scholarly journals A Study on the Design of the Technology Reference Model Based on Technologies of Interoperability in Digital Libraries

2007 ◽  
Vol 24 (4) ◽  
pp. 239-254
Author(s):  
Seong-Hee Kim ◽  
Jeong-Soo Lee
Keyword(s):  
Digital Libraries ◽  
Reference Model ◽  
Model Based

scholarly journals A MODEL BASED ON FUZZY LINGUISTIC INFORMATION TO EVALUATE THE QUALITY OF DIGITAL LIBRARIES

2010 ◽  
Vol 09 (03) ◽  
pp. 455-472 ◽  
Author(s):  
F. J. CABRERIZO ◽  
J. LÓPEZ-GIJÓN ◽  
A. A. RUÍZ ◽  
E. HERRERA-VIEDMA
Keyword(s):  
Digital Libraries ◽  
User Satisfaction ◽  
Information Access ◽  
Great Influence ◽  
Linguistic Information ◽  
Model Based ◽  
Global Quality ◽  
Fuzzy Linguistic ◽  
The Web

The Web is changing the information access processes and it is one of the most important information media. Thus, the developments on the Web are having a great influence over the developments on others information access instruments as digital libraries. As the development of digital libraries is to satisfy user need, user satisfaction is essential for the success of a digital library. The aim of this paper is to present a model based on fuzzy linguistic information to evaluate the quality of digital libraries. The quality evaluation of digital libraries is defined using users' perceptions on the quality of digital services provided through their Websites. We assume a fuzzy linguistic modeling to represent the users' perception and apply automatic tools of fuzzy computing with words based on the LOWA and LWA operators to compute global quality evaluations of digital libraries. Additionally, we show an example of application of this model where three Spanish academic digital libraries are evaluated by fifty users.

Reference model based adaptive control of a hybrid suspension system

2010 ◽  
Vol 43 (7) ◽  
pp. 312-317 ◽  
Author(s):  
Guido Koch ◽  
Sebastian Spirk ◽  
Boris Lohmann
Keyword(s):  
Adaptive Control ◽  
Reference Model ◽  
Suspension System ◽  
Model Based

scholarly journals A Reference-Model-Based Neural Network Control Method for Multi-Input Multi-Output Temperature Control System

Processes ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1365
Author(s):  
Yuan Liu ◽  
Song Xu ◽  
Seiji Hashimoto ◽  
Takahiro Kawaguchi
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Reference Model ◽  
Mimo System ◽  
Network Control ◽  
Temperature Control System ◽  
Neural Network Control ◽  
Model Based ◽  
Neural Network Controller ◽  
Highly Nonlinear

Neural networks (NNs), which have excellent ability of self-learning and parameter adjusting, has been widely applied to solve highly nonlinear control problems in industrial processes. This paper presents a reference-model-based neural network control method for multi-input multi-output (MIMO) temperature system. In order to improve the learning efficiency of the NN control, a reference model is introduced to provide the teaching signal for the NN controller. The control inputs for the MIMO system are given by the sum of the output of the conventional integral-proportional-derivative (I-PD) controller and the outputs of the neural network controller. The proposed NN control method can not only improve the transient response of the system, but can also realize temperature uniformity in MIMO temperature systems. To verify the proposed method, simulations are carried out in MATLAB/SIMULINK environment and experiments are carried out on the DSP (Digital Signal Processor)-based experimental platform, respectively. Both results are quantitatively compared to those obtained from the conventional I-PD control systems. The effectiveness of the proposed method has been successfully verified.

scholarly journals A Reference-Model-Based Artificial Neural Network Approach for a Temperature Control System

Processes ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 50
Author(s):  
Song Xu ◽  
Seiji Hashimoto ◽  
YuQi Jiang ◽  
Katsutoshi Izaki ◽  
Takeshi Kihara ◽  
...  
Keyword(s):  
Control System ◽  
Temperature Control ◽  
Digital Signal Processor ◽  
Reference Model ◽  
Learning Performance ◽  
Temperature Control System ◽  
Neural Network Approach ◽  
Model Based ◽  
Artificial Neural ◽  
Self Learning

Artificial neural networks (ANNs), which have excellent self-learning performance, have been applied to various applications, such as target detection and industrial control. In this paper, a reference-model-based ANN controller with integral-proportional-derivative (I-PD) compensation has been proposed for temperature control systems. To improve the ANN self-learning efficiency, a reference model is introduced for providing the teaching signal for the ANN. System simulations were carried out in the MATLAB/SIMULINK environment and experiments were carried out on a digital-signal-processor (DSP)-based experimental platform. The simulation and experimental results were compared with those for a conventional I-PD control system. The effectiveness of the proposed method was verified.

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