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Categorize Level of Crystal Sugar Making with Recurrent Neural Network
(2022-01-01)
Ounsrimuang, Pimolrat
;
Nootyaskool, Supakit
This research presents the study of recurrent neural networks to predict industrial crystal sugar making. The recurrent neural network trains on six parameters consisting of liquid in the pan, Brix levels, vacuum in the pan, liquor temperatures, water steam supplier, and current for mix-motor agitator. The input variables were the trained model to predict by categorizing data in three levels high, middle, and low which the data came from human control the sugar boiler machine. The trained model for the future can be extended to make an experience meter to indicate the ability of workers to control the machine.
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Reinforcement learning applied to scrum team towards large-scale global optimization
(2019-02-02)
Nootyaskool, Supakit
;
Ounsrimuang, Pimolrat
Large-scale problems have size of problem over a thousand dimensions in finding a best solution that uses long computation times. In this work, we use an idea of scrum methodology that is a well-known in software development companies, to create an optimization algorithm. The scrum methodology describing about the team organization likes as rugby team management that player have expert in game. The proposed algorithm is developed based on concept of the evolutionary computation by this work added agent specifics in leaning environment of the problem. The specific of the agent is reinforcement learning by taking an action and getting reward. The proposed algorithm was experimented on a large-scale global optimization finding optimum point of numerical function, comparing between with and without reinforcement learning. The experiment result showed that the usage of reinforcement learning has good results.
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Introducing scrum process optimization
(2017-11-14)
Ounsrimuang, Pimolrat
;
Nootyaskool, Supakit
Scrum is a well-known methodology in software development describing as organization in a developer team. A scrum team is a small size with expert members, rich communication, sharing knowledge, self-organization, and self-planning. Scrum process in this version developed by an attempt to create a self-organization team, which an action-reward function integrated in the proposed algorithm. This paper proposed a scrum process creating an optimization algorithm in the class of evolutionary computation. The proposed algorithm experimented on 30 numerical functions by the benchmark coding from the CEC2017 competition problems. The experiment results indicate that uses action-reward function to organize operation planning can help the proposed algorithm finding the best result.
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Roulette wheel selection applied to PSO on numerical function in discrete and continuous space
(2016-07-22)
Ounsrimuang, Pimolrat
;
Nootyaskool, Supakit
Particle Swarm Optimization (PSO) successfully finds a solution as shown in various literatures. In some problems creating on discrete space, adjustment control-parameter may be difficult to modify a reach of optimum solution. The paper proposes an approach applying roulette wheel selection to PSO, which can help PSO escape from a local solution. This approach tested on both continuous and discrete space by finding solution of 12-numerical functions and an engineering-problem. The experiment result showed that the proposed technique can help PSO getting the best result both problem spaces, the performance improvement but also maintain easily to implementation.
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Optimization algorithm using scrum process
(2016-07-02)
Nootyaskool, Supakit
;
Ounsrimuang, Pimolrat
Scrum process is methodology for software development. Members in a scrum team have self-organizing team by planning and sharing knowledge. This paper introduces optimization algorithm using the population as scrum team doing the scrum process to find an optimum solution. The proposed algorithm maintains the level of exploration and the exploitation search by specific of the scrum-team. The experiment has compared the proposed approach with GA and PSO by finding an optimal solution of five numerical functions. The experiment result indicates that the proposed algorithm provides the best solution and finds the result quickly.
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Roulette wheel selection to encourage discrete Particle Swarm Optimization solving toll-keeper scheduling problem
(2015-12-30)
Ounsrimuang, Pimolrat
;
Nootyaskool, Supakit
Particle Swarm Optimization (PSO) has been proven to solve various applications by most applications using the real problem-space. In some specific problem, the discrete problem-space is selected on PSO that getting the solution result slowly by cause of sticky on local solution, and also the researcher cannot modify or difficult to understand how to improve the performance finding solution. The researcher many be tried to adjust velocity value by giving a new c1, c2 and weight to be a smaller or a larger value. This research proposed how to apply roulette wheel select to improve PSO on the discrete problem-space. Experiment tested the idea by a toll-keeper scheduling and a numerical function. Both problems created parameters inform discrete problem-space. The experiment result showed that PSO with roulette wheel selection taking the solution quickly.
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Mutual information rough sets feature selection and classification for microarray data analysis
(2014-01-01)
Ounsrimuang, Pimolrat
;
Boonjing, Veera
The feature selection (FS) techniques aim to reduce the subset size of an original data set, which are retained in the most useful information by selecting the most informative feature instead of irrelevant or redundant features. The benefits of FS for classification analysis can reduce the input data, improved predictive accuracy, learned knowledge is that easily understood, and reduced execution time. Many approaches based on rough set theory up to now, have operated the dependency function for measuring the goodness of the feature. However, there is not tolerance to noisy or inconsistency data, especially on high dimensional data microarray data sets. Moreover, mostly relevant information could be invisible by using only information from a positive region but neglecting a boundary region, mostly relevant may be invisible. Therefore, this paper proposes the maximal positive region and minimal boundary region criterion, based on rough set and mutual information, which use the different values among the information contained in the positive region, and the information contained in the boundary region. The experimental results indicate that our proposed method can increase the classification accuracy. © 2014 Pushpa Publishing House, Allahabad, India.
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The proposed algorithm for feature selection based on rough set and mutual information
(2014-01-01)
Ounsrimuang, Pimolrat
;
Boonjing, Veera
The feature selection approaches based on rough set theory aim to reduce the input data for improvement classification accuracy. Most existing approaches have concerned the discernibility relation to find the features, and have employed the dependency function for measuring the goodness of feature. The most relevant information cannot be visible by using information from discernibility relation only, so that neglecting indiscernibility relation, mostly relevant may be invisible. Moreover, their results are not tolerant to noisy or inconsistency data. Therefore, this paper proposes new algorithm based on rough set theory, which concerned both the discernibility and indiscernibility relations. The experimental results show that our approach gives higher classification accuracy than existing approaches. © 2014 Pushpa Publishing House, Allahabad, India.

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