• Journal of the Korean Society for Precision Engineering
• /
• v.13 no.6
• /
• pp.152-160
• /
• 1996

This paper focuses on development and evaluation of a performance management algorithm for IEEE 802.4 token bus networks to serve large-scale integrated manufacturing systems. Such factory automation networks have to satisfy delay constraints imposed on time-critical messages while maintaining as much network capacity as possible for non-time-critical messages. This paper presents a network performance manager that adjusts queue capacity as well as timers by using a set of fuzzy rules and fuzzy inference mechanism. The efficacy of the performance management has been demonstrated by a series of simulation experiments.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.20 no.8
• /
• pp.2436-2448
• /
• 1996

This paper presents the development of three types of network performance manager for IEEE 802.4 token bus networks that are a part of Manufacturing Automation Protocol(MAP). The performance managers attempt to keep the average data latency below a certain level specified for each priority class. All of the three performance managers are based on a set of fuzzy rules incorporating the knowledge on the relationship between data latency and parameters of the priority mechanism. These Fuzzy Network Performance Managers(FNPMs) have been evaluated via discrete event simulation to demonstrate their efficacy.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2002.10a
• /
• pp.511-514
• /
• 2002

In may automated systems such as manufacturing systems and process plants, an industrial network or fieldbus is a very important component for the exchange of various and sometimes crucial information. Some of the information has a tendency to rapidly lose its value as time elapses after its creation. Such information or data is called real-time data that includes sensor values and control commands. In order to deliver these data in time, the fieldbus network should be tailored to have short delay with respect to the individual time limit of various data. Fine-tuning the network for a given traffic requires the knowledge on the relationship between the protocol parameters such as timer values and the performance measure such as network delay. This paper presents a mathematical performance model to calculate communication delays of the Profibus FMS network when the timer value TTR and the traffic characteristics are given. The results of this model is compared to those from experiments to assess the model's validity.

• Journal of the Korean Society for Precision Engineering
• /
• v.14 no.4
• /
• pp.46-55
• /
• 1997

This paper presents the development of intelligent performance management of computer communication networks for larger-scale integrated systems and the demonstration of its efficacy using computer simula- tion. The innermost core of the performance management is based on fuzzy set theory. This fuzzy perfor- mance manager has learning ability by using principles of neuro-fuzzy model, neuralnetwork, genetic algo- rithm(GA). Two types of performance managers are described in this paper. One is the Neuro-Fuzzy Per- formance Manager(NFPM) of which learning ability is based on the conventional gradient method, and the other is GA-based Neuro-Fuzzy Performance Manager(GNFPM)with its learning ability based on a genetic algorithm. These performance managers have been evaluated via discrete event simulation of a computer network.

• 제어로봇시스템학회:학술대회논문집
• /
• 1989.10a
• /
• pp.524-529
• /
• 1989

In this paper, an approximate sojourn time distribution is obtained for cyclic service systems. We consider symmetric and limited service systems in which each queue has an infinite capacity. The combined service time is defined which consists of the frame service time and server waiting time that is approximated by two cases of the uniform and exponential distributions. The approximate sojourn time distribution is obtained from the Pollaczek-Khinchine formula where the combined service time is used for the service time in the M/G/I model. And some numerical examples are given to validate the suggested approximate analysis.