• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.290-295
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• 2005

Hydraulic actuators are important in modern industry due to high power, fast response, and high stiffness. In recent years, hybrid actuation system, which combines electric and hydraulic technology in a compact unit, can be adapted to a wide variety of force, speed and torque requirements. Moreover, the hybrid actuation system has dealt with the energy consumption and noise problem existed in the conventional hydraulic system. Therefore, hybrid actuator has a wide range of application fields such as plastic injection-molding and metal forming technology, where force or pressure control is the most important technology. In this paper, the solution for force control of hybrid system is presented. However, some limitations still exist such as deterioration of the performance of transient response due to the variable environment stiffness. Therefore, intelligent switching control using Learning Vector Quantization Neural Network (LVQNN) is newly proposed in this paper in order to overcome these limitations. Experiments are carried out to evaluate the effectiveness of the proposed algorithm with large variation of stiffness of external environment. In addition, it is understood that the new system has energy saving effect even though it has almost the same response as that of valve controlled system.

• Journal of Mechanical Science and Technology
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• v.15 no.5
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• pp.544-554
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• 2001

A shop floor can be considered as an important level to develop Computer Integrated Manufacturing system (CIMs). However, a shop floor is a dynamic environment where unexpected events continuously occur, and impose changes to the planned activities. To deal with this problem, a shop floor should adopt an appropriate control system that is responsible for the coordination and control of the manufacturing physical flow and information flow. In this paper, a hybrid control system is described with a shop floor activity methodology called Multi-Layered Task Initiation Diagram (MTD). The architecture of the control model contains three levels: i.e., he shop floor controller (SFC), the intelligent agent controller (IAC) and the equipment controller (EC). The methodology behind the development of the control system is an intelligent multi-agent paradigm that enables the shop floor control system to be an independent, an autonomous, and distributed system, and to achieve an adaptability to change of the manufacturing environment.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.984-988
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• 2005

To accomplish various and complex tasks by intelligent robots, improvement is needed not only in mechanical system architecture but also in control system architecture. Hybrid control architecture has been suggested as a mutually complementing architecture of the weak points of a deliberative and a reactive control. This paper addresses a control architecture of robots, and a behavior representation methodology. The suggested control architecture consists of three layers of deliberative, sequencing, and reactive as hybrid control architecture. Multi-layer behavior model is employed to represent desired tasks. 3D simulation will be conducted to verify the applicability of suggested control architecture and behavior representation method.

• Journal of Intelligence and Information Systems
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• v.14 no.1
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• pp.101-115
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• 2008

Combining collaborative filtering with some other technique is most common in hybrid recommender systems. As many recommended items from collaborative filtering seem to be similar with respect to content, the collaborative-content hybrid system suffers in terms of quality recommendation and recommending new items as well. To alleviate such problem, we have developed a novel method that uses a diversity metric to select the dissimilar items among the recommended items from collaborative filtering, which together with the input when fed into content space let us improve and include new items in the recommendation. We present experimental results on movielens dataset that shows how our approach performs better than simple content-based system and naive hybrid system.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2880-2882
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• 2000

In the case of designing autonomous robot architecture using deliberative and reactive control methods, we can use mixed hybrid form as well as purely reactive scheme or purely deliberative scheme respectively according to its own goal and environment within the robot operates, It needs time and endeavors to design robot control architecture in either case above. In our research, we implemented a 3-dimensional robot simulator in order to help designing reactive/deliberative autonomous robot control architecture by offering methods which is capable of selecting design parameters and confirming its performances. It can be used, of course, to design purely reactive or purely deliberative architecture. The architecture and performance of simulator is shown and a sample hybrid robot architecture designed with the simulator is introduced in this article.

• Journal of Korea Multimedia Society
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• v.22 no.2
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• pp.281-291
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• 2019

In order to reflect complex and diverse building types, resident characteristics and disaster factors, it is necessary to introduce a flexible situation-based response system based on real-time information. Intelligent CCTV, hybrid sensor, location scanner, and customized broadcasting device were examined to introduce for the real-time response intelligent response system and its feasibility was verified through field test. In addition, based on the real-time information, the evacuation simulation was executed by assuming the dormitory building and the resident of the school, and the safety of the evacuation and the shortening of the pinnacle time were confirmed. The feasibility of real time information based evacuation comparing with the existing evacuation system were verified in the case of evacuation.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.10
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• pp.690-696
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• 2004

This paper is proposed a hybrid intelligent controller based on the vector controlled surface permanent magnet synchronous motor(SPMSM) drive system. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of SPMSM using neural network-fuzzy(NNF) control and speed estimation using artificial neural network(ANN) Controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the theoretical analysis as well as the simulation results to verify the effectiveness of the new method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.433-439
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• 1998

In this paper, we present an approach to the structure identification based on the input space partition methods and to the parameter identification by hybrid learning method in neuro-fuzzy system. The structure identification can automatically estimate the number of membership function and fuzzy rule using grid partition, tree partition, scatter partition from numerical input-output data. And then the parameter identification is carried out by the hybrid learning scheme using back-propagation and least squares estimate. Finally, we sill show its usefulness for neuro-fuzzy modeling to truck backer-upper control.

• Journal of Intelligence and Information Systems
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• v.6 no.2
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• pp.15-27
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• 2000

The paper proposes a hybrid fire fighting control intelligent system(H-FFIS) using rules and cases to detect fire in Integrated Platform Management System. By far most conventional systems have been based on rule-based system in which expert knowledges are expressed with production rules. It is hard to express the knowledges to detect fire with production rules only. The knowledges of fire detection are often based on previously encountered situations of fires. For improvement of system capability renewing and adding of rules is needed in an already build-up system and such adding and renewing procedures could hinder users from fluent utilization of the system. We design and implement H-FFIS. Compared with rule-based FFIS(Fire Fighting control Intelligent System), H-FFIS extended with case-based reasoning shows that the system proposed here can lead to an improvement in fire detection rate.

• Journal of Digital Convergence
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• v.6 no.4
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• pp.177-183
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• 2008

This paper aims to offer a solution based on intelligent document classification to create a user-centric information retrieval system allowing user-centric linguistic expression. So, structures expressing user intention and fine document classifying process using EBL, similarity, knowledge base, user intention, are proposed. To overcome the problem requiring huge and exact semantic information, a hybrid process is designed integrating keyword, thesaurus, probability and user intention information. User intention tree hierarchy is build and a method of extracting group intention between key words and user intentions is proposed. These structures and processes are implemented in HDCI(Hybrid Document Classification with Intention) system. HDCI consists of analyzing user intention and classifying web documents stages. Classifying stage is composed of knowledge base process, similarity process and hybrid coordinating process. With the help of user intention related structures and hybrid coordinating process, HDCI can efficiently categorize web documents in according to user's complex linguistic expression with small priori information.