| 1. |
- Norbury, John W., et al.
(författare)
-
Are Further Cross Section Measurements Necessary for Space Radiation Protection or Ion Therapy Applications? Helium Projectiles
- 2020
-
Ingår i: Frontiers in Physics. - : Frontiers Media SA. - 2296-424X. ; 8
-
Forskningsöversikt (refereegranskat)abstract
- The helium ((Formula presented.) He) component of the primary particles in the galactic cosmic ray spectrum makes significant contributions to the total astronaut radiation exposure. (Formula presented.) He ions are also desirable for direct applications in ion therapy. They contribute smaller projectile fragmentation than carbon ((Formula presented.) C) ions and smaller lateral beam spreading than protons. Space radiation protection and ion therapy applications need reliable nuclear reaction models and transport codes for energetic particles in matter. Neutrons and light ions ((Formula presented.) H, (Formula presented.) H, (Formula presented.) H, (Formula presented.) He, and (Formula presented.) He) are the most important secondary particles produced in space radiation and ion therapy nuclear reactions; these particles penetrate deeply and make large contributions to dose equivalent. Since neutrons and light ions may scatter at large angles, double differential cross sections are required by transport codes that propagate radiation fields through radiation shielding and human tissue. This work will review the importance of (Formula presented.) He projectiles to space radiation and ion therapy, and outline the present status of neutron and light ion production cross section measurements and modeling, with recommendations for future needs.
|
|
| 2. |
- Romeo, Luca, et al.
(författare)
-
An Innovative Design Support System for Industry 4.0 Based on Machine Learning Approaches
- 2018
-
Ingår i: 2018 5TH INTERNATIONAL SYMPOSIUM ON ENVIRONMENT-FRIENDLY ENERGIES AND APPLICATIONS (EFEA). - : IEEE. - 9781538655177
-
Konferensbidrag (refereegranskat)abstract
- Electric machines together with power electronic converters are the major components in industrial and automotive applications. The frequent situation in the engineering practice is that designers, final or intermediate users have to roughly estimate some basic performance data or specification data or other metrics related to the specific task they have, on the basis of few data available at a particular instant of time or at the time of use. This paper addresses this problem in the Industry 4.0 scenario by introducing innovative Design support system (DesSS), originated from the Decision Support System (DSS), for the prediction and estimation of machine specification data such as machine geometry and machine design on the basis of other heterogeneous parameters (i.e. motor performance, field of application, geographic market, and range of cost). For the development of the DesSS different machine learning techniques were compared such as Decision/Regression Tree (DT/RT), Nearest Neighbors (NN), and Neighborhood Component Features Selection (NCFS). Experimental results obtained on the real use case demonstrated the appropriateness of the application of the machine learning approaches as the main core of the DesSS used for the estimation of the machine parameters. In particular, the results show high reliability in terms of accuracy and macro-F1 score of the 1-NN+NCFS and RT for solving respectively the classification and regression task. This approach can viably replace the model-based tools used for the parameters prediction, being it more accurate and with higher computational speed.
|
|
| 3. |
- Romeo, Luca, et al.
(författare)
-
Machine learning-based design support system for the prediction of heterogeneous machine parameters in industry 4.0
- 2020
-
Ingår i: Expert systems with applications. - : PERGAMON-ELSEVIER SCIENCE LTD. - 0957-4174 .- 1873-6793. ; 140
-
Tidskriftsartikel (refereegranskat)abstract
- In the engineering practice, it frequently occurs that designers, final or intermediate users have to roughly estimate some basic performance or specification data on the basis of input data available at the moment, which can be time-consuming. There is the need for a tool that will fill the missing gap in the optimization problems in engineering design processes, by making use of the advances in the artificial intelligence field. This paper aims to fill this gap by introducing an innovative Design Support System (DesSS), originated from the Decision Support System, for the prediction and estimation of machine specification data such as machine geometry and machine design on the basis of heterogeneous input parameters. As the main core of the developed DesSS, we introduced different machine learning (ML) approaches based on Decision/Regression Tree, k-Nearest Neighbors, and Neighborhood Component Features Selection. Experimental results obtained on a real use case and using two different real datasets demonstrated the reliability and the effectiveness of the proposed approach. The innovative machine learning-based DesSS meant for supporting the designing choice, can bring various benefits such as the easier decision-making, conservation of the company's knowledge, savings in man-hours, higher computational speed and accuracy.
|
|
