewa Kristiansen ◽  
Emil Krabbe Nielsen ◽  
Lasse Hansen ◽  
David Bourne
Keyword(s):  
Error Detection ◽  
Active Vision ◽  
Error Recovery ◽  
Robotic Assembly ◽  
Classification Rate ◽  
A Cell ◽  
Error Classification ◽  
Novel Strategy ◽  
Assembly Tasks ◽  
Automatic Error

AbstractIn this article, we develop a novel strategy for automatic error classification and recovery in robotic assembly tasks. The strategy does not require error diagnosis. It allows for effective reduction of an undetermined number of error states to 4, without the need for further operator updates of error space. The strategy integrates existing methods for computer vision, active vision and active manipulation. Our solution is implemented in a generic software framework, which is independent from software and hardware for implementing error detection and allows for application in other assembly types and components. The value of our strategy was experimentally validated on a simple case, where we inserted a battery into a cell phone. The experiment was performed on 1500 assembly attempts and included 500 detected errors. The whole experiment ran for 42 hours, with no need for operator assistance or supervision. The resulting classification rate is 99.6% and the resulting recovery rate is 98.8%. The 6 unrecovered errors were successfully resolved in a successive assembly attempt.

scholarly journals Efficient Estimation of CFO-Affected OFDM BER Floor in Small Cells with Resource-Limited IoT End-Points

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3747
Author(s):  
Adriana Lipovac ◽  
Vlatko Lipovac ◽  
Borivoj Modlic
Keyword(s):  
Orthogonal Frequency Division Multiplexing ◽  
Carrier Frequency Offset ◽  
Signal To Noise Ratio ◽  
Low Cost ◽  
Error Recovery ◽  
Efficient Estimation ◽  
Analytical Models ◽  
Small Cells ◽  
End Points ◽  
Power Delay Profile

Contemporary wireless networks dramatically enhance data rates and latency to become a key enabler of massive communication among various low-cost devices of limited computational power, standardized by the Long-Term Evolution (LTE) downscaled derivations LTE-M or narrowband Internet of Things (NB IoT), in particular. Specifically, assessment of the physical-layer transmission performance is important for higher-layer protocols determining the extent of the potential error recovery escalation upwards the protocol stack. Thereby, it is needed that the end-points of low processing capacity most efficiently estimate the residual bit error rate (BER) solely determined by the main orthogonal frequency-division multiplexing (OFDM) impairment–carrier frequency offset (CFO), specifically in small cells, where the signal-to-noise ratio is large enough, as well as the OFDM symbol cyclic prefix, preventing inter-symbol interference. However, in contrast to earlier analytical models with computationally demanding estimation of BER from the phase deviation caused by CFO, in this paper, after identifying the optimal sample instant in a power delay profile, we abstract the CFO by equivalent time dispersion (i.e., by additional spreading of the power delay profile that would produce the same BER degradation as the CFO). The proposed BER estimation is verified by means of the industry-standard LTE software simulator.

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