• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1366-1369
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• 1993

This paper presents a simulation study on two self-learning control systems for a fuzzy prediction model of CO (carbon monoxide) concentration:linear control and fuzzy control. The self-learning control systems are realized by using Widrow-Hoff learning rule which is a basic learning method in neural networks. Simulation results show that the learning efficiency of fuzzy controller is superior to that of linear controller.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.4
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• pp.129-134
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• 2018

Recently, the artificial neural network (ANN) model is a promising technique in the prediction, numerical control, robot control and pattern recognition. We predicted the outside temperature of greenhouse using ANN and utilized the model in greenhouse control. The performance of ANN model was evaluated and compared with multiple regression model(MRM) and support vector machine (SVM) model. The 10-fold cross validation was used as the evaluation method. In order to improve the prediction performance, the data reduction was performed by correlation analysis and new factor were extracted from measured data to improve the reliability of training data. The backpropagation algorithm was used for constructing ANN, multiple regression model was constructed by M5 method. And SVM model was constructed by epsilon-SVM method. As the result showed that the RMSE (Root Mean Squared Error) value of ANN, MRM and SVM were 0.9256, 1.8503 and 7.5521 respectively. In addition, by applying the prediction model to greenhouse heating load calculation, it can increase the income by reducing the energy cost in the greenhouse. The heating load of the experimented greenhouse was 3326.4kcal/h and the fuel consumption was estimated to be 453.8L as the total heating time is $10000^{\circ}C/h$. Therefore, data mining technology of ANN can be applied to various agricultural fields such as precise greenhouse control, cultivation techniques, and harvest prediction, thereby contributing to the development of smart agriculture.

• Journal of Korean Tunnelling and Underground Space Association
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• v.16 no.4
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• pp.361-372
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• 2014

A measurement of tunnel displacement plays an important role for stability analysis and prediction of possible fault zone ahead of tunnel face. In this study, we evaluated characteristics of tunnel behaviour due to the existence and orientation of fault zone based on 3-dimensional finite element numerical analysis. The crown settlement representing tunnel behaviour is acquired at 5 m away from tunnel face in combination with x-Rs control chart analysis based on statistics for trend line and L/C (longitudinal/crown displacement) ratio in order to propose risk management method for fault zone. As a result, x-Rs control chart analysis can enable to predict fault zone in terms of existence and orientation in tunnelling.

• Journal of Korean Society of Forest Science
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• v.109 no.4
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• pp.438-445
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• 2020

The characteristics of forest environmental factors were analyzed using the quantification theory (I) for prediction of the suitable area of erosion control dams. The results indicated that sediment discharge in small forest catchments was significantly correlated with dredging passage (0.7495) and age class (0.6000). In contrast, area (0.3416), slope gradient (0.3207), rainfall (0.3160), altitude (0.2990) and soil type (0.2192) were poorly correlated. Following quantification theory (I), we developed a selection decision table for erosion control dams based on sediment discharge rate as class I (highly suitable site, greater than 2.2496), class II (suitable site, 1.1248~2.2495), and class III (poorly suited site, lower than 1.1247).

• ETRI Journal
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• v.5 no.2
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• pp.17-28
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• 1983

This paper deals with the problem of optimally controlling traffic flows in urban transportation traffic networks. For this, a nonlinear discrete-time model of urban traffic network is first suggested in order to handle the phenomenon of traffic flows such as oversaturatedness and/or undersaturatedness. Then an optimal control problem is formulated and a hierarchical optimization technique is applied, which is based upon a prediction-type two-level method of Hirvonen and Hakkala.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2007.05a
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• pp.229-232
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• 2007

In hot strip rolling, the work roll profile is one of the main factors in predicting and correcting the strip profile. Various studies concerning the wear profile and the thermal crown of work roll have been performed, and the results of these studies have shown that the work roll profile must be predicted accurately so as to efficiently control the strip qualities such as thickness, crown, flatness, and camber. Therefore, a precise prediction model of roll profile is called for in a perfect shape control system. In this paper, a genetic algorithm was applied to improve on the roll profile prediction model in hot strip rolling. In this approach, the optimal design problem is formulated on the basis of a numerical model so as to cover the diverse design variables and objective functions. A genetic algorithm was adopted for conducting design iteration for optimization to determine the coefficient of the numerical model for minimization of errors in the result of the calculated value and the measured data. A comparative analysis showed a satisfactory conformity between them..

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.2
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• pp.784-789
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• 2012

In this paper, a number of anode and reference electrodes that are installed around the target anti-corrosion objects to monitor, appropriate to your situation of a fixed potential and polarization methods can be applied automatically in the system was developed. In particular, this system was configured with a remote electric anti-corrosion automatic control device that have automatically adjust function to uniformal anti-corrosion in all parts of target objects, a corrosion monitoring device to perform the function of corrosion potential detection and a web-based operating program to perform the function of real-time monitoring, control and prediction. Using this system, by preventing oxidative corrosion phenomena can maximize the life of the target anti-corrosion objects.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.6
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• pp.550-557
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• 2005

The multivariate statistical analysis methods, using both multiple linear regression(MLR) and partial least square(PLS), have been applied to predict the reactor properties and the product quality of a direct esterification reactor for polyethylene terephthalate(PET) synthesis. On the basis of the set of data including the flow rate of water vapor, the flow rate of EG vapor, the concentration of acid end groups of a product and other operating conditions such as temperature, pressure, reaction times and feed monomer mole ratio, two multi-variable analysis methods have been applied. Their regression and prediction abilities also have been compared. The prediction results are critically compared with the actual plant data and the other mathematical model based results in reliability. This paper shows that PLS method approach can be used for the reasonably accurate prediction of a product quality of a direct esterification reactor in PET synthesis process.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2137-2142
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• 2003

One of the important performance metrics of the World Wide Web is how fast and precise a request from users will be serviced successfully. Shared Web Caching (SWC) is one of the techniques to improve the performance of the network system. In Shared Web Caching Systems, the key issue is on deciding when and where an item is cached, and also how to transfer the correct and reliable information to the users quickly. Such SWC distributes the items to the proxies which have sufficient capacity such as the processing time and the cache sizes. In this study, the Hot Spot Prediction Algorithm (HSPA) has been suggested to improve the consistent hashing algorithm in the point of the load balancing, hit rate with a shorter response time. This method predicts the popular hot spots using a prediction model. The hot spots have been patched to the proper proxies according to the load-balancing algorithm. Also a simulator is developed to utilize the suggested algorithm using PERL language. The computer simulation result proves the performance of the suggested algorithm. The suggested algorithm is tested using the consistent hashing in the point of the load balancing and the hit rate.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2003.05a
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• pp.176-179
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• 2003

The mean temperature prediction of strip is very important in hot strip finishing mill because of affecting on product quality and shape. Also, temperature can be used by basic information in other on-line control models with affecting control accuracy in factory. So, FE based on-line temperature model was developed for predicting strip mean temperature accurately in various process conditions and factory environments. There are many variables in affecting strip mean temperature in on-line states of factory. But some problems are occurred in considering all variables for making temperature model because of the bad efficiency of regression or fitting analysis. In this report, we have adopted dimensional analysis for solving these problems. We have many variables with dimensions affecting strip temperature but we are able to make non-dimensional variables less than dimensional variables from the combination of dimensional variables caused by PI-Theorem in fluid mechanics. The developed models are divided by two parts. The one is interstand temperature prediction model. The other is roll gap temperature model.