@article {,
	title = {Woodwind Instrument Design Optimization based on Impedance Characteristics with Geometric Constraints},
	journal = {Journal of the Acoustical Society of America},
	volume = {148},
	year = {2020},
	month = {11/2020},
	chapter = {2864},
	abstract = {<p>Computational optimization algorithms coupled with acoustic models of wind instruments provide instrument makers with an opportunity to explore new designs. Specifically, they enable the automatic discovery of geometries exhibiting desired resonance characteristics. In this paper, the design optimization of woodwind instruments with complex geometrical features (e.g., non-cylindrical bore profile and side holes with various radii and chimney heights) is investigated. Optimal geometric designs are searched so that their acoustic input impedance has peaks with specific target frequencies and amplitudes. However, woodwind instruments exhibit complex input impedance whose features, such as resonances, might have non-smooth evolution with respect to design variables, thus hampering gradient-based optimization. For this reason, this paper introduces new formulations of the impedance characteristics (resonance frequencies and amplitudes) using a regularized unwrapped angle of the reflection function. The approach is applied to an illustrative instrument subjected to geometric constraints similar to the ones encountered by manufacturers (a key-less pentatonic clarinet with two-registers). Three optimization problems are considered, demonstrating a strategy to simultaneously adjust several impedance characteristics on all fingerings.</p>
},
	doi = {10.1121/10.0002449},
	url = {https://doi.org/10.1121/10.0002449},
	author = {Augustin Ernoult and Christophe Vergez and Samy Missoum and Philippe Guillemain and Michael Jousserand}
}
@conference {,
	title = {Comment l{\textquoteright}optimisation peut-elle {\^e}tre une aide {\`a} la facture instrumentale ?},
	booktitle = {Congr{\`e}s Fran{\c c}ais d{\textquoteright}Acoustique},
	year = {2018},
	address = {Le Havre, France},
	author = {Ernoult, Augustin and Missoum, Samy and Jousserand, Michael and Guillemain, Phillipe and Vergez, Christophe and Sanchez, Patrick}
}
@article {jiang2015fusion,
	title = {Fusion of Clinical and Stochastic Finite Element Data for Hip Fracture Risk Prediction},
	journal = {Journal of Biomechanics},
	volume = {48},
	year = {2015},
	month = {26 November 2015},
	pages = {4043{\textendash}4052},
	doi = {doi: 10.1016/j.jbiomech.2015.09.044},
	author = {Jiang, Peng and Missoum, Samy and Chen, Zhao}
}
@conference {jiang2015hybrid,
	title = {A Hybrid Risk Model for Hip Fracture Prediction Using Clinical and Stochastic Finite Element Data},
	booktitle = {Summer Biomechanics, Bioengineering, and Biotransport Conference, Snowbird, Utah, June 17-20,},
	year = {2015},
	month = {06/2015},
	doi = {10150/579112},
	url = {http://codes.arizona.edu/sites/default/files/pdf/Jiang2015a.pdf},
	author = {Jiang, Peng and Missoum, Samy and Chen, Zhao}
}
@conference {jiang2014hip,
	title = {Hip Fracture Prediction Using Finite Element Modeling and Machine Learning},
	booktitle = {7th World Congress of Biomechanics, Boston, MA, Jul. 6 - 11},
	year = {2014},
	month = {07/2014},
	url = {http://codes.arizona.edu/sites/default/files/pdf/Jiang2014a.pdf},
	author = {Jiang, Peng and Chen, Zhao and Missoum, Samy}
}
@article {jiang2014optimal,
	title = {Optimal SVM Parameter Selection for Non-separable and Unbalanced Datasets},
	journal = {Structural and Multidisciplinary Optimization},
	volume = {50},
	year = {2014},
	month = {10/2014},
	pages = {523-535},
	abstract = {<p>This article presents a study of three validation metrics used for the selection of optimal parameters of a support vector machine (SVM) classifier in the case of nonseparable and unbalanced datasets. This situation is often encountered when the data is obtained experimentally or clinically. The three metrics selected in this work are the area under the ROC curve (AUC), accuracy, and balanced accuracy. These validation metrics are tested using computational data only, which enables the creation of fully separable sets of data. This way, non-separable datasets, representative of a real-world problem, can be created by projection onto a lower dimensional sub-space. The knowledge of the separable dataset, unknown in real-world problems, provides a reference to compare the three validation metrics using a quantity referred to as the {\textquotedblleft}weighted likelihood{\textquotedblright}. As an application example, the study investigates a classification model for hip fracture prediction. The data is obtained from a parameterized finite element model of a femur. The performance of the various validation metrics is studied for several levels of separability, ratios of unbalance, and training set sizes.</p>
},
	keywords = {Cross validation, Non-separable and unbalanced datasets, Support vector machines, Validation metrics},
	doi = {10.1007/s00158-014-1105-z},
	url = {http://codes.arizona.edu/sites/default/files/pdf/Jiang2014.pdf},
	author = {Jiang, Peng and Missoum, Samy and Chen, Zhao}
}
@conference {jiang2013estimation,
	title = {Estimation of Probability of Failure with Dependent Variables using Copulas and Support Vector Machines},
	booktitle = {Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 8: 22nd Reliability, Stress Analysis, and Failure Prevention Conference},
	year = {2013},
	publisher = {American Society of Mechanical Engineers},
	organization = {American Society of Mechanical Engineers},
	address = {Portland, OR},
	doi = {10.1115/DETC2013-13383},
	url = {https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings-abstract/IDETC-CIE2013/55997/V008T12A007/256675},
	author = {Jiang, Peng and Missoum, Samy}
}
@conference {jiang2013optimal,
	title = {Optimal Parameter Selection of an SVM Model: Application to Hip Fracture Risk Prediction},
	booktitle = {AIAA-2013-1935. Proceedings of the 54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, Boston, MA, April 8-11},
	year = {2013},
	doi = {10.2514/6.2013-1935},
	url = {http://codes.arizona.edu/sites/default/files/pdf/Jiang2013a.pdf},
	author = {Jiang, Peng and Missoum, Samy and Hu, Chengcheng and Chen, Zhao}
}
@conference {missoum2013towards,
	title = {Towards Hip Fracture Prediction Using Finite Element Analysis and Machine Learning},
	booktitle = {Poster presentation. 2013 Annual meeting of the American Society for Bone and Mineral Research (ASBMR)},
	year = {2013},
	address = { Baltimore, MD},
	url = {http://codes.arizona.edu/sites/default/files/pdf/Jiang2013b.pdf},
	author = {Missoum, Samy and Jiang, Peng and Hu, Chengcheng and Jennifer, Nicholas and Chen, Zhao}
}
@conference {chen2012incorporation,
	title = {The Incorporation of Support Vector Machines and Hip Geometric Structure Assessments in the Development of Hip Fracture Risk Prediction Model},
	booktitle = {2012 Annual meeting of the American Society for Bone and Mineral Research (ASBMR)},
	year = {2012},
	url = {http://codes.arizona.edu/sites/default/files/pdf/Jiang2012.pdf},
	author = {Chen, Zhao and Jiang, Peng and Hu, Chengcheng and Arendell, Leslie and Robbins, John and Missoum, Samy}
}
@conference {basudhar2011reliability,
	title = {Reliability Assessment with Correlated Variables Using Support Vector Machines},
	booktitle = {Proceedings of the 52nd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference},
	year = {2011},
	doi = {10.2514/6.2011-1905},
	url = {http://codes.arizona.edu/sites/default/files/pdf/Basudhar2011.pdf},
	author = {Jiang, Peng and Basudhar, Anirban and Missoum, Samy}
}
