• Proceedings of the KIEE Conference
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• 2006.07a
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• pp.103-104
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• 2006

Since the first proposition of IEC61850 object model at 1993, many questions about making a seamless model have been issued. the reason which they have worry about is that the functions of the equipment are supposed to be changed properly and new equipment and scheme are need to be introduced according to user's application. To handle those issues, TC57 which is a IEC committee for power control and communication has continuously updated the object model. Nowadays along with the new object model involving power quality, distribution resource and wind power, the committee has a plan to announce the revision of IEC61850-7-4. In the study, authors will present the prediction and diagnosis object models for transformer. Transformer models for protection and control have already been dealt with in the international standard but the models for prediction and diagnosis have never mentioned until now. Designing the prediction and diagnosis functions with the existing IEC61850-7-4, it'll be shown what is a proper object model for prediction and diagnosis.

• Proceedings of the Korea Society for Simulation Conference
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• 2005.05a
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• pp.29-33
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• 2005

Grid computing is widely applicable to various fields of industry including process control and manufacturing, military command and control, transportation management, and so on. In a viewpoint of application area, grid computing can be classified to three aspects that are computational grid, data grid and access grid. This paper focuses on computational grid which handles complex and large-scale computing problems. Computational grid is characterized by system dynamics which handles a variety of processors and jobs on continuous time. To solve problems of system complexity and reliability due to complex system dynamics, computational grid needs scheduling policies that allocate various jobs to proper processors and decide processing orders of allocated jobs. This paper proposes the service prediction-based job scheduling model and present its algorithm that is applicable for computational grid. The service prediction-based job scheduling model can minimize overall system execution time since the model predicts a processing time of each processing component and distributes a job to processing component with minimum processing time. This paper implements the job scheduling model on the DEVSJAVA modeling and simulation environment and simulates with a case study to evaluate its efficiency and reliability Empirical results, which are compared to the conventional scheduling policies such as the random scheduling and the round-robin scheduling, show the usefulness of service prediction-based job scheduling.

• Environmental Engineering Research
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• v.24 no.3
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• pp.404-411
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• 2019

In this study, we designed a data-driven model to predict chlorophyll-a using M5P model tree and extreme learning machine (ELM). The Juksan weir in the Youngsan River has high chlorophyll-a, which is the primary indicator of algal bloom every year. Short-term algal bloom prediction is important for environmental management and ecological assessment. Two models were developed and evaluated for short-term algal bloom prediction. M5P is a classification and regression-analysis-based method, and ELM is a feed-forward neural network with fast learning using the least square estimate for regression. The dataset used in this study includes water temperature, rainfall, solar radiation, total nitrogen, total phosphorus, N/P ratio, and chlorophyll-a, which were collected on a daily basis from January 2013 to December 2016. The M5P model showed that the prediction model after one day had the highest performance power and dropped off rapidly starting with predictions after three days. Comparing the performance power of the ELM model with the M5P model, it was found that the performance power of the 1-7 d chlorophyll-a prediction model was higher. Moreover, in a period of rapidly increasing algal blooms, the ELM model showed higher accuracy than the M5P model.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.18 no.34
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• pp.107-114
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• 1995

The objective of this study is to design a one-stage dynamic prediction model with Kalman state space model. For a model verification, it is compared with EWMA(Exponentially Weighed Moving Average) model. The model designed in this research can be extended to process prevention control and quality monitoring.

• Biotechnology and Bioprocess Engineering:BBE
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• v.10 no.2
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• pp.142-148
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• 2005

This paper proposes a cell age model for Penicillium chrysogenum fed-batch cultivation to supply a qualitative insight into morphology-associated dynamics. The average ages of the segregated cell populations, such as growing cells, non-growing cells and intact productive cells, were estimated by this model. A combined model was obtained by incorporating the aver-age ages of the cell sub-populations into a known but modified segregated kinetic model from literature. For simulations, no additional effort was needed for parameter identification since the cell age model has no internal parameters. Validation of the combined model was per-formed by 20 charges of industrial-scale penicillin cultivation. Meanwhile, only two charge-dependent parameters were required in the combined model among approximately 20 parameters in total. The model is thus easily transformed into an adaptive model for a further application in on-line state variables prediction and optimal scheduling.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.5
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• pp.52-61
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• 1993

This paper deals with adaptive control method of a robot manipulator with one-flexible link. ARMA model is used as a prediction and estimation model, and adaptive control scheme consists of parameter estimation part and adaptive controller. Parameter estimation part estimates ARMA model's coefficients by using recursive least-squares(RLS) algorithm and generates the predicted output. Variable forgetting factor (VFF) is introduced to achieve an efficient estimation, and adaptive controller consists of reference model, error dynamics model and minimum prediction error controller. An optimal input is obtained by minimizing input torque, it's successive input change and the error between the predicted output and the reference output.

• Advances in robotics research
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• v.1 no.1
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• pp.41-59
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• 2014

This paper deals with the problem of steering a group of mobile robots along a reference path while maintaining a desired geometric formation. To solve this problem, the overall formation is decomposed into numerous geometric patterns composed of pairs of robots, and the state of the geometric patterns is defined. A control algorithm for the problem is proposed based on the Nash equilibrium strategies incorporating receding horizon control (RHC), also known as model predictive control (MPC). Each robot calculates a control input over a finite prediction horizon and transmits this control input to its neighbor. Considering the motion of the other robots in the prediction horizon, each robot calculates the optimal control strategy to achieve its goals: tracking a reference path and maintaining a desired formation. The performance of the proposed algorithm is validated using numerical simulations.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.227-233
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• 2021

Predictive maintenance has been one of important applications of data science technology that creates a predictive model by collecting numerous data related to management targeted equipment. It does not predict equipment failure with just one or two signs, but quantifies and models numerous symptoms and historical data of actual failure. Statistical methods were used a lot in the past as this predictive maintenance method, but recently, many machine learning-based methods have been proposed. Such proposed machine learning-based methods are preferable in that they show more accurate prediction performance. However, with the exception of some learning models such as decision tree-based models, it is very difficult to explicitly know the structure of learning models (Black-Box Model) and to explain to what extent certain attributes (features or variables) of the learning model affected the prediction results. To overcome this problem, a recently proposed study is an explainable artificial intelligence (AI). It is a methodology that makes it easy for users to understand and trust the results of machine learning-based learning models. In this paper, we propose an explainable AI method to further enhance the explanatory power of the existing learning model by targeting the previously proposedpredictive model [5] that learned data from a core facility (Hyper Compressor) of a domestic chemical plant that produces polyethylene. The ensemble prediction model, which is a black box model, wasconverted to a white box model using the Explainable AI. The proposed methodology explains the direction of control for the major features in the failure prediction results through the Explainable AI. Through this methodology, it is possible to flexibly replace the timing of maintenance of the machine and supply and demand of parts, and to improve the efficiency of the facility operation through proper pre-control.

• Journal of the Korea Concrete Institute
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• v.15 no.1
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• pp.87-94
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• 2003

Construction plan and strength control have limitations in construction production field because it is difficult to predict the form removal strength and development of specified concrete strength. However, we can have reasonable construction plan and strength control if prediction of concrete strength is available. In this study, firstly, the newly proposed strength prediction model with maturity method was compared with the logistic model to test the adaptability. Secondly, the determination of time of form removal was verified through the new strength prediction model. As the results, it is found that investigation of the activation energy that are used to calculate equivalent age is necessary, and new strength prediction model was proved to be more accurate in the strength prediction than logistic model in the early age. Moreover, the use of new model was more reasonable because it has low SSE and high decisive factor. If we adopt new strength prediction model at construction field, we can expect the reduced period of work through the reduced time of form removal.

• Journal of Korean Society of Water and Wastewater
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• v.21 no.5
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• pp.601-607
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• 2007

In order to maintain constant residual chlorine in sedimentation basin, It is necessary to develop real time prediction model of residual chlorine considering water treatment plant data such as water qualities, weather, and plant operation conditions. Based on the operation data acquired from K water treatment plant, prediction models of residual chlorine in sediment basin were accomplished. The input parameters applied in the models were water temperature, turbidity, pH, conductivity, flow rate, alkalinity and pre-chlorination dosage. The multiple regression models were established with linear and non-linear model with 5,448 data set. The corelation coefficient (R) for the linear and non-linear model were 0.39 and 0.374, respectively. It shows low correlation coefficient, that is, these multiple regression models can not represent the residual chlorine with the input parameters which varies independently with time changes related to weather condition. Artificial neural network models are applied with three different conditions. Input parameters are consisted of water quality data observed in water treatment process based on the structure of auto-regressive model type, considering a time lag. The artificial neural network models have better ability to predict residual chlorine at sediment basin than conventional linear and nonlinear multi-regression models. The determination coefficients of each model in verification process were shown as 0.742, 0.754, and 0.869, respectively. Consequently, comparing the results of each model, neural network can simulate the residual chlorine in sedimentation basin better than mathematical regression models in terms of prediction performance. This results are expected to contribute into automation control of water treatment processes.