High Performance SiGe HBT Performance Variability Learning by Utilizing Neural Networks and Technology Computer Aided Design

2020 ◽  
Vol 98 (5) ◽  
pp. 127-134
Author(s):  
Henry Lee Aldridge ◽  
Jeffrey B Johnson ◽  
Rajendran Krishnasamy ◽  
Vibhor Jain ◽  
Rahul Mishra ◽  
...  
Keyword(s):  
Neural Networks ◽  
Computer Aided Design ◽  
High Performance ◽  
Sige Hbt ◽  
Performance Variability ◽  
Computer Aided ◽  
Aided Design

High Performance SiGe HBT Performance Variability Learning by Utilizing Neural Networks and Technology Computer Aided Design

2020 ◽  
Vol MA2020-02 (24) ◽  
pp. 1699-1699
Author(s):  
Henry Lee Aldridge ◽  
Jeffrey B Johnson ◽  
Rajendran Krishnasamy ◽  
Vibhor Jain ◽  
Rahul Mishra ◽  
...  
Keyword(s):  
Neural Networks ◽  
Computer Aided Design ◽  
High Performance ◽  
Sige Hbt ◽  
Performance Variability ◽  
Computer Aided ◽  
Aided Design

Intelligent Automated Welding for Shipyard Applications

1992 ◽  
Vol 8 (02) ◽  
pp. 77-88
Author(s):  
S. Madden ◽  
H. H. Vanderveldt ◽  
J. Jones
Keyword(s):  
Neural Networks ◽  
Computer Technology ◽  
Computer Aided Design ◽  
Data Systems ◽  
Fully Integrated ◽  
Computer Aided Process Planning ◽  
Computer Aided ◽  
Process Operation ◽  
Aided Design ◽  
Computerized System

Computer Aided Process Planning (CAPP) integrated with Computer Aided Design (CAD) and Computer Aided Manufacturing (CAM) will form the basis of engineering/planning systems of the future. These systems will have the capability to operate in a paperless environment and provide highly optimized process operation plans. The WELDEXCELL System is a prototype of such a system for welding in shipyards. The paper discusses three significant computer technology advances which have been in into the WELDEXCELL prototype. First is a computerized system for allowing multiple knowledge sources (expert systems, humans, data systems, etc.) to work together to solve a common problem (the weld plan). This system is called a "blackboard." The second is a methodology for the blackboard to communicate to the human user. This interface includes full interactive graphics fully integrated to CAD as well as data searches and automatic completion of routine engineering tasks. The third is artificial neural networks (ANS's), which are based on biological neural networks (such as the human brain) and which can do neural reasoning tasks about difficult problems. ANS's offer the opportunity to model highly complex multivariable and nonlinear processes (for example, welding) and provide a means for an engineer to quantitatively assess the process and its operation.

scholarly journals Computer-aided design and Computer-aided engineering

2018 ◽  
Vol 170 ◽  
pp. 01115 ◽  
Author(s):  
Alexander Kolbasin ◽  
Oksana Husu
Keyword(s):  
Computer Aided Design ◽  
High Performance ◽  
New Technologies ◽  
Production Systems ◽  
Computer Aided Engineering ◽  
Qualified Personnel ◽  
Computer Aided ◽  
Major Factors ◽  
Engineering Modeling ◽  
Aided Design

In modern industrial production some of the major factors of successful development include: cost reduction of the production, im-provement of its quality, as well as help to minimise the time in market en-try. Computer-aided design and Computer-aided engineering (CAD / CAE - systems) are the most effective for implementation of these requirements. Possible use of this engineering modeling simulation in conjunction with the power and speed of high performance computing could reduce costs and time of each cycle of designing, and also significantly reduce devel-opment time. The introduction of new technologies, the use of high quality products and engagement of qualified personnel would allow businesses and organizations to get on a path of innovative development of design and production systems.

The Research on Automatic Liquor Blending Systems Based on Wavelet Neural Networks

2013 ◽  
Vol 765-767 ◽  
pp. 1019-1022
Author(s):  
Lian Jun Hu ◽  
Xiao Hui Zeng ◽  
Hong Song ◽  
Qian Li
Keyword(s):  
Neural Networks ◽  
Computer Aided Design ◽  
Mathematic Model ◽  
Wavelet Neural Networks ◽  
Time Frequency ◽  
The Neural Network ◽  
Computer Aided ◽  
Time Frequency Localization ◽  
Aided Design ◽  
Self Learning

The blending of liquors is a key process in the production of liquors. According to time-frequency localization characteristics of the wavelet transform and advantages of the neural network such as ability to develop, fault-tolerance, self-adaptability, self-learning, and robustness, a mathematic model based on wavelet neural networks is proposed in liquor blending processes with the help of computer-aided design technologies, which makes liquor blending technologies more scientific.

scholarly journals Computer aided production process design work methods

Mechanik ◽  
2017 ◽  
Vol 90 (8-9) ◽  
pp. 805-807
Author(s):  
Izabela Rojek
Keyword(s):  
Neural Networks ◽  
Expert System ◽  
Production Process ◽  
Process Design ◽  
Computer Aided Design ◽  
Design Work ◽  
Production Processes ◽  
Computer Aided ◽  
Research Procedure ◽  
Aided Design

The article presents the computer aided design methods as applied for arrangement of production processes in the range from the simplest to the most advanced ones. The idea behind the research procedure as conducted by the author was to develop a method, models and expert system that would resemble a human expert in the field. This goal was achieved using neural networks.

Neural Networks for Modeling the Contact Foot-Shoe Upper

2010 ◽  
pp. 561-573
Author(s):  
M. J. Rupérez ◽  
J. D. Martín ◽  
C. Monserrat ◽  
M. Alcañiz
Keyword(s):  
Neural Networks ◽  
Computer Aided Design ◽  
Characteristic Curve ◽  
Neural Model ◽  
Computer Aided ◽  
Materials Used ◽  
Aided Design ◽  
Footwear Design ◽  
The Relationship

Recently, important advances in virtual reality have made possible real improvements in computer aided design, CAD. These advances are being applied to all the fields and they have reached to the footwear design. The majority of the interaction foot-shoe simulation processes have been focused on the interaction between the foot and the sole. However, few efforts have been made in order to simulate the interaction between the shoe upper and the foot surface. To simulate this interaction, flexibility tests (characterization of the relationship between exerted force and displacement) are carried out to evaluate the materials used for the shoe upper. This chapter shows a procedure based on artificial neural networks (ANNs) to reduce the number of flexibility tests that are needed for a comfortable shoe design. Using the elastic parameters of the material as inputs to the ANN, it is possible to find a neural model that provides a unique equation for the relationship between force and displacement instead of a different characteristic curve for each material. Achieved results show the suitability of the proposed approach.

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