• KIPS Transactions on Software and Data Engineering
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• v.2 no.7
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• pp.479-486
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• 2013

The data model that can be said as skeleton of the information system constitutes important 2 axles in the information system together with the process model. There is entity, properties, relation as key factors of the data model, and entity is the most fundamental factor in the data model, and thus total data model becomes vague if not deriving entity definitely. This study dealt with entity deduction only. Deducing methods of existing entity depended on experiences, task knowledge of designers and clear procedures were not suggested, so there were many difficulties in approaching them from beginners or unskilled persons. For giving helps in solving the problem, this study proposes entity- deducing procedures based on tasks that can derive entity with a systematic process at previously derived target businesses through suggested methods from advancing researches. And the study enabled proposing procedures on imaginary tasks to be applied, objecting to undergraduates who had not experiences on the data modeling, and then verified suggesting process through a similarity checking between best answers with deduced entity by students after taking impossible points of comparing existing methods with suggesting process into consideration. By doing so, deducing entity closely to the best answer was confirmed accordingly. Therefore, a fact could be confirmed that beginners were able to deduce entity closely to the best answer even if letting beginners who had not experiences on the data modeling be applied to unfamiliar tasks. Regarding researches on properties and relation deduction besides entity, this study leaves them to next time.

• Archives of design research
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• v.19 no.5 s.67
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• pp.213-222
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• 2006

This paper presents MIDAS (Media Interaction Design Authoring System), an authoring toolkit for designers and artists to develop working prototypes in new interaction design projects. Field research were conducted to identify the requirements and a case study of designing new interactive products was carried out to examine the feasibility of the new tool. MIDAS provides easier ways of integrating hardware and software, to manage a wide range of electric input and output elements and to employ 3D Augmented Reality technology within conventional multimedia authoring tools, such as Director and Flash, which are popularly used by designers. MIDAS was used in case study projects of design education as well as by voluntary designers for evaluation. From the result of case studies, it was found that many design projects were successfully accomplished using MIDAS. Designers who participated in the projects reported that MIDAS not only helped them to concentrate more on ideation but also was very easy to use as they implemented the physical interface concepts without advanced engineering skills. It is expected that MIDAS can also support prototyping in interactive media an, tangible user interface development and related human computer interaction fields.

• Korean Journal of Clinical Laboratory Science
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• v.50 no.3
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• pp.275-283
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• 2018

Helicobacter pylori is a causative organism of various gastrointestinal diseases, including chronic gastritis, gastric ulcer, or gastric adenocarcinoma. Pathogenic factors, such as cytotoxin-associated protein A (CagA) and vacuolating cytotoxic protein A (VacA), play a role. This study analyzed qualitatively and quantitatively the effects of zerumbone on the changes in the protein expression levels of various H. pylori proteins, including CagA and VacA. Approximately 200 significant proteins were screened for the H. pylori 60190 (VacA positive / CagA positive; Eastern type) strain, and proteomic analysis was performed on 13 protein molecules that were clinically significant. After two-dimensional electrophoresis (2-DE), $ImageMaster^{TM}$ 2-DE Platinum software was used for quantitative measurements of protein spots. Matrix-assisted laser desorption/ionization-mass spectrometry (MALDI-TOF-MS) and liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS) were used for protein identification. After intensive analysis of the proteins that showed significant changes, a reverse transcription-polymerase chain reaction was performed as required to verify the results. In this study, the significance of zerumbone as a therapeutic agent for H. pylori infection was examined by screening a new pharmacological activity mechanism of zerumbone.

• Journal of KIISE:Software and Applications
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• v.35 no.9
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• pp.572-587
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• 2008

The Self-organizing Feature Map(SOFM) that is one of unsupervised neural networks is a very powerful tool for data clustering and visualization in high-dimensional data sets. Although the SOFM has been applied in many engineering problems, it needs to cluster similar weights into one class on the trained SOFM as a post-processing, which is manually performed in many cases. The traditional clustering algorithms, such as t-means, on the trained SOFM however do not yield satisfactory results, especially when clusters have arbitrary shapes. This paper proposes automatic clustering on trained SOFM, which can deal with arbitrary cluster shapes and be globally optimized by graph cuts. When using the graph cuts, the graph must have two additional vertices, called terminals, and weights between the terminals and vertices of the graph are generally set based on data manually obtained by users. The Proposed method automatically sets the weights based on mode-seeking on a distance matrix. Experimental results demonstrated the effectiveness of the proposed method in texture segmentation. In the experimental results, the proposed method improved precision rates compared with previous traditional clustering algorithm, as the method can deal with arbitrary cluster shapes based on the graph-theoretic clustering.

• Journal of the Korean Applied Science and Technology
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• v.35 no.1
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• pp.299-305
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• 2018

Perception refers to the process of acquiring all the information about the environment through various sensory organs such as the visual, auditory, tactile, and olfactory senses and integrating and interpreting the information transmitted to the brain. The ability to use these perceptions efficiently is called perceptual skill, and perceptual skill is an important factor for improving performance in the field of sports. As a result, many researchers have developed various perceptual training programs to maximize these perceptual skills while they have also progressed on attempting to verify their effects. The perceptual skill training introduced in this study is a training method that focuses on visual perception and is a training method that is applied in the United States and Europe. to improve sports performance. As a result of carrying out the perceptual skills training based on the kicker's important clue (the kicker's hip - the angle of the body and foot before kicking) to the goalkeeper in the situation of a soccer penalty kick improved the ability of predicting the direction of the ball while even in tennis, carrying out the perceptual skills training based on the server's important clue (position, ball, racket) improved the accuracy of the ability to predict in the direction of serve. Recently, there have been numerous research studies that were carried out on such perceptual skills training, but the number of studies conducted are insufficient, especially in Korea where research studies on perceptual training seem to be in a relatively neglected state. In addition, extensive studies need to be carried out to investigate whether the improvement of perceptual skills in the laboratory situation can be transitioned to an actual performance situation. Therefore, in order to elevate sports performance, researchers need to examine the perceptual training program's extent of necessity as well as the research direction regarding its effects.

• Journal of KIISE:Software and Applications
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• v.37 no.1
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• pp.60-73
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• 2010

This paper aims to describe KorLexClas 1.5 which provides us with a very large list of Korean numeral classifiers, and with the co-occurring noun categories that select each numeral classifier. Differently from KorLex of other POS, of which the structure depends largely on their reference model (Princeton WordNet), KorLexClas 1.0 and its extended version 1.5 adopt a direct building method. They demand a considerable time and expert knowledge to establish the hierarchies of numeral classifiers and the relationships between lexical items. For the efficiency of construction as well as the reliability of KorLexClas 1.5, we use following processes: (1) to use various language resources while their cross-checking for the selection of classifier candidates; (2) to extend the list of numeral classifiers by using a shallow parsing techniques; (3) to set up the hierarchies of the numeral classifiers based on the previous linguistic studies; and (4) to determine LUB(Least Upper Bound) of the numeral classifiers in KorLexNoun 1.5. The last process provides the open list of the co-occurring nouns for KorLexClas 1.5 with the extensibility. KorLexClas 1.5 is expected to be used in a variety of NLP applications, including MT.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.326-338
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• 2003

The objective of a neural network design and model selection is to construct an optimal network with a good generalization performance. However, training data include noises, and the number of training data is not sufficient, which results in the difference between the true probability distribution and the empirical one. The difference makes the teaming parameters to over-fit only to training data and to deviate from the true distribution of data, which is called the overfitting phenomenon. The overfilled neural network shows good approximations for the training data, but gives bad predictions to untrained new data. As the complexity of the neural network increases, this overfitting phenomenon also becomes more severe. In this paper, by taking statistical viewpoint, we proposed an integrative process for neural network design and model selection method in order to improve generalization performance. At first, by using the natural gradient learning with adaptive regularization, we try to obtain optimal parameters that are not overfilled to training data with fast convergence. By adopting the natural pruning to the obtained optimal parameters, we generate several candidates of network model with different sizes. Finally, we select an optimal model among candidate models based on the Bayesian Information Criteria. Through the computer simulation on benchmark problems, we confirm the generalization and structure optimization performance of the proposed integrative process of teaming and model selection.

• Journal of KIISE:Software and Applications
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• v.29 no.11
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• pp.837-850
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• 2002

In this paper, we propose an EEG-based response time prediction method during a yes/no cognitive decision task. In the experimental task, a subject goes through responding of visual stimulus, understanding the given problem, controlling hand motions, and hitting a key. Considering the subject's varying brain activities, we model subjects' mental states with defining CT (cut time), ST (selection time), and RP (repeated period). Based on the assumption between ST and RT in the mental model, we predict subjects' response time by detection of selection time. To recognize the subjects' selection time ST, we extract 3 types of feature from the filtered brain waves at frequency bands of $\alpha$, $\beta$, ${\gamma}$ waves in 4 electrode pairs combined by spatial relationships. From the extracted features, we construct specific rules for each subject and meta rules including common factors in all subjects. Applying the ST detection rules to 8 subjects gives 83% success rates and also shows that the subjects will hit a key in 0.73 seconds after ST detected. To validate the detection rules and parameters, we test the rules for 2 subjects among 8 and discuss about the experimental results. We expect that the proposed detection method can be a basic technology for brain-computer-interface by combining with left/right hand movement or yes/no discrimination methods.

• Journal of Navigation and Port Research
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• v.36 no.10
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• pp.825-832
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• 2012

GPS signal delay that caused by dry gases and water vapor in troposphere is a main error source of GPS point positioning and it must be eliminated for precise point positioning. In this paper, we implemented to generate tropospheric delay grid map over the Korean Peninsula based on post-processing method by using the GPS permanent station network in order to determine the availability of tropospheric delay generation algorithm. GIPSY 5.0 was used for GPS data process and nationwide AWS observation network was used to calculate the amount of dry delay and wet delay separately. As the result of grid map's accuracy analysis, the RMSE between grid map data and GPS site data was 0.7mm in ZHD, 7.6mm in ZWD and 8.5mm in ZTD. After grid map accuracy analysis, we applied the calculated tropospheric delay grid map to single frequency relative positioning algorithm and analyzed the positioning accuracy enhancement. As the result, positioning accuracy was improved up to 36% in case of relative positioning of Suwon(SUWN) and Mokpo(MKPO), that the baseline distance is about 297km.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.327-331
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• 2018

The purpose of this study was to design a competence teaching-learning model that could help college students improve their computational thinking among core competences in SW education. A competence teaching-learning model, UDDPAAP (Unplugged-Demonstration-Decomposition-Pattern Recognition-Abstraction-Algorithm-Programming), was designed by analyzing competences of learners with no experience in software coding, by reconstructing DMM, DDD, and DPAA among the five existing SW-based teaching-learning models, and by analyzing unplugged activity and the Bebras challenge computational thinking scale carefully. The unplugged activity partially adapted to instruction for college students and some items chosen from the Bebras challenge computational thinking scale were applied to the existing teaching-learning model. To determine the effects of the study, pretest was conducted in freshmen for computational thinking and self-confidence on the basis of the experience in SW and computer information literacy education, and posttest following instruction applying the UDDPAAP teaching-learning model. The students provided with SW education based on the UDDPAAP teaching-learning model saw their computational thinking competence improved.