• Genomics & Informatics
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• v.19 no.1
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• pp.11.1-11.8
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• 2021

For the novel coronavirus disease 2019 (COVID-19), predictive modeling, in the literature, uses broadly susceptible exposed infected recoverd (SEIR)/SIR, agent-based, curve-fitting models. Governments and legislative bodies rely on insights from prediction models to suggest new policies and to assess the effectiveness of enforced policies. Therefore, access to accurate outbreak prediction models is essential to obtain insights into the likely spread and consequences of infectious diseases. The objective of this study is to predict the future COVID-19 situation of Korea. Here, we employed 5 models for this analysis; SEIR, local linear regression (LLR), negative binomial (NB) regression, segment Poisson, deep-learning based long short-term memory models (LSTM) and tree based gradient boosting machine (GBM). After prediction, model performance comparison was evelauated using relative mean squared errors (RMSE) for two sets of train (January 20, 2020-December 31, 2020 and January 20, 2020-January 31, 2021) and testing data (January 1, 2021-February 28, 2021 and February 1, 2021-February 28, 2021) . Except for segmented Poisson model, the other models predicted a decline in the daily confirmed cases in the country for the coming future. RMSE values' comparison showed that LLR, GBM, SEIR, NB, and LSTM respectively, performed well in the forecasting of the pandemic situation of the country. A good understanding of the epidemic dynamics would greatly enhance the control and prevention of COVID-19 and other infectious diseases. Therefore, with increasing daily confirmed cases since this year, these results could help in the pandemic response by informing decisions about planning, resource allocation, and decision concerning social distancing policies.

• The Korean journal of internal medicine
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• v.33 no.6
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• pp.1119-1128
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• 2018

Background/Aims: In multicenter clinical trials, laboratory tests are performed in the laboratory of each center, mostly using different measuring methodologies. The purpose of this study was to evaluate coefficients of variation (CVs) of laboratory results produced by various measuring methods and to determine whether mathematical data adjustment could achieve harmonization between the methods. Methods: We chose 10 clinical laboratories, including Green Cross Laboratories (GC Labs), the central laboratory, for the measurement of total cholesterol, high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C), serum triglycerides, creatinine, and glucose. The serum panels made with patient samples referred to GC Labs were sent to the other laboratories. Twenty serum samples for each analyte were prepared, sent frozen, and analyzed by each participating laboratory. Results: All methods used by participating laboratories for the six analytes had traceability by reference materials and methods. When the results from the nine laboratories were compared with those from GC Labs, the mean CVs for total cholesterol, HDL-C, LDL-C, and glucose analyzed using the same method were 1.7%, 3.7%, 4.3%, and 1.7%, respectively; and those for triglycerides and creatinine analyzed using two different methods were 4.5% and 4.48%, respectively. After adjusting data using Deming regression, the mean CV were 0.7%, 1.4%, 1.8%, 1.4%, 1.6%, and 0.8% for total cholesterol, HDL-C, LDL-C, triglyceride, creatinine, and glucose, respectively. Conclusions: We found that more comparable results can be produced by laboratory data harmonization using commutable samples. Therefore, harmonization efforts should be undertaken in multicenter trials for accurate data analysis (CRIS number; KCT0001235).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.3917-3941
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• 2019

The recent interest in artificial intelligence and machine learning has partly contributed to an interest in the use of such approaches for hyperspectral remote sensing (HRS) imagery classification, as evidenced by the increasing number of deep framework with deep convolutional neural networks (CNN) structures proposed in the literature. In these approaches, the assumption of obtaining high quality deep features by using CNN is not always easy and efficient because of the complex data distribution and the limited sample size. In this paper, conventional handcrafted learning-based multi features based on expert knowledge are introduced as the input of a special designed CNN to improve the pixel description and classification performance of HRS imagery. The introduction of these handcrafted features can reduce the complexity of the original HRS data and reduce the sample requirements by eliminating redundant information and improving the starting point of deep feature training. It also provides some concise and effective features that are not readily available from direct training with CNN. Evaluations using three public HRS datasets demonstrate the utility of our proposed method in HRS classification.

• Journal of Technologic Dentistry
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• v.41 no.1
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• pp.31-41
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• 2019

Purpose: This provides basic data for improving educational effectiveness by selecting and applying basic vocational competency applicable to dental technology's sub-major curricula by determining and researching dental technicians' awareness levels concerning the basic vocational competency's importance. Methods: 170 dental technicians working in Daegu, Korea were surveyed using a self-administered questionnaire between 9 January 2018 and 23 February 2018. Results: 1. The highest level of the basic vocational competency elements by the awareness levels of importance in dental technology laboratories was shown in the occupational ethics, followed by technological skills, interpersonal skills, self-development levels, problem-solving skills, information skills, resource management skills and skills for understanding groups, communication skills, and mathematical skills. 2. Female dental technicians put relatively higher importance on writing documents and managing time, while males stressing on basic statistics. 3. Dental laboratory CEOs and managers put high values on resource management skills, while head technicians valuing information skills more. 4. The awareness levels concerning the basic vocational competency importance in some dental technology fields did not match those of the dental technology laboratories in the fields of porcelain, crown and bridge, and orthodontics. Conclusion : This study demonstrates that the awareness levels concerning basic vocational competency's importance in some fields of dental technology differ from those of dental technology laboratories. Developing a basic vocational competency curriculum, it seems necessary to find ways to increase educational effectiveness by selecting and applying the basic vocational competency for the respective fields of dental technology.

• Journal of Elementary Mathematics Education in Korea
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• v.22 no.4
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• pp.369-384
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• 2018

Multiplication is able to be described by using repeated addition, a Cartesian product, a scalar operation, rectangular array and area in many various context. Multiplication in various problem situations is learned by various of the teaching method and the order of teaching more than any other mathematical concepts and operations in elementary school. Nevertheless, the context of multiplication leaves further room for improvement. The purpose of this study is to examine the similarities and differences between the conceptual aspects of multiplication through the literature and to analyze the appropriateness of the teaching method and the order of teaching through textbook analysis. As a result of the study, it was found that multiplication of a scalar operation was introduced too early and did not properly reflect of meaning of multiplication as a scalar operation. There is also a need to use the concept of the rectangular array or area as a meaning of multiplication two quantities.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.4
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• pp.119-128
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• 2019

Due to the nature of the 3D graphics processor system, many mathematical calculations are required and parallel processing research using GPU (Graphics Processing Unit) is being performed for high-speed processing. In this paper, we propose a 3D graphics processor using MAMS, a parallel processor that does not use cache memory, to solve the GPU problem of increasing bandwidth caused by cache memory miss and the problem that 3D shader processing speed is not constant. The 3D graphics processor using MAMS proposed in this paper designed Vertex shader, Pixel shader, Tiling and Rasterizing structure using DirectX command analysis, the FPGA(Xilinx Virtex6@100MHz) board for MAMS was constructed and designed using Verilog. We compared the processing time of the developed FPGA (100Mhz) and nVidia GeForce GTX 660 (980Mhz), the processing time using GTX 660 was not constant and suing MAMS was constant.

• Convergence Security Journal
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• v.18 no.4
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• pp.27-33
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• 2018

Stream cipher is an one-time-pad type encryption algorithm that encrypt plaintext using simple operation such as XOR with random stream of bits (or characters) as symmetric key and its security depends on the randomness of used stream. Therefore we can design more secure stream cipher algorithm by using mathematical analysis of the stream such as period, linear complexity, non-linearity, correlation-immunity, etc. The key stream in stream cipher is generated in linear feedback shift register(LFSR) having characteristic polynomial. The primitive polynomial is the characteristic polynomial which has the best security property. It is used widely not only in stream cipher but also in SEED, a block cipher using 8-degree primitive polynomial, and in Chor-Rivest(CR) cipher, a public-key cryptosystem using 24-degree primitive polynomial. In this paper we present the concept and various properties of primitive polynomials in Galois field and prove the theorem finding the number of irreducible polynomials and primitive polynomials over $F_p$ when p is larger than 2. This kind of research can be the foundation of finding primitive polynomials of higher security and developing new cipher algorithms using them.

• Steel and Composite Structures
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• v.31 no.5
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• pp.503-516
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• 2019

This work presents a dynamic investigation of functionally graded (FG) plates resting on elastic foundation using a simple quasi-3D higher shear deformation theory (quasi-3D HSDT) in which the stretching effect is considered. The culmination of this theory is that in addition to taking into account the effect of thickness extension (${\varepsilon}_z{\neq}0$), the kinematic is defined with only 4 unknowns, which is even lower than the first order shear deformation theory (FSDT). The elastic foundation is included in the formulation using the Pasternak mathematical model. The governing equations are deduced through the Hamilton's principle. These equations are then solved via closed-type solutions of the Navier type. The fundamental frequencies are predicted by solving the eigenvalue problem. The degree of accuracy of present solutions can be shown by comparing it to the 3D solution and other closed-form solutions available in the literature.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.5
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• pp.621-626
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• 2019

The driver's steering wheel maneuver is a typical disturbance that causes excessive body motion and traveling instability of a vehicle. Abrupt and extreme operation can cause rollover depending on the geometric and dynamic characteristics, e.g., SUV vehicles. In this study, to cope with the performance limitation of conventional cars, fundamental research on the structurization of a control system was performed as follows. Mathematical modeling of the lateral behavior induced by driver input was carried out. A controller was designed to reduce the body motion based on this model. An algorithm was applied to secure robust control performance against modeling errors due to parameter uncertainty, $H_{\infty}$. Using the decoupled 1/4 car, a dynamic load simulating model considering the body moment was suggested. The simulation result showed the validity of the load-simulating model. The framework for a lateral behavior control system is proposed, including an experimental 1/4 vehicle unit, load simulating module, suspension control module, and hardware-in-the-loop simulation technology.

• Steel and Composite Structures
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• v.29 no.6
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• pp.717-734
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• 2018

This paper presents the flexural performance of steel beam-to-column joints composed of hollow structural section beams and columns. A finite element (FE) model was developed incorporating geometrical and material nonlinearities to evaluate the behaviour of joints subjected to bending moments. The numerical outcomes were validated with experimental results and compared with EN1993-1-8. The demountability of the structure was discussed based on the tested specimen. A parametric analysis was carried out to investigate the effects of steel yield strength, end-plate thickness, beam thickness, column wall thickness, bolt diameter, number of bolts and location. Consequently, an analytical model was derived based on the component method to predict the moment-rotation relationships for the sub-assemblies with extended end-plates. The accuracy of the proposed model was calibrated by the experimental and numerical results. It is found that the FE model is fairly reliable to predict the initial stiffness and moment capacity of the joints, while EN1993-1-8 overestimates the initial stiffness extensively. The beam-to-column joints are shown to be demountable and reusable with a moment up to 53% of the ultimate moment capacity. The end-plate thickness and column wall thickness have a significant influence on the joint behaviour, and the layout of double bolt-rows in tension is recommended for joints with extended end-plates. The derived analytical model is capable of predicting the moment-rotation relationship of the structure.