• The Journal of Korean Medical History
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• v.18 no.1
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• pp.103-126
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• 2005

1) Objective This research is a process of examining the Traditional Acupunctural Methods left in our documents. The objectives of this paper are explaining the process of researching documents and reporting several points revealed in this process. 2) Process This study was done through the following steps: (1) We located the Acupunctural Methods in the original text database on the web. (2) We sorted them according to criterion. (3) We set up some standard clauses in order to organize the selected information. (4) We re-organized the information following the standards within the categories. (5) As for clauses that were not fully explained in the documents, we tried to restore them through other documents. 3) Results (1) 11 standard clauses were set up in order to organize the Traditional Acupunctural Methods. (2) Traditional Acupunctural Methods were extracted from 22 documents, (3) 24 Traditional Acupunctural Methods were extracted and organized by standards. (4) We learned of similar methods with resembling ways of drawing blood and methods with deep interest in the depth of needle insertion. Also, we were able to gain a clue to the exchange of Acupunctural Methods among the three Asian countries and the possibility of research using the Korean Medicine Database.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.555-559
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• 2005

Spinning process is a chipless metal forming method for axis-symmetric parts, which is more economical, efficient and versatile method for producing parts than other sheet metal forming process such as stamping or deep drawing. The large-sized spindle for spinning machine is the equipment to ferm a high-pressure vessel into the demanded shape. The important problem in the spindle system fur spinning machines is to reduce and minimize the thermal effect by motor and bearings. In this study, the effect of heat generation of bearings for the large-sized spindle is considered. Temperature distribution and thermal displacement of the spindle system for spinning machine can be analyzed by using the finite element method. The numerical results are compared with the measured data. The results show that temperature distribution and thermal displacement can be reasonably estimated by using the finite element method and the three dimensional model.

• The Journal of the Petrological Society of Korea
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• v.23 no.3
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• pp.249-259
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• 2014

Proxies for paleo-circulation are drawing much interest with the recognition that ocean circulation plays an important part in the redistribution of heat and climate change on orbital and millennial timescales. In this review, we will introduce how neodymium isotope ratios of the authigenic fraction of marine sediments can be used as a proxy for ocean circulation along with analytical methods and two case studies. The first case study shows how the North Atlantic Deep Water (NADW) has varied over the glacial-interglacial and stadial-interstadial periods. The second case study shows how the freshwater budget and water circulation within the Arctic Ocean can be reconstructed for the last glacial period.

• Journal of the Semiconductor & Display Technology
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• v.20 no.4
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• pp.6-9
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• 2021

The 3D industry is drawing attention for its applications in various markets, including architecture, media, VR/AR, metaverse, imperial broadcast, and etc.. The current feature of the architecture we are introducing is to make 3D models more easily created and modified than conventional ones. Existing methods for generating 3D models mainly obtain values using specialized equipment such as RGB-D cameras and Lidar cameras, through which 3D models are constructed and used. This requires the purchase of equipment and allows the generated 3D model to be verified by the computer. However, our framework allows users to collect data in an easier and cheaper manner using cell phone cameras instead of specialized equipment, and uses 2D data to proceed with 3D modeling on the server and output it to cell phone application screens. This gives users a more accessible environment. In addition, in the 3D modeling process, object classification is attempted through deep learning without user intervention, and mesh and texture suitable for the object can be applied to obtain a lively 3D model. It also allows users to modify mesh and texture through requests, allowing them to obtain sophisticated 3D models.

• Journal of the Microelectronics and Packaging Society
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• v.29 no.3
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• pp.1-12
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• 2022

With the rapid development of artificial intelligence technology that gives existing sensors functions similar to human intelligence is drawing attention. Previously, researches were mainly focused on an improvement of fundamental performance indicators as sensors. However, recently, attempts to combine artificial intelligence such as classification and prediction with sensors have been explored. Based on this, intelligent sensor research has been actively reported in almost all kinds of sensing fields such as disease detection, motion detection, and gas sensor. In this paper, we introduce the basic concepts, types, and driving mechanisms of artificial intelligence and review some examples of its use.

• Journal of Platform Technology
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• v.11 no.1
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• pp.72-84
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• 2023

With the rise of the Internet of Things, the security of such lightweight computing environments has become a hot topic. Lightweight block ciphers that can provide efficient performance and security by having a relatively simpler structure and smaller key and block sizes are drawing attention. Due to these characteristics, they can become a target for new attack techniques. One of the new cryptanalytic attacks that have been attracting interest is Neural cryptanalysis, which is a cryptanalytic technique based on neural networks. It showed interesting results with better results than the conventional cryptanalysis method without a great amount of time and cryptographic knowledge. The first work that showed good results was carried out by Aron Gohr in CRYPTO'19, the attack was conducted on the lightweight block cipher SPECK-/32/64 and showed better results than conventional differential cryptanalysis. In this paper, we first apply the Differential Neural Distinguisher proposed by Aron Gohr to the block ciphers HIGHT and GOST to test the applicability of the attack to ciphers with different structures. The performance of the Differential Neural Distinguisher is then analyzed by replacing the neural network attack model with five different models (Multi-Layer Perceptron, AlexNet, ResNext, SE-ResNet, SE-ResNext). We then propose a Related-key Neural Distinguisher and apply it to the SPECK-/32/64, HIGHT, and GOST block ciphers. The proposed Related-key Neural Distinguisher was constructed using the relationship between keys, and this made it possible to distinguish more rounds than the differential distinguisher.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.12
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• pp.86-98
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• 1996

An combined implicit/explicit scheme for the analysis of sheet forming problems has been proposed in this work. In finite element simulation of sheet metal forming processes, the robustness and stability of computation are important requirements since the computation time and convergency become major points of consideration besides the solution accuracy due to the complexity of geometry and boundary conditions. The implicit scheme dmploys a more reliable and rigorous scheme in considering the equilibrium at each step of deformation, while in the explict scheme the problem of convergency is elimented at thecost of solution accuracy. The explicit approach and the implicit approach have merits and demerits, respectively. In order to combine the merits of these two methods a step-wise combined implici/explicit scheme has been developed. In the present work, the rigid-plastic finite element method using bending energy augmented membraneelements(BEAM)(1) is employed for computation. Computations are carried out for some typical sheet forming examples by implicit, combined implicit/explicit schemes including deep drawing of an oil pan, front fender and fuel tank. From the comparison between the methods the advantages and disadvantages of the methods are discussed.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.358-377
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• 2023

During the initial period of the COVID-19 pandemic, governments around the world implemented non-pharmaceutical interventions. For these policy interventions to be effective, authorities engaged in the political discourse of legitimising their activity to generate positive public attitudes. To understand effective COVID-19 policy, this study investigates public attitudes in South Korea, the United Kingdom, and the United States and how they reflect different legitimisation of policy intervention. We adopt a big data approach to analyse public attitudes, drawing from public comments posted on Twitter during selected periods. We collect the number of tweets related to COVID-19 policy intervention and conduct a sentiment analysis using a deep learning method. Public attitudes and sentiments in the three countries show different patterns according to how policy interventions were implemented. Overall concern about policy intervention is higher in South Korea than in the other two countries. However, public sentiments in all three countries tend to improve following implementation of policy intervention. The findings suggest that governments can achieve policy effectiveness when consistent and transparent communication take place during the initial period of the pandemic. This study contributes to the existing literature by applying big data analysis to explain which policies engender positive public attitudes.

• Journal of IKEEE
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• v.24 no.2
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• pp.529-535
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• 2020

As the main technology of the 4th industrial revolution, immersive 360-degree video contents are drawing attention. The market size of immersive 360-degree video contents worldwide is projected to increase from $6.7 billion in 2018 to approximately $70 billion in 2020. However, most of the immersive 360-degree video contents are distributed through illegal distribution networks such as Webhard and Torrent, and the damage caused by illegal reproduction is increasing. Existing 2D video industry uses copyright filtering technology to prevent such illegal distribution. The technical difficulties dealing with immersive 360-degree videos arise in that they require ultra-high quality pictures and have the characteristics containing images captured by two or more cameras merged in one image, which results in the creation of distortion regions. There are also technical limitations such as an increase in the amount of feature point data due to the ultra-high definition and the processing speed requirement. These consideration makes it difficult to use the same 2D filtering technology for 360-degree videos. To solve this problem, this paper suggests a feature point extraction and identification technique that select object identification areas excluding regions with severe distortion, recognize objects using deep learning technology in the identification areas, extract feature points using the identified object information. Compared with the previously proposed method of extracting feature points using stitching area for immersive contents, the proposed technique shows excellent performance gain.

• Korean Journal of Radiology
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• v.23 no.12
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• pp.1251-1259
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• 2022

Objective: T1 mapping provides valuable information regarding cardiomyopathies. Manual drawing is time consuming and prone to subjective errors. Therefore, this study aimed to test a DL algorithm for the automated measurement of native T1 and extracellular volume (ECV) fractions in cardiac magnetic resonance (CMR) imaging with a temporally separated dataset. Materials and Methods: CMR images obtained for 95 participants (mean age ± standard deviation, 54.5 ± 15.2 years), including 36 left ventricular hypertrophy (12 hypertrophic cardiomyopathy, 12 Fabry disease, and 12 amyloidosis), 32 dilated cardiomyopathy, and 27 healthy volunteers, were included. A commercial deep learning (DL) algorithm based on 2D U-net (Myomics-T1 software, version 1.0.0) was used for the automated analysis of T1 maps. Four radiologists, as study readers, performed manual analysis. The reference standard was the consensus result of the manual analysis by two additional expert readers. The segmentation performance of the DL algorithm and the correlation and agreement between the automated measurement and the reference standard were assessed. Interobserver agreement among the four radiologists was analyzed. Results: DL successfully segmented the myocardium in 99.3% of slices in the native T1 map and 89.8% of slices in the post-T1 map with Dice similarity coefficients of 0.86 ± 0.05 and 0.74 ± 0.17, respectively. Native T1 and ECV showed strong correlation and agreement between DL and the reference: for T1, r = 0.967 (95% confidence interval [CI], 0.951-0.978) and bias of 9.5 msec (95% limits of agreement [LOA], -23.6-42.6 msec); for ECV, r = 0.987 (95% CI, 0.980-0.991) and bias of 0.7% (95% LOA, -2.8%-4.2%) on per-subject basis. Agreements between DL and each of the four radiologists were excellent (intraclass correlation coefficient [ICC] of 0.98-0.99 for both native T1 and ECV), comparable to the pairwise agreement between the radiologists (ICC of 0.97-1.00 and 0.99-1.00 for native T1 and ECV, respectively). Conclusion: The DL algorithm allowed automated T1 and ECV measurements comparable to those of radiologists.