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Journal articles on the topic 'OpenMDAO'

Author: Grafiati

Published: 30 May 2022

Last updated: 31 May 2022

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1

Chung, Hayoung, John T. Hwang, Justin S. Gray, and H. Alicia Kim. "Topology optimization in OpenMDAO." Structural and Multidisciplinary Optimization 59, no. 4 (February 14, 2019): 1385–400. http://dx.doi.org/10.1007/s00158-019-02209-7.

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2

Vidner, Olle, Robert Pettersson, Johan A. Persson, and Johan Ölvander. "MULTIDISCIPLINARY DESIGN OPTIMIZATION OF A MOBILE MINER USING THE OPENMDAO PLATFORM." Proceedings of the Design Society 1 (July 27, 2021): 2207–16. http://dx.doi.org/10.1017/pds.2021.482.

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AbstractThis paper proposes an optimization framework based on the OpenMDAO software library intended for engineer-to-order products and applies it to the conceptual design of a Mobile Miner. A Mobile Miner is a complex machine and a flexible alternative to Tunnel Boring Machines for small-scale tunneling and mining applications. The proposed framework is intended for use in early design and quotation stages with the objective to get fast estimates of important product characteristics, such as excavation rate and cutter lifetime. The ability to respond fast to customer requests is vital when offering customized products for specific applications and thereby to stay competitive on the global market. This is true for most engineer-to-order products and especially for mining equipment where each construction project is unique with different tunnel geometries and rock properties. The presented framework is applied to a specific use-case where the design of the miner's cutter wheel is in focus and a set of Pareto optimal designs are obtained. Furthermore, the framework extends the capabilities of OpenMDAO by including support for mixed-variable formulations and it supports an exploratory approach to design optimization.

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3

Gray, Justin S., John T. Hwang, Joaquim R. R. A. Martins, Kenneth T. Moore, and Bret A. Naylor. "OpenMDAO: an open-source framework for multidisciplinary design, analysis, and optimization." Structural and Multidisciplinary Optimization 59, no. 4 (March 1, 2019): 1075–104. http://dx.doi.org/10.1007/s00158-019-02211-z.

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4

Delbecq, Scott, Marc Budinger, and Aurélien Reysset. "Benchmarking of monolithic MDO formulations and derivative computation techniques using OpenMDAO." Structural and Multidisciplinary Optimization 62, no. 2 (March 11, 2020): 645–66. http://dx.doi.org/10.1007/s00158-020-02521-7.

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5

MIYAZAWA, Naoya, Toru TAKAHASHI, Masaki TANIGAWA, Hiroshi ISAKARI, and Toshiro MATSUMOTO. "Parametric optimisation for 3D acoustic problems using time-domain fast boundary element method and OpenMDAO." Proceedings of Conference of Tokai Branch 2020.69 (2020): 119. http://dx.doi.org/10.1299/jsmetokai.2020.69.119.

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6

He, Jiang-Zhou, Wen-Guang Chen, Guang-Ri Chen, Wei-Min Zheng, Zhi-Zhong Tang, and Han-Dong Ye. "OpenMDSP: Extending OpenMP to Program Multi-Core DSPs." Journal of Computer Science and Technology 29, no. 2 (March 2014): 316–31. http://dx.doi.org/10.1007/s11390-014-1433-x.

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7

Rapf, Dietmar. "Standards im Datenmanagement ASAM ODS und openMDM." ATZextra 21, S2 (May 2016): 32–39. http://dx.doi.org/10.1007/s35778-016-0005-9.

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8

Ramírez-Acosta, AlejandroA, MireyaS García-Vázquez, and Sunil Kumar. "Streaming Media Portability with the Emerging Support OpenMAX." IETE Technical Review 28, no. 2 (2011): 146. http://dx.doi.org/10.4103/0256-4602.76139.

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9

Barba, Jesus, David La Fuente, Fernando Rincon, Francisco Moya, and Juan Lopez. "Openmax hardware native support for efficient multimedia embedded systems." IEEE Transactions on Consumer Electronics 56, no. 3 (August 2010): 1722–29. http://dx.doi.org/10.1109/tce.2010.5606318.

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10

Wang, Chao, Bailing Wang, Yunxiao Sun, Yuliang Wei, Kai Wang, Hui Zhang, and Hongri Liu. "Intrusion Detection for Industrial Control Systems Based on Open Set Artificial Neural Network." Security and Communication Networks 2021 (August 18, 2021): 1–14. http://dx.doi.org/10.1155/2021/4027900.

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The security of industrial control systems (ICSs) has received a lot of attention in recent years. ICSs were once closed networks. But with the development of IT technologies, ICSs have become connected to the Internet, increasing the potential of cyberattacks. Because ICSs are so tightly linked to human lives, any harm to them could have disastrous implications. As a technique of providing protection, many intrusion detection system (IDS) studies have been conducted. However, because of the complicated network environment and rising means of attack, it is difficult to cover all attack classes, most of the existing classification techniques are hard to deploy in a real environment since they cannot deal with the open set problem. We propose a novel artificial neural network based-methodology to solve this problem. Our suggested method can classify known classes while also detecting unknown classes. We conduct research from two points of view. On the one hand, we use the openmax layer instead of the traditional softmax layer. Openmax overcomes the limitations of softmax, allowing neural networks to detect unknown attack classes. During training, on the other hand, a new loss function termed center loss is implemented to improve detection ability. The neural network model learns better feature representations with the combined supervision of center loss and softmax loss. We evaluate the neural network on NF-BoT-IoT-v2 and Gas Pipeline datasets. The experiments show our proposed method is comparable with the state-of-the-art algorithm in terms of detecting unknown classes. But our method has a better overall classification performance.

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11

Kayser, Hendrik, Tobias Herzke, Paul Maanen, Chaslav Pavlovic, and Volker Hohmann. "Open Master Hearing Aid (openMHA)—An integrated platform for hearing aid research." Journal of the Acoustical Society of America 146, no. 4 (October 2019): 2879. http://dx.doi.org/10.1121/1.5136988.

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12

Vázquez Gallego, F., J. Alonso-Zarate, C. Liss, and Ch Verikoukis. "OpenMAC: a new reconfigurable experimental platform for energy-efficient medium access control protocols." IET Science, Measurement & Technology 6, no. 3 (2012): 139. http://dx.doi.org/10.1049/iet-smt.2011.0069.

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13

Kayser, Hendrik, Tobias Herzke, Paul Maanen, Max Zimmermann, Giso Grimm, and Volker Hohmann. "Open community platform for hearing aid algorithm research: open Master Hearing Aid (openMHA)." SoftwareX 17 (January 2022): 100953. http://dx.doi.org/10.1016/j.softx.2021.100953.

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14

Zhao, Xin Ye, Ying Cai, Shan Liang Yang, and Ke Di Huang. "Lessons Learned from Design and Development of Military Scenario Definition Language Scenario Editing Toolset." Advanced Materials Research 748 (August 2013): 1041–45. http://dx.doi.org/10.4028/www.scientific.net/amr.748.1041.

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The Military Scenario Definition Language (MSDL) is an approved SISO standard for describing components of military scenarios that can be shared across a variety of modeling and simulation systems. However, the “last mile problem” for MSDL development is to have a user interface that represents information flowing to/from C2 and simulation systems. We have developed an open-source Toolset for this purpose: MSDL Scenario Editing Toolset (MSDLSET), providing an easy-to-use graphical user interface to MSDL developers that can serve as a surrogate input/output GUI or alternately to generating MSDL file. MSDLSET is developed using other open-source Tools: Xcentric's JaxFront and BBN's OpenMap. MSDLSET provides easy and efficient means for the end user to edit validate and add MSDL components to the MSDL file. Numerous initiatives are in progress to employ the new Toolset and to realize the benefits of exchanging scenarios files across diverse systems.

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15

Huang, Keju, Junan Yang, Pengjiang Hu, and Hui Liu. "A Novel Framework for Open-Set Authentication of Internet of Things Using Limited Devices." Sensors 22, no. 7 (March 30, 2022): 2662. http://dx.doi.org/10.3390/s22072662.

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The Internet of Things (IoT) is promising to transform a wide range of fields. However, the open nature of IoT makes it exposed to cybersecurity threats, among which identity spoofing is a typical example. Physical layer authentication, which identifies IoT devices based on the physical layer characteristics of signals, serves as an effective way to counteract identity spoofing. In this paper, we propose a deep learning-based framework for the open-set authentication of IoT devices. Specifically, additive angular margin softmax (AAMSoftmax) was utilized to enhance the discriminability of learned features and a modified OpenMAX classifier was employed to adaptively identify authorized devices and distinguish unauthorized ones. The experimental results for both simulated data and real ADS–B (Automatic Dependent Surveillance–Broadcast) data indicate that our framework achieved superior performance compared to current approaches, especially when the number of devices used for training is limited.

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16

de la Fuente, D., J. Barba, J. C. López, P. Peñil, H. Posadas, and P. Sánchez. "Synthesis of simulation and implementation code for OpenMAX multimedia heterogeneous systems from UML/MARTE models." Multimedia Tools and Applications 76, no. 6 (March 30, 2016): 8195–226. http://dx.doi.org/10.1007/s11042-016-3448-5.

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17

NOLAN, JAMES J., ARUN K. SOOD, and ROBERT SIMON. "DISCOVERING AND COMPOSING DISTRIBUTED ATOMIC AGENTS FOR IMAGERY AND GEOSPATIAL PROBLEM SOLVING." International Journal of Pattern Recognition and Artificial Intelligence 16, no. 08 (December 2002): 995–1019. http://dx.doi.org/10.1142/s021800140200212x.

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This paper describes our approach to building a scalable, flexible agent-based architecture for imagery and geospatial processing. Central to this approach is the agent discovery and composition mechanism which scales to support networks with thousands of agents. The agent architecture implements over 100 imagery and geospatial processing agents based on the Java Advanced Imaging and OpenMap(TM) APIs. The agents are distributed over a Jini enabled network, and communicate with one another via JavaSpaces. We discuss our "atomic" approach in this paper: developing low-level processing agents that are used by application of specific agents. We discuss several concepts in this approach: agent lookup and discovery through traditional information retrieval techniques, the ability to rapidly prototype agents based on commercial software products, and a knowledge management approach that reuses prior processing approaches and results. We present results demonstrating the scalability of our agent discovery and composition mechanism to compare them with other traditional discovery mechanisms, and demonstrate how the discovery mechanism scales to support thousands of agents.

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18

Mellado, Diego, Carolina Saavedra, Steren Chabert, Romina Torres, and Rodrigo Salas. "Self-Improving Generative Artificial Neural Network for Pseudorehearsal Incremental Class Learning." Algorithms 12, no. 10 (October 1, 2019): 206. http://dx.doi.org/10.3390/a12100206.

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Deep learning models are part of the family of artificial neural networks and, as such, they suffer catastrophic interference when learning sequentially. In addition, the greater number of these models have a rigid architecture which prevents the incremental learning of new classes. To overcome these drawbacks, we propose the Self-Improving Generative Artificial Neural Network (SIGANN), an end-to-end deep neural network system which can ease the catastrophic forgetting problem when learning new classes. In this method, we introduce a novel detection model that automatically detects samples of new classes, and an adversarial autoencoder is used to produce samples of previous classes. This system consists of three main modules: a classifier module implemented using a Deep Convolutional Neural Network, a generator module based on an adversarial autoencoder, and a novelty-detection module implemented using an OpenMax activation function. Using the EMNIST data set, the model was trained incrementally, starting with a small set of classes. The results of the simulation show that SIGANN can retain previous knowledge while incorporating gradual forgetfulness of each learning sequence at a rate of about 7% per training step. Moreover, SIGANN can detect new classes that are hidden in the data with a median accuracy of 43 % and, therefore, proceed with incremental class learning.

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19

Barre, Arnaud, Fabien Leboeuf, and Stéphane Armand. "OpenMA : une nouvelle bibliothèque open source pour homogénéiser et faciliter le traitement des données de l’analyse quantifiée de la marche." Neurophysiologie Clinique/Clinical Neurophysiology 46, no. 4-5 (November 2016): 243–44. http://dx.doi.org/10.1016/j.neucli.2016.09.018.

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20

Darvishi, M., and G. Ahmadi. "Validation techniques of agent based modelling for geospatial simulations." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-2/W3 (October 22, 2014): 91–95. http://dx.doi.org/10.5194/isprsarchives-xl-2-w3-91-2014.

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One of the most interesting aspects of modelling and simulation study is to describe the real world phenomena that have specific properties; especially those that are in large scales and have dynamic and complex behaviours. Studying these phenomena in the laboratory is costly and in most cases it is impossible. Therefore, Miniaturization of world phenomena in the framework of a model in order to simulate the real phenomena is a reasonable and scientific approach to understand the world. Agent-based modelling and simulation (ABMS) is a new modelling method comprising of multiple interacting agent. They have been used in the different areas; for instance, geographic information system (GIS), biology, economics, social science and computer science. The emergence of ABM toolkits in GIS software libraries (e.g. ESRI’s ArcGIS, OpenMap, GeoTools, etc) for geospatial modelling is an indication of the growing interest of users to use of special capabilities of ABMS. Since ABMS is inherently similar to human cognition, therefore it could be built easily and applicable to wide range applications than a traditional simulation. But a key challenge about ABMS is difficulty in their validation and verification. Because of frequent emergence patterns, strong dynamics in the system and the complex nature of ABMS, it is hard to validate and verify ABMS by conventional validation methods. Therefore, attempt to find appropriate validation techniques for ABM seems to be necessary. In this paper, after reviewing on Principles and Concepts of ABM for and its applications, the validation techniques and challenges of ABM validation are discussed.

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21

Vavitsas, Konstantinos. "OpenMTA, a paradigm shift in exchanging biological material." Synthetic Biology 3, no. 1 (January 1, 2018). http://dx.doi.org/10.1093/synbio/ysy021.

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22

"OpenMA: A new open source library for working and altering the Conventional Gait Model." Gait & Posture 49 (September 2016): 260. http://dx.doi.org/10.1016/j.gaitpost.2016.07.314.

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23

Pollak, Bernardo, Tamara Matute, Isaac Nuñez, Ariel Cerda, Constanza Lopez, Valentina Vargas, Anton Kan, et al. "Universal loop assembly: open, efficient and cross-kingdom DNA fabrication." Synthetic Biology 5, no. 1 (January 1, 2020). http://dx.doi.org/10.1093/synbio/ysaa001.

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Abstract Standardized type IIS DNA assembly methods are becoming essential for biological engineering and research. These methods are becoming widespread and more accessible due to the proposition of a ‘common syntax’ that enables higher interoperability between DNA libraries. Currently, Golden Gate (GG)-based assembly systems, originally implemented in host-specific vectors, are being made compatible with multiple organisms. We have recently developed the GG-based Loop assembly system for plants, which uses a small library and an intuitive strategy for hierarchical fabrication of large DNA constructs (>30 kb). Here, we describe ‘universal Loop’ (uLoop) assembly, a system based on Loop assembly for use in potentially any organism of choice. This design permits the use of a compact number of plasmids (two sets of four odd and even vectors), which are utilized repeatedly in alternating steps. The elements required for transformation/maintenance in target organisms are also assembled as standardized parts, enabling customization of host-specific plasmids. Decoupling of the Loop assembly logic from the host-specific propagation elements enables universal DNA assembly that retains high efficiency regardless of the final host. As a proof-of-concept, we show the engineering of multigene expression vectors in diatoms, yeast, plants and bacteria. These resources are available through the OpenMTA for unrestricted sharing and open access.

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24

Wong, Alexander, Jack Lu, Adam Dorfman, Paul McInnis, Mahmoud Famouri, Daniel Manary, James Ren Hou Lee, and Michael Lynch. "Fibrosis-Net: A Tailored Deep Convolutional Neural Network Design for Prediction of Pulmonary Fibrosis Progression From Chest CT Images." Frontiers in Artificial Intelligence 4 (November 3, 2021). http://dx.doi.org/10.3389/frai.2021.764047.

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Pulmonary fibrosis is a devastating chronic lung disease that causes irreparable lung tissue scarring and damage, resulting in progressive loss in lung capacity and has no known cure. A critical step in the treatment and management of pulmonary fibrosis is the assessment of lung function decline, with computed tomography (CT) imaging being a particularly effective method for determining the extent of lung damage caused by pulmonary fibrosis. Motivated by this, we introduce Fibrosis-Net, a deep convolutional neural network design tailored for the prediction of pulmonary fibrosis progression from chest CT images. More specifically, machine-driven design exploration was leveraged to determine a strong architectural design for CT lung analysis, upon which we build a customized network design tailored for predicting forced vital capacity (FVC) based on a patient’s CT scan, initial spirometry measurement, and clinical metadata. Finally, we leverage an explainability-driven performance validation strategy to study the decision-making behavior of Fibrosis-Net as to verify that predictions are based on relevant visual indicators in CT images. Experiments using a patient cohort from the OSIC Pulmonary Fibrosis Progression Challenge showed that the proposed Fibrosis-Net is able to achieve a significantly higher modified Laplace Log Likelihood score than the winning solutions on the challenge. Furthermore, explainability-driven performance validation demonstrated that the proposed Fibrosis-Net exhibits correct decision-making behavior by leveraging clinically-relevant visual indicators in CT images when making predictions on pulmonary fibrosis progress. Fibrosis-Net is able to achieve a significantly higher modified Laplace Log Likelihood score than the winning solutions on the OSIC Pulmonary Fibrosis Progression Challenge, and has been shown to exhibit correct decision-making behavior when making predictions. Fibrosis-Net is available to the general public in an open-source and open access manner as part of the OpenMedAI initiative. While Fibrosis-Net is not yet a production-ready clinical assessment solution, we hope that its release will encourage researchers, clinicians, and citizen data scientists alike to leverage and build upon it.

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