• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.4
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• pp.830-837
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• 2011

A turbine control system which has been operated for years in a nuclear power plant was retrofitted with a newly developed digital control system. After completion of the retrofit, turbine valve tests were performed to ensure the integrity of each valve's control function. The sequence of each valve test is composed of a closing process and a reopening process. To minimize megawatt variation which normally occurs during the test sequence, we employed a kind of compensator algorithm in the new digital control system which also have been used in the old system. There were difficulties finding optimal parameter settings for our new compensator algorithm because the power plant didn't allow us to perform necessary tuning procedures while the turbine is on load operation. Therefore an alternative measure for the compensator tuning which is independent of the turbine actual operation had to be implemented. So, a process model for the test was required to overcome this situation. We analyzed the operation data of the test and implemented the process model by use of input and output variable relations. Also we verified the process model by use of another condition's operating data. The result shows that the output of model is similar to the actual operation data.

• International Journal of Computer Science & Network Security
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• v.23 no.6
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• pp.115-120
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• 2023

Plant leaf diseases and destructive insects are major challenges that affect the agriculture production of the country. Accurate and fast prediction of leaf diseases in crops could help to build-up a suitable treatment technique while considerably reducing the economic and crop losses. In this paper, Convolutional Neural Network based model is proposed to detect leaf diseases of a plant in an efficient manner. Convolutional Neural Network (CNN) is the key technique in Deep learning mainly used for object identification. This model includes an image classifier which is built using machine learning concepts. Tensor Flow runs in the backend and Python programming is used in this model. Previous methods are based on various image processing techniques which are implemented in MATLAB. These methods lack the flexibility of providing good level of accuracy. The proposed system can effectively identify different types of diseases with its ability to deal with complex scenarios from a plant's area. Predictor model is used to precise the disease and showcase the accurate problem which helps in enhancing the noble employment of the farmers. Experimental results indicate that an accuracy of around 93% can be achieved using this model on a prepared Data Set.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.107-112
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• 2024

Plant leaf diseases and destructive insects are major challenges that affect the agriculture production of the country. Accurate and fast prediction of leaf diseases in crops could help to build-up a suitable treatment technique while considerably reducing the economic and crop losses. In this paper, Convolutional Neural Network based model is proposed to detect leaf diseases of a plant in an efficient manner. Convolutional Neural Network (CNN) is the key technique in Deep learning mainly used for object identification. This model includes an image classifier which is built using machine learning concepts. Tensor Flow runs in the backend and Python programming is used in this model. Previous methods are based on various image processing techniques which are implemented in MATLAB. These methods lack the flexibility of providing good level of accuracy. The proposed system can effectively identify different types of diseases with its ability to deal with complex scenarios from a plant's area. Predictor model is used to precise the disease and showcase the accurate problem which helps in enhancing the noble employment of the farmers. Experimental results indicate that an accuracy of around 93% can be achieved using this model on a prepared Data Set.

• Transactions of the Korean hydrogen and new energy society
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• v.24 no.6
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• pp.487-494
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• 2013

In this study, solutions for a liquid pool spreading model with continuous and instantaneous release are discussed based on the model used in the FERC's report. The effects of the release time on the liquid pool volume and radius are investigated for the continuous release. For the continuous release with the frictional resistance force in the liquid pool spreading model, the vaporization time decreases as the release time increases. On the other hand, for the continuous release without the frictional resistance force in the liquid pool spreading model, the vaporization time increases as the release time increases. These phenomena are deeply related to the pool radius. In addition, the effects of the initial pool radius for the instantaneous release in the liquid pool spreading model are discussed. For the case with the frictional resistance force in the liquid pool spreading model, as reducing release time in the model with the frictional resistance force for the continuous release, the solution for a continuous release approaches to that for an instantaneous release. On the contrary to this, the pool volume and radius for the instantaneous release without the frictional resistance force are totally different from those for the continuous release without the frictional resistance force.

• The Journal of Information Systems
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• v.29 no.1
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• pp.75-91
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• 2020

Purpose Retrofit, defined to be addition of new technologies or features to the old system to increase efficiency or to abate GHG emissions, is considered as an important alternative for the old coal-fired power plant. The purpose of this study is to propose mathematical method to model multiple alternative retrofit in Generation Capacity Expansion Planning(GCEP) problem, and to get insight to the retrofit patterns from realistic case studies. Design/methodology/approach This study made a multi-alternative retrofit GECP model by adopting some new variables and equations to the existing GECP model. Added variables and equations are to ensure the retrofit feature that the life time of retrofitted plant is the remaining life time of the old power plant. We formulated such that multiple retrofit alternatives are simultaneously compared and the best retrofit alternative can be selected. And we found that old approach to model retrofit has a problem that old plant with long remaining life time is retrofitted earlier than the one with short remaining life time, fixed the problem by some constraints with some binary variables. Therefore, the proposed model is formulated into a mixed binary programming problem, and coded and run using the GAMS/cplex. Findings According to the empirical analysis result, we found that approach to model the multiple alternative retrofit proposed in this study is comparing simultaneously multiple retrofit alternatives and select the best retrofit satisfying the retrofit features related to the life time. And we found that retrofit order problem is cleared. In addition, the model is expected to be very useful in evaluating and developing the national policies concerning coal-fired power plant retrofit.

• Industry Promotion Research
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• v.8 no.4
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• pp.47-60
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• 2023

The purpose of this study is to examine the causal relationship between self-efficacy and safety consciousness of health belief factors and how they affect the disaster prevention behavior of construction workers using TBM. To this end, a research model is presented that applies the main variables of the Health Belief Theory, a social psychological health behavior change model developed to predict and explain health-related behaviors. To empirically verify the research model of this study, a survey was conducted among construction workers who have experience in using TBMs for chemical plant construction. The results showed that, first, the perceived severity of construction workers utilizing chemical plant construction has a significant effect on self-efficacy and safety consciousness; second, the perceived probability of construction workers utilizing chemical plant construction has a significant effect on self-efficacy and safety consciousness. Third, the perceived obstacles of construction workers utilizing chemical plant construction have a significant effect on self-efficacy and safety consciousness. Fourth, the perceived benefits of construction workers utilizing chemical plant construction were found to have a significant effect on self-efficacy and safety awareness. The purpose of this study is to reduce critical accidents through disaster prevention behavior of chemical plant construction workers through TBM.

• Journal of Biosystems Engineering
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• v.20 no.3
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• pp.262-274
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• 1995

A rice mill pilot plant was designed and developed in the basis of the simulation results on the mill plants. The performance of the developed rice mill plant was evaluated, and the simulation model on the mill system was validated with the experimental data in the mill plant. The results of this study were as followings : 1. A rice mill pilot plant with the capacity of 0.5 t/h was designed and developed. 2. The hulled ratio of the mill plant was 87.3%, and the milled rice recovery and the head rice recovery of the cleaned rice were 74% and 87% , respectively. The degree of milling of the cleaned rice was 10.6% with a high polish. The intensity of the cleaned rice appeared high compared with that of the milled rice in the analysis of whiteness test using an image processing system. 3. The bottleneck, processing time, and production amount of the developed mill system almost coincided with those of the simulation of the rice mill plant. The developed simulation model of the rice mill plant was proven to be applicable to the design of a rice mill plant through experiments.

• Journal of the Korea Society for Simulation
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• v.31 no.1
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• pp.29-41
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• 2022

As one of the alternatives to solve the problem of unstable food supply and demand imbalance caused by abnormal climate change, the need for plant factories is increasing. Airflow in plant factory is recognized as one of important factor of plant which influence transpiration and heat transfer. On the other hand, Digital Twin (DT) is getting attention as a means of providing various services that are impossible only with the real system by replicating the real system in the virtual world. This study aimed to develop a digital twin model for airflow prediction that can predict airflow in various situations by applying the concept of digital twin to a plant factory in operation. To this end, first, the mathematical formalism of the digital twin model for airflow analysis in plant factories is presented, and based on this, the information necessary for airflow prediction modeling of a plant factory in operation is specified. Then, the shape of the plant factory is implemented in CAD and the DT model is developed by combining the computational fluid dynamics (CFD) components for airflow behavior analysis. Finally, the DT model for high-accuracy airflow prediction is completed through the validation of the model and the machine learning-based calibration process by comparing the simulation analysis result of the DT model with the actual airflow value collected from the plant factory.

• Journal of the Society of Disaster Information
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• v.16 no.4
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• pp.842-848
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• 2020

Purpose: It is important to operate safely and economically in obsolescent power plant facilities. Economical operation is related in the balance of the supply and demand. Safety operation predicts the possible risks in the facilities and then, takes measures to the facilities. For the monitoring of the power plant facilities, we needs several kinds of the sensing system. From the sensors data, we can predict the possible risk. Method: We installed the acoustic, vibration, electric and smoke sensors in the power plant facilities. Using the data, we developed 3 kinds of prediction models, such as, demand prediction, plant engine abnormal prediction model, and risk prediction model. Results: Accuracy of the demand prediction model is over 90%. The other models make a stable operation of the system. Conclusion: For the sustainable operation of the obsolescent power plant, we developed 3 kinds of AI prediction models. The model apply to JB company's power plant facilities.

• Journal of Veterinary Science
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• v.24 no.4
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• pp.53.1-53.12
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• 2023

Background: Mammalian orthoreovirus type 3 (MRV3), which is responsible for gastroenteritis in many mammalian species including pigs, has been isolated from piglets with severe diarrhea. However, the use of pig-derived cells as an infection model for swine-MRV3 has rarely been studied. Objectives: This study aims to establish porcine intestinal organoids (PIOs) and examine their susceptibility as an in vitro model for intestinal MRV3 infection. Methods: PIOs were isolated and established from the jejunum of a miniature pig. Established PIOs were characterized using polymerase chain reaction (PCR) and immunofluorescence assays (IFAs) to confirm the expression of small intestine-specific genes and proteins, such as Lgr5, LYZI, Mucin-2, ChgA, and Villin. The monolayered PIOs and three-dimensional (3D) PIOs, obtained through their distribution to expose the apical surface, were infected with MRV3 for 2 h, washed with Dulbecco's phosphate-buffered saline, and observed. Viral infection was confirmed using PCR and IFA. We performed quantitative real-time reverse transcription-PCR to assess changes in viral copy numbers and gene expressions linked to intestinal epithelial genes and antiviral activity. Results: The established PIOs have molecular characteristics of intestinal organoids. Infected PIOs showed delayed proliferation with disruption of structures. In addition, infection with MRV3 altered the gene expression linked to intestinal epithelial cells and antiviral activity, and these effects were observed in both 2D and 3D models. Furthermore, viral copy numbers in the supernatant of both models increased in a time-dependent manner. Conclusions: We suggest that PIOs can be an in vitro model to study the infection mechanism of MRV3 in detail, facilitating pharmaceutical development.