• Nuclear Engineering and Technology
• /
• v.50 no.1
• /
• pp.54-67
• /
• 2018

There have been recent efforts to establish methods for high-fidelity and multi-physics simulation with coupled thermal-hydraulic (T/H) and neutronics codes for the entire core of a light water reactor under accident conditions. Considering the computing power necessary for a pin-by-pin analysis of the entire core, subchannel-scale T/H analysis is considered appropriate to achieve acceptable accuracy in an optimal computational time. In the present study, the applicability of in-house code CUPID of the Korea Atomic Energy Research Institute was extended to the subchannel-scale T/H analysis. CUPID is a component-scale T/H analysis code, which uses three-dimensional two-fluid models with various closure models and incorporates a highly parallelized numerical solver. In this study, key models required for a subchannel-scale T/H analysis were implemented in CUPID. Afterward, the code was validated against four subchannel experiments under unheated and heated single-phase incompressible flow conditions. Thereafter, a subchannel-scale T/H analysis of the entire core for an Advanced Power Reactor 1400 reactor core was carried out. For the high-fidelity simulation, detailed geometrical features and individual rod power distributions were considered in this demonstration. In this study, CUPID shows its capability of reproducing key phenomena in a subchannel and dealing with the subchannel-scale whole core T/H analysis.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.44 no.2 s.314
• /
• pp.116-125
• /
• 2007

In this paper, we propose a illumination environment adaptive real-time surveillance system for security of important area such as military bases, prisons, and strategic infra structures. The proposed system recognizes movement of objects on the bright environments as well as in dark illumination. The procedure of proposed system may be summarized as follows. First, the system discriminates between bright and dark with input image distribution. Then, if the input image is dark, the system has a pre-processing. The Multi-scale Retinex Color Restoration(MSRCR) is processed to enhance the contrast of image captured in dark environments. Secondly, the enhanced input image is subtracted with the revised background image. And then, we take a morphology image processing to obtain objects correctly. Finally, each bounding box enclosing each objects are tracked. The center point of each bounding box obtained by the proposed algorithm provides more accurate tracking information. Experimental results show that the proposed system provides good performance even though an object moves very fast and the background is quite dark.

• Proceedings of the Korean Vacuum Society Conference
• /
• 2012.08a
• /
• pp.440-440
• /
• 2012

Nanomaterials have emerged as new building blocks to construct light energy harvesting assemblies. Size dependent properties provide the basis for developing new and effective systems with semiconductor nanoparticles, quantized charging effects in metal nanoparticle or their combinations in 2 and 3 dimensions for expanding the possibility of developing new strategies for photovoltaic system. As top-down approach, we developed a simple and effective method for the large scale formation of self-assembled Cu(In,Ga)$Se_2$ (CIGS) nanostructures by ion beam irradiation. The compositional changes and morphological evolution were observed as a function of the irradiation time. As the ion irradiation time increased, the nano-dots were transformed into a nano-ridge structure due to the difference in the sputtering yields and diffusion rates of each element and the competition between sputtering and diffusion processes during irradiation. As bottom-up approach, we developed the growth of CIGS nanowires using thermal-chemical vapor deposition (CVD) method. Vapor-phase synthesis is probably the most extensively explored approach to the formation of 1D nanostructures such as whiskers, nanorods, and nanowires. However, unlike binary or ternary chalcogenides, the synthesis of quaternary CIGS nanostructures is challenging because of the difficulty in controlling the stoichiometry and phase structure. We introduced a method for synthesis of the single crystalline CIGS nanowires in the form of chalcopyrite using thermal-CVD without catalyst. It was confirmed that the CIGS nanowires are epitaxially grown on a sapphire substrate, having a length ranged from 3 to 100 micrometers and a diameter from 30 to 500 nm.

• Steel and Composite Structures
• /
• v.4 no.6
• /
• pp.471-487
• /
• 2004

Modular steel scaffolds are commonly used as supporting scaffolds in building construction, and traditionally, the load carrying capacities of these scaffolds are obtained from limited full-scale tests with little rational design. Structural failure of these scaffolds occurs from time to time due to inadequate design, poor installation and over-loads on sites. In general, multi-storey modular steel scaffolds are very slender structures which exhibit significant non-linear behaviour. Hence, secondary moments due to both $P-{\delta}$ and $P-{\Delta}$ effects should be properly accounted for in the non-linear analyses. Moreover, while the structural behaviour of these scaffolds is known to be very sensitive to the types and the magnitudes of restraints provided from attached members and supports, yet it is always difficult to quantify these restraints in either test or practical conditions. The problem is further complicated due to the presence of initial geometrical imperfections in the scaffolds, including both member out-of-straightness and storey out-of-plumbness, and hence, initial geometrical imperfections should be carefully incorporated. This paper presents an extensive numerical study on three different approaches in analyzing and designing multi-storey modular steel scaffolds, namely, a) Eigenmode Imperfection Approach, b) Notional Load Approach, and c) Critical Load Approach. It should be noted that the three approaches adopt different ways to allow for the non-linear behaviour of the scaffolds in the presence of initial geometrical imperfections. Moreover, their suitability and accuracy in predicting the structural behaviour of modular steel scaffolds are discussed and compared thoroughly. The study aims to develop a simplified and yet reliable design approach for safe prediction on the load carrying capacities of multi-storey modular steel scaffolds, so that engineers can ensure safe and effective use of these scaffolds in building construction.

• Journal of the Korea Society of Computer and Information
• /
• v.24 no.11
• /
• pp.51-59
• /
• 2019

Due to the exponential growth of access information on the web, the need for predicting web users' next access has increased. Various models such as markov models, deep neural networks, support vector machines, and fuzzy inference models were proposed to handle web access prediction. For deep learning based on neural network models, training time on large-scale web usage data is very huge. To address this problem, deep neural network models are trained on cluster of computers in parallel. In this paper, we investigated impact of several important spark parameters related to data partitions, shuffling, compression, and locality (basic spark parameters) for training Multi-Layer Perceptron model on Spark standalone cluster. Then based on the investigation, we tuned basic spark parameters for training Multi-Layer Perceptron model and used it for tuning Spark when training Multi-Layer Perceptron model for web access prediction. Through experiments, we showed the accuracy of web access prediction based on our proposed web access prediction model. In addition, we also showed performance improvement in training time based on our spark basic parameters tuning for training Multi-Layer Perceptron model over default spark parameters configuration.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.18 no.9
• /
• pp.2358-2367
• /
• 1994

This paper presents techniques for evaluation and compensation of total measuring errors in a commercial CMM. The probe errors as well as the machine geometric errors are assessed from probing of the mechanical artefacts such as shpere, step, and rings. For the error compensation, the integrated volumetric error equations are considered, including the probe error adn the machine geometric error. The error compensation is performed on the absolute scale coordinate system, in order to overcome the redundant degree of freedom in the CMM with multi-axis probe. A interface box and corresponding software driver are developed for data intercepting/correction between the machine controller and machine, thus the volumetric errors can be compensated in real time with minimum interference to the operating software and hardware of a commercial CMM. The developed system applied to a practical CMM installed on the shop floor, and demonstrated its performance.

• Current Photovoltaic Research
• /
• v.2 no.3
• /
• pp.95-102
• /
• 2014

Light trapping techniques can change the propagation direction of incident light and keep the light longer in the absorption layers of solar cells to enhance the power conversion efficiency. In thin film silicon (Si) solar cells, the thickness of absorption layer is generally not enough to absorb entire available photons because of short carrier life time, and light induced degradation effect, which can be compensated by the light trapping techniques. These techniques have been adopted as textured transparent conduction oxide (TCO) layers randomly or periodically textured, intermediate reflection layers of tandem and triple junction, and glass substrates etched by various patterning methods. We reviewed the light trapping techniques for thin film Si solar cells and mainly focused on the commercially available techniques applicable to textured TCO on patterned glass substrates. We described the characterization methods representing the light trapping effects, texturing of TCO and showed the results of multi-scale textured TCO on etched glass substrates. These methods can be used tandem and triple thin film Si solar cells to enhance photo-current and power conversion efficiency of long term stability.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.12 no.7
• /
• pp.1243-1248
• /
• 2008

As development in technology of bioinformatics recently mates it possible to operate micro-level experiments, we can observe the expression pattern of total genome through on chip and analyze the interactions of thousands of genes at the same time. In this thesis, we used CDNA microarrays of 3840 genes obtained from neuronal differentiation experiment of cortical stem cells on white mouse with cancer. It analyzed and compared performance of each of the experiment result using existing DT, NB, SVM and multi-perceptron neural network classifier combined the similar scale combination method after constructing class classification model by extracting significant gene list with a similar scale combination method proposed in this paper through normalization. Result classifying in Multi-Perceptron neural network classifier for selected 200 genes using combination of PC(Pearson correlation coefficient) and ED(Euclidean distance coefficient) represented the accuracy of 98.84%, which show that it improve classification performance than case to experiment using other classifier.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.5
• /
• pp.1431-1445
• /
• 2022

We construct a deep neural network model named ECGResNet. This model can diagnosis diseases based on 12-lead ECG data of eight common cardiovascular diseases with a high accuracy. We chose the 16 Blocks of ResNet50 as the main body of the model and added the Squeeze-and-Excitation module to learn the data information between channels adaptively. We modified the first convolutional layer of ResNet50 which has a convolutional kernel of 7 to a superposition of convolutional kernels of 8 and 16 as our feature extraction method. This way allows the model to focus on the overall trend of the ECG signal while also noticing subtle changes. The model further improves the accuracy of cardiovascular and cerebrovascular disease classification by using a fully connected layer that integrates factors such as gender and age. The ECGResNet model adds Dropout layers to both the residual block and SE module of ResNet50, further avoiding the phenomenon of model overfitting. The model was eventually trained using a five-fold cross-validation and Flooding training method, with an accuracy of 95% on the test set and an F1-score of 0.841.We design a new deep neural network, innovate a multi-scale feature extraction method, and apply the SE module to extract features of ECG data.

• Journal of the Korea Society for Simulation
• /
• v.27 no.3
• /
• pp.1-11
• /
• 2018

Discrete event system specification (DEVS) has been used in many simulations including hybrid systems featuring both discrete and continuous behavior that require a lot of time to get results. Therefore, in this study, we proposed the acceleration of a DEVS-based hybrid system simulation using multi-cores and GPUs tightly coupled computing. We analyzed the proposed heterogeneous computing of the simulation in terms of the configuration of the target device, changing simulation parameters, and power consumption for efficient simulation. The result revealed that the proposed architecture offers an advantage for high-performance simulation in terms of execution time, although more power consumption is required. With these results, we discovered that our approach is applicable in hybrid system simulation, and we demonstrated the possibility of optimized hardware distribution in terms of power consumption versus execution time via experiments in the proposed architecture.