• Nuclear Engineering and Technology
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• v.49 no.7
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• pp.1537-1546
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• 2017

Many thermal-hydraulic tests have been conducted at the Korea Atomic Energy Research Institute for verification of the SMART (System-integrated Modular Advanced ReacTor) design, the standard design approval of which was issued by the Korean regulatory body. In this paper, the contributions of these tests to the standard design approval of SMART are discussed. First, an integral effect test facility named VISTA-ITL (Experimental Verification by Integral Simulation of Transients and Accidents-Integral Test Loop) has been utilized to assess the TASS/SMR-S (Transient and Set-point Simulation/Small and Medium) safety analysis code and confirm its conservatism, to support standard design approval, and to construct a database for the SMART design optimization. In addition, many separate effect tests have been performed. The reactor internal flow test has been conducted using the SCOP (SMART COre flow distribution and Pressure drop test) facility to evaluate the reactor internal flow and pressure distributions. An ECC (Emergency Core Coolant) performance test has been carried out using the SWAT (SMART ECC Water Asymmetric Two-phase choking test) facility to evaluate the safety injection performance and to validate the thermal-hydraulic model used in the safety analysis code. The Freon CHF (Critical Heat Flux) test has been performed using the FTHEL (Freon Thermal Hydraulic Experimental Loop) facility to construct a database from the $5{\times}5$ rod bundle Freon CHF tests and to evaluate the DNBR (Departure from Nucleate Boiling Ratio) model in the safety analysis and core design codes. These test results were used for standard design approval of SMART to verify its design bases, design tools, and analysis methodology.

• Journal of Power Electronics
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• v.15 no.5
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• pp.1305-1317
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• 2015

Distributed generation systems (DGSs) have been getting more and more attention in terms of renewable energy use and new generation technologies in the past decades. The self-excited induction generator (SEIG) occupies an important role in the area of energy conversion due to its low cost, robustness and simple control. Unlike synchronous generators, the SEIG has to absorb capacitive reactive power from the outer device aiming to stabilize the terminal voltage at load changes. This paper presents a novel static VAR compensator (SVC) called a magnetic energy recovery switch (MERS) to serve as a voltage controller in SEIG powered DGSs. In addition, many small scale SEIGs, instead of a single large one, are applied and devoted to promote the generation efficiency. To begin with, an expandable mathematic model based on a d-q equivalent circuit is created for parallel SEIGs. The control method of the MERS is further improved with the objective of broadening its operating range and restraining current harmonics by parameter optimization. A hybrid control strategy is developed by taking both of the stand-alone and grid-connected modes into consideration. Then simulation and experiments are carried out in the case of single and double SEIG(s) generation. Finally, the measurement results verify that the proposed DGS with SVC-MERS achieves a better stability and higher feasibility. The major advantages of the mentioned variable reactive power supplier, when compared to the STATCOM, include the adoption of a small DC capacitor, line frequency switching, simple control and less loss.

• Journal of Digital Contents Society
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• v.16 no.4
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• pp.605-613
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• 2015

This paper addresses the problem to make sparse the projection matrix in pattern recognition method. Recently, the size of computer program is often restricted in embedded systems. It is very often that developed programs include some constant data. For example, many pattern recognition programs use the projection matrix for dimension reduction. To improve the recognition performance, very high dimensional feature vectors are often extracted. In this case, the projection matrix can be very big. Recently, RSR(roated sparse regression) method[1] was proposed. This method has been proved one of the best algorithm that obtains the sparse matrix. We propose three methods to improve the RSR; outlier removal, sampling and elastic net RSR(E-RSR) in which the penalty term in RSR optimization function is replaced by that of the elastic net regression. The experimental results show that the proposed methods are very effective and improve the sparsity rate dramatically without sacrificing the recognition rate compared to the original RSR method.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.28 no.5
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• pp.217-221
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• 2018

Selective laser melting (SLM) technique is one of the additive manufacturing processes, in which functional, complex parts can be directly manufactured by selective melting layers of powder. SLM technique has received great attention due to offering a facile part-manufacturing route and utilizing a hard-to-manufacturing material (e.g. Ti6Al4V). The SLM process allows the accurate fabrication of near-net shaped parts and the significant reduction in the consumption of raw materials when compared to the traditional manufacturing processes such as casting and/or forging. In this study, we focus the high-speed additive manufacturing of Ti6Al4V parts in the aspect of manufacturing time, controlling various process parameters.

• Journal of Korean Association for Spatial Structures
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• v.20 no.2
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• pp.51-58
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• 2020

Recently, deep learning that is the most popular and effective class of machine learning algorithms is widely applied to various industrial areas. A number of research on various topics about structural engineering was performed by using artificial neural networks, such as structural design optimization, vibration control and system identification etc. When nonlinear semi-active structural control devices are applied to building structure, a lot of computational effort is required to predict dynamic structural responses of finite element method (FEM) model for development of control algorithm. To solve this problem, an artificial neural network model was developed in this study. Among various deep learning algorithms, a recurrent neural network (RNN) was used to make the time history response prediction model. An RNN can retain state from one iteration to the next by using its own output as input for the next step. An eleven-story building structure with semi-active tuned mass damper (TMD) was used as an example structure. The semi-active TMD was composed of magnetorheological damper. Five historical earthquakes and five artificial ground motions were used as ground excitations for training of an RNN model. Another artificial ground motion that was not used for training was used for verification of the developed RNN model. Parametric studies on various hyper-parameters including number of hidden layers, sequence length, number of LSTM cells, etc. After appropriate training iteration of the RNN model with proper hyper-parameters, the RNN model for prediction of seismic responses of the building structure with semi-active TMD was developed. The developed RNN model can effectively provide very accurate seismic responses compared to the FEM model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.12
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• pp.8350-8356
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• 2015

The demand for user convenient devices in automotive applications are fast growing, mainly driven by the customer's requirement for higher efficiency and more features. In important such feature is the cold module for cars, which are convenient luxuries that warm or cool drinks placed in the cup holder by means of a thermoelectric element. In present study, we would like to find out the optimal thickness of the cup holder and mounting position of the thermoelectric element through experiments under various testing conditions and thermal analysis. The resulting thermal distribution of the primary area of thermal analysis was found to be lowest when the thickness was 2.5 mm. The temperature distribution was also lowest when the thermal element was positioned underneath the holder (A-type).

• The Journal of the Society of Korean Medicine Diagnostics
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• v.19 no.3
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• pp.141-149
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• 2015

Objectives Recently the fuzzy logic is widely used in the decision making, identification, pattern recognition, optimization in various fields. In this study, we propose the fuzzy logic as the objective method of distinguishing hot and cold, the basis of diagnosis in Korean medicine. Methods We developed fuzzy inference system to distinguish whether the subjects had hot or cold. The cold and hot questionnaire of Korean traditional university textbook, the pulse rate and the DITI value of face used in the system. These three kinds of information were defined as 'fuzzy sets,' and 54 fuzzy rules were established on the basis of clinical practitioners' knowledge. The fuzzy inference was performed by using the Mamdani's method. To evaluate the usefulness of the fuzzy inference system, 200 cases of data measured in the Woosuk university hospital of oriental medicine were used to compare the determining hot, normal, cold results obtained from the experts and from the proposed system. Results As a result, 100 cases of "cold", 54 cases of "normal", and 34 cases of "hot" were matched between the experts and the proposed system. This fuzzy system showed the conformity degree of 94%(${\kappa}=0.853$). Conclusions In this study, we could express the process of distinguishing hot-cold using the fuzzy logic for objectification and quantification of hot-cold identification. This is the first study that introduce a fuzzy logic for distinguish pattern identification. The degree of the heat characteristic of the patients inferred by this system could provide a more objective basis for diagnosing the hot-cold of patients.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.9
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• pp.431-437
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• 2002

Low hemolysis is one of the key factors in the production of successful rotary blood pumps. It is, however, difficult to identify the areas where hemolysis occurs. Computational fluid dynamics(CFD) analysis enables the engineer to predict hemolysis on a computer Fluid dynamics in five different axial flow pumps was analyzed 3-dimensionally using CFD software. The impeller was rotated at a speed which supplied a flow of 5L/min at a pressure difference of 100mmHg. Changes in the turbulent kinetic energy along streamlines through the pumps were computed. Reynolds' shear stress( (equation omitted) ) was calculated using the turbulent kinetic energy. Hemolysis was evaluated based on Reynolds'shear stress and its exposure time(t) : dHb/Hb=3.62$\times$10$^{-5}$ $t^{0.785}$$\tau$$^{2.416}$ . Hemolysis of the pumps was measured in vitro using fresh bovine blood to which citrate phosphate dextrose was added to prevent clotting. A pump flow of 5L/min was maintained at a pressure difference of 100mmHg for 3h. The normalized index of hemolysis(NIH) as measured. Reynolds' shear stress was high behind the impellers. The measured NIH and the calculated hemolysis(dHb/Hb) shoed a good correlation; NIH=0.0003(dHb/Hb) (r=0.90, n=6) in the range of NIH between 0.003 and 1.1. CFD analysis can predict the in vitro results of hemolysis as well as the areas where hemolysis occurs.ysis occurs.

• Journal of the Korea Convergence Society
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• v.9 no.8
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• pp.33-39
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• 2018

In the Internet of Things (IoT) era, objects are connected to each other by wired and wireless networks, and information is exchanged whenever necessary. Channel and network environments change over time; thus, a carrier sensing function that identifies whether signals containing information are present in the channel is essential. The carrier sensing circuit of a wireless communication system determines the receiver sensitivity, and the receiver sensitivity is closely related to the service coverage and service quality of the system. As the receiver sensitivity decreases, the service coverage increases but it becomes sensitive to noise. However, as the receiver sensitivity increases, the service coverage decreases but it becomes insensitive to the noise. Therefore, carrier sensing design and optimization are very important from the viewpoint of the receiver sensitivity and noise sensitivity. This paper proposes an effective carrier sensing technique from the viewpoint of the receiver sensitivity for the long range IoT.

• Journal of the Korea Convergence Society
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• v.9 no.8
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• pp.171-177
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• 2018

In 3D printing technologies, many parameters should be optimized for obtaining a part with higher quality. FDM (fused deposition modeling) printer has also diverse parameters to be optimized. Among them, it can be said that nozzle temperature and moving speed of nozzle are fundamental parameters. Thus, it should be preceded to know the optimal combination of the two parameters in the use of FDM 3D printer. In this paper, a new method is proposed to estimate the range of the stable combinations of the two parameters, based on the single line quality. The proposed method was verified by comparing the results between single line printing and multi-layered single line printing. Based on the comparison, it can be said that the proposed method is very meaningful in that it has a simple test approach and can be easily implemented. In addition, it is very helpful to provide the basic data for the optimization of process parameters.