Assessing empathy using static and dynamic behavior models based on therapist's language in addiction counseling

2015 ◽  
Author(s):  
Sandeep Nallan Chakravarthula ◽  
Bo Xiao ◽  
Zac E. Imel ◽  
David C. Atkins ◽  
Panayiotis G. Georgiou
Keyword(s):  
Dynamic Behavior ◽  
Addiction Counseling ◽  
Behavior Models

Modeling for Dynamic Topology of Equipment Support Information Network with Certain Equipment Materials

2012 ◽  
Vol 485 ◽  
pp. 417-420
Author(s):  
Xiao Sun ◽  
Bin Liu ◽  
Gui Qi Wang ◽  
Qi Gen Zhong
Keyword(s):  
Dynamic Behavior ◽  
Dynamic Network ◽  
Information Network ◽  
Dynamic Behaviors ◽  
Dynamic Topology ◽  
Behavior Function ◽  
Modern Warfare ◽  
Behavior Models ◽  
Support Information

The importance of the equipment support had been recognized in the modern warfare and the dynamic network is an important characteristic of equipment support. Firstly, the dynamic reasons of the equipment support information network were analyzed. Then the dynamic behavior models were built and all dynamic behaviors were sorted generalized to four kinds of models. Meanwhile, a simulation instance was given, which had shown the dynamic behavior function had eye-catching effect to network statistics parameters changed, and the dynamic behavior importance for the equipment support information network was validated.

Aspect Mining Based on Object-Oriented Systems’ Dynamic Behavior Models

2010 ◽  
Vol 40-41 ◽  
pp. 873-876
Author(s):  
Hua Chu ◽  
Qing Shan Li ◽  
Shen Ming Hu ◽  
Ping Chen
Keyword(s):  
Reverse Engineering ◽  
Dynamic Behavior ◽  
Object Oriented ◽  
Engineering Process ◽  
Aspect Mining ◽  
Crosscutting Concerns ◽  
Systematic Experiment ◽  
Statechart Diagram ◽  
Object Oriented Systems ◽  
Behavior Models

Aspect mining is a reverse engineering process that aims at finding crosscutting concerns in existing systems. This paper describes an aspect mining approach making use of the results of reverse engineering, statechart diagram, to aid in the understanding of an object-oriented software system’s behaviors. An aspect based on the recovered statechart diagram is defined as a set of states and an event. These states will transit to the same state after they send the event. Finally, systematic experiment is conducted in the paper in order to verify the correctness and validity of this approach.

Feature Model Approach for Managing Variablility of Dynamic Behavior Models in Mechatronic Systems

2015 ◽  
Author(s):  
Farisoroosh Abrishamchian ◽  
Felix Oestersötebier ◽  
Ansgar Trächtler
Keyword(s):  
Dynamic Behavior ◽  
Simulation Models ◽  
Specific Model ◽  
Product Line ◽  
Feature Model ◽  
Mechatronic Systems ◽  
Modeling Tools ◽  
Levels Of Detail ◽  
Complex Interactions ◽  
Behavior Models

The design of mechatronic products requires cooperation and coordination of the involved disciplines. To analyze the dynamic behavior of the product’s subsystems and their components, multiple dynamic behavior models (DBM) are developed in different levels of detail (modeling depths) and domains. However, in order to simulate the complex interactions and dependencies between them, models of the whole system are needed, which fit the varying modeling objectives and analysis goals. These comprehensive models are often extensive and the manual construction presupposes deep insight in the specific model approaches and modeling tools. Furthermore, consistency needs to be ensured. The paper describes a way to automatically configure simulation models of the system adopting a Software Product Line (SPL) approach. With the use of feature models, SPL approach provides a structured method for managing variability. The particular focus of this paper is on handling of components in different tools with more than one level of detail through deployment of feature modeling. Also, it presents the concept of a multifunctional model client (MMC), which facilitates integration of solution and system knowledge.

Dynamic Behavior Models and Their Modeling Depth in the Design Process of Mechatronic Systems

2014 ◽  
Author(s):  
M. Lochbichler ◽  
F. Oestersötebier ◽  
A. Trächtler
Keyword(s):  
Dynamic Behavior ◽  
Design Process ◽  
Design Method ◽  
State Variables ◽  
Mechatronic Systems ◽  
Model Based ◽  
Levels Of Detail ◽  
Combined Models ◽  
Four Levels ◽  
Behavior Models

Model-based design is an important part in the development of mechatronic systems. Models are used in various fields of this process, for example, to specify the actuators and sensors, as well as to design the controller, or to describe and analyze the dynamic behavior of the system. These models consist of mechanical, electrical and software components and many more. Hence, developers need a discipline-spanning knowledge in order to model and analyze mechatronic systems, as well as methods for the development of these systems are needed. We divide the design process of mechatronic systems into a discipline-spanning design phase and a concurrent engineering phase in the respective disciplines [1]. Both phases rely on model-based design. The selection of an appropriate modeling depth is essential, and one of the most challenging parts during modeling process [2,3]. An aim of the design method [1] is the preparation of solution knowledge in order to reuse it in future developments. The challenge is to find and select an appropriate dynamic behavior model with a sufficient modeling depth in order to fulfill all modeling objectives. A quantification of modeling depth is required in order to be able to classify models for reuse, and to automate their selection. Therefore, our first step is to classify models by defining four levels of detail. Based on the modeling objectives a level of detail is recommended to the developer. However, models typically consist of many elements from several disciplines and of different levels of detail. In this case, mapping to the four levels of modeling depth is not obvious. That is why our second step defines an index to describe the modeling depth of combined models. This index is based on the state variables of the dynamic behavior models and independent from the composition/modularization of the system model. With these two criteria, it is possible to classify the modeling depth of dynamic behavior models. Our method is shown and validated with the help of an application example.

Ball bearing turbocharger vibration management: application on high speed balancer

2020 ◽  
Vol 21 (6) ◽  
pp. 619
Author(s):  
Kostandin Gjika ◽  
Antoine Costeux ◽  
Gerry LaRue ◽  
John Wilson
Keyword(s):  
Dynamic Behavior ◽  
High Speed ◽  
Internal Combustion Engines ◽  
Ball Bearing ◽  
Squeeze Film ◽  
Design And Technology ◽  
Squeeze Film Damper ◽  
Damping Coefficients ◽  
Bearing Loads ◽  
Stiffness And Damping

Today's modern internal combustion engines are increasingly focused on downsizing, high fuel efficiency and low emissions, which requires appropriate design and technology of turbocharger bearing systems. Automotive turbochargers operate faster and with strong engine excitation; vibration management is becoming a challenge and manufacturers are increasingly focusing on the design of low vibration and high-performance balancing technology. This paper discusses the synchronous vibration management of the ball bearing cartridge turbocharger on high-speed balancer and it is a continuation of papers [1–3]. In a first step, the synchronous rotordynamics behavior is identified. A prediction code is developed to calculate the static and dynamic performance of “ball bearing cartridge-squeeze film damper”. The dynamic behavior of balls is modeled by a spring with stiffness calculated from Tedric Harris formulas and the damping is considered null. The squeeze film damper model is derived from the Osborne Reynolds equation for incompressible and synchronous fluid loading; the stiffness and damping coefficients are calculated assuming that the bearing is infinitely short, and the oil film pressure is modeled as a cavitated π film model. The stiffness and damping coefficients are integrated on a rotordynamics code and the bearing loads are calculated by converging with the bearing eccentricity ratio. In a second step, a finite element structural dynamics model is built for the system “turbocharger housing-high speed balancer fixture” and validated by experimental frequency response functions. In the last step, the rotating dynamic bearing loads on the squeeze film damper are coupled with transfer functions and the vibration on the housings is predicted. The vibration response under single and multi-plane unbalances correlates very well with test data from turbocharger unbalance masters. The prediction model allows a thorough understanding of ball bearing turbocharger vibration on a high speed balancer, thus optimizing the dynamic behavior of the “turbocharger-high speed balancer” structural system for better rotordynamics performance identification and selection of the appropriate balancing process at the development stage of the turbocharger.

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