• Journal of Digital Contents Society
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• v.19 no.4
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• pp.749-756
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• 2018

In this study, we developed a prediction model for greenhouse control using machine learning technique. The prediction model was developed using measured data (2016) on greenhouse in the Protected Horticulture Research Institute. In order to improve the predictive performance of model and to ensure the reliability of data, the dimension of the data was reduced by correlation analysis. The dataset were divided into spring, summer, autumn, and winter considering the seasonal characteristics. An artificial neural network, recurrent neural network, and multiple regression model were constructed as a machine leaning based prediction model and evaluated by comparative analysis with real dataset. As a result, ANN showed good performance in selected dataset, while MRM showed good performance in full dataset.

• Korean Journal of Agricultural Science
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• v.49 no.3
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• pp.679-690
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• 2022

As the supply and demand of pork has become a significant concern in Korea, controlling it has become a critical challenge for the industry. However, compared to the demand for pork, which has relatively stable consumption, it is not easy to maintain a stable supply. As the preparation of measures for a supply-demand crisis response and supply control in the pig industry has emerged as an important task, it has become necessary to establish a stable supply model and create an appropriate manual. In this study, a pork supply prediction model is constructed using reported data from the pig traceability system. Based on the derived results, a method for determining the supply-demand crisis stage using a statistical approach was proposed. From the results of the analysis, working days, African swine fever, heat wave, and Covid-19 were shown to affect the number of pigs graded in the market. A test of the performance of the model showed that both in-sample error rate and out-sample error rate were between 0.3 - 7.6%, indicating a high level of predictive power. Applying the forecast, the distribution of the confidence interval of the predicted value was established, and the supply crisis stage was identified, evaluating supply-demand conditions.

• Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
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• 2007.05a
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• pp.83-125
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• 2007

Increasing energy costs have caused profitability problems for paper suppliers. Therefore unprofitable lines are being closed down. The actions aiming for improved profits are focused either on cost savings or on increasing the capacity of the remaining machines. The runnability of a paper machine and its total efficiency have a significant effect on energy consumption. Producing one ton of waste paper consumes at least as much energy as producing the same amount of sellable end product. New automation solutions enable significant cost-effective improvements to the total efficiency of a line without large investment projects. The measures focus on minimizing changes, interruptions, interruption recovery times and grade change times. Newest actuators, online quality measurements and wet end analysators create an improvement potential, which can be optimally implemented with the latest machine direction control solutions, based on model predictive control concepts. Equally, drying management is significant to the energy consumption. The newest control strategies optimize the use of various drying actuators for different situations; either by responding to changes as efficiently as possible or by using only the cheapest energy sources in stable situations. An even steam supply, which is vital for paper machines, is achieved with control for the power plant steam network. This makes possible to avoid the delays upon starting the paper machine and assure an even steam supply for the drying section and the actuators. This document describes means which have brought significant energy and raw material savings for paper machines. Metso Automation has provided efficiency improvement packages, which are usually based on optimized control of dry weight and drying in all running conditions. The solutions are based on performance analysis, on which the estimations for improvement potential and the necessary actions are based on. Typically benefits on an annual level have been from hundreds of thousands of euros to over one million euro. For example, variations in dry weight have been decreased more than 50%. The results are presented with a few examples. Additionally, the analysis models, adjustment solutions and the changes in running methods with which the results were achieved, are presented.

• Journal of Drive and Control
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• v.13 no.4
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• pp.59-67
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• 2016

This paper proposes a model-prediction-based collision-avoidance algorithm for excavators for which the recursive-least-squares (RLS) estimation of the excavator's rotational inertia is used. To estimate the rotational inertia of the excavator, the RLS estimation with multiple forgetting and two updating rules for the nominal parameter and the forgetting factors was conducted based on the excavator-swing dynamics. The average value of the estimated rotational inertia that is for the minimizing effects of the estimation error was computed using the recursive-average method with forgetting. Based on the swing dynamics, the computed average of the rotational inertia, the damping coefficient for braking, and the excavator's braking angle were predicted, and the predicted braking angle was compared with the detected-object angle for a safety evaluation. The safety level defined in this study consists of the three levels safe, warning, and emergency braking. The analytical rotational-inertia-based performance evaluation of the designed estimation algorithm was conducted using a typical working scenario. The results of the safety evaluation show that the predictive safety-evaluation algorithm of the proposed model can evaluate the safety level of the excavator during its operation.

• Journal of Intelligence and Information Systems
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• v.5 no.1
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• pp.35-48
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• 1999

Many organizational contexts should be considered in designing EDI controls to make control systems effective and efficient. This paper gives a description of the neural network model for suggesting the extent of effective EDI controls for a company that has specific organizational environment. Feedforward backpropagation neural network models are designed to predict the state of 12 modes of EDI controls from the sate of environment. The predictive power of the system is compared with that of multivariate regression analysis to evaluate the effectiveness of using neural network model in predicting the level of EDI controls. The results show that the neural network model outperforms regression analysis in predictive accuracy. The controls that have high estimated value in the model are likely to be critical controls and EDI auditor or management can enhance investment of IS resources to enhance these controls.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.5
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• pp.153-160
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• 2020

Energy Consumption Predictions for Industries has a prominent role to play in the energy management and control system as dynamic and seasonal changes are occurring in energy demand and supply. This paper introduces and explores the steel industry's predictive models of energy consumption. The data used includes lagging and leading reactive power lagging and leading current variable, emission of carbon dioxide (tCO2) and load type. Four statistical models are trained and tested in the test set: (a) Linear Regression (LR), (b) Radial Kernel Support Vector Machine (SVM RBF), (c) Gradient Boosting Machine (GBM), and (d) Random Forest (RF). Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used for calculating regression model predictive performance. When using all the predictors, the best model RF can provide RMSE value 7.33 in the test set.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.5
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• pp.435-444
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• 2023

Suitable environmental conditions in Saemangeum frequently favor phytoplankton growth. There have been occurrences of sudden phytoplankton blooms, surpassing the algae management standards. A model was designed to prevent such blooms using scientific predictive techniques to forecast and regulate the possibility of phytoplankton blooms. We propose effective and efficient algae control measures concerning every phytoplankton species optimized through the policy control of nutrients (DIN, PO4-P) from rivers and controlling lake salinity using gate operations. The probability of phytoplankton blooms was initially forecast using an artificial neural network algorithm based on observations. The model's Kappa number fluctuated from 0.7889 to 1.0000, indicating good to excellent predictive power. The Garson algorithm was then utilized to assess the significance of explanatory variables for every species. Meanwhile, the probability of phytoplankton blooms was anticipated depending on the DIN and salinity value changes. Therefore, the model predicted the precise DIN and salinity concentrations to inhibit phytoplankton blooms for each species. Hence, the green algae model can create effective proactive measures to avoid future phytoplankton blooms in enormous artificial lakes.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.291-295
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• 2022

The transition to wartime operational control during the term of office, which was the promise of the Moon Jae Inn administration, fell through. More than 70 years after it was transferred during the Korean War in 1950, the policy of converting wartime operational control has been repeatedly decided and reversed several times. This conversion of wartime operational control is a national policy directly related to our security, and it is most important to understand the determinants of the administration's conversion to wartime operational control. This paper selects two cases of adjustment of wartime operational control policy during the Lee Myung Bak administration in 2006 and 2010 during the Roh Moo Hyun administration as the subject of the study and expects to gain not only policy predictive power but also successful policy execution at the time of the two administration' policy changes.

• Journal of the Korean Society of Safety
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• v.29 no.1
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• pp.47-53
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• 2014

The application of advanced Main Control Room(MCR) is accompanied with lots of changes and different forms and features through the virtue of new digital technologies. The characteristics of these digital technologies and devices give many opportunities to the interface management, and can be integrated into a compact single workstation in advanced MCR so that workers can operate the plant with minimum physical burden under any operation conditions. However, these devices may introduce new types of human errors and thus a means to evaluate and prevent such errors is needed, especially those related to characteristics of digital devices. This paper reviewed the new type of human error hazards of tasks based on digital devices and surveyed researches on physiological assessment related to human error. An experiment was performed to verify human error hazards by physiological responses such as EEG which was measured to evaluate the cognitive workload of operators. And also, the performances of four tasks which are representative in human error hazard tasks based on digital devices were compared. Response time, ${\beta}$ power spectrum rate of each task by EEG, and mental workload by NASA-TLX were evaluated. In the results of the experiment, the rate of the ${\beta}$ power was increased in the task 1 and task 4 which are searching and navigating task and memory task of hierarchical information, respectively. In case of the mental workload, in most of evaluation items, task 1 and 4 were highly rated comparatively. In this paper, human error hazards might be identified by highly cognitive workload. Conclusively, it was concluded that the predictive method which is utilized in this paper and an experimental verification can be used to ensure the safety when applying the digital devices in Nuclear Power Plants (NPPs).

• Nuclear Engineering and Technology
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• v.54 no.2
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• pp.608-616
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• 2022

Power peaking factors (PPF) is an important parameter for safe and efficient reactor operation. There are several methods to calculate the PPF at TRIGA research reactors such as MCNP and TRIGLAV codes. However, these methods are time-consuming and required high specifications of a computer system. To overcome these limitations, artificial intelligence was introduced for parameter prediction. Previous studies applied the neural network method to predict the PPF, but the publications using the ANFIS method are not well developed yet. In this paper, the prediction of PPF using the ANFIS was conducted. Two input variables, control rod position, and neutron flux were collected while the PPF was calculated using TRIGLAV code as the data output. These input-output datasets were used for ANFIS model generation, training, and testing. In this study, four ANFIS model with two types of input space partitioning methods shows good predictive performances with R² values in the range of 96%-97%, reveals the strong relationship between the predicted and actual PPF values. The RMSE calculated also near zero. From this statistical analysis, it is proven that the ANFIS could predict the PPF accurately and can be used as an alternative method to develop a real-time monitoring system at TRIGA research reactors.