Very fast simulated annealing for pattern detection and seismic applications

Huang, Kou‐Yuan; Hsieh, Yueh-Hsun

doi:10.1109/igarss.2011.6049174

Cited by 10 publications

(5 citation statements)

References 13 publications

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“…According to equations (1) -(4), we calculate the two constrains between every two adjacent picked points inside vector s, so the picked points does not violate the constraints. We pick the polyline that has high energy peaks and satisfies the constraints (3) and (4) (5) where E sem (s) is the summation of semblance value of picked points. Constant is a factor for the two penalty terms.…”

Section: Proposed Method: Simulated Annealing For Velocity Pickingmentioning

confidence: 99%

“…Constant is a factor for the two penalty terms. In the SA algorithm, it is to find the best solution with the lowest energy, so we take a negative symbol for E(s) as in (5).…”

Section: Proposed Method: Simulated Annealing For Velocity Pickingmentioning

confidence: 99%

“…Calculate energy ' s E by (5 Step 3: Cool the system Using the cooling function t T to the next cooling cycle, and then go to Step 2 until the preset maximum iteration is reached.…”

Section: Proposed Method: Simulated Annealing For Velocity Pickingmentioning

confidence: 99%

“…By using Metropolis criterion [3], the higher energy state could be accepted with certain condition. In 2006 and 2009, Huang et al had adopted SA algorithm on seismic pattern detection [4]- [5].…”

Section: Introductionmentioning

confidence: 99%

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Seismic velocity picking by simulated annealing

Huang

Chen

Yang

2012

2012 IEEE International Geoscience and Remote Sensing Symposium

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Velocity picking is to pick several peaks on seismic semblance image (stacking energy) that is very important in seismic data processing. In time-velocity semblance image, these chosen peaks are representing the relation of stacking velocity and time. In this study, we apply simulated annealing (SA) on seismic velocity picking problem and use two constraints in the energy function of SA algorithm for searching a best peak point combination. After obtaining the best polyline, we use the picking result to perform further seismic data processing including normal move-out (NMO) correction and common midpoint stacking. This method automates the velocity picking procedure with short period of time and improves the further seismic data interpretation.

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Section: Proposed Method: Simulated Annealing For Velocity Pickingmentioning

confidence: 99%

“…Constant is a factor for the two penalty terms. In the SA algorithm, it is to find the best solution with the lowest energy, so we take a negative symbol for E(s) as in (5).…”

Section: Proposed Method: Simulated Annealing For Velocity Pickingmentioning

confidence: 99%

“…Calculate energy ' s E by (5 Step 3: Cool the system Using the cooling function t T to the next cooling cycle, and then go to Step 2 until the preset maximum iteration is reached.…”

Section: Proposed Method: Simulated Annealing For Velocity Pickingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Seismic velocity picking by simulated annealing

Huang

Chen

Yang

2012

2012 IEEE International Geoscience and Remote Sensing Symposium

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“…SAO guarantees to converge to the global-minima if the cooling is slowly performed [31][32][33][34]. SAO have been applied to numerous engineering and real life applications and achieved promising results [28,33,[35][36][37][38][39][40][41][42][43][44].…”

Section: Simulated Annealing Optimizationmentioning

confidence: 99%

Simulated Annealing Optimization of PMBLDC Motor Speed and Current Controllers

Shatnawi

Bayoumi

2020

Wseas Transactions on Power Systems

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Brushless Direct-Current (BLDC) motors have several advantages including high efficiency and high speed ranges and accordingly are commonly used in a broad range of industrial applications. The optimization of the proportional-integral (PI) and proportional-integral-derivative (PID) controller parameters are highly explored and a number of tuning techniques have been proposed. This work demonstrates a permanent magnet brushless Direct-Current (PMBLDC) motor controller design method by employing a simulated annealing optimization (SAO) algorithm to optimize the current controller and speed controller parameters to concurrently minimize over-shoot, rise-time, and settling-time. We compared the proposed approach with the Particle Swarm Optimization and the Ziegler-Nichols approaches and showed that the proposed approach outperforms both of the other two approaches.

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Contextual Simulated Annealing Q-Learning for Pre-negotiation of Agent-Based Bilateral Negotiations

Pinto¹,

Vale

2019

Progress in Artificial Intelligence

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Electricity markets are complex environments, which have been suffering continuous transformations due to the increase of renewable based generation and the introduction of new players in the system. In this context, players are forced to rethink their behavior and learn how to act in this dynamic environment in order to get as much benefit as possible from market negotiations. This paper introduces a new learning model to enable players identifying the expected prices of future bilateral agreements, as a way to improve the decision-making process in deciding the opponent players to approach for actual negotiations. The proposed model introduces a con-textual dimension in the well-known Q-Learning algorithm, and includes a simulated annealing process to accelerate the convergence process. The proposed model is integrated in a multi-agent decision support system for electricity market players negotiations, enabling the experimentation of results using real data from the Iberian electricity market.

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Very fast simulated annealing for pattern detection and seismic applications

Cited by 10 publications

References 13 publications

Seismic velocity picking by simulated annealing

Seismic velocity picking by simulated annealing

Simulated Annealing Optimization of PMBLDC Motor Speed and Current Controllers

Contextual Simulated Annealing Q-Learning for Pre-negotiation of Agent-Based Bilateral Negotiations

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