Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 3

Data Modeling Related Abstracts

3 From Modeling of Data Structures towards Automatic Programs Generating

Authors: Valentin P. Velikov

Abstract:

Automatic program generation saves time, human resources, and allows receiving syntactically clear and logically correct modules. The 4-th generation programming languages are related to drawing the data and the processes of the subject area, as well as, to obtain a frame of the respective information system. The application can be separated in interface and business logic. That means, for an interactive generation of the needed system to be used an already existing toolkit or to be created a new one.

Keywords: Computer Science, Data Modeling, Graphical User Interface, user dialog interface, dialog frames, subject area modeling

Procedia PDF Downloads 140
2 Foundation of the Information Model for Connected-Cars

Authors: Yong-Gu Lee, Hae-Won Seo

Abstract:

Recent progress in the next generation of automobile technology is geared towards incorporating information technology into cars. Collectively called smart cars are bringing intelligence to cars that provides comfort, convenience and safety. A branch of smart cars is connected-car system. The key concept in connected-cars is the sharing of driving information among cars through decentralized manner enabling collective intelligence. This paper proposes a foundation of the information model that is necessary to define the driving information for smart-cars. Road conditions are modeled through a unique data structure that unambiguously represent the time variant traffics in the streets. Additionally, the modeled data structure is exemplified in a navigational scenario and usage using UML. Optimal driving route searching is also discussed using the proposed data structure in a dynamically changing road conditions.

Keywords: Data Modeling, Route Planning, navigation system, connected-car

Procedia PDF Downloads 254
1 Data Modeling and Calibration of In-Line Pultrusion and Laser Ablation Machine Processes

Authors: David F. Nettleton, Elodie Bugnicourt, Christian Wasiak, Alejandro Rosales, Jonas Dorissen, David Gillen, Alexandr Tretyak

Abstract:

In this work, preliminary results are given for the modeling and calibration of two inline processes, pultrusion, and laser ablation, using machine learning techniques. The end product of the processes is the core of a medical guidewire, manufactured to comply with a user specification of diameter and flexibility. An ensemble approach is followed which requires training several models. Two state of the art machine learning algorithms are benchmarked: Kernel Recursive Least Squares (KRLS) and Support Vector Regression (SVR). The final objective is to build a precise digital model of the pultrusion and laser ablation process in order to calibrate the resulting diameter and flexibility of a medical guidewire, which is the end product while taking into account the friction on the forming die. The result is an ensemble of models, whose output is within a strict required tolerance and which covers the required range of diameter and flexibility of the guidewire end product. The modeling and automatic calibration of complex in-line industrial processes is a key aspect of the Industry 4.0 movement for cyber-physical systems.

Keywords: Machine Learning, Data Modeling, Industrial Processes, Calibration

Procedia PDF Downloads 90