• Journal of the Korea Society for Simulation
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• v.18 no.3
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• pp.103-112
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• 2009

In a nuclear power plant (NPP), periodic sensor calibrations are required to assure sensors are operating correctly. However, only a few faulty sensors are found to be calibrated. For the safe operation of an NPP and the reduction of unnecessary calibration, on-line calibration monitoring is needed. In this paper, principal component-based Auto-Associative support vector regression (PCSVR) was proposed for the sensor signal validation of the NPP. It utilizes the attractive merits of principal component analysis (PCA) for extracting predominant feature vectors and AASVR because it easily represents complicated processes that are difficult to model with analytical and mechanistic models. With the use of real plant startup data from the Kori Nuclear Power Plant Unit 3, SVR hyperparameters were optimized by the response surface methodology (RSM). Moreover the statistical techniques are integrated with PCSVR for the failure detection. The residuals between the estimated signals and the measured signals are tested by the Shewhart Control Chart, Exponentially Weighted Moving Average (EWMA), Cumulative Sum (CUSUM) and generalized likelihood ratio test (GLRT) to detect whether the sensors are failed or not. This study shows the GLRT can be a candidate for the detection of sensor drift.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.5 no.6
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• pp.583-591
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• 2015

In order to evaluate the performance of TAS applied to the hybrid vehicle of the soft belt driven, acceleration performance and fuel consumption performance is to be superior to the existing vehicle. The key components of belt driven TAS(Torque Assist System), such as the engine, the motor and the battery, The key components of the driven belt TAS, such as the engine, the motor, and the battery, have a significant impact on fuel consumption performance of the vehicle. Therefore, in order to improve the efficiency at the point of view based on the overall system, the study of the power distribution algorithm for controlling the main source powers is necessary. In this paper, we propose the power distribution algorithm, applied the homogeneous analysis method in terms of fuel equivalent, for minimizing the fuel consumption. We have confirmed that the proposed algorithm is contribute to improving the fuel consumption performance satisfied the constraints considering the vehicle status information and the required power through the control parameters to minimize the fuel consumption of the engine. The optimization process of the proposed driving strategy can reduce the trial and error in the research and development process and monitor the characteristics of the control parameter quickly and accurately. Therefore, it can be utilized as a way to derive the operational strategy to minimize the fuel consumption.

• Economic and Environmental Geology
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• v.56 no.3
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• pp.331-341
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• 2023

When creating a soil contamination map using geostatistical techniques, there are various sources that can affect prediction errors. In this study, a grid-based soil contamination map was created from the sampling data of heavy metal concentrations in soil in abandoned mine areas using Ordinary Kriging. Five factors that were judged to affect the prediction error of the soil contamination map were selected, and the variation of the root mean squared error (RMSE) between the predicted value and the actual value was analyzed based on the Leave-one-out technique. Then, using a machine learning algorithm, derived the top three factors affecting the RMSE. As a result, it was analyzed that Variogram Model, Minimum Neighbors, and Anisotropy factors have the largest impact on RMSE in the Standard interpolation. For the variogram models, the Spherical model showed the lowest RMSE, while the Minimum Neighbors had the lowest value at 3 and then increased as the value increased. In the case of Anisotropy, it was found to be more appropriate not to consider anisotropy. In this study, through the combined use of geostatistics and machine learning, it was possible to create a highly reliable soil contamination map at the local scale, and to identify which factors have a significant impact when interpolating a small amount of soil heavy metal data.

• Economic and Environmental Geology
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• v.56 no.5
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• pp.515-532
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• 2023

Bentonite, predominantly consists of expandable clay minerals, is considered to be the suitable buffering material in high-level radioactive waste disposal repository due to its large swelling property and low permeability. Additionally, the bentonite has large cation exchange capacity and specific surface area, and thus, it effectively retards the transport of leaked radionuclides to surrounding environments. This study aims to review the thermodynamic sorption models for four radionuclides (U, Am, Se, and Eu) and eight bentonites. Then, the thermodynamic sorption models and optimized sorption parameters were precisely analyzed by considering the experimental conditions in previous study. Here, the optimized sorption parameters showed that thermodynamic sorption models were related to experimental conditions such as types and concentrations of radionuclides, ionic strength, major competing cation, temperature, solid-to-liquid ratio, carbonate species, and mineralogical properties of bentonite. These results implied that the thermodynamic sorption models suggested by the optimization at specific experimental conditions had large uncertainty for application to various environmental conditions.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.6
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• pp.172-181
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• 2023

In this study, real-time damage evaluation of cable-stayed bridges was conducted for cable damage. ICP type acceleration sensors were used for real-time damage assessment of cable-stayed bridges, and Kinetic Energy Optimization Techniques (KEOT) were used to select the optimal conditions for the location and quantity of the sensors. When a structure vibrates by an external force, KEOT measures the value of the maximum deformation energy to determine the optimal measurement position and the quantity of sensors. The damage conditions in this study were limited to cable breakage, and cable damage was caused by dividing the cable-stayed bridge into four sections. Through FE structural analysis, a virtual model similar to the actual model was created in the real-time damage evaluation method of cable. After applying random oscillation waves to the generated virtual model and model structure, cable damage to the model structure was caused. The two data were compared by defining the response output from the virtual model as a corruption-free response and the response measured from the real model as a corruption-free data. The degree of damage was evaluated by applying the data of the damaged cable-stayed bridge to the Improved Mahalanobis Distance (IMD) theory from the data of the intact cable-stayed bridge. As a result of evaluating damage with IMD theory, it was identified as a useful damage evaluation technology that can properly find damage by section in real time and apply it to real-time monitoring.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.245-250
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• 2023

Digital twin technology, which copies information from real space into virtual space, is being used in a variety of fields.Interest in digital twins is increasing, especially in advanced manufacturing fields such as Industry 4.0-based smart manufacturing. Operating a digital twin system generates a large amount of data, and the data generated has different characteristics depending on the technology field, so it is necessary to efficiently manage resources and use an optimized digital twin platform technology. Research on digital twin pipelines has continued, mainly in the advanced manufacturing field, but research on high-speed pipelines suitable for data in the plant field is still lacking. Therefore, in this paper, we propose a pipeline design method that is specialized for digital twin data in the plant field that is rapidly poured through Apache Kafka. The proposed model applies plant information on a Revit basis. and collect plant-specific data through Apache Kafka. Equipped with a lightweight CFD engine, it is possible to create a digital twin model that is more suitable for the plant field than existing digital twin technology for the manufacturing field.

• Journal of Navigation and Port Research
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• v.46 no.6
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• pp.483-490
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• 2022

Floating LiDAR is a system that provides a new paradigm for wind condition observation, which is essential when creating an offshore wind farm. As it can save time and money, minimize environmental impact, and even reduce backlash from local communities, it is emerging as the industry standard. However, the design and verification of a stable platform is very important, as disturbance factors caused by fluctuations of the buoy affect the reliability of observation data. In Korea, due to the nation's late entry into the technology, a number of foreign equipment manufacturers are dominating the domestic market. The west coast of Korea is a shallow sea environment with a very large tidal difference, so strong currents repeatedly appear depending on the region, and waves of strong energy that differ by season are formed. This paper conducted a study examining buoys suitable for LiDAR operation in the waters of Korea, which have such complex environmental characteristics. In this paper, we will introduce examples of optimized design and verification of ship-type buoys, which were applied first, and derive important concepts that will serve as the basis for the development of various platforms in the future.

• Composites Research
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• v.36 no.6
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• pp.429-434
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• 2023

In this study, aimed at improving the existing acoustic emission sensor for real time monitoring, a macro-fiber composite (MFC) transducer was employed as the acoustic emission sensor in the gas leak detection system. Prior to implementation, structural analysis was conducted to optimize the MFC's design. Consequently, the flexibility of the MFC facilitated excellent adherence to curved pipes, enabling the reception of acoustic emission (AE) signals without complications. Analysis of AE signals revealed substantial variations in parameter values for both high-pressure and low-pressure leaks. Notably, in the parameters of the Fast Fourier Transform (FFT) graph, the change amounted to 120% to 626% for high-pressure leaks compared to the case without leaks, and approximately 9% to 22% for low-pressure leaks. Furthermore, depending on the distance from the leak site, the magnitude of change in parameters tended to decrease as the distance increased. As the results, in the future, not only will it be possible to detect a leak by detecting the amount of parameter change in the future, but it will also be possible to identify the location of the leak from the amount of change.

• KIPS Transactions on Software and Data Engineering
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• v.13 no.1
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• pp.1-16
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• 2024

Over recent years, 6 Sigma has become a key methodology in manufacturing for quality improvement and cost reduction. However, challenges have arisen due to the difficulty in analyzing large-scale data generated by smart factories and its traditional, formal application. To address these limitations, a big data-based 6 Sigma approach has been developed, integrating the strengths of 6 Sigma and big data analysis, including statistical verification, mathematical optimization, interpretability, and machine learning. Despite its potential, the practical impact of this big data-based 6 Sigma on manufacturing processes and management performance has not been adequately verified, leading to its limited reliability and underutilization in practice. This study investigates the efficiency impact of DX SS, a big data-based 6 Sigma, on manufacturing processes, and identifies key success policies for its effective introduction and implementation in enterprises. The study highlights the importance of involving all executives and employees and researching key success policies, as demonstrated by cases where methodology implementation failed due to incorrect policies. This research aims to assist manufacturing companies in achieving successful outcomes by actively adopting and utilizing the methodologies presented.

• Composites Research
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• v.37 no.1
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• pp.7-14
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• 2024

Due to the structural characteristic of a double roller, the double roller can have various deformation behaviors depending on a gap block design, even if dimensions and loading conditions for the double roller are the same. Based on this feature, we propose a strategy for designing the gap block of the carbon-fiber reinforced plastic (CFRP) double roller to minimize defects (e.g., sagging and wrinkling), which can be raised during the product conveying process, with the pursue of the lightweight design. In the suggested strategy, analysis cases are first selected by considering main design parameters and engineering tolerances of the gap block, and then deformation behaviors of these selected cases are extracted using the finite element method (FEM). Here, to obtain the optimal gap block parameters that satisfy the purpose of this study, deformation deviations in the contact area are calculated and compared using the extracted deformation behaviors. Note that the contact area in this work is located between the product and the roller. As a result, through the design method of the gap block proposed in this work, it is possible to construct the CFRP double roller that can significantly decrease the defects without changing the overall sizes of the roller. A detailed method is suggested herein, and the results are evaluated in a numerical way.