• Molecules and Cells
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• v.39 no.5
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• pp.382-388
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• 2016

Inherited peripheral neuropathies (IPN), which are a group of clinically and genetically heterogeneous peripheral nerve disorders including Charcot-Marie-Tooth disease (CMT), exhibit progressive degeneration of muscles in the extremities and loss of sensory function. Over 70 genes have been reported as genetic causatives and the number is still growing. We prepared a targeted gene panel for IPN diagnosis based on next generation sequencing (NGS). The gene panel was designed to detect mutations in 73 genes reported to be genetic causes of IPN or related peripheral neuropathies, and to detect duplication of the chromosome 17p12 region, the major genetic cause of CMT1A. We applied the gene panel to 115 samples from 63 non-CMT1A families, and isolated 15 pathogenic or likelypathogenic mutations in eight genes from 25 patients (17 families). Of them, eight mutations were unreported variants. Of particular interest, this study revealed several very rare mutations in the SPTLC2, DCTN1, and MARS genes. In addition, the effectiveness of the detection of CMT1A was confirmed by comparing five 17p12-nonduplicated controls and 15 CMT1A cases. In conclusion, we developed a gene panel for one step genetic diagnosis of IPN. It seems that its time- and cost-effectiveness are superior to previous tiered-genetic diagnosis algorithms, and it could be applied as a genetic diagnostic system for inherited peripheral neuropathies.

• Journal of the Korean Society of Visualization
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• v.15 no.3
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• pp.20-26
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• 2017

The two-fluid equations are widely used to simulate two-phase flows in a nuclear reactor. For the two-fluid momentum equation, the wall and interfacial drag terms play an important role in predicting a two-phase flow behavior. Since the bubble density is much smaller than the water density, the bubble accelerates faster than the liquid in a nozzle. As a result, the bubble phase becomes faster than the liquid phase in the nozzle. In contrast, the opposite phenomena occur in the diffuser. The purpose of our study is to experimentally show these behaviors in an area-varying channel such as nozzle and diffuser. Experiments were made of turbulent bubbly flows in an area-varying horizontal channel. The velocities of the bubble and liquid phases were measured by the PIV technique. It was shown that the two-phase velocities were no longer close to each other in the area-varying regions. The bubble was faster than the liquid in the nozzle; in contrast, the bubble was slower than the liquid in the diffuser. Code simulations were also performed using the MARS code. By replacing the original wall drag model in the MARS code with Kim (1)'s wall drag partition model, we obtained the simulation results being consistent with experimental observations.

• The KIPS Transactions:PartB
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• v.15B no.2
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• pp.95-102
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• 2008

The interactive virtual reality technology has used in scientific inquiry learning since it can overcome the restriction of real world and it draws user's interest and foster active participation. However, prior works are mostly designed for a specific inquiry learning lesson and it is quite difficult to use them for constructing other inquiry learning environments. Hence, we developed the integrated virtual reality system, SASILE (System for Augmenting Scientific Inquiry Learning Environments), that helps ease the development of the scientific inquiry learning environment. In this paper, we first describe the related works on supporting VR scientific inquiry learning systems, followed by the SASILE system architecture and implementation. Then, we illustrate the use of this system to develop a Virtual Moyangsung application for teaching a scientific structure of Korean traditional house by exploring and observing the convection currents as well as a Mars Rover application for estimating the asteroid impacts on Mars by measuring rock properties. Finally, we will discuss the future research directions for this system.

• Journal of Advanced Navigation Technology
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• v.24 no.2
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• pp.101-106
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• 2020

In order to smoothly control the speed of the induction motor, it is necessary to obtain the required rotor speed information. In order to obtain the speed information, it must be obtained using a sensor, but it can also be obtained using an appropriate algorithm without using a sensor. In order to obtain speed information, a system was designed using a model reference adaptive system (MARS). Indirect vector control, one of the speed control methods of induction motors, was calculated from the motor current and rotor parameter values. The method of obtaining the position information of the magnetic flux by combining the slip frequency with the rotor speed was used. It is possible to simply perform instantaneous current control in a wide speed range without actual magnetic flux information, and has the advantage that the structure of the controller is simple. Therefore, in this paper, the control system was constructed based on the indirect vector control method, and the speed control system of the induction motor was developed by estimating the required rotor speed information as an intelligent algorithm developed without using it as a sensor.

• Nuclear Engineering and Technology
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• v.52 no.4
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• pp.755-763
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• 2020

The critical flow phenomenon has been studied because of its significant effect for design basis accidents in nuclear power plants. Transition points from thermal non-equilibrium to equilibrium are different according to the geometric effect on the critical flow. This study evaluates the uncertainty parameters of the critical flow model for analysis of DBA (Design Basis Accident) with the MARS-KS (Multi-dimensional Analysis for Reactor Safety-KINS Standard) code used as an independent regulatory assessment. The uncertainty of the critical flow model is represented by three parameters including the thermal non-equilibrium factor, discharge coefficient, and length to diameter (L/D) ratio, and their ranges are determined using large-scale Marviken test data. The uncertainty range of the thermal non-equilibrium factor is updated by the MCDA (Model Calibration through Data Assimilation) method. The updated uncertainty range is confirmed using an LBLOCA (Large Break Loss of Coolant Accident) experiment in the LOFT (Loss of Fluid Test) facility. The uncertainty ranges are also used to calculate an LBLOCA of the APR (Advanced Power Reactor) 1400 NPP (Nuclear Power Plants), focusing on the effect of the PCT (Peak Cladding Temperature). The results reveal that break flow is strongly dependent on the degree of the thermal non-equilibrium state in a ruptured pipe with a small L/D ratio. Moreover, this study provides the method to handle the thermal non-equilibrium factor, discharge coefficient, and length to diameter (L/D) ratio in the system code.

• Nuclear Engineering and Technology
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• v.52 no.8
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• pp.1611-1625
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• 2020

We propose a scaled-down experimental model of vertical air-natural convection channels by applying the modified Ishii-Kataoka scaling method with the assistance of numerical analyses to the Reactor Vault Cooling System (RVCS) of the Proto-type Gen-IV Sodium-cooled fast reactor (PGSFR) being developed in Korea. Two major non-dimensional numbers (modified Richardson and Friction number) from the momentum equation and Stanton number from the energy balance equation were identified to design the scaled-down experimental model to assimilate thermal-hydraulic behaviors of the natural convective air-cooling channel of RVCS. The ratios of the design parameters in the PGSFR RVCS between the prototype and the scaled-down model were determined by setting Richardson and Stanton number to be unity. The friction number which cannot be determined by the Ishii-Kataoka method was estimated by numerical analyses using the MARS-KS system code. The numerical analyses showed that the friction number with the form loss coefficient of 2.0 in the scale-down model would result in an acceptable prediction of the thermal-hydraulic behavior in RVCS. We also performed experimental benchmarking using the scaled-down model with the MARS-KS simulations to verify the appropriateness of the scale-down model, which demonstrated that the temperature rises and the average air flow velocity measured in the scale-down model.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.11
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• pp.1700-1713
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• 2018

Objective: The aim of this study was to verify the usefulness of artificial neural networks (ANN), multivariate adaptive regression splines (MARS), naïve Bayes classifier (NBC), general discriminant analysis (GDA), and logistic regression (LR) for dystocia detection in Polish Holstein-Friesian Black-and-White heifers and cows and to indicate the most influential predictors of calving difficulty. Methods: A total of 1,342 and 1,699 calving records including six categorical and four continuous predictors were used. Calving category (difficult vs easy or difficult, moderate and easy) was the dependent variable. Results: The maximum sensitivity, specificity and accuracy achieved for heifers on the independent test set were 0.855 (for ANN), 0.969 (for NBC), and 0.813 (for GDA), respectively, whereas the values for cows were 0.600 (for ANN), 1.000 and 0.965 (for NBC, GDA, and LR), respectively. With the three categories of calving difficulty, the maximum overall accuracy for heifers and cows was 0.589 (for MARS) and 0.649 (for ANN), respectively. The most influential predictors for heifers were an average calving difficulty score for the dam's sire, calving age and the mean yield of the farm, where the heifer was kept, whereas for cows, these additionally included: calf sex, the difficulty of the preceding calving, and the mean daily milk yield for the preceding lactation. Conclusion: The potential application of the investigated models in dairy cattle farming requires, however, their further improvement in order to reduce the rate of dystocia misdiagnosis and to increase detection reliability.

• Applied Chemistry for Engineering
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• v.10 no.8
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• pp.1161-1168
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• 1999

The redox property of oxide materials of the 3rd period transition metals(Cr~Zn), V, Mo, and W was studied with temperature-programmed reduction/temperature-programmed oxidation(TPR/TPO) experiment. The peak temperatures of TPO spectra were equal to or lower than those of TPR spectra. And the peak shapes of TPO spectra were broader than those of TPR ones. The activation energies of TPR/TPO for the oxides of the 3rd period transition metals showed in the range of 33~149 kJ/mol, while for the oxides of V, Mo, and W, they showed relatively higher values. The change of activation energies of TPR/TPO with various metal oxides showed a similar trend to the change of their metal-oxygen bond strengths. The change of activation energies of o-xylene oxidation for various metal oxides was proportional to the difference (${\Delta}E_a$) between the activation energy of TPR and that of TPO. From these results, we concluded that the oxidation of o-xylene over various metal oxide catalysts follows the Mars-van Krevelen mechanism including the surface reduction-oxidation of the metal oxide itself.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.5
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• pp.712-717
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• 2007

Using home network system including home server, home service robot, a variety of device, it is generally known that application of Multi Agent System for performing variously distributed process that can be occur in home environment, is efficient method. In this system, it is intelligent service robot that a key of human interface and physical service offer Therefore, using application of established multi agent system, we can defined Multi Agent Robot System. In 'open' home environment, between all agent data interaction and cooperation are needed for Multi Agent System offer to user that more efficient service. For this, we focus our attention on define as agent that can autonomic drive and offer to user that physical service robots and, design, suggest the simulator can display that between robot agents communication or between other agents, like home server, and robot agents communication information to user interface.

• Journal of the Korean Society of Systems Engineering
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• v.16 no.2
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• pp.97-109
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• 2020

On March 11, 2011, an earthquake followed by a tsunami caused an extended station blackout (SBO) at the Fukushima Dai-ichi NPP Units. The accident was initiated by a total loss of both onsite and offsite electrical power resulting in the loss of the ultimate heat sink for several days, and a consequent core melt in some units where proper mitigation strategies could not be implemented in a timely fashion. To enhance the plant's coping capability, the Diverse and Flexible Strategies (FLEX) were proposed to append the Emergency Operation Procedures (EOPs) by relying on portable equipment as an additional line of defense. To assess the success window of FLEX strategies, all sources of uncertainties need to be considered, using a physics-based model or system code. This necessitates conducting a large number of simulations to reflect all potential variations in initial, boundary, and design conditions as well as thermophysical properties, empirical models, and scenario uncertainties. Alternatively, data-driven models may provide a fast tool to predict the success window of FLEX strategies given the underlying uncertainties. This paper explores the applicability of Artificial Intelligence (AI) to identify the success window of FLEX strategy for extended SBO. The developed model can be trained and validated using data produced by the lumped parameter thermal-hydraulic code, MARS-KS, as best estimate system code loosely coupled with Dakota for uncertainty quantification. A Systems Engineering (SE) approach is used to plan and manage the process of using AI to predict the success window of FLEX strategies under extended SBO conditions.