• Journal of the Korea Society for Simulation
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• v.30 no.4
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• pp.1-8
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• 2021

Deep learning is one of the most widely accepted methods for the forecasting of time series data which have the complexity and non-linear behavior. In this paper, we investigate the modification of a state-of-art WaveNet deep learning architecture and walk forward validation (WFV) in order to forecast electric power consumption data 24-hour-ahead. WaveNet originally designed for raw audio uses 1D dilated causal convolution for long-term information. First of all, we propose a modified version of WaveNet which activates real numbers instead of coded integers. Second, this paper provides with the training process with tuning of major hyper-parameters (i.e., input length, batch size, number of WaveNet blocks, dilation rates, and learning rate scheduler). Finally, performance evaluation results show that the prediction methodology based on WFV performs better than on the traditional holdout validation.

• International Area Studies Review
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• v.12 no.3
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• pp.25-47
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• 2008

This paper tries to explore the impact of economic globalization on social capital accumulation. To investigate direct effect, we build a model and derive a proposition which can explain the relative decline in social capital brought about by market expansion. Besides direct effect, we also explore channel effect through democracy, government size, education attainment, and inequality. We estimate direct and channel effect of globalization using cross section of 65 countries data time period 1980-1999 using three-stage least squares(3SLS). Results are in line with predictions and clearly support that globalization significantly and negatively affects social capital accumulation. Channel effect also shows that globalization has a negative effect through aggravating income inequality while it has a positive effect through higher education attainment, higher level of democracy and larger government spending. Such a net negative channel effect reinforces our prediction. As a robustness check we estimate other sets of data and the result strongly supports our theory.

• Biomedical Engineering Letters
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• v.8 no.4
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• pp.383-392
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• 2018

For prompt gamma ray imaging for biomedical applications and environmental radiation monitoring, we propose herein a multiple-scattering Compton camera (MSCC). MSCC consists of three or more semiconductor layers with good energy resolution, and has potential for simultaneous detection and differentiation of multiple radio-isotopes based on the measured energies, as well as three-dimensional (3D) imaging of the radio-isotope distribution. In this study, we developed an analytic simulator and a 3D image generator for a MSCC, including the physical models of the radiation source emission and detection processes that can be utilized for geometry and performance prediction prior to the construction of a real system. The analytic simulator for a MSCC records coincidence detections of successive interactions in multiple detector layers. In the successive interaction processes, the emission direction of the incident gamma ray, the scattering angle, and the changed traveling path after the Compton scattering interaction in each detector, were determined by a conical surface uniform random number generator (RNG), and by a Klein-Nishina RNG. The 3D image generator has two functions: the recovery of the initial source energy spectrum and the 3D spatial distribution of the source. We evaluated the analytic simulator and image generator with two different energetic point radiation sources (Cs-137 and Co-60) and with an MSCC comprising three detector layers. The recovered initial energies of the incident radiations were well differentiated from the generated MSCC events. Correspondingly, we could obtain a multi-tracer image that combined the two differentiated images. The developed analytic simulator in this study emulated the randomness of the detection process of a multiple-scattering Compton camera, including the inherent degradation factors of the detectors, such as the limited spatial and energy resolutions. The Doppler-broadening effect owing to the momentum distribution of electrons in Compton scattering was not considered in the detection process because most interested isotopes for biomedical and environmental applications have high energies that are less sensitive to Doppler broadening. The analytic simulator and image generator for MSCC can be utilized to determine the optimal geometrical parameters, such as the distances between detectors and detector size, thus affecting the imaging performance of the Compton camera prior to the development of a real system.

• Journal of Convergence for Information Technology
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• v.9 no.8
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• pp.35-44
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• 2019

The purpose of this study is to find out quantitative vulnerability assessment about COTS(Commercial Off The Shelf) O/S based I&C System. This paper analyzed vulnerability's lifecycle and it's impact. this paper is to develop a quantitative assessment of overall cyber security risks and vulnerabilities I&C System by studying the vulnerability analysis and prediction method. The probabilistic vulnerability assessment method proposed in this study suggests a modeling method that enables setting priority of patches, threshold setting of vulnerable size, and attack path in a commercial OS-based measurement control system that is difficult to patch an immediate vulnerability.

• Geomechanics and Engineering
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• v.18 no.1
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• pp.11-20
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• 2019

The purpose of this study was to propose a new approach for quantifying in situ rock mass damage, which would include a degree-of-damage and the degraded strength of a rock mass, along with its prediction based on real-time Acoustic Emission (AE) observations. The basic approach for quantifying in-situ rock mass damage is to derive the normalized value of measured AE energy with the maximum AE energy, called the degree-of-damage in this study. With regard to estimation of the AE energy, an AE crack source location algorithm of the Wigner-Ville Distribution combined with Biot's wave dispersion model, was applied for more reliable AE crack source localization in a rock mass. In situ AE wave attenuation was also taken into account for AE energy correction in accordance with the propagation distance of an AE wave. To infer the maximum AE energy, fractal theory was used for scale-independent AE energy estimation. In addition, the Weibull model was also applied to determine statistically the AE crack size under a jointed rock mass. Subsequently, the proposed methodology was calibrated using an in situ test carried out in the Underground Research Tunnel at the Korea Atomic Energy Research Institute. This was done under a condition of controlled incremental cyclic loading, which had been performed as part of a preceding study. It was found that the inferred degree-of-damage agreed quite well with the results from the in situ test. The methodology proposed in this study can be regarded as a reasonable approach for quantifying rock mass damage.

• Journal of The Korean Society of Emergency Medicine
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• v.29 no.6
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• pp.641-648
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• 2018

Objective: This study examined the incidence and amount of air inflow during central venous catheter (CVC) insertion. Methods: This study was an experimental study aimed at designing an apparatus to implement blood vessel and blood flow in the human body. A 1.5-m long core tube with a Teflon tube, suction rubber tube, and polyvinyl chloride tube were made. This core tube was assumed to be the blood vessel of the human body. Blood was replaced with a saline solution. The saline solution was placed higher than the core tube and flowed into the inside of the tube by gravity. The CVC was injected 15-cm deep into the core tube. The air was collected through a 3-way valve into the upper tube. The experiments were carried out by differentiating the pressure in the tube, CVC insertion step, and diameter of the end of the catheter. The experiment was repeated 10 times under the same conditions. Results: The amount of air decreased with increasing pressure applied to the tube. Air was not generated when the syringe needle was injected, and the amount of air increased with increasing size of the distal end catheter. Conclusion: To minimize the possibility of air embolism, it is necessary to close the distal end catheter at the earliest point as soon as possible.

• Journal of Digital Convergence
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• v.19 no.5
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• pp.307-313
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• 2021

In this paper, a lightweight network with fewer parameters compared to the existing object detection method is proposed. In the case of the currently used detection model, the network complexity has been greatly increased to improve accuracy. Therefore, the proposed network uses EfficientNet as a feature extraction network, and the subsequent layers are formed in a pyramid structure to utilize low-level detailed features and high-level semantic features. An attention process was applied between pyramid structures to suppress unnecessary noise for prediction. All computational processes of the network are replaced by depth-wise and point-wise convolutions to minimize the amount of computation. The proposed network was trained and evaluated using the PASCAL VOC dataset. The features fused through the experiment showed robust properties for various objects through a refinement process. Compared with the CNN-based detection model, detection accuracy is improved with a small amount of computation. It is considered necessary to adjust the anchor ratio according to the size of the object as a future study.

• Journal of Korean Neurosurgical Society
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• v.64 no.2
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• pp.229-237
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• 2021

Objective : Expansion in the spinal canal area (SCA) after laminoplasty is one of the critical factors to relieve the preoperative symptoms. No previous study has compared the increases in SCA achieved by open-door laminoplasty (ODL) and double door laminoplasty (DDL) according to the preoperative lamina angle (LA). This study was designed to clarify the relationship between the laminoplasty opening angle (OA)/laminoplasty opening size (OS) and increases in the SCA following ODL and DDL according to the preoperative LA using a simulation model. Methods : The simulation model was constructed and validated by comparing the clinical data of 64 patients who had undergone C3-C6 laminoplasty (43 patients with ODL and 21 patients with DDL). SCA expansion was predicted with a verified simulation model at various preoperative LAs (from 28° to 32°) with different OAs (40° to 44°) and OSs (10 mm to 14 mm) recruited from patient data. Results : The constructed simulation model was validated by comparing clinical data and revealed a very high degree of correlation (r=0.935, p<0.001). In this validated model, at the same OA, the increase in SCA was higher following ODL than following DDL in the usual LA (p<0.05). At the same OS, the increase in SCA was slightly larger following DDL than following ODL, but the difference was not significant (p>0.05). The difference was significant when the preoperative LA was narrower or much wider. Conclusion : Based on clinical data, a simulation model was constructed and verified that could predict increases in the SCA following ODL and DDL. When applying this model, prediction in SCA increase using the OS parameter was more practical and compatible with clinical data. Both laminoplasties achieved enough SCA, and there was no significant difference between them in the usual range.

• Journal of agriculture & life science
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• v.53 no.5
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• pp.37-49
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• 2019

Environmental conditions are important in increasing the fruit sugar content and productivity of the new cultivar Autumn Sense of Actinidia arguta. We analyzed various soil properties at experimental sites in South Korea. A Pearson's correlation analysis was performed between the soil properties and sugar content or productivity of Autumn Sense. Further, a decision tree was used to determine the optimal soil conditions. The difference in the fruit size, sugar content, and productivity of Autumn Sense across sites was significant, confirming the effects of soil properties. The decision tree analysis showed that a soil C/N ratio of over 11.49 predicted a sugar content of more than 7°Bx at harvest time, and soil electrical capacity below 131.83 µS/cm predicted productivity more than 50 kg/vine at harvest time. Our results present the soil conditions required to increase the sugar content or productivity of Autumn Sense, a new A. arguta cultivar in South Korea.

• Smart Media Journal
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• v.10 no.1
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• pp.32-38
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• 2021

Image classification through deep learning on the image from photographs has been a very active research field for the past several years. In this paper, we propose a method of automatically discriminating stone images from domestic source through deep learning, which is to use Python's hash library to scan 300×300 pixel photo images of granites such as Hwangdeungseok, Goheungseok, and Pocheonseok, performing data preprocessing to create learning images by examining duplicate images for each stone, removing duplicate images with the same hash value as a result of the inspection, and deep learning by stone. In addition, to utilize VGGNet, the size of the images for each stone is resized to 224×224 pixels, learned in VGG16 where the ratio of training and verification data for learning is 80% versus 20%. After training of deep learning, the loss function graph and the accuracy graph were generated, and the prediction results of the deep learning model were output for the three kinds of stone images.