scholarly journals Fast Design Closure of Compact Microwave Components by Means of Feature-Based Metamodels

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 10
Author(s):  
Anna Pietrenko-Dabrowska ◽  
Slawomir Koziel
Keyword(s):  
Network Models ◽  
Gradient Estimation ◽  
Passive Components ◽  
Simulation Tools ◽  
Design Procedures ◽  
Warm Start ◽  
Microwave Components ◽  
Feature Based ◽  
And Performance ◽  
Design Closure

Precise tuning of geometry parameters is an important consideration in the design of modern microwave passive components. It is mandatory due to limitations of theoretical design methods unable to quantify certain phenomena that are important for the operation and performance of the devices (e.g., strong cross-coupling effects in miniaturized layouts). Consequently, the initial designs obtained using analytical or equivalent network models require further adjustment. For reliability reasons, it has to be conducted using electromagnetic (EM) simulation tools, which entails considerable computational expenses whenever conventional numerical optimization algorithms are employed. Accelerating EM-driven design procedures is therefore highly desirable. This work discusses a surrogate-based algorithm for fast design closure and dimension scaling of miniaturized microwave passives. Our approach involves a small database of previously obtained designs as well as two metamodels, an inverse one, employed to yield a high-quality initial design, and the forward surrogate that provides predictions of the system sensitivities. The second model is constructed at the level of response features, which enables a more accurate gradient estimation and leads to improved reliability and a faster convergence of the optimization process. The presented technique is validated using two compact microstrip couplers and benchmarked against the state-of-the-art warm-start optimization frameworks.

Geometric Reasoning Based on Graph Grammar Parsing

1994 ◽  
Vol 116 (3) ◽  
pp. 763-769 ◽  
Author(s):  
Z. Fu ◽  
A. de Pennington
Keyword(s):  
Intelligent Design ◽  
Feature Recognition ◽  
Geometric Reasoning ◽  
Geometric Constraints ◽  
Graph Grammar ◽  
Feature Interaction ◽  
Knowledge Based ◽  
Feature Based ◽  
Feature Information ◽  
And Performance

It has been recognized that future intelligent design support environments need to reason about the geometry of products and to evaluate product functionality and performance against given constraints. A first step towards this goal is to provide a more robust information model which directly relates to design functionality or manufacturing characteristics, on which reasoning can be carried out. This has motivated research on feature-based modelling and reasoning. In this paper, an approach is presented to geometric reasoning based on graph grammar parsing. Our approach is presented to geometric reasoning based on graph grammar parsing. Our work combines methodologies from both design by features and feature recognition. A graph grammar is used to represent and manipulate features and geometric constraints. Geometric constraints are used within symbolical definitions of features constraints. Geometric constraints are used within symbolical definitions of features and also to define relative position and orientation of features. The graph grammar parsing is incorporated with knowledge-based inference to derive feature information and propagate constraints. This approach can be used for the transformation of feature information and to deal with feature interaction.

Automatic Parametric Modeling From Non-Feature Based Designs for Additive Manufacturing

2021 ◽  
Author(s):  
Xinyi Xiao ◽  
Byeong-Min Roh
Keyword(s):  
Additive Manufacturing ◽  
3D Models ◽  
Parametric Modeling ◽  
Parametric Models ◽  
Performance Simulation ◽  
Cad Modeling ◽  
3D Cad ◽  
Feature Based ◽  
And Performance ◽  
The Given

Abstract The integration of Topology optimization (TO) and Generative Design (GD) with additive manufacturing (AM) is becoming advent methods to lightweight parts while maintaining performance under the same loading conditions. However, these models from TO or GD are not in a form that they can be easily edited in a 3D CAD modeling system. These geometries are generally in a form with no surface/plane information, thus having non-editable features. Direct fabricate these non-feature-based designs and their inherent characteristics would lead to non-desired part qualities in terms of shape, GD&T, and mechanical properties. Current commercial software always requires a significant amount of manual work by experienced CAD users to generate a feature-based CAD model from non-feature-based designs for AM and performance simulation. This paper presents fully automated shaping algorithms for building parametric feature-based 3D models from non-feature-based designs for AM. Starting from automatically decomposing the given geometry into “formable” volumes, which is defined as a sweeping feature in the CAD modeling system, each decomposed volume will be described with 2D profiles and sweeping directions for modeling. The Boolean of modeled components will be the final parametric shape. The volumetric difference between the final parametric form and the original geometry is also provided to prove the effectiveness and efficiency of this automatic shaping methodology. Besides, the performance of the parametric models is being simulated to testify the functionality.

Implementing the Mechanistic–Empirical Design Guide Procedure for a Hot-Mix Asphalt–Rehabilitated Pavement in Indiana

2005 ◽  
Vol 1919 (1) ◽  
pp. 121-133 ◽  
Author(s):  
Khaled A. Galal ◽  
Ghassan R. Chehab
Keyword(s):  
Hot Mix Asphalt ◽  
Pavement Design ◽  
Design Parameters ◽  
Design Procedures ◽  
Design Concepts ◽  
Asphalt Overlay ◽  
Design Guide ◽  
Strategic Goals ◽  
And Performance

One of the Indiana Department of Transportation's (INDOT's) strategic goals is to improve its pavement design procedures. This goal can be accomplished by fully implementing the 2002 mechanistic–empirical (M-E) pavement design guide (M-E PDG) once it is approved by AASHTO. The release of the M-E PDG software has provided a unique opportunity for INDOT engineers to evaluate, calibrate, and validate the new M-E design process. A continuously reinforced concrete pavement on I-65 was rubblized and overlaid with a 13–in.-thick hot-mix asphalt overlay in 1994. The availability of the structural design, material properties, and climatic and traffic conditions, in addition to the availability of performance data, provided a unique opportunity for comparing the predicted performance of this section using the M-E procedure with the in situ performance; calibration efforts were conducted subsequently. The 1993 design of this pavement section was compared with the 2002 M-E design, and performance was predicted with the same design inputs. In addition, design levels and inputs were varied to achieve the following: ( a) assess the functionality of the M-E PDG software and the feasibility of applying M-E design concepts for structural pavement design of Indiana roadways, ( b) determine the sensitivity of the design parameters and the input levels most critical to the M-E PDG predicted distresses and their impact on the implementation strategy that would be recommended to INDOT, and ( c) evaluate the rubblization technique that was implemented on the I-65 pavement section.

Towards a Multi-Formalism Multi-Solution Framework for Model-Driven Performance Engineering

2013 ◽  
pp. 34-55
Author(s):  
Catalina M. Lladó ◽  
Pere Bonet ◽  
Connie U. Smith
Keyword(s):  
Queueing Network ◽  
Network Models ◽  
Performance Model ◽  
Analysis Model ◽  
Model Driven ◽  
Performance Engineering ◽  
Performance Specifications ◽  
Interchange Format ◽  
And Performance ◽  
Model Interchange

Model-Driven Performance Engineering (MDPE) uses performance model interchange formats among multiple formalisms and tools to automate performance analysis. Model-to-Model (M2M) transformations convert system specifications into performance specifications and performance specifications to multiple performance model formalisms. Since a single tool is not good for everything, tools for different formalisms provide multiple solutions for evaluation and comparison. This chapter demonstrates transformations from the Performance Model Interchange Format (PMIF) into multiple formalisms: Queueing Network models solved with Java Modeling Tools (JMT), QNAP, and SPE·ED, and Petri Nets solved with PIPE2.

scholarly journals Information Extraction of Cybersecurity Concepts: An LSTM Approach

2019 ◽  
Vol 9 (19) ◽  
pp. 3945 ◽  
Author(s):  
Houssem Gasmi ◽  
Jannik Laval ◽  
Abdelaziz Bouras
Keyword(s):  
Neural Network ◽  
Domain Knowledge ◽  
Conditional Random Fields ◽  
Short Term Memory ◽  
Network Models ◽  
Relation Extraction ◽  
Entity Recognition ◽  
Feature Engineering ◽  
Neural Network Models ◽  
Feature Based

Extracting cybersecurity entities and the relationships between them from online textual resources such as articles, bulletins, and blogs and converting these resources into more structured and formal representations has important applications in cybersecurity research and is valuable for professional practitioners. Previous works to accomplish this task were mainly based on utilizing feature-based models. Feature-based models are time-consuming and need labor-intensive feature engineering to describe the properties of entities, domain knowledge, entity context, and linguistic characteristics. Therefore, to alleviate the need for feature engineering, we propose the usage of neural network models, specifically the long short-term memory (LSTM) models to accomplish the tasks of Named Entity Recognition (NER) and Relation Extraction (RE). We evaluated the proposed models on two tasks. The first task is performing NER and evaluating the results against the state-of-the-art Conditional Random Fields (CRFs) method. The second task is performing RE using three LSTM models and comparing their results to assess which model is more suitable for the domain of cybersecurity. The proposed models achieved competitive performance with less feature-engineering work. We demonstrate that exploiting neural network models in cybersecurity text mining is effective and practical.

Simulation of Longitudinal Train Dynamics: Case Studies Using the Train Energy and Dynamics Simulator (TEDS)

2015 ◽  
Author(s):  
Monique F. Stewart ◽  
S. K. (John) Punwani ◽  
David R. Andersen ◽  
Graydon F. Booth ◽  
Som P. Singh ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Case Studies ◽  
State Of The Art ◽  
Longitudinal Dynamics ◽  
Simulation Tools ◽  
Power Operations ◽  
Real World Applications ◽  
Train Dynamics ◽  
And Performance ◽  
Train Safety

Longitudinal dynamics influence several measures of train performance, including schedules and energy efficiency, stopping distances, run-in/run-out forces, etc. Therefore, an effective set of tools for studying longitudinal dynamics is essential to improving the safety and performance of train operations. Train Energy and Dynamics Simulator (TEDS) is a state-of-the-art software program designed and developed by the Federal Railroad Administration (FRA), for studying and simulating train safety and performance, and can be used for modeling train performance under a wide variety of equipment, track, and operating configurations [1]. Several case studies and real-world applications of TEDS, including the investigation of multiple train make-up and train handling related derailments, a study of train stopping distances, evaluations of the safety benefits of Electronically Controlled Pneumatic (ECP) brakes, Distributed Power operations, and a study of alternate train handling methodologies are described in this paper. These studies demonstrate the effectiveness of using the appropriate simulation tools to quantify and enhance a better understanding of train dynamics, and the resultant safety benefits.

scholarly journals An evaluation of DEVS simulation tools

SIMULATION ◽  
2016 ◽  
Vol 93 (2) ◽  
pp. 103-121 ◽  
Author(s):  
Yentl Van Tendeloo ◽  
Hans Vangheluwe
Keyword(s):  
Programming Language ◽  
Dynamic Systems ◽  
Discrete Event ◽  
Complex Dynamic ◽  
Language Implementation ◽  
Simulation Tools ◽  
Complex Dynamic Systems ◽  
Current State ◽  
And Performance

DEVS is a popular formalism for modeling complex dynamic systems using a discrete-event abstraction. Owing to its popularity, and the simplicity of the simulation kernel, a number of tools have been constructed by academia and industry. However, each of these tools has distinct design goals and a specific programming language implementation. Consequently, each supports a specific set of formalisms, combined with a specific set of features. Performance differs significantly between different tools. We provide an overview of the current state of eight different DEVS simulation tools: ADEVS, CD++, DEVS-Suite, MS4 Me, PowerDEVS, PythonPDEVS, VLE, and X-S-Y. We compare supported formalisms, compliance, features, and performance. This paper aims to help modelers in deciding which tools to use to solve their specific problems. It further aims to help tool builders, by showing the aspects of their tools that could be extended in future tool versions.

Sign in / Sign up

Export Citation Format

Share Document