A Robotic Driver on Roller Dynamometer with Vehicle Performance Self Learning Algorithm

1991 ◽  
Author(s):  
Akinobu Moriyama ◽  
Isao Murase ◽  
Akira Shimozono ◽  
Tohru Takeuchi
Keyword(s):  
Learning Algorithm ◽  
Vehicle Performance ◽  
Self Learning

Operon Prediction Based On an Iterative Self-learning Algorithm*

2011 ◽  
Vol 38 (7) ◽  
pp. 642-651
Author(s):  
Wen-Qi Wu ◽  
Xiao-Bin ZHENG ◽  
Yong-Chu LIU ◽  
Kai TANG ◽  
Huai-Qiu ZHU
Keyword(s):  
Learning Algorithm ◽  
Operon Prediction ◽  
Self Learning

scholarly journals Reinforcement learning-based link adaptation in long delayed underwater acoustic channel

2019 ◽  
Vol 283 ◽  
pp. 07001 ◽  
Author(s):  
Jingxi Wang ◽  
Chau Yuen ◽  
Yong Liang Guan ◽  
Fengxiang Ge
Keyword(s):  
Reinforcement Learning ◽  
Learning Strategy ◽  
Learning Algorithm ◽  
Optimal Parameters ◽  
Underwater Acoustic Channel ◽  
Transfer Data ◽  
Data Packets ◽  
Significant Area ◽  
Channel Environment ◽  
Self Learning

In this paper, we apply reinforcement learning, a significant area of machine learning, to formulate an optimal self-learning strategy to interact in an unknown and dynamically variable underwater channel. The dynamic and volatile nature of the underwater channel environment makes it impossible to employ pre-knowledge. In order to select the optimal parameters to transfer data packets, by using reinforcement learning, this problem could be resolved, and better throughput could be achieved without any environmental pre-information. The slow sound velocity in an underwater scenario, means that the delay of transmitting packet acknowledgement back to sender from the receiver is material, deteriorating the convergence speed of the reinforcement learning algorithm. As reinforcement learning requires a timely acknowledgement feedback from the receiver, in this paper, we combine a juggling-like ARQ (Automatic Repeat Request) mechanism with reinforcement learning to minimize the long-delayed reward feedback problem. The simulation is accomplished by OPNET.

scholarly journals RNA-Sequencing of Tumor-Educated Platelets, a Novel Biomarker for Blood-Based Sarcoma Diagnostics

Cancers ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1372 ◽  
Author(s):  
Kimberley M. Heinhuis ◽  
Sjors G. J. G. In ’t Veld ◽  
Govert Dwarshuis ◽  
Daan van den Broek ◽  
Nik Sol ◽  
...  
Keyword(s):  
Learning Algorithm ◽  
Expression Patterns ◽  
Area Under The Curve ◽  
Disease Recurrence ◽  
Cancer Diagnostics ◽  
Messenger Rnas ◽  
Liquid Biopsies ◽  
Recurrence Rates ◽  
Healthy Donors ◽  
Self Learning

Sarcoma is a heterogeneous group of rare malignancies arising from mesenchymal tissues. Recurrence rates are high and methods for early detection by blood-based biomarkers do not exist. Hence, development of blood-based liquid biopsies as disease recurrence monitoring biomarkers would be an important step forward. Recently, it has been shown that tumor-educated platelets (TEPs) harbor specific spliced ribonucleic acid(RNA)-profiles. These RNA-repertoires are potentially applicable for cancer diagnostics. We aim to evaluate the potential of TEPs for blood-based diagnostics of sarcoma patients. Fifty-seven sarcoma patients (active disease), 38 former sarcoma patients (cancer free for ≥3 years) and 65 healthy donors were included. RNA was isolated from platelets and sequenced. Quantified read counts were processed with self-learning particle-swarm optimization-enhanced thromboSeq analysis and subjected to analysis of variance (ANOVA) statistics. Highly correlating spliced platelet messenger RNAs (mRNAs) of sarcoma patients were compared to controls (former sarcoma + healthy donors) to identify a quantitative sarcoma-specific signature measure, the TEP-score. ANOVA analysis identified distinctive platelet RNA expression patterns of 2647 genes (false discovery rate <0.05) in sarcoma patients as compared to controls. The self-learning algorithm reached a diagnostic accuracy of 87% (validation set only; n = 53 samples, area under the curve (AUC): 0.93, 95% confidence interval (CI): 0.86–1). Our data indicates that TEP RNA-based liquid biopsies may enable for sarcoma diagnostics.

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