• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.903-906
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• 2018

본 논문에서는 동적 프로젝션 매핑 콘텐츠 저작 도구의 설계를 위해 동적 프로젝션 매핑의 유형과 그에 따른 인터렉션, 이펙트 효과, 그리고 적용되고 있는 객체 추적 방법을 분석하였다. 움직임이나 형태가 고정된 대상체에 영상을 투영하던 정적 프로젝션 매칭의 방법과는 다르게 최근 소개되고 있는 동적 프로젝션 매핑 콘텐츠들은 다양한 객체 추적 방법을 적용하여 프로젝션 매핑의 적용 대상 및 내용이 다양화되고 있다. 그러나 이에 따른 동적 프로젝션 매핑 콘텐츠 저작도구의 개발이 요구되고 있다. 이에 향우 저작도구의 설계를 위한 사전연구로 동적 프로젝션 매핑 콘텐츠의 특성을 분석하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.5
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• pp.642-646
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• 2006

The fuzzy TAM(Topographical Attentive Mapping) network is a supervised method of pattern analysis which is composed of input layer, category layer, and output layer. But if we don't know the target value of the pattern, the network can not be trained. In this case, the target value can be replaced by a result induced by using an unsupervised neural network as the SOM (Self-organizing Map). In this paper, we apply the results of SOM to fuzzy TAM network and show its usefulness through the case study.

• The Journal of Korean Physical Therapy
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• v.28 no.5
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• pp.321-327
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• 2016

Purpose: The purpose was to link items of questionnaires that measure functioning and disability of persons with Low Back Pain (LBP) into the International Classification of Functioning, Disability and Health (ICF). Methods: The Oswestry Disability Index (ODI), Roland and Morris Disability Questionnaire (RM), Fear-Avoidance Beliefs Questionnaire (FABQ), and Short Form-36 health survey (SF-36) were evaluated to map items of those questionnaires into the ICF categories. The linking rule was employed and linking was performed independently by 10 health professionals. One-hundred and two patients with LBP were recruited from 19 medical institutes to this study for a field test to examine relations between the scale and its linked ICF category set. Pearson correlation coefficient was used to analyze their relationships. Results: Walking was only found to be one-to-one linking between the scale and the ICF. Sixty questions in FABQ were to be linked to 9 of ICF categories. Ten and 14 ICF categories were able to be linked to RM and ODI respectively. It was found that majority of items from ODI and RM scale had similar concept and linked to the same ICF category. SF-36 had only 15 categories of the ICF linked. Strong relationship was observed between measurement scales and linked ICF code sets (r=0.79, r=0.65, r=0.47, and r=-0.31 for ODI, RM, FABQ and SF-36 respectively). Conclusion: It was found that commonly used clinical tools for LBP may be linked to ICF. The study results may suggest that clinical data can be standardized to communicate between related professionals.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1464-1479
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• 2018

In face recognition, sometimes the number of available training samples for single category is insufficient. Therefore, the performances of models trained by convolutional neural network are not ideal. The small sample face recognition algorithm based on novel Siamese network is proposed in this paper, which doesn't need rich samples for training. The algorithm designs and realizes a new Siamese network model, SiameseFacel, which uses pairs of face images as inputs and maps them to target space so that the $L_2$ norm distance in target space can represent the semantic distance in input space. The mapping is represented by the neural network in supervised learning. Moreover, a more lightweight Siamese network model, SiameseFace2, is designed to reduce the network parameters without losing accuracy. We also present a new method to generate training data and expand the number of training samples for single category in AR and labeled faces in the wild (LFW) datasets, which improves the recognition accuracy of the models. Four loss functions are adopted to carry out experiments on AR and LFW datasets. The results show that the contrastive loss function combined with new Siamese network model in this paper can effectively improve the accuracy of face recognition.

• Journal of The Geomorphological Association of Korea
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• v.26 no.3
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• pp.53-67
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• 2019

The purpose of this study is to identify the quality of landslide susceptibility in a landslide-prone area (Jinbu-myeon, Gangwon-do, South Korea) by spatial prediction modeling approach and compare the results obtained. For this goal, a landslide inventory map was prepared mainly based on past historical information and aerial photographs analysis (Daum Map, 2008), as well as some field observation. Altogether, 550 landslides were counted at the whole study area. Among them, 182 landslides are debris flow and each group of landslides was constructed in the inventory map separately. Then, the landslide inventory was randomly selected through Excel; 50% landslide was used for model analysis and the remaining 50% was used for validation purpose. Total 12 contributing factors, such as slope, aspect, curvature, topographic wetness index (TWI), elevation, forest type, forest timber diameter, forest crown density, geology, landuse, soil depth, and soil drainage were used in the analysis. Moreover, to find out the co-relation between landslide causative factors and incidents landslide, pixels were divided into several classes and frequency ratio for individual class was extracted. Eventually, six landslide susceptibility maps were constructed using the Bayesian Predictive Discriminant (BPD), Empirical Likelihood Ratio (ELR), and Linear Regression Method (LRM) models based on different category dada. Finally, in the cross validation process, landslide susceptibility map was plotted with a receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC) and tried to extract success rate curve. The result showed that Bayesian, likelihood and linear models were of 85.52%, 85.23%, and 83.49% accuracy respectively for total data. Subsequently, in the category of debris flow landslide, results are little better compare with total data and its contained 86.33%, 85.53% and 84.17% accuracy. It means all three models were reasonable methods for landslide susceptibility analysis. The models have proved to produce reliable predictions for regional spatial planning or land-use planning.

• Journal of Gifted/Talented Education
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• v.26 no.2
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• pp.389-404
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• 2016

This research aims to build a concept map reflecting gifted elementary students' perception on fun factors of the classes for the gifted. Data for this research was gathered through brainstorming of 80 students currently attending classes for the gifted. 10 of them were selected as focus group for classification and evaluation, and 140 gifted-class students were asked of the fun factors for level of agreement after concept map analysis. Results of the research were, first, 46 final statements about fun factors which are categorized into 6 sub-categories ('the gifted class teacher's encouragement and feedback', 'confidence and chance', 'teamwork and intimacy with gifted friends', 'fulfilling, beneficial, and rewarding feeling', 'new and special experiments', 'qualitatively different class level and learning environment'). Second, the gifted students showed highest level of agreement on 'new and special experiments' category among the 6 sub-categories. Implication of this research on the field has been discussed.

• The Korean Journal of Archival Studies
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• no.32
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• pp.127-176
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• 2012

Metadata is well recognized as one of the foundational factors in archiving and long-term preservation of digital resources. There are several metadata standards for records management, archives and preservation, e.g. ISAD(G), EAD, AGRkMs, PREMIS, and OAIS. Consideration is important in selecting appropriate metadata standards in order to design metadata schema that meet the requirements of a particular archival system. Interoperability of metadata with other systems should be considered in schema design. In our previous research, we have presented a feature analysis of metadata standards by identifying the primary resource lifecycle stages where each standard is applied. We have clarified that any single metadata standard cannot cover the whole records lifecycle for archiving and preservation. Through this feature analysis, we analyzed the features of metadata in the whole records lifecycle, and we clarified the relationships between the metadata standards and the stages of the lifecycle. In the previous study, more detailed analysis was left for future study. This paper proposes to analyze the metadata schemas from the viewpoint of tasks performed in the lifecycle. Metadata schemas are primarily defined to describe properties of a resource in accordance with the purposes of description, e.g. finding aids, records management, preservation and so forth. In other words, the metadata standards are resource- and purpose-centric, and the resource lifecycle is not explicitly reflected in the standards. There are no systematic methods for mapping between different metadata standards in accordance with the lifecycle. This paper proposes a method for mapping between metadata standards based on the tasks contained in the resource lifecycle. We first propose a Task Model to clarify tasks applied to resources in each stage of the lifecycle. This model is created as a task-centric model to identify features of metadata standards and to create mappings among elements of those standards. It is important to categorize the elements in order to limit the semantic scope of mapping among elements and decrease the number of combinations of elements for mapping. This paper proposes to use 5W1H (Who, What, Why, When, Where, How) model to categorize the elements. 5W1H categories are generally used for describing events, e.g. news articles. As performing a task on a resource causes an event and metadata elements are used in the event, we consider that the 5W1H categories are adequate to categorize the elements. By using these categories, we determine the features of every element of metadata standards which are AGLS, AGRkMS, PREMIS, EAD, OAIS and an attribute set extracted from DPC decision flow. Then, we perform the element mapping between the standards, and find the relationships between the standards. In this study, we defined a set of terms for each of 5W1H categories, which typically appear in the definition of an element, and used those terms to categorize the elements. For example, if the definition of an element includes the terms such as person and organization that mean a subject which contribute to create, modify a resource the element is categorized into the Who category. A single element can be categorized into one or more 5W1H categories. Thus, we categorized every element of the metadata standards using the 5W1H model, and then, we carried out mapping among the elements in each category. We conclude that the Task Model provides a new viewpoint for metadata schemas and is useful to help us understand the features of metadata standards for records management and archives. The 5W1H model, which is defined based on the Task Model, provides us a core set of categories to semantically classify metadata elements from the viewpoint of an event caused by a task.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.2
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• pp.238-243
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• 2007

The TAM(Topographic Attentive Mapping) network neural network model is an especially effective one for pattern analysis. It is composed of of Input layer, category layer, and output layer. Fuzzy rule, lot input and output data are acquired from it. The TAM network with three pruning rules for reducing links and nodes at the layer is called fuzzy TAM network. In this paper, we apply fuzzy TAM network to pattern analysis of leadership type for organizational leader and show its usefulness. Here, criteria of input layer and target value of output layer are the value and leadership related personality type variables of the Egogram and Enneagram, respectively.

• The Journal of Korean Physical Therapy
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• v.28 no.5
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• pp.341-347
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• 2016

Purpose: The aim of the study was to investigate items of commonly used questionnaires that measure functioning status of persons with stroke and map to the International Classification of Functioning, Disability and Health (ICF). Methods: Eighty-six patients with stroke were recruited from 12 medical institutes for the study. Each item of the Modified Bathel Index (MBI), Stroke Impact Scale (SIS), Mini Mental Status Evaluation (MMSE) and SF-36 were examined and compared its concept with the ICF. Concept linking was performed by 10 health professionals independently. A field test was performed to assess its correlation between those of scales and their linked ICF category sets. Results: It was found that 11 items in MBI was linked to 14 ICF categories, whereas 27 items of MMSE had 10 categories of ICF linked. 60 items of SIS were to be linked with 35 ICF categories. Agreement between professionals in linking was found to be high: 97.5% for MBI items, 78.0%, 78.0%, and 74.8% for MMSE, SIS, and SF-36 respectively. Strong relationship was observed between measurement scales and linked ICF code sets (r=-0.76 for SIS, r=-0.78 for MBI, r=-0.47 for MMSE) whereas there was no relationship was found between SF-36 and its ICF code set (r=-0.06) from the field test. Conclusion: It was found that items of SIS, MMSE and MBI may be linked to ICF categories. Those of linking concept between clinical tools and the ICF could be helpful for clinical data standardization.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.1
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• pp.86-93
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• 2006

The TAM(Topographic Attentive Mapping) network based on a biologically-motivated neural network model is an especially effective one for pattern analysis. It is composed of of input layer, category layer, and output layer. Fuzzy rule, for input and output data are acquired from it. The TAM network with three pruning rules for reducing links and nodes at the layer is called fuzzy TAM network. In this paper, we apply fuzzy TAM network to pattern analysis of core competency model for subcontractors of construction companies and show its usefulness.