Classifier-assisted constrained evolutionary optimization for automated geometry selection of orthodontic retraction spring

Lim, Dudy; Ong, Yew-Soon; Setiawan, Rachman; Idris, Muhammad

doi:10.1109/cec.2010.5586460

Cited by 10 publications

(4 citation statements)

References 21 publications

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“…On the other hand, if computationally expensive realworld problems, such as parametric engineering design (Saridakis and Dentsoras 2009), are handled, and the optimisation framework has to be made much more efficient, techniques of the surrogate-assisted evolutionary algorithm (SAEA) (Sastry, Goldberg, and Pelikan 2001;Jin 2003;Lim, Jin, Ong, and Sendhoff 2010a;Lim, Ong, Setiawan, and Idris 2010b) may be adopted and utilised.…”

Section: Discussionmentioning

confidence: 99%

Inductive linkage identification on building blocks of different sizes and types

Chen

Chuang

Yuan-wei

2012

International Journal of Systems Science

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The goal of linkage identification is to obtain the dependencies among decision variables. Such information or knowledge can be applied to design crossover operators and/or the encoding schemes in genetic and evolutionary methods. Thus, promising sub-solutions to the problem will be disrupted less likely, and successful convergence may be achieved more likely. To obtain linkage information, a linkage identification technique, called Inductive Linkage Identification (ILI), was proposed recently. ILI was established upon the mechanism of perturbation and the idea of decision tree learning. By constructing a decision tree according to decision variables and fitness difference values, the interdependent variables will be determined by the adopted decision tree learning algorithm. In this article, we aim to acquire a better understanding on the characteristics of ILI, especially its behaviour under problems composed of different-sized and different-type building blocks (BBs) which are not overlapped. Experiments showed that ILI can efficiently handle BBs of different sizes and is insensitive to BB types. Our experimental observations indicate the flexibility and the applicability of ILI on various elementary BB types that are commonly adopted in related experiments.

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Section: Discussionmentioning

confidence: 99%

Inductive linkage identification on building blocks of different sizes and types

Chen

Chuang

Yuan-wei

2012

International Journal of Systems Science

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“…[42] Compared to the selections made by eight expert orthodontists, the system correctly identified the appropriate headgears 95.6% of the time. Recently, decision support systems were developed to determine the geometry of orthodontic springs used to close extraction spaces [43] and to determine the forces needed to align teeth, [44] but neither system has been applied clinically.…”

Section: Decision Treesmentioning

confidence: 99%

Applications of artificial intelligence and machine learning in orthodontics

Asiri

Tadlock

Schneiderman

et al. 2020

APOS

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Over the past two decades, artificial intelligence (AI) and machine learning (ML) have undergone considerable development. There have been various applications in medicine and dentistry. Their application in orthodontics has progressed slowly, despite promising results. The available literature pertaining to the orthodontic applications of AI and ML has not been adequately synthesized and reviewed. This review article provides orthodontists with an overview of AI and ML, along with their applications. It describes state-of-the-art applications in the areas of orthodontic diagnosis, treatment planning, growth evaluations, and in the prediction of treatment outcomes. AI and ML are powerful tools that can be utilized to overcome some of the clinical problems that orthodontists face daily. With the availability of more data, better AI and ML systems should be expected to be developed that will help orthodontists practice more efficiently and improve the quality of care.

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“…In this report, a mixed numerical-experimental approach to the design methodology is formulated and demonstrated for designing composite brakes for train and motorcycles. It is called Knowledge-Based Design methodology as has been used for various application, such as composite plate mechanical property characterization [5], and design of orthodontic retraction springs [6]. Similar design methodology was also developed independently by Wang et al, that is called Metamodel-Based Design Optimization (MBDO) [7].…”

Section: Introductionmentioning

confidence: 99%

Design of Composite Brakes Using Knowledge-Based Design Methodology

Setiawan

Yulianto

2012

AMR

Self Cite

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In designing modern friction materials that are normally made of composite materials, a more systematical approach is needed in order to select the most appropriate composition of raw materials for specific applications. Here, knowledge-based design methodology has been formulated for composite brake design application, that consists of: design database generation from experiments, metamodelling, and global optimization processes. The objective function has incorporated, among others, the uncertainties introduced in the experimentally-obtained design database, so that providing the optmization results with the information of variation. The application of the methodology has been demonstrated to two cases, i.e. train brake and motorcycle brake pad, with the optimization results close to the targetted values and the information of output variation, as intended.

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Classifier-assisted constrained evolutionary optimization for automated geometry selection of orthodontic retraction spring

Cited by 10 publications

References 21 publications

Inductive linkage identification on building blocks of different sizes and types

Inductive linkage identification on building blocks of different sizes and types

Applications of artificial intelligence and machine learning in orthodontics

Design of Composite Brakes Using Knowledge-Based Design Methodology

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