• Advances in Computational Design
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• v.8 no.2
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• pp.147-177
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• 2023

Elliptical concrete-filled steel tubular (CFST) column is widely used in modern structures for both aesthetical appeal and structural performance benefits. The ultimate axial load is a critical factor for designing the elliptical CFST short columns. However, there are complications of geometric and material interactions, which make a difficulty in determining a simple model for predicting the ultimate axial load of elliptical CFST short columns. This study aims to propose an efficient adaptive neuro-fuzzy inference system (ANFIS) model for predicting the ultimate axial load of elliptical CFST short columns. In the proposed method, the ANFIS model is used to establish a relationship between the ultimate axial load and geometric and material properties of elliptical CFST short columns. Accordingly, a total of 188 experimental and simulation datasets of elliptical CFST short columns are used to develop the ANFIS models. The performance of the proposed ANFIS model is compared with that of existing design formulas. The results show that the proposed ANFIS model is more accurate than existing empirical and theoretical formulas. Finally, an explicit formula and a Graphical User Interface (GUI) tool are developed to apply the proposed ANFIS model for practical use.

• Proceedings of the Korea Water Resources Association Conference
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• 1992.07a
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• pp.443-447
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• 1992

This study describes the strategy for advanced decision support system (ADSS) development for integrated management of water resources and quality in reservoir systems. The developed ADSS consists of database that contain hydrologic data, observed operational data, and data to support specific reservoir operations simulation, optimization models, and water quality models. The optimization model, mass balance simulation model and water quality models are used in a general prototype ADSS, menu driven controlling framework that assists the user to specify and evaluate the alternative operational scenarios at one time. These alternative scenarios are evaluated by the models and the results are compared through the use of a graphical based display system. This graphical based system uses an icon based schematic representation of the system to organize the presentation of the results. The ADSS includes the ability to use monthly or weekly time periods of analysis for the models and it can use monthly historical or stochastically generated inflows.

• Proceedings of the Korea Society for Simulation Conference
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• 2004.05a
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• pp.136-140
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• 2004

A power-plant simulation tool has been developed for training the plant operators and testing a plant control system. The simulation tool is composed of a graphic editor, a component model builder and a system simulation solver. Such new programing techniques as object-oriented modeling and GUI(Graphical User Interface) are employed in developing the simulation tool. The graphic editor is based on the OpenGL library for effective implementation of GUI while the component model builder is based on object-oriented programming for efficient generalization of component models. The developed tool has been verified through the simulation of a real power plant.

• Communications for Statistical Applications and Methods
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• v.5 no.1
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• pp.1-18
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• 1998

Since 1960s, in which the computer graphics system first appeared, various graphical techniques have been introduced for regression diagnostics and they have been remarkably developed. In particular, animation, one of the dynamic graphical methods which Cook and Weisberg (1989) proposed helps to show the effect of adding variables or observations to a model, or removing them from a model on the regression results. We present the added variable plots (AVP) with animation, which can be used as an optical tool of understanding the affect of some variables or observations on other variables, and the detrended added-variable plots (DAVP) with animation, through which it is possible to find out whether specific variables or observations have an effect on the nonlinearity of other variables or not.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.426-428
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• 2012

In order to identify proteins that are present in biological samples, these samples are separated and analyzed under the sequential procedure as follows: protein purification and digestion, peptide fragmentation by tandem mass spectrometry (MS/MS) which breaks peptides into fragments, peptide identification, and protein identification. One of the widely used methods for protein identification is based on probabilistic approaches such as ProteinProphet and BaysPro. However, they do not consider the difference in peptide identification probabilities according to their length. Here, we propose a probabilistic graphical model-based approach to protein identification from MS/MS data considering peptide identification probabilities, number of sibling peptides, and peptide length. We compared our approach with ProteinProphet using a yeast MS/MS dataset. As a result, our model identified 27 more proteins than ProteinProphet at 1% of FDR (false discovery rate), confirming the importance of peptide length information in protein identification.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.1
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• pp.100-112
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• 1996

This paper presents a design experience of a control language for coordination of a multi-robot system. To effectively program job commands, a Petrinet-type Graphical Robot Language(PGRL) is proposed, where some functions, such as concurrency and synchronization, for coordination among tasks can be easily programmed.In our system, the proposed task commands of PGRL are implemented by employing formal model languages, which are composed of three modules, sensory, data handling, and action module. It is expected that by using our proposed PGRL and formal languages, one can easily describe a job or task, and hence can effectively operate a complex real-time and concurrent system. The control system is being implemented by using VME-based 32-bit microprocessor boards for supervisory, each module controller(arm, hand, leg, sensor data processing module) and a real time multi-tasking operating system(VxWorks). (author). 17 refs., 16 figs., 2 tabs.

• Communications for Statistical Applications and Methods
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• v.26 no.5
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• pp.463-471
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• 2019

The variable transformations are useful ways to guarantee the functional relationships in the model. However, the presence of outliers may undermine the accuracy of transformation. This paper deals with response transformations in the partial linear models under the existence of outliers. A new procedure for response transformation and outliers detection is proposed. The procedure uses a sequential method for identifying outliers and dynamic graphical methods for an appropriate transformation. The graphical tools make it possible to catch diagnostic information by monitoring the movement of points in the data. The procedure is illustrated with several examples. Examples show that visual clues regarding the optimal transformation, the fittness of the model and the outlyness of the observations can be checked from the series of plots.

• Journal of Multimedia Information System
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• v.8 no.4
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• pp.259-266
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• 2021

Security vulnerabilities have been reported in major design software systems such as Adobe Photoshop and Illustrator, which are recognized as de facto standard design tools in most of the design industries. Companies need to evaluate and manage their risk levels posed by those vulnerabilities, so that they could mitigate the potential security bridges in advance. In general, security vulnerabilities are discovered throughout their life cycles repeatedly if software systems are continually used. Hence, in this study, we empirically analyze risk levels for the three major graphical design software systems, namely Photoshop, Illustrator and GIMP with respect to a software vulnerability discovery model. The analysis reveals that the Alhazmi-Malaiya Logistic model tends to describe the vulnerability discovery patterns significantly. This indicates that the vulnerability discovery model makes it possible to predict vulnerability discovery in advance for the software systems. Also, we found that none of the examined vulnerabilities requires even a single authentication step for successful attacks, which suggests that adding an authentication process in software systems dramatically reduce the probability of exploitations. The analysis also discloses that, for all the three software systems, the predictions with evenly distributed and daily based datasets perform better than the estimations with the datasets of vulnerability reporting dates only. The observed outcome from the analysis allows software development managers to prepare proactively for a hostile environment by deploying necessary resources before the expected time of vulnerability discovery. In addition, it can periodically remind designers who use the software systems to be aware of security risk, related to their digital work environments.

• The Journal of the Korea Contents Association
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• v.13 no.1
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• pp.55-63
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• 2013

NUI(Natural User Interfaces) has been developed through CLI(Command Line Interfaces) and GUI(Graphical User Interfaces). NUI uses many different input modalities, including multi-touch, motion tracking, voice and stylus. In order to adopt NUI to legacy GUI applications, he/she must add device libraries, modify relevant source code and debug it. In this paper, we propose a gesture-based interface model that can be applied without modification of the existing event-based GUI applications and also present the XML schema for the specification of the model proposed. This paper shows a method of using the proposed model through a prototype.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.3
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• pp.97-110
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• 2017

This study investigates the efficiencies of machine learning models, including artificial neural network (ANN), generalized regression neural network (GRNN), adaptive neuro-fuzzy inference system (ANFIS) and random forest (RF), for reservoir water level forecasting in the Chungju Dam, South Korea. The models' efficiencies are assessed based on model efficiency indices and graphical comparison. The forecasting results of the models are dependent on lead times and the combination of input variables. For lead time t = 1 day, ANFIS1 and ANN6 models yield superior forecasting results to RF6 and GRNN6 models. For lead time t = 5 days, ANN1 and RF6 models produce better forecasting results than ANFIS1 and GRNN3 models. For lead time t = 10 days, ANN3 and RF1 models perform better than ANFIS3 and GRNN3 models. It is found that ANN model yields the best performance for all lead times, in terms of model efficiency and graphical comparison. These results indicate that the optimal combination of input variables and forecasting models depending on lead times should be applied in reservoir water level forecasting, instead of the single combination of input variables and forecasting models for all lead times.