• IE interfaces
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• v.23 no.4
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• pp.311-318
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• 2010

In the daily multi-reservoir operating problem, monthly storage targets can be used as principal operational guidelines. In this study, we tested the use of a simple back-propagation Artificial Neural Network (ANN) model to derive monthly storage guideline for daily Coordinated Multi-reservoir Operating Model (CoMOM) of the Han-River basin. This approach is based on the belief that the optimum solution of the daily CoMOM has a good performance, and the ANN model trained with the results of daily CoMOM would produce effective monthly operating guidelines. The optimum results of daily CoMOM is used as the training set for the back-propagation ANN model, which is designed to derive monthly reservoir storage targets in the basin. For the input patterns of the ANN model, we adopted the ratios of initial storage of each dam to the storage of Paldang dam, ratios of monthly expected inflow of each dam to the total inflow of the whole basin, ratios of monthly demand at each dam to the total demand of the whole basin, ratio of total storage of the whole basin to the active storage of Paldang dam, and the ratio of total inflow of the whole basin to the active storage of the whole basin. And the output pattern of ANN model is the optimal final storages that are generated by the daily CoMOM. Then, we analyzed the performance of the ANN model by using a real-time simulation procedure for the multi-reservoir system of the Han-river basin, assuming that historical inflows from October 1st, 2004 to June 30th, 2007 (except July, August, September) were occurred. The simulation results showed that by utilizing the monthly storage target provided by the ANN model, we could reduce the spillages, increase hydropower generation, and secure more water at the end of the planning horizon compared to the historical records.

• Nuclear Engineering and Technology
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• v.17 no.4
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• pp.247-255
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• 1985

This paper describes a new method for the design of variable structure model-following control systems(VSMFC). This design concept is developed using the theory of variable structure systems (VSS) and slide mode. The new results are presented on the sliding control methodology to achieve accurate tracking for a class of nonlinear, multi-input multi-output(MIMO), time varying systems in the presence of parameter variations. The design requires little computational effort. The dynamic response is insensitive to parameter variations. The feasibility and the advantages of the method are illustrated by applying it to a 1000 MWe boiling water reactor(BWR). The control is studied in the range of 85%∼90% of rated power for load-following control. A set of 12 nonlinear differential equations is used to simulate the total plant. A 6-th order linear model has been developed from these equations at 85% of rated power. The obtained controller is shown by simulations to be able to compensate for a plant parameter variation over a wide power range.

• Journal of the Korean Association of Geographic Information Studies
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• v.12 no.4
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• pp.95-112
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• 2009

The river cross section in the input/output data which are needed in the area of river flow analysis is very important factor. The bottom elevation of actual river cross section has to be correctly reflected to obtain correct results when two dimensional flow analysis is conducted for natural river. But to reflect virtually the bottom elevation of river cross section is impossible. The objective of this study is to suggest a method for creating equi-distance river cross section by using both HEC section and ArcView and constructing the finite element mesh. The main channels of Han and Nakdong river were selected and equi-distance river cross sections constructed in this study have shown good agreement with the observed river cross sections. In addition, high quality finite element meshes can be applied to many areas of study such as finite element analysis for water quality and two dimensional flow analysis using the suggested method for equi-distance river cross sections in this study.

• Advances in concrete construction
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• v.13 no.2
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• pp.123-132
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• 2022

This paper aims to adapt Multilinear regression (MLR) to predict the strength and toughness of SIFCON containing various pozzolanic materials. Slurry Infiltrated Fibrous Concrete (SIFCON) is one of the most common terms used in concrete manufacturing, known for its benefits such as high ductility, toughness and high ultimate strength. Assessment of compressive strength (CS.), flexural strength (F.S.), splitting tensile strength (STS), dynamic elasticity modulus (DME) and impact energy (I.E.) using the experimental approach is too costly. It is time-consuming, and a slight error can lead to a repeat of the test and, to solve this, alternative methods are used to predict the strength and toughness properties of SIFCON. In the present study, the experimentally investigated SIFCON data about various mix proportions are used to predict the strength and toughness properties using regression analysis-multilinear regression (MLR) models. The input parameters used in regression models are cement, fibre, fly ash, Metakaolin, fine aggregate, blast furnace slag, bottom ash, water-cement ratio, and the strength and toughness properties of SIFCON at 28 days is the output parameter. The models are developed and validated using data obtained from the experimental investigation. The investigations were done on 36 SIFCON mixes, and specimens were cast and tested after 28 days of curing. The MLR model yields correlation between predicted and actual values of the compressive strength (C.S.), flexural strength, splitting tensile strength, dynamic modulus of elasticity and impact energy. R-squared values for the relationship between observed and predicted compressive strength are 0.9548, flexural strength 0.9058, split tensile strength 0.9047, dynamic modulus of elasticity 0.8611 for impact energy 0.8366. This examination shows that the MLR model can predict the strength and toughness properties of SIFCON.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.297-299
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• 2021

Predicting future productivity of tight oil is an important task for analyzing residual oil recovery and reservoir behavior. In general, productivity prediction is made using the decline curve analysis(DCA). In this study, we intend to propose an effective model for predicting future production using deep learning-based recurrent neural networks(RNN), LSTM, and GRU algorithms. As input variables, the main parameters are oil, gas, water, which are calculated during the production of tight oil, and the type curve calculated through various cluster analyzes. the output variable is the monthly oil production. Existing empirical models, the DCA and RNN models, were compared, and an optimal model was derived through hyperparameter tuning to improve the predictive performance of the model.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.37 no.1
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• pp.106-111
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• 2024

A triboelectric nanogenerator is a promising energy harvester operated by the combined mechanism of electrostatic induction and contact electrification. It has attracting attention as eco-friendly and sustainable energy generators by harvesting wasting mechanical energies. However, the power generated in the natural environment is accompanied by low frequencies, so that the output power under such input conditions is normally insufficient amount for a variety of industrial applications. In this study, we introduce a non-contact rotational triboelectric nanogenerator using pedaling and gear systems (called by P-TENG), which has a mechanism that produces high power by using rack gear and pinion gear when a large force by a pedal is given. We design the system can rotate the shaft to which the rotor is connected through the conversion of vertical motion to rotational motion between the rack gear and the pinion gear. Furthermore, the system controls the one directional rotation due to the engagement rotation of the two pinion gears and the one-way needle roller bearing. The TENG with a 2 mm gap between the rotor and the stator produces about the power of 200 ㎼ and turns on 82 LEDs under the condition of 800 rpm. We expect that P-TENG can be used in a variety of applications such as operating portable electronics or sterilizing contaminated water.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.20 no.1
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• pp.1-6
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• 2024

Crud is a corrosion deposit that forms in equipments and piping of nuclear reactor's primary systems. When crud circulates through the reactor's primary system coolant and adheres to the surface of the nuclear fuel cladding tube, it can lead to the Axial Offset Anomaly (AOA) phenomenon. This occurrence is known to potentially reduce the output of a nuclear power plant or to necessitate an early shutdown. Consequently, worldwide nuclear power plants have employed ultrasonic cleaning methods since 2000 to mitigate crud deposition, ensuring stable operation and economic efficiency. This paper details the system configuration of ultrasonic nuclear fuel cleaning equipment, outlining the function of each component. The objective is to contribute to the local domestic production of ultrasonic nuclear fuel cleaning equipment. Additionally, the paper introduces a method for accurately measuring the weight of removed crud, a crucial factor in assessing cleaning effectiveness and providing input data for the BOA code used in core safety evaluations. Accurate measurement of highly radioactive filters containing crud is essential, and weighing them underwater is a common practice. However, the buoyancy effect during underwater weighing may lead to an overestimation of the collected crud's weight. To address this issue, the paper proposes a formula correcting for buoyancy errors, enhancing measurement accuracy. This improved weight measurement method, accounting for buoyancy effects in water, is expected to facilitate the quantitative assessment of filter weights generated during chemical decontamination and system operations in nuclear power plants.

• Journal of Digital Convergence
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• v.13 no.8
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• pp.321-329
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• 2015

This study investigates effects of hardware and telecommunication and software service divided by ICT service on each 5 transportations to explore convergence of ICT and Transportation. Research models are production inducing effects, Added Value inducing effects of Demand-Driven model and Shortage cost effects of Supply-Driven model by using data for 2010~2012 of Input-Output Table. Results are that network and software service effects are more impact than hardware effects on transportations. Especially, hardware is impacted heavily on production inducing effect, telecommunications and software services has had a significant impact on the production inducing effect and Shortage cost effects. In addition, by each detail the transportation industries, packages and other transport and road transport is influenced greatly from ICT. On the other hand, rail and water transport are relatively lower impact by ICT, However, the effects of rail and water transport by ICT is grater than investment ratio of ICT. As a result, increasing investment in the ICT services could contribute to development of rail and water transport development.

• Journal of Korea Water Resources Association
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• v.43 no.6
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• pp.507-516
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• 2010

The purpose of this study is to propose the alternative development method by means of determining the optimal project size from the economic viewpoint, improving the existing method depending on engineering aspects. To this end, this study defined the flood mitigation projects as the production activities carried out by inputs and outputs, and proposed the alternative development method on the basis of optimizing input and output combinations. This paper, as the case study of the proposed method, developed alternatives for the flood mitigation planning of Youngsan River Basin by determining the optimal project scale. As the result of determining optimal project size, the net benefit of the optimal alternative tended to be dependent on the net benefits of the large individual proposals. Due to such problem, the effect of relatively small individual proposals are underestimated and possibly be excluded from the optimal alternative, which may result in exclusion of the potential damaged regions protected by them from the flood mitigation project. Thus for the selective flood protection by region, individual proposals need to be categorized into the global measures and local measures according to the flood protection area.

• Journal of Korea Water Resources Association
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• v.53 no.4
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• pp.273-283
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• 2020

Quantitative forecasting of groundwater levels for the assessment of groundwater variation and vulnerability is very important. To achieve this purpose, various time series analysis and machine learning techniques have been used. In this study, we developed a prediction model based on LSTM (Long short term memory), one of the artificial neural network (ANN) algorithms, for predicting the daily groundwater level of 11 groundwater wells in Hankyung-myeon, Jeju Island. In general, the groundwater level in Jeju Island is highly autocorrelated with tides and reflected the effects of precipitation. In order to construct an input and output variables based on the characteristics of addressing data, the precipitation data of the corresponding period was added to the groundwater level data. The LSTM neural network was trained using the initial 365-day data showing the four seasons and the remaining data were used for verification to evaluate the fitness of the predictive model. The model was developed using Keras, a Python-based deep learning framework, and the NVIDIA CUDA architecture was implemented to enhance the learning speed. As a result of learning and verifying the groundwater level variation using the LSTM neural network, the coefficient of determination (R²) was 0.98 on average, indicating that the predictive model developed was very accurate.