• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.951-952
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• 2006

Railway signaling systems are so vital to ensure the safe operation of railroad and the assurance and demonstration of the safety is so important. The safety management process shall consist of a number of phases and activities, which are linked to form the safety life-cycle. The basic processes of safety management and safety activity throughout the lifecycle are 'risk analysis' and 'hazard control'. The safety managements and activities for the two kinds of aspects are implemented throughout the whole steps of system lifecycle. The risk analyses and hazard controls like those are needed, these activities have to be carried out through the whole of system lifecycle.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.217-220
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• 2007

This paper studies method of controlling dynamic process with Fuzzy Cognitive Map (FCM) built in NI LabVIEW software. FCM is the hybrid methodology that combines fuzzy logic and neural networks. A FCM will be developed using NI LabVIEW software to model and control a process of dynamic system. Nowadays more autonomous and intelligent systems are very useful in many areas of people lives especially related with Complex Systems.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.2
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• pp.264-269
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• 2005

Advances in sensors, actuators, and computers and developments In information systems offer unprecedented opportunities to implement highly ambitious automation, control and decision strategies. There are also new challenges and demands for control and automation in modern industrial practices. There is a growing need for an active participation from the information systems in industrial, manufacturing and process industry environments because currently there are many control problems. This paper provides pattern recognition to the monitoring system for solvent manufacturing process and shows performance in real-time response with multiple input signals. Data is teamed by a multilayer feedforward network trained by error-backpropagation. The two kinds of test results show that the trained network has the ability to show the current system status with different input data sets.

• Proceedings of the Korea Association of Information Systems Conference
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• 2003.11a
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• pp.55-63
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• 2003

Workflow Management System (WFMS) is a software system to support an efficient execution, control and management of complex business processes. Since traditional commercial systems mainly focus on automating processes, they don't have methods for enhancing the task performer's efficiency. In this paper, we propose a new method of executing business processes more efficiently in that a whole process is scheduled considering the degree of the participants' workload. The method allows managing the largest constraints among constituent resources of the process. We utilize DBR scheduling techniques to develop the method. We first consider the differences between workflow process models and DBR application models, and then develop the modified drum, buffer and rope. This leads us to develop WF-DBR (WorkFlow-DBR) that can control the proper size of the task performers' work list and arrival rate of process instances. Use of WF-DBR improves the efficiency of the whole process as well as the participants' working condition. We then carry out a set of simulation experiments and compare the effectiveness of our approach with that of scheduling techniques used in existing systems.

• Transactions on Control, Automation and Systems Engineering
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• v.3 no.3
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• pp.190-195
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• 2001

The advance of computer, network, and Internet technology enables the control systems to process the massive data in the distributed computing environments. To implement and maintain the software in distributed environment, the component-based methodology is widely used. This paper proposes the middleware architecture for the distributed computer control system. With the proposed middleware services, it is relatively easy to maintain compatibility between products and to implement a portable control application. To achieve the compatibility between heterogeneous systems, the proposed architecture provides the communication protocols based on the XML with lightweight event-based service.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.3
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• pp.148-157
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• 1996

Availability of input trajectories corresponding to desired output trajectories is often important in designing control systems for batch and other transient processes. In this paper, we propose a predictive control-type model-based iterative learning algorithm which is applicable to finding the nominal input trajectories of a linear time-invariant batch process. Unlike the other existing learning control algorithms, the proposed algorithm can be applied to nonsquare systems and has an ability to adjust noise sensitivity as well as convergence rate. A simple model identification technique with which performance of the proposed learning algorithm can be significantly enhanced is also proposed. Performance of the proposed learning algorithm is demonstrated through numerical simulations.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.467-470
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• 2004

MANPro-based control is a novel control paradigm aimed at intelligent manufacturing systems on the basis of mobile agent-based negotiation process (MANPro). MANPro is a negotiation mechanism based on the agent-based control architecture and, especially, it adapts a mobile agent system called N-agent for the negotiation process. N-agent travels around the network of distributed manufacturing systems to acquire information, and it makes a decision for system control according to the obtained information. MANPro includes communication architecture and information architecture for intelligent shop floor control. MANPro also considers the following issues: (1) negotiation mechanism, (2) single-agent internal strategic policies, and (3) information model. Communication architecture concerns the first issue of the negotiation mechanism. It provides information exchanging mechanism with functional modules. In specific, N-agent is equipped with an intelligent reasoning engine with a built-in knowledge base. This reasoning engine is closely related to the single-agent internal strategic policies of the second issue. Finally, ontology-based information architecture addresses information models and provides a framework for information modeling on negotiation. In this paper, these three issues are addressed in detail and a framework of MANPro-based control is also proposed.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.4
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• pp.433-440
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• 1998

The purpose of this study is to provide an integrated process control system for plasma etching. The control system is designed to employ neural network for the modeling of plasma etching process and to utilize genetic algorithm to search for the appropriate selection of control input variables, and to provide a control chart to maintain the process output within a desired range in the real plasma etching process. The target equipment is the one operating in DRAM production lines. The result shows that the integrated system developed is practical value in the improved performance of plasma etching process.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.463-467
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• 1992

This paper describes the automatic generation of sequence control programs for DCS(Distributed Control System), PLC(Programable Logic Controller) and so on. Since there is no same manufacturing process, it is difficult to standardize sequence programs. We propose the automatic sequence control program generator which is CAD software using knowledge engineering technique.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.401-405
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• 1993

This paper presents a neuro adaptive control method for nonlinear dynamical systems based on artificial neural network systems. The proposed neuro adaptive controller consists of 3 layers artificial neural network system and parallel PD controller. At the early stage in learning or identification process of the system characteristics the PD controller works mainly in order to compensate for the inadequacy of the learning process and then gradually the neuro contrller begins to work instead of the PD controller after the learning process has proceeded. From the simulation studies the neuro adaptive controller is seen to be robust and works effectively for nonlinear dynamical systems from a practical applicational points of view.