Equivox: An Example of Adaptation Using an Artificial Neural Network on a Case-Based Reasoning Platform

Henriet, Julien; Chebel-Morello, Brigitte; Salomon, Michel; Farah, Jad; Laurent, R.; Sauget, Marc; Broggio, David; Franck, Didier; Makovicka, Libor

doi:10.4015/s1016237213500270

Cited by 4 publications

(4 citation statements)

References 26 publications

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“…However, the parametric design is a complex problem when there are massive parameters in the process of design, and the utilization of classical rule-based adaptation method in this situation demands a significant knowledge engineering effort to capture abundant adaptation rules. This prompted some studies to research machine learning-based adaptation under k-NN principle, and several learning methods have been employed in this area, for example, neural networks, [19][20][21][22]36,37 SVR, 38 genetic algorithm, 12,[14][15][16] and partial-order planning. 39 But insufficient knowledge badly affects the selection of an appropriate machine learning algorithm and its performance in featureoriented adaptation.…”

Section: Case Adaptation In Cbdmentioning

confidence: 99%

Integrating multiple adaptation results by utilization of support vector regression in case-based mechanical product design

2018

Advances in Mechanical Engineering

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Case adaptation is crucial for a good and reasonable case-based design which is a common method in computer-aided design, because the solution of old case is not always the exact answer for the encountered new designing problem. Recently, a statistical feature-oriented adaptation method in the principle of k-nearest neighbors has been employed widely in case-based design because of its easily understandable mechanism for designers, but its adaptation accuracy is relatively low compared with knowledge-intensive method. This article presents a new method by integrating with multiple adaptation values from statistical feature-oriented adaptation methods to improve the adaptation accuracy. First, two simple statistical feature-oriented adaptation methods including mean and median approaches and other four statistical feature-oriented adaptation methods based on Euclidean distance, Manhattan distance, Gaussian transformation, and gray coefficient are used as individual statistical feature-oriented adaptation mode in this article, and each statistical feature-oriented adaptation method generates pre-adapting value under k-nearest neighbor. Then, support vector regression is utilized to make a combined adaptation which takes pre-adapting values as its inputs, and the new multiple statistical feature-oriented adaptation method integration based on support vector regression is named as MSFA-SVR. Furthermore, to validate the feasibility and superiority of MSFA-SVR, it was applied to the power transformer design and was compared with the classical statistical feature-oriented adaptation methods. Empirical comparison results indicated that MSFA-SVR achieves the better adaptation performance under k-nearest neighbor than other statistical feature-oriented adaptation methods in terms of adaptation accuracy.

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Section: Case Adaptation In Cbdmentioning

confidence: 99%

Integrating multiple adaptation results by utilization of support vector regression in case-based mechanical product design

2018

Advances in Mechanical Engineering

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“…Using Artificial Neural Networks (ANN), we proposed two methods: for the simulation of pulmonary movement [11], and for the adaptation of the IRSN (French radiation protection and nuclear safety institute) phantoms [16,17]. These two projects aim at developing adapted tools for a customized and accurate modelization of the human body, either in a radiation protection context (anthroporadiametry), or for delivering external beam treatment more accurately.…”

Section: Introductionmentioning

confidence: 99%

Development of a 4D numerical chest phantom with customizable breathing

et al. 2016

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Respiratory movement information is useful for radiation therapy, and is generally obtained using 4D scanners (4DCT). In the interest of patient safety, reducing the use of 4DCT could be a significant step in reducing radiation exposure, the effects of which are not well documented. The authors propose a customized 4D numerical phantom representing the organ contours. Firstly, breathing movement can be simulated and customized according to the patient's anthroporadiametric data. Using learning sets constituted by 4D scanners, artificial neural networks can be trained to interpolate the lung contours corresponding to an unknown patient, and then to simulate its respiration. Lung movement during the breathing cycle is modeled by predicting the lung contours at any respiratory phases. The interpolation is validated comparing the obtained lung contours with 4DCT via Dice coefficient. Secondly, a preliminary study of cardiac and œsophageal motion is also presented to demonstrate the flexibility of this approach. The application may simulate the position and volume of the lungs, the œsophagus and the heart at every phase of the respiratory cycle with a good accuracy: the validation of the lung modeling gives a Dice index greater than 0.93 with 4DCT over a breath cycle.

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“…EquiVox is a tool implementing artificial intelligence concepts in order to find and customize numerical phantoms of human beings [1]. This paper aims at presenting and analyzing the different improvements which have been made in this tool.…”

Section: Introductionmentioning

confidence: 99%

“…This phantom is then automatically adapted by the system after the retrieval phase. The adaptation phase implements an ANN [1,4] : the target case coordinates of each point of each organ contour is interpolated by taking into account the coordinates of the same point on the closest phantom and having the same size as that of the target case. The adapted phantom is then revised by the expert, then capitalized.…”

Section: Introductionmentioning

confidence: 99%

A Self-Adaptable Distributed CBR Version of the Equivox System

Henriet

Lang

Pottayya

et al. 2016

Biomed. Eng. Appl. Basis Commun.

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Three dimensional (3D) voxel phantoms are numerical representations of human bodies, used by physicians in very different contexts. In the controlled context of hospitals, where from 2 to 10 subjects may arrive per day, phantoms are used to verify computations before therapeutic exposure to radiation of cancerous tumors. In addition, 3D phantoms are used to diagnose the gravity of accidental exposure to radiation. In such cases, there may be from 10 to more than 1000 subjects to be diagnosed simultaneously. In all of these cases, computation accuracy depends on a single such representation. In this paper, we present EquiVox which is a tool composed of several distributed functions and enables to create, as quickly and as accurately as possible, 3D numerical phantoms that fit anyone, whatever the context. It is based on a multi-agent system. Agents are convenient for this kind of structure, they can interact together and they may have individual capacities. In EquiVox, the phantoms adaptation is a key phase based on artificial neural network (ANN) interpolations. Thus, ANNs must be trained regularly in order to take into account newly capitalized subjects and to increase interpolation accuracy. However, ANN training is a time-consuming process. Consequently, we have built Equivox to optimize this process. Thus, in this paper, we present our architecture, based on agents and ANN, and we put the stress on the adaptation module. We propose, next, some experimentations in order to show the efficiency of the EquiVox architecture.

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Equivox: An Example of Adaptation Using an Artificial Neural Network on a Case-Based Reasoning Platform

Cited by 4 publications

References 26 publications

Integrating multiple adaptation results by utilization of support vector regression in case-based mechanical product design

Integrating multiple adaptation results by utilization of support vector regression in case-based mechanical product design

Development of a 4D numerical chest phantom with customizable breathing

A Self-Adaptable Distributed CBR Version of the Equivox System

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