• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.368-368
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• 2000

In this paper, we present forecasting ozone concentration with decision support system. Since the mechanism of ozone concentration is highly complex, nonlinear, and nonstationary, modeling of ozone prediction system has many problems and results of prediction are not good performance so far. Forecasting ozone concentration with decision support system is acquired to information from human knowledge and experiment data. Fuzzy clustering method uses the acquisition and dynamic polynomial neural network gives us a good performance for ozone prediction with ability of superior data approximation and self-organization.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.2A
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• pp.139-148
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• 2011

This paper presents the minimum-cost network selection scheme which determines the transmission instance in the multi-band maritime communication system, so that the shipment-related real-time information can be transmitted within the maximum allowed period. The transmission instances and the corresponding network selection process are modeled by a Markov Decision Process (MDP), for the channel model in the 2-state Markov chain, which can be solved by stochastic dynamic programming. It derives the minimum-cost network selection rule, which can reduce the network cost significantly as compared with the straight-forward scheme with a periodic transmission.

• Journal of KIISE
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• v.43 no.7
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• pp.801-811
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• 2016

Self-adaptive systems (SASs) can change their goals and behaviors to achieve its ultimate goal in a dynamic execution environment. Existing approaches have designed, at the design time, utility functions to evaluate and predict the goal satisfaction, and set policies that are crucial to achieve each goal. The systems can be adapted to various runtime environments by utilizing the pre-defined utility functions and policies. These approaches, however, may or may not guarantee the proper adaptability, because system designers cannot assume and predict all system environment perfectly at the design time. To cope with this problem, this paper proposes a new method of dynamic decision making, which takes the following steps: firstly we design a Dynamic Decision Network (DDN) with environmental data and goal model that reflect system contexts; secondly, the goal satisfaction is evaluated and predicted with the designed DDN and real-time environmental information. We furthermore propose a dynamic reflection method that changes the model by using newly generated data in real-time. The proposed method was actually applied to ROBOCODE, and verified its effectiveness by comparing to conventional static decision making.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.2
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• pp.115-121
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• 2013

LED-ID (Light Emitting Diode-Identification) network is envisioned to be the next generation indoor wireless communication medium by which simultaneously high speed data transmission, identification, and illumination are possible. In spite of being extremely promising, it suffers from much impairment. Signals having different propagation paths can suffer from delays, and phase shifts which will eventually result interference. The probability of interference is also increased when communication links are established between a tag and several readers. Therefore it is necessary to reduce the interference in LED-ID network to ensure quality of service. It is possible to avoid interference by knowing the information about readers prior to assign the available spectrum. In this paper, we have proposed dynamic spectrum decision using cognitive radio concept. The simulation results justify that the proposed scheme is better than the conventional scheme.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.3
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• pp.35-40
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• 2014

Many researches have been conducted to achieve improvement in distributed system that connects multiple computer systems via communication lines. Among others, the load balancing and file migration are considered to have significant impact on the performance of distributed system. The dynamic file migration algorithm common in distributed processing system involved complex calculations of decision function necessary for file migration and required migration of control messages for the performance of decision function. However, the performance of this decision function puts significant computational strain on computer. As one single network is shared by all computers, more computers connected to network means migration of more control messages from file migration, causing the network to trigger bottleneck in distributed processing system. Therefore, it has become imperative to carry out the research that aims to reduce the number of control messages that will be migrated. In this study, the learning automata was used for file migration which would requires only the file reference-related information to determine whether file migration has been made or determine the time and site of file migration, depending on the file conditions, thus reflecting the status of current system well and eliminating the message transfer and additional calculation overhead for file migration. Moreover, mathematical model for file migration was described in order to verify the proposed model. The results from mathematical model and simulation model suggest that the proposed model is well-suited to the distributed system.

• Journal of Korean Society of Rural Planning
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• v.3 no.1
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• pp.96-104
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• 1997

The purpose of this study is to develop a dynamic DSS (Decision Support System) interfaced with GIS (Geographic Information System) and agriculture information network, In this study, DSS was developed to assist services which required tremendous and real time data in national scale. The data transmitte'd from the local area by the agriculture network were stored in DBMS (Data Base Management System) and analyzed by GIS. GIS and database tools used in this study were ARC/INFO 7.1.1 and INFORMIX 4.0. ACSAS (Agriculture Calamity Service Asist System) by the system prototype was constructed to solve the problem about the drought counterplan service which was to take the responsibility in the Ministry of Agriculture. It was easy to transfer, process, and analyze the information using the system. Specially, the meteological, the reservoir storage rate and the drought counterplan information were spatially analyzed by the functions of GIS.

• Asia pacific journal of information systems
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• v.13 no.2
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• pp.145-170
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• 2003

Open Decision Support System(DSS) is an automated and transparent system that is built to be shared both within and across organizations. The open DSS has been thought to be a set of decision components that are communicating through Web protocols. These characteristics intuitively invite the Web services concepts, which are currently one of the new trends in network-based business services. However, web services still are not active enough to be autonomous, and to provide for composing functionalities. These lead to the motivation on building a sophisticated web service to contain these features and to utilize web services on behalf of the user. This paper aims to propose a new concept of Meta Web Service, a web service-based open DSS. Decision modules in a dynamic and open DSS can be viewed as a web service. The Meta Web Service understands the users problem statement with ontology, performs web service discovery, web service composition, and automatically generates codes for composite web service execution. A prototype of example web service has been developed to show the feasibility of the proposed idea.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.8B
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• pp.691-698
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• 2003

Throughput response characteristics depending on the network bandwidth allocation need to be modeled to devise adaptive control mechanism to support QoS of the network. Thus, two models are proposed in this study. The first one is a dynamic system model and the other one is a stochastic model. The dynamic system model is developed to represent dynamic characteristics of the network and the stochastic model is developed to represent distribution of measured throughput data. An optimization technique is used for decision of proposed model's factor. The result confirms that the characteristics of proposed models are similar with actual network's characteristics.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.4
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• pp.126-137
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• 1994

Performance management of computer networks is intended to improve a given network performance in order for more efficient information exchange between subsystems of an integrated large-scale system. Importance of perfomance management is growing as many functions of the large- scale system depend on the quality of communication services provided by the network. The role of performance management is to manipulate the adjustable protocol parameters on line so that the network can adapt itself to a dynamic environment. This can be divided into two subtasks : performance evaluation to find how changes in protocol parameters affect the network performance and decision making to determine the magnitude and direction of parameter adjustment. This paper is the first part of the two papers focusing on conceptual design, development, and evaluation of performance management for token bus networks. This paper specifically deals with the task of performance evaluation which utilizes the principle of perturbation analysis of discrete event dynamic systems. The developed algorithm can estimate the network performance under a perturbed protocol parameter setting from observations of the network operations under a nominal parameter setting.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.19-22
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• 2000

In this paper, we present forecasting ozone concentration with decision support system. Forecasting ozone concentration with decision support system is acquired to information from human knowledge and experiment data. Fuzzy clustering method uses the acquisition and dynamic polynomial neural network gives us a good performance for ozone prediction with ability of superior data approximation and self-organization.