• Journal of Digital Convergence
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• v.19 no.9
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• pp.241-255
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• 2021

The purpose of this study was to verify the effects of the online lifelong education program which is a key words card production and demonstration on the job preparation skills for work for part-time developmental disabilities. The subjects of this study were three developmental disabilities workers working in a restaurant business on a part-time basis, and the experimental environment was composed of the home and subway stations where they reside. The study method used a single subject study, and the baseline, intervention, and maintenance were reflected as the design conditions of the experiment. As a result, the subjects of the study effectively acquired and maintained the job preparation skills for work work through the online lifelong education program for the production and demonstration of key words cards. Through the results of this study, it was possible to discuss and conclude that the significant functional relationship between the independent variables and the dependent variables is valid.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.6
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• pp.1489-1497
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• 2018

Web application services have diversified. At the same time, research on intrusion detection is continuing due to the surge of cyber threats. Also, As a single-defense system evolves into multi-level security, we are responding to specific intrusions by correlating security events that have become vast. However, it is difficult to check the OS, service, web application type and version of the target system in real time, and intrusion detection events occurring in network-based security devices can not confirm vulnerability of the target system and success of the attack A blind spot can occur for threats that are not analyzed for problems and associativity. In this paper, we propose the validation of effectiveness for intrusion detection events using TF-IDF. The proposed scheme extracts the response traffics by mapping the response of the target system corresponding to the attack. Then, Response traffics are divided into lines and weights each line with an TF-IDF weight. we checked the valid intrusion detection events by sequentially examining the lines with high weights.

• Computers and Concrete
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• v.23 no.1
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• pp.1-10
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• 2019

The use of non-linear analysis of structures in a functional way for evaluating the structural seismic behavior has attracted the attention of the engineering community in recent years. The most commonly used functional method for analysis is a non-linear static method known as the "pushover method". In this study, for the first time, a cyclic pushover analysis with different loading protocols was used for seismic investigation of curved bridges. The finite element model of 8-span curved bridges in plan created by the ZEUS-NL software was used for evaluating different pushover methods. In order to identify the optimal loading protocol for use in astatic non-linear cyclic analysis of curved bridges, four loading protocols (suggested by valid references) were used. Along with cyclic analysis, conventional analysis as well as adaptive pushover analysis, with proven capabilities in seismic evaluation of buildings and bridges, have been studied. The non-linear incremental dynamic analysis (IDA) method has been used to examine and compare the results of pushover analyses. To conduct IDA, the time history of 20 far-field earthquake records was used and the 50% fractile values of the demand given the ground motion intensity were computed. After analysis, the base shear vs displacement at the top of the piers were drawn. Obtained graphs represented the ability of a cyclic pushover analysis to estimate seismic capacity of the concrete piers of curved bridges. Based on results, the cyclic pushover method with ISO loading protocol provided better results for evaluating the seismic investigation of concrete piers of curved bridges in plan.

• Journal of the Korean Society of Safety
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• v.37 no.2
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• pp.1-9
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• 2022

Most factories deal with toxic or flammable chemicals in their industrial processes. These hazardous substances pose a risk of leakage due to accidents, such as fire and explosion. In the event of chemical release, massive casualties and property damage can result; hence, quantitative risk prediction and assessment are necessary. Several methods are available for evaluating chemical dispersion in the atmosphere, and most analyses are considered neutral in dispersion models and under far-field wind condition. The foregoing assumption renders a model valid only after a considerable time has elapsed from the moment chemicals are released or dispersed from a source. Hence, an initial dispersion model is required to assess risk quantitatively and predict the extent of damage because the most dangerous locations are those near a leak source. In this study, the dispersion model for initial consequence analysis was developed with three-dimensional unsteady advective diffusion equation. In this expression, instantaneous leakage is assumed as a puff, and wind velocity is considered as a coordinate transform in the solution. To minimize the buoyant force, ethane is used as leaked fuel, and two different diffusion coefficients are introduced. The calculated concentration field with a molecular diffusion coefficient shows a moving circular iso-line in the horizontal plane. The maximum concentration decreases as time progresses and distance increases. In the case of using a coefficient for turbulent diffusion, the dispersion along the wind velocity direction is enhanced, and an elliptic iso-contour line is found. The result yielded by a widely used commercial program, ALOHA, was compared with the end point of the lower explosion limit. In the future, we plan to build a more accurate and general initial risk assessment model by considering the turbulence diffusion and buoyancy effect on dispersion.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.1
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• pp.11-18
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• 2022

De novo drug design is the process of developing new drugs that can interact with biological targets such as protein receptors. Traditional process of de novo drug design consists of drug candidate discovery and drug development, but it requires a long time of more than 10 years to develop a new drug. Deep learning-based methods are being studied to shorten this period and efficiently find chemical compounds for new drug candidates. Many existing deep learning-based drug design models utilize recurrent neural networks to generate a chemical entity represented by SMILES strings, but due to the disadvantages of the recurrent networks, such as slow training speed and poor understanding of complex molecular formula rules, there is room for improvement. To overcome these shortcomings, we propose a deep learning model for SMILES string generation using variational autoencoders with self-attention mechanism. Our proposed model decreased the training time by 1/26 compared to the latest drug design model, as well as generated valid SMILES more effectively.

• Korean Journal of Exercise Nutrition
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• v.25 no.1
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• pp.30-34
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• 2021

[Purpose] Many studies have observed a high prevalence of erectile dysfunction among individuals performing physical activity in less leisure-time. However, this relationship in patients with type 2 diabetic patients is not well studied. In exposure outcome studies with ordinal outcome variables, investigators often try to make the outcome variable dichotomous and lose information by collapsing categories. Several statistical models have been developed to make full use of all information in ordinal response data, but they have not been widely used in public health research. In this paper, we discuss the application of two statistical models to determine the association of physical inactivity with erectile dysfunction among patients with type 2 diabetes. [Methods] A total of 204 married men aged 20-60 years with a diagnosis of type 2 diabetes at the outpatient unit of the Department of Endocrinology at PSG hospitals during the months of May and June 2019 were studied. We examined the association between physical inactivity and erectile dysfunction using proportional odds ordinal logistic regression models and continuation ratio models. [Results] The proportional odds model revealed that patients with diabetes who perform leisure time physical activity for over 40 minutes per day have reduced odds of erectile dysfunction (odds ratio=0.38) across the severity categories of erectile dysfunction after adjusting for age and duration of diabetes. [Conclusion] The present study suggests that physical inactivity has a negative impact on erectile function. We observed that the simple logistic regression model had only 75% efficiency compared to the proportional odds model used here; hence, more valid estimates were obtained here.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.831-838
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• 2022

Machine Learning is a process of using computer algorithms to extract information from raw data to solve complex problems in a data-rich environment. It has been used in the construction industry by both academics and practitioners for multiple applications to improve the construction process. The Construction Industry Institute, a leading construction research organization has twenty-five years of experience in benchmarking capital projects in the industry. The organization is at an advantage to develop useful machine learning applications because it possesses enormous real construction data. Its benchmarking programs have been actively used by owner and contractor companies today to assess their capital projects' performance. A credible benchmarking program requires statistically valid data without subjective interference in the program administration. In developing the next-generation benchmarking program, the Data Warehouse, the organization aims to use machine learning algorithms to minimize human effort and to enable rapid data ingestion from diverse sources with data validity and reliability. This research effort uses a focus group comprised of practitioners from the construction industry and data scientists from a variety of disciplines. The group collaborated to identify the machine learning requirements and potential applications in the program. Technical and domain experts worked to select appropriate algorithms to support the business objectives. This paper presents initial steps in a chain of what is expected to be numerous learning algorithms to support high-performance computing, a fully automated performance benchmarking system.

• The Journal of the Acoustical Society of Korea
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• v.27 no.3
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• pp.140-148
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• 2008

In this study, a numerical method for a time-domain acoustic wave backscattering analysis is established based on a physical optics and a Fourier transform. The frequency responses of underwater targets are calculated based on physical optics derived from the Kirchhoff-Helmholtz integral equation by applying Kirchhoff approximation and the time-domain signals are simulated taking inverse fast Fourier transform to the obtained frequency responses. Particularly, the adaptive triangular beam method is introduced to calculate the areas impinged directly by acoustic incident wave and the virtual surface concept is adopted to consider the multiple reflection effect. The numerical analysis result for an acoustic plane wave field incident normally upon a square flat plate is coincident with the result by the analytic time-domain physical optics derived theoretically from a conventional physical optics. The numerical simulation result for a hemi-spherical end-capped cylinder model is compared with the measurement result, so that it is recognized that the presented method is valid when the specular reflection effect is predominant, but, for small targets, gives errors due to higher order scattering components. The numerical analysis of an idealized submarine shows that the established method is effectively applicable to large and complex-shaped underwater targets.

• Journal of the Earthquake Engineering Society of Korea
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• v.7 no.1
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• pp.9-16
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• 2003

To evaluate the seismic performance of multistory building structures use an equivalent SDOF model to represent the resistance of the structure to deformation as it respond in its predominant mode. This paper presents a method of converting a MDOF system into an equivalent SDOF model. The principal objective of this investigation is to evaluate appropriateness of converting method through perform nonlinear time history analysis of a multistory building structures and an equivalent SDOF model. The hysteresis rules to be used an equivalent SDOF model is obtained from the pushover analysis. Comparing the peak inelastic response of a moment resisting reinforced concrete frames and an equivalent SDOF model, the adequacy and the validity of the converting method is verified. The conclusion of this study is following; A method of converting a MDOF system into an equivalent SDOF model through the nonlinear time history response analysis is valid. The representative lateral displacement of a moment resisting reinforced concrete frames is close to the height of the first modal participation vector \ulcorner$_1{\beta}$${_1{\mu}}=1$. It can be found that the hysteresis rule of an equivalent SDOF model have influence on the time history response. Therefore, it necessary for selecting hysteresis rules to consider hysteresis characteristics of a moment resisting reinforced concrete frames.

• Journal of Korea Multimedia Society
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• v.13 no.3
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• pp.417-429
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• 2010

Though background subtraction has been widely studied for last decades, it is still a poorly solved problem especially when it meets real environments. In this paper, we first address some common problems for background subtraction that occur in real environments and then those problems are resolved by improving an existing GMM-based background modeling method. First, to achieve low computations, fixed point operations are used. Because background model usually does not require high precision of variables, we can reduce the computation time while maintaining its accuracy by adopting fixed point operations rather than floating point operations. Secondly, to avoid erroneous backgrounds that are induced by high pedestrian traffic, static levels of pixels are examined using shot-time statistics of pixel history. By using a lower learning rate for non-static pixels, we can preserve valid backgrounds even for busy scenes where foregrounds dominate. Finally, to adapt rapid illumination changes, we estimated the intensity change between two consecutive frames as a linear transform and compensated learned background models according to the estimated transform. By applying the fixed point operation to existing GMM-based method, it was able to reduce the computation time to about 30% of the original processing time. Also, experiments on a real video with high pedestrian traffic showed that our proposed method improves the previous background modeling methods by 20% in detection rate and 5~10% in false alarm rate.