• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.393-397
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• 2001

This paper addresses an Intelligent Agent-based Open Architecture Cell Controller for Intelligent Manufacturing System(IMS). With an Intelligent Agent approach, the IMS will be a independent, autonomous, distributed system and achieve a adaptability to change of manufacturing environment. As the development methodology of Open Architecture Cell Controller, an object-oriented modeling technique is employed for building models associated with IMS operation, such as resource model, product model, and control model. Intelligent Agent-based Open Architecture Cell Controller consists of two kinds of dependant agents, that are the active agent and the coordinator agent. The Active agent is contributed to control components of IMS in real-time. The coordinator agent has great role in scheduling and planning of IMS. It communicates with other active agents to get information about status on system and generates the next optimal task through the making-decision logic and dispatch it to other active agent.

• Journal of Korea Multimedia Society
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• v.7 no.4
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• pp.550-558
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• 2004

Previous researches on scalability problem of distributed virtual environment (DVE) have been mainly focused on spatial partitioning of area of interest (AOI). Congestion phenomena by avatar groups in AOI have been neglected relatively However, AOI congestion is highly related to scalability of DVE because it exhausts system resources such as network bandwidth and rendering time, and could be a bar to perform collaboration among participants. In this paper, this will be defined as the problem that must be solved for the realization of the scalable DVE, and a model will be proposed to measure and control congestion situation in AOI. The purposes of the proposed model are to prevent high density of participants in AOI, and to protect stable collaboration in DVE. For evaluation of the performance it is compared with a previous method by defining the resource cost model which is dynamically activated to AOI congestion.

• Environmental and Resource Economics Review
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• v.20 no.2
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• pp.357-379
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• 2011

The problem of invasive species has been recently emerged as one of complicated issues due to increasing globalisation and its consequence of species immigrations. Since in most cases of invasive species it is less likely to fully eradicate them through human efforts, it is often interested in reducing the possibility of ecological disaster caused by the invasive species. This paper provides an optimal control model to minimize such possibility while allowing the stochastic nature of biological growth of the invasive species. Conditions under which the partial eradication effort is optimal are derived. Simple numerical illustration is provided using H1N1 data which is categorized as an invasive disease in microorganism level.

• Environmental and Resource Economics Review
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• v.14 no.4
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• pp.867-891
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• 2005

The purpose of this study is estimating the recreation benefits of the largest 10 dam reservoirs in Korea. A mixed logit or random parameters log it model is constructed and estimated. Not only the recreation value of each dam lake but also the values of the main characteristics of the lakes such as the amount of water reserved, and the availability of boating and fishing are estimated. It is shown that recreation value is not less than other benefit such as irrigation, industrial, municipal use, hydro power, or even flood control benefit.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.134-134
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• 2022

Streamflow forecasting plays a crucial role in water resource control, especially in highly urbanized areas that are very vulnerable to flooding during heavy rainfall event. In addition to providing the accurate prediction, the evaluation of effects and importance of the input predictors can contribute to water manager. Recently, machine learning techniques have applied their advantages for modeling complex and nonlinear hydrological processes. However, the techniques have not considered properly the importance and uncertainty of the predictor variables. To address these concerns, we applied the GA-BART, that integrates a genetic algorithm (GA) with the Bayesian additive regression tree (BART) model for hourly streamflow forecasting and analyzing input predictors. The Jungrang urban basin was selected as a case study and a database was established based on 39 heavy rainfall events during 2003 and 2020 from the rain gauges and monitoring stations. For the goal of this study, we used a combination of inputs that included the areal rainfall of the subbasins at current time step and previous time steps and water level and streamflow of the stations at time step for multistep-ahead streamflow predictions. An analysis of multiple datasets including different input predictors was performed to define the optimal set for streamflow forecasting. In addition, the GA-BART model could reasonably determine the relative importance of the input variables. The assessment might help water resource managers improve the accuracy of forecasts and early flood warnings in the basin.

• The KIPS Transactions:PartC
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• v.8C no.1
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• pp.51-59
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• 2001

In order to support various QoS, IETF has proposed the Differentiated Services Model which provides discrimination service according to t the user’s requirements and payment intention intention for each traffic characteristic. This model is an excellent mechanism, which is not too c complicated in terms of the management for service and network model. Also, it has scalability that satisfies the requirement of Differentiated Services. In this paper, We define the Differentiated Services Model using feedback control, propose its control procedure, and analyze its p performance. In conventional model, non-adaptive traffic, such as UDP traffic, is more occupied the network resource than adaptive traffic, such a as TCP traffic. On the other hand, the Differentiated Services Model using feedback control fairly utlizes the network resources and even p prevents congestion occurrence due to its ability of congestion expectation.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.30-30
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• 2023

This study aims to establish the multi-reservoir operation system model in the Upper Mun River Basin which includes 5 main dams namely, Mun Bon (MB), Lamchae (LC), Lam Takhong (LTK), Lam Phraphoeng (LPP), and Lower Lam Chiengkrai (LLCK) Dams. The knowledge and AI technology were applied aiming to develop innovative prototype for SMART dam-reservoir operation in future. Two different sorts of reservoir operation system model namely, Fuzzy Logic (FL) and Constraint Programming (CP) as well as the development of rainfall and reservoir inflow prediction models using Machine Learning (ML) technique were made to help specify the right amount of daily reservoir releases for the Royal Irrigation Department (RID). The model could also provide the essential information particularly for the Office of National Water Resource of Thailand (ONWR) to determine the short-term and long-term water resource management plan and strengthen water security against flood and drought in this region. The simulated results of base case scenario for reservoir operation in the Upper Mun from 2008 to 2021 indicated that in the same circumstances, FL and CP models could specify the new release schemes to increase the reservoir water storages at the beginning of dry season of approximately 125.25 and 142.20 MCM per year. This means that supplying the agricultural water to farmers in dry season could be well managed. In other words, water scarcity problem could substantially be moderated at some extent in case of incapability to control the expansion of cultivated area size properly. Moreover, using AI technology to determine the new reservoir release schemes plays important role in reducing the actual volume of water shortfall in the basin although the drought situation at LTK and LLCK Dams were still existed in some periods of time. Meanwhile, considering the predicted inflow and hydrologic factors downstream of 5 main dams by FL model and minimizing the flood volume by CP model could ensure that flood risk was considerably minimized as a result of new release schemes.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.4
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• pp.176-181
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• 2001

Due to the increasing complexity to handle conflicts and interruptions caused by resource failures and rush orders, shop control is obliged to redesign its organization according to the changing demands of the manufacturing control. These demands are leading to the development of decentralization and gradually to their permanent optimization. As a result, a powerful modeling method which can be adapted efficiently is required. The use of agent theory enables specific modeling of the relevant shop planning activities. The planning activities are modeled in a so-called activity modeling through the definition of three classes of agents; Plan Agent, Manufacturing System Agent and Control Agent as well as the description of the cooperative relationship among these agents. On the basis of the activity model the agent-based shop control method is developed which emphasizes the distributed problem-solving and the cooperation with relevant agents.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.3989-4006
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• 2020

The intelligent agriculture monitoring is based on the perception and analysis of environmental data, which enables the monitoring of the production environment and the control of environmental regulation equipment. As the scale of the application continues to expand, a large amount of data will be generated from the perception layer and uploaded to the cloud service, which will bring challenges of insufficient bandwidth and processing capacity. A fog-based offline and real-time hybrid data analysis architecture was proposed in this paper, which combines offline and real-time analysis to enable real-time data processing on resource-constrained IoT devices. Furthermore, we propose a data process-ing algorithm based on the incremental principal component analysis, which can achieve data dimensionality reduction and update of principal components. We also introduce the concept of Squared Prediction Error (SPE) value and realize the abnormal detection of data through the combination of SPE value and data fusion algorithm. To ensure the accuracy and effectiveness of the algorithm, we design a regular-SPE hybrid model update strategy, which enables the principal component to be updated on demand when data anomalies are found. In addition, this strategy can significantly reduce resource consumption growth due to the data analysis architectures. Practical datasets-based simulations have confirmed that the proposed algorithm can perform data fusion and exception processing in real-time on resource-constrained devices; Our model update strategy can reduce the overall system resource consumption while ensuring the accuracy of the algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.5
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• pp.1567-1587
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• 2014

Virtual Machine (VM) resource management is crucial to satisfy the Quality of Service (QoS) demands of various multimedia services in a media cloud platform. To this end, this paper presents a VM resource allocation model that dynamically and optimally utilizes VM resources to satisfy QoS requirements of media-rich cloud services or applications. It additionally maintains high system utilization by avoiding the over-provisioning of VM resources to services or applications. The objective is to 1) minimize the number of physical machines for cost reduction and energy saving; 2) control the processing delay of media services to improve response time; and 3) achieve load balancing or overall utilization of physical resources. The proposed VM allocation is mapped into the multidimensional bin-packing problem, which is NP-complete. To solve this problem, we have designed a Mixed Integer Linear Programming (MILP) model, as well as heuristics for quantitatively optimizing the VM allocation. The simulation results show that our scheme outperforms the existing VM allocation schemes in a media cloud environment, in terms of cost reduction, response time reduction and QoS guarantee.