• Nuclear Engineering and Technology
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• v.51 no.6
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• pp.1487-1503
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• 2019

The methods and performance of a pin-level nuclear reactor core thermal-hydraulics (T/H) code ESCOT employing the drift-flux model are presented. This code aims at providing an accurate yet fast core thermal-hydraulics solution capability to high-fidelity multiphysics core analysis systems targeting massively parallel computing platforms. The four equation drift-flux model is adopted for two-phase calculations, and numerical solutions are obtained by applying the Finite Volume Method (FVM) and the Semi-Implicit Method for Pressure-Linked Equation (SIMPLE)-like algorithm in a staggered grid system. Constitutive models involving turbulent mixing, pressure drop, and vapor generation are employed to simulate key phenomena in subchannel-scale analyses. ESCOT is parallelized by a domain decomposition scheme that involves both radial and axial decomposition to enable highly parallelized execution. The ESCOT solutions are validated through the applications to various experiments which include CNEN $4{\times}4$, Weiss et al. two assemblies, PNNL $2{\times}6$, RPI $2{\times}2$ air-water, and PSBT covering single/two-phase and unheated/heated conditions. The parameters of interest for validation include various flow characteristics such as turbulent mixing, spacer grid pressure drop, cross-flow, reverse flow, buoyancy effect, void drift, and bubble generation. For all the validation tests, ESCOT shows good agreements with measured data in the extent comparable to those of other subchannel-scale codes: COBRA-TF, MATRA and/or CUPID. The execution performance is examined with a mini-sized whole core consisting of 89 fuel assemblies and for an OPR1000 core. It turns out that it is about 1.5 times faster than a subchannel code based on the two-fluid three field model and the axial domain decomposition scheme works as well as the radial one yielding a steady-state solution for the OPR1000 core within 30 s with 104 processors.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.4
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• pp.85-91
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• 2019

In this paper, we propose a new logging and recovery scheme that is suited for the high-performance transaction processing system base on flash memory storage. The proposed scheme is designed by considering flash's I/O characteristic of asymmetric costs between page update/read operations. That is, we substitute the costly update operation with writing and real-time usage of snapshot log, which is for the page-level physical redo. From this, we can avoid costly rewriting of a dirty page when it is evicted form a buffering pool. while supporting efficient revery procedure. The proposed scheme would be not lucrative in the case of HDD-based system. However, the proposed scheme offers the performance advance sush as a reduced number of updates and the fast system recovery time, in the case of flash storage such as SSD (solid state drive). Because the proposed scheme can easily be applied to existing systems by saving our snapshot records and ordinary log records together, our scheme can be used for improving the performance of upcoming SSD-based database systems through a tiny modification to existing REDO algorithms.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.5
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• pp.380-388
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• 2019

Jet engine modulation(JEM) signals are widely used for target recognition. These signals coming from a potentially hostile aircraft provide specific information about the jet engine. In order to obtain the number of blades, which is uniquely provided by the JEM signal, one must extract the spool rate, which is the rotation speed of the blades. In this paper, we propose an algorithm to extract the spool rate from a weak JEM signal. A criterion is developed to extract the spool rate from the JEM signal by analyzing the intensity of the JEM signal component. The weak signal is first subjected to a high-pass filtering-based process, which modifies it to facilitate spool rate extraction. We then apply a peak detection process and extract the spool rate. The technique is simpler than the existing CEMD or WD method, is accurate, and greatly reduces the time required.

• The Journal of the Acoustical Society of Korea
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• v.38 no.4
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• pp.387-395
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• 2019

TMA (Target Motion Analysis) in passive sonar is generally conducted with the bearing only or the bearing frequency. In order to conduct TMA fast and accurately, it is essential to estimate a initial target maneuver precisely. The accuracy of TMA can be improved by using SNR (Signal to Noise Ratio) information and acoustic propagation model additionally. This method assumes that the radiated noise level of the target is known, but the accuracy of TMA can be degraded due to a mismatch between the assumed radiated noise level and the actual radiated noise level. In this paper, TMA with the acoustic propagation model, bearing measurements, and SNR information is conducted in the ocean environment of South Korea (East Sea/ Yellow Sea/ South Sea). And the performance analysis of TMA for the mismatch in the radiated noise is presented.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.54.2-54.2
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• 2019

Removing noise which occurs inevitably when taking image data has been a big concern. There is a way to raise signal-to-noise ratio and it is regarded as the only way, image stacking. Image stacking is averaging or just adding all pixel values of multiple pictures taken of a specific area. Its performance and reliability are unquestioned, but its weaknesses are also evident. Object with fast proper motion can be vanished, and most of all, it takes too long time. So if we can handle single shot image well and achieve similar performance, we can overcome those weaknesses. Recent developments in deep learning have enabled things that were not possible with former algorithm-based programming. One of the things is generating data with more information from data with less information. As a part of that, we reproduced stacked image from single shot image using a kind of deep learning, conditional generative adversarial network (cGAN). r-band camcol2 south data were used from SDSS Stripe 82 data. From all fields, image data which is stacked with only 22 individual images and, as a pair of stacked image, single pass data which were included in all stacked image were used. All used fields are cut in $128{\times}128$ pixel size, so total number of image is 17930. 14234 pairs of all images were used for training cGAN and 3696 pairs were used for verify the result. As a result, RMS error of pixel values between generated data from the best condition and target data were $7.67{\times}10^{-4}$ compared to original input data, $1.24{\times}10^{-3}$. We also applied to a few test galaxy images and generated images were similar to stacked images qualitatively compared to other de-noising methods. In addition, with photometry, The number count of stacked-cGAN matched sources is larger than that of single pass-stacked one, especially for fainter objects. Also, magnitude completeness became better in fainter objects. With this work, it is possible to observe reliably 1 magnitude fainter object.

• Health Communication
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• v.12 no.1
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• pp.51-72
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• 2017

Purpose: Adolescents use emoticons to express their emotions in an online environment. Hence, medical experts can understand the emotions of adolescents by emoticons. The goal of this study was to investigate the relationship between various emotions and emoticons among the Korean adolescents. Methods: The questionnaire survey was conducted between September 1 and 30, 2014, involving 3,272 students in elementary schools, middle schools, and high schools affiliated in the Department of Education of the metropolitan city of Busan. A total of 1,717 students responded to the survey. The participants consisted of 806 males (46.9%), and 911 females (53.1%). Among these, there were 557 elementary school students (32.4%), 617 middle school students (35.9%), and 543 high school students (31.6%). A social networking analysis was conducted using NodeXL. Results: The frequency of emoticon use among adolescents runs in the order of joy, sadness, fear, surprise, anger, disgust, and then depression. Elementary school females mainly use emoticons to express joy; middle school females use emoticons to express sadness, surprise, anger, disgust, and depression; and high school females use emoticons to express fear. Age- and gender-specific emoticon networks were visualized by using the Haren-Korel fast multiscale algorithm. Commonly used emoticons by age and gender were expressed in the networks. Results of age- and gender-specific emoticon networks visualization show similar results of centrality of seven emoticons. Conclusion: In the digital communication environment, emoticons could be used to catch the emotions of adolescents in Korea.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1345-1352
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• 2021

Most of weather radar systems are used to monitor the whole weather situation for the very wide and medium-to-long range area. However, as the likelihood of occurrence of the local weather hazards is increased in recent days, it is very important to detect these wether phenomena with a local weather radar. For this purpose, it is necessary to detect the fast varying low altitude weather conditions and the effect of the ground surface clutter is more evident. Therefore, in this paper, the newly suggested method is explained and analyzed for detection of weather hazards such as the gust and wind shear using the fluctuation of wind velocities and the gradient of wind velocities among range cells. It is shown that the suggested method can be used efficiently in the future for faster detection of weather change through the simple algorithm implementation and also the effect of the ground clutter can be minimized in the detection procedure.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.8
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• pp.1058-1063
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• 2020

This paper presents a new insulation resistance calculation technique to prevent electric shock and fire accidents due to the dielectric breakdown in the primary insulation section of the IT ground system. The solar power generation market is growing rapidly due to the recent expansion of renewable energy and energy storage systems, but as the insulation is destroyed and fire accidents frequently occur, a device for monitoring the insulation resistance state is indispensable to the IT grounding method. Compared to the conventional algorithm that use a method of multiplying a time constant to a fixed coefficient, the proposed insulation resistance calculation method has a fast response time and high accuracy over a wide insulation resistance range by applying a different coefficient according to the values of the insulation impedance. The proposed dynamic time constant based insulation resistance calculation technique reduces the response time by up to 39.29 seconds and improves the error rate by 20.11%, compared to the conventional method.

• Journal of IKEEE
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• v.24 no.4
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• pp.1141-1147
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• 2020

The hornet species are so similar in shape that they are difficult for non-experts to classify, and because the size of the objects is small and move fast, it is more difficult to detect and classify the species in real time. In this paper, we developed a system that classifies hornets species in real time based on a deep learning algorithm using a boundary box. In order to minimize the background area included in the bounding box when labeling the training image, we propose a method of selecting only the head and body of the hornet. It also experimentally compares existing boundary box-based object recognition algorithms to find the best algorithms that can detect wasps in real time and classify their species. As a result of the experiment, when the mish function was applied as the activation function of the convolution layer and the hornet images were tested using the YOLOv4 model with the Spatial Attention Module (SAM) applied before the object detection block, the average precision was 97.89% and the average recall was 98.69%.

• International Journal of Advanced Culture Technology
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• v.9 no.2
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• pp.100-105
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• 2021

Contactless service is rapidly emerging as a new growth strategy due to consumers who are reluctant to the face-to-face situation in the global pandemic of coronavirus disease 2019 (COVID-19), and various technologies are being developed to support the fast-growing contactless service market. In particular, the restaurant industry is one of the most desperate industrial fields requiring technologies for contactless service, and the representative technical case should be a kiosk, which has the advantage of reducing labor costs for the restaurant owners and provides psychological relaxation and satisfaction to the customer. In this paper, we propose a solution to the restaurant's store operation through the unmanned kiosk using a state-of-the-art artificial intelligence (AI) technology of image recognition. Especially, for the products that do not have barcodes in bakeries, fresh foods (fruits, vegetables, etc.), and autonomous restaurants on highways, which cause increased labor costs and many hassles, our proposed system should be very useful. The proposed system recognizes products without barcodes on the ground of image-based AI algorithm technology and makes automatic payments. To test the proposed system feasibility, we established an AI vision system using a commercial camera and conducted an image recognition test by training object detection AI models using donut images. The proposed system has a self-learning system with mismatched information in operation. The self-learning AI technology allows us to upgrade the recognition performance continuously. We proposed a fully automated payment system with AI vision technology and showed system feasibility by the performance test. The system realizes contactless service for self-checkout in the restaurant business area and improves the cost-saving in managing human resources.