Reinforcement Learning Based on Active Learning Method

Sagha, Hesam; Shouraki, Saeed Bagheri; Khasteh, Seyed Hossein; Kiaei, Ali Akbar

doi:10.1109/iita.2008.565

Cited by 7 publications

(4 citation statements)

References 5 publications

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“…Each narrow path shows the behaviour of an output relative to the input. Spread of data pointing around this path shows the importance degree of that input in overall system behaviour [24].…”

Section: Ink Drop Spreadmentioning

confidence: 99%

RLIDS Based Goal Improvement in Emotional Control

Zade¹,

Afrakoti²,

Shuraki³

2013

AJIS

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In this paper, a goal improvement approach based on experiences for handling external disturbances is proposed. The suggested method uses reinforcement learning by an actor-critic mechanism in order to adapt the system to disturbance and uncertainty. Goal improvement is achieved via IDS. IDS is a powerful method in information-handling processes. It has a fast convergence and simple underlying, away from complex formulas. Temporal Difference (TD) learning is utilized in updating both Stress-Evaluator and the goal of emotional controller. As a benchmark to evaluate our method, inverted pendulum and simple submarine systems are used. Results show that the proposed method responds better to external tackling disturbances in comparison with the former fixed goal model.

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Section: Ink Drop Spreadmentioning

confidence: 99%

RLIDS Based Goal Improvement in Emotional Control

Zade¹,

Afrakoti²,

Shuraki³

2013

AJIS

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“…This method can greatly improve the clarity of images [ 9 ]. Deep learning is used for medical applications to diagnose and improve chronic disease consequences [ 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

Fusion of medical images using Nabla operator; Objective evaluations and step-by-step statistical comparisons

Behrouzi,

Basiri,

Pourgholi

et al. 2023

PLoS ONE

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Since vectors include direction and magnitude, they have more information than scalars. So, converting the scalar images into the vector field leads achieving much information about the images that have been hidden in the spatial domain. In this paper, the proposed method fuses images after transforming the scalar field of images to a vector one. To transform the field, it uses Nabla operator. After that, the inverse transform is implemented to reconstruct the fused medical image. To show the performance of the proposed method and to evaluate it, different experiments and statistical comparisons were accomplished. Comparing the experimental results with the previous works, shows the effectiveness of the proposed method.

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“…Narrow paths are estimated by applying Ink Drop Spread (IDS) on data points and Center of Gravity (COG) on data planes (Bagheri Shouraki, 2000;Bagheri Shouraki and Honda, 1999). IDS and COG are two main operators of ALM (Bagheri Shouraki and Honda, 1997;Sagha et al, 2008).…”

Section: Active Learning Methodsmentioning

confidence: 99%

Prediction of drilling pipe sticking by active learning method (ALM)

Naraghi¹,

Ezzatyar²,

Jamshidi³

2013

J. Petroleum Gas Eng.

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Stuck piping is a common problem with tremendous impact on drilling efficiency and costs in oil industry. Generally, the stuck pipe troubles are solved after their occurrences by using some standard techniques; here we attempt to predict the causes of occurrence of such problems to eschew risks and excessive drilling costs. If these risks are identified in advance, better solutions can be provided to reduce the associated consequences. Based on the literature, this problem is caused by numerous parameters, such as drilling fluid properties and the characteristics of the mud cake that is formed while drilling. In this study, an attempt is made to develop a model for stuck pipe prediction. To consider all aspects of pipe sticking and behavior of the involved variables, the fuzzy logic and active learning method (ALM) can be used as a primary predictive tool. Active Learning Method is a robust recursive fuzzy modeling without computational complexity. These methods are broadly used in many industries; including oil and gas. This paper proposes a systematic approach for pipe stuck prediction based on ALM. The results of this method are more accurate than other methods and prediction accuracy is close to perfect either in stuck or non-stuck cases. This study presents a case study in which the ALM is used successfully to estimate pipe sticking. Thus, the proposed method possesses reliable results for prediction of pipe stuck, and can be used in order to minimize the risk of pipe sticking.

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Reinforcement Learning Based on Active Learning Method

Abstract: Abstract

Cited by 7 publications

References 5 publications

RLIDS Based Goal Improvement in Emotional Control

RLIDS Based Goal Improvement in Emotional Control

Fusion of medical images using Nabla operator; Objective evaluations and step-by-step statistical comparisons

Prediction of drilling pipe sticking by active learning method (ALM)

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