• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.374-385
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• 2022

In order to investigate the application of 3D base-seismic isolation system in nuclear power plants (NPPs), comprehensive analysis of constitution and design theory for 3-dimensional combined isolation bearing (3D-CIB) was presented and derived. Four different vertical stiffness of 3D-CIB was designed to isolate the nuclear island (NI) building. This paper aimed at investigating the isolation effectiveness of 3D-CIB through modal analysis and dynamic time-history analysis. Numerical results in terms of dynamic response of 3D-CIB, relative displacement response, acceleration and floor response spectra (FRS) of the superstructure were compared to validate the reliability of 3D-CIB in mitigating seismic response. The results showed that 3D-CIB can significantly attenuate the horizontal acceleration response, and a fair amount of the vertical acceleration response reduction of the upper structure was still observed. 3D-CIB plays a significant role in reducing the horizontal and vertical FRS, the vertical FRS basically do not vary with the floor height. The smaller the vertical stiffness of 3D-CIB is, the better the vertical isolation effectiveness is, whereas, it will increase the displacement and the rocking effect of superstructure. Although the advantage of 3D-CIB is that the vertical stiffness can be flexibly adjusted, it should be designed by properly accounting for the balance between the isolation effectiveness and displacement control including rocking effect. The results of this study can provide the technical basis and guidance for the application of 3D-CIB to engineering structure.

• Nuclear Engineering and Technology
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• v.53 no.1
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• pp.148-163
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• 2021

Timely fault identification is important for safe and reliable operation of the electric valve system. Many research works have utilized different data-driven approach for fault diagnosis in complex systems. However, they do not consider specific characteristics of critical control components such as electric valves. This work presents an integrated shallow-deep fault diagnostic model, developed based on signals extracted from DN50 electric valve. First, the local optimal issue of particle swarm optimization algorithm is solved by optimizing the weight search capability, the particle speed, and position update strategy. Then, to develop a shallow diagnostic model, the modified particle swarm algorithm is combined with support vector machine to form a hybrid improved particle swarm-support vector machine (IPs-SVM). To decouple the influence of the background noise, the wavelet packet transform method is used to reconstruct the vibration signal. Thereafter, the IPs-SVM is used to classify phase imbalance and damaged valve faults, and the performance was evaluated against other models developed using the conventional SVM and particle swarm optimized SVM. Secondly, three different deep belief network (DBN) models are developed, using different acoustic signal structures: raw signal, wavelet transformed signal and time-series (sequential) signal. The models are developed to estimate internal leakage sizes in the electric valve. The predictive performance of the DBN and the evaluation results of the proposed IPs-SVM are also presented in this paper.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.94-107
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• 2022

Accidents prevention and mitigation is the highest priority of nuclear power plant (NPP) operation, particularly in the aftermath of the Fukushima Daiichi accident, which has reignited public anxieties and skepticism regarding nuclear energy usage. To deal with accident scenarios more effectively, operators must have ample and precise information about key safety parameters as well as their future trajectories. This work investigates the potential of machine learning in forecasting NPP response in real-time to provide an additional validation method and help reduce human error, especially in accident situations where operators are under a lot of stress. First, a base-case SGTR simulation is carried out by the best-estimate code RELAP5/MOD3.4 to confirm the validity of the model against results reported in the APR1400 Design Control Document (DCD). Then, uncertainty quantification is performed by coupling RELAP5/MOD3.4 and the statistical tool DAKOTA to generate a large enough dataset for the construction and training of neural-based machine learning (ML) models, namely LSTM, GRU, and hybrid CNN-LSTM. Finally, the accuracy and reliability of these models in forecasting system response are tested by their performance on fresh data. To facilitate and oversee the process of developing the ML models, a Systems Engineering (SE) methodology is used to ensure that the work is consistently in line with the originating mission statement and that the findings obtained at each subsequent phase are valid.

• Journal of Advanced Navigation Technology
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• v.27 no.1
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• pp.102-110
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• 2023

The MATS(Missile Assembly Test Set) is manufactured and used to check the function of the missile during the period of development for the guided missile system, and the requirements for power and communication are managed for equipment production. The MATS developer implements software according to the proposed communication standard to guarantee the reliability of the data that communicates with the guided missile. The test bed is built and self-performance evaluation is performed after implementation. And the verification process is performed using the standard equipment. The characteristics of periodic delay for data transmission must be reflected when building a test bed. This paper describes a test bed construction method for data processing with periodic delay. Also This paper compares and evaluates the performance by changing the previously designed algorithm.

• Nuclear Engineering and Technology
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• v.55 no.4
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• pp.1439-1447
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• 2023

The fuel cycle characteristics of the IVG.1M reactor were studied within the framework of the research reactor conversion program to modernize the IVG.1M reactor. Optimum use of the nuclear fuel and reactor was achieved through routine methods which included partial fuel reloading combined with scheduled maintenance operations. Since, the additional problem in planning the fuel cycle of the IVG.1M reactor was the poisoning of the beryllium parts of the core, reflector, and control system. An assessment of the residual power and composition of spent fuel is necessary for the selection and justification of the technology for its subsequent management. Computational studies were performed using the MCNP6.1 program and the neutronics model of the IVG.1M reactor. The proposed scheme of annual partial fuel reloading allows for maintaining a high reactor reactivity margin, stabilizing it within 2-4 β_eff for 20 years, and achieving a burnup of 9.9-10.8 MW × day/kg U in the steady state mode of fuel reloading. Spent fuel immediately after unloading from the reactor can be placed in a transport packaging cask for shipping or safely stored in dry storage at the research reactor site.

• Journal of Marine Life Science
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• v.2 no.2
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• pp.49-55
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• 2017

Four chemical treatments with hydrogen peroxide (H₂O₂), copper sulfate (CuSO₄), potassium permanganate (KMnO₄) and chlorine (Cl₂) were applied to the effluent pond water of a hybrid striped bass saltwater recirculating aquaculture system to compare their oxidation power. Four chemicals were applied at concentrations of 0 (control), 1, 5, 10 and 20 mg/l. An additional concentration of 40 mg/l was included in the chlorine treatment. Water samples from four hybrid striped bass ponds were tested with KMnO₄ and Cl₂. H₂O₂ did not reduce any of BOD, COD and chlorophyll-a, and copper sulfate was only effective on chlorophyll-a for the effluent pond. Removal efficiencies for chlorophyll-a by copper sulfate were 19.2%, 37.5%, 54.2% and 74.1% dose-dependently. Potassium permanganate effectively removed the BOD, COD and chlorophyll-a. The COD removal rates in four fish ponds varied from 15.9% to 31.6% at the concentration of 10 mg/l. Interestingly, Cl₂ did not reduce the BOD and COD at all, but the BOD and COD instead increased drastically with increasing the Cl₂ concentration. The pond water with the highest initial BOD and COD values among the fish ponds tested increased by 350% in the BOD and 150% in the COD at 20 mg/l. Furthermore, Cl₂ did not significantly reduce any types of solid matter in this study, while KMnO₄ seemed to reduce some extent volatile dissolved solid in the fish pond.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.35-40
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• 2021

People counting data is crucial for most business owners, since they can derive meaningful information about customers movement within their businesses. For example, owners of the supermarkets can increase or decrease the number of checkouts counters depending on number of occupants. Also, it has many applications in smart buildings, too. Where it can be used as a smart controller to control heating and cooling systems depending on a number of occupants in each room. There are advanced technologies like camera-based people counting system, which can give more accurate counting result. But they are expensive, hard to deploy and privacy invasive. In this paper, we propose a method and a hardware sensor for counting people passing through a passage or an entrance using IR Infrared sensors. Proposed sensor operates at low voltage, so low power consumption ensure long duration on batteries. Moreover, we propose a new method that distinguishes human body and other objects. Proposed method is inexpensive, easy to install and most importantly, it is real-time. The evaluation of our proposed method showed that when counting people passing one by one without overlapping, recall was 96% and when people carrying handbag like objects, the precision was 88%. Our proposed method outperforms IR Infrared based people counting systems in term of counting accuracy.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.841-852
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• 2023

Gas turbines, which are used as generators for frequency regulation of the domestic power system, are increasing in use due to the carbon-neutral policy, quick startup and shutdown, and high thermal efficiency. Since the gas turbine rotates the turbine using high-temperature flame, the turbine inlet temperature is acting as a key factor determining the performance and lifespan of the device. However, since the inlet temperature cannot be directly measured, the temperature calculated by the manufacturer is used or the temperature predicted based on field experience is applied, which makes it difficult to operate and maintain the gas turbine in a stable manner. In this study, we present a model that can predict the inlet temperature of a reheat gas turbine based on Deep Neural Network (DNN), which is widely used in artificial neural networks, and verify the performance of the proposed DNN based on actual data.

• Journal of Advanced Technology Convergence
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• v.3 no.2
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• pp.9-15
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• 2024

Due to the high inflation rate of dining out, along with changes in group meals or cafeteria services, office workers are increasingly using workplace cafeterias to reduce their meal expenses even slightly. With the recent development of ICT technology, various fields are realizing that not only are smartphones becoming more popular, but they are also becoming an integration of the latest technologies. In this paper, we analyze the current status of cafeterias with a large number of customers and propose ways to improve problems or difficulties. Since most people always carry their smartphones for urgent communication or work tasks, we aim to develop a cafeteria management system that utilizes the NFC function of smartphones. By presenting the process from customer entry to menu selection, it will enable more efficient use of the cafeteria.

• Journal of Drive and Control
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• v.21 no.1
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• pp.22-30
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• 2024

Gearboxes designed for reciprocating motion operating mechanisms operate under conditions where both the load and speed undergo continuous variations. When conducting durability tests on gearboxes designed for such applications, operating the target gearbox under conditions similar to the intended usage is essential. The gearbox must be operated for the required number of cycles to validate its durability under conditions mirroring its intended usage. This study devised an accelerated test method for gearboxes, which reduces operating angles and operational strokes. The reliability of the accelerated test was verified by comparing the stresses imposed on the gears under general and acceleration conditions through multi-body dynamic simulations. The results confirmed that the maximum contact stress levels under normal and accelerated conditions were within a 0.1% error range, indicating a minimal difference in the gear damage rates. However, a difference in the maximum contact stress results between the normal and accelerated conditions was observed when inertial forces acted on the output shaft due to the operational acceleration of the gearbox. Therefore, when conducting this acceleration test, caution should be exercised to ensure that the operational load on the gearbox, which affects inertia, does not significantly deviate from the conditions observed under normal operating conditions.